This study forecasts and assesses drought situations in various regions of India (the Araveli region, the Bundelkhand region, and the Kansabati river basin) based on seven simulated climates in the near future (2015–2044). The self-calibrating Palmer Drought Severity Index (scPDSI) was used based on its fairness in identifying drought conditions that account for the temperature as well. Gridded temperature and rainfall data of spatial resolution of 1 km were used to bias correct the multi-model ensemble mean of the Global Climatic Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data, which were corrected on a monthly scale. The outcome of the forecast suggests multiple severe-to-extreme drought events of appreciable durations, mostly after the 2030s, under most climate scenarios in all the three study areas. The severe-to-extreme drought duration was found to last at least 20 to 30 months in the near future in all three study areas. A high-resolution drought index was developed and proven to be a key to assessing the drought situation.

Urbanization leads to increase in impervious area and eventually increasing vulnerability to urban flooding. Deer Creek in the city of Brentwood, Missouri, has been experiencing major significant high flows in the past and has flooded the neighboring communities several times. A major step in mitigating the flood is to evaluate the flooding extent. The current research utilizes the Hydrologic Engineering Center River Analysis System (HEC-RAS) and hydrodynamic modelling software—MIKE 21 to study the extent of floodplain in the urbanized area. A comparative study between the models to evaluate the modelling advantages of each model is performed. Moreover, the study focuses on two-dimensional (2D) modelling capabilities of each model as 2D models are more useful in urban areas with more numbers of discrete features. The tradeoff between the accuracy and resources is evaluated while comparing the 2D floodplain modelling capabilities of each models. Channel morphology and flow characteristics for different return period will be incorporated while modeling and the USGS gage data will be utilized for calibration of the models. The models will be calibrated for peak stages along the Deer Creek, with downstream boundary condition of normal depth and the unsteady flow simulations will be performed using the relevant flow hydrographs. The streamflow properties simulated will be derived from the measurements of USGS gaging site near Brentwood, MO.

Regional assessments of droughts are limited, and meticulous assessments over larger spatial scales are generally not substantial. Understanding drought variability on a regional scale is crucial for enhancing the resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and projection of droughts based on regional climate across various spatial and temporal scales. This work focuses on drought analysis using a high-resolution dataset for three drought-prone regions of India between 1950 and 2016. This study also uses monthly values of the self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith approximation, which is physically based on potential evapotranspiration. Climate data are statistically downscaled and formulated to form a timeline for characterizing major drought events. The downscaled climate data hold a good statistical agreement with station data with correlation coefficients (R) ranging from 0.91 to 0.96. Drought analysis indicates and identifies several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in reports of various literatures for those regions.

Groundwater is one of the important hydrological cycles that has a major role in balancing the ecosystem. The drastic change in climate in the twenty-first century has adversely affected the groundwater storage (GWS), later the hydrological cycle. The limited availability of groundwater data poses difficulty to investigate the effects of changing the climate on GWS. This research addressed the impact of climate variability in GWS variability in Northern India. The underlying goal was to assess the relationship between GWS and climate variability. Here, El Niño-Southern oscillation (ENSO) was used as an indicator of long-term climate change which can be categorized into three climatic phases—El Niño, La Niña, and neutral phases. Gravity recovery and climate experiment (GRACE) derived GWS anomaly was calculated to understand groundwater temporal variability as an effect of ENSO phases. Pearson correlation analysis was done to assess the relationship between GWS, precipitation, and ENSO. A significant positive correlation was observed between precipitation, GWS, and ENSO. A decreasing trend in GWSA was observed. Increased storage during La Niña and relatively less groundwater storage during El Nino were observed. This study highlights the research gap between GWS and climate variability. A better understanding of the climatological effect on GWS might be more useful to water managers in addressing the complex issues related to groundwater management.