Water Hazard Analysis and Monitoring Using Satellite and Radar

Abstract

Soil moisture estimates from satellite retrievals and models are known to have large biases and uncertainties among different products. The satellite and model data have complementary advantages and disadvantages and they can be merged to produce an optimally merged soil moisture product. First, the Korea Land Data Assimilation System (KLDAS) has been established for agricultural drought (i.e. soil moisture deficit) monitoring in South Korea, running the Noah-MP land surface model within the NASA Land Information System (LIS) framework with the added value of local precipitation forcing dataset and soil texture maps. This study compares KLDAS products with two benchmark LDAS products and one remote sensing product (ESA CCI) and examines the performance of KLDAS agricultural drought area percentages in the four major river. Second, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on surface soil moisture (SSM) simulations for the years, 2002–2017. This study analyzes the impact of GRACE DA on modeled SSM and presents the qualitative comparisons of seasonality and annual trend of these SSM estimates in West Africa.