Abstract
Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.
Highlights
Precipitation is a key driver in the hydrologic cycle and associated modeling efforts
Multiple quantitative precipitation estimation (QPE) products were generated at a 5-min, 600-m resolution for model input, including: uncorrected Level III radar data, mean field bias (MFB)-corrected Level III radar data, kriged rain gauge data (10 gauges), and a watershed uniform depth based on measurements at the KROA rain gauge
From the 5-min incremental depths, cumulative depths were calculated over the study period for gauge, uncorrected radar, and MFB corrected radar QPEs at each gauge location (Figure 7)
Summary
Precipitation is a key driver in the hydrologic cycle and associated modeling efforts. Remains a key component of model uncertainty, regardless of the resolution of the remaining model components. QPE uncertainty is exacerbated in areas with orographic or convective precipitation due to heterogeneity in rainfall spatiotemporal distribution [6,7]. Some studies have shown that even small, fragmented urbanized areas can cause significant increases and/or decreases in precipitation due to impacts on temperature and wind [8,9]. With growing urbanization and climatic changes increasing the frequency and magnitude of hydrologic extremes [10], the ability of QPEs to simulate and predict hydrologic response accurately at high spatiotemporal resolutions is becoming increasingly important
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