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The UnCommons Project, as the name implies, introduces an alternative building model to traditional inefficient and environmentally destructive building stock, making it a beacon of sustainability. This state-of-the-art structure is a localized, bio-based structure reflecting concentric rings of impact from the site level, to regional, and to liberating materials from global supply chain constraints. This project’s approach integrates restorative renewable resources, water harvesting, recycling, and conservation measures to minimize its footprints. This 1,000 square foot structure, serving as a student common area at UCI, showcases high-tech, low-impact sustainable solutions through the seamless integration of AI for temperature regulation, structural wall-adjustments, and predictive maintenance systems. Given its proximity to the UCI Ecological Preserve, this structure aims to augment local biodiversity, cultivate ecological resilience, and enable students to blend with and foster an enhanced coexistence with nature. The UnCommons Project envisions buildings as living structures that harmonize with their environment.Industry Advisors: James Bucknam, Brett Kaufmann

Flooding rice (Oryza sativa) agricultural fields during winter to facilitate rice straw decomposition has mitigated the loss of some of the natural wetlands in California’s Central Valley. We conducted bird surveys in 253 rice checks (2,158 ha) within 177 rice fields in the Sacramento Valley during the fall and winter of 2021–2022 and 2022–2023 to evaluate factors that influence bird use of winter-flooded, post-harvest rice fields enrolled in the California Winter Rice Habitat Incentive Program. We counted 143,932 birds from 57 species, including dabbling ducks (86.4%), geese (8.0%), shorebirds (0.9%), wading birds (0.7%), and other birds (4.0%). Extrapolating from the lowest densities observed in rice fields during the 70-day mandatory flooding period, we estimated that properties enrolled in this public–private partnership provided habitat for at least 271,312 birds day-1 (16,248 ha; 2021–2022) and 147,315 birds day-1 (8,448 ha; 2022–2023), totaling >10 million bird-use-days each winter. Water depth had the greatest influence on bird abundance and diversity. Relatively shallow water depths (≤13 cm) had greater abundance of shorebirds, wading birds, and geese, and higher diversity, whereas intermediate depths (~23 cm) resulted in the greatest dabbling duck abundance. Duck, goose, and wading bird abundances were greatest—and species richness and family diversity were highest—8 days after the onset of flooding in rice fields (typically late October), followed by a decline in bird use until 65 to 87 days post-flooding, after which bird use increased slightly. Bird abundance and species diversity were lowest in rice fields with the greatest hunting intensity (≥3 days week-1). We identified several habitat variables that could be managed and prioritized by land-owner incentive programs to increase bird use of winter-flooded rice, including water depth, variation in emergent vegetation height, mudflat habitat availability, rice check shape, hunting intensity, and post-harvest treatment of residual rice straw.

Understanding the hydrological impacts of climate change is essential for robust and sustainable water management. This study assessed the hydrologic conditions under changing climate in the Jinshajiang River basin, the source region of the Yangtze River, using the hydrological model SWAT with the historical observations and the future climate simulations under two Shared Socioeconomic Pathways (SSP2‐4.5 and SSP5‐8.5). For the historical period, with an increasing trend of precipitation, evapotranspiration, and snowmelt, streamflow increases in upstream region but keeps decreasing in the downstream catchment. For future scenarios, a warmer and wetter climate is projected for the basin throughout the 21st century, leading to an overall increase in mean and extreme streamflow. The streamflow magnitude increases more significantly in the far future than in the near future, and more significant under SSP5‐8.5 than SSP2‐4.5. The projected remarkable increase in precipitation causes the transition in changing trend of streamflow compared with the historical period. The projected warming leads to a continuing decline in snowfall and snow water equivalent, followed by an earlier snowmelt and higher peak streamflow, especially at the upstream catchment. Ultimately, reservoirs in the basin are expected to gain more inflows, however, with greater variability including higher likelihoods of flood and drought events, which impose potential challenges on reservoir operations. These outcomes indicate the importance of adaptive water resources management in the melting water contributed basin to sustain and enhance its services under global warming. [ABSTRACT FROM AUTHOR]

