Drought forecasting for improved water management and

Droughts have become increasingly frequent in recent years across Central Asia, posing significant challenges to water management, agriculture, and socio-economic stability in the region. Accurate forecasting of droughts is crucial for mitigating their impacts, yet effective prediction relies on comprehensive and high-quality datasets from the source areas. In Central Asia, however, such datasets are often sparse or incomplete, limiting traditional monitoring and forecasting approaches. To address this challenge, we employ remote sensing datasets to forecast potential drought occurrence across the region. By leveraging satellite-derived indicators of snow cover, vegetation index (NDVI), and precipitation anomalies, we develop predictive models capable of identifying areas at risk of drought even under limited ground-based observations. The results demonstrate the potential for remote sensing approaches to fill critical data gaps, providing timely and actionable information for decision-makers. Implementation of these forecasts at the policy level can support proactive drought management, resource allocation, and adaptation strategies, ultimately enhancing regional resilience to increasing drought frequency.

We have integrated the developed methodology of drought forecasting into MODSNOW-Tool as an additional functionality of forecasting droughts.