SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation

Abstract. As a key component in the water and energy cycle, estimates of precipitation with high resolution accuracy is great significance for hydrological, meteorological, ecological studies. However, current satellite-based products have coarse spatial (from 10 to 50 km) not meeting needs several applications (e.g., flash floods landslides). The implementation downscaling methods can be suitable approach overcome this shortcoming. In study, we developed soil moisture-based (SMPD) method spatially integrated multisatellite retrievals global measurement (IMERG) V06B daily product over complex topographic climatic area southwestern Europe (Iberian Peninsula) period 2016–2018. By exploiting soil-water balance equation, high-resolution surface moisture (SSM) normalized difference vegetation index (NDVI) were used as auxiliary variables. IMERG was downscaled from 1 km. An evaluation using 1027 rain gauge stations highlighted good performance km compared original product, correlation coefficient 0.61, root mean square error (RMSE) 4.83 mm relative bias 5 %. Meanwhile, results also capture typical temporal variation behaviors study during dry wet seasons. Overall, SMPD greatly improves details slight enhancement accuracy. It shows potential applied development high-quality any region interest.