A Soil and Water Assessment Tool (SWAT) model is adopted to investigate the variability of BW and GW for baseline (1995-2019) and future (2020-2100) periods in a semi-arid Dharoi catchment of the Sabarmati River basin, India. Several non-parametric tests are employed to ascertain statistically significant changes in the hydrologic variables between future and baseline periods. Further, a multi-criteria decision-making approach is adopted to prioritize the watersheds for implementing climate change adaptation measures. The year 2040 is established as a ‘tipping point’ for climate-induced changes in the Dharoi catchment, wherein a distinct reversal in the hydroclimatic regime is evident before and after 2040. (Sharma et al., 2023).

The impact of climate change (CC) and land use land cover change (LULCC) on model parameter variability and alterations in streamflow and water balance components of a semi-arid river basin (i.e., Dharoi catchment of Sabarmati basin) in western India. The results showed that CC has a more significant impact on SWAT model parameter variability than LULCC. Thus, CC is the primary driver of streamflow changes in the Dharoi catchment, wherein a four-fold increase in the streamflow is noticed in the period 2005–2014 vis-a-vis 1995–2004 (Sharma et al., 2022).

A new frequency-based performance measure (FBPM) that can incorporate application-specific information for model evaluation is proposed to address the limitations of existing measures. FBPM is derived using a data classification scheme to partition the observed data into several classes and evaluate frequencies of each class's chronologically paired observed and forecasted values. A variant of FBPM, composite performance measure (CPM), is also developed to include additional indices that evaluate error, variance, and other statistical characteristics of the observed and forecasted series. The FBPM and CPM are scale-independent, informative, interpretable, and outlier-resistant (Teegavarapu et al., 2022).

Individual and joint influences of Atlantic multidecadal oscillation (AMO) and El Niño Southern Oscillation (ENSO) on precipitation (P) and temperature (T) variability in a Florida are investigated.  To understand the variability of daily P-T correlation (or association) within various temperature ranges or intervals, a Latin hypercube bootstrap sampling (LHBS) procedure is adopted. The results suggest that climate variability has a stronger influence than local meteorological conditions in generating higher precipitation (Teegavarapu and Sharma, 2021).