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Impacts of climate change may vary from location to location for various reasons and may exhibit unique features in some regions. In this study, we considered India's Northeast which is geographically and hydro-meteorologically unique. The Gomati River catchment is the largest and one of the important river catchments in Tripura, a state in the northeastern region of India. Due to changes in climatic conditions over the previous few decades, the flow pattern of this catchment has changed significantly. The study examines the effect of climate change on the climatology of precipitation and streamflow using the simulation output from the Earth System Model (ESM) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) into two different conceptual hydrological models for streamflow simulation. Findings indicate that water availability is projected to be reduced in the future due to a reduction in the average streamflow volume by approximately 12–30% (varies from model to model and scenario to scenario). Moreover, the water demands for other hydrological processes, i.e., evaporation/evapotranspiration, are expected to increase due to a significant increase in temperature (∼1.4–2.1 °C). A sustainable management of water resources will benefit from the research outcomes of this study.

Conversion of natural to urban land forms imparts influence on local and regional hydroclimate via modification of the surface energy and water balance, and consideration of such effects due to rapidly expanding megapolitan areas is necessary in light of the growing global share of urban inhabitants. Based on a suite of ensemble-based, multi-year simulations using the Weather Research and Forecasting (WRF) model, we quantify seasonally varying hydroclimatic impacts of the most rapidly expanding megapolitan area in the US: Arizona's Sun Corridor, centered upon the Greater Phoenix metropolitan area. Using a scenario-based urban expansion approach that accounts for the full range of Sun Corridor growth uncertainty through 2050, we show that built environment induced warming for the maximum development scenario is greatest during the summer season (regionally averaged warming over AZ exceeds 1 °C). Warming remains significant during the spring and fall seasons (regionally averaged warming over AZ approaches 0.9 °C during both seasons), and is least during the winter season (regionally averaged warming over AZ of 0.5 °C). Impacts from a minimum expansion scenario are reduced, with regionally averaged warming ranging between 0.1 and 0.3 °C for all seasons except winter, when no warming impacts are diagnosed. Integration of highly reflective cool roofs within the built environment, increasingly recognized as a cost-effective option intended to offset the warming influence of urban complexes, reduces urban-induced warming considerably. However, impacts on the hydrologic cycle are aggravated via enhanced evapotranspiration reduction, leading to a 4% total accumulated precipitation decrease relative to the non-adaptive maximum expansion scenario. Our results highlight potentially unintended consequences of this adaptation approach within rapidly expanding megapolitan areas, and emphasize the need for undeniably sustainable development paths that account for hydrologic impacts in addition to continued focus on mean temperature effects.

A mesoscale meteorological model (MM5) is linked to a hydrologic model to simulate river-basin response to single-storm events. MM5 uses a nested-domain configuration, with grid increments of 36, 12 and 4 km, to produce high-resolution precipitation fields for input to the hydrologic model. A problem that will arise when performing longer-term simulations is the extraordinary computational demands of the nested MM5. To evaluate the effect that the MM5 resolution has on the simulation of direct surface runoff in the linked-model experiments, and with the goal of decreasing the computational intensity of these experiments, 3 single-storm events and their basin response were simulated with MM5 using 3 domain set-ups: 36-12-4, 36-12, and 36 km. The results show that the 36-12 km set-up generates similar patterns of precipitation and direct surface runoff to those of the 36-12-4 km domain set-up. The 36 km domain set-up produces unrepresentative precipitation distributions in time and space. It is concluded that 12 km precipitation fields may be a suitable compromise, providing sufficient resolution for simulating the basin response to climate variation and change.

Regional hydroclimatic variability and change can affect water resources and hydropower generation. It is essential to assess hydropower potential under current and future climatic conditions to inform the design and operation of hydropower infrastructures. Here, we employ an integrated modeling framework to assess the impact of projected hydroclimatic conditions on water resource systems and hydropower generation. The integrated framework samples climate model outputs under different scenarios to force a hydrologic model and produces streamflow projections. The projected streamflows are inputs for the future hydropower potential assessment. We implement the framework in the central Himalayan river basin. Our results demonstrate substantial spatiotemporal variability in different water balance components (precipitation, evapotranspiration, and water yield) under current and future climatic conditions. For the Himalayan Tila river basin, the annual average energy production is expected to increase under future hydroclimatic conditions (up to 39% in Tila-2 hydropower project, suggested by ensemble mean). This increase in energy is driven mainly by the increased streamflow projections, particularly during the dry season and in the late century. Our results highlight the impacts of hydroclimatic variability in hydropower productions and are of practical use to provide decision-relevant information for designing and operating hydropower infrastructures. The integrated modeling framework presented here is region-specific; however, the approach is reproducible, and the overall insights are generalizable across the Himalayan region.

