Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model

Abstract

Abstract. Early warning of agricultural drought can enable decision makers to act to improve food security. Land-surface models are useful tools to inform such monitoring systems, but model errors are problematic. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land-surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find a 44% reduction in root-mean-squared error for a 5-year hindcast after assimilating a single year of soil moisture ob-5 servations to update model parameters. The use of an improved remotely-sensed rainfall dataset contributes to 10% of this reduction in error. Improved rainfall data has the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying down. The significant reduction in root-mean-squared error we find after assimilating a single year of observations bodes well for the production of improved soil moisture forecasts over sub-Saharan Africa where subsistence farming remains prevalent.

Representation of Stony Surface‐Atmosphere Interactions in WRF Reduces Cold and Wet Biases for the Southern Tibetan Plateau

“…Pinnington et al. (2017) significantly improved model estimation of soil moisture using satellite‐derived estimates of shallow soil moisture. However, satellite data has not been widely used in model improvement yet, especially for parameters such as soil type.…”

“…Yang et al (2016) estimated soil parameter values by assimilating Advanced Microwave Scanning Radiometer for Earth Observing System brightness temperature data. Pinnington et al (2017) significantly improved model estimation of soil moisture using satellite-derived estimates of shallow soil moisture. However, satellite data has not been widely used in model improvement yet, especially for parameters such as soil type.…”