Editor’s Choice Articles

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Climate change affects streamflow through changes in precipitation, temperature, and extreme weather events. These changes will impact water resource availability significantly. Thus, understanding the impacts of climate change on hydrology is essential for sustainable water management. This study investigated the potential effects of climate change on streamflow in the Olifants River basin under shared socioeconomic pathways (SSPs), utilizing the restructured version of the Soil and Water Assessment Tool (SWAT+) model. Projected precipitation and temperature (Tmax and Tmin) were analyzed for the near (2030–2060) and far (2070–2100) future to simulate and analyze streamflow variations under SSP245 and SSP585 scenarios using bias-corrected CMIP6 data and the SWAT+ model. The SWAT+ model was calibrated and validated successfully, with Nash–Sutcliffe efficiency (NSE) values of 0.76 and 0.77, and coefficient of determination (R²) values of 0.78 and 0.82 during the calibration and validation periods, respectively. Climate model ensemble projections show a consistent decline in precipitation and increases in Tmax and Tmin, with Tmin increasing more significantly. These changes are projected to reduce streamflow, with annual declines of 43.08% and 50.89% under SSP245 and 57.79% and 58.82% under SSP585 for the near and far future, respectively. Moreover, climate change reduces streamflow across all seasons in the Olifants River basin. Therefore, adopting water management strategies such as enhancing integrated water resource management and investing in climate-resilient infrastructure is essential for sustainable water resource management under changing climate conditions in the basin. Full article

Climate change is expected to reduce water availability during cropping season, while growing populations and rising living standards will increase the global water demand. This creates an urgent need for national water management tools to optimize water allocation. In particular, agriculture requires targeted approaches to improve efficiency. Alongside field measurements and remote sensing, agro-hydrological models have emerged as a particularly valuable resource for assessing and managing agricultural water demand. This study introduces WaterCROPv2, a state-of-the-art agro-hydrological model designed to estimate national-scale irrigation water demand while effectively balancing accuracy with practical data requirements. WaterCROPv2 incorporates innovative features such as hourly time-step computations, advanced rainwater canopy interception modeling, detailed soil-dependent leakage dynamics, and localized daily evapotranspiration patterns based on meteorological data. Through comprehensive analyses, WaterCROPv2 demonstrates significantly enhanced reliability in estimating irrigation water needs across various climatic regions, particularly under contrasting dry and wet conditions. Validation against independent data from the Italian National Institute of Statistics (ISTAT) for maize cultivation in Italy in 2010 confirms the model’s accuracy and underscores its potential for broader international applications. A spatial analysis further reveals that the estimation errors align closely with regional precipitation patterns: the model tends to slightly underestimate irrigation needs in the wetter northern regions, whereas it somewhat overestimates demand in the drier southern areas. WaterCROPv2 has also been used to analyze irrigation water requirements for maize cultivation in Italy from 2005 to 2015, highlighting its significant potential as a strategic decision-support tool. The model identifies optimal cultivation areas, such as the Pianura Padana, where the irrigation requirements do not exceed 200 mm for the entire maize growing period, and unsuitable regions, such as Salentino, where over 500 mm per season are required due to the local climatic conditions. In addition, estimates of the water volumes required for the current extent of maize cultivation show that the Pianura Padana region demands nearly three times the amount of water used in the Salentino area. The model has also been used to identify regions where adopting efficient irrigation technologies could lead to substantial water savings. With micro-irrigation currently covering less than 18% of irrigated land, simulations suggest that a complete transition to this system could reduce the national water demand by 21%. Savings could reach 30–40% in traditionally water-rich regions that rely on inefficient irrigation practices but are expected to be increasingly exposed to temperature increases and precipitation shifts. The analysis shows that those regions currently lacking adequate irrigation infrastructure stand to gain the most from targeted irrigation system investments but also highlights how incentives where micro-irrigation is already widespread can provide further 5–10% savings. Full article

Wastewater overflows (WOs) are a growing concern for water quality and ecological health in urban estuaries. This study provides a robust water quality and ecological assessment of WOs from four pump stations discharging into the Waimea Estuary, Aotearoa, New Zealand. Using overflow scenario modelling, baseline and event-based water quality sampling, and whole effluent toxicity testing, we assessed the potential impacts under conservative (2 h) and worst-case (24 h) overflow durations. Results showed that, even under worst-case conditions, the estuary’s natural dilution capacity exceeded the median dilution required to meet the 95% ecological protection level. Ecotoxicity was site- and season-specific, with amphipods and mussels showing sensitivity at some sites, while algal assays indicated nutrient enrichment rather than toxicity. Impacts were spatially limited and unlikely to persist beyond one or two tidal cycles. The estuary’s tidal exchange and resilient biota further mitigated risks. This method provides a transferable framework for assessing intermittent discharges in other coastal systems, especially those with high ecological value and infrequent discharge events. Full article

This study aims to analyse the effect of partially clogged inlets on the behaviour of urban drainage systems at the city scale, particularly regarding intercepted volumes and flood depths. The main challenges were to represent the inlet network in detail at a rather large scale and to avoid the effect of sewer network surcharging on the draining capacity of inlets. This goal has been achieved through a 1D/2D coupled hydraulic model of the whole urban drainage system in La Almunia de Doña Godina (Zaragoza, Spain). The model focuses on the interaction between grated drain inlets and the sewer network under partial clogging conditions. The model is fed with data obtained on field surveys. These surveys identified 948 inlets, classified into 43 types based on geometry and grouped into 7 categories for modelling purposes. Clogging patterns were derived from field observations or estimated using progressive clogging trends. The hydrological model combines a semi-distributed approach for micro-catchments (buildings and courtyards) and a distributed “rain-on-grid” approach for public spaces (streets, squares). The model assesses the impact of inlet clogging on network performance and surface flooding during four rainfall scenarios. Results include inlet interception volumes, flooded surface areas, and flow hydrographs intercepted by single inlets. Specifically, the reduction in intercepted volume ranged from approximately 7% under a mild inlet clogging condition to nearly 50% under severe clogging conditions. Also, the model results show the significant influence of the 2D mesh detail on flood depths. For instance, a mesh with high resolution and break lines representing streets curbs showed a 38% increase in urban areas with flood depths above 1 cm compared to a scenario with a lower-resolution 2D mesh and no curbs. The findings highlight how inlet clogging significantly affects the efficiency of urban drainage systems and increases the surface flood hazard. Further novelties of this work are the extent of the analysis (city scale) and the approach to improve the 2D mesh to assess flood depth. Full article

