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<title><![CDATA[Illinois State Water Survey Staff Bibliography]]></title>
<description><![CDATA[Citations and abstracts of works by Illinois State Water Survey staff. "Discover" finds full text, when available. Covers 1942 to the present, with record count of >1300 as of April 2009. New records added weekly. Citations may be downloaded in a variety of formats. For more information, please contact Susan Braxton (braxton@illinois.edu). ISWS author contact information may be found here: http://www.isws.illinois.edu/staff/swsstaff.asp]]></description>
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<title><![CDATA[Analysis of temperature structure for persistent disasterous freezing rain and snow over southern China in early 2008]]></title>
<refworks:t3><![CDATA[Acta Meteorol. Sin. (China)]]></refworks:t3>
<dc:creator><![CDATA[Mingjian,Z.]]></dc:creator>
<dc:creator><![CDATA[ Weisong,L.]]></dc:creator>
<dc:creator><![CDATA[ Xinzhong,Liang]]></dc:creator>
<dc:creator><![CDATA[ Haiying,W.]]></dc:creator>
<dc:creator><![CDATA[ Meijuan,P.]]></dc:creator>
<dc:creator><![CDATA[ Dongping,Yin]]></dc:creator>
<description><![CDATA[Early 2008, a large-scale and persistent low-temperature, freezing rain and snow weather occurred in southern China. The disaster caused by freezing rain and snow storm was unprecedented, and broke the 50 years record in many areas. In this paper, we mainly discussed the geographical distribution characteristics of the three different types of precipitation, especially freezing rain, and reasons from temperature stratification status and horizontal distribution of surface and ground temperature. Analysis showed that the distributive characteristics of rain, freezing rain and snow from south to north was determined by different stratification status in different regions of northward inclined cold front zone in the mid-low troposphere and surface temperature conditions. Under above front conditions, there must also be a specific vertical temperature structure in the mid-lower troposphere, i.e. the existence of obvious inversion layer and lower surface or ground temperature conditions. The inversion layer exceeding 0C should possess suitable strength, thickness and height, it should be neither too thick and low nor thin and high. The type of precipitation will be rain if the inversion layer is too thick and low, and it will be snow or ice vice versa. It seemed that a subfreezing layer below 0C existed beneath the warm layer of 0-6C between 650 and 850 hPa. And under colder conditions in the subfreezing layer, freezing might occur even if the surface temperature was between 0-1C . Besides, if there is no suitable inversion layer for freezing rain, the ice-covered water drop, supercooled water or melted snow will freeze rapidly under conditions of lower surface and ground temperatures.]]></description>
<dc:publisher><![CDATA[Editorial Board of Acta Meteorologica Sinica]]></dc:publisher>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Acta Meteorologica Sinica]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[6]]></prism:number>
<prism:volume><![CDATA[66]]></prism:volume> 
<prism:startingPage><![CDATA[1043]]></prism:startingPage>
<prism:endingPage><![CDATA[52]]></prism:endingPage> 
<refworks:created><![CDATA[11/20/2009 10:35:49 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/20/2009 10:41:09 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3008</link>
<refworks:FD><![CDATA[12]]></refworks:FD>
<refworks:k1><![CDATA[ ice]]></refworks:k1>
<refworks:k1><![CDATA[ land surface temperature]]></refworks:k1>
<refworks:k1><![CDATA[ rain]]></refworks:k1>
<refworks:k1><![CDATA[ snow]]></refworks:k1>
<refworks:no><![CDATA[M1: Copyright 2009, The Institution of Engineering and Technology; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined]]></refworks:no>
<refworks:pp><![CDATA[China]]></refworks:pp>
<refworks:sn><![CDATA[0577-6619]]></refworks:sn>
<refworks:ad><![CDATA[Nanjing Univ. of Inf. Sci. Technol., Nanjing, China]]></refworks:ad>
<refworks:ds><![CDATA[Engineering Village: INSPEC]]></refworks:ds>
<refworks:id><![CDATA[3008]]></refworks:id>
<refworks:u1><![CDATA[10920204]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3007">
<title><![CDATA[WRF-chem simulation of east Asian air quality: sensitivity to temporal and vertical emissions distributions]]></title>
<dc:creator><![CDATA[Wang,Xueyuan]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<dc:creator><![CDATA[ Jiang,W.]]></dc:creator>
<dc:creator><![CDATA[ Tao,Zhining]]></dc:creator>
<dc:creator><![CDATA[ Wang,J. X. L.]]></dc:creator>
<dc:creator><![CDATA[ Liu,H.]]></dc:creator>
<dc:creator><![CDATA[ Han,Z.]]></dc:creator>
<dc:creator><![CDATA[ Liu,Shuyan]]></dc:creator>
<dc:creator><![CDATA[ Zhang,Yuyan]]></dc:creator>
<dc:creator><![CDATA[ Grell,G. A.]]></dc:creator>
<dc:creator><![CDATA[ Peckham,S. E.]]></dc:creator>
<description><![CDATA[This study develops fine temporal (seasonal, day-of-week, diurnal) and vertical allocations of anthropogenic emissions for the TRACE-P inventory and evaluates their impacts on the East Asian air quality prediction using WRF-Chem simulations in July 2001 at 30-km grid spacing against available surface measurements from EANET and NEMCC. For NO2 and SO2, the diurnal and vertical redistributions of emissions play essential roles, while the day-of-week variation is less important. When all incorporated, WRF-Chem best simulates observations of surface NO2 and SO2 concentrations, while using the default emissions produces the worst result. The sensitivity is especially large over major cities and industrial areas, where surface NO2 and SO2 concentrations are reduced by respectively 3-7 and 6-12 ppbv when using the scaled emissions. The incorporation of all the three redistributions of emissions simulates surface O3 concentrations higher by 4-8 ppbv at night and 2-4 ppbv in daytime over broad areas of northern, eastern and central China. To this sensitivity, the diurnal redistribution contributes more than the other two.]]></description>
<prism:publicationName><![CDATA[Atmospheric Environment]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:volume><![CDATA[In Press, Accepted Manuscript]]></prism:volume> 
<refworks:created><![CDATA[11/18/2009 7:08:30 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/18/2009 7:13:01 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3007</link>
<refworks:FD><![CDATA[Available online 12 November 2009]]></refworks:FD>
<refworks:k1><![CDATA[ WRF-Chem]]></refworks:k1>
<refworks:k1><![CDATA[ emissions]]></refworks:k1>
<refworks:k1><![CDATA[ diurnal cycle]]></refworks:k1>
<refworks:k1><![CDATA[ vertical redistribution]]></refworks:k1>
<refworks:k1><![CDATA[ air quality modeling]]></refworks:k1>
<refworks:k1><![CDATA[ East Asia]]></refworks:k1>
<refworks:sn><![CDATA[1352-2310]]></refworks:sn>
<refworks:do><![CDATA[DOI: 10.1016/j.atmosenv.2009.11.011]]></refworks:do>
<refworks:ds><![CDATA[Science Direct]]></refworks:ds>
<refworks:id><![CDATA[3007]]></refworks:id>
<refworks:ul><![CDATA[http://www.sciencedirect.com/science/article/B6VH3-4XNW4BJ-2/2/66c781d95f758fe7aee658ee61969242]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3006">
<title><![CDATA[Differences in kulti-sensor and rain-gauge precipitation amounts]]></title>
<dc:creator><![CDATA[Westcott,Nancy]]></dc:creator>
<description><![CDATA[A comparison of multi-sensor (radar and gauge) and gauge precipitation estimates at a monthly temporal resolution and a county spatial resolution was undertaken for the midwestern USA. Precipitation data were collected from February 2002 to October 2006 from two sources: (a) multi-sensor precipitation estimates (MPE) based on the stage III/IV algorithm developed by the US National Oceanic and Atmospheric Administration (NOAA), national weather service (NWS) office of hydrology and NWS river forecast centres; and (b) quality-controlled NWS cooperative rain-gauge (QC-Coop) data from the NOAA national climatic data centre (NCDC). The gauge data were employed as the reference standard. The monthly median of the percentage differences in countyaveraged monthly precipitation estimated by MPE and QC-Coop in the midwestern USA, for around 750 counties, was mainly within 12.5%, with a median percentage difference of +6%. The positive difference indicates that, overall, the MPE values tend to be smaller than the QC-Coop values. ME values more closely correspond with QC-Coop values at all latitudes in the summer months when convective precipitation is dominant, and in the winter months for latitudes where non-frozen precipitation is most prevalent.]]></description>
<dc:publisher><![CDATA[Thomas Telford Services Ltd]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Proceedings of the Institution of Civil Engineers: Water Management]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[162]]></prism:volume> 
<prism:startingPage><![CDATA[73]]></prism:startingPage>
<prism:endingPage><![CDATA[81]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 6:26:01 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:26:51 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3006</link>
<refworks:k1><![CDATA[ Rain]]></refworks:k1>
<refworks:k1><![CDATA[ Climatology]]></refworks:k1>
<refworks:k1><![CDATA[ Composite micromechanics]]></refworks:k1>
<refworks:k1><![CDATA[ Gages]]></refworks:k1>
<refworks:k1><![CDATA[ Hydrology]]></refworks:k1>
<refworks:k1><![CDATA[ Quality control]]></refworks:k1>
<refworks:k1><![CDATA[ Quantum theory]]></refworks:k1>
<refworks:k1><![CDATA[ Sensors]]></refworks:k1>
<refworks:k1><![CDATA[ Water]]></refworks:k1>
<refworks:k1><![CDATA[ Water analysis]]></refworks:k1>
<refworks:k1><![CDATA[ Water resources]]></refworks:k1>
<refworks:k1><![CDATA[ Weather forecasting]]></refworks:k1>
<refworks:pp><![CDATA[1 Heron Quay, London, E14 4JD, United Kingdom]]></refworks:pp>
<refworks:sn><![CDATA[17417589]]></refworks:sn>
<refworks:ad><![CDATA[Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois, Champaign, IL, United States]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org/10.1680/wama.2009.162.2.73]]></refworks:lk>
<refworks:ds><![CDATA[Engineering Village: Compendex]]></refworks:ds>
<refworks:id><![CDATA[3006]]></refworks:id>
<refworks:u1><![CDATA[20094512421747]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3003">
<title><![CDATA[Evaluation of alternative geospatial models using image ranking and machine learning: An application in shallow groundwater recharge and discharge]]></title>
<refworks:t2><![CDATA[World Environmental and Water Resources Congress 2009: Great Rivers]]></refworks:t2>
<dc:creator><![CDATA[Lin,Y. -F]]></dc:creator>
<dc:creator><![CDATA[ Bajcsy,P.]]></dc:creator>
<dc:creator><![CDATA[ Yahja,A.]]></dc:creator>
<dc:creator><![CDATA[ Kim,C.]]></dc:creator>
<description><![CDATA[This paper addresses the problem of accurate estimation of geospatial models from a set of groundwater recharge and discharge maps and from auxiliary remote sensing and terrestrial raster measurements. The motivation for our work is driven by the cost of field measurements, and by the limitations of currently available physics-based modeling techniques that do not include all relevant variables and allow accurate predictions only at coarse spatial scales. The goal is to improve our understanding of the underlying physical phenomena and increase the accuracy of geospatial models - with a combination of remote sensing, field measurements and physics-based modeling. Our approach is to process a set of recharge and discharge maps generated from interpolated sparse field measurements using existing physics-based models, and identify the recharge and discharge map that would be the most suitable for extracting a set of rules between the auxiliary variables of interest and the recharge and discharge map labels. We implemented this approach by ranking recharge and discharge maps using information entropy and mutual information criteria, and then by deriving a set of rules using a machine learning technique, such as the decision tree method. The novelty of our work is in developing a general framework for building geospatial models with the ultimate goal of minimizing cost and maximizing model accuracy. The framework is demonstrated for groundwater recharge and discharge rate models but could be applied to other similar studies, for instance, to understanding hypoxia based on physics-based models and remotely sensed variables. Furthermore, our key contribution is in designing a ranking method for recharge and discharge maps that allows us to analyze multiple plausible recharge and discharge maps with a different number of zones. This JAVA based software package, Spatial Pattern To Learn (SP2Learn), is designed to be user-friendly and universal utilities for pattern learning to improve model predictions from sparse measurements by computer-assisted integration of spatially dense geospatial image data and machine learning of model dependencies. The reliability indices from SP2Learn will improve the traditionally subjective approach to initiating conceptual models by providing objectively quantifiable conceptual bases for further probabilistic and uncertainty analyses. This new approach has been tested using the dataset from Buena Vista Groundwater Basin, a thoroughly understood system in the Central Sand Plains of Wisconsin. This project was supported by the National Center for Supercomputing Applications - Faculty Fellows Program and the Illinois Water Supply Planning Project. © 2009 ASCE.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:volume><![CDATA[342]]></prism:volume> 
<prism:startingPage><![CDATA[1753]]></prism:startingPage>
<prism:endingPage><![CDATA[1756]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 6:05:09 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3003</link>
<refworks:FD><![CDATA[17 May 2009 through 21 May 2009]]></refworks:FD>
<refworks:no><![CDATA[Conference code: 77351]]></refworks:no>
<refworks:ed><![CDATA[Kansas City, MO]]></refworks:ed>
<refworks:sn><![CDATA[9780784410363 (ISBN)]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey/Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Dr., Champaign, IL 61820, United States; Affiliation: National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 West Clark Street, Urbana, IL 61801, United States; Correspondence Address: Lin, Y.-F.; Illinois State Water Survey/Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Dr., Champaign, IL 61820, United States; email: yflin@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:lk><![CDATA[http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=ASCECP000342041036000174000001&idtype=cvips&gifs=yes]]></refworks:lk>
<refworks:do><![CDATA[10.1061/41036(342)174]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[3003]]></refworks:id>
<refworks:jo><![CDATA[Proc. World Env. Water Resour. Congr. - World Environ. Water Resour. Congr.: Great Rivers]]></refworks:jo>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3004">
<title><![CDATA[Modeling the hydrology and hydraulics of the cache river system]]></title>
<refworks:t2><![CDATA[World Environmental and Water Resources Congress 2009: Great Rivers]]></refworks:t2>
<dc:creator><![CDATA[Demissie,M.]]></dc:creator>
<dc:creator><![CDATA[ Keefer,L.]]></dc:creator>
<dc:creator><![CDATA[ Lian,Y.]]></dc:creator>
<dc:creator><![CDATA[ Yue,F.]]></dc:creator>
<dc:creator><![CDATA[ Bekele,E.]]></dc:creator>
<description><![CDATA[The Cache River is located in the extreme southern part of Illinois, just north of the confluence of the Ohio and Mississippi Rivers. Major floods in both of the two rivers have significant impact on the hydrology and hydraulics of the Cache River. In 1915, a cutoff to the Ohio River was constructed east of Karnak that resulted in sub-dividing the Cache River watershed into the Upper and Lower Cache River watersheds. The Upper Cache River watershed consists of the eastern part of the watershed draining directly to the Ohio River through the Post Creek Cutoff. The Lower Cache River watershed consists of the western part of the watershed draining to the Mississippi River through a diversion channel at the outlet. However, part of the Lower Cache River can at times reverse flow, draining to the Upper Cache River and Post Creek Cutoff. Hydrologic and hydraulic models have been developed to simulate the hydrology and hydraulics of the Cache River system to evaluate flow and water level conditions along the river under different alternative scenarios that can be implemented as part of a restoration effort for the Cache River. The hydrologic model that simulates the rainfall-runoff process for tributary watersheds is based on the HEC-HMS model. The model is used to compute runoff from tributary watersheds for selected storm events. Outputs from the HEC-HMS model are then used as inputs to the hydraulic model, UNET. The UNET model, a one-dimensional unsteady-flow dynamic wave routing model, is capable of modeling the complex hydraulics of the Cache River System that experiences flow reversals during flood events in the Lower Cache River. © 2009 ASCE.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:volume><![CDATA[342]]></prism:volume> 
<prism:startingPage><![CDATA[6010]]></prism:startingPage>
<prism:endingPage><![CDATA[6019]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 6:05:09 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3004</link>
<refworks:FD><![CDATA[17 May 2009 through 21 May 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Flood event]]></refworks:k1>
<refworks:k1><![CDATA[ Flow dynamics]]></refworks:k1>
<refworks:k1><![CDATA[ Flow reversals]]></refworks:k1>
<refworks:k1><![CDATA[ HEC-HMS]]></refworks:k1>
<refworks:k1><![CDATA[ Hydrologic and hydraulic models]]></refworks:k1>
<refworks:k1><![CDATA[ Hydrologic models]]></refworks:k1>
<refworks:k1><![CDATA[ Illinois]]></refworks:k1>
<refworks:k1><![CDATA[ Mississippi river]]></refworks:k1>
<refworks:k1><![CDATA[ Ohio River]]></refworks:k1>
<refworks:k1><![CDATA[ Rainfall-runoff process]]></refworks:k1>
<refworks:k1><![CDATA[ Reverse flow]]></refworks:k1>
<refworks:k1><![CDATA[ River systems]]></refworks:k1>
<refworks:k1><![CDATA[ River watersheds]]></refworks:k1>
<refworks:k1><![CDATA[ Routing model]]></refworks:k1>
<refworks:k1><![CDATA[ Significant impacts]]></refworks:k1>
<refworks:k1><![CDATA[ Storm events]]></refworks:k1>
<refworks:k1><![CDATA[ Tributary watersheds]]></refworks:k1>
<refworks:k1><![CDATA[ Floods]]></refworks:k1>
<refworks:k1><![CDATA[ Hydraulic models]]></refworks:k1>
<refworks:k1><![CDATA[ Hydraulic structures]]></refworks:k1>
<refworks:k1><![CDATA[ Landforms]]></refworks:k1>
<refworks:k1><![CDATA[ Rain]]></refworks:k1>
<refworks:k1><![CDATA[ Runoff]]></refworks:k1>
<refworks:k1><![CDATA[ Water levels]]></refworks:k1>
<refworks:k1><![CDATA[ Water resources]]></refworks:k1>
<refworks:k1><![CDATA[ Watersheds]]></refworks:k1>
<refworks:k1><![CDATA[ River diversion]]></refworks:k1>
<refworks:no><![CDATA[Conference code: 77351]]></refworks:no>
<refworks:ed><![CDATA[Kansas City, MO]]></refworks:ed>
<refworks:sn><![CDATA[9780784410363 (ISBN)]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Center for Watershed Science, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820, United States; Affiliation: Fluvial Geomorphologist, Center for Watershed Science, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820, United States; Affiliation: Graduate Research Assistant, Center for Watershed Science, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820, United States; Correspondence Address: Demissie, M.; Center for Watershed Science, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820, United States; email: demissie@uiuc.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:lk><![CDATA[http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=ASCECP000342041036000608000001&idtype=cvips&gifs=yes]]></refworks:lk>
<refworks:do><![CDATA[10.1061/41036(342)608]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[3004]]></refworks:id>
<refworks:cr><![CDATA[Allgire, R., Comparison of 1987 and 1989 Bed Profile Surveys of the Lower Cache River (1991) Illinois State Water Survey Contract Report, 508;; Demissie, M., Keefer, L., Lian, Y., Yue, F., Larson, B., (2001) Hydrologic and Hydraulic Modeling and Analyses for the Cache River for the Purposes of Evaluating Current Conditions and Alternative Restoration MeasuRes, , Illinois State Water Survey Contract Report, Champaign, IL. 2008;; Demissie, M., Knapp, H.V., Parmar, P., Kriesant, D.J., (2006) Hydrology of the Big Creek Watershed and Its Influence on the Lower Cache River, , Illinois State Water Survey Contract Report, 2001;; Demissie, M., Soong, T.W., Allgire, R., Keefer, L., Makowski, P., Cache River Basin: Hydrology, Hydraulics, and Sediment Transport. Volume 1: Background, Data Collection, and Analysis, , 1990a, Illinois State Water Survey Contract Report 484;; Demissie, M., Soong, T.W., Camacho, R., Cache River Basin: Hydrology, Hydraulics, and Sediment Transport. Volume 2: Mathematical Modeling, , 1990b, Illinois State Water Survey Contract Report 485;; Huff, F.A., Angel, J.R., Frequency Distributions and Hydroclimatic Characteristics of Heavy Rainstorms in Illinois (1989) Illinois State Water Survey Bulletin, 70;; (1997) Cache River Area Assessment. Volume 1, Part 1: Hydrology, Air Quality, and Climate, , Illinois Department of Natural Resources IDNR, IDNR, Office of Scientific Research and Analysis, Springfield, IL;; (2004) Upper Mississippi River System Flow Frequency Study, , U.S. Army Corps of Engineers, Final Report, USACE Rock Island District, Rock Island, IL;; UNET, One-Dimensional Unsteady Flow Through a Full Network of Open Channels, User's Manual (1997) USACE, Hydrologic Engineering Center, CPD-66, , U.S. Army Corps of Engineers, Version 3.2, Davis, CA]]></refworks:cr>
<refworks:jo><![CDATA[Proc. World Env. Water Resour. Congr. - World Environ. Water Resour. Congr.: Great Rivers]]></refworks:jo>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3005">
<title><![CDATA[Potential effects of climate and emissions changes on surface ozone in the Chicago area]]></title>
<dc:creator><![CDATA[Lin,J. -T]]></dc:creator>
<dc:creator><![CDATA[ Wuebbles,D. J.]]></dc:creator>
<dc:creator><![CDATA[ Huang,H. -C]]></dc:creator>
<dc:creator><![CDATA[ Tao,Zhining]]></dc:creator>
<dc:creator><![CDATA[ Caughey,Michael A.]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin Zhong]]></dc:creator>