China's Losses Plateau (LP) is one of the ecologically vulnerable and the most severe soil erosion regions. Thus, knowing spatiotemporal changes in evapotranspiration (ET) and its components (soil evaporation, E; transpiration, T; and vegetation interception evaporation, EI) and revealing the underlying mechanisms are vital for ecosystem and water resources sustainability for this region. Here, we investigate the spatiotemporal changes in ET and its components and then quantify the impacts of climate variables (i.e., precipitation, radiation, temperature, and relative humidity) and vegetation dynamics (e.g., land use/cover changes [LUCC] and changes in leaf area index [LAI]) on their annual trends, by using a process‐based terrestrial ecosystem model and a joint‐solution method with multiple sensitivity numerical experiments. Results show that over 67% of the study region experienced significant (p < 0.05) increases in annual ET, T, and EI, with regional average rises of 4.05, 3.67, and 0.74 mm·year−1, respectively. However, there are significant (p < 0.05) decreases in regional mean E of 0.38 mm·year−1, and the negative trend covers 35.8% of the study area. E, T, and EI changes dominate the annual ET trends over 11.8%, 87.3%, and 0.9% of the study area, respectively. Attribution analyses highlight the increased LAI as the critical factor governing these trends across most of the LP (>58%). At the same time, precipitation and LUCC play a more dominant role in the remaining areas. This study emphasizes the spatial heterogeneity in the drivers of changes in ET and its components and highlights the critical role of vegetation dynamics. These findings provide valuable insights for understanding the ET processes and guiding sustainable water resource management in the LP. [ABSTRACT FROM AUTHOR]

The rational design of the microporous layer (MPL) is very important for the water management of proton exchange membrane fuel cells (PEMFC). The uncertainty of pore structure distribution of MPL originated from the random stacking of particles. To facilitate the water transfer at high current density, it is necessary to improve the capillary force of the gas diffusion layer (GDL). The hierarchical porous structure MPL was designed by coating carbon nanotubes (CNTs) with different length-diameter ratios. In the process of constructing the network structure of CNTs, the polyvinylidene fluoride (PVDF)/N-methylpyrrolidone binder/solvent system alleviates the structural and performance defects caused by the agglomeration via an integrated method of dispersion and binding. The optimal current density of PEMFC at 0.2 V can reach 1913.8 mA/cm2 under the optimal MPL structure. The peak power density of GDL-G PVDF prepared with PVDF is 831.8 W/cm2 which is 8.85% and 15.74% higher than that of GDL-1 PVDF and GDL-2 PVDF with single pore structure, respectively. The lowest mass transfer resistance of GDL-G PVDF is 23.71 mΩ, indicating that it can discharge excess water at the catalyst layer/MPL interface to avoid flooding. This design provided a novel strategy for the stable operation of PEMFC at high current density. [Display omitted] • Two types of carbon nanotubes were used to construct MPL with hierarchical network structure. • Enhanced performance of PEMFC was achieved through enhancing capillary pressure. • The optimized properties of MPL ensure ultra-low mass transport resistance. • The MPLs with PVDF/NMP binder/solvent system facilitate the water and gas transport. [ABSTRACT FROM AUTHOR]