Stream temperatures are expected to increase with warming air temperatures, yet the extent and aquatic health impacts vary significantly across heterogeneous landscapes. This study was conducted in a 3360-ha multi-land-use watershed in the Pacific Northwest region of the USA to assess and compare the driving factors for stream temperature heating, cooling, and cool-water refugia along a 12-km mainstem stream longitudinal profile. Study objectives were to (1) determine yearlong stream temperature variability along the entire stream longitudinal profile, and (2) assess stream-environment relationships influencing stream temperature dynamics across forest, agriculture, and urban landscapes within the watershed. Stream and riparian air temperatures, solar radiation, shade, and related stream-riparian characteristics were measured over six years at 21 stations to determine changes, along the longitudinal profile, of thermal sensitivity, maximum and minimum stream temperatures, and correlation between solar radiation and temperature increases, and potential causal factors associated with these changes. Solar radiation was a primary heating factor for an exposed agricultural land use reach with 57% effective shade, while southern stream aspects and incoming tributary conditions were primary factors for forested reaches with greater than 84% effective shade. Potential primary cooling factors were streambank height, groundwater inflows, and hyporheic exchange in an urban reach with moderate effective shade (79%) and forest riparian width (16 m). Combining watershed-scale analysis with on-site stream-environmental data collection helps assess primary temperature heating factors, such as solar radiation and shade, and potential cooling factors, such as groundwater and cool tributary inflows, as conditions change along the longitudinal profile. Full article

Recent flooding events in Spain have highlighted the need to develop real-time flood forecasts to estimate streamflows over the next few hours and days. Therefore, a meteorological forecast that provides possible precipitation for the upcoming hours combined with a hydrological model to simulate the rainfall-runoff processes in the basin and its flood response are needed. In this paper, a probabilistic flood forecasting tool is proposed for the Francolí river basin, located in Catalonia (Spain). For this purpose, the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model was calibrated in this basin for a set of flood events. Then, a series of rainfall field forecasts based on the analog method have been used as input data in the hydrological model, obtaining a set of hydrographs for given flood events as output. Finally, a probabilistic forecast that supplies the probability distribution of the possible response flows of the Francolí river is provided for a set of episodes. Full article

This study evaluates and corrects ECMWF precipitation forecasts (Set VI-ENS extended) over the confluence of Asian monsoons and westerlies, deriving a time series of correction factors for medium- and long-term hydrological forecasting. Based on a 15-year dataset (2008–2023), a dominant spatial and temporal bias pattern was identified: ~50% of the study area—in particular, the entire Tibetan Plateau—experienced overestimated precipitation, with larger relative errors in dry seasons than in wet seasons. Daily correction factors were derived using the linear scaling method and applied to distributed hydrological models for the Mekong, Salween, and Brahmaputra river basins. The results demonstrated substantial efficacy in correcting streamflow forecasts, particularly in the Brahmaputra basin at the Nuxia station, where the relative error in the total water volume over a 32-day period was reduced from 25% to 10% during the calibration period (2008–2020) and from 20% to 9% in the validation period (2021–2023). Furthermore, over 90% (calibration) and 85% (validation) of hydrological forecast events were successfully corrected at Nuxia. Comparable improvements were observed in key stations across the Salween and Mekong basins, with the combined success rates exceeding 70% and 65%, demonstrating the method’s regional robustness. Challenges remain in areas with weak linear relationships between forecasted and observed data, highlighting the need for further investigation. Full article

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article

To date, few studies have been published for cities in Germany that take into account climate change and changing hydrologic patterns due to increases in building density. This study investigates the efficiency of LID for past and future climate in the polycentric agglomeration area Frankfurt, Main (Central Germany) using observed and projected climate (model) data for a standard reference period (1961–1990) and a high emission scenario (RCP 8.5) as well as a climate protection scenario (RCP 2.6), under 40 to 75 percent building density. LID elements included green roofs, permeable pavement and bioretention cells. SWMM₅ was used as model for simulation purposes. A holistic evaluation of simulation results showed that effectiveness increases incrementally with LID implementation percentage and inverse to building density if implemented onto at least 50 percent of available impervious area. Building density had a higher adverse effect on LID efficiency than climate change. The results contribute to the understanding of localized effects of climate change and the implementation of adaption strategies to that end. The results of this study can be helpful for the scientific community regarding future investigations of LID implementation efficiency in dense residential areas and used by local governments to provide suggestions for urban water balance revaluation. Full article

Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes problematic. Autoregressive integrated moving average (ARIMA) models offer a promising alternative; however, severe volatility, nonlinearity, and trends in hydrological time series can still lead to significant errors. To address these challenges, this study introduces a new adaptive hybrid model, ARIMA-iGARCH, designed to account volatility, variance inconsistency, and nonlinear behavior in short-term hydrological datasets. We apply the model to four hourly discharge time series from the Schwarzbach River at the Nauheim gauge in Hesse, Germany, under the assumption of normally distributed residuals. The results demonstrate that the specialized parameter estimation method achieves lower complexity and higher accuracy. For the four events analyzed, R² values reached 0.99, 0.96, 0.99, and 0.98; RMSE values were 0.031, 0.091, 0.023, and 0.052. By delivering accurate short-term discharge predictions, the ARIMA-iGARCH model provides a basis for enhancing water resource planning and flood risk management. Overall, the model significantly improves modeling long memory, nonlinear, nonstationary shifts in short-term hydrological datasets by effectively capturing fluctuations in variance. Full article

A comprehensive analysis of the evolution of rainfall characteristics in Catalonia, NE Spain, was conducted using monthly data from 72 rain gauges over the period 1950–2022. A moving-window approach was applied at annual, seasonal, and monthly scales, calculating mean values, coefficients of variation (CV), and trends across 43 overlapping 31-year periods. To assess trends in these moving statistics, a modified Mann–Kendall test was applied to both the 31-year means and CVs. Results revealed a significant 10% decrease in annual rainfall, with summer showing the most pronounced decline, as nearly 90% of stations exhibited negative trends, while the CV showed negative trends in coastal areas and mostly positive trends inland. At the monthly scale, February, March, June, August, and December exhibited negative trends at more than 50% of stations, with rainfall reductions ranging from 20% to 30%. Additionally, the temporal evolution of Mann–Kendall trend coefficients within each 31-year moving window displayed a fourth-degree polynomial pattern, with a periodicity of 30–35 years at annual and seasonal scales, and for some months. Finally, at the annual scale and in two centennial series, the 80-year oscillations found were inversely correlated with the large-scale climate indices North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO). Full article

Poyang Lake, a large floodplain lake, plays a crucial role in the ecological safety and quality of life in surrounding areas. Over the past decade (2013–2022), amid economic development and environmental changes, the water environment of Poyang Lake has encountered complex challenges. This study evaluated the water quality of Poyang Lake in a recent 10-year span by the water quality index (WQI), trophic level index (TLI) and a newly constructed comprehensive evaluation index, and it analyzed the trend of water quality change under extreme events. Meanwhile, the main factors affecting the water quality of Poyang Lake were analyzed by partial least squares (PLS), a multivariate statistical method that accounts for multicollinearity. The results indicate that: (1) The water quality of Poyang Lake in summer and autumn is slightly worse than that in spring and winter. Each water quality index reflects the distinct states of the water environment in Poyang Lake. (2) Each water quality evaluation index responds differently to influencing factors. (3) Extreme flood and drought events have markedly different impacts on the water environment of Poyang Lake, exhibiting significant spatial heterogeneity. Domestic sewage discharge and total water resources have a relatively great impact on the water environment of Poyang Lake. The results of this study provide important insights for water quality management and policy formulation in Poyang Lake, supporting sustainable regional development. Full article

