Watershed Data Management (WDM) Database for Salt Creek Streamflow Simulation, DuPage County, Illinois

Murphy, Elizabeth A.; Ishii, Audrey L.

doi:10.3133/ofr20061248

Cited by 3 publications

(12 citation statements)

References 1 publication

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“…• Freezing/nonfreezing days: Data were sorted into two temperature-indexed categories based on the hourly temperature data from the ANL temperature record (Murphy and Ishii, 2006;Over and others, 2010) using the criteria from Over and others (2007).…”

Section: Methodsmentioning

confidence: 99%

“…Data were checked by comparison with values at neighboring stations, and, during some years, missing and freezing-affected data were estimated by comparison with values at neighboring stations. See Murphy and Ishii (2006), Bera (2014), and Bera (2017) for more information.…”

Section: Rain Gage Network Datamentioning

confidence: 99%

“…Some missing data were estimated from nearby gages (Murphy and Ishii, 2006;Bera, 2014). These estimated data were included in the analysis.…”

Section: Rain Gage Network Datamentioning

confidence: 99%

“…• Hourly temperature data from the Argonne National Laboratory (ANL, figs. 2, 3) temperature record processed by the USGS (Murphy and Ishii, 2006;Over and others, 2010) were used (table 1).…”

Section: Temperature Datamentioning

confidence: 99%

See 3 more Smart Citations

Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12

Spies¹,

Over²,

Ortel³

2018

Open-File Report

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In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks-an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network-in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD-MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD-MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD-MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD-MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix provides an analysis of spatially combined NEXRAD-MPE precipitation data as a function of temperature at an hourly time scale and indicates, among other results, that most precipitation in the study area occurs at moderate temperatures of 30 to 74 degrees Fahrenheit. However, when precipitation does occur, its intensity increases with temperature to about 86 degrees Fahrenheit.

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Section: Methodsmentioning

confidence: 99%

Section: Rain Gage Network Datamentioning

confidence: 99%

“…Some missing data were estimated from nearby gages (Murphy and Ishii, 2006;Bera, 2014). These estimated data were included in the analysis.…”

Section: Rain Gage Network Datamentioning

confidence: 99%

Section: Temperature Datamentioning

confidence: 99%

See 2 more Smart Citations

Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12

Spies¹,

Over²,

Ortel³

2018

Open-File Report

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“…The Hydrological Simulation Program-FORTRAN (HSPF) hydrologic model (Bicknell and others, 2000) and Full Equations (FEQ) hydraulic model (Franz and Melching, 1997) are currently (2013) used by the USGS and DuPage County to develop simulations of the watershed rainfall runoff and routed streamflow, respectively, at a 1-hour time step. The meteorologic and hydrologic data are collected at various sites (Murphy and Ishii, 2006). The precipitation data are from a watershed-and county-wide network of real-time tipping-bucket rain gages maintained by the USGS and DuPage County.…”

Section: Introductionmentioning

confidence: 99%

Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois, water years 2005-11

Bera¹

2014

Data Series

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The U.S. Geological Survey (USGS), in cooperation with DuPage County Stormwater Management Division, maintains a USGS database of hourly meteorologic and hydrologic data for use in a near real-time streamflow simulation system, which assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek watershed in DuPage County, Illinois. Most of the precipitation data are collected from a tipping-bucket rain-gage network located in and near DuPage County. The other meteorologic data (wind speed, solar radiation, air temperature, and dewpoint temperature) are collected at Argonne National Laboratory in Argonne, Ill. Potential evapotranspiration is computed from the meteorologic data. The hydrologic data (discharge and stage) are collected at USGS streamflow-gaging stations in DuPage County. These data are stored in a Watershed Data Management (WDM) database. An earlier report (Murphy and Ishii, 2006) describes in detail the WDM database development including the processing of data from

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User's guide for MAGIC-Meteorologic and hydrologic genscn (generate scenarios) input converter

Ortel¹,

Martin²

2010

Open-File Report

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Meteorologic and hydrologic data used in watershed modeling studies are collected by various agencies and organizations, and stored in various formats. Data may be in a raw, un-processed format with little or no quality control, or may be checked for validity before being made available. Flood-simulation systems require data in near real-time so that adequate flood warnings can be made. Additionally, forecasted data are needed to operate flood-control structures to potentially mitigate flood damages. Because real-time data are of a provisional nature, missing data may need to be estimated for use in floodsimulation systems. The Meteorologic and Hydrologic GenScn (Generate Scenarios) Input Converter (MAGIC) can be used to convert data from selected formats into the Hydrologic Simulation System-Fortran hourly-observations format for input to a Watershed Data Management database, for use in hydrologic modeling studies. MAGIC also can reformat the data to the Full Equations model time-series format, for use in hydraulic modeling studies. Examples of the application of MAGIC for use in the flood-simulation system for Salt Creek in northeastern Illinois are presented in this report.

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Watershed Data Management (WDM) Database for Salt Creek Streamflow Simulation, DuPage County, Illinois

Cited by 3 publications

References 1 publication

Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12

Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12

Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois, water years 2005-11

User's guide for MAGIC-Meteorologic and hydrologic genscn (generate scenarios) input converter

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