With the rapid development of global economy and industrialization, the management and protection of water resources have become increasingly important. In this paper, a novel three-dimensional Chitosan/Fe 3 O 4 /TiO 2 @MXene photocatalyst with excellent degradation and bacterial inhibition properties was prepared by a simple one-step hydrothermal method using FeCl 3 ·6H 2 O, Chitosan (CS) and MXene (Ti 3 C 2) as main raw materials. TiO 2 was generated in-situ on the surface of Ti 3 C 2 nanosheets and modified by loading Fe 3 O 4 nanoparticles and CS films. The conformations and band gaps of the composites were modified, and then photocatalytic mechanisms, phase compositions, morphological structures, magnetic properties and photoelectrochemical properties of the photocatalysts were systematically analyzed. The degradation efficiency of the CS/Fe 3 O 4 /TiO 2 @Ti 3 C 2 composite photocatalyst can reach 95.9 % when the ration of CS to FeCl 3 ·6H 2 O is 1:3 and the hydrothermal temperature is 180 °C, which showing excellent photocatalytic performance. Meanwhile, the inhibition rate of Staphylococcus aureus (S. aureus) is close to 98.91 % under the light condition. This study further reveals that visible light is a cost-effective way to treat pollution in terms of degradation and bacteriostatic inhibition, and the composite photocatalysts can be efficiently recycled and reused under the action of a magnetic field. [Display omitted] • CS/Fe 3 O 4 /TiO 2 @MXene with excellent degradation and bacteriostatic properties was synthesized by one-step hydrothermal method. • Fe 3 O 4 formed Schottky heterojunction with TiO 2 generated in situ, CS formed a double-layer film regulation system with Ti 3 C 2. • Bacteriostatic system was built: Nanoparticles brought ROS, loaded on sharp sheets, wrapped by CS with positive charges. • The material also has outstanding cyclic degradation performance, can be easily recycled and reused due to its magnetism. [ABSTRACT FROM AUTHOR]

Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales. Plain Language Summary: Global flash drought risks are prevailing currently and will continue to be an important concern under climate change. Skillful prediction of global flash drought onset and termination events can provide critical early warning information for agricultural preparations and water resources management. However, current weather forecasting models suffer from large errors in root‐zone soil moisture and flash drought predictions. Here, we develop a machine learning‐based framework that could predict 33% and 24% global flash drought onset and termination events at 7 day lead time, better than the state‐of‐the‐art dynamic model. The machine learning‐based framework can reduce soil moisture prediction errors in dynamic coupled models and thus enhances global flash drought prediction skills. The proposed machine learning forecasting approach may provide scientific advances for flash drought early warning at global scales. Key Points: Machine learning is used for global flash drought prediction up to 2 week lead time33% and 24% global flash drought onset and termination events can be correctly predicted at 7 day lead timeMachine learning improves global flash drought prediction over dynamic model [ABSTRACT FROM AUTHOR]

Monsoon precipitation variability over the Indo‐China Peninsula (ICP) has become more complicated affected by global warming. In this study, the modulation of Pacific decadal oscillation (PDO) on the relationship between El Niño–Southern Oscillation (ENSO) and the rainy season onset over the ICP were investigated. The results showed that the ICP rainy season onset were predominantly correlated with winter sea surface temperature anomalies (SSTAs) in the East Pacific Ocean, with late and early onsets following El Niño and La Niña events, respectively. During the warm and cold PDO phase, the correlations tended to be substantially strengthened and weakened, respectively. Further analysis indicates that PDO significantly influenced the effects of ENSO on the ICP rainy season onset by modulating SSTAs and low‐level wind fields. During the El Niño events, abnormal easterlies over the Bay of Bengal (BoB) and southern ICP suppressed water vapour transporting to the ICP, which may be related to the zonal SST anomaly gradient between the Indian Ocean and the Northwest Pacific Ocean. When the El Niño occurred during the warm PDO phase, the rainy season onsets were later. The anomalous easterlies became stronger corresponds to the increasing zonal sea surface temperature anomaly (SSTA) gradient between the Indian Ocean and the Northwest Pacific Ocean. There was no significant anomaly on the rainy season onset during the cold PDO phase. During the La Niña events, the abnormal westerlies in BOB accelerated water vapour transport, and the rainy season onset were earlier during the warm and cold PDO phase. The modulating effects of PDO on La Niña were less than those on El Niño. These results suggest that the predictability of rainy season onset over the ICP can be improved through PDO and thus help agricultural planning and water resources management. [ABSTRACT FROM AUTHOR]

