Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010–2011)

Abstract

Despite the importance of the ecological and agricultural aspects of severe droughts, no drought monitoring and prediction framework based on a land data assimilation system (LDAS) has been developed to monitor and predict vegetation dynamics in the middle of droughts. In this study, we applied a LDAS that can simulate surface soil moisture, root-zone soil moisture, and vegetation dynamics to the Horn of Africa drought in 2010-2011 caused by the precipitation deficit in two consecutive rainy seasons. We successfully simulated the ecohydrological drought quantified by the model-estimated soil moistures and leaf area index (LAI). The root-zone soil moisture and LAI are good indicators of prolonged droughts because they reflect the long-term effects of past precipitation deficit. The precipitation deficit in 2010 significantly affected the land surface condition of the next rainy season in 2011, which indicated the importance of obtaining accurate initial soil moisture and LAI values for prediction of multiseasonal droughts. In addition, the general circulation model-based seasonal meteorological prediction showed good performance in predicting land surface conditions of the Horn of Africa drought.In this study, we followed the strategy of Sawada et al. [2014] and used model-estimated soil moistures and LAI as drought indicators. The type of drought quantified by soil moistures and LAI is identified as an ecohydrological drought in this paper. SAWADA AND KOIKELDAS FOR ECOHYDROLOGICAL DROUGHT 8229

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

“…A variety of land data assimilation systems (LDAS) have been developed in recent decades, such as the NLDAS in the United States (Mitchell et al, ; Xia, Peters‐Lidard, et al, ; Xia, Ek, et al, ) and the Global Land Data Assimilation System (GLDAS) (Rodell et al, ), and they play an important role in drought early warning. For example, the recently developed Coupled Land and Vegetation Data Assimilation System (CLVDAS) can generate initial conditions of both soil moisture and leaf area index (LAI), and thus can provide unique opportunities to predict ecohydrological droughts (Sawada & Koike, ). Improving data assimilations is expected to generate better initializations of climatic and hydrologic models and obtain improved prediction skill for drought.…”

Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

“…Zhou et al [4] detected the widespread decline of Congo rainforest by analyzing VOD data from AMSR-E. In addition, datasets of passive microwave satellite observations have been assimilated to land surface models in order to improve their performance of simulating water and energy fluxes (e.g., [5][6][7][8][9][10][11]). …”

Thermal Inertia-Based Method for Estimating Soil Moisture

“…Data assimilation procedures are downscaled schemes of microwave-based soil moisture, which has a scale of several tens of kilometers, to one to a couple of kilometers. These schemes have recently been improved [47][48][49][50] using visible, near-infrared, and thermal-infrared satellite data, which have more precise spatial resolution than microwaves. These procedures can be competitive with thermal inertia procedures to derive surface soil moisture.…”