The Efficiency of Data Assimilation

Short-term satellite soil moisture for agricultural drought characterization over Godavari basin, India

Soil moisture (SM) is crucial for identifying agricultural drought, but due to the limited availability of large scale and high-resolution SM data, drought indices based on hydro-meteorological variables are commonly used. Recent advancements in microwave remote sensing, such as the Soil Moisture Active Passive (SMAP) satellite, provide global daily SM at 9-km spatial resolution making it suitable for agricultural drought monitoring. However, while SMAP offers relatively high spatial resolution, it lacks long-term records (available since April 2015), whereas other long-term satellite products have coarser spatial resolution. So, this study evaluates the potential of short-term satellite SM data, specifically SMAP-SM, for agricultural drought characterization using two SM-based indices: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI). The Godavari basin is selected as study area, which is not so well gauged for SM data, and the agricultural drought in the basin is assessed from 2016 to 2021. The results revealed that both SWDI and SMDI indices effectively captured inter-annual/seasonal variations, demonstrating their robustness in comparison to precipitation-based indices rainfall anomalies (RA) and Standardized Precipitation Index (SPI). Spatial analysis reveals that western basin consistently experiences drought conditions, while the eastern region remains relatively wet. The drought area ratio (DAR) analysis across agro-ecological zones (AEZs) of basin reveals that SWDI is more sensitive to severe and extreme droughts, whereas SPI is more responsive to mild and moderate droughts. Zone-wise DAR showed SWDI and SMDI identified distinct drought conditions across all AEZs, whereas SPI and RA showed evenly distributed drought levels across AEZs, underscoring their broader, less soil-specific focus. These findings emphasize the potential of short-term satellite-based SM, as well as SM-derived indices in advancing agricultural drought characterization, offering valuable insights for policymakers in developing region-specific mitigation strategies and improving drought preparedness in other poorly gauged river basins.

OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.