This paper presents a high-resolution gridded dataset of daily minimum (TN), mean (TG) and maximum (TX) near-surface air temperatures over Poland, covering the period from 1951 to 2020, with a spatial resolution of 1 km2. PL1GD-T dataset was developed using radial basis functions (RBFs) applied to quality-controlled observations from ground weather stations from the Institute of Meteorology and Water Management – National Research Institute. Cross-validation methods evaluated the gridding procedure on a monthly basis. The linear RBF was employed by hold-out cross-validation (HO-CV) as the most suitable for the gridding procedure among other RBFs. The leave-one-out cross-validation (LOO-CV) was performed to ensure the ability to reproduce the original characteristics variability. The values of the scores averaged over all stations for individual months are in range 0.3–0.2, 0.3–0.2, 0.1–0.2 for the bias, in range 1,23–1,46, 0.69–0.92, 0.84–0.99 the root-mean-squared difference (RMSD) and in range 0.91–0.97, 0.98–0.99, 0.98–0.99 for the correlation for TN, TG and TX, respectively. The RSMD is clearly altitude dependent, increasing from low-land to mountainous regions. The dataset's scope and resolution allowed robust estimation of local climate variability characteristics and observed trends. The availability of high-resolution datasets in both spatial and temporal contexts is essential for climate change impact analysis on a smaller scale. This new dataset provides a quality-validated, high-resolution, and open-access dataset that could be utilised by society, administrative bodies, or research institutions for further analysis. The dataset is publicly available from the repository of the Institute of Meteorology and Water Management – National Research Institute under https://doi.org/10.26491/imgw%5frepo/PL1GD-T (Jaczewski et al., 2024). [ABSTRACT FROM AUTHOR]

The effluent discharge of domestic wastewater and industrial effluents into watercourses has necessitated the development of monitoring systems with the purpose of characterizing levels of pollution. Substantial levels of pollution in emerging nations have been exacerbated by rapidly growing populations and inefficient enforcement of sustainable management initiatives. The Water Quality Index (WQI) can help fill the gap between water quality reporting and monitoring by offering a simple and effective way to assimilate and communicate results from a large amount of data. WQIs are the kind of communication tools that can facilitate knowledge sharing between scholars and the general public. The degradation of natural water resources, such as lakes, streams, and estuaries, is the most significant problem facing humanity. Water that is not clean has far-reaching effects on all aspects of life. Water resource management is therefore essential if it is to maximize water quality. If data are analyzed and water quality can be predicted in advance, water pollution may be efficiently addressed. Although this subject has been the subject of numerous earlier studies, additional research is still required to fully understand the effectiveness, reliability, accuracy, and application of the present approaches to water quality management. In this research work, a comprehensive review has been conducted on water quality assessment using artificial techniques. It reviews 75 research works on water quality analysis. The data collected in each research work have been analyzed. Moreover, the type of water resource surface water, groundwater, drinking water as well has been analyzed. The parameters considered in each work are “Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD), Total Dissolved Solids (TDS), and Chemical Oxygen Demand (COD), chloride, hardness, alkalinity, nitrate–nitrite”. In addition, the Artificial Intelligence technique utilized for water quality analysis is also assessed. Finally, the research gaps identified in water quality detection are exhibited. [ABSTRACT FROM AUTHOR]

Accurate and reliable monthly runoff forecasting is crucial for water resource management, but the increasing non-stationarity of runoff series poses new challenges to the development of forecasting models. To overcome these problems, this study proposes a novel climate-driven hybrid model based on two-stage decomposition and. optimization, called ICEEMDAN-SVMD-ESPSO-SVR. Firstly, the original runoff is decomposed into a set of components by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and high-frequency components identified through entropy analysis are further decomposed into multiple subcomponents by successive variational mode decomposition (SVMD). Secondly, the input variables are selected from the candidate set consisting of runoff, subcomponents and key climate factors by using correlation coefficient and mutual information, respectively. Thirdly, support vector regression (SVR) is employed to forecast each sub-component, and particle swarm optimization algorithm using eagle strategy (ESPSO) is used to select each model's parameters. Finally, the forecasted values of all sub-components are aggregated as the final result. Monthly runoff data from three different hydrological stations in China's Poyang Lake Basin are employed to evaluate the performance of the proposed hybrid model and other comparable models. Results indicate that the proposed hybrid model outperforms single models and other combination models based on single-stage decomposition, and the correlation coefficient method is more suitable for input variable selection in climatedriven models, thus confirming the effectiveness of two-stage decomposition, the ESPSO algorithm, and input variable selection in enhancing modeling accuracy. Therefore, the proposed hybrid model is a feasible and promising new method for monthly runoff forecasting. [ABSTRACT FROM AUTHOR]

