Improving Runoff Estimates by Increasing Catchment Subdivision Complexity and Resolution of Rainfall Data in the Upper Ping River Basin, Thailand

Abstract

This study investigated the effect of different subdivision schemes and two rainfall data types-gauge and radar-on the accuracy of runoff forecasting using a semi-distributed hydrological URBS model in a large river basin with a limited network of rainfall gauges. The entire catchments at three runoff stations in the Upper Ping River Basin, Thailand, were employed initially as a single lumped unit, and each catchment was thereafter divided into four increasingly complex subdivision schemes. Model performance was compared using areal gauge rainfall data (from the sparse rain gauge network) and estimated, highresolution, radar rainfall data across all catchment schemes over three periods; June-October 2003, May-September 2004, and May-July 2005. The results indicated that the accuracy of runoff estimates increased with increasing catchment subdivision complexity when using the high-resolution radar rainfall, but did not improve with the rain gauge data.

Citizen rain gauge improves hourly radar rainfall bias correction using a two-step Kalman filter

“…Weather radar, which can better capture the 25 variation of rainfall fields at fine spatial and temporal resolutions could be used as an alternative rainfall product for improving the accuracy of flash flood estimates and warning. (Collinge and Kirby, 1987;Sun et al, 2000;Uijlenhoet 2001;Bedient et al, 2003;Creutin and Borga, 2003;Mapiam et al, 2009aMapiam et al, , 2014Mapiam and Chautsuk, 2018;Corral et al, 2019). However, weather radar provides indirect measurement of backscattered electromagnetic waves called radar reflectivity data (Z).…”