A deep interaction between groundwater and surface hydrology characterizes karst environments. These settings feature closed depressions, whose hydrological role varies depending on whether they have genetic and hydraulic relationships with overland–subsurface flow (epigenic) or deep groundwater circulation (hypogenic). Epigenic dolines and poljes are among the diagnostic landforms of karst terrains. In this study, we applied a hydrological criterion to map closed depressions—including dolines—across the Alburni karst massif, in southern Italy. A GIS-based, semi-automatic approach was employed, combining the sink-filling method (applied to a 5 m DEM) with the visual interpretation of various informative layers. This process produced a raster representing the location and depth of karst closed depressions. This raster was then used to automatically delineate endorheic areas using classic GIS tools. The resulting map reveals a thousand dolines and hundreds of adjacent endorheic areas. Endorheic areas form a complex mosaic across the massif, a feature that had been poorly emphasized in previous works. The main morphometric features of the dolines and endorheic areas were statistically analyzed and compared with the structural characteristics of the massif. The results of the proposed mapping approach provide valuable insights for groundwater management, karst area protection, recharge modeling, and tracer test planning. Full article

The mean annual water balance is essential for evaluating water availability in a catchment and planning water resources. Climate change alters this balance by affecting precipitation, evapotranspiration, and overall water availability. This study analyses the impact of climate change on the mean annual water balance in the Catamayo catchment, a key water source for irrigation and hydropower in southern Ecuador and northern Peru. A Budyko-based approach was employed due to its conceptual simplicity and proven robustness for estimating long-term water balances under changing climatic conditions. Using outputs from 23 Global Circulation Models (GCMs) under CMIP6’s SSP2-4.5 and SSP8.5 scenarios, the results indicate increasing aridity, particularly in the lower and middle parts of the catchment, which correspond to arid and semi-arid zones. Water availability may decrease by 26.3 ± 12.3% to 33.3 ± 17% until 2080 due to negligible changes (statistically speaking) in average precipitation but rising evapotranspiration. However, historical precipitation analysis (1961–2020) reveals an increasing trend over this historical period which can be attributed to natural climatic variability associated to the El Nino-Southern Oscillation (ENSO), possibly enhanced by anthropogenic climate change. A novel hybrid method combining the statistics of historical precipitation deficits with GCM mean projections provides estimates of future precipitation deficits. These findings suggest potential reductions in crop yields and hydropower capacity, which (although not quantitatively assessed in this study) are inferred based on the projected decline in water availability. Such impacts could lead to higher energy costs, increased reliance on fossil fuels, and intensified competition for water. Mitigation measures, including water-saving strategies, energy diversification, and integrated water resource management, are recommended to address these challenges. Full article

Globally, groundwater resources are experiencing a decline in hydraulic heads resulting from the dual effects of urbanization and climate change, highlighting the need for integrated and sustainable water resources management. Urban development in the cities of Kansai region, western Japan, presents a significant challenge to the sustainability of groundwater resources. This study aims to assess the combined influence of urbanization and climate change on the groundwater resources of the Nara Basin using MODFLOW 6 for two distinct periods: The Pre-Urbanization Period (PreUP: 1980–1988), and the Post-Urbanization Period (PostUP, 2000–2008) with an emphasis on spatiotemporal distribution of recharge in a multi-layer aquifer system. Simulated hydraulic heads were evaluated under three different recharge scenarios: uniformly, spatiotemporally and spatially distributed. The uniform recharge scenario both overestimates and underestimates hydraulic heads, while the spatially distributed scenario produced a simulated heads distribution similar to the spatiotemporally distributed recharge scenario, underscoring the importance of incorporating spatiotemporal variability in recharge input for accurate groundwater flow simulation. Moreover, our results highlight the relevance of spatial distribution of recharge input than temporal distribution. Our findings indicate a significant decrease in hydraulic heads of approximately 5 m from the PreUP to PostUP in the unconfined aquifer, primarily driven by changes in land use and climate. In contrast, the average head decline in deep confined aquifers is about 4 m and is mainly influenced by long-term climatic variations. The impervious land use types experienced more decline in hydraulic heads than the permeable areas under changing climate because of the impedance to infiltration and percolation exacerbating the climate variability effect. These changes in hydraulic heads were particularly evident in the interactions between surface and groundwater. The cumulative volume of groundwater discharge to the river decreased by 27%, while the river seepage into the aquifer increased by 16%. Sustainable groundwater resources management under conditions of urbanization and climate change necessitates a holistic and integrated approach. Full article

The sharp decline in streamflow prediction accuracy with increasing lead times remains a persistent challenge for effective water resources management and flood mitigation. In this study, we developed a coupled deep learning model for daily streamflow prediction in the Hanjiang River Basin, China. The proposed model integrates self-attention (SA), a one-dimensional convolutional neural network (1D-CNN), and bidirectional long short-term memory (BiLSTM). The model’s effectiveness was assessed during flood events, and its predictive uncertainty was quantified using kernel density estimation (KDE). The results demonstrate that the proposed model consistently outperforms baseline models across all lead times. It achieved Nash-Sutcliffe Efficiency (NSE) scores of 0.92, 0.86, and 0.79 for 1-, 3-, and 5-days, respectively, showing particular strength at these extended lead time predictions. During major flood events, the model demonstrated an enhanced capacity to capture peak magnitudes and timings. It achieved the highest NSE values of 0.924, 0.862, and 0.797 for the 1-, 3-, and 5-day forecasting horizons, respectively, thereby showcasing the strengths of integrating CNN and SA mechanisms for recognizing local hydrological patterns. Furthermore, KDE-based uncertainty analysis identified a high prediction interval coverage in different forecast periods and a relatively narrow prediction interval width, indicating the strong robustness of the proposed model. Overall, the proposed SA-CNN-BiLSTM model demonstrates significantly improved accuracy, especially for extended lead times and flood events, and provides robust uncertainty quantification, thereby offering a more reliable tool for reservoir operation and flood risk management. Full article

Crop water use (ET_c) is typically estimated as the product of crop evapotranspiration (ET_o) and a crop coefficient (K_c). However, the estimation of ET_o requires various meteorological data, which are often unavailable or of poor quality, particularly in countries such as Guinea-Bissau, where the maintenance of weather stations is frequently inadequate. The present study aimed to assess alternative approaches, as outlined in the revised FAO56 guidelines, for estimating ET_o when only temperature data is available. These included the use of various predictors for the missing climatic variables, referred to as the Penman–Monteith temperature (PMT) approach. New approaches were developed, with a particular focus on optimizing the predictors at the cluster level. Furthermore, different gridded weather datasets (AgERA5 and MERRA-2 reanalysis) were evaluated for ET_o estimation to overcome the lack of ground-truth data and upscale ET_o estimates from point to regional and national levels, thereby supporting water management decision-making. The results demonstrate that the PMT is generally accurate, with RMSE not exceeding 26% of the average daily ET_o. With regard to shortwave radiation, using the temperature difference as a predictor in combination with cluster-focused multiple linear regression equations for estimating the radiation adjustment coefficient (k_Rs) yielded accurate results. ET_o estimates derived using raw (uncorrected) reanalysis data exhibit considerable bias and high RMSE (1.07–1.57 mm d⁻¹), indicating the need for bias correction. Various correction methods were tested, with the simple bias correction delivering the best overall performance, reducing RMSE to 0.99 mm d⁻¹ and 1.05 mm d⁻¹ for AgERA5 and MERRA-2, respectively, and achieving a normalized RMSE of about 22%. After implementing bias correction, the AgERA5 was found to be superior to the MERRA-2 for all the studied sites. Furthermore, the PMT outperformed the bias-corrected reanalysis in estimating ET_o. It was concluded that PMT-ET_o can be recommended for further application in countries with limited access to ground-truth meteorological data, as it requires only basic technical skills. It can also be used alongside reanalysis data, which demands more advanced expertise, particularly for data retrieval and processing. Full article

Dam breaks can lead to important economic and human losses. Design floods, which are useful to assess possible dam breaks, are usually estimated through statistical analysis of rainfall and streamflow observed data. However, such available samples are commonly limited and, consequently, high uncertainties are associated with the design flood estimates. In addition, climate change is expected to increase the frequency and magnitude of extreme rainfall and flood events in the future. Therefore, a methodology based on a stochastic rainfall generator is proposed to assess hydrological dam safety by considering climate change. We selected the Eugui Dam on the Arga river in the north of Spain as a case study that has a spillway operated by gates with a maximum capacity of 270 m³/s. The stochastic rainfall generator STORAGE is used to simulate long time series of 15-min precipitation in both current and future climate conditions. Precipitation projections of 12 climate modeling chains, related to the usual three 30-year periods (2011–2024; 2041–2070 and 2071–2100) and two emission scenarios of AR5 (RCP 4.5 and 8.5), are used to consider climate change in the STORAGE model. The simulated precipitation time series are transformed into runoff time series by using the continuous COSMO4SUB hydrological model, supplying continuous 15-min runoff time series as output. Annual maximum flood hydrographs are selected and considered as inflows to the Eugui Reservoir. The Volume Evaluation Method is applied to simulate the operation of the Eugui Dam spillway gates, obtaining maximum water levels in the reservoir and outflow hydrographs. The results show that the peak outflows at the Eugui Dam will be lower in the future. Therefore, maximum reservoir water levels will not increase in the future. The methodology proposed could allow practitioners and dam managers to check the hydrological dam safety requirements, accounting for climate change. Full article

Existing wet tropical urban drainage systems often fail to accommodate runoff generated during extreme rainfall. Water-sensitive urban design (WSUD) systems have the potential to retrofit the existing urban drainage system by enhancing infiltration and retention functions. However, studies supporting this assumption were based on temperate or arid climatic conditions, raising questions about its relevance in wet tropical catchments. To answer these questions, in this study a comprehensive modelling study of WSUD effectiveness in a tropical environment was implemented. Engineers Park, a small sub-catchment of 0.27 km² at Saltwater Creek, Cairns, Queensland, Australia was the study site in which the flood mitigation capabilities of grey and WSUD systems under major (1% Annual Exceedance Probability—AEP), moderate (20% AEP), and minor (63.2% AEP) magnitudes of rainfall were evaluated. A detailed one-dimensional (1D) and coupled 1D2D hydrodynamic model in MIKE+ were developed and deployed for this study. The results highlighted that the existing grey infrastructure within the catchment underperformed during major events resulting in high peak flows and overland flow, while minor rainfall events increased channel flow and shifted the location of flooding. However, the integration of WSUD with grey infrastructure reduced peak flow by 0% to 42%, total runoff volume by 0.9% to 46%, and the flood extent ratio to catchment area from 0.3% to 1.1%. Overall, the WSUD integration positively contributed to reduced flooding in this catchment, highlighting its potential applicability in tropical catchments subject to intense rainfall events. However, careful consideration is required before over-generalization of these results, since the study area is small. The results of this study can be used in similar study sites by decision-makers for planning and catchment management purposes, but with careful interpretation. Full article

River bends and confluences are critical features in fluvial environments where complex flow patterns, including secondary currents, turbulence, and surface changes, strongly influence sediment transport, river morphology, and water quality. The accurate prediction of these flow characteristics is essential for hydraulic engineering applications. In this study, we present a numerical simulation of turbulent flow in river bends and confluences, with special consideration given to the dynamic interaction between free-surface variations and closed-surface constraints. The simulations were performed using OpenFOAM, an open-source computational fluid dynamics (CFDs) platform, with the k-ω SST (Shear Stress Transport) turbulence model, which is well-suited for capturing boundary layer behavior and complex turbulence structures. The finite volume method (FVM) is used to simulate and examine the behavior of the secondary current in channel bends and confluences. Two sets of experimental data, one with a sharply curved channel and the other with a confluent channel, were used to compare the numerical results and to evaluate the validity of the model. This study focuses on investigating to what extent the k-ω SST turbulence model can capture the effects of secondary flow and surface changes on flow hydrodynamics, analyzing velocity profiles and turbulence effects. The results are validated against experimental data, demonstrating the model’s ability to reasonably replicate flow features under both free- and closed-surface conditions. This study provides insights into the performance of the k-ω SST model in simulating the impact of geometrical constraints on flow regimes, offering a computationally robust and reasonable tool for river engineering and water resources management, particularly in the context of hydraulic structure design and erosion control in curved and confluence regions. Full article

Rapid suburbanization can alter catchment flow regime and increase stormwater runoff, posing threats to sensitive ecosystems. Applications of Nature-based Solutions (NbS) have increasingly been adopted as part of integrated water management efforts to tackle the hydrological impact of urbanization with co-benefits for improved urban resilience, sustainability, and community well-being. However, the implementation of NbS can be hindered by gaps in performance assessment. This paper introduces a physically based dynamic modeling approach to assess the performance of a nature-based storage facility designed to capture excess runoff from an urbanizing catchment (Armstrong Creek catchment) in Geelong, Australia. The study adopts a numerical modelling approach, supported by extensive field monitoring of water levels over a 2.5-year period. The model provides a decision support tool for Geelong local government in managing stormwater runoff to protect Lake Connewarre, a Ramsar-listed wetland under the Port Phillip Bay (Western Shoreline) and Bellarine Peninsula. Runoff is currently managed via a set of operating rules governing gate operations that prevents flows into the ecological sensitive downstream waterbody from December to April (drier periods in summer and most of autumn). Comparison with observed water level data at three monitoring stations for a continuous simulation period of May 2022 to October 2024 demonstrates satisfactory to excellent model performance (NSE: 0.55–0.79, R²: 0.80–0.89, ISE rating: excellent). Between 1670 × 10³ m³ and 2770 × 10³ m³ of runoff was intercepted by the nature-based storage facility, representing a 56–70% reduction in stormwater discharge into Lake Connewarre. Our model development underscores the importance of understanding and incorporating user interventions (gate operations and emergency pumping) from the standard operation plan to better manage catchment runoff. As revealed by the seasonal flow analysis for consecutive years, adaptive runoff management practices, capable of responding to rainfall variability, should be incorporated. Full article