Climate change threatens agricultural sustainability, especially water resource management. This study comprehensively analyses how climate change impacts irrigation water needs in India's vital Girna command area, which lies in a semi-arid region, where a decision support tool with climate scenario analysis was integrated, incorporating five different climate models with region-specific agricultural data to evaluate future irrigation demands under various climate change scenarios. The maximum and minimum temperatures will rise under two representative concentration pathways (RCP) scenarios, with RCP 8.5 projecting a more significant increase than the RCP 4.5 scenario. Reference evapotranspiration (ETo) will also increase compared to the base period, reaching its maximum in May and minimum in December across all climate models. However, crop water requirements should remain stable or slightly decrease, with a maximum occurring in October and a minimum in March across all climate models. Depending on the climate model and RCP scenario, maximum effective precipitation will vary and most likely occur in July. Maximum gross irrigation requirements may decrease under RCP 8.5 for the base period, particularly in November. These findings significantly impact agricultural planning, water management, and climate change adaptation strategies. This study can assist stakeholders in making informed decisions for sustainably managing water resources in the Girna command area and beyond. [ABSTRACT FROM AUTHOR]

The reasonable selection of water resource management alternatives is crucial for decision-makers to develop effective management measures ensuring the sustainable use of water resources. A hybrid decision framework is proposed to address incomplete decision data and dynamic updates of criteria. This framework integrates generalized regression neural networks (GRNN), affinity propagation clustering (AP), probabilistic neural networks (PNN), and a linear weighting method, aiming to achieve long-term socio-economic sustainability. First, based on the PNN model, the missing matrix is filled to obtain a complete evaluation matrix. Then, AP is used to cluster the evaluation criteria to obtain the decision matrix of the cluster center, effectively reducing the dimension of the matrix. For the new evaluation criteria, PNN classifies and updates the cluster center matrix, addressing criteria updates effectively. The alternatives are ranked using the linear weighting method. The framework's effectiveness and reliability are verified through two cases: urban water supply and reservoir optimal operation. Results demonstrate that this hybrid decision-making framework aids managers in selecting optimal schemes. [ABSTRACT FROM AUTHOR]

Virtual water transfer is considered as an important pathway to alleviate water shortage in arid regions due to limited water resources and unbalanced distribution. It is essential to explore the hidden mechanism of virtual water transfers among multiple regions to support future water resources allocation and management. In this study, a Disaggregated Multi-Region Virtual Water Flow and Interaction (DrWIn) model is developed to facilitate the analysis of virtual water inflow, outflow, transfer balance, and the related interactions. In addition, a factorial analysis is integrated to quantify the impacts of industrial water consumption and their interactive effects. A special case study of China is conducted to illustrate the applicability and superiority of the DrWIn model. It is found that interaction effects of any two industries are negative, indicating that high freshwater consumption in two industries is not the best choice. The obtained results provide a solid scientific basis for identifying the key industries and regions across a multi-region study system and supporting water resources utilization management in the future. [ABSTRACT FROM AUTHOR]