This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM and hybrid ELM-based methods including ELM with salp swarm algorithm (SSA), ELM with equilibrium optimizer (EO) and ELM with reptile search algorithm (RSA). The methods were evaluated using different lagged inputs of temperature, precipitation and river flow data obtained from Upper Indus Basin located in Pakistan. Models performance evaluation was based on common statistics such as root mean square errors (RMSE), mean absolute errors, determination coefficient and Nash–Sutcliffe Efficiency. The prediction accuracy of single ELM model with respect to RMSE was improved by 2.8%, 7.7%, 15% and 20.7% using SSA, EO, RSA and IRSA metaheuristic algorithms in the test period, respectively. The ELM-IRSA model with lagged temperature and river flow inputs provided the best predictions with the RMSE improvement of 20.7%.

Weighting climate models has recently become a more accepted approach. However, it remains a topic of ongoing discussion, especially for analyses needed at regional scales, such as hydrological assessments. Various studies have evaluated the weighting approaches for climate simulations. Yet, few case studies have assessed the impacts of weighting climate models on streamflow projections. Additionally, the methodological and location limitations of previous studies make it difficult to extrapolate their conclusions over regions with contrasting hydroclimatic regimes, highlighting the need for further studies. Thus, this study evaluates the effects of different climate model’s weighting approaches on hydrological projections over hydrologically diverse basins. An ensemble of 24 global climate model (GCM) simulations coupled with a lumped hydrological model is used over 20 North American basins to generate 24 GCM-driven streamflow projections. Six unequal-weighting approaches, comprising temperature-, precipitation-, and streamflow-based criteria, were evaluated using an out-of-sample approach during the 1976–2005 reference period. Moreover, the unequal-weighting approaches were compared against the equal-weighting approach over the 1976–2005, 2041–2070, and 2070–2099 periods. The out-of-sample assessment showed that unequally weighted ensembles can improve the mean hydrograph representation under historical conditions compared to the common equal-weighting approach. In addition, results revealed that unequally weighting climate models not only impacted the magnitude and climate change signal, but also reduced the model response uncertainty spread of hydrological projections, particularly over rain-dominated basins. These results underline the need to further evaluate the adequacy of equally weighting climate models, especially for variables with generally larger uncertainty at regional scale.

Hydrological models are used to assess natural and man-made changes in watersheds worldwide. Proper input data collection and handling are essential to reduce simulation uncertainty. Thus, this study reviews the sources of physical and hydroclimatic data, used in the last 5 years, from 55 articles that applied the SWAT model in Brazil. Most studies took place in the Atlantic Forest biome (20), followed by Cerrado (14), Amazon (11), and Caatinga (10). Worth noting that there are no studies published in the Pantanal and Pampa biomes. National databases (INPE, INMET, EMPRAPA, and ANA) are the most used in data acquisition process, followed by regional databases, more applied in smaller basins. Global databases are more sought after in studies of large basins due to their low spatial resolution. National climate data have low spatial density and are only available in five states at the regional level, so satellite data and reanalysis are viable alternatives in regions with little climate monitoring. Future research directions include (1) evaluating and comparing available data, (2) using high-resolution imagery to map land use in small catchments, (3) expanding the model’s database of vegetation parameters to cover all classes identified in high-resolution images, (4) create a database at regional level in the states, (5) develop software to manage hydroclimatic information, and (6) continuously monitor the quality of water bodies.

Closed-basin lakes are highly sensitive to climatic variability, yet for the Lake Van Basin (Türkiye), the dynamic and spatially heterogeneous linkages among atmospheric drivers and lake-level changes (particularly their lag structure and predictive directionality) remain insufficiently quantified in a unified multivariate setting. This study examines how temperature and precipitation jointly influence hydrological behavior in the Lake Van Basin using a multi-station Vector Autoregression (VAR) framework. By integrating long-term observations from multiple meteorological stations, the analysis explicitly captures the spatial heterogeneity that characterizes this complex endorheic system and provides a consistent basis for comparing station-specific dynamics. The results show strong persistence in lake-level dynamics across specifications, with lagged lake-level coefficients of 0.2595 to 0.3685 (p < 0.01), indicating a buffered endorheic response. Temperature exhibits a highly consistent seasonal dependence across stations, reflected by a uniformly negative and significant four-month temperature lag in the temperature equations (−0.34 to −0.42, p < 0.01). Granger-causality tests further indicate robust bidirectional coupling between temperature and precipitation in all station specifications (p < 0.01 and typically p ≤ 0.05), while climate-to-lake-level linkages remain spatially heterogeneous but are statistically supported across both Tatvan-based and Gevas-based specifications (Tatvan-Tatvan: p < 0.01 for both climate variables; Tatvan-Ahlat: temperature p = 0.000; Gevas-Van, Gevas-Ercis, and Gevas-Muradiye: temperature p = 0.000 and precipitation p = 0.013, 0.008, and 0.015, respectively). Distinct station-level patterns further demonstrate that topographical differences modulate the strength and direction of climate–hydrology linkages across the basin. By providing a coherent, causally consistent understanding of these interactions and explicitly incorporating season-specific VAR and Granger-causality evidence, this study offers a transferable methodological framework for analyzing climate-sensitive lake systems and highlights the need to incorporate temperature-driven processes into water-management and climate-adaptation strategies in endorheic basins.

Reconstruction of lake-level fluctuations from landform and outcrop evidence typically involves characterizing periods with relative high stands. We developed a new approach to provide water-level estimates in the absence of shoreline evidence for Owens Lake in eastern California by integrating landform, outcrop, and existing lake-core data with wind-wave and sediment entrainment modeling of lake-core sedimentology. We also refined the late Holocene lake-level history of Owens Lake by dating four previously undated shoreline features above the water level (1096.4 m) in AD 1872. The new ages coincide with wetter and cooler climate during the Neopluvial (~3.6 ka), Medieval Pluvial (~0.8 ka), and Little Ice Age (~0.35 ka). Dates from stumps below 1096 m also indicate two periods of low stands at ~0.89 and 0.67 ka during the Medieval Climatic Anomaly. The timing of modeled water levels associated with 22 mud and sand units in lake cores agree well with shoreline records of Owens Lake and nearby Mono Lake, as well as with proxy evidence for relatively wet and dry periods from tree-ring and glacial records within the watershed. Our integrated analysis provides a continuous 4000-yr lake-level record showing the timing, duration, and magnitude of hydroclimate variability along the south-central Sierra Nevada.

Burundi is susceptible to future water-related disasters, but examining the influence of climate change on regional hydroclimatic features is challenging due to a lack of local data and adaptation planning. This study investigated the influence of climate change on hydroclimate-focused changes in the climatology of heavy precipitation (and streamflow) means and extremes based on the multi-model ensemble mean of earth system models in the sixth phase of the Coupled Model Intercomparison Project (CMIP). For runoff analysis, hydrologic responses to future climate conditions were simulated using the Soil and Water Assessment Tool (SWAT) model over the Ruvubu River basin, Burundi. Temperature increases by 5.6 °C, with strong robustness, under future climate conditions. The mean annual precipitation (and runoff) undergoes large seasonal variations, with weak robustness. Precipitation (and streamflow) changes between the wet and dry seasons differ in signal and magnitude. However, alterations in both the amount and frequency of precipitation reveal the intensification of the water cycle due to anthropogenic climate change. Thus, the highest variability in the maximum daily streamflow is shown in months of long wet seasons, especially in the far future (2085). Without considering the regional climate characteristics and shared socioeconomic pathway (SSP) scenarios, this behavior is expected to be enhanced in 2085 (compared with 2045) and increase the severity of extreme precipitation and flood risk. Climate change will cause alterations in the magnitude and seasonal distributions of extreme precipitation (and streamflow). These findings could be important for flood planning and mitigation measures to cope with climate change in Burundi.