<dc:creator><![CDATA[ Zhu,Jin-Hong]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<description><![CDATA[Future changes in climate and precursor emissions will likely have important consequences on ground-level ozone concentrations for the City of Chicago and its surrounding suburban/rural areas. Here we use a regional climate-air quality modeling system to evaluate the combined and individual effects of climate warming (and resulting biogenic emissions increases) and anthropogenic emissions perturbations from 1996-2000 to 2048-2052 and 2095-2099 in this region. Two scenarios are considered, including A1FI (higher warming with increasing anthropogenic emissions) and B1 (less warming with reduced anthropogenic emissions). Relative to 1996-2000, projected changes in climate and anthropogenic emissions together lead to little ozone change for the City of Chicago under A1FI but 5.0-7.8 ppb increases under B1 by 2048-2052 and 2095-2099. For A1FI, the decreasing ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx) reduces ozone concentrations over Chicago, despite the increasing emissions for both NOx and VOCs. Averaged over the Chicago urban and surrounding suburban area, however, surface ozone increase 2.3-7.1 ppb under A1FI by 2095-2099. Additionally, the seasonal ozone variation is projected to increase 84-127% under A1FI but decrease 23-30% under B1 over the Chicago area. By comparison, projected climate warming alone increases the surface ozone by 2.1-8.7 ppb and its seasonal variation by 22-89% over the Chicago area from 1996-2000 to 2095-2099 under both scenarios. Therefore, effective emission regulation and climate considerations are both important to pollution mitigation in the Chicago area. © 2009 Elsevier Inc. All rights reserved.]]></description>
<prism:publicationName><![CDATA[Journal of Great Lakes Research]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<refworks:created><![CDATA[11/17/2009 6:05:09 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3005</link>
<refworks:k1><![CDATA[ Change]]></refworks:k1>
<refworks:k1><![CDATA[ Chicago]]></refworks:k1>
<refworks:k1><![CDATA[ Climate]]></refworks:k1>
<refworks:k1><![CDATA[ Emissions]]></refworks:k1>
<refworks:k1><![CDATA[ Ozone]]></refworks:k1>
<refworks:no><![CDATA[Article in Press]]></refworks:no>
<refworks:sn><![CDATA[03801330]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, 105 S. Gregory St., Urbana, IL 61801, USA; Affiliation: School of Engineering and Applied Sciences, Harvard University, Cruft Laboratory 211A, 19 Oxford St., Cambridge, MA 02138, USA; Affiliation: Science Applications International Corporation, on assignment to NOAA/NWS/NCEP/EMC W/NP2, NOAA, WWB#207, 5200 Auth Road, Camp Springs, MD 20746-4304, USA; Affiliation: Illinois State Water Survey, Institute for Natural Resource Sustainability, University of Illinois at Urbana-Champaign 2204 Griffith Dr., Champaign, IL 61820-7495, USA; Affiliation: Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison, 1710 University Ave., Room 201A, WI, USA; Correspondence Address: Wuebbles, D.J.; Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, 105 S. Gregory St.,email: wuebbles@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1016/j.jglr.2009.09.004]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[3005]]></refworks:id>
<refworks:an><![CDATA[CODEN: JGLRD]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3002">
<title><![CDATA[Smart Pipe - Nanosensors for monitoring water quantity and quality in public water systems]]></title>
<refworks:t2><![CDATA[World Environmental and Water Resources Congress 2009: Great Rivers(Proceedings of World Environmental and Water Resources Congress 2009)]]></refworks:t2>
<dc:creator><![CDATA[Lin,Yu-Feng]]></dc:creator>
<dc:creator><![CDATA[ Liu,C.]]></dc:creator>
<dc:creator><![CDATA[ Whisler,J.]]></dc:creator>
<description><![CDATA[A 2005 study by the American Society of Civil Engineers showed that six billion gallons of clean, treated drinking water disappears every day, mostly due to old, leaky pipes and mains. The amount is enough to serve the population of California. The approximate dollar cost, given varied water rates in different U.S. regions, is $12.5 million - $92 million. Moreover, leaking systems have wasted not only dollars but also priceless natural and energy resources for future generations. A current research project funded by the US Environmental Protection Agency - Midwest Technology Assistance Center is designed to improve water supply infrastructure via a highly-advanced, cost-efficient monitoring system. A research group led by the Illinois State Water Survey, in collaboration with the Department of Mechanical Engineering at Northwestern University, has been developing a "Smart Pipe" prototype: a multi-sensor array to monitor water flow and quality using state-of-the-art nanotechnology. Each sensor unit in the array will include sensors for pressure, flow velocity and temperature on a 2.5mm by 2.5 mm silicon skin. The Smart Pipe will be equipped with a wireless processor and antenna to transfer monitoring data via commercial wireless communication systems. © 2009 ASCE.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:volume><![CDATA[342]]></prism:volume> 
<prism:startingPage><![CDATA[356]]></prism:startingPage>
<prism:endingPage><![CDATA[363]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 5:57:16 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/20/2009 8:49:06 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3002</link>
<refworks:FD><![CDATA[17 May 2009 through 21 May 2009]]></refworks:FD>
<refworks:k1><![CDATA[ American Society of Civil Engineers]]></refworks:k1>
<refworks:k1><![CDATA[ California]]></refworks:k1>
<refworks:k1><![CDATA[ Cost-efficient]]></refworks:k1>
<refworks:k1><![CDATA[ Drinking water]]></refworks:k1>
<refworks:k1><![CDATA[ Future generations]]></refworks:k1>
<refworks:k1><![CDATA[ Illinois]]></refworks:k1>
<refworks:k1><![CDATA[ Multi-sensor arrays]]></refworks:k1>
<refworks:k1><![CDATA[ Northwestern University]]></refworks:k1>
<refworks:k1><![CDATA[ Public water systems]]></refworks:k1>
<refworks:k1><![CDATA[ Research groups]]></refworks:k1>
<refworks:k1><![CDATA[ Sensor units]]></refworks:k1>
<refworks:k1><![CDATA[ US Environmental Protection Agency]]></refworks:k1>
<refworks:k1><![CDATA[ Water flows]]></refworks:k1>
<refworks:k1><![CDATA[ Water quantities]]></refworks:k1>
<refworks:k1><![CDATA[ Water rates]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply infrastructures]]></refworks:k1>
<refworks:k1><![CDATA[ Wireless communication system]]></refworks:k1>
<refworks:k1><![CDATA[ Cellular telephone systems]]></refworks:k1>
<refworks:k1><![CDATA[ Communication systems]]></refworks:k1>
<refworks:k1><![CDATA[ Design]]></refworks:k1>
<refworks:k1><![CDATA[ Energy resources]]></refworks:k1>
<refworks:k1><![CDATA[ Environmental Protection Agency]]></refworks:k1>
<refworks:k1><![CDATA[ Global system for mobile communications]]></refworks:k1>
<refworks:k1><![CDATA[ Mobile computing]]></refworks:k1>
<refworks:k1><![CDATA[ Nanosensors]]></refworks:k1>
<refworks:k1><![CDATA[ Pipe]]></refworks:k1>
<refworks:k1><![CDATA[ Potable water]]></refworks:k1>
<refworks:k1><![CDATA[ Sensor arrays]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply]]></refworks:k1>
<refworks:k1><![CDATA[ Water treatment]]></refworks:k1>
<refworks:k1><![CDATA[ Water resources]]></refworks:k1>
<refworks:no><![CDATA[Conference code: 77351]]></refworks:no>
<refworks:ed><![CDATA[Kansas City, MO]]></refworks:ed>
<refworks:sn><![CDATA[9780784410363 (ISBN)]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Dr., Champaign, IL 61820, United States; Affiliation: Department of Mechanical Engineering, Northwestern University, Tech Institute, 2145 Sheridan Rd, Evanston IL 60201, United States; Correspondence Address: Lin, Y.-F.; Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Dr., Champaign, IL 61820, United States; email: yflin@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:lk><![CDATA[http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=ASCECP000342041036000034000001&idtype=cvips&gifs=yes]]></refworks:lk>
<refworks:do><![CDATA[10.1061/41036(342)34]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[3002]]></refworks:id>
<refworks:cr><![CDATA[Card for America's Infrastructure. Press Conference Remarks by William P. Henry, President, ASCE (2005) American Society of Civil Engineers (2005) Report, , http://www.asce.org/reportcard/2005/page.cfm?id=147, March 9]]></refworks:cr>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3001">
<title><![CDATA[Analysis of model sensitivity and uncertainty for chlorine transport and decay in a water distribution system]]></title>
<refworks:t2><![CDATA[World Environmental and Water Resources Congress 2009: Great Rivers (Proceedings of World Environmental and Water Resources Congress 2009)]]></refworks:t2>
<dc:creator><![CDATA[Dawsey,Wesley J.]]></dc:creator>
<dc:creator><![CDATA[ Minsker,B. S.]]></dc:creator>
<dc:creator><![CDATA[ Ostfeld,A.]]></dc:creator>
<description><![CDATA[There are a number of sources of uncertainty in drinking water distribution system modeling. Uncertain parameters include pipe diameters, consumer demands, hydraulic energy loss coefficients, reaction coefficients and others. Understanding the relative importance of these sources of uncertainty can improve the allocation of resources for model refinement and calibration, as well as, aid knowledge inference from monitoring data. This paper presents an analysis of uncertainty and model sensitivity for chlorine transport and decay in a water distribution system. A clustering and global variance-based sensitivity methodology is proposed to account for spatial inconsistencies found in the results of previous studies of this problem. Results are presented from small and large scalecase studies. This methodology is then used to explore the occurrence of intrusion events in a water distribution system, and the potential to detect such events through online monitoring of chlorine residual concentrations. Noise present in the chlorine monitoring signal has the potential to overwhelm the detection of an upstream intrusion and its associated chlorine demand. Results are presented from simulated intrusion events of varying magnitude and duration. © 2009 ASCE.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:volume><![CDATA[342]]></prism:volume> 
<prism:startingPage><![CDATA[655]]></prism:startingPage>
<prism:endingPage><![CDATA[665]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 5:55:06 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3001</link>
<refworks:FD><![CDATA[17 May 2009 through 21 May 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Chlorine demand]]></refworks:k1>
<refworks:k1><![CDATA[ Chlorine residuals]]></refworks:k1>
<refworks:k1><![CDATA[ Consumer demands]]></refworks:k1>
<refworks:k1><![CDATA[ Drinking water distribution system]]></refworks:k1>
<refworks:k1><![CDATA[ Hydraulic energy]]></refworks:k1>
<refworks:k1><![CDATA[ Intrusion events]]></refworks:k1>
<refworks:k1><![CDATA[ Model refinement]]></refworks:k1>
<refworks:k1><![CDATA[ Model sensitivity]]></refworks:k1>
<refworks:k1><![CDATA[ Monitoring signals]]></refworks:k1>
<refworks:k1><![CDATA[ Online monitoring]]></refworks:k1>
<refworks:k1><![CDATA[ Pipe diameter]]></refworks:k1>
<refworks:k1><![CDATA[ Relative importance]]></refworks:k1>
<refworks:k1><![CDATA[ Sources of uncertainty]]></refworks:k1>
<refworks:k1><![CDATA[ Uncertain parameters]]></refworks:k1>
<refworks:k1><![CDATA[ Chlorine]]></refworks:k1>
<refworks:k1><![CDATA[ Energy dissipation]]></refworks:k1>
<refworks:k1><![CDATA[ Intrusion detection]]></refworks:k1>
<refworks:k1><![CDATA[ Potable water]]></refworks:k1>
<refworks:k1><![CDATA[ Resource allocation]]></refworks:k1>
<refworks:k1><![CDATA[ Uncertainty analysis]]></refworks:k1>
<refworks:k1><![CDATA[ Water analysis]]></refworks:k1>
<refworks:k1><![CDATA[ Water distribution systems]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply]]></refworks:k1>
<refworks:k1><![CDATA[ Water resources]]></refworks:k1>
<refworks:no><![CDATA[Conference code: 77351]]></refworks:no>
<refworks:ed><![CDATA[Kansas City, MO]]></refworks:ed>
<refworks:sn><![CDATA[9780784410363 (ISBN)]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey, Institute of Natural Resources Sustainability, University of Illinois, 2204 Griffith Drive, Champaign, IL 61820-7495, United States; Affiliation: Department of Civil and Environmental Engineering, National Center for Supercomputing Applications, University of Illinois; 3230d Newmark Lab, MC-250 205 N. Mathews Ave., Urbana, IL 61801, United States; Affiliation: Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel; Correspondence Address: Dawsey, W. J.; Illinois State Water Survey, Institute of Natural Resources Sustainability, University of Illinois, 2204 Griffith Drive, Champaign, IL 61820-7495, United States; email: dawsey@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:lk><![CDATA[http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=ASCECP000342041036000064000001&idtype=cvips&gifs=yes]]></refworks:lk>
<refworks:do><![CDATA[10.1061/41036(342)64]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[3001]]></refworks:id>
<refworks:cr><![CDATA[Babeyan, A.V., Kapelan, Z.S., Savic, D.A., Walters, G.A., Comparison of two approaches for the least cost design of water distribution systems under uncertain demands (2004) Proceedings of the ASCE World Water and Environmental Resources Congress, June 27-July 1, 2004, , Salt Lake City, Utah, USA;; Babayan, A.V., Savic, D.A., Walters, G.A., Multiobjective Optimization for the Last-Cost Design of Water Distribution System Under Correlated Uncertain Parameters (2005) Proceedings of the ASCE World Water and Environmental Resources Congress 2005, , Anchorage, Alaska, USA;; Babayan, A.V., Kapelan, Z.S., Savic, D.A., Walters, G.A., Comparison of two methods for the stochastic least cost design of water distribution systems (2006) Engineering Optimization, 38 (3), pp. 281-297;; Barkdoll, B.D., Didigam, H., Effect of user demand on water quality and hydraulics of distribution systems (2004) Proceedings of the ASCE World Water and Environmental Resources Congress, June 27-July 1, 2004, , Salt Lake City, Utah, USA;; Bao, Y., Mays, L.W., Model for water distribution system reliability (1990) Journal of Hydraulic Engineering, ASCE, 116 (9), pp. 1119-1137;; Branisavljevic, N., Ivetic, M., Fuzzy approach in the uncertainty analysis of the water distribution network of Becej (2006) Civil Engineering and Environmental Systems, 23 (3), pp. 221-236;; Janković-Nišić, B., Graham, N.J.D., Maksimović, C., Butler, D., Cost-effective leakage reduction through district metering Proceedings of the Institution of Civil Engineers: Water Management, 160 (3), pp. 181-187;; Khanal, N., Steven, G., Buchberger, S.G., McKenna, S.A., Distribution System Contamination Events: Exposure, Influence, and Sensitivity (2006) Journal of Water Resources Planning and Management, ASCE, 132 (4), pp. 283-292;; Kang, D.S., Pasha, M.F.K., Lansey, K., Approximate methods for analyzing water quality prediction uncertainty in water distribution systems (2007) Proceedings of the ASCE World Environmental and Water Resources Congress 2007, , Tampa, Florida, USA;; Lansey, K.E., Duan, N., Mays, L.W., Tung, Y.K., Water distribution system design under uncertainties (1989) Journal of Water Resources Planning and Management, ASCE, 115 (5), pp. 630-645;; Pasha, M.F.K., Lansey, K.E., Analysis of uncertainty on water distribution hydraulics and water quality (2005) Proceedings of the ASCE World Water and Environmental Resources Congress, May 15-19, 2005, , Anchorage, Alaska, USA;; Rossman, L. A. (2002). Epanet User's Manual, Environmental Protection Agency, EPA, CincinnatiCR Rossman, L.A., Clark, R.M., Grayman, W.M., Modeling chlorine residuals in drinking-water distribution systems (1994) Journal of Environmental Engineering, ASCE, 120 (4), pp. 803-820;; Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M., (2004) Sensitivity analysis in practice: A guide to assessing scientific models, , Wiley, Chichester, England;; Saltelli, A., Chan, K., Scot, E., (2000) Sensitivity analysis, , Wiley, Chichester, England;; Wagner, J.M., Shamir, U., Marks, D.H., Water distribution reliability: Analytical methods (1988) Journal of Water Resources Planning and Management, ASCE, 114 (3), pp. 253-275;; Wagner, J.M., Shamir, U., Marks, D.H., Water distribution reliability: Simulation methods (1988) Journal of Water Resources Planning and Management, ASCE, 114 (3), pp. 276-294;; Xu, C., Goulter, I., Reliability-based optimal design of water distribution networks (1999) Journal of Water Resources Planning and Management, ASCE, 125 (6), pp. 352-362;; Kapelan, Z.S., Savic, D.A., Walters, G.A., Calibration of Water Distribution Hydraulic Models Using a Bayesian-Type Procedure (2007) Journal of Hydraulic Engineering, ASCE, 133 (8), pp. 927-936]]></refworks:cr>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3000">
<title><![CDATA[The Mississippi river: a national resource]]></title>
<refworks:t2><![CDATA[World Environmental and Water Resources Congress 2009: Great Rivers (Proceedings of World Environmental and Water Resources Congress 2009)]]></refworks:t2>
<dc:creator><![CDATA[Bhowmik,Nani G.]]></dc:creator>
<description><![CDATA[The Mississippi River with its tributaries is the third largest river of the world. The river drains about 40 of the continental Unites States. The main stem of the river is about 3,500 km long travelling from Minnesota in the north to the Gulf of Mexico in the south. During this travel the river crosses also through 10 states. The present day river was formed during the last glacial melt, consequently the present day size and shape is too large for the flow and the sediment load. Thus the river valley has been filling up with sediments. For identification and management purposes the river has been divided into two segments, Upper Mississippi River and the Lower Mississippi river. The Upper Mississippi River extends from its headwater to its confluence with the Ohio River near Cairo, Illinois. Upper Mississippi River does not include Missouri River for management purposes even though the Missouri river do come and join the main stem of the river upstream of St. Louis. The river is also the main inland transportation artery of the United States. Over the year the river and its watershed has altered significantly. Currently, the Mississippi River States, the Federal Government and many no-governmental entities are working together to enhance the ecosystem of this great river. © 2009 ASCE.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:volume><![CDATA[342]]></prism:volume> 
<prism:startingPage><![CDATA[6020]]></prism:startingPage>
<prism:endingPage><![CDATA[6027]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 5:51:37 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=3000</link>
<refworks:FD><![CDATA[17 May 2009 through 21 May 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Federal governments]]></refworks:k1>
<refworks:k1><![CDATA[ Gulf of Mexico]]></refworks:k1>
<refworks:k1><![CDATA[ Head waters]]></refworks:k1>
<refworks:k1><![CDATA[ Illinois]]></refworks:k1>
<refworks:k1><![CDATA[ Last glacial]]></refworks:k1>
<refworks:k1><![CDATA[ Minnesota]]></refworks:k1>
<refworks:k1><![CDATA[ Mississippi river]]></refworks:k1>
<refworks:k1><![CDATA[ Missouris]]></refworks:k1>
<refworks:k1><![CDATA[ Ohio River]]></refworks:k1>
<refworks:k1><![CDATA[ River valley]]></refworks:k1>
<refworks:k1><![CDATA[ Sediment loads]]></refworks:k1>
<refworks:k1><![CDATA[ Size and shape]]></refworks:k1>
<refworks:k1><![CDATA[ Upper Mississippi]]></refworks:k1>
<refworks:k1><![CDATA[ Landforms]]></refworks:k1>
<refworks:k1><![CDATA[ Sedimentology]]></refworks:k1>
<refworks:k1><![CDATA[ Water resources]]></refworks:k1>
<refworks:k1><![CDATA[ Rivers]]></refworks:k1>
<refworks:ed><![CDATA[Kansas City, MO]]></refworks:ed>
<refworks:sn><![CDATA[9780784410363 (ISBN)]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey Institute, Natural Resource Sustainability University of Illinois, Urbana-Champaign, IL, United States; Correspondence Address: Bhowmik, N. G.; Illinois State Water Survey Institute, Natural Resource Sustainability University of Illinois, Urbana-Champaign, IL, United States]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:lk><![CDATA[http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=ASCECP000342041036000609000001&idtype=cvips&gifs=yes]]></refworks:lk>
<refworks:do><![CDATA[10.1061/41036(342)609]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[3000]]></refworks:id>
<refworks:cr><![CDATA[Dobney, F. J, Undated, River Engineers on the Middle Mississippi. Superintendent of Documents, U. S. Government Printing Office, Washington, DCCR Bhowmik, N. G., A. G. Buck, S. A. Changnon, S. A. Dalton, R. H. Durgunoglu, S. M. Demissie, A. R. Juhl, h. V. Knapp, K. E. Kunkel, S. A. McConkey, R. W. Scott, K. P. Singh, Ta-Wei D. Soong, R. E. Sparks, A. P. Visocky, D. R. Vonnahme, and W. M. Wendland. (1995). The Great Flood on the Mississippi River in Illinois. Illinois State Water Survey Miscellaneous Publication 151, 165 p, 2nd PrintingNO Conference code: 77351]]></refworks:cr>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2999">
<title><![CDATA[Nontornadic convective wind fatalities in the United States [Article in press]]]></title>
<dc:creator><![CDATA[Black,Alan W.]]></dc:creator>
<dc:creator><![CDATA[ Ashley,W. S.]]></dc:creator>
<description><![CDATA[A database was compiled for the period 1977-2007 to assess the threat to life in the conterminous United States from nontornadic convective wind events. This study reveals the number of fatalities from these wind storms, their causes, and their unique spatial distributions. Nontornadic convective wind fatalities occur most frequently outdoors, in vehicles including aircraft, or while boating. Fatalities are most common in the Great Lakes and Northeast, with fewer fatalities observed in the central United States despite the climatological peak in severe thunderstorms in this region. Differences in fatality locations between tornadoes and nontornadic convective wind events highlight the unique combination of physical and social vulnerabilities involved in these deaths. Understanding these vulnerabilities is important to future reduction of nontornadic convective wind fatalities. © 2009 Springer Science+Business Media B.V.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Natural Hazards]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:startingPage><![CDATA[1]]></prism:startingPage>
<prism:endingPage><![CDATA[12]]></prism:endingPage> 
<refworks:created><![CDATA[11/17/2009 5:48:35 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2999</link>
<refworks:FD><![CDATA[Available online October 26, 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Fatalities]]></refworks:k1>
