Assimilation of Soil Moisture Data into Agricultural Drought and Hydrological Predictions

Presented by:
Dr. Di Liu
Associate Professor
HoHai University, China

Date: Wednesday January 31, 2024
Time: 10 am MST / 11am CST
Location: Zoom (Click here to register)

Soil moisture plays an important role in the global water and energy cycle. The anomalies of soil moisture may exert great impact on the subsequent climate variables, thus, leading to the evolution of climate extremes (e.g., flood, drought and heat wave) by linking hydroclimatic fluxes at different spatio-temporal scales. With the development of satellite and remote sensing techniques, as well as the machine learning and data assimilation technique, multi-sources of soil moisture products are available for the application in the agricultural and hydrological fields. This topic shares some cases about the soil moisture data assimilation at different soil depth from surface to root zone, and the application of multi-sources of soil moisture in the agricultural and hydrological fluxes simulation and forecast. It is found that machine learning technique (i.e., support vector machine) combined with data assimilation technique (i.e., Ensemble Kalman Filtering) could improve the soil moisture estimation at deep zone. The assimilation of remote sensing soil moisture could efficiently improve the forecasting of near-real time agricultural drought (quantified using soil water deficit index (SWDI)) at most stations. Such improvement can persist up to 2-4-week lead time. The assimilation of remote sensing soil mositure into the community land surface model (CLM4.5) model can improve the SM simulations, as well as other hydrological fluxes (i.e., ET, surface runoff), especially over the climate transition zones in Africa, East Australia, South South America, Southeast Asia, and East North America in summer season. The Local Ensemble Kalman Filter (LEnKF) technique improves the performance of CLM4.5 model compared to the directly substituted method.

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