Monitoring and Predicting Agricultural Droughts for a Water-Limited Subcontinental Region by Integrating a Land Surface Model and Microwave Remote Sensing

Abstract

Agricultural drought monitoring and prediction technology are urgently needed. We applied an ecohydrological land data assimilation system (LDAS), which can simulate soil moisture and leaf area index (LAI) by data assimilation of microwave brightness temperature into a land surface model (LSM), to monitor and predict agricultural droughts in North Africa. We successfully monitor nationwide crop failures, which are characterized by the declines of the nationwide wheat production, in Morocco, Algeria, and Tunisia using LAI and soil moisture calculated by the LDAS. Our simulated LAI is well correlated with the nationwide wheat production ( r= 0.70, 0.65, and 0.72 in Morocco, Algeria, and Tunisia, respectively). A general circulation model (GCM)-based seasonal meteorological prediction significantly contributes to accurately predicting LAI and agricultural droughts in 2-3-month lead time. In addition, it is found that initial conditions have an important role in predicting LAI. We demonstrate the capability of our framework to monitor and predict agricultural drought in North Africa. Our proposed framework can contribute to mitigating the negative impact of drought on agriculture in poorly gauged water-limited subcontinental regions.