<refworks:k1><![CDATA[ Nontornadic convective wind]]></refworks:k1>
<refworks:k1><![CDATA[ Thunderstorm]]></refworks:k1>
<refworks:no><![CDATA[Article in Press]]></refworks:no>
<refworks:sn><![CDATA[0921030X]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Midwestern Regional Climate Center/Illinois State Water Survey, Institute of Natural Resource Sustai, University of Illinois Urbana-Champaign, 2204 Griffith Drive, Champaign, 61820-7495, United States; Affiliation: Meteorology Program, Department of Geography, Northern Illinois University, #118 Davis Hall, DeKalb, 60115, United States; Correspondence Address: Black, A.W.; Midwestern Regional Climate Center/Illinois State Water Survey, Institute of Natural Resource Sustai, University of Illinois Urbana-Champaign, 2204 Griffith Drive, Champaign, 61820-7495, IL, United States; email: awblack@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s11069-009-9472-2]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 November 2009]]></refworks:rd>
<refworks:id><![CDATA[2999]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2997">
<title><![CDATA[Potential effects of climate and emissions changes on surface ozone in the Chicago area]]></title>
<dc:creator><![CDATA[Lin,Jin-Tai]]></dc:creator>
<dc:creator><![CDATA[ Wuebbles,Donald J.]]></dc:creator>
<dc:creator><![CDATA[ Huang,Ho-Chun]]></dc:creator>
<dc:creator><![CDATA[ Tao,Zhining]]></dc:creator>
<dc:creator><![CDATA[ Caughey,Michael]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<dc:creator><![CDATA[ Zhu,Jin-Hong]]></dc:creator>
<dc:creator><![CDATA[ Holloway,Tracey]]></dc:creator>
<description><![CDATA[Future changes in climate and precursor emissions will likely have important consequences on ground-level ozone concentrations for the City of Chicago and its surrounding suburban/rural areas. Here we use a regional climate–air quality modeling system to evaluate the combined and individual effects of climate warming (and resulting biogenic emissions increases) and anthropogenic emissions perturbations from 1996–2000 to 2048–2052 and 2095–2099 in this region. Two scenarios are considered, including A1FI (higher warming with increasing anthropogenic emissions) and B1 (less warming with reduced anthropogenic emissions). Relative to 1996–2000, projected changes in climate and anthropogenic emissions together lead to little ozone change for the City of Chicago under A1FI but 5.0–7.8 ppb increases under B1 by 2048–2052 and 2095–2099. For A1FI, the decreasing ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx) reduces ozone concentrations over Chicago, despite the increasing emissions for both NOx and VOCs. Averaged over the Chicago urban and surrounding suburban area, however, surface ozone increase 2.3–7.1 ppb under A1FI by 2095–2099. Additionally, the seasonal ozone variation is projected to increase 84–127% under A1FI but decrease 23–30% under B1 over the Chicago area. By comparison, projected climate warming alone increases the surface ozone by 2.1–8.7 ppb and its seasonal variation by 22–89% over the Chicago area from 1996–2000 to 2095–2099 under both scenarios. Therefore, effective emission regulation and climate considerations are both important to pollution mitigation in the Chicago area.]]></description>
<prism:publicationName><![CDATA[Journal of Great Lakes Research]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:volume><![CDATA[In Press, Corrected Proof]]></prism:volume> 
<refworks:created><![CDATA[10/30/2009 11:01:49 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[11/17/2009 6:25:25 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2997</link>
<refworks:FD><![CDATA[Available online 25 October 2009.]]></refworks:FD>
<refworks:k1><![CDATA[ Climate]]></refworks:k1>
<refworks:k1><![CDATA[ Emissions]]></refworks:k1>
<refworks:k1><![CDATA[ Change]]></refworks:k1>
<refworks:k1><![CDATA[ Ozone]]></refworks:k1>
<refworks:k1><![CDATA[ Chicago]]></refworks:k1>
<refworks:sn><![CDATA[0380-1330]]></refworks:sn>
<refworks:do><![CDATA[DOI: 10.1016/j.jglr.2009.09.004]]></refworks:do>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:id><![CDATA[2997]]></refworks:id>
<refworks:ul><![CDATA[http://www.sciencedirect.com/science/article/B984D-4XJ13XJ-1/2/4fedc1c092617ea36e89075506988362]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2998">
<title><![CDATA[The response of Great Lakes water levels to future climate scenarios with an emphasis on Lake Michigan-Huron]]></title>
<dc:creator><![CDATA[Angel,James R.]]></dc:creator>
<dc:creator><![CDATA[ Kunkel,Kenneth E.]]></dc:creator>
<description><![CDATA[Future climate change and its impact on Lake Michigan is an important issue for water supply planning in Illinois. To estimate possible future levels of the Great Lakes due to climate change, the output of 565 model runs from 23 Global Climate Models were applied to a lake-level model developed by the Great Lakes Environmental Research Laboratory (GLERL). In this study, three future emission scenarios were considered: the B1, A1B, and A2 emission scenarios representing relatively low, moderate, and high emissions, respectively. The results showed that the A2 emission scenario yielded the largest changes in lake levels of the three emission scenarios. Of the three periods examined, lake levels in 2080–2094 exhibited the largest changes. The response of Lake Superior was the smallest of the Great Lakes, while lakes Michigan-Huron, Erie, and Ontario were similar in their response over time and between emission scenarios. For Lake Michigan-Huron, the median changes in lake levels at 2080–2094 were − 0.25, − 0.28, and − 0.41 m for the B1, A1B, and A2 emission scenarios, respectively. However, the range in lake levels was considerable. The wide range of results is due to the differences in emission scenarios and the uncertainty in the model simulations. Selecting model simulations based on their historical performance does little to reduce the uncertainty. The wide range of lake-level changes found here make it difficult to envision the level of impacts that change in future lake levels would cause.]]></description>
<prism:publicationName><![CDATA[Journal of Great Lakes Research]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:volume><![CDATA[In Press, Corrected Proof]]></prism:volume> 
<refworks:created><![CDATA[10/30/2009 11:01:49 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/30/2009 11:05:15 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2998</link>
<refworks:FD><![CDATA[Available online 21 October 2009.]]></refworks:FD>
<refworks:k1><![CDATA[ Climate change]]></refworks:k1>
<refworks:k1><![CDATA[ Great Lakes]]></refworks:k1>
<refworks:k1><![CDATA[ Water levels]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply]]></refworks:k1>
<refworks:sn><![CDATA[0380-1330]]></refworks:sn>
<refworks:do><![CDATA[DOI: 10.1016/j.jglr.2009.09.006]]></refworks:do>
<refworks:id><![CDATA[2998]]></refworks:id>
<refworks:ul><![CDATA[http://www.sciencedirect.com/science/article/B984D-4XH56H2-1/2/31d6a752279b2667e1f77d503bd20a23]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2996">
<title><![CDATA[China summer precipitation simulations using an optimal ensemble of cumulus schemes]]></title>
<refworks:t3><![CDATA[Front. Earth Sci. China (China)]]></refworks:t3>
<dc:creator><![CDATA[Liu,Shuyan]]></dc:creator>
<dc:creator><![CDATA[ Gao,Wei]]></dc:creator>
<dc:creator><![CDATA[ Xu,Min]]></dc:creator>
<dc:creator><![CDATA[ Wang,Xueyuan]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<description><![CDATA[RegCM3 (REGional Climate Model) simulations of precipitation in China in 1991 and 1998 are very sensitive to the cumulus parameterization. Among the four schemes available, none has superior skills over the whole of China, but each captures certain observed signals in distinct regions. The Grell scheme with the Fritsch-Chappell closure produces the smallest biases over the North; the Grell scheme with the Arakawa-Schubert closure performs the best over the southeast of 100E; the Anthes-Kuo scheme is superior over the northeast; and the Emanuel scheme is more realistic over the southwest of 100E and along the Yangtze River Basin. These differences indicate a strong degree of independence and complementarity between the parameterizations. As such, an ensemble is developed from the four schemes, whose relative contributions or weights are optimized locally to yield overall minimum root-mean-square errors from observed daily precipitation. The skill gain is evaluated by applying the identical distribution of the weights in a different period. It is shown that the ensemble always produces gross biases that are smaller than the individual schemes in both 1991 and 1998. The ensemble, however, cannot eliminate the large rainfall deficits over the southwest of 100E and along the Yangtze River Basin that are systematic across all schemes. Further improve-ments can be made by a super-ensemble based on more cumulus schemes and/or multiple models.]]></description>
<dc:publisher><![CDATA[Springer]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Frontiers of Earth Science in China]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[3]]></prism:volume> 
<prism:startingPage><![CDATA[248]]></prism:startingPage>
<prism:endingPage><![CDATA[57]]></prism:endingPage> 
<refworks:created><![CDATA[10/30/2009 10:54:34 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/30/2009 10:57:39 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2996</link>
<refworks:FD><![CDATA[June 2009; available online March 22, 2009]]></refworks:FD>
<refworks:k1><![CDATA[ atmospheric humidity]]></refworks:k1>
<refworks:k1><![CDATA[ atmospheric precipitation]]></refworks:k1>
<refworks:k1><![CDATA[ mean square error methods]]></refworks:k1>
<refworks:k1><![CDATA[ rivers]]></refworks:k1>
<refworks:no><![CDATA[M1: Copyright 2009, The Institution of Engineering and Technology; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined]]></refworks:no>
<refworks:pp><![CDATA[China]]></refworks:pp>
<refworks:sn><![CDATA[1673-7385]]></refworks:sn>
<refworks:ad><![CDATA[Div. of Illinois State Water Survey, Univ. of Illinois, Champaign, IL, USA]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org/10.1007/s11707-009-0022-8]]></refworks:lk>
<refworks:id><![CDATA[2996]]></refworks:id>
<refworks:u1><![CDATA[10912917]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2995">
<title><![CDATA[Tornado losses in the United states]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<description><![CDATA[Insurance data for tornado damages during 1949-2006 revealed 793 tornado events that each caused 1 million in losses. The average annual loss of these tornado catastrophes is 982 million, an amount that greatly exceeds the existing average of 462 million based on estimates from government records. Tornado losses typically occurred in only one state but when tornadoes occurred with floods or hurricanes, the losses occurred in four or five states. Tornado catastrophes and losses were most frequent in Texas, Oklahoma, and Kansas, and relatively frequent in many Midwestern states. The temporal distribution of tornado catastrophes revealed large interannual variability with a few years of major loss and many years with none. Tornado-only catastrophes and their losses had flat trends for 1949-2006 but trends were upward for cases of tornadoes with floods and cases when tornadoes occurred with hurricanes. These result from upward trends in flooding across the nation and the tornado-hurricane temporal increase results from time-related increases in hurricane-prone storm conditions and from coastal society's growing vulnerability to storm damages. 2009 ASCE.]]></description>
<dc:publisher><![CDATA[American Society of Civil Engineers]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Natural Hazards Review]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[4]]></prism:number>
<prism:volume><![CDATA[10]]></prism:volume> 
<prism:startingPage><![CDATA[145]]></prism:startingPage>
<prism:endingPage><![CDATA[150]]></prism:endingPage> 
<refworks:created><![CDATA[10/30/2009 10:54:33 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/30/2009 11:00:19 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2995</link>
<refworks:FD><![CDATA[November 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Tornadoes]]></refworks:k1>
<refworks:k1><![CDATA[ Damage detection]]></refworks:k1>
<refworks:k1><![CDATA[ Decision making]]></refworks:k1>
<refworks:k1><![CDATA[ Disasters]]></refworks:k1>
<refworks:k1><![CDATA[ Floods]]></refworks:k1>
<refworks:k1><![CDATA[ Hurricanes]]></refworks:k1>
<refworks:k1><![CDATA[ Risk analysis]]></refworks:k1>
<refworks:k1><![CDATA[ Risk management]]></refworks:k1>
<refworks:k1><![CDATA[ Storms]]></refworks:k1>
<refworks:pp><![CDATA[1801 Alexander Graham Bell Drive, Reston, VA 20191-4400, United States]]></refworks:pp>
<refworks:sn><![CDATA[15276988]]></refworks:sn>
<refworks:ad><![CDATA[University of Illinois, Illinois State Water Survey, 2204 Griffith Dr., Champaign, IL 61820, United States]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org/10.1061/(ASCE)1527-6988(2009)10:4(145)]]></refworks:lk>
<refworks:id><![CDATA[2995]]></refworks:id>
<refworks:u1><![CDATA[20094312399980]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
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<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2994">
<title><![CDATA[Anthropogenic-induced changes in twenty-first century summertime hydroclimatology of the Northeastern US [article in press]]]></title>
<dc:creator><![CDATA[Anderson,B. T.]]></dc:creator>
<dc:creator><![CDATA[ Hayhoe,K.]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<description><![CDATA[Potential changes in summertime hydroclimatology over the northeastern (NE) region of the USA induced by increases in greenhouse gas (GHG) concentrations are investigated using a state-of-the-art regional climate modeling system. Results for a higher emissions scenario illustrate changes that may occur if dependence on fossil fuels continues over the coming century. Summertime precipitation is projected to decrease across much of the central NE, but increase over the southernmost and northernmost portions of the domain. Evaporation is expected to increase across the entire domain. The balance between these two results in a decrease in soil moisture content across most of the domain (by approximately 10 mm) and an increase in the summertime soil-moisture depletion rate (by approximately 10 mm/month). At the same time, an increase in both atmospheric near-surface specific and saturation specific humidity is projected, resulting in an increase in relative humidity across the southern portion of the domain, with slight decreases over the northern portion. Combined with an average increase in summer temperatures of 3.5°C, the projected increase in relative humidity results in a marked increase in the average daily maximum heat index for the region on the order of 3.9°C, as well as a 350-400% increase in the number of days with heat index values exceeding 32.2°C (90°F)-the level of "extreme caution". Taken together, these high-resolution, dynamically-generated projections confirm the potential for significant summertime climate change impacts on the NE over the coming century as suggested by previous studies. © 2009 Springer Science+Business Media B.V.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Climatic Change]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:startingPage><![CDATA[1]]></prism:startingPage>
<prism:endingPage><![CDATA[21]]></prism:endingPage> 
<refworks:created><![CDATA[10/29/2009 8:48:07 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/29/2009 8:49:47 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2994</link>
<refworks:FD><![CDATA[Available online: October 08, 2009]]></refworks:FD>
<refworks:no><![CDATA[Article in Press]]></refworks:no>
<refworks:sn><![CDATA[01650009]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Department of Geography and Environment, Boston University, Boston, 02215, United States; Affiliation: Department of Geosciences, Texas Tech University, Lubbock, 70409, United States; Affiliation: Illinois Department of Natural Resources, Illinois State Water Survey, University of Illinois, Urbana-Champaign, United States; Correspondence Address: Anderson, B.T.; Department of Geography and Environment, Boston University, Boston, 02215, MA, United States; email: brucea@bu.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s10584-009-9674-3]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[29 October 2009]]></refworks:rd>
<refworks:id><![CDATA[2994]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com/inward/record.url?eid=2-s2.0-70349744306&partnerID=40]]></refworks:ul>
<refworks:an><![CDATA[CODEN: CLCHD]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2993">
<title><![CDATA[Lake Pittsfield Illinois National Monitoring  Program project]]></title>
<dc:creator><![CDATA[White,William P.]]></dc:creator>
<dc:creator><![CDATA[ Beardsley,John]]></dc:creator>
<dc:creator><![CDATA[ Devotta,Denise]]></dc:creator>
<dc:creator><![CDATA[ Tomkins,Scott]]></dc:creator>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[NCSU Water Quality Group Newsletter]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Magazine Article]]></refworks:rwtype>
<prism:volume><![CDATA[129]]></prism:volume> 
<refworks:created><![CDATA[10/27/2009 4:24:11 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/30/2009 11:07:20 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2993</link>
<refworks:FD><![CDATA[September 2008]]></refworks:FD>
<refworks:k1><![CDATA[ Lake Pittsfield]]></refworks:k1>
<refworks:k1><![CDATA[ Sediment deposition]]></refworks:k1>
<refworks:k1><![CDATA[ Blue Creek watershed]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply]]></refworks:k1>
<refworks:sn><![CDATA[1062-9149]]></refworks:sn>
<refworks:id><![CDATA[2993]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2992">
<title><![CDATA[Watershed assessment]]></title>
<dc:creator><![CDATA[White,William P.]]></dc:creator>
<dc:creator><![CDATA[ Phillips,Andrew C.]]></dc:creator>
<dc:creator><![CDATA[ Devotta,Denise]]></dc:creator>
<description><![CDATA[Describes efforts to evaluate the Illinois River watershed, including tributary streams with a goal of restoring the Illinois River.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Outdoor Illinois]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Magazine Article]]></refworks:rwtype>
<prism:number><![CDATA[3]]></prism:number>
<prism:volume><![CDATA[17]]></prism:volume> 
<refworks:created><![CDATA[10/27/2009 4:20:42 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/30/2009 11:07:44 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2992</link>
<refworks:FD><![CDATA[March 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Restoration]]></refworks:k1>
<refworks:k1><![CDATA[ Illinois River]]></refworks:k1>
<refworks:k1><![CDATA[ Degraded streams]]></refworks:k1>
<refworks:k1><![CDATA[ Erosion]]></refworks:k1>
<refworks:k1><![CDATA[ Sedimentation]]></refworks:k1>
<refworks:no><![CDATA[photos by Jon Rodsater]]></refworks:no>
<refworks:id><![CDATA[2992]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2991">
<title><![CDATA[The development of knowledge about the climate of Illinois]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Transactions of the Illinois State Academy of Science]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[3-4]]></prism:number>
<prism:volume><![CDATA[101]]></prism:volume> 
<prism:startingPage><![CDATA[205]]></prism:startingPage>
<prism:endingPage><![CDATA[216]]></prism:endingPage> 
<refworks:created><![CDATA[10/22/2009 8:38:28 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:35 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2991</link>
<refworks:k1><![CDATA[ climate]]></refworks:k1>
<refworks:k1><![CDATA[ weather stations]]></refworks:k1>
<refworks:k1><![CDATA[ climate data]]></refworks:k1>
<refworks:k1><![CDATA[ weather monitoring, history]]></refworks:k1>
<refworks:ds><![CDATA[Hand entered from source publication]]></refworks:ds>
<refworks:id><![CDATA[2991]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2990">
<title><![CDATA[Record hail loss in Illinois and adjacent states]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Transactions of the Illinois State Academy of Science]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[3-4]]></prism:number>
<prism:volume><![CDATA[101]]></prism:volume> 
<prism:startingPage><![CDATA[201]]></prism:startingPage>
<prism:endingPage><![CDATA[204]]></prism:endingPage> 
<refworks:created><![CDATA[10/22/2009 8:36:11 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:35 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2990</link>
<refworks:k1><![CDATA[ hail]]></refworks:k1>
<refworks:k1><![CDATA[ economic impact]]></refworks:k1>
<refworks:ds><![CDATA[Hand entered from source publication]]></refworks:ds>
<refworks:id><![CDATA[2990]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2989">
<title><![CDATA[Record heavy rains in August 2007: cause, magnitude, and impacts]]></title>
<dc:creator><![CDATA[Angel,James R.]]></dc:creator>
<dc:creator><![CDATA[ Changnon,Stanley A.]]></dc:creator>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Transactions of the Illinois State Academy of Science]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[3-4]]></prism:number>
<prism:volume><![CDATA[101]]></prism:volume> 
<prism:startingPage><![CDATA[187]]></prism:startingPage>
<prism:endingPage><![CDATA[199]]></prism:endingPage> 
<refworks:created><![CDATA[10/8/2009 10:01:55 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:34 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2989</link>
<refworks:k1><![CDATA[ rainfall]]></refworks:k1>
<refworks:k1><![CDATA[ economic impact]]></refworks:k1>
<refworks:id><![CDATA[2989]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2988">
<title><![CDATA[Storm event and continuous modeling of an illinois watershed to evaluate surface water supplies]]></title>
<refworks:t2><![CDATA[2005 ASAE Annual International Meeting]]></refworks:t2>
<dc:creator><![CDATA[Borah,Deva K.]]></dc:creator>
<dc:creator><![CDATA[ Krug,Edward C.]]></dc:creator>
<dc:creator><![CDATA[ Bera,Maitreyee]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<dc:creator><![CDATA[ Arnold,J. G.]]></dc:creator>
