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Event-based hydrological models are extensively adopted for the estimation of design floods and in operational flood forecasting frameworks. However, an accurate estimation of the initial hydrologic condition (IHC) is essential in enhancing the predictive capability of an event-based hydrological model. Hence, in this study, IHCs of an event-based conceptual model are estimated using two different methods: (1) assimilation of observed variables such as streamflow and soil moisture using an ensemble Kalman filter and (2) states obtained from the continuous model calibrated using four different calibration metrics. The observed flood events at the Jagdalpur catchment are simulated using a conceptual hydrologic model setup at two spatial resolutions (lumped and semi-distributed). The results of the study demonstrate that IHCs estimated by the continuous models perform better than those obtained through data assimilation. The performance of semi-distributed event-based models was found to be outperforming their lumped counterparts demonstrating the advantage of increased model resolution. The states obtained from the continuous models calibrated using Nash–Sutcliffe Efficiency (NSE) are performing well in initialising the event-based models. The median efficiency of the semi-distributed event-based model (based on states from the NSE calibrated continuous model) is 0.91 and 0.77 during calibration and validation periods, respectively.

This study analyses the variability of daytime-only, nighttime-only, and compound heat waves (HWs) and their impact on population exposure across India using shared socioeconomic pathways (SSPs) scenarios (SSP126, SSP245, SSP370, and SSP585) from the Coupled Model Intercomparison Project Phase 6 experiment. The research questions addressed are: (1) what effects might compound heatwaves have under climate change scenarios? (2) How are compound heatwaves expected to impact the population in the future? The outcomes indicate that the compound HWs may increase by 4.6 events annually in Northwest India (NWI) under the SSP585 scenario. In contrast, daytime-only HWs are expected to decline after 2060, except in the Himalayan region, possibly due to changes in monsoon patterns and increased evaporative cooling. It is anticipated that nighttime-only heatwaves will uniformly increase across all regions and scenarios, with the most substantial rises observed in the Central Northeast India (CNI) and NWI. Under the SSP370 scenario during 2061–2100, the population exposure to compound heatwaves and nighttime-only heatwaves is projected to increase substantially across all regions. Specifically, exposure to compound heatwaves is anticipated to exceed historical levels by more than 30 times in most regions. Both the CNI and NWI regions show the highest rise in compound and nighttime-only heatwave extremes. The outcomes provide a substantial scientific foundation for policymakers to inform and enhance heat action plans at the national, state, and local levels.

Climate change significantly impacts the natural systems, accelerating the global water cycle, and impacting various ecosystem services. However, the expected effects of climate change on the frequency and severity of extreme events on hydrological systems vary significantly with location. The present study investigates the uncertainties engulfed in hydrological predictions for the Tungabhadra River Basin. The ensemble streamflow projections were generated using four hydrological models, five climate models, and four climate scenarios to illustrate the associated uncertainties. The uncertainty in hydrological components such as streamflow (QQ), water availability (WA), and potential evapotranspiration (PET) was analysed in the future period (2015–2100). The results suggest that, in the monsoon period, precipitation projections increase by about 10.43–222.5%, whereas QQ projections show an increment between 34.50 and 377.7%. The analysis of variance (ANOVA) technique is used to further quantify the contribution of different sources to the total uncertainty. Furthermore, the ensemble spread is optimized using quantile regression forests (QRF), and the post-processed flows are likely to decrease up to 7% in June and increase up to 70% in September. This study is envisaged to give insights into the quantification of uncertainties in the prediction of future streamflow for rational and sustainable policymaking.

Accurate streamflow simulation is crucial for effective hydrologic forecasting and water resource management. This study introduces a nested discretization scheme aimed at refining catchment delineation based on the spatial heterogeneity of its characteristics. The scheme aims to align with the assumption of spatial homogeneity within a hydrologic model, enhancing simulation accuracy. Investigating the impact of discretization, the study evaluates lumped, semi-lumped, and semi-distributed conceptual model structures, both in continuous and event-based simulations of flood events in Jagdalpur and Wardha basins of India. Results indicate superior performance by continuous semi-distributed and semi-lumped models (efficiency > 0.77), followed by continuous lumped models (efficiency > 0.68) during both calibration and validation periods at both basins. Event-based models, particularly semi-distributed and semi-lumped, exhibit higher median efficiency (> 0.71 at Jagdalpur and > 0.67 at Wardha) compared to their lumped counterparts (0.57 at Jagdalpur and 0.27 at Wardha), showcasing their proficiency in capturing spatial variability. However, a marginal performance increase in semi-lumped models with increased spatial discretization is observed, accompanied by a significant rise in computational time. This research contributes insights into the trade-offs associated with the proposed discretization scheme and emphasizes the balance between model complexity and efficiency for optimal streamflow simulations.