Evaluate Spatiotemporal Characteristics Retrieved from High-Resolution Radar Rainfall Data and Corresponding Hydrologic Impacts

The dependence between spatiotemporal variability of rainfall and its hydrologic impact has not been fully understood. A previously developed Dynamic Moving Storm (DMS) generator is a multivariate rainfall model simulating spatiotemporal variability as well as storm movement. In this study, the DMS generator has been upgraded to generate realistic precipitation field. Rain cell segmentation and tracking algorithms were respectively developed and applied on high resolution radar rainfall data (1) to spatially determine the rain cells within individual radar image and (2) to temporally analyze their dynamic behavior. The dependence of hydrologic responses on rainfall features was investigated by dissecting the precipitation field into rain cells and modifying their spatio-temporal specifics individually. The study area was the Upper Trinity River Basin (UTRB) watershed, Texas; and two kinds of high resolution radar data i.e. the Next-Generation Radar (NEXRAD) level III Digital Hybrid Reflectivity (DHR) product and Multi-Radar Multi-Sensor (MRMS) precipitation rate product, were utilized to establish parameter statistics and to recreate/remodel historical events respectively. Empirical distributions of the DMS parameters were calculated by processing a long record of rainfall data (10 years), which would reveal preferential patterns and seasonality under the regional climate. Subsequently, the WRF-Hydro model forced by the remodeled and modified precipitation was used for hydrologic simulation. The results demonstrated that rainfall duration is a significant linkage between DMS parameters and their hydrologic impacts—any combination of spatiotemporal characteristics that render rain cells to stay longer over the catchment will produce higher peak discharge.