<description><![CDATA[Based on recent reviews of leading watershed-scale hydrologie and nonpoint-source pollution models, the long-term continuous model SWAT was selected to enhance with storm event simulation algorithms from a storm event model to be used as a source-water protection and assessment tool for small public water supply systems. This enhanced SWA T will simulate hydrology, soil erosion, and transport of sediment and agrochemicals during storm events with short time intervals (minutes or hours) to capture rapid changes, especially during severe events causing most of the environmental damages, in addition to long-term simulations with longer time intervals (days, months, and years) while studying long-term impacts. The 8,400 km2 Little Wabash River watershed in Illinois was selected for this study because of its favorable small drinking water supply and watershed attributes. Using multi-year period (1995-2002) of observed precipitation, stream flow, and concentrations of sediment and water quality data, the continuous model is being calibrated and validated. Established statistical indicators (coefficient of determination and Nash-Sutcliffe coefficient) are used to measure and improve model predictions. Using storm event rainfall and flow data at smaller (15 minute) time intervals, the storm event hydrology model is also being calibrated and validated. The calibrated and validated model will be used for both long-term and storm event water quantity and quality evaluations throughout the watershed, including at intakes of small public water supply systems under existing and alternative land use and management practices. Flow calibration results at an upstream station reveal that the storm event hydrology model with less parameter predicts more accurate flows, especially peak flows, than the continuous daily model.]]></description>
<dc:date><![CDATA[2005]]></dc:date>
<prism:publicationName><![CDATA[2005 ASAE Annual International Meeting]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<refworks:created><![CDATA[10/8/2009 8:26:59 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:48:29 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2988</link>
<refworks:FD><![CDATA[17 July 2005 through 20 July 2005]]></refworks:FD>
<refworks:k1><![CDATA[ Continuous model]]></refworks:k1>
<refworks:k1><![CDATA[ Hydrology]]></refworks:k1>
<refworks:k1><![CDATA[ Storm event model]]></refworks:k1>
<refworks:k1><![CDATA[ SWAT]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply]]></refworks:k1>
<refworks:no><![CDATA[Sponsors: ASAE; Conference code: 77080]]></refworks:no>
<refworks:ed><![CDATA[Tampa, FL]]></refworks:ed>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey, 2204 Griffith Dr, Champaign, IL 61820, United States; Affiliation: USDA-ARS, 808 East Blackland Rd, Temple, TX 76502, United States; Correspondence Address: Borah, D. K.; Illinois State Water Survey, 2204 Griffith Dr, Champaign, IL 61820, United States]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[8 October 2009]]></refworks:rd>
<refworks:id><![CDATA[2988]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com/inward/record.url?eid=2-s2.0-70349127757&partnerID=40]]></refworks:ul>
<refworks:cr><![CDATA[Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., Large area hydrologic modeling and assessment part I: Model development (1998) Journal of the American Water Resources Association, 34 (1), pp. 73-89;; Barker, B., Carlisle, J.B., Nyberg, R., (1967) Little Wabash River Basin Study: A Comprehensive Plan for Water Resource Development., , Illinois Department of Public Works and Buildings, Division of Waterways, Springfield, IL;; Borah, D.K., Runoff simulation model for small watersheds (1989) Trans. ASAE, 32 (3), pp. 881-886;; Borah, D.K., Bera, M., Watershed-scale hydrologie and nonpoint-source pollution models: Review of mathematical Bases (2003) Trans. ASAE, 46 (6), pp. 1553-1566;; Borah, D.K., Bera, M., Watershed scale hydrologie and nonpoint source pollution models: Review of applications (2004) Trans. ASAE, 47 (3), pp. 789-803;; Borah, D.K., Prasad, S.B., Alonso, C.V., Kinematic wave routing incorporating shock fitting (1980) Water Resources Research, 16 (3), pp. 529-541;; Borah, D.K., Xia, R., Bera, M., DWSM-A dynamic watershed simulation model. Chapter 5 (2002) Mathematical Models of Small Watershed Hydrology and Applications, pp. 113-166. , V.P. Singh and D.K. Frevert eds. Water Resources Publications, LLC, Highlands Ranch, CO;; Borah, D.K., Bera, M., Shaw, S., Water, sediment, nutrient, and pesticide measurements in an agricultural watershed in Illinois during storm events (2003) Trans. ASAE, 46 (3), pp. 657-674;; Borah, D.K., Bera, M., Xia, R., Storm event flow and sediment simulations in agricultural watersheds using DWSM (2004) Trans. ASAE, 47 (5), pp. 1539-1559;; (2002) Source Water Assessment of the Public Water Supply Well for the Pit Restaurant, Laurel, Delaware., , Delaware Division of Water Resources. PWS ID: DE00A0335. Division of Water Resources - Source Water Assessment and Protection Program, Delaware Department of Natural Resources and Environmental Control, Dover, DE;; Doering, O.C., Diaz-Hermelo, F., Howard, C., Heimlich, R., Hitzhusen, F., Kazmierczak, R., Lee, J., Ribaudo, M., Evaluation of economic costs and benefits of methods for reducing nutrient loads to the Gulf of Mexico (1999) Task Group 6 Report, Gulf of Mexico Hypoxia Assessment. White House Committee on Environment and Natural Resources, , Washington, D.C;; Eimers, J.L., Weaver, J.C., Terziotti, S., Midget, R.W., (2000) Methods of Rating Unsaturated Zone and Watershed Characteristics of Public Water Supplies in North Carolina., pp. 99-4283. , WaterResources Investigations Report USGS, Raleigh, NC;; Fitzpatrick, W.P., Bogner, W.C., Bhowmik, N.G., (1985) Sedimentation Investigation of Lake Springfield, Springfield, Illinois., , Contract Report 363, Illinois State Water Survey, Champaign, IL;; Fitzpatrick, W.P., Bogner, W.C., Bhowmik, N.G., (1987) Sedimentation and Hydrologic Processes in Lake Decatur and Its Watershed., , Report of Investigation 107, Illinois State Water Survey, Champaign, IL;; Goolsby, D.A., Battaglin, W.A., Lawrence, G.B., Artz, R.S., Aulenbach, B.T., Hooper, R.P., Keeney, D.R., Stensland, G.J., Flux and sources of nutrients in the MississippiAtchafalaya River Basin (1999) Task Group 3 Report, Gulf of Mexico Hypoxia Assessment., , White House Committee on Environment and Natural Resources, Washington, D.C;; Green, W.H., Ampt, G.A., Studies on soil physics (1911) J. Agrie. Sci., 4 (1), pp. 1-24;; Hite, R.L., Matson, M.R., Bickers, C.A., (1993) An Intensive Survey of the Little Wabash River Basin. Summer 1989., , IEPA/WPC/92-053. Illinois Environmental Protection Agency, Springfield, IL;; Hjelmfelt Jr., A.T., Cassidy, J.J., (1975) Hydrology for Engineers and Planners., , Iowa State University Press, Ames, IA;; (2001) Critical Trends in Illinois Ecosystems. Critical Trend Assessment Program, Illinois, , IDNR. Department of Natural Resources, Springfield, IL;; (2003) An Intensive Survey of the Little Wabash River Basin and Lower Wabash Tributaries, 1996/1999: Data Summary., , IEPA. IEPA/BOW/02-023. Illinois Environmental Protection Agency, Springfield, IL;; (2004) Illinois 2004 Section 303(d) List., , http://www.epa.state.il.us/water/tmdl/303dlist.html, IEPA. Bureau of Water, Illinois Environmental Protection Agency, Springfield, Illinois. Available at: Accessed December 29, 2004;; Keefer, L., Demissie, M., Mayer, D., Nichols, K., Shaw, S., (1996) Watershed Monitoring and Land Use Evaluation for the Vermilion River Watershed., , Illinois State Water Survey Miscellaneous Publication 176. Illinois State Water Survey, Champaign, IL;; King, K.W., Arnold, J.G., Bingner, R.L., Comparison of Green-Ampt and curve number methods on Goodwin Creek watershed using SWAT (1999) Trans. ASAE, 42 (4), pp. 919-925;; Liang, X.-Z., Li, L., Kunkel, K.E., Ting, M., Wang, J.X.L., Regional climate model simulation of U.S. precipitation during 1982-2002. Part 1: Annual cycle (2004) J. Climate, 17, pp. 3510-3528;; Lighthill, M.J., Whitham, C.B., On kinematic waves, 1, flood movement in long rivers (1955) Proc. Royal Society Ser. A, 229, pp. 281-316;; Luzio, M.D., Srinivasan, R., Arnold, J.G., Integration of watershed tools and SWAT model into BASINS (2002) J. AWRA, 38 (4), pp. 1127-1141;; Mitchell, J.K., McIsaac, G.F., Walker, S.E., Hirschi, M.C., Nitrate in river and subsurface drainage flows from an east central Illinois watershed (2000) Transactions of the American Society of Agricultural Engineers, 43 (2), pp. 337-342;; Nash, J.E., Sutcliffe, J.V., River flow forecasting through conceptual models: Part 1. A discussion of principles (1970) J. Hydrology, 10 (3), pp. 282-290;; Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Srinivasan, R., Williams, J.R., (2002) Soil and Water Assessment Tool User's Manual Version 2000., pp. 02-06. , GSWRL Report 02-02, BRC Report;; Saleh, A., Du, B., Evaluation of SWAT and HSPF within BASINS program for the Upper North Bosque River watershed in Central Texas (2004) Trans. ASAE, 47 (4), pp. 1039-1049;; (1972) National Engineering Handbook., , SCS. Hydrology. Section 4 in USDA Soil Conservation Service, Washington, D.C;; Shasteen, S.P., Matson, M.R., King, M.M., Levesque, J.M., Minton, G.L., Tripp, S.J., Muir, D.B., (2003) An Intensive Survey of the Little Wabash River Basin and the Lower Wabash Tributaries: Data Summary. Summer 1996 and 1999., , IEPA/BOW/02-023. Illinois Environmental Protection Agency, Springfield, IL;; Shasteen, S.P., Hite, R.L., Matson, M.R., King, M.M., Lesesque, J.M., Minton, G.L., Muir, D.B., (2002) An Intensive Survey of the Skillet Fork Basin: Data Summary. Summer 1998., , IEPA/BOW/00-003. Illinois Environmental Protection Agency, Springfield, IL;; Simons, D.B., Li, R.M., Stevens, M.A., (1975) Development of Models for Predicting Water and Sediment Routing and Yield from Storms on Small Watersheds., , Colorado State University, Fort Collins, CO;; Sloan, P.G., Moore, I.D., Coltharp, G.B., Eigel, J.D., (1983) Modeling Surface and Subsurface Stormflow on Steeply-sloping Forested Watersheds., , Water Resources Institute Report No. 142. University of Kentucky, Lexington, KY;; Smith, R.E., Parlange, J.Y., A parameter-efficient hydrologie infiltration model (1978) Water Resources Research, 14 (3), pp. 533-538;; (1948) Water Resources of Southern Illinois., , State Water Survey Staff. Joint Committee on Southern Illinois, University of Illinois, Urbana-Champaign, IL;; (1979) Final Environmental Impact Statement: Louisville Lake, Little Wabash River Basin, Illinois., , U.S. Army Corps of Engineers. U.S. Army Corps of Engineers, Louisville District, Louisville, KY;; U.S. Bureau of Reclamation (1949) Flood Hydrology, Pt. 6, in Water Studies,., 4. , Flood routing. Chapter 6.10 in U.S. Bureau of Reclamation, Washington, D.C;; (2003) Source Water Assessment Program for Illinois., , http://il.water.usqs.gov/factsheets/, USGS and IEPA. Available at;; Van Liew, M.W., Arnold, J.G., Garbrecht, J.D., Hydrologic simulation on agricultural watersheds: Choosing between two models (2003) Trans. ASAE, 46 (6), pp. 1539-1551;; Warner, K.L., (2000) Analysis of Nutrients, Selected Inorganic Constituents, and Trace Elements in Water from Illinois Community-Supply Wells, 1984-91., , Water-Resources Investigations Report 99-4152. USGS, Urbana, IL]]></refworks:cr>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2987">
<title><![CDATA[Storm event and continuous modeling of an Illinois watershed to evaluate surface water supplies]]></title>
<refworks:t2><![CDATA[2005 ASAE Annual International Meeting, July 17, 2005 - July 20]]></refworks:t2>
<dc:creator><![CDATA[Borah,Deva K.]]></dc:creator>
<dc:creator><![CDATA[ Krug,Edward C.]]></dc:creator>
<dc:creator><![CDATA[ Bera,Maitreyee]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<dc:creator><![CDATA[ Arnold,J. G.]]></dc:creator>
<description><![CDATA[Based on recent reviews of leading watershed-scale hydrologie and nonpoint-source pollution models, the long-term continuous model SWAT was selected to enhance with storm event simulation algorithms from a storm event model to be used as a source-water protection and assessment tool for small public water supply systems. This enhanced SWA T will simulate hydrology, soil erosion, and transport of sediment and agrochemicals during storm events with short time intervals (minutes or hours) to capture rapid changes, especially during severe events causing most of the environmental damages, in addition to long-term simulations with longer time intervals (days, months, and years) while studying long-term impacts. The 8,400 km2 Little Wabash River watershed in Illinois was selected for this study because of its favorable small drinking water supply and watershed attributes. Using multi-year period (1995-2002) of observed precipitation, stream flow, and concentrations of sediment and water quality data, the continuous model is being calibrated and validated. Established statistical indicators (coefficient of determination and Nash-Sutcliffe coefficient) are used to measure and improve model predictions. Using storm event rainfall and flow data at smaller (15 minute) time intervals, the storm event hydrology model is also being calibrated and validated. The calibrated and validated model will be used for both long-term and storm event water quantity and quality evaluations throughout the watershed, including at intakes of small public water supply systems under existing and alternative land use and management practices. Flow calibration results at an upstream station reveal that the storm event hydrology model with less parameter predicts more accurate flows, especially peak flows, than the continuous daily model.]]></description>
<dc:publisher><![CDATA[American Society of Agricultural and Biological Engineers]]></dc:publisher>
<dc:date><![CDATA[2005]]></dc:date>
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:startingPage><![CDATA[ASAE]]></prism:startingPage>
<refworks:created><![CDATA[10/2/2009 8:43:26 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:48:29 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2987</link>
<refworks:FD><![CDATA[July 17 - July 20 2005]]></refworks:FD>
<refworks:k1><![CDATA[ Hydraulic models]]></refworks:k1>
<refworks:k1><![CDATA[ Agricultural chemicals]]></refworks:k1>
<refworks:k1><![CDATA[ Erosion]]></refworks:k1>
<refworks:k1><![CDATA[ Land use]]></refworks:k1>
<refworks:k1><![CDATA[ Landforms]]></refworks:k1>
<refworks:k1><![CDATA[ Potable water]]></refworks:k1>
<refworks:k1><![CDATA[ Quality control]]></refworks:k1>
<refworks:k1><![CDATA[ Sedimentology]]></refworks:k1>
<refworks:k1><![CDATA[ Simulators]]></refworks:k1>
<refworks:k1><![CDATA[ Storms]]></refworks:k1>
<refworks:k1><![CDATA[ Stream flow]]></refworks:k1>
<refworks:k1><![CDATA[ Surface waters]]></refworks:k1>
<refworks:k1><![CDATA[ Water piping systems]]></refworks:k1>
<refworks:k1><![CDATA[ Water pollution]]></refworks:k1>
<refworks:k1><![CDATA[ Water quality]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply]]></refworks:k1>
<refworks:k1><![CDATA[ Water supply systems]]></refworks:k1>
<refworks:k1><![CDATA[ Watersheds]]></refworks:k1>
<refworks:pp><![CDATA[Tampa, FL, United states]]></refworks:pp>
<refworks:ad><![CDATA[Illinois State Water Survey, 2204 Griffith Dr, Champaign, IL 61820, United States]]></refworks:ad>
<refworks:ds><![CDATA[Engineering Village, Compendex]]></refworks:ds>
<refworks:id><![CDATA[2987]]></refworks:id>
<refworks:u1><![CDATA[20093912332385]]></refworks:u1>
<refworks:ul><![CDATA[http://www.isws.illinois.edu/iswsdocs/journals/ASAE052153.pdf]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2986">
<title><![CDATA[Charging Properties of cassiterite (alpha-SnO2) surfaces in NaCl and RbCl ionic media]]></title>
<dc:creator><![CDATA[Rosenqvist,Joergen]]></dc:creator>
<dc:creator><![CDATA[ Machesky,Michael L.]]></dc:creator>
<dc:creator><![CDATA[ Vlcek,Lukas]]></dc:creator>
<dc:creator><![CDATA[ Cummings,Peter T.]]></dc:creator>
<dc:creator><![CDATA[ Wesolowski,David J.]]></dc:creator>
<description><![CDATA[The acid-base properties of cassiterite (alpha-SnO2) surfaces at 10-50 degrees C were studied using potentiometric titrations of powder suspensions in aqueous NaCl and RbCl media. The proton sorption isotherms exhibited common intersection points in the pH range of 4.0-4.5 tinder all conditions, and the magnitude of charging was similar but not identical in NaCl and RbCl. The hydrogen bonding configuration at the oxide-water interface, obtained from classical molecular dynamics (MD) simulations, was analyzed in detail, and the results were explicitly incorporated in calculations of protonation constants for the reactive surface sites using the revised MUSIC model. The calculations indicated that the terminal SnOH2 group is more acidic than the bridging Sn2OH group, with protonation constants (log K-H) of 3.60 and 5.13 at 25 degrees C, respectively. This is contrary to the situation on the isostructural, alpha-TiO2 (rutile), apparently because of the difference in electronegativity between Ti and Sn. MD simulations and speciation calculations indicated considerable differences in the speciation of Na+ and Rb+, despite the similarities in overall charging. Adsorbed sodium ions are almost exclusively found in bidentate surface complexes, whereas adsorbed rubidium ions form comparable numbers of bidentate and tetradentate complexes. Also, the distribution of adsorbed Na+ between the different complexes shows a considerable dependence oil the surface charge density (pH), whereas the distribution of adsorbed Rb+ is almost independent of pH. A surface complexation model (SCM) capable of accurately describing both the measured surface charge and the MD-predicted speciation of adsorbed Na+/Rb+ was formulated. According to the SCM, the deprotonated terminal group (SnOH-0.40) and the protonated bridging group (Sn2OH+0.36) dominate the surface speciation over the entire pH range of this study (2.7-10). The complexation of medium cations increases significantly with increasing negative surface charge, and at pH 10, roughly 40% of the terminal sites are predicted to form cation complexes, whereas anion complexation is minor throughout the studied pH range.]]></description>
<dc:publisher><![CDATA[AMER CHEMICAL SOC]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Langmuir]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[18]]></prism:number>
<prism:volume><![CDATA[25]]></prism:volume> 
<prism:startingPage><![CDATA[10852]]></prism:startingPage>
<prism:endingPage><![CDATA[10861]]></prism:endingPage> 
<refworks:created><![CDATA[9/18/2009 8:49:00 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:14:40 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2986</link>
<refworks:FD><![CDATA[SEP 15]]></refworks:FD>
<refworks:k1><![CDATA[ RUTILE-WATER INTERFACE]]></refworks:k1>
<refworks:k1><![CDATA[ PROTONATION EQUILIBRIUM-CONSTANTS]]></refworks:k1>
<refworks:k1><![CDATA[ DENSITY-FUNCTIONAL THEORY]]></refworks:k1>
<refworks:k1><![CDATA[ ELECTRIC DOUBLE-LAYER]]></refworks:k1>
<refworks:k1><![CDATA[ METAL-OXIDE SURFACES]]></refworks:k1>
<refworks:k1><![CDATA[ MOLECULAR-DYNAMICS]]></refworks:k1>
<refworks:k1><![CDATA[ MUSIC MODEL]]></refworks:k1>
<refworks:k1><![CDATA[ SNO2 SURFACES]]></refworks:k1>
<refworks:k1><![CDATA[ ADSORPTION]]></refworks:k1>
<refworks:k1><![CDATA[ (HYDR)OXIDES]]></refworks:k1>
<refworks:k1><![CDATA[ Chemistry, Physical]]></refworks:k1>
<refworks:no><![CDATA[PT: J; NR: 37; TC: 0; J9: LANGMUIR; PG: 10; GA: 492KR]]></refworks:no>
<refworks:pp><![CDATA[WASHINGTON; 1155 16TH ST, NW, WASHINGTON, DC 20036 USA]]></refworks:pp>
<refworks:sn><![CDATA[0743-7463]]></refworks:sn>
<refworks:ad><![CDATA[[Machesky, Michael L.] Illinois State Water Survey, Champaign, IL 61820 USA. [Vlcek, Lukas; Cummings, Peter T.] Vanderbilt Univ, Dept Chem Engn, Nashville, TN 37235 USA. [Vlcek, Lukas] Acad Sci Czech Republic, Inst Chem Proc Fundamentals, CR-16502 Prague, Czech Republic. [Cummings, Peter T.] Oak Ridge Natl Lab, Ctr Nanophase Mat Sci, Oak Ridge, TN 37831 USA. [Rosenqvist, Joergen; Wesolowski, David J.] Oak Ridge Natl Lab, Div Chem Sci, Oak Ridge, TN 37831 USA.; Rosenqvist, J, Univ Leeds, Sch Earth & Environm, Leeds LS2 9LT, W Yorkshire, England.; j.rosenqvist@leeds.ac.uk]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:sf><![CDATA[Article]]></refworks:sf>
<refworks:do><![CDATA[10.1021/la901396w]]></refworks:do>
<refworks:ds><![CDATA[Web of Science]]></refworks:ds>
<refworks:id><![CDATA[2986]]></refworks:id>
<refworks:an><![CDATA[000269655000059]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2985">
<title><![CDATA[Field application of processed manure upon water quality and crop productivity]]></title>
<dc:creator><![CDATA[Walker,P.]]></dc:creator>
<dc:creator><![CDATA[ Kelly,Walton R.]]></dc:creator>
<dc:creator><![CDATA[ Smiciklas,K.]]></dc:creator>
<dc:creator><![CDATA[ Kelley,T.]]></dc:creator>
<description><![CDATA[The purpose of this study was to conduct an applied field study investigating the feasibility of utilizing processed liquid swine manure in crop production. Four treatments were evaluated; unprocessed liquid swine manure, processed liquid effluent, inorganic nitrogen fertilizer and zero-rate control. For shallow subsurface water (as measured by lysimeters), the inorganic nitrogen fertilizer treatment had the greatest levels of nitrate-No However, there were no significant differences for any measured chemical parameter for groundwater (as measured by sampling wells) among the four treatments. In general, the zero-rate control treatment was the lowest yielding treatment for com (Zea mays L.), in contrast to the equivalent response of the other treatments. Nutrient accumulation was similar for the four treatments, with the exception of greater plant manganese content of the inorganic nitrogen fertilizer treatment. For soybean (Glycine max L.), all four treatments responded in a similar fashion. After 5 years of annual treatment application the processed liquid effluent and unprocessed manure treatments were similar for most soil parameters. In addition, soil and plant tissue samples were evaluated for pathogenic organisms (total coliform and Escherichia coli) and non-detectable levels were found for all treatments. The results of this study indicate the processed liquid swine effluent produced in this study, inorganic nitrogen fertilizer and unprocessed manure had similar effects on crop characteristics and subsurface water quality. © 2009 Asian Network for Scientific Information.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Agronomy]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[8]]></prism:volume> 
<prism:startingPage><![CDATA[49]]></prism:startingPage>
<prism:endingPage><![CDATA[59]]></prism:endingPage> 
<refworks:created><![CDATA[9/12/2009 8:32:57 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:21:31 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2985</link>
<refworks:k1><![CDATA[ Bacteria]]></refworks:k1>
<refworks:k1><![CDATA[ Grain yield]]></refworks:k1>
<refworks:k1><![CDATA[ Groundwater]]></refworks:k1>
<refworks:k1><![CDATA[ Soil quality]]></refworks:k1>
<refworks:sn><![CDATA[18125379]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Department of Agriculture, Illinois State University, Campus Box 5020, Normal, IL 61790-5020, United States; Affiliation: Illinois State Water Survey, Institute of Natural Resources Sustainability, University of Illinois, 2204 Griffith Drive, Champaign, IL 61820, United States; Affiliation: Department of Health Education and Promotion, Eastern Carolina University, Greenville, NC 27858, United States; Correspondence Address: Smiciklas, K.; Department of Agriculture, Illinois State University, Campus Box 5020, Normal, IL 61790-5020, United States]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.3923/ja.2009.49.59]]></refworks:do>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[12 September 2009]]></refworks:rd>
<refworks:id><![CDATA[2985]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-68749118243&partnerID=40]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2984">
<title><![CDATA[Surface speciation of yttrium at the rutile-water interface: incorporation of structural information and charge distribution within the MUSIC model [meeting abstract]]]></title>
<dc:creator><![CDATA[Ridley,M. K.]]></dc:creator>
<dc:creator><![CDATA[ Hiemstra,T.]]></dc:creator>
<dc:creator><![CDATA[ Machesky,Michael L.]]></dc:creator>
<dc:creator><![CDATA[ Wesolowski,D. J.]]></dc:creator>
<dc:creator><![CDATA[ van Riemsdijk,W. H.]]></dc:creator>
<dc:publisher><![CDATA[PERGAMON-ELSEVIER SCIENCE LTD]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Geochimica et Cosmochimica Acta]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:number><![CDATA[13 Supplement]]></prism:number>
<prism:volume><![CDATA[73]]></prism:volume> 
<prism:startingPage><![CDATA[A1101]]></prism:startingPage>
<prism:endingPage><![CDATA[A1101]]></prism:endingPage> 
<refworks:created><![CDATA[8/3/2009 2:33:41 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/22/2009 8:17:11 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2984</link>
<refworks:FD><![CDATA[JUN]]></refworks:FD>
<refworks:k1><![CDATA[ Geochemistry & Geophysics]]></refworks:k1>
<refworks:pp><![CDATA[OXFORD; THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND]]></refworks:pp>
<refworks:sn><![CDATA[0016-7037]]></refworks:sn>
<refworks:ad><![CDATA[[Ridley, M. K.] Texas Tech Univ, Dept Geosci, Lubbock, TX 79409 USA. [Hiemstra, T.; van Riemsdijk, W. H.] Wageningen Univ, Dept Soil Qual, Wageningen, Netherlands. [Machesky, M. L.] Illinois State Water Survey, Champaign, IL 61820 USA. [Wesolowski, D. J.] Oak Ridge Natl Lab, Oak Ridge, TN USA.; moira.ridley@ttu.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:sf><![CDATA[Meeting Abstract]]></refworks:sf>
<refworks:ds><![CDATA[Web of Science]]></refworks:ds>
<refworks:id><![CDATA[2984]]></refworks:id>
<refworks:an><![CDATA[000267229902659]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2983">
<title><![CDATA[Physical and chemical parameters]]></title>
<dc:creator><![CDATA[Larson,T. E.]]></dc:creator>
<dc:publisher><![CDATA[: U.S. Dept. of Health, Education, and Welfare, Public Health Service, Bureau of State Services, Division of Water Supply & Pollution Control]]></dc:publisher>
<dc:date><![CDATA[1961]]></dc:date>
<prism:publicationName><![CDATA[Water Quality Measurement and Instrumentation]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Conference Proceedings]]></refworks:rwtype>
<prism:volume><![CDATA[W61-2]]></prism:volume> 
<prism:startingPage><![CDATA[31]]></prism:startingPage>
<prism:endingPage><![CDATA[36]]></prism:endingPage> 
<refworks:created><![CDATA[7/22/2009 9:56:09 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[7/22/2009 9:59:18 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2983</link>
<refworks:FD><![CDATA[August 29-31, 1960.]]></refworks:FD>
<refworks:k1><![CDATA[ Freshwater environment]]></refworks:k1>
<refworks:k1><![CDATA[ Pollution, toxicity]]></refworks:k1>
<refworks:k1><![CDATA[ Water chemistry]]></refworks:k1>
<refworks:k1><![CDATA[ Water resources]]></refworks:k1>
<refworks:k1><![CDATA[ North America]]></refworks:k1>
<refworks:k1><![CDATA[ United States]]></refworks:k1>
<refworks:k1><![CDATA[ Illinois]]></refworks:k1>
<refworks:k1><![CDATA[ freshwater environments]]></refworks:k1>
<refworks:k1><![CDATA[ water pollution]]></refworks:k1>
<refworks:k1><![CDATA[ water quality]]></refworks:k1>
<refworks:k1><![CDATA[ drinking water]]></refworks:k1>
<refworks:k1><![CDATA[ water usages]]></refworks:k1>
<refworks:k1><![CDATA[ physical properties]]></refworks:k1>
<refworks:k1><![CDATA[ chemical properties]]></refworks:k1>
<refworks:k1><![CDATA[ trace elements]]></refworks:k1>
<refworks:k1><![CDATA[ sampling]]></refworks:k1>
<refworks:k1><![CDATA[ water quality data]]></refworks:k1>
<refworks:k1><![CDATA[ resource management]]></refworks:k1>
<refworks:k1><![CDATA[ waterlit]]></refworks:k1>
<refworks:no><![CDATA[TY: CONF; Database Contributor: FISHLIT . Database Contributor ID: FSLT-152487. Language: English. Document Type: Proceeding. Publication Type: Conference / Workshop Article. Accession Number: FSLT-152487. Author Affiliation: [1961] Chemistry Section, Illinois State Water Survey, Urbana, Illinois, USA 1;]]></refworks:no>
<refworks:ed><![CDATA[Cincinnati, Ohio,]]></refworks:ed>
<refworks:pp><![CDATA[Cincinnati, OH]]></refworks:pp>
<refworks:sn><![CDATA[0500-3563]]></refworks:sn>
<refworks:lk><![CDATA[http://search.ebscohost.com/login.aspx?direct=true&db=ffw&AN=FSLT-152487&site=ehost-live]]></refworks:lk>
<refworks:ds><![CDATA[Fish & Fisheries WorldWide (EBSCOhost)]]></refworks:ds>
<refworks:id><![CDATA[2983]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2982">
<title><![CDATA[Influence of Lake Michigan on squall line rainfall]]></title>
<dc:creator><![CDATA[Stout,G. E.]]></dc:creator>
<dc:creator><![CDATA[ Wilk,K. E.]]></dc:creator>
<dc:date><![CDATA[1962]]></dc:date>
<prism:publicationName><![CDATA[Great Lakes Research Division Publication [University of Michigan]]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[9]]></prism:number>
<prism:startingPage><![CDATA[111]]></prism:startingPage>
<prism:endingPage><![CDATA[115]]></prism:endingPage> 
<refworks:created><![CDATA[7/22/2009 9:54:06 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[8/6/2009 8:55:06 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2982</link>
<refworks:FD><![CDATA[01/01]]></refworks:FD>
<refworks:k1><![CDATA[ Freshwater environment]]></refworks:k1>
<refworks:k1><![CDATA[ Climate, weather, current, tide]]></refworks:k1>
<refworks:k1><![CDATA[ Hydrological cycle]]></refworks:k1>
<refworks:k1><![CDATA[ Distribution, geography, biogeography]]></refworks:k1>
<refworks:k1><![CDATA[ Ecology]]></refworks:k1>
<refworks:k1><![CDATA[ North America]]></refworks:k1>
<refworks:k1><![CDATA[ Great Lakes]]></refworks:k1>
<refworks:k1><![CDATA[ waterlit]]></refworks:k1>
<refworks:k1><![CDATA[ lake michigan]]></refworks:k1>
<refworks:k1><![CDATA[ squall]]></refworks:k1>
<refworks:k1><![CDATA[ rainfall]]></refworks:k1>
<refworks:k1><![CDATA[ thunderstorm]]></refworks:k1>
<refworks:k1><![CDATA[ radar synoptic analysis]]></refworks:k1>
<refworks:k1><![CDATA[ conective activity]]></refworks:k1>
<refworks:k1><![CDATA[ meterological research]]></refworks:k1>
<refworks:k1><![CDATA[ climatic variables]]></refworks:k1>
<refworks:k1><![CDATA[ ecological modelling]]></refworks:k1>
<refworks:k1><![CDATA[ precipitation]]></refworks:k1>
<refworks:no><![CDATA[Database Contributor: FISHLIT . Database Contributor ID: FSLT-110391. Language: English. Document Type: Article. Publication Type: Article. Accession Number: FSLT-110391. Author Affiliation: [1983] Water Resources Center, University of Illinois at Urbana Champaign, Urbana, Illinois, USA 1; [1962] Illinois State Water Survey, Urbana, Illinois 2;]]></refworks:no>
<refworks:lk><![CDATA[http://search.ebscohost.com/login.aspx?direct=true&db=ffw&AN=FSLT-110391&site=ehost-live]]></refworks:lk>
<refworks:ds><![CDATA[Fish & Fisheries WorldWide (EBSCOhost)]]></refworks:ds>
<refworks:id><![CDATA[2982]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2981">
<title><![CDATA[Adsorption of phosphate by river particulate matter]]></title>
<dc:creator><![CDATA[Wang,WunCheng]]></dc:creator>
<description><![CDATA[Adsorption of phosphate on particulate matter from the Illinois and Spoon Rivers was investigated. Adsorption reached equilibrium after 5 to 6 days and adsorption isotherms were linear for both constant and varying amounts of particulate matter. Adsorption was maximum at pH 8.3-8.4 and minimum at 6.0. Rates of adsorption were influenced by the equilibrium concentration of phosphate-P and were essentially the same for both rivers. However, the quantity of phosphate-P adsorbed per unit weight of particulate matter differed from the two streams, with the Illinois River adsorbing capacity seven times that of the Spoon River]]></description>
<dc:date><![CDATA[1974]]></dc:date>
<prism:publicationName><![CDATA[Water Resources Bulletin]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[4]]></prism:number>
<prism:volume><![CDATA[10]]></prism:volume> 
<prism:startingPage><![CDATA[662]]></prism:startingPage>
<prism:endingPage><![CDATA[671]]></prism:endingPage> 
<refworks:created><![CDATA[7/22/2009 9:48:58 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[8/6/2009 8:55:09 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2981</link>
<refworks:k1><![CDATA[ Illinois River]]></refworks:k1>
<refworks:k1><![CDATA[ Spoon River]]></refworks:k1>
<refworks:k1><![CDATA[ phosphates]]></refworks:k1>
<refworks:no><![CDATA[Note: August; UNIV WIS WATER RESOUR CENT EUTROPHICATION PROGRAM, EUTROPHICATION: A Bimonthly Summary of Current Literature, Issue No.44, September-October, 1974. Database Contributor ID: ABC0806-5753. Document Type: Article. Publication Type: Article. Accession Number: ABC0806-5753. Author Affiliation: Wang, WC, Wun Cheng: Water Quality Section, Illinois State Water Survey, P.O. Box 717, Peoria, Illinois 61601 1;]]></refworks:no>
<refworks:sn><![CDATA[00431370]]></refworks:sn>
<refworks:lk><![CDATA[http://search.ebscohost.com/login.aspx?direct=true&db=ffw&AN=ABC0806-5753&site=ehost-live]]></refworks:lk>
<refworks:ds><![CDATA[Fish & Fisheries WorldWide (EBSCOhost)]]></refworks:ds>
<refworks:id><![CDATA[2981]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2980">
<title><![CDATA[Wet deposition of mercury in the U.S. and Canada, 1996-2005: Results and analysis of the NADP mercury deposition network (MDN)]]></title>
<dc:creator><![CDATA[Prestbo,E. M.]]></dc:creator>
<dc:creator><![CDATA[ Gay,David A.]]></dc:creator>
<description><![CDATA[One of the most critical measurements needed to understand the biogeochemical cycle of mercury, and to verify atmospheric models, is the rate of mercury wet-deposition. The Mercury Deposition Network (MDN) operates sites across North America to monitor total mercury in wet-deposition. MDN's primary goal is to provide both spatial and temporal continental-scale observations of mercury wet-deposition fluxes to support researchers, modelers, policy-makers and the public interest. MDN represents the only continental-scale mercury deposition database with a >10-year record of continuous values. This study provides analysis and interpretation of MDN observations at 10 years (1996-2005) with an emphasis on investigating whether rigorous, statistically-significant temporal trends and spatial patterns were present and where they occurred. Wet deposition of mercury ranges from more than 25 μg m-2 yr in south Florida to less than 3 μg m-2 yr in northern California. Volume-weighted total mercury concentrations are statistically different between defined regions overall (Southeast ≈ Midwest > Ohio River > Northeast), with the highest in Florida, Minnesota, and several Southwest locations (10-16 ng L-1). Total mercury wet-deposition is significantly different between defined regions (Southeast > Ohio River > Midwest > Northeast). Mercury deposition is strongly seasonal in eastern North America. The average mercury concentration is about two times higher in summer than in winter, and the average deposition is approximately more than three times greater in summer than in winter. Forty-eight sites with validated datasets of five years or more were tested for trends using the non-parametric seasonal Kendall trend test. Significant decreasing mercury wet-deposition concentration trends were found at about half of the sites, particularly across Pennsylvania and extending up through the Northeast. © 2009 Elsevier Ltd. All rights reserved.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Atmospheric Environment]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[27]]></prism:number>
<prism:volume><![CDATA[43]]></prism:volume> 
<prism:startingPage><![CDATA[4223]]></prism:startingPage>
<prism:endingPage><![CDATA[4233]]></prism:endingPage> 
<refworks:created><![CDATA[7/17/2009 3:57:37 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:09:41 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2980</link>
<refworks:FD><![CDATA[September 2009]]></refworks:FD>
<refworks:k1><![CDATA[ MDN]]></refworks:k1>
<refworks:k1><![CDATA[ Mercury]]></refworks:k1>
<refworks:k1><![CDATA[ Temporal trends]]></refworks:k1>
<refworks:k1><![CDATA[ Wet deposition]]></refworks:k1>
<refworks:sn><![CDATA[13522310]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Tekran Research and Development, 330 Nantucket Blvd., Toronto, ON, Canada M1P2P4; Affiliation: Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois, 2204 Griffith Drive, Champaign, IL 61820, USA; Correspondence Address: Gay, D.A.; Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois, 2email: dgay@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1016/j.atmosenv.2009.05.028]]></refworks:do>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[17 July 2009]]></refworks:rd>
<refworks:id><![CDATA[2980]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-67649625167&partnerID=40]]></refworks:ul>
<refworks:an><![CDATA[CODEN: AENVE]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2979">
<title><![CDATA[Differences in multi-sensor and rain-gauge precipitation amounts]]></title>
<dc:creator><![CDATA[Westcott,Nancy E.]]></dc:creator>
<description><![CDATA[A comparison of multi-sensor (radar and gauge) and gauge precipitation estimates at a monthly temporal resolution and a county spatial resolution was undertaken for the midwestern USA. Precipitation data were collected from February 2002 to October 2006 from two sources: (a) multi-sensor precipitation estimates (MPE) based on the stage III/IV algorithm developed by the US National Oceanic and Atmospheric Administration (NOAA), national weather service (NWS) office of hydrology and NWS river forecast centres; and (b) quality-controlled NWS cooperative rain-gauge (QC_Coop) data from the NOAA national climatic data centre (NCDC). The gauge data were employed as the reference standard. The monthly median of the percentage differences in county-averaged monthly precipitation estimated by MPE and QC_Coop in the midwestern USA, for around 750 counties, was mainly within +/- 12.5%, with a median percentage difference of +6%. The positive difference indicates that, overall, the MPE values tend to be smaller than the QC_Coop values. ME values more closely correspond with QC_Coop values at all latitudes in the summer months when convective precipitation is dominant, and in the winter months for latitudes where non-frozen precipitation is most prevalent.]]></description>
<dc:publisher><![CDATA[THOMAS TELFORD PUBLISHING]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Proceedings of the Institution of Civil Engineers-Water Management]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[162]]></prism:volume> 
<prism:startingPage><![CDATA[73]]></prism:startingPage>
<prism:endingPage><![CDATA[81]]></prism:endingPage> 
<refworks:created><![CDATA[7/2/2009 8:34:34 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:21:59 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2979</link>
<refworks:FD><![CDATA[April 2009]]></refworks:FD>
<refworks:k1><![CDATA[ hydrology & water resource]]></refworks:k1>
<refworks:k1><![CDATA[ statistical analysis]]></refworks:k1>
<refworks:k1><![CDATA[ weather]]></refworks:k1>
<refworks:k1><![CDATA[ UNITED-STATES]]></refworks:k1>
<refworks:k1><![CDATA[ RIVER-BASIN]]></refworks:k1>
<refworks:k1><![CDATA[ RADAR]]></refworks:k1>
<refworks:k1><![CDATA[ GAGE]]></refworks:k1>
<refworks:k1><![CDATA[ Engineering, Civil]]></refworks:k1>
<refworks:k1><![CDATA[ Water Resources]]></refworks:k1>
<refworks:no><![CDATA[PT: J; NR: 25; TC: 0; J9: PROC INST CIVIL ENG-WATER MAN; PG: 9; GA: 452NR]]></refworks:no>
<refworks:pp><![CDATA[LONDON; THOMAS TELFORD HOUSE, 1 HERON QUAY, LONDON E14 4JD, ENGLAND]]></refworks:pp>
<refworks:sn><![CDATA[1741-7589]]></refworks:sn>
<refworks:ad><![CDATA[Univ Illinois, Illinois State Water Survey, Inst Nat Resource Sustainabil, Champaign, IL 61820 USA.; Westcott, NE, Univ Illinois, Illinois State Water Survey, Inst Nat Resource Sustainabil, Champaign, IL 61820 USA.]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:sf><![CDATA[Article]]></refworks:sf>
<refworks:do><![CDATA[10.1680/wama.2009.162.2.73]]></refworks:do>
<refworks:ds><![CDATA[Web of Science]]></refworks:ds>
<refworks:id><![CDATA[2979]]></refworks:id>
<refworks:an><![CDATA[000266548100003]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2977">
<title><![CDATA[Predicting the risk of soybean rust in Minnesota based on an integrated atmospheric model [Article in press]]]></title>
<dc:creator><![CDATA[Tao,Zhining]]></dc:creator>
<dc:creator><![CDATA[ Malvick,D.]]></dc:creator>
<dc:creator><![CDATA[ Claybrooke,Roger]]></dc:creator>
<dc:creator><![CDATA[ Floyd,C.]]></dc:creator>
<dc:creator><![CDATA[ Bernacchi,Carl J.]]></dc:creator>
<dc:creator><![CDATA[ Spoden,G.]]></dc:creator>
<dc:creator><![CDATA[ Kurle,J.]]></dc:creator>
<dc:creator><![CDATA[ Gay,David A.]]></dc:creator>
<dc:creator><![CDATA[ Bowersox,Van C.]]></dc:creator>
<dc:creator><![CDATA[ Krupa,S.]]></dc:creator>
<description><![CDATA[To minimize crop loss by assisting in timely disease management and reducing fungicide use, an integrated atmospheric model was developed and tested for predicting the risk of occurrence of soybean rust in Minnesota. The model includes a long-range atmospheric spore transport and deposition module coupled to a leaf wetness module. The latter is required for spore germination and infection. Predictions are made on a daily basis for up to 7 days in advance using forecast data from the United States National Weather Service. Complementing the transport and leaf wetness modules, bulk (wet plus dry) atmospheric deposition samples from Minnesota were examined for soybean rust spores using a specific DNA test and sequence analysis. Overall, the risk prediction worked satisfactorily within the bounds of the uncertainty associated with the use of modeled 7-day weather forecasts, with more than 65% agreement between the model forecast and the DNA test results. The daily predictions are available as an advisory to the user community through the University of Minnesota Extension. However, users must take the actual decision to implement the disease management strategy. © 2009 ISB.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[International journal of biometeorology]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:startingPage><![CDATA[1]]></prism:startingPage>
<prism:endingPage><![CDATA[13]]></prism:endingPage> 
<refworks:created><![CDATA[7/2/2009 8:11:00 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:17:28 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2977</link>
<refworks:FD><![CDATA[Available online June 15, 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Atmospheric deposition]]></refworks:k1>
<refworks:k1><![CDATA[ Atmospheric model]]></refworks:k1>
<refworks:k1><![CDATA[ Risk prediction]]></refworks:k1>
<refworks:k1><![CDATA[ Source-receptor relationship]]></refworks:k1>
<refworks:k1><![CDATA[ Soybean rust]]></refworks:k1>
<refworks:no><![CDATA[Article in Press]]></refworks:no>
<refworks:sn><![CDATA[00207128]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Drive, Champaign, 61820, United States; Affiliation: Department of Plant Pathology, University of Minnesota, 495 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, 55108, United States; Affiliation: State of Minnesota Office of the Climatologist, Soil, Water, and Climate, University of Minnesota, 439 Borlaug Hall, 1991 Upper Buford Circle, St Paul, 55108, United States; Affiliation: 1811 Clover Lane, Champaign, 61821, United States; Correspondence Address: Tao, Z.; Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana-Champaign, 2204 Griffith Drive, Champaign, 61820, IL, United States; email: ztao@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s00484-009-0239-y]]></refworks:do>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[2 July 2009]]></refworks:rd>
<refworks:id><![CDATA[2977]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-66749085585&partnerID=40]]></refworks:ul>
<refworks:an><![CDATA[CODEN: IJBMA]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2978">
<title><![CDATA[Thomas A. "Tom" Prickett (1935 to 2007)--ground water modeling pioneer.]]></title>
<dc:creator><![CDATA[Wehrmann,H. Allen]]></dc:creator>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Ground Water]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[6]]></prism:number>
<prism:volume><![CDATA[46]]></prism:volume> 
<prism:startingPage><![CDATA[910]]></prism:startingPage>
<prism:endingPage><![CDATA[914]]></prism:endingPage> 
<refworks:created><![CDATA[7/2/2009 8:11:00 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:37 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2978</link>
<refworks:FD><![CDATA[November–December 2008]]></refworks:FD>
<refworks:k1><![CDATA[ fresh water]]></refworks:k1>
<refworks:k1><![CDATA[ article]]></refworks:k1>
<refworks:k1><![CDATA[ awards and prizes]]></refworks:k1>
<refworks:k1><![CDATA[ environmental monitoring]]></refworks:k1>
<refworks:k1><![CDATA[ history]]></refworks:k1>
<refworks:k1><![CDATA[ theoretical model]]></refworks:k1>
<refworks:k1><![CDATA[ United States]]></refworks:k1>
<refworks:k1><![CDATA[ water flow]]></refworks:k1>
<refworks:k1><![CDATA[ water supply]]></refworks:k1>
<refworks:k1><![CDATA[ History, 20th Century]]></refworks:k1>
<refworks:k1><![CDATA[ Illinois]]></refworks:k1>
<refworks:k1><![CDATA[ Models, Theoretical]]></refworks:k1>
<refworks:k1><![CDATA[ Water Movements]]></refworks:k1>