Increasing pressures on groundwater in the last decades have led to a deterioration in the quality of groundwater for human consumption around the world. Beyond the essential evaluation of groundwater dynamics and quality, analyzing the situation from the perspective of the Ecosystem Services Assessment (ESA) approach can be useful to support aquifer management plans aiming to recover aquifers’ capacity to provide good quality water. This work illustrates how to implement the ESA using groundwater flow and nitrate transport modelling for evaluating future trends of the provisioning service Groundwater of Good Quality for Human Supply. It has been applied to the Medina del Campo Groundwater Body (Spain), where the intensification of agricultural activities and groundwater exploitation since the 1970s caused severe nitrate pollution. Nitrate status and future trends under different fertilizer and aquifer exploitation scenarios were modelled with MT3DMS coupled to a MODFLOW model calibrated with piezometric time series. Historical land use and fertilizer data were compiled to assess nitrogen loadings. Besides the uncertainties of the model, the results clearly show that: (i) managing fertilizer loads is more effective than managing aquifer exploitation; and (ii) only the cessation of nitrogen application by the year 2030 would improve the evaluated provisioning service in the long term. The study illustrates how the ESA can be incorporated to evaluate the expected relative impact of different management actions aimed at improving significant groundwater services to humans. Full article

This study quantifies the atmospheric deposition (AD) of nutrient loads into the Farmington Bay ecosystem via wet deposition over a three-year period. We analyzed nutrient concentrations from 509 total phosphorus (TP), 507 orthophosphate (OP), and 511 total nitrogen (TN) samples collected at seven locations around the Bay. We estimated AD loads using two different spatial interpolation methods, Kriging and Inverse Distance Weighting (IDW), as well as average concentrations. The loads computed using Kriging and IDW were similar, but the loads computed using sample averages were about 70% smaller. We estimated that annual atmospherically deposited nutrient loads range from 306 to 594 Mg for TN, 73 to 195 Mg for TP, and 43 to 144 Mg for OP. The loads in 2023 were significantly higher than those in 2021 and 2022, a phenomenon we attribute to higher precipitation and a major loading event that occurred on 13 April 2023. Based on comparison with studies concerning nearby Utah Lake, the total loads could be two to three times larger than our estimates. These studies suggest that fine particulate matter may significantly contribute to AD nutrient loads, but these loads are not captured by our sampling method. However, the inclusion of non-water surfaces in Farmington Bay may mitigate this difference. Full article

The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance. Full article

This study presents a general workflow for creating a priority map for blue–green infrastructure (BGI) placement at the city scale, incorporating model-based benefit analysis. This workflow generates a BGI priority map, combining flood hazard and urban heat island maps, that guarantees multi-functional requirements are met. This approach was applied at a small study site in Feldbach, Austria. In the second part, we used the priority map generated to implement six BGI strategies in an integrated 1D-2D urban flood model and a semi-distributed hydrological model at high-priority and low-priority locations. The use of the efficiency index (EI_mod) enabled a multi-objective assessment. The results indicate that all the strategies led to a higher EI_mod when implemented in high-priority locations compared to low-priority locations. Our findings demonstrate that priority maps support decision making regarding where strategies should be implemented, providing remarkable benefits for water management objectives. Additionally, the findings highlight the importance of incorporating potential flooding areas to enhance prioritisation regarding flood hazard indicators. In future assessments, economic parameters, such as cost considerations, should also be integrated in order to optimise BGI placement efficiency. Full article

Managed Aquifer Recharge (MAR) is increasingly being adopted across Europe to enhance water security in semiarid regions, with over 230 operational sites. The Akrotiri MAR system in Limassol, Cyprus, comprises 17 recharge ponds operating since 2016 to counteract saltwater intrusion. This study evaluates MAR effectiveness by analyzing spatial and temporal variations in water quality from 2016 to 2020. Parameters analyzed include nutrients, metals, pesticides, pharmaceuticals, fecal indicators, physicochemical characteristics, recharge and pumping volumes, and groundwater levels. The results show that soil aquifer treatment (SAT) generally improves groundwater quality but certain boreholes exhibited elevated nitrate (range 12.70–31 mg/L), electrical conductivity (range 936–10,420 μs/cm), and chloride concentrations (range 117–1631 mg/L), attributed to recharge water quality, seawater intrusion, and nearby agricultural activities. Tertiary treated wastewater used for recharge occasionally exceeds permissible limits, particularly in E. coli (up to 2420/100 mL), chloride (up to 385 mg/L), and nitrogen (up to 41 mg/L). Supplementing recharge with dam-supplied freshwater improves groundwater quality and raises water levels. These findings underline the importance of continuous monitoring and effective management, adopting sustainable farming practices, and the strict control of recharge water quality. The study offers valuable insights for optimizing MAR systems and supports integrating MAR into circular water management frameworks to mitigate pollution and seawater intrusion, enhancing long-term aquifer sustainability. Full article

Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model. Full article

This study assesses the functioning of the karst aquifer system located on the Croatian coast of the Adriatic Sea, where saltwater intrusion often presents a major problem for freshwater supply. We use two years of sensor data collected from two coastal springs to conduct a range of time-invariant and time-variant statistical analyses over various timescales. We perform separate analyses of the within-day and longer-term variation in the data as well as the interactions between the spring levels, salinity, rainfall, and sea levels. Such comprehensive analyses provide a greater understanding into the inner functioning of the intricate, heavily karstified aquifers. Time-invariant time-series analyses of the hourly data indicate that the spring levels and salinity are strongly controlled by sea levels. Furthermore, time-variant wavelet analyses demonstrate that the variation in spring levels in both springs has two modes defined by flow regime. Increases in the delay of the spring response to sea level indicate that aquifer diffusivity decreases in low flow conditions. Analyses facilitated the development of a conceptual model of the karst subsurface in the discharge zone. Using daily data, we constructed a linear mixed model of the spring levels. This model identified long-term sea level changes, rainfall from previous weeks, and seasonal recharge patterns as the primary factors influencing longer-term spring dynamics. Full article

