Advanced Hybrid Deep Learning for Sub-Seasonal to Seasonal Forecasting of Soil Moisture Drought Over India

Soil Moisture drought (SMD), characterized by insufficient soil moisture, affects water resources, crop yields, and economic stability across various temporal scales. India is an agrarian nation with ~70% of population dependent on agriculture. Forecasting SMD at sub-seasonal to seasonal (S2S) scales will support crop and water management, optimizing yields and averting losses. Traditionally, dynamical models like North American Multi Model Ensemble (NMME), CFSv2, and ECMWF's SEAS5 provide S2S predictions up to ten months, predicting drought onset and intensity. These models require post-processing through bias correction and downscaling due to uncertainties in initial conditions and parameterizations. Although dynamical forecasts show considerable skill in predicting extremes, forecast accuracy needs refinement to improve reliability and utilization in operational systems. In recent years, advancements in deep learning have shown potential to meet or surpass the quality of dynamical forecasts.

Recognizing the skill of dynamical S2S forecasts, this study develops a hybrid deep learning framework to predict SMDs in India at 1-3-month lead times. We combined dynamical forecasts from CFSv2 and SEAS5 with antecedent land-atmosphere conditions, climate drivers, and static features to predict SMDs using Graph Neural Networks (GNNs) with an extreme-aware custom loss function. GNNs have a better ability to learn spatial and temporal patterns and offer advantages over conventional models like ConvLSTM. Land-atmosphere variables include precipitation, maximum-minimum temperature, vapour pressure deficit, evapotranspiration, vegetation index, soil moisture, and wind speed. Large-scale climate drivers that influence rainfall patterns over India include El Niño, NAO, IOD, PDO, and MJO. Static features comprise soil type, position vector, elevation, and land use for essential contextual information. The model training was performed from June 1981–May 2015, and testing from June 2015–May 2022. The model performance is evaluated using metrics like probability of detection, percentage correct, false alarm rate, and equitable threat score. We also compare the model with dynamical forecasts and other benchmark deep learning algorithms to develop functional drought early warning systems.