<refworks:sn><![CDATA[17456584]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Center for Groundwater Science, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820, USA.; Correspondence Address: Wehrmann, H.A.email: alex@illinois.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1111/j.1745-6584.2008.00492]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[2 July 2009]]></refworks:rd>
<refworks:id><![CDATA[2978]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2976">
<title><![CDATA[Flash floods in Illinois]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<dc:creator><![CDATA[ Osman,Paul]]></dc:creator>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Outdoor Illinois]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Magazine Article]]></refworks:rwtype>
<prism:number><![CDATA[7]]></prism:number>
<prism:volume><![CDATA[27]]></prism:volume> 
<prism:startingPage><![CDATA[14]]></prism:startingPage>
<prism:endingPage><![CDATA[16]]></prism:endingPage> 
<refworks:created><![CDATA[7/2/2009 7:51:14 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/22/2009 8:17:26 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2976</link>
<refworks:k1><![CDATA[ flash floods]]></refworks:k1>
<refworks:sn><![CDATA[1072-7175]]></refworks:sn>
<refworks:ds><![CDATA[smb]]></refworks:ds>
<refworks:id><![CDATA[2976]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2975">
<title><![CDATA[Impacts of long-range transport of global pollutants and precursor gases on U.S. air quality under future climatic conditions]]></title>
<refworks:t3><![CDATA[J. Geophys. Res., F, Solid Earth (USA)]]></refworks:t3>
<dc:creator><![CDATA[Huang,Ho-Chun]]></dc:creator>
<dc:creator><![CDATA[ Lin,Jintai]]></dc:creator>
<dc:creator><![CDATA[ Tao,Zhining]]></dc:creator>
<dc:creator><![CDATA[ Choi,Hyun I.]]></dc:creator>
<dc:creator><![CDATA[ Patten,K.]]></dc:creator>
<dc:creator><![CDATA[ Kunkel,Kenneth E.]]></dc:creator>
<dc:creator><![CDATA[ Xu,Min]]></dc:creator>
<dc:creator><![CDATA[ Zhu,Jinhong]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<dc:creator><![CDATA[ Williams,A.]]></dc:creator>
<dc:creator><![CDATA[ Caughey,Michael]]></dc:creator>
<dc:creator><![CDATA[ Wuebbles,D. J.]]></dc:creator>
<dc:creator><![CDATA[ Wang,J.]]></dc:creator>
<description><![CDATA[The U.S. air quality is impacted by emissions both within and outside the United States. The latter impact is manifested as long-range transport (LRT) of pollutants across the U.S. borders, which can be simulated by lateral boundary conditions (LBC) into a regional modeling system. This system consists of a regional air quality model (RAQM) that integrates local-regional source emissions and chemical processes with remote forcing from the LBC predicted by a nesting global chemical transport model (model for ozone and related chemical tracers (MOZART)). The present-day simulations revealed important LRT effects, varying among the five major regions with ozone problems, i.e., northeast United States, midwest United States, Texas, California, and southeast United States. To determine the responses of the LRT impacts to projected global climate and emissions changes, the MOZART and RAQM simulations were repeated for future periods (2048-2052 and 2095-2099) under two emissions scenarios (IPCC AlFi and Bl). The future U.S. air quality projected by the MOZART is less sensitive to the emissions scenarios than that simulated by the RAQM with or without incorporating the LRT effects via the LBC from the MOZART. The result of RAQM with the LRT effects showed that the southeast United States has the largest sensitivity of surface ozone mixing ratio to the emissions changes in the 2095-2099 climate (-24% to +25%) followed by the northeast and midwest United States. The net increase due to the LRT effects in 2095-2099 ranges from +4% to +13% in daily mean surface ozone mixing ratio and +4% to +11% in mean daily maximum 8-h average ozone mixing ratios. Correspondingly, the LRT effects in 2095-2099 cause total column O3 mixing ratio increases, ranging from +7% to +16%, and also 2 to 3 more days with the surface ozone exceeding the national standard. The results indicate that future U.S. air quality changes will be substantially affected by global emissions.]]></description>
<dc:publisher><![CDATA[American Geophysical Union]]></dc:publisher>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Journal of Geophysical Research - Part D: Atmospheres,]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[19]]></prism:number>
<prism:volume><![CDATA[113]]></prism:volume> 
<prism:startingPage><![CDATA[D19307]]></prism:startingPage>
<refworks:created><![CDATA[6/7/2009 2:01:44 AM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:35 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2975</link>
<refworks:FD><![CDATA[October 16, 2008]]></refworks:FD>
<refworks:k1><![CDATA[ air pollution]]></refworks:k1>
<refworks:k1><![CDATA[ atmospheric composition]]></refworks:k1>
<refworks:k1><![CDATA[ ozone]]></refworks:k1>
<refworks:no><![CDATA[M1: Copyright 2009, The Institution of Engineering and Technology; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined]]></refworks:no>
<refworks:pp><![CDATA[USA]]></refworks:pp>
<refworks:sn><![CDATA[0148-0227]]></refworks:sn>
<refworks:ad><![CDATA[Illinois State Water Survey, Champaign, IL, USA]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org.proxy2.library.uiuc.edu/10.1029/2007JD009469]]></refworks:lk>
<refworks:id><![CDATA[2975]]></refworks:id>
<refworks:u1><![CDATA[10618616]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2972">
<title><![CDATA[Increasing major hail losses in the U.S. ]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<description><![CDATA[Property losses due to hailstorms on April 13-14, 2006, resulted in Midwestern property losses that totaled $1.822 billion, an amount considerably more than the previous record high of $1.5 billion set by an April 2001 hail event. The huge April 2006 loss was largely due to multiple severe storms with frequent large hail hitting major metropolitan areas. A highly unstable air mass that developed on April 13 led to several supercell storms and they then produced large hailswaths across portions of Iowa, Illinois, Indiana, and Wisconsin during a 30-h period. This storm event and prior recent major hail losses occurred when several major hailstorms developed and then traveled for hundreds of kilometers. The nation's top ten loss events during 1950-2006 reveal a notable temporal increase with most losses in the 1992-2006 period. Causes for the increases could be an increasing frequency of very unstable atmospheric conditions leading to bigger, longer lasting storms, and/or a greatly expanded urban society that has become increasingly vulnerable to hailstorms. © 2009 Springer Science+Business Media B.V.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Climatic Change]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[1-2]]></prism:number>
<prism:volume><![CDATA[96]]></prism:volume> 
<prism:startingPage><![CDATA[161]]></prism:startingPage>
<prism:endingPage><![CDATA[166]]></prism:endingPage> 
<refworks:created><![CDATA[6/7/2009 1:42:07 AM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 3:58:45 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2972</link>
<refworks:FD><![CDATA[September 2009; available online in advance of print]]></refworks:FD>
<refworks:sn><![CDATA[01650009]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Illinois State Water Survey, Champaign, United States; Correspondence Address: Changnon, S.A.; Illinois State Water Survey, Champaign, IL, United States; email: schangno@uiuc.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s10584-009-9597-z]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[6 June 2009]]></refworks:rd>
<refworks:id><![CDATA[2972]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-65549150397&partnerID=40]]></refworks:ul>
<refworks:an><![CDATA[CODEN: CLCHD]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2971">
<title><![CDATA[Temporal and spatial distributions of wind storm damages in the United States]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<description><![CDATA[High wind caused catastrophes, storms causing property losses >$1 million, during 1952-2006 averaged 3.1 events per year in the U.S. The average loss per event was $90 million, and the annual average loss was $354 million. High wind catastrophes were most frequent in the Northeast, Central, and West Coast areas. Storm losses on the West Coast were the nation's highest, averaging $115 million per event. High wind losses are the nation's only form of severe weather that maximizes on the West Coast. High wind catastrophes were most frequent in winter, and were infrequent in the late spring and early fall seasons. Loss areas were frequently confined to one state. Losses in the western U.S. and nationally have increased during the 1952-2006 period, both with statistically significant upward trends.]]></description>
<dc:publisher><![CDATA[SPRINGER]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Climatic Change]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[3-4]]></prism:number>
<prism:volume><![CDATA[94]]></prism:volume> 
<prism:startingPage><![CDATA[473]]></prism:startingPage>
<prism:endingPage><![CDATA[482]]></prism:endingPage> 
<refworks:created><![CDATA[6/4/2009 9:06:05 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 3:57:48 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2971</link>
<refworks:FD><![CDATA[June 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Environmental Sciences]]></refworks:k1>
<refworks:k1><![CDATA[ Meteorology & Atmospheric Sciences]]></refworks:k1>
<refworks:pp><![CDATA[DORDRECHT; VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS]]></refworks:pp>
<refworks:sn><![CDATA[0165-0009]]></refworks:sn>
<refworks:ad><![CDATA[Illinois State Water Survey, Champaign, IL 61820 USA.; Changnon, SA, Illinois State Water Survey, Champaign, IL 61820 USA.; schangno@uiuc.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:sf><![CDATA[Article]]></refworks:sf>
<refworks:do><![CDATA[10.1007/s10584-008-9518-6]]></refworks:do>
<refworks:ds><![CDATA[Web of Science]]></refworks:ds>
<refworks:id><![CDATA[2971]]></refworks:id>
<refworks:an><![CDATA[000266079200015]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2968">
<title><![CDATA[Streamflow Forecasting Based on Artificial Neural Networks]]></title>
<refworks:t2><![CDATA[Artificial Neural Networks in Hydrology]]></refworks:t2>
<dc:creator><![CDATA[Salas,J. D.]]></dc:creator>
<dc:creator><![CDATA[ Markus,Momcilo]]></dc:creator>
<dc:creator><![CDATA[ Tokar,A. S.]]></dc:creator>
<refworks:a2><![CDATA[Govindaraju,R. S.]]></refworks:a2>
<refworks:a2><![CDATA[ Rao,A. Ramachandra]]></refworks:a2>
<dc:publisher><![CDATA[Kluwer Academic Publishers]]></dc:publisher>
<dc:date><![CDATA[2000]]></dc:date>
<refworks:rwtype><![CDATA[Book, Section]]></refworks:rwtype>
<prism:startingPage><![CDATA[348 pp.]]></prism:startingPage>
<prism:endingPage><![CDATA[348 pp.]]></prism:endingPage> 
<refworks:created><![CDATA[6/4/2009 6:52:20 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[6/4/2009 9:37:26 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2968</link>
<refworks:pp><![CDATA[Amsterdam]]></refworks:pp>
<refworks:sn><![CDATA[0792362268; 978-0792362265]]></refworks:sn>
<refworks:id><![CDATA[2968]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2967">
<title><![CDATA[Precipitation-runoff modeling using artificial neural networks and conceptual models]]></title>
<dc:creator><![CDATA[Tokar,A. S.]]></dc:creator>
<dc:creator><![CDATA[ Markus,Momcilo]]></dc:creator>
<description><![CDATA[Inspired by the functioning of the brain and biological nervous systems, artificial neural networks (ANNs) have been applied to various hydrologic problems in the last 10 years. In this study, ANN models are compared with traditional conceptual models in predicting watershed runoff as a function of rainfall, snow water equivalent, and temperature. The ANN technique was applied to model watershed runoff in three basins with different climatic and physiographic characteristics-the Fraser River in Colorado, Raccoon Creek in Iowa, and Little Patuxent River in Maryland. In the Fraser River watershed, the ANN technique was applied to model monthly streamflow and was compared to a conceptual water balance (Watbal) model. The ANN technique was used to model the daily rainfall-runoff process and was compared to the Sacramento soil moisture accounting (SAC-SMA) model in the Raccoon River watershed. The daily rainfall-runoff process was also modeled using the ANN technique in the Little Patuxent River basin, and the training and testing results were compared to those of a simple conceptual rainfall-runoff (SCRR) model. In all cases, the ANN models provided higher accuracy, a more systematic approach, and shortened the time spent in training of the models. For the Fraser River, the accuracy of monthly streamflow forecasts by the ANN model was significantly higher compared to the accuracy of the Watbal model. The best-fit ANN model performed as well as the SAC-SMA model in the Raccoon River. The testing and training accuracy of the ANN model in Little Patuxent River was comparatively higher than that of the SCRR model. The initial results indicate that ANNs can be powerful tools in modeling the precipitation-runoff process for various time scales, topography, and climate patterns. Inspired by the functioning of the brain and biological nervous systems, artificial neural networks (ANNs) have been applied to various hydrologic problems in the last 10 years. In this study, ANN models are compared with traditional conceptual models in predicting watershed runoff as a function of rainfall, snow water equivalent, and temperature. The ANN technique was applied to model watershed runoff in three basins with different climatic and physiographic characteristics - the Fraser River in Colorado, Raccoon Creek in Iowa, and Little Patuxent River in Maryland. In the Fraser River watershed, the ANN technique was applied to model monthly streamflow and was compared to a conceptual water balance (Watbal) model. The ANN technique was used to model the daily rainfall-runoff process and was compared to the Sacramento soil moisture accounting (SAC-SMA) model in the Raccoon River watershed. The daily rainfall-runoff process was also modeled using the ANN technique in the Little Patuxent River basin, and the training and testing results were compared to those of a simple conceptual rainfall-runoff (SCRR) model. In all cases, the ANN models provided higher accuracy, a more systematic approach, and shortened the time spent in training of the models. For the Fraser River, the accuracy of monthly streamflow forecasts by the ANN model was significantly higher compared to the accuracy of the Watbal model. The best-fit ANN model performed as well as the SAC-SMA model in the Raccoon River. The testing and training accuracy of the ANN model in Little Patuxent River was comparatively higher than that of the SCRR model. The initial results indicate that ANNs can be powerful tools in modeling the precipitation-runoff process for various time scales, topography, and climate patterns.]]></description>
<dc:publisher><![CDATA[ASCE]]></dc:publisher>
<dc:date><![CDATA[2000]]></dc:date>
<prism:publicationName><![CDATA[Journal of Hydrologic Engineering]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[2]]></prism:number>
<prism:volume><![CDATA[5]]></prism:volume> 
<prism:startingPage><![CDATA[156]]></prism:startingPage>
<prism:endingPage><![CDATA[161]]></prism:endingPage> 
<refworks:created><![CDATA[6/4/2009 6:49:44 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[8/6/2009 8:55:07 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2967</link>
<refworks:k1><![CDATA[ Mathematical models]]></refworks:k1>
<refworks:k1><![CDATA[ Moisture]]></refworks:k1>
<refworks:k1><![CDATA[ Neural networks]]></refworks:k1>
<refworks:k1><![CDATA[ Precipitation (meteorology)]]></refworks:k1>
<refworks:k1><![CDATA[ Runoff]]></refworks:k1>
<refworks:k1><![CDATA[ Soils]]></refworks:k1>
<refworks:k1><![CDATA[ Watbal model]]></refworks:k1>
<refworks:k1><![CDATA[ Hydrology]]></refworks:k1>
<refworks:k1><![CDATA[ artificial neural network]]></refworks:k1>
<refworks:k1><![CDATA[ conceptual framework]]></refworks:k1>
<refworks:k1><![CDATA[ hydrological modeling]]></refworks:k1>
<refworks:k1><![CDATA[ rainfall-runoff modeling]]></refworks:k1>
<refworks:k1><![CDATA[ United States]]></refworks:k1>
<refworks:no><![CDATA[Cited By (since 1996): 62]]></refworks:no>
<refworks:pp><![CDATA[Reston, VA, United States]]></refworks:pp>
<refworks:sn><![CDATA[10840699]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Nat. Weather Service, 1325 East-West Hwy., Silver Spring, MD 20910-3283, United States; Affiliation: Michael Baker, Jr., Inc., Alexandria, VA, United States; Correspondence Address: Tokar, A.S.; Nat. Weather Service, 1325 East-West Hwy., Silver Spring, MD 20910-3283, United States]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1061/(ASCE)1084-0699(2000)5:2(156)]]></refworks:do>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[4 June 2009]]></refworks:rd>
<refworks:id><![CDATA[2967]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com/inward/record.url?eid=2-s2.0-0034174397&partnerID=40]]></refworks:ul>
<refworks:cr><![CDATA[Caudill, M., Neural network primer: Part I (1987) AI Expert, pp. 46-52. , December;; French, M.N., Krajewski, W.F., Cuykendall, R.R., Rainfall forecasting in space and time using a neural network (1992) J. Hydro., 137, pp. 1-13;; The great flood of 1993 (1994) NOAA Natural Disaster Survey Rep., , National Weather Service, Office of Hydrology, Silver Spring, Md;; Hecht-Nielsen, (1990) Neurocomputing, , Addison-Wesley, Reading, Mass;; Hsu, K., Gupta, V.H., Sorooshian, S., Artificial neural network modeling of the rainfall-runoff process (1995) Water Resour. Res., 31 (10), pp. 2571-12530;; Leaf, C.F., Alexander, R.R., Simulating timber yields and hydrologic impact resulting from timber harvest on subalpine watersheds (1975) USDA Forest Service Res. Paper RM-133, , U.S. Department of Agriculture, Washington, D.C;; Leaf, C.F., Brink, G.E., Hydrologic simulation model of Colorado subalpine forest (1973) USDA Forest Service Res. Paper RM-107, , U.S. Department of Agriculture, Washington, D.C;; Leaf, C.F., Brink, G.E., Land use simulation model of the subalpine coniferous forest zone (1975) USDA Forest Service Res. Paper RM-135, , U.S. Department of Agriculture, Washington, D.C;; Markus, M., (1997) Application of Neural Networks in Streamflow Forecasting, , doctoral dissertation, Colorado State University, Fort Collins, Colo;; Markus, M., Baker, D., The fraser river: Streamflow forecasting and simulation computer package (1994) Tech. Rep., , Northern Colorado Water Conservancy District, Loveland, Colo;; Markus, M., Salas, J.D., Shin, H., Predicting streamflows based on neural networks (1995) 1st Int. Conf. on Water Resour. Engrg., , ASCE, New York;; McCuen, R.H., Snyder, M.W., (1986) Hydrologic Modeling: Statistical Methods and Applications, , Prentice-Hall, Englewood Cliffs, N.J;; Muller, B., Reinhardt, J., (1990) Neural Networks: An Introduction, , Springler, New York;; (1996) National Weather Service River Forecast System, , National Weather Service, Office of Hydrology, Silver Spring, Md;; Poff, L.N., Tokar, A.S., Johnson, P.A., Stream hydrological and ecological responses to climatic changes assessed with an artificial neural network (1996) Limnology and Oceanography, 41 (5), pp. 857-863;; (1993) Professional II/PLUS and NeuralWorks Explorer, , NeuralWare, Pittsburgh;; Roger, L.L., Dowla, F.U., Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling (1994) Water Resour. Res., 30 (2), pp. 457-481;; Rumelhart, D.E., McClelland, J.L., (1986) Parallel Distributed Processing - Volume I: Foundations, 1. , MIT Press, Cambridge, Mass;; Shamseldin, A., Application of a neural network technique to rainfall-runoff modeling (1997) J. Hydro., 199, pp. 272-294;; Tokar, A.S., (1996) Rainfall-runoff Modeling in an Uncertain Environment, , doctoral dissertation, University of Maryland, College Park, Md;; Tokar, A.S., Johnson, P.A., Rainfall-runoff modeling using artificial neural networks (1999) J. Hydrologic Engrg., ASCE, 4 (3), pp. 232-239;; Trent, R., Molinas, A., Gagarin, N., An artificial neural network for computing sediment transport (1993) Proc., ASCE Hydr. Conf., , ASCE, New York;; Trent, R., Molinas, A., Gagarin, N., Estimating pier scour with artificial neural networks (1993) Proc., ASCE Hydr. Conf., , ASCE, New York;; (1972) Tech. Memo. NWS HYDRO-14, , National Weather Service, Silver Spring, Md;; Wasserman, P.D., (1989) Neural Computing Theory and Practice, , Van Nostrand Reinhold, New York]]></refworks:cr>
<refworks:an><![CDATA[CODEN: JHYEF]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2966">
<title><![CDATA[Tornadoes in Illinois]]></title>
<dc:creator><![CDATA[Changnon,Stanley A.]]></dc:creator>
<description><![CDATA[Basic information about the occurrence of tornadoes in the state.  Tornado protection technologies are described.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Outdoor Illinois]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Magazine Article]]></refworks:rwtype>
<prism:number><![CDATA[5]]></prism:number>
<prism:volume><![CDATA[17]]></prism:volume> 
<prism:startingPage><![CDATA[17]]></prism:startingPage>
<prism:endingPage><![CDATA[19]]></prism:endingPage> 
<refworks:created><![CDATA[6/4/2009 6:40:20 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 4:11:21 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2966</link>
<refworks:FD><![CDATA[May 2009]]></refworks:FD>
<refworks:no><![CDATA[photos by Stanley Changnon]]></refworks:no>
<refworks:id><![CDATA[2966]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2965">
<title><![CDATA[Hydroinformatics : data integrative approaches in computation, analysis, and modeling]]></title>
<dc:creator><![CDATA[Kumar,Praveen]]></dc:creator>
<dc:creator><![CDATA[ Folk,Mike]]></dc:creator>
<dc:creator><![CDATA[ Markus,Momcilo]]></dc:creator>
<dc:creator><![CDATA[ Alameda,Jay C.]]></dc:creator>
<description><![CDATA[Publisher summary: Modern hydrology is more interdisciplinary than ever. Staggering  amounts and varieties of information pour in from GIS and remote  sensing systems every day, and this information must be collected,  interpreted, and shared efficiently. Hydroinformatics: Data Integrative  Approaches in Computation, Analysis, and Modeling introduces the tools,  approaches, and system considerations necessary to take full advantage  of the abundant hydrological data available today.