The US state of Iowa has experienced profound historical changes in its streamflow and baseflow. While several studies have noted increasing baseflow and baseflow index (BFI) values throughout the 20th century, analyses quantifying BFI trends in recent years or exploring spatial differences in watersheds marked by varying land use and geologic properties have not been conducted. This study calculated annual values for BFI (and several other hydrologic metrics) using flow records from 42 Iowa stream gauges containing at least 50 years of uninterrupted measurements. While BFI overwhelmingly rose throughout the mid-1900s, circa 1990 it began to level off. In some areas of Iowa (e.g., the southwest), BFI has continued to rise over the past 30 years—albeit at a slower rate; in other regions, it has become stationary or declined. One site failed to follow this trend (Walnut Cr), the only basin to experience large-scale urbanization. Furthermore, BFI demonstrated a strong negative correlation to streamflow flashiness, indicating that rising baseflow has also made Iowa streams less dynamic. BFI was largely independent of overall streamflow. These results may suggest the increased influence of conservation practices and the diminishing impacts of tile drainage on the delivery of water to Iowa’s rivers. Full article

The relationship between global warming and heatwaves contributes to environmental risks. We investigate lake heatwaves (LHWs) in the Eastern Mediterranean, where dust intrusions are frequently observed. The dust intrusions are characterized by the arrival of warm air masses containing dust pollution from the desert. In saline lakes, LHWs caused by dust intrusions have not been investigated in previous studies. In our study we focus on this point. It was found for the first time that, in the hypersaline Dead Sea, a severe dust intrusion (aerosol optical depth of over 3) caused the formation of LHWs, as appeared in September 2015. At the water surface, the LHWs were represented by abnormally high daily maximal and minimal surface water temperature (SWT) in comparison with their seasonally varied 90th percentile thresholds for 10 consecutive days (7–17 September). The surface LHWs’ intensity was up to 3 °C. Satellite (MODIS-Terra and METEOSAT) SWT did not detect the LHWs. Surface LHWs were accompanied by subsurface LHWs down to a depth of 20 m. The subsurface LHWs lasted longer (16 days) than the surface LHWs (10 days). There was a 4-day delay between the first date of the surface LHWs (7 September) and the start date of the subsurface LHWs (11 September). The maximal intensity of the subsurface LHWs decreased with depth from 1 m (0.6 °C) down to 5 m (0.3 °C), followed by an increase (up to 0.6 °C) at the deeper layers (from 10 m to 20 m). Taking into account that, over the Eastern Mediterranean, desert dust has increased during the past several decades, one can expect frequent occurrence of dust-related intense persistent heatwaves in the Dead Sea in the coming years. This will contribute to additional water heating and further drying up of the Dead Sea. Full article

The internal nutrient load of natural and artificial lakes is a worldwide problem. To minimize its potential risks, the dredging of the highly eutrophic shallow first reservoir of Kis-Balaton (Lake Hídvégi) is planned in the near future. Our study aimed to evaluate the potential effects of dredging and desiccation on water and sediment quality. Experimental dredging was carried out in the northernmost part of Lake Hídvégi (2023). The physical and chemical characteristics of the sediment and nutrient loss during desiccation were examined in a column experiment. The relationships between the properties of leachate and sediment were identified using principal component analysis (SPSS). Spatial variations in sediment particle size distribution, nutrient content, and other chemical parameters (e.g., organic matter) suggest that deeper core sampling than the depth of preliminary dredging is necessary for a more comprehensive assessment of potential impacts. We found that spatiotemporally varying the dominance of chemical and biological processes affects the amount of and changes in phosphorus fractions under lake-/sediment-specific conditions. The readily available calcium- and iron-bound phosphorus, texture, and organic matter content of the sediment play an important role in phosphorus fixation/release. Based on our results, dredging and desiccation are feasible within the intended operating parameters. The sediment’s composition does not preclude potential agricultural disposal. Full article

Accurately determining the hydraulic properties of soilless growing media is essential for optimizing water management in container-based horticulture and agriculture. The very rapid estimation of hydraulic properties using a Mini Disk Infiltrometer has great potential for practical use compared to the very time-consuming standard methods. The objectives of this study were (1) to calibrate simulated cumulative stepwise infiltration under different suctions with the measured data from Mini Disk Infiltrometer, (2) to evaluate the efficiency of the Hydrus-2D inverse model to predict water dynamics through substrates, (3) to compare the substrate hydraulic parameters obtained through the numerical inversion model to those obtained via laboratory methods, and (4) to provide recommendations on how to effectively use the MDI-based method for practical applications. This study employs numerical inversion of Mini Disk Infiltrometer (MDI) data to estimate the hydraulic parameters of three different growing media, namely white peat, thermally treated wood fibre (WF4), and Seedling substrate. Infiltration experiments were conducted under suction-controlled conditions using varying initial moisture contents, followed by numerical simulations using the Hydrus-2D model and the Van Genuchten equation to describe the hydraulic parameters. The results demonstrated strong agreement between observed and simulated infiltration data, particularly under moistened conditions, with high R² > 0.9 values indicating the model’s effectiveness. However, discrepancies were observed for substrates in their initial dry state, suggesting limitations in capturing early-stage infiltration dynamics. The findings highlighted the potential of numerical inversion methods for estimating substrate hydraulic properties but also revealed the need for methodological refinements. Modifying the Van Genuchten model or exploring alternative approaches such as the Brooks and Corey model may enhance accuracy. Extending the suction range of measurement techniques is also recommended to improve parameter estimation. This study provides important evidence that the inverse method based on MDI is an effective tool for rapidly determining the hydraulic functions of substrates, which are important in promoting sustainable horticultural practices. Future research should focus on refining parameter estimation methods and addressing model limitations to enhance the reliability of hydraulic property assessments in soilless growing media. Full article

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the sensitivity and complexity of flood generation attributes. This study explores the application of Recurrent Neural Networks (RNNs)—specifically Vanilla Recurrent Neural Networks (VRNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in flood prediction and assessment. By integrating catchment-specific hydrological and meteorological variables, the RNN models leverage sequential data processing to capture the temporal dynamics and seasonal patterns characteristic of flooding. These models were employed across diverse terrains, including mountainous watersheds in the state of South Carolina, USA, to examine their robustness and adaptability. To identify significant hydrological events for flash flood analysis, a discharge frequency analysis was conducted using the Pearson Type III distribution. The 1-year and 2-year return period flows were estimated based on this analysis, and the 1-year return flow was selected as a conservative threshold for flash flood event identification to ensure a sufficient number of training instances. Comparative benchmarking with the National Water Model (NWM v3.0) revealed that the RNN-based approaches offer notable enhancements in capturing the intensity and timing of flood events, particularly for short-duration and high-magnitude floods (flash floods). Comparison of predicted disharges with the discharge recorded at the gauges revealed that GRU had the best performance as it achieved the highest mean NSE values and exhibited low variability across diverse watersheds. LSTM results were slightly less consistent compared to the GRU albeit achieving satisfactory performance, proving its value in hydrological forecasting. In contrast, VRNN had the highest variability and the lowest NSE values among the three. The NWM model trailed the machine learning-based models. The study highlights the efficacy of the RNN models in advancing hydrological predictions. Full article