The examination of the spatiotemporal characteristics and developmental trends of drought is crucial for enhancing water resource management, bolstering drought resistance, and improving disaster prevention capabilities. This study employs the Standardized Precipitation Evapotranspiration Index (SPEI) and grain yield data across various time scales, in conjunction with methodologies such as Run Theory, Mann-Kendall, and Standardized Yield Residual Series, to conduct an in-depth investigation into the spatiotemporal variation characteristics of meteorological drought in Hunan Province and its impact on grain yield. The findings suggest that: (1) Since 1960, the likelihood of seasonal drought occurrence in Hunan Province has been ranked as autumn > winter > spring > summer, with mild drought occurring most frequently, followed by moderate drought, while the frequency of severe and extreme drought remains low. (2) Meteorological drought in Hunan Province exhibits spatial differences at the seasonal scale, with the overall drought changes in spring and summer displaying a non-significant upward trend; the western and southern regions exhibit a trend of aridification in autumn; and in winter, the Zhangjiajie and Xiangxi regions show an insignificant downward trend. (3) From 1960 to 2022, grain production in Hunan Province has demonstrated a pattern of fluctuation and increase. The meteorological yield of grain crops displays a high-low-high spatial distribution from south to north. Concurrently, there is a positive correlation between short-term climate change and meteorological output, while long-term climate change is not evident. (4) El Niño Southern Oscillation (ENSO) is a significant circulation factor affecting meteorological drought in Hunan Province, and the meteorological drought in autumn and winter in Hunan Province is significantly influenced by ENSO. The research findings can provide reference significance and a scientific basis for drought research and comprehensive management in Hunan Province, and offer data and theoretical support for promoting economic development. [ABSTRACT FROM AUTHOR]

ABSTRACT The impact of extreme events in risk analysis depends on factors such as magnitude, duration, timing and whether the system recovers fully before the next event occurs. While previous studies have primarily examined the drivers and characteristics of individual extremes, less focus has been given to the concurrent or compounding nature of extremes across adjacent seasons. Thus, understanding the dynamics of such compound extremes, particularly dryness and wetness, is crucial. To address these concerns, a Multi Scalar Drought Index (MSDI) is formulated using precipitation and temperature data from three river basins (Brahmani, Baitarani and Cauvery) of eastern and southern India. The combinations of dryness and wetness, such as Dry‐Dry, Dry‐Wet, Wet‐Dry and Wet‐Wet, between consecutive seasons are analysed across four seasons (summer, rainy, autumn and winter). The prolonged dryness/wetness along with dry/wet year are evaluated from baseline (1979–2018) to projected COrdinated Regional Climate Downscaling Experiment (CORDEX) future period (2020–2099). The spatio‐temporal variations in intra‐annual dry‐wet extremes are identified using the Mann–Kendall test. The results suggest that the eastern Indian river basins, particularly the Brahmani and Baitarani basins, experience more frequent occurrences of compounding dryness‐wetness compared to Cauvery river which is a southern Indian basin. Future scenarios indicate a trend towards dryness during the monsoon season in Brahmani and Baitarani basins, with frequent wet extremes in late autumn and winter. Abrupt transitions between dryness and wetness are prevalent during the Rainy‐Autumn and Autumn‐Winter seasons in Brahmani and Baitarani basins. The increased frequency of compound dry‐wet extremes poses significant socio‐economic risks, including reduced agricultural productivity, water management challenges and heightened vulnerability of local livelihoods dependent on consistent water availability. The results of this study provide a scientific reference for sustainable agriculture and water resource management to predict future seasonal dry and wet alternations and develop effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River's source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN's runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs' output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt's fossil aquifers during high flood years. [ABSTRACT FROM AUTHOR]

ABSTRACT The reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution. [ABSTRACT FROM AUTHOR]

The implement of water resources spatial equilibrium (WRSE) schemes is fundamental task of integrated water resources management in China, in which, the evaluation and simulation analysis of WRSE system is of great concern for understanding the overall variation and feedback characteristics of WRSE system. Therefore, we utilized the ordered degree, entropy and connection number coupling model to evaluate the variation of WRSE system, and also employed system dynamics (SD) and scenario simulation integrated method to reveal the feedback characteristics between different equilibrium variables and subsystems, and thus the set pair analysis‐SD based approach for structure simulation and variation trend evaluation of WRSE system was constructed. The application results in Anhui province, China demonstrated that, the overall variation of provincial spatial equilibrium situation of WRSE system presented obvious improving trend, 2009–2019, the index of ordered degree and connection number entropy in Hefei, increased from the minimum of 0.6434 (Grade 3, unequilibrium) to maximum of 0.9985 (Grade 1, equilibrium). Moreover, the future variation of WRSE system will display stable improving trend during 2020 to 2029, in which, annual water resources availability and water resources utilization efficiency will have significant influence on WRSE system. And the research findings can be favorable to formulate water resources development and utilization strategies. [ABSTRACT FROM AUTHOR]