Linking  hydrological science with computer engineering, networking, and  database science, this book lays a pedagogical foundation in the  concepts underlying developments in hydroinformatics. It begins with an  introduction to data representation through Unified Modeling Language  (UML), followed by digital libraries, metadata, the basics of data  models, and Modelshed, a new hydrological data model. Building on this  platform, the book discusses integrating and managing diverse data in  large datasets, data communication issues such as XML and Grid  computing, the basic principles of data processing and analysis  including feature extraction and spatial registration, and modern  methods of soft computing such as neural networks and genetic  algorithms.

Today, hydrological data are increasingly rich,  complex, and multidimensional. Providing a thorough compendium of  techniques and methodologies, Hydroinformatics: Data Integrative  Approaches in Computation, Analysis, and Modeling is the first  reference to supply the tools necessary to confront these challenges  successfully.]]></description>
<dc:publisher><![CDATA[CRC Press]]></dc:publisher>
<dc:date><![CDATA[2005]]></dc:date>
<refworks:rwtype><![CDATA[Book, Whole]]></refworks:rwtype>
<refworks:created><![CDATA[5/28/2009 8:44:20 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:48:30 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2965</link>
<refworks:FD><![CDATA[November 2, 2005]]></refworks:FD>
<refworks:k1><![CDATA[ Hydrology -- Data processing]]></refworks:k1>
<refworks:pp><![CDATA[Boca Raton, Fla]]></refworks:pp>
<refworks:sn><![CDATA[0849328942 (cased); 9780849328947 (cased)]]></refworks:sn>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:sf><![CDATA[WorldCat; Book; OCLC; WorldCat]]></refworks:sf>
<refworks:cn><![CDATA[Dewey: 551.480285]]></refworks:cn>
<refworks:id><![CDATA[2965]]></refworks:id>
<refworks:an><![CDATA[62760185]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2963">
<title><![CDATA[Adsorption of ions on zirconium oxide surfaces from aqueous solutions at high temperatures.]]></title>
<dc:creator><![CDATA[Palmer,D. A.]]></dc:creator>
<dc:creator><![CDATA[ Machesky,Michael L.]]></dc:creator>
<dc:creator><![CDATA[ Bénézeth,P.]]></dc:creator>
<dc:creator><![CDATA[ Wesolowski,D. J.]]></dc:creator>
<dc:creator><![CDATA[ Anovitz,L. M.]]></dc:creator>
<dc:creator><![CDATA[ Deshon,J. C.]]></dc:creator>
<description><![CDATA[Surface titrations were carried out on suspensions of monoclinic ZrO2 from 25 to 290 °C slightly above saturation vapor pressure at ionic strengths of 0.03, 0.1 and 1.0 mol{dot operator}kg-1(NaCl). A typical increase in surface charge was observed with increasing temperature. There was no correlation between the radius of the cations, Li+, Na+, K+ and (CH3)4N+, and the magnitude of their association with the surface. The combined results were treated with a 1-pKa MUSIC model, which yielded association constants for the cations (and chloride ion at low pH) at each temperature. The pH of zero-point-charge, pHzpc, decreased with increasing temperature as found for other metal oxides, reaching an apparent minimum value of 4.1 by 250 °C. Batch experiments were performed to monitor the concentration of LiOH in solutions containing suspended ZrO2 particles from 200 to 360 °C. At 350 and 360 °C, Li+ and OH- ions were almost totally adsorbed when the pressure was lowered to near saturation vapor pressure. This reversible trend has implications not only to pressure-water reactor, PWR, operations, but is also of general scientific and other applied interest. Additional experiments probed the feasibility that boric acid/borate ions adsorb reversibly onto ZrO2 surfaces at near-neutral pH conditions as indicated in earlier publications. © 2009 Springer Science+Business Media, LLC.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Solution Chemistry]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[7]]></prism:number>
<prism:volume><![CDATA[38]]></prism:volume> 
<prism:startingPage><![CDATA[907]]></prism:startingPage>
<prism:endingPage><![CDATA[924]]></prism:endingPage> 
<refworks:created><![CDATA[5/28/2009 8:15:44 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:08:56 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2963</link>
<refworks:FD><![CDATA[July 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Adsorption]]></refworks:k1>
<refworks:k1><![CDATA[ Batch experiments]]></refworks:k1>
<refworks:k1><![CDATA[ Borate anions]]></refworks:k1>
<refworks:k1><![CDATA[ High temperature]]></refworks:k1>
<refworks:k1><![CDATA[ Lithium ions]]></refworks:k1>
<refworks:k1><![CDATA[ MUSIC model]]></refworks:k1>
<refworks:k1><![CDATA[ Potentiometry]]></refworks:k1>
<refworks:k1><![CDATA[ PWR applications]]></refworks:k1>
<refworks:k1><![CDATA[ Zirconium oxide]]></refworks:k1>
<refworks:sn><![CDATA[00959782]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Chemical Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, 37831, United States; Affiliation: Illinois State Water Survey, 2204 Griffith Dr., Champaign, 61820, United States; Affiliation: Laboratoire des Mécanismes et Transferts en Géologie, CNRS/IRD/Université de Toulouse, Toulouse, 31400, France; Affiliation: Electrical Power Research Institute, 3420 Hillview Avenue, Palo Alto, 94304, United States; Correspondence Address: Palmer, D.A.; Chemical Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, 37831, TN, United States; email: Solution_Chemistry@comcast.net]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s10953-009-9415-2]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[28 May 2009]]></refworks:rd>
<refworks:id><![CDATA[2963]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-65449167567&partnerID=40]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2964">
<title><![CDATA[Identifying and quantifying Phakopsora pachyrhizi spores in rain]]></title>
<dc:creator><![CDATA[Barnes,C. W.]]></dc:creator>
<dc:creator><![CDATA[ Szabo,L. J.]]></dc:creator>
<dc:creator><![CDATA[ Bowersox,Van C.]]></dc:creator>
<description><![CDATA[In summers of 2005 and 2006, rain was collected weekly at over 100 selected National Atmospheric Deposition Program/National Trends Network sites across the soybean-growing region of the central and eastern United States. Rain samples were screened for Phakopsora pachyrhizi (causal agent of soybean rust) DNA using a nested real-time polymerase chain reaction assay. Over this time frame, P. pachyrhizi spores were detected in every state in the study, but more frequently in states along the Gulf and Atlantic coasts and along the Ohio River Valley westward to Kansas. A bimodal temporal distribution of samples testing positive for P. pachyrhizi was found in both years. However, there was a greater than threefold increase in the number of samples testing positive for P. pachyrhizi in 2006 compared with 2005, with the most significant increase in August. There was also an increase in the average number of spores per sample in 2006 relative to 2005. Sequence analysis of a subset of positive samples was used to validate the assay results. From the sequence analysis, two reliable polymorphic regions were found, resulting in six distinct genotypes. One genotype was found in 56% of the samples tested, whereas the other genotypes were found less frequently.]]></description>
<dc:publisher><![CDATA[American Phytopathological Society]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Phytopathology]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[4]]></prism:number>
<prism:volume><![CDATA[99]]></prism:volume> 
<prism:startingPage><![CDATA[328]]></prism:startingPage>
<prism:endingPage><![CDATA[338]]></prism:endingPage> 
<refworks:created><![CDATA[5/28/2009 8:15:44 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 3:54:35 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2964</link>
<refworks:FD><![CDATA[April 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Long-distance dispersal (LDD)]]></refworks:k1>
<refworks:k1><![CDATA[ Pest Information Platform for Extension and Education (PIPE)]]></refworks:k1>
<refworks:k1><![CDATA[ OLYMERASE-CHAIN-REACTION]]></refworks:k1>
<refworks:k1><![CDATA[ SOYBEAN RUST]]></refworks:k1>
<refworks:k1><![CDATA[ UNITED-STATES]]></refworks:k1>
<refworks:k1><![CDATA[ PATHOGENS]]></refworks:k1>
<refworks:k1><![CDATA[ EPIDEMIOLOGY]]></refworks:k1>
<refworks:k1><![CDATA[ AFRICA]]></refworks:k1>
<refworks:k1><![CDATA[ SPREAD]]></refworks:k1>
<refworks:k1><![CDATA[ FUNGI]]></refworks:k1>
<refworks:k1><![CDATA[ Plant Sciences]]></refworks:k1>
<refworks:sn><![CDATA[0031949X]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Cereal Disease Laboratory, U.S. Department of Agriculture-Agricultural Research Service, University of Minnesota, St. Paul, MN; Affiliation: Illinois State Water Survey, National Atmospheric Deposition Program, Champaign, IL; Correspondence Address: Barnes, C. W.; Cereal Disease Laboratory, U.S. Department of Agriculture-Agricultural Research Service, University of Minnesota, St. Paul, MN; email: barn0107@umn.edu]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1094/PHYTO-99-4-0328]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[28 May 2009]]></refworks:rd>
<refworks:id><![CDATA[2964]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-65249117441&partnerID=40]]></refworks:ul>
<refworks:cr><![CDATA[Anikster, Y., Szabo, L.J., Eilam, T., Manisterski, J., Koike, S.T., Bushnell, W.R., Morphology, life cycle biology, and DNA sequence analysis of rust on garlic and chives from California (2004) Phytopathology, 94, pp. 569-577;; Barnes, C.W., Szabo, L.J., Detection and identification of four common rust pathogens of cereals and grasses using real-time polymerase chain reaction (2007) Phytopathology, 97, pp. 717-727;; Bromfield, K.R., Soybean rust: Some considerations relevant to threat analysis (1980) Prot. Ecol, 2, pp. 251-257;; Bromfield, K. R. 1984. Soybean Rust. Monogr. No. 11. American Phytopathological Society, St. Paul, MNCR Brown, J.K.M., Hovmøller, M.S., Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease (2002) Science, 297, pp. 537-541;; Christensen, J.J., (1942) Long distance dissemination of plant pathogens, pp. 78-87. , Aerobiology. F. R. Moulten, ed. AAAS, Washington, DC;; Dossett, S.R., Bowersox, V.C., National Trends Network Site Operations Manual. National Atmospheric Deposition Program Office at the Illinois State Water Survey (1999) NADP Manual 1999-01, , Champaign, IL;; Frederick, R.D., Snyder, C.L., Peterson, G.L., Bonde, M.R., Polymerase chain reaction assays for the detection and discrimination of the soybean rust pathogens Phakopsora pachyrhizi and P. meibomiae (2002) Phytopathology, 92, pp. 217-227;; Gregory, P.H., (1973) Microbiology of the Atmosphere, , 2nd ed. Wiley, New York;; Hirst, J.M., Spore liberation and dispersal (1959) in: Plant Pathology: Problems and Progress, 1908-1958, , C. S. Holton, G. W. Fischer, R. W. Fulton, H. Hart, and S. E. A. McCallan, eds. The University of Wisconsin Press, Madison;; Isard, S.A., Russo, J.M., Ariatti, A., The Integrated Aerobiology Modeling System applied to the spread of soybean rust into the Ohio River valley during September 2006 (2007) Aerobiologia, 23, pp. 271-282;; Killgore, E., Heu, R., Gardner, D.E., First report of soybean rust in Hawaii (1994) Plant Dis, 78, p. 1216;; Krupa, S., Bowersox, V., Claybrooke, R., Barnes, C.W., Szabo, L., Harlin, K., Kurle, J., Introduction of Asian soybean rust urediniospores into the Midwestern United States - A case study (2006) Plant Dis, 90, pp. 1254-1259;; Levy, C., Epidemiology and chemical control of soybean rust in southern Africa (2005) Plant Dis, 89, pp. 669-674;; Melching, J.S., Dowler, W.M., Koogle, D.L., Royer, M.H., Effects of duration, frequency, and temperature of leaf wetness periods on soybean rust (1989) Plant Dis, 73, pp. 117-112;; Mims, S.A., Mims III, F.M., Fungal spores are transported long distances in smoke from biomass fires (2004) Atmos. Environ, 38, pp. 651-655;; Morel, W., Yorinori, J.T., Situacion de la roja de la soja en el Paraguay (2002) Bol de Diulgacion, (44). , Ministerio de Agricultura y Granaderia, Centro Regional de Investigacion Agricola, Capitan Miranda, Paraguay;; Nagarajan, S., Singh, D.V., Long-distance dispersion of rust pathogens (1990) Annu. Rev. Phytopathol, 28, pp. 139-153;; Ono, Y., Buritica, P., Hennen, J., Delimitation of Phakopsora, Physopella, and Cerotelium and their species on Leguminosae (1992) Mycol. Res, 96, pp. 825-850;; Pivonia, S., Yang, X.B., Assessment of the potential year-round establishment of soybean rust throughout the world (2004) Plant Dis, 88, pp. 523-529;; Prospero, J.M., Blades, E., Mathison, G., Naidu, R., Interhemispheric transport of viable fungi and bacteria from Africa to the Caribbean with soil dust (2005) Aerobiologia, 21, pp. 1-19;; Purdy, L.H., Krupa, S.V., Dean, J.L., Introduction of sugarcane rust into the Americas and its spread to Florida (1985) Plant Dis, 69, pp. 689-693;; Roelfs, A.P., Rowell, J.B., Romig, R.W., Sampler for monitoring cereal rust uredospores in rain (1970) Phytopathology, 60, pp. 187-188;; Rowell, J. B. 1984. Controlled infection by Puccinia graminis f. sp. tritici under artificial conditions. Pages 291-332 in: The Cereal Rusts, 1. A. P. Roelfs and W. R. Bushnell, eds. Academic Press, New YorkCR Rowell, J.B., Romig, R.W., Detection of urediospores of wheat rusts in spring rains (1966) Phytopathology, 56, pp. 807-811;; Schneider, R.W., Hollier, C.A., Whitam, H.K., Palm, M.E., McKemy, J.M., Hernandez, J.R., Levy, L., DeVries-Paterson, R., First report of soybean rust caused by Phakopsora pachyrhizi in the continental United States (2005) Plant Dis, 89, p. 774;; Stakman, E.C., (1942) The field of extramural aerobiology, pp. 1-7. , Aerobiology. F. R. Moulten, ed. AAAS, Washington, DC;; Stakman, E.C., Christensen, C.M., Aerobiology in relation to plant disease (1946) Bot. Rev, 12, pp. 205-253]]></refworks:cr>
<refworks:an><![CDATA[CODEN: PHYNB]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2961">
<title><![CDATA[Effect of amines on the surface charge properties of iron oxides]]></title>
<dc:creator><![CDATA[Bénézeth,P.]]></dc:creator>
<dc:creator><![CDATA[ Wesolowski,D. J.]]></dc:creator>
<dc:creator><![CDATA[ Palmer,D. A.]]></dc:creator>
<dc:creator><![CDATA[ Machesky,Michael L.]]></dc:creator>
<description><![CDATA[Specific studies detailing the effects of amines, used as pH control agents for corrosion inhibition in power plants, on the surface charge of iron oxides provide data to assess the mechanism of how these amines impact deposition rate. The current study was undertaken in order to determine accurately the dissociation constants of the relevant amines at Pressurized Water Reactor (PWR) operating conditions and to investigate the effect of sorption of two of these amines (morpholine and dimethylamine) by magnetite. The acid-dissociation equilibria of morpholine (MOR), dimethylamine (DMA) and ethanolamine (ETA) were measured potentiometrically with a hydrogen-electrode concentration cell (HECC) from 0 to 290 °C in sodium trifluoromethanesulfonate (NaTr) solutions at ionic strengths up to 1 mol{dot operator}kg-1. Magnetite surface titrations were performed at an ionic strength of 0.03 mol{dot operator}kg-1 (NaTr medium) in the presence or absence of morpholine and dimethylamine buffers over a wide range of pH and total amine concentrations at 150-250 °C. © 2009 Springer Science+Business Media, LLC.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Solution Chemistry]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[7]]></prism:number>
<prism:volume><![CDATA[38]]></prism:volume> 
<prism:startingPage><![CDATA[925]]></prism:startingPage>
<prism:endingPage><![CDATA[945]]></prism:endingPage> 
<refworks:created><![CDATA[5/28/2009 8:15:43 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 3:30:42 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2961</link>
<refworks:FD><![CDATA[July 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Acid-dissociation constants]]></refworks:k1>
<refworks:k1><![CDATA[ Amines]]></refworks:k1>
<refworks:k1><![CDATA[ Corrosion]]></refworks:k1>
<refworks:k1><![CDATA[ High temperature]]></refworks:k1>
<refworks:k1><![CDATA[ Magnetite]]></refworks:k1>
<refworks:k1><![CDATA[ Surface charge]]></refworks:k1>
<refworks:sn><![CDATA[00959782]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Université de Toulouse, UPS (OMP), LMTG, 14 Avenue Edouard Belin, Toulouse, 31400, France; Affiliation: CNRS, LMTG, Toulouse, 31400, France; Affiliation: IRD, LMTG, Toulouse, 31400, France; Affiliation: Chemical Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, 37831-6110, United States; Affiliation: Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, 37831-6110, United States; Affiliation: Illinois State Water Survey, 2204 Griffith Drive, Champaign, 61820-7495, United States; Correspondence Address: Bénézeth, P.; Université de Toulouse, UPS (OMP), LMTG, 14 Avenue Edouard Belin, Toulouse, 31400, France; email: benezeth@lmtg.obs-mip.fr]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s10953-009-9419-y]]></refworks:do>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[28 May 2009]]></refworks:rd>
<refworks:id><![CDATA[2961]]></refworks:id>
<refworks:ul><![CDATA[http://www.scopus.com.proxy2.library.uiuc.edu/inward/record.url?eid=2-s2.0-65449129638&partnerID=40]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2962">
<title><![CDATA[Comparison of precipitation chemistry measurements obtained by the Canadian Air and Precipitation Monitoring Network and National Atmospheric Deposition Program for the period 1995-2004 [Article in press]]]></title>
<dc:creator><![CDATA[Wetherbee,G. A.]]></dc:creator>
<dc:creator><![CDATA[ Shaw,M. J.]]></dc:creator>
<dc:creator><![CDATA[ Latysh,N. E.]]></dc:creator>
<dc:creator><![CDATA[ Lehmann,Christopher M. B.]]></dc:creator>
<dc:creator><![CDATA[ Rothert,Jane E.]]></dc:creator>
<description><![CDATA[Precipitation chemistry and depth measurements obtained by the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program/National Trends Network (NADP/NTN) were compared for the 10-year period 1995-2004. Colocated sets of CAPMoN and NADP instrumentation, consisting of precipitation collectors and rain gages, were operated simultaneously per standard protocols for each network at Sutton, Ontario and Frelighsburg, Ontario, Canada and at State College, PA, USA. CAPMoN samples were collected daily, and NADP samples were collected weekly, and samples were analyzed exclusively by each network's laboratory for pH, H + , Ca2 + , Mg2 + , Na + , K + , {Mathematical expression}, Cl - , {Mathematical expression}, and {Mathematical expression}. Weekly and annual precipitation-weighted mean concentrations for each network were compared. This study is a follow-up to an earlier internetwork comparison for the period 1986-1993, published by Alain Sirois, Robert Vet, and Dennis Lamb in 2000. Median weekly internetwork differences for 1995-2004 data were the same to slightly lower than for data for the previous study period (1986-1993) for all analytes except {Mathematical expression}, {Mathematical expression}, and sample depth. A 1994 NADP sampling protocol change and a 1998 change in the types of filters used to process NADP samples reversed the previously identified negative bias in NADP data for hydrogen-ion and sodium concentrations. Statistically significant biases (α = 0.10) for sodium and hydrogen-ion concentrations observed in the 1986-1993 data were not significant for 1995-2004. Weekly CAPMoN measurements generally are higher than weekly NADP measurements due to differences in sample filtration and field instrumentation, not sample evaporation, contamination, or analytical laboratory differences. © 2009 US Government.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Environmental monitoring and assessment]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:startingPage><![CDATA[1]]></prism:startingPage>
<prism:endingPage><![CDATA[22]]></prism:endingPage> 
<refworks:created><![CDATA[5/28/2009 8:15:43 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:29:20 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2962</link>
<refworks:FD><![CDATA[Available online May 06, 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Acid rain]]></refworks:k1>
<refworks:k1><![CDATA[ Acidic deposition]]></refworks:k1>
<refworks:k1><![CDATA[ CAPMoN]]></refworks:k1>
<refworks:k1><![CDATA[ Composite samples]]></refworks:k1>
<refworks:k1><![CDATA[ NADP/NTN]]></refworks:k1>
<refworks:k1><![CDATA[ Network comparability]]></refworks:k1>
<refworks:k1><![CDATA[ Precipitation chemistry]]></refworks:k1>
<refworks:sn><![CDATA[01676369]]></refworks:sn>
<refworks:ad><![CDATA[Affiliation: Branch of Quality Systems, US Geological Survey, Denver Federal Center, Mail Stop 401, Bldg. 95, Denver, 80225, United States; Affiliation: Environment Canada, 4905 Dufferin Street, Toronto, M3H 5T4, Canada; Affiliation: Geospatial Information Office, US Geological Survey, Denver Federal Center, Mail Stop 306, Bldg. 810, Denver, 80225, United States; Affiliation: Illinois State Water Survey, 2204 Griffith Drive, Champaign, 61820, United States; Correspondence Address: Wetherbee, G.A.; Branch of Quality Systems, US Geological Survey, Denver Federal Center, Mail Stop 401, Bldg. 95, Denver, 80225, CO, United States; email: wetherbe@usgs.gov]]></refworks:ad>
