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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Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations.

An increase in heavy precipitation associated with climate change has exacerbated flooding in the Eastern U.S. To assess regional flood risk with changing climatic conditions, we demonstrate the application of a novel hydrologic modeling framework that integrates climate projections with a coupled Noah‐MP land surface model and a two‐dimensional HEC‐RAS hydrodynamic model. We employ this framework along a 41 km reach of the Susquehanna River near Harrisburg, Pennsylvania, where recent flood damages exceeded $2 billion (2011 Irene and Lee floods). Historical and future 30‐year and 100‐year peak‐discharge estimates were compared to assess how flood risk might be altered due to climate change. Results indicate that precipitation increases from climate change do not always lead to increases in flood risk, because interplay of hydrological components in the watershed, which are considered by Noah‐MP, largely controls flooding severity. However, climate change is expected to increase the severity of extreme events; if a 50‐year flood (the recurrence interval of Tropical Storm Lee) occurred toward the end of the 21st century in the worst‐case emission scenario, then flood volume would increase by 40% and flood extent by 15%, due to an increase in soil moisture from a wetter overall climate.

<p>Extreme precipitation is projected to increase in many parts of the world, leading to concern over flooding and associated hazards. Despite the intuitive causal link between extreme precipitation and extreme flooding, observations and models suggest that other factors, in particular, low antecedent moisture may obscure or offset this relationship. Low antecedent soil moisture can arise through evaporation—driven by atmospheric warming that is projected to continue—but moisture is also reduced through an increase in the length of the time between storms, i.e., ‘storm intermittency’, which has been less studied and which can be derived directly from precipitation data. In this presentation, we focus on the modulating role of storm intermittency on the relationship between extreme precipitation and both flood potential, i.e., both flood peaks and volumes. We create an observation-based historical baseline of storm intermittency, 1950-2015, from a set of case-study basins to understand the relationship across a range of hydrometeorological settings. Next, the storm intermittency from a set of 16 CMIP6 climate models is evaluated relative to the baseline, and the evaluation is used to project future intermittency and likely outcomes on flood potential for the period 2016-2100. This projected flood potential is compared with hydrologic simulations from downscaled land surface models forced by the same CMIP6 models and differences between the projected and modeled outcomes are assessed in the context of simulated soil moisture and other hydrologic factors. Overall, we seek to understand the utility of storm intermittency as a predictor of flood potential and to understand the impacts of projected intermittency on future flood hazards.</p>