India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socioeconomic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India

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Despite the advances in climate change modeling, extreme events pose a challenge to develop approaches that are relevant for urban stormwater infrastructure designs and best management practices. The study first investigates the statistical methods applied to the land‐based daily precipitation series acquired from the Global Historical Climatology Network‐Daily (GHCN‐D). Additional analysis was carried out on the simulated Multivariate Adaptive Constructed Analogs (MACA)‐based downscaled daily extreme precipitation of 15 General Circulation Models and Weather Research and Forecasting‐based hourly extreme precipitation of North American Regional Reanalysis to discern the return period of 24‐hr and 48‐hr events. We infer that the GHCN‐D and MACA‐based precipitation reveals increasing trends in annual and seasonal extreme daily precipitation. Both BCC‐CSM1‐1‐m and GFDL‐ESM2M models revealed that the magnitude and frequency of extreme precipitation events are projected to increase between 2016 and 2099. We conclude that the future scenarios show an increase in magnitudes of extreme precipitation up to three times across southeastern Virginia resulting in increased discharge rates at selected gauge locations. The depth‐duration‐frequency curve predicted an increase of 2–3 times in 24‐ and 48‐h precipitation intensity, higher peaks, and indicated an increase of up to 50% in flood magnitude in future scenarios.

Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western US. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April-July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gages and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, i.e., years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, i.e., below-median years (P15, P57.5]), minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid to late April for colder regions. We report similar findings using a modified NRCS procedure in nine large UCRB basins, highlighting the importance of the snowpack-streamflow relationship in streamflow predictability. We propose an ‘adaptive sampling’ approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of upto 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning.

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