<refworks:la><![CDATA[English]]></refworks:la>
<refworks:do><![CDATA[10.1007/s10661-009-0879-8]]></refworks:do>
<refworks:db><![CDATA[SCOPUS]]></refworks:db>
<refworks:ds><![CDATA[Scopus]]></refworks:ds>
<refworks:rd><![CDATA[28 May 2009]]></refworks:rd>
<refworks:id><![CDATA[2962]]></refworks:id>
<refworks:an><![CDATA[CODEN: EMASD]]></refworks:an>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY10]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2960">
<title><![CDATA[Stream classification system based on susceptibility to algal growth in response to nutrients]]></title>
<dc:creator><![CDATA[Lin,Lian-Shin]]></dc:creator>
<dc:creator><![CDATA[ Markus,Momcilo]]></dc:creator>
<dc:creator><![CDATA[ Russell,Amy]]></dc:creator>
<description><![CDATA[We developed a stream classification system that is based on stream's susceptibility to algal growth using a two-step approach. The model portrays algal biomass as a result of stream's response to nutrient concentrations and the response is governed by various stream factors. In the first step, a nutrient-chlorophyll a relationship was developed to characterize nutrient's effects on algal biomass. Residuals of the relationship were attributed to stream's susceptibility to algal growth in response to nutrients and referred to as "observed" susceptibility. In the second step, conditions of other contributing factors were used to explain the variation in the residuals and the developed relationship was used to generate "predicted" susceptibility. Existing data compiled from various monitoring projects of Illinois streams and rivers were used to illustrate the approach. Streams were classified into three (high, medium, and low) categories based on their observed and predicted susceptibility values, respectively. With the available data, the model showed a 40-50% success rate for classifying the streams based on three observed and predicted susceptibility categories. Model entropy also was calculated for selecting the best model. The results show the important role of both nutrients and other contributing factors in explaining the variation of algal biomass. The study also suggests ways to fine tune the model and improve its accuracy, which would make the presented model a more viable tool for stream classification for establishing nutrient criteria to prevent surface streams from eutrophication. 2007 ASCE.]]></description>
<dc:publisher><![CDATA[ASCE - American Society of Civil Engineers]]></dc:publisher>
<dc:date><![CDATA[2007]]></dc:date>
<prism:publicationName><![CDATA[Journal of Environmental Engineering]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[7]]></prism:number>
<prism:volume><![CDATA[133]]></prism:volume> 
<prism:startingPage><![CDATA[692]]></prism:startingPage>
<prism:endingPage><![CDATA[697]]></prism:endingPage> 
<refworks:created><![CDATA[5/26/2009 9:11:31 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:16 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2960</link>
<refworks:k1><![CDATA[ alga]]></refworks:k1>
<refworks:k1><![CDATA[ biomass]]></refworks:k1>
<refworks:k1><![CDATA[ chlorophyll a]]></refworks:k1>
<refworks:k1><![CDATA[ eutrophication]]></refworks:k1>
<refworks:k1><![CDATA[ growth response]]></refworks:k1>
<refworks:k1><![CDATA[ stream]]></refworks:k1>
<refworks:no><![CDATA[Compilation and indexing terms, Copyright 2008 Elsevier Inc.; undefined]]></refworks:no>
<refworks:sn><![CDATA[07339372]]></refworks:sn>
<refworks:ad><![CDATA[Dept. of Civil and Environmental Engineering, West Virginia Univ., Morgantown, WV 26506-6103]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org/10.1061/(ASCE)0733-9372(2007)133:7(692)]]></refworks:lk>
<refworks:do><![CDATA[10.1061/(ASCE)0733-9372(2007)133:7(692)]]></refworks:do>
<refworks:id><![CDATA[2960]]></refworks:id>
<refworks:u1><![CDATA[3045792]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2959">
<title><![CDATA[Hydrologic applications of MRAN algorithm]]></title>
<dc:creator><![CDATA[Amenu,Geremew G.]]></dc:creator>
<dc:creator><![CDATA[ Markus,Momcilo]]></dc:creator>
<dc:creator><![CDATA[ Kumar,Praveen]]></dc:creator>
<dc:creator><![CDATA[ Demissie,Misganaw]]></dc:creator>
<description><![CDATA[Applications of artificial neural networks in simulation and forecasting of hydrologic systems have a long record and generally promising results. Most of the earlier applications were based on the back-propagation (BP) feed-forward method, which used a trial-and-error to determine the final network parameters. The minimal resource allocation network (MRAN) is an on-line adaptive method that automatically configures the number of hidden nodes based on the input-output patterns presented to the network. Numerous MRAN applications in various fields such as system identification and signal processing demonstrated flexibility of the MRAN approach and higher or similar accuracy with more compact networks, compared to other learning algorithms. This research introduces MRAN and assesses its performance in hydrologic applications. The technique was applied to an agricultural watershed in central Illinois to predict daily runoff and nitrate-nitrogen concentration, and the predictions were more accurate compared to the BP model. 2007 ASCE.]]></description>
<dc:publisher><![CDATA[American Society of Civil Engineers]]></dc:publisher>
<dc:date><![CDATA[2007]]></dc:date>
<prism:publicationName><![CDATA[Journal of Hydrologic Engineering]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[1]]></prism:number>
<prism:volume><![CDATA[12]]></prism:volume> 
<prism:startingPage><![CDATA[124]]></prism:startingPage>
<prism:endingPage><![CDATA[129]]></prism:endingPage> 
<refworks:created><![CDATA[5/26/2009 8:25:00 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:08 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2959</link>
<refworks:k1><![CDATA[ algorithm]]></refworks:k1>
<refworks:k1><![CDATA[ artificial neural network]]></refworks:k1>
<refworks:k1><![CDATA[ hydrological modeling]]></refworks:k1>
<refworks:k1><![CDATA[ resource allocation]]></refworks:k1>
<refworks:k1><![CDATA[ runoff]]></refworks:k1>
<refworks:k1><![CDATA[ signal processing]]></refworks:k1>
<refworks:k1><![CDATA[ watershed]]></refworks:k1>
<refworks:no><![CDATA[Compilation and indexing terms, Copyright 2008 Elsevier Inc.]]></refworks:no>
<refworks:sn><![CDATA[10840699]]></refworks:sn>
<refworks:ad><![CDATA[Environmental Hydrology and Hydraulic Engineering, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL 61801, United States]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org/10.1061/(ASCE)1084-0699(2007)12:1(124)]]></refworks:lk>
<refworks:id><![CDATA[2959]]></refworks:id>
<refworks:u1><![CDATA[2955388]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2958">
<title><![CDATA[Trends in twentieth-century U.S. snowfall using a quality-controlled dataset]]></title>
<dc:creator><![CDATA[Kunkel,Kenneth E.]]></dc:creator>
<dc:creator><![CDATA[ Palecki,Michael A.]]></dc:creator>
<dc:creator><![CDATA[ Ensor,Leslie]]></dc:creator>
<dc:creator><![CDATA[ Hubbard,Kenneth G.]]></dc:creator>
<dc:creator><![CDATA[ Robinson,David]]></dc:creator>
<dc:creator><![CDATA[ Redmond,Kelly]]></dc:creator>
<dc:creator><![CDATA[ Easterling,David]]></dc:creator>
<description><![CDATA[A quality assessment of daily manual snowfall data has been undertaken for all U.S. long-term stations and their suitability for climate research. The assessment utilized expert judgment on the quality of each station. Through this process, the authors have identified a set of stations believed to be suitable for analysis of trends. Since the 1920s, snowfall has been declining in the West and the mid-Atlantic coast. In some places during recent years the decline has been more precipitous, strongly trending downward along the southern margins of the seasonal snow region, the southern Missouri River basin, and parts of the Northeast. Snowfall has been increasing since the 1920s in the lee of the Rocky Mountains, the Great Lakes - northern Ohio Valley, and parts of the north-central United States. These areas that are in opposition to the overall pattern of declining snowfall seem to be associated with specific dynamical processes, such as upslope snow and lake-effect snow that may be responding to changes in atmospheric circulation. 2009 American Meteorological Society.]]></description>
<dc:publisher><![CDATA[American Meteorological Society]]></dc:publisher>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Atmospheric and Oceanic Technology]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[1]]></prism:number>
<prism:volume><![CDATA[26]]></prism:volume> 
<prism:startingPage><![CDATA[33]]></prism:startingPage>
<prism:endingPage><![CDATA[44]]></prism:endingPage> 
<refworks:created><![CDATA[5/26/2009 3:15:48 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 4:06:32 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2958</link>
<refworks:FD><![CDATA[January 2009]]></refworks:FD>
<refworks:k1><![CDATA[ Snow]]></refworks:k1>
<refworks:k1><![CDATA[ Climatology]]></refworks:k1>
<refworks:k1><![CDATA[ Forestry]]></refworks:k1>
<refworks:k1><![CDATA[ Landforms]]></refworks:k1>
<refworks:k1><![CDATA[ Quality control]]></refworks:k1>
<refworks:no><![CDATA[Compilation and indexing terms, Copyright 2008 Elsevier Inc.; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined; undefined]]></refworks:no>
<refworks:pp><![CDATA[45 Beacon Street, Boston, MA 02108-3693, United States]]></refworks:pp>
<refworks:sn><![CDATA[07390572]]></refworks:sn>
<refworks:ad><![CDATA[Illinois State Water Survey, University of Illinois at Urbana-Champaign, 2204 Griffith Dr., Champaign, IL 61820-7495, United States]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org.proxy2.library.uiuc.edu/10.1175/2008JTECHA1138.1]]></refworks:lk>
<refworks:do><![CDATA[10.1175/2008JTECHA1138.1]]></refworks:do>
<refworks:id><![CDATA[2958]]></refworks:id>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2957">
<title><![CDATA[Impacts of urbanization and climate variability on floods in northeastern Illinois]]></title>
<dc:creator><![CDATA[Hejazi,Mohamad I.]]></dc:creator>
<dc:creator><![CDATA[ Markus,Momcilo]]></dc:creator>
<description><![CDATA[Trend analysis of annual flood peaks on 12 small urbanizing watersheds in northeastern Illinois indicated that annual peaks, and thus frequency and impact of flooding, increased over the past several decades. An increase in flood peaks could be attributed to intensive urbanization and increasing incidences of heavy rainfall. Average urbanization of the 12 watersheds increased significantly from 10.6% in 1954 to 61.8% in 1996. In addition, numerous studies have reported increasing frequency and intensity of heavy rainfall in the region. This outcome is consistent with lower design rainfall estimates produced by older studies, such as U.S. Weather Bureau Technical Paper No. 40 (TP-40), compared to more recent sources, such as National Oceanic and Atmospheric Administration Atlas-14. This study used a design storm approach and the Hydrologic Engineering Center for Hydrologic Modeling System (HEC-HMS) model to calculate design flood peaks. Hydrologic model parameters were calibrated using hourly rainfall-runoff data of two large regional floods, observed in 1954 and 1996 at 12 small urbanizing watersheds in the metropolitan Chicago area. A sensitivity analysis was performed to evaluate the effects of urbanization and climate variability on increasing flood peaks. Results indicated that, on average, urbanization caused a 34% greater increase in peak flows than climate variability. In addition, this study indicated that present discharges are, on average, at least 19% larger than regulatory discharges. Ongoing urbanization may cause flood peaks to become even higher. The proposed framework can be used to provide input for flood study prioritization by comparing published regulatory discharges and flood discharges computed for current conditions, and investigating potential impacts of future land use changes and precipitation on flood peaks.]]></description>
<dc:date><![CDATA[2009]]></dc:date>
<prism:publicationName><![CDATA[Journal of Hydrologic Engineering]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[6]]></prism:number>
<prism:volume><![CDATA[14]]></prism:volume> 
<prism:startingPage><![CDATA[606]]></prism:startingPage>
<prism:endingPage><![CDATA[616]]></prism:endingPage> 
<refworks:created><![CDATA[5/26/2009 2:45:00 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/25/2009 3:59:57 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2957</link>
<refworks:FD><![CDATA[June 2009]]></refworks:FD>
<refworks:k1><![CDATA[ climatic changes]]></refworks:k1>
<refworks:k1><![CDATA[ urban development]]></refworks:k1>
<refworks:k1><![CDATA[ rainfall-runoff relationships]]></refworks:k1>
<refworks:k1><![CDATA[ flood frequency]]></refworks:k1>
<refworks:do><![CDATA[10.1061/$ASCE$HE.1943-5584.0000020]]></refworks:do>
<refworks:ds><![CDATA[Author]]></refworks:ds>
<refworks:id><![CDATA[2957]]></refworks:id>
<refworks:ul><![CDATA[http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000020]]></refworks:ul>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2948">
<title><![CDATA[Dependence of land surface albedo on solar zenith angle: observations and model parameterization]]></title>
<dc:creator><![CDATA[Yang,F.]]></dc:creator>
<dc:creator><![CDATA[ Mitchell,K.]]></dc:creator>
<dc:creator><![CDATA[ Hou,Y. -T]]></dc:creator>
<dc:creator><![CDATA[ Dai,Y.]]></dc:creator>
<dc:creator><![CDATA[ Zeng,X.]]></dc:creator>
<dc:creator><![CDATA[ Wang,Z.]]></dc:creator>
<dc:creator><![CDATA[ Liang,Xin-Zhong]]></dc:creator>
<description><![CDATA[This study examines the dependence of surface albedo on solar zenith angle (SZA) over snow-free land surfaces using the intensive observations of surface shortwave fluxes made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and the National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) in 1997-2005. Results are used to evaluate the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) parameterization and several new parameterizations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. The influence of clouds on surface albedo and the albedo difference between morning and afternoon observations are also investigated. A new approach is taken to partition the observed upward flux so that the direct-beam and diffuse albedos can be separately computed. The study focused first on the ARM Southern Great Plains Central Facility site. It is found that the diffuse albedo prescribed in the NCEP GFS matched closely with the observations. The direct-beam albedo parameterized in the GFS is largely underestimated at all SZAs. The parameterizations derived from the MODIS product underestimated the direct-beam albedo at large SZAs and slightly overestimated it at small SZAs. Similar results are obtained from the analyses of observations at other stations. It is also found that the morning and afternoon dependencies of direct-beam albedo on SZA differ among the stations. Attempts are made to improve numerical model algorithms that parameterize the direct-beam albedo as a product of the direct-beam albedo at SZA = 60 (or the diffuse albedo), which varies with surface type or geographical location and/or season, and a function that depends only on SZA. A method is presented for computing the direct-beam albedos over these snow-free land points without referring to a particular land-cover classification scheme, which often differs from model to model. 2008 American Meteorological Society.]]></description>
<dc:publisher><![CDATA[American Meteorological Society]]></dc:publisher>
<dc:date><![CDATA[2008]]></dc:date>
<prism:publicationName><![CDATA[Journal of Applied Meteorology and Climatology]]></prism:publicationName> 
<refworks:rwtype><![CDATA[Journal Article]]></refworks:rwtype>
<prism:number><![CDATA[11]]></prism:number>
<prism:volume><![CDATA[47]]></prism:volume> 
<prism:startingPage><![CDATA[2963]]></prism:startingPage>
<prism:endingPage><![CDATA[2982]]></prism:endingPage> 
<refworks:created><![CDATA[5/15/2009 10:37:51 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[10/27/2009 7:49:38 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2948</link>
<refworks:FD><![CDATA[November 2008]]></refworks:FD>
<refworks:k1><![CDATA[ Solar radiation]]></refworks:k1>
<refworks:k1><![CDATA[ Atmospheric radiation]]></refworks:k1>
<refworks:k1><![CDATA[ Atmospherics]]></refworks:k1>
<refworks:k1><![CDATA[ Parameterization]]></refworks:k1>
<refworks:k1><![CDATA[ Scanning]]></refworks:k1>
<refworks:k1><![CDATA[ Snow]]></refworks:k1>
<refworks:k1><![CDATA[ Solar energy]]></refworks:k1>
<refworks:k1><![CDATA[ Spectrometers]]></refworks:k1>
<refworks:k1><![CDATA[ Surface measurement]]></refworks:k1>
<refworks:k1><![CDATA[ Weather forecasting]]></refworks:k1>
<refworks:pp><![CDATA[45 Beacon Street, Boston, MA 02108-3693, United States]]></refworks:pp>
<refworks:sn><![CDATA[15588424]]></refworks:sn>
<refworks:ad><![CDATA[Science Applications International Corporation, NCEP Environmental Modeling Center, 5200 Auth Road, Camp Springs, MD 20746, United States]]></refworks:ad>
<refworks:lk><![CDATA[http://dx.doi.org.proxy2.library.uiuc.edu/10.1175/2008JAMC1843.1]]></refworks:lk>
<refworks:ds><![CDATA[Engineering Village: Compendex]]></refworks:ds>
<refworks:id><![CDATA[2948]]></refworks:id>
<refworks:u1><![CDATA[20091912069711]]></refworks:u1>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:u15><![CDATA[FY09]]></refworks:u15>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>
<item rdf:about="http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2949">
<title><![CDATA[NADP (National Atmospheric Deposition Program) Technical Committee Meeting Proceedings held at Saratoga Springs, New York on October 17-20, 2000]]></title>
<dc:creator><![CDATA[Art,R. S.]]></dc:creator>
<dc:creator><![CDATA[ Douglas,K. E.]]></dc:creator>
<dc:creator><![CDATA[ Bedient,P. S.]]></dc:creator>
<description><![CDATA[In 2000, scientists, students, educators, and others interested in National Atmospheric Deposition Program (NADP) data viewed more than 84,000 maps and made nearly 17,000 on-line data retrievals from the NADP Internet site. These data are used to address important questions about the impact of the wet deposition of nutrients on eutrophication in coastal estuarine environments; the relationship between wet deposition, the health of unmanaged forests, and the depletion of base cations from forest soils; the impact of pollutant emissions changes on precipitation chemistry; and the rate at which precipitation delivers mercury to remote lakes and streams.]]></description>
<dc:date><![CDATA[2000]]></dc:date>
<refworks:rwtype><![CDATA[Report]]></refworks:rwtype>
<prism:startingPage><![CDATA[122]]></prism:startingPage>
<refworks:created><![CDATA[5/15/2009 10:37:51 PM GMT ]]></refworks:created>
<refworks:modified><![CDATA[5/15/2009 10:40:58 PM GMT ]]></refworks:modified><link>http://www.refworks.com/refshare?site=023461151737200000/RWWS4A1359546/081901228415187000&amp;rn=2949</link>
<refworks:FD><![CDATA[10]]></refworks:FD>
<refworks:k1><![CDATA[ Atmospheric chemistry]]></refworks:k1>
<refworks:k1><![CDATA[ Deposition]]></refworks:k1>
<refworks:k1><![CDATA[ Meetings]]></refworks:k1>
<refworks:k1><![CDATA[ Air pollution]]></refworks:k1>
<refworks:k1><![CDATA[ Information retrieval]]></refworks:k1>
<refworks:k1><![CDATA[ Nutrients]]></refworks:k1>
<refworks:k1><![CDATA[ Eutrophication]]></refworks:k1>
<refworks:k1><![CDATA[ Coastal areas]]></refworks:k1>
<refworks:k1><![CDATA[ Estuarines]]></refworks:k1>
<refworks:k1><![CDATA[ Forests]]></refworks:k1>
<refworks:k1><![CDATA[ Soils]]></refworks:k1>
<refworks:k1><![CDATA[ Precipitation]]></refworks:k1>
<refworks:k1><![CDATA[ Pollutants]]></refworks:k1>
<refworks:k1><![CDATA[ Emission]]></refworks:k1>
<refworks:k1><![CDATA[ Mercury]]></refworks:k1>
<refworks:k1><![CDATA[ Lakes]]></refworks:k1>
<refworks:k1><![CDATA[ Streams]]></refworks:k1>
<refworks:no><![CDATA[Compiled and Distributed by the NTIS, U.S. Department of Commerce. It contains copyrighted material. All rights reserved. 2008]]></refworks:no>
<refworks:pp><![CDATA[United States]]></refworks:pp>
<refworks:ad><![CDATA[National Atmospheric Deposition Program, Champaign, IL.;National Oceanic and Atmospheric Administration, Silver Spring, MD. Air Resources Lab.]]></refworks:ad>
<refworks:ds><![CDATA[Engineering Village: NTIS]]></refworks:ds>
<refworks:id><![CDATA[2949]]></refworks:id>
<refworks:u1><![CDATA[PB2009109664]]></refworks:u1>
<refworks:u5><![CDATA[Prepared in cooperation with National Oceanic and Atmospheric Administration, Silver Spring, MD. Air Resources Lab. Sponsored by Illinois State Water Survey Div., Champaign.]]></refworks:u5>Anonymous 
<refworks:ol><![CDATA[Unknown(0)]]></refworks:ol>
<refworks:sr><![CDATA[Print(0)]]></refworks:sr></item>

</rdf:RDF>