Floating photovoltaic systems (FPV) are one of the emerging technologies that are able to support the “green” energy transition. In Greece, the environmental impact assessment of such projects is still under early development. The scope of the present study was to provide insights into the potential impacts of a small-scale FPV system on the water quality of the oligotrophic Kremasta Lake, an artificial reservoir. For this reason, a hydrodynamic and water quality model was employed. The results showed that the water quality parameter variations were insignificant and limited only in the immediate area of the FPV construction and gradually disappeared toward the shoreline. Likewise, this variation was restricted to the first few meters of depth of the water column and was eliminated onwards. The water temperature slightly decreased only in the area of close proximity to the installation. Average annual dissolved oxygen, chlorophyll-a, and nutrient concentrations were predicted not to change considerably after the panels’ construction. FPV systems can provide an attractive alternative for energy production in artificial reservoirs, especially in regions of land use conflicts that are associated with land allocation for alternative energy development. Given the limited data on the long-term impact of such projects, robust monitoring programs are essential. These initiatives rely on public support, making collaboration between stakeholders and the local community crucial. Full article

Nitrate contamination poses a significant global environmental threat, impacting the water quality in surface and groundwater systems. Despite its considerable impact, there remains a lack of comprehensive understanding of nitrate sources and discharge patterns, particularly in the Lake Victoria basin of East Africa. To address this gap, a study was conducted in the Kagera River basin, responsible for 33% of Lake Victoria’s surface inflow. This study utilized δ¹⁵N and δ¹⁸O isotope analysis in nitrate, hydrochemistry, and the Bayesian mixing model (MixSIAR) to identify and quantify nitrate sources. Spatiotemporal data were collected across three seasons: long rains, dry season, and short rains, in areas with diverse land uses. Nitrate isotopic data from water and potential sources were integrated into a Bayesian mixing model to determine the relative contributions of various nitrate sources. Notable spatial variations were observed at sampling sites with concentrations ranging from 0.004 to 3.31 mg L⁻¹. Spatially and temporally, δ¹⁵N-NO₃⁻ values ranged from +6.0% to +10.2‰, whereas δ¹⁸O-NO₃⁻ displayed significant spatial differences with mean ranges from −1% to +7‰. MixSIAR analysis revealed important contributions from manure and sewage sources ranging between 49% and 73%. A boron analysis revealed manure was the main source of nitrates in the manure and sewage. These results show that it is necessary to implement improved manure and sewage management practices, especially through proper waste treatment and disposal systems, to enable informed policy decisions to enhance nitrogen management strategies in riparian East Africa, and to safeguard the region’s water resources and ecosystems. Full article

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance. Full article

Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments. Full article

Pinios river basin constitutes the most important agricultural production area in Greece but contributes to the degradation of the quality and quantity of surface water and groundwater bodies. Bioenergy crops implemented as part of the existing cropping systems could be a novel and efficient mitigation strategy against water degradation, contributing to the production of energy through renewable sources. This study uses the Soil and Water Assessment Tool (SWAT) to first develop a representative model of Pinios river basin and evaluate its current state with respect to water availability and nitrate water pollution. A low-input perennial bioenergy crop, switchgrass, is then simulated closely to the Greek conditions to investigate its potential effects on water in three implementation scenarios: the installation and growth of switchgrass in the entire irrigated cropland, exclusively in irrigated sloping (slopes > 1.5%) cropland, and exclusively in irrigated non-sloping cropland. The simulated results demonstrate that under all scenarios, the water quality improvements with respect to the nitrate loads entering surface water and groundwater bodies were significant, with their reduction being directly affected by the extent to which switchgrass replaced resource-demanding conventional crops. Specifically, the reduction in the annual nitrate loads in the surface water under these three scenarios varied from 7% to 18% at the river basin scale, while in certain cropland areas, the respective reduction even exceeded a level of 80%. The potential to improve the water status was also considerable, as the implementation of the bioenergy crop reduced the irrigation water used annually in the basin by 10% (64 Mm³) when switchgrass replaced the conventional crops only on the sloping land and by almost 30% (187 Mm³) when it replaced them throughout the irrigated land. At the same time, significant biomass production above 18 t/ha/y applied in all of the simulations. This study also highlights the contribution of the bioenergy crop to the rehabilitation of the groundwater levels across the basin, with the possibility of increasing them by >50% compared to the baseline, implying that the adoption of switchgrass could be a promising means against water scarcity. Full article

Vegetation and subsequent ecosystem services can recover over time in forest headwaters devastated by massive disasters. However, in cold regions, their recovery rates are typically slow and often imperceptible, which makes it difficult to evaluate how much ecosystem services have recovered. This study targeted dissolved organic matter (DOM), which plays a central role in biogeochemical processes in forest ecosystems, and aimed to examine whether vegetation conditions affect the quality of stream DOM from cool-temperate forest headwaters in northern Japan. To achieve this, hydrological observations and stream water sampling were conducted monthly from May to December 2021 in three small forest catchments with different landslide coverage. Dissolved organic carbon (DOC) concentration in stream water was measured, and the molecular composition of DOM was analyzed using ultrahigh-resolution mass spectrometry and compared among the three catchments. The peak-intensity-weighted average aromaticity index (AI_wa) increased with DOC concentration. We found that AI_wa was the highest in the undisturbed catchment, followed by the catchments with landslide coverages of 16% and 52% at a given DOC level. These results indicate that the quality of DOM could dramatically change depending not only on DOC concentration but also on vegetation disturbance in cool-temperate forest headwaters. Full article

The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest in AI, a technology with a well-established history spanning several decades. The California Department of Water Resources (DWR) has been at the forefront of this field, leveraging Artificial Neural Networks (ANNs), a core technique in machine learning (ML), which is a subfield of AI, for water and environmental modeling (WEM) since the early 1990s. While protocols for WEM exist in California, they were designed primarily for traditional statistical or process-based models that rely on predefined equations and physical principles. In contrast, ML models learn patterns from data and require different development methodologies, which existing protocols do not address. This study, drawing on DWR’s extensive experience in ML, addresses this gap by developing standardized protocols for the development and implementation of ML models in WEM in California. The proposed protocols cover four key phases of ML development and implementation: (1) problem definition, ensuring clear objectives and contextual understanding; (2) data preparation, emphasizing standardized collection, quality control, and accessibility; (3) model development, advocating for a progression from simple models to hybrid and ensemble approaches while integrating domain knowledge for improved accuracy; and (4) model deployment, highlighting documentation, training, and open-source practices to enhance transparency and collaboration. A case study is provided to demonstrate the practical application of these protocols step by step. Once implemented, these protocols can help achieve standardization, quality assurance, interoperability, and transparency in water and environmental modeling using machine learning in California. Full article

