Abstract
This study investigated the propagation of errors in input satellite-based precipitation products (SPPs) on streamflow and water quality indicators simulated by a hydrological model in the Occoquan Watershed, located in the suburban Washington, D.C. area. A dense rain gauge network was used as reference to evaluate three SPPs which are based on different retrieval algorithms. A Hydrologic Simulation Program-FORTRAN (HSPF) hydrology and water quality model was forced with the three SPPs to simulate output of streamflow (Q), total suspended solids (TSS), stream temperature (TW), and dissolved oxygen (DO). Results indicate that the HSPF model may have a dampening effect on the precipitation-to-streamflow error. The bias error propagation of all three SPPs showed a positive dependency on basin scale for streamflow and TSS, but not for TW and DO. On a seasonal basis, bias error propagation varied by product, with larger values generally found in fall and winter. This study demonstrated that the spatiotemporal variability of SPPs, along with their algorithms to estimate precipitation, have an influence on water quality simulations in a hydrologic model.
Highlights
It is well understood that precipitation is the most important forcing input in a hydrologic model, as it influences both watershed hydrology and water quality processes
AWSA values are compared to gauge data rather than a pixel-to-point comparison to assess the error propagation of different precipitation forcing datasets used in the model to simulated streamflow and water quality indicators on a seasonal basis and by basin scale
Results show that the Probability density functions (PDFs) of CMORPH is closer to the one of rain gauge observations with respect to TMPA and PERSIANN
Summary
It is well understood that precipitation is the most important forcing input in a hydrologic model, as it influences both watershed hydrology and water quality processes. While the traditional approach has been to measure precipitation using ground-based rain gauges, the use of satellite-based precipitation products (SPPs) in hydrologic modeling has been gaining more popularity due to their continuous geographic coverage with high spatial and temporal resolution. Whilst these products offer a viable resource, seasonal precipitation patterns, storm type, resolution of measurement, and background surface, all have an influence on the performance of SPPs and impact the output of hydrologic models [4,5]. Relevant literature to this study addressed includes: (1) SPP uncertainty associated with hydrologic modeling, (2) propagation of error from precipitation to streamflow simulated by a hydrologic model, and (3) precipitation product resolution impact on water quality simulation error
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