Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales

Abstract

Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higherresolution data sources are available, but they are associated with greater computational requirements and expertise. Here, we investigate whether the Multisensor Precipitation Estimator (MPE or Stage IV Next-Generation Radar) data improve the accuracy of streamflow simulations using the Soil and Water Assessment Tool (SWAT), compared with rain gauge data. Simulated flows from 2002 to 2010 at five timesteps were compared with observed flows for four nested subwatersheds of the Neuse River basin in North Carolina (21-, 203-, 2979-, and 10 100-km 2 watershed area), using a multi-objective function, informal likelihood-weighted calibration approach. Across watersheds and timesteps, total gauge precipitation was greater than radar precipitation, but radar data showed a conditional bias of higher rainfall estimates during large events (>25-50 mm/day). Model parameterization differed between calibrations with the two datasets, despite the fact that all watershed characteristics were the same across simulation scenarios. This underscores the importance of linking calibration parameters to realistic processes. SWAT simulations with both datasets underestimated median and low flows, whereas radar-based simulations were more accurate than gauge-based simulations for high flows. At coarser timesteps, differences were less pronounced. Our results suggest that modelling efforts in watersheds with poor rain gauge coverage can be improved with MPE radar data, especially at short timesteps. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Runoff Simulation by SWAT Model Using High-Resolution Gridded Precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau

“…Thus, the grids of gridded precipitation data are treated as a virtual precipitation station, and a virtual station is a common method that grids data input to the SWAT model [21,22,43,44]. However, the SWAT model only uses station data closest to the centroid of each sub-basin.…”

Improvement of Hydrological Simulations by Applying Daily Precipitation Interpolation Schemes in Meso-Scale Catchments

“…Its temporal variability is fundamental for hydrological modelling and has been discussed many times elsewhere. Likewise, there is a large and still growing body of literature on the role and effect of the spatial distribution of precipitation in hydrological modelling [3][4][5][6][7][8]. In general, there are several types of potential data sources: (1) station data from precipitation gauges; (2) reanalysis data and (3) radar data.…”

“…Reanalysis data products such as the WATCH Forcing Data (Water and Global Change, "WFD"; [12]) are promising in that they usually cover long time periods (100 years in the case of the WFD), but their spatial resolution is not sufficient for modelling of small and medium-sized catchments. Finally, (high-resolution) radar data do not have drawbacks of gauge and reanalysis data and are a valuable asset for the hydrological modelling community [5,[13][14][15]. Nevertheless, their use often implies using gauge data as well in order to find the optimal parameters of the transformation equation [16].…”

Quantitative Precipitation Estimation and Hydrological Modeling in Japan

“…Furthermore, limited and interpolated rain gauge data can introduce large uncertainties into predictions made by hydrological models (Chow et al, 1988). However, point measurements (i.e., rain gauge data) are still widely used for calibration and validation purposes (Price et al, 2014;P.C. et al, 2013;P.C.…”

“…Austin et al (2002) showed, for example, that during a storm, rainfall may vary by tens of millimeters per hour, from minute to minute, and over distances of only a few tens of meters. For this reason, there has been a considerable interest in recent years in developing high spatial and temporal resolution gridded rainfall datasets (Chen et al, 2002;Mitra et al, 2003;Nesbitt and Anders, 2009;Price et al, 2014).…”