Global Optimization of Soil Texture Maps From Satellite-Observed Soil Moisture Drydowns and Its Implementation in Noah-MP Land Surface Model

Abstract

Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large-scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in-situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite-estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite-optimized soil texture maps is improved at both grid and in-situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation-oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs).