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Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). Global hydrological reanalyses are powerful datasets that can fill the observational gap in river discharge observation. They make wide ranging downstream applications possible worldwide, from water resources to ocean monitoring and early warning systems, through earth system model diagnostic, hydrological extreme understanding and model calibration and training. The GloFAS hydrological reanalysis dataset is a product of the Copernicus Emergency Management Service freely available from the Copernicus Climate Data store, offering daily time series from early 1980 until recent, updated daily with a 3‐ to 5‐day delay.

Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. The analysis is performed for 471 gauging stations across the globe for the period 1971-2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%-73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications.

Hydrological signatures (HS) have proven to be highly effective in calibrating physically‐based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)‐based hydrological models has been limited. To address this gap, we propose a novel HS‐informed framework that dynamically integrates HS into DL parameterization through a multi‐task learning approach. This study evaluates the impact of HS integration on model performance using a large‐scale, global hydrological data set. The HS‐informed model achieved a significant performance improvement, with a median Nash‐Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow‐dominated (+0.135), drought‐prone (+0.148), and flood‐prone basins (+0.159). Sensitivity analysis further revealed that the HS‐informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30‐day forecast period. Shapley Additive Explanations analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long‐term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non‐climatic attributes. These findings demonstrate that integrating HS offers a superior approach to traditional point‐error‐based calibration in AI‐driven hydrological modeling.

The resultant impact of climate change and urbanization has caused extensive disruption to natural hydrological processes, thus enhancing the flood risk in susceptible areas. This study evaluated flood processes in the Pallikaranai Marshland–Buckingham Canal corridor using detailed flood inundation modeling and risk assessment methodology. Important geospatial factors and variables, such as rainfall, Digital Elevation Model (DEM), slope, Land Use Land Cover (LULC), river distance, flow length, and Normalized Difference Water Index (NDWI), were weighed and ranked. These weighted parameters were assimilated to estimate the Flood Hazard Index (FHI), which was subsequently applied to create an intricately mapped flood hazard. The analysis and testing of the involved parameters by assessing flood susceptibility has been facilitated with hydrological modeling, Geographic Information System (GIS), as well as with remote sensing procedures. Deep-learning frameworks, particularly convolutional neural networks, have also shown high predictive capability for regional flood susceptibility (Kalantar et al. 2021). The findings suggest that urban growth has resulted in extensive wetland degradation, elevated surface runoff, and more frequent flooding, particularly during intense rainfall. The FHI-based flood hazard map identifies critical areas at risk of flooding, highlighting the explicit role of land cover changes in flood intensity and frequency. This study underscores the urgent need for sustainable urban planning, wetland conservation, and climate-resilient infrastructure to mitigate flood hazards and enhance longterm urban flood resilience in the region. These results help to better understand urban flood hazards and offer a scientific foundation for future flood management.

Hydrological dynamics in glacierized catchments of the Alps are shaped by temperature‐driven processes, including snow and ice melt as well as precipitation, leading to diel streamflow cycles that vary in intensity within‐ and among‐the seasons. During the summer melt period, the amplitude of these diel cycles increases due to diminished snow storage and the emergence of efficient subglacial drainage systems. Accurately modeling these sub‐daily cycles remains difficult, due to a lack of high‐resolution meteorological input data for melt simulations and due to challenges in parameterizing meltwater routing through dynamic glacial systems. This research develops an approach for downscaling daily streamflow timeseries to sub‐daily timescales (daily flow duration curves) in alpine glacierized catchments influenced by snow and ice melt runoff. We adapt a maximum entropy framework (POME) to the specificities of glacial systems, that we calibrate on a 45‐year data set of 15‐min discharge records from seven glacier‐fed catchments in the southwestern Swiss Alps. The calibrated method is then applied to the outputs of a semi‐lumped hydrological model that simulates daily discharge and provides hydrological variables such as snow depth and ice melt to inform the downscaling, and the results are evaluated against observed discharge. Our results reveal that a sigmoid function effectively represents seasonally varying daily flow duration curves in glacierized catchments and highlight the influence of climate warming on sub‐daily flow dynamics over recent decades. This downscaling method offers a robust tool for reconstructing sub‐daily discharge in catchments with limited data, opening new perspectives for hydrological modeling at finer scales.

The predominant philosophy underlying most environmental modelling is a form of pragmatic realism. The limitations of this approach in practical applications are discussed, in particular, in relation to questions of scale, nonlinearity and uniqueness of place. A new approach arising out of the concept of equifinality of models (structures and parameter sets) in application is outlined in the form of an uncertain 'landscape space' to model space mapping. The possibility of hypothesis testing within this framework is proposed as a means of refining the mapping, with a focus on the differentiation of function within the model space. The approach combines elements of instrumentalism, relativism, Bayesianism and pragmatism, while allowing the realist stance that underlies much of the practice of environmental modelling as a fundamental aim. It may be an interim philosophy that is awaiting developments in measurement technique to allow further refinement, but allows some coherent guidance about how to be specific in presenting predictions to end users.

Irrigation plays a critical role in stabilizing agricultural productivity under increasing climatic variability. However, the intensification of droughts and extreme weather events is revealing the vulnerability of irrigation systems, particularly due to mismatches between peak water needs and natural water availability. This study introduces a probabilistic framework that integrates distributed agro‐hydrological modeling with stochastically generated climate inputs to assess irrigation water needs (blue water, BW) and its associated risks. The framework is applied to eight climatically diverse, high‐productivity regions in Italy, highlighting the near absence of interannual memory, quantified through cross‐correlation analyses, and a steep increase in return period associated with small increases in BW. This behavior reflects the concentration of extreme irrigation needs during rare combinations of prolonged dry spells and peak crop water requirements (CWRs). Systemic challenges emerge in Northern Italy, which exhibits lower mean BW but higher interannual variability, driven by water‐intensive crops in highly variable precipitation regimes. Conversely, Southern Italy shows higher but more stable BW patterns, associated with chronic water scarcity, drought‐adapted cropping systems, and long‐standing reliance on irrigation. The results underscore the need to incorporate irrigation risk into water management strategies—similar to flood risk planning—and provide actionable insights for designing resilient irrigation infrastructure. Moving beyond deterministic simulations, the proposed approach enables the estimation of irrigation return periods, supports probabilistic forecasting, and uncovers key interdependencies among hydrological and agronomic variables. This approach provides a robust foundation for sustainable agricultural planning under uncertainty and climate change.

The recent floods in the Kedarnath area, Uttarakhand are a classic example of flash floods in the Mandakini River that devastated the country by killing thousands of people besides livestock. Though the duration of the event was small compared to other flood disasters in the country, it resulted in severe damage to property and life. Post-disaster satellite images depict that the river banks were eroded completely along the Kedarnath valley due to the flash floods and few new channels were visible. Extreme erosion took place in the upstream portion of Kedarnath, besides the breach of Chorabari Lake and deposition of debris/sediments in the valley. Hydrological and hydraulic simulation study was carried out in the Mandakini River using space-based inputs to quantify the causes of the flash floods and their impact. Chorabari Lake breach analysis was carried out using Froehlich theory. Flood inundation simulations were done using CARTO DEM of 10 m posting in which the combined effect of lake breach and high-intensity rainfall flood was examined. As the slopes are very steep in the upstream catchment area, lag-time of the peak flood was found to be less and washed-off the Kedarnath valley without any alert. The study reveals quantitative parameters of the disaster which was due to an integrated effect of high rainfall intensity, sudden breach of Chorabari Lake and very steep topography.