You are here:
Using Remote Sensing and Radar MET Data to Support Watershed Assessments Comprising IEM
Citation:
Kim, K., K. Price, G. Whelan, M. Galvin, K. Wolfe, P. Duda, M. Gray, AND Y. Pachepsky. Using Remote Sensing and Radar MET Data to Support Watershed Assessments Comprising IEM. In Proceedings, 7th Intl. Congress on Env. Modelling and Software, San Diego, CA, June 15 - 19, 2014. International Environmental Modelling and Software Society, Manno, Switzerland, 940-947, (2014).
Impact/Purpose:
Proceedings for International Environmental Modelling and Software Society (iEMSs) 7th Intl. Congress on Env. Modelling and Software, San Diego, CA, USA
Description:
Meteorological (MET) data required by watershed assessments that comprise Integrated Environmental Modeling (IEM) have traditionally been provided by land-based weather (gauge) stations; although these data may not be most appropriate for describing adequate spatial and temporal resolution if the MET stations are too few, too far away, or operating improperly. To complement land-based stations, remote sensing and radar satellite data are being increasingly used in obtaining synoptic data with the spatial and temporal resolution required for site-specific and/or event-based assessments. This study compares and contrasts the viability of automating the use of radar satellite data and land-based gauge stations to support MET data collection for IEM applications, especially at those locations where gauge stations prove to be inadequate. Specifically, the North American Land Data Assimilation System (NLDAS) and NEXRAD (NEXt generation RADar)Multisensor Precipitation Estimates (MPE) are compared with gauge data at Milwaukee and Manitowoc, Wisconsin USA. NLDAS contains automatic quality control (QC), uses hourly gauge station data and modeled precipitation, provides estimates at hourly intervals with a 1/8th-degree resolution, and provides time series at specified locations. MPE contains data QC’ed by human forecasters, combines radar-based estimates with hourly gauge station data on a 4-km grid, provides all spatial data by time increment, and is based on newer algorithms than NLDAS. Results of the comparison showed excellent correlation between gauge and radar data at Milwaukee, while the Manitowoc results strongly suggested using radar over gauge data.