The Abelar pilot basin in Coruña (northwestern Spain) has been monitored for hydrological and hydrochemical data to assess the effects of eucalyptus plantation and manure applications on water resources, water quality, and nitrate contamination. Here, we report the machine learning analysis of hydrological and hydrochemical data from the Abelar basin. K-means cluster analysis (CA) is used to relate nitrate concentrations at the outlet of the basin with daily interflows and groundwater flows calculated with a hydrological balance. CA identifies three linearly separable clusters. Times series Gaussian process regression (TS-GPR) is employed to predict surface water nitrate concentration by incorporating hydrological variables as additional input parameters using a time series shifting. TS-GPR allows modelling nitrate concentrations based on shifted interflows and groundwater flows and chemical concentrations with R² = 0.82 and 0.80 for training and testing, respectively. Groundwater flow from five days prior to the current date, $Q_{g5}$, is the most important input parameter of the TS-GPR model. Interaction effects between the variables are found. TS-GPR validation with recent data provides results consistent with those of testing (R² = 0.85). Model inspection by permutation feature importance and partial dependence plots shows interactions between $Q_{g5}$ and Cl, and between Ca and Mg. Full article

Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate. Full article

Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. However, for the urban hydrology area, the coupling of these models is necessary in order to forecast the upcoming inundation area maps and is, therefore, of the utmost importance for successful flood risk management. In this paper, three deep-learning-based models are coupled in a systematic modular approach with the aim to analyze the performance of this model chain in an operative setup for urban pluvial flooding nowcast: precipitation nowcasting with an adapted version of the NowcastNet model, the forecast of manhole overflow hydrographs with a Seq2Seq model, and the generation of a spatiotemporal sequence of inundation areas in an urban catchment for the upcoming hour with an encoder–decoder model. It can be concluded that the forecast quality still largely depends on the accuracy of the precipitation nowcasting model. With the increasing development of DL models for both precipitation and flood nowcasting, the presented modular approach for model coupling enables the substitution of individual blocks for better and newer models in the model chain without jeopardizing the operation of the flooding forecast system. Full article

In coastal regions, groundwater is often the only freshwater resource available for human consumption, agriculture, and other productive activities. From a management point of view, it is essential to understand the processes that occur in a coastal aquifer affected by seawater intrusion and upconing processes and evaluate their potential response to climate change as these scenarios usually indicate a decrease in aquifer recharge. Therefore, the dynamics of seawater intrusion and the upconing process in the Plana de Castellón aquifer on the Mediterranean coast were analysed by building and calibrating a new numerical model of flow and transport using the MODFLOW and SEAWAT codes. The model was used to examine two Shared Socioeconomic Pathway (SSP) climate change scenarios (SSP1–2.6 and SSP5–8.5) when considering field data with constant extraction conditions. The results suggest that by 2050, groundwater levels could rise by 0.18 m (on average) in the SSP1–2.6 scenario and by 0.12 m for the SSP5–8.5 scenario. In these cases, aquifer recharge and groundwater discharge to the sea could increase compared to the historical period, as precipitation is not expected to decrease significantly during this timeframe, even in the most unfavourable scenario (SSP5–8.5). The result would be the attenuation of seawater intrusion and a decrease in the volume of the aquifer that is affected by the upconing process, resulting in total dissolved solids values below 2000 mg/L. The innovation of this research lies in the fact that the numerical model allowed the dynamics of seawater intrusion and the upconing process to be adequately represented, especially in the latter process, as it was not possible to model it with real data in another study. These results can improve and facilitate decision-making for the management of the aquifer and contribute to plans for future exploitation strategies. Full article

Arctic tidewater glaciers are retreating, serving as key indicators of global warming. This study aims to assess how subglacial hydrology affects glacier front retreat by comparing two glacier–fjord models of the Hansbreen glacier: one incorporating a detailed subglacial hydrology model and another simplifying the subglacial discharge to a single channel centered in the flow line. We first validate the subglacial hydrology model by comparing its discharge channels with observations of plume activity. Simulations conducted from April to December 2010 revealed that the glacier front position aligns more closely with the observations in the coupled model than in the simplified version. Furthermore, the mass loss due to calving and submarine melting is greater in the coupled model, with the calving mass loss reaching 6 Mt by the end of the simulation compared to 4 Mt in the simplified model. These findings highlight the critical role of subglacial hydrology in predicting glacier dynamics and emphasize the importance of detailed modeling in understanding the responses of Arctic tidewater glaciers to climate change. Full article

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be $1271\times {10}^6 {\mathrm{m}}^3$, and that for D = 0.5 was $634.5\times {10}^6 {\mathrm{m}}^3$. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal $[315,5679]\times {10}^6 {\mathrm{m}}^3$ and for D = 0.5 the value is interval $[158,2839]\times {10}^6 {\mathrm{m}}^3$, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions. Full article

Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs (${\tau }_2$-Precipitation and ${\tau }_2$-Discharge: R² = 0.675, p-values < 0.001; ${\tau }_3$-Precipitation and ${\tau }_3$-Discharge: R² = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming $\alpha$ = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future. Full article

Groundwater contamination poses a major challenge to water supplies around the world. Assessing groundwater vulnerability is crucial to protecting human livelihoods and the environment. This research explores a machine learning-based variation of the classic DRASTIC method to map groundwater vulnerability. Our approach is based on the application of a large number of tree-based machine learning algorithms to optimize DRASTIC’s parameter weights. This contributes to overcoming two major issues that are frequently encountered in the literature. First, we provide an evidence-based alternative to DRASTIC’s aprioristic approach, which relies on static ratings and coefficients. Second, the use of machine learning approaches to compute DRASTIC vulnerability maps takes into account the spatial distribution of groundwater contaminants, which is expected to improve the spatial outcomes. Despite offering moderate results in terms of machine learning metrics, the machine learning approach was more accurate in this case than a traditional DRASTIC application if appraised as per the actual distribution of nitrate data. The method based on supervised classification algorithms was able to produce a mapping in which about 45% of the points with high nitrate concentrations were located in areas predicted as high vulnerability, compared to 6% shown by the original DRASTIC method. The main difference between using one method or the other thus lies in the availability of sufficient nitrate data to train the models. It is concluded that artificial intelligence can lead to more robust results if enough data are available. Full article

Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as flood management, water level monitoring, agricultural water discharge, and water pollution management. We evaluated these MLLMs on hydrology-specific tasks, testing their response generation and real-time suitability in complex real-world scenarios. Prompts were designed to enhance the models’ visual inference capabilities and contextual comprehension from images. Our findings reveal that GPT-4 Vision demonstrated exceptional proficiency in interpreting visual data, providing accurate assessments of flood severity and water quality. Additionally, MLLMs showed potential in various hydrological applications, including drought prediction, streamflow forecasting, groundwater management, and wetland conservation. These models can optimize water resource management by predicting rainfall, evaporation rates, and soil moisture levels, thereby promoting sustainable agricultural practices. This research provides valuable insights into the potential applications of advanced AI models in addressing complex hydrological challenges and improving real-time decision-making in water resource management Full article