In the south-eastern region of Nigeria, the application of rainfall (P) and runoff (Q) data directly into the Natural Resources Conservation Service Curve Number (NRCS-CN) method hasn't been thoroughly studied. This research aimed to determine representative values of the initial abstraction ratio (λ) and corresponding curve number (CN), fit the P and Q data using theoretical probability distributions, and establish confidence intervals for CN. The least squares minimization method and Kolmogorov-Smirnov test were employed on data from 129 sub-basins across 4 major basins. Findings revealed optimal initial abstraction ratio (λopt) = 0.24 and optimal CN (CNopt) = 80, with rainfall best fitted by Gamma, runoff by Weibull, and CN by Normal distributions. However, the study was limited to the available 8-year record period with 96 storm events. The 96 storm events over an 8-year period may seem limited for a humid tropical region, the available data were the most comprehensive and reliable dataset for this study area. Additional data collection over a longer time frame could enhance future studies. The localized CN value and associated confidence intervals can enhance runoff prediction accuracy for flood mitigation and water resources management in this humid tropical region, though further validation is recommended. [ABSTRACT FROM AUTHOR]

The freshwater ecological characteristics in terms of the daily inventory of thermal stratification, spatial variation of O2 distribution, and the mobility of potentially toxic elements (PTEs) at the water sediment interface (WSI) are prudent freshwater assessment indices for water quality management protocol. The study conducted daily observations within a monsoon-influenced region, utilizing high-resolution techniques such as HR Peeper, Yellow Spring Instrument (YSI), and ZrO-Chelex diffusive gradient technology (DGT) to analyze PTEs, specifically phosphorus (P) and iron (Fe),within the water–sediment interface (WSI) under different temperatures and oxygen conditions. The 66-day field study showed that high thermal structure contributed significantly to production Fe ions and P from sediment under reductive dissolution of FeOOH. The study also revealed that P and Fe exhibited comparable spatial distribution patterns at the WSI, indicating a linked relationship between these PTEs. This correlation was reinforced by high Pearson correlation coefficients ranging from 0.7 to 0.9 (bilateral, p < 0.05) indicating that the concentrations of labile P were predominantly influenced by the release of phosphorus bound to iron. The fluxes of the PTEs were positive with a range of Fe, 3.3–81.5 mg/m2 day and P, 0.03–0.5 mg/m2 day showing the sediments liberated the PTEs into the benthic water. Again, high positive fluxes (Fe≈60 mg/m2 day, P≈0.5 mg/m2 day) for PTEs were obtained when stratification was high (anoxic conditions) and low (Fe≈5 mg/m2 day, P≈0.08 mg/m2 day) when stratification did not exist. This depicts that Fe/P dynamics were hinged mainly on hypoxic conditions in the benthic water under the reductive dissolution of FeOOH. The findings showed that organic materials (both solid and dissolved) correlated (> 0.7) significantly with (positive high values) Fe. This indicates that their interaction contributed to the reservoir water deterioration. However, Ca2⁺ and Mg2⁺ had little impact on the liberation of Fe-DOC-P from sediments due to their inability to compete with Fe for binding to DOC and P, as shown by their low correlation values. The research provides in-depth insights into the dynamics of PTEs on a daily timescale and offers valuable information for water management practices in inland reservoirs, particularly concerning the cycling of phosphorus (P) and its effects on ecosystem health. [ABSTRACT FROM AUTHOR]

This study investigates the hydrogeochemical characteristics and water quality of the Puebla Valley aquifer, a volcano-sedimentary system predominantly influenced by water-rock interactions and ion exchange processes. The research assesses the suitability of groundwater for agricultural irrigation by applying various water quality indices, including salinity and sodicity indices and Wilcox diagrams and USSL. Seventy-one water samples were analyzed to determine key physicochemical parameters and dominant ion concentrations, revealing that the primary hydrogeochemical facies are HCO 3 -Mg and HCO 3 -Ca. The results indicate a heightened risk of salinization, particularly in agricultural and urban areas, underscoring the need for ongoing monitoring. Natural processes such as water-rock interaction and ion exchange are the main factors influencing groundwater quality. However, anthropogenic activities, particularly the use of fertilizers and irrigation return flows from the Atoyac River, exacerbate groundwater quality degradation. This study emphasizes the necessity of sustainable water resource management to ensure the long-term viability of the aquifer. [ABSTRACT FROM AUTHOR]

Terrestrial crabs (brachyurans and anomurans) have invaded land following a variety of pathways from marine and/or via freshwater environments. This transition from water to land requires physiological, ecological, and behavioral adaptations to allow the exploitation of these new environmental conditions. Arguably, the management of salt and water balance (e.g., osmoregulation) is integral for their survival and success in an environment where predominantly low-salinity aquatic (e.g., freshwater) water sources are found, sometimes in only minimal amounts. This requires a suite of morphological and biochemical modifications, especially at the branchial chamber of semi-terrestrial and terrestrial crabs to allow reprocessing of urine to maximize ion uptake. Using knowledge gained from electrophysiology, biochemistry, and more recent molecular biology techniques, we present summarized updated models for ion transport for all major taxonomic groups of terrestrial crabs. This is an exciting and fast-moving field of research, and we hope that this review will stimulate further study. Terrestrial crabs retain their crown as the ideal model group for studying the evolutionary pathways that facilitated terrestrial invasion. [ABSTRACT FROM AUTHOR]

Recent satellite maps have reported that India is experiencing extreme heat waves, surpassing even Middle Eastern countries. This study addresses a critical gap in understanding how land use land cover (LULC) changes impact land surface temperature (LST), urban heat intensity (UHI), and water spread area (WSA) in rapidly growing cities such as Vellore and Katpadi over three decades (1997–2024). We used Landsat thermal bands and the support vector machine (SVM) algorithm to investigate LULC and LST patterns, examining the effects of urbanization and water body reduction on local climate dynamics. The LULC results showed an increase in built-up lands from 5.89 to 25.89%, while zooming water areas shrank from 3.15 to 1.02%. LST showed a significant increasing trend, with temperatures for water bodies and vegetation ranging from 17.4°C to 26°C, and for barren and built-up areas from 28°C to 42.6°C. The results of the multivariate analysis revealed a positive correlation between LST and the Normalized Difference Built-up Index (NDBI) and negative correlations between LST and the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Modified Normalized Difference Water Index (MNDWI). Moreover, spatial and time series analyses of WSAs indicated a significant increase in LST. Furthermore, a strong negative correlation was found between WSA and LST, with a 10% decrease in WSA potentially increasing LST by 0.12°C to 0.55°C in surrounding regions. This study offers important contributions to improving land use policy and water resource management in urban areas, while addressing environmental concerns related to rising temperatures. The findings underscore the urgency of mitigating heat impacts and managing water resources in rapidly expanding cities. Our results provide valuable insights for policymakers and practitioners aiming to develop more sustainable, resilient, and livable urban environments. [ABSTRACT FROM AUTHOR]

Traditional edible, spicy and medicinal herbs rarely are included in studies on water requirements, evapotranspiration and crop coefficients (Kc) of vegetable crops. While the latter refer to large markets and are used worldwide, the former has small and specialized markets. They are very rarely grown in large farms but rather in small farming systems, or just harvested in the wild, and related evapotranspiration (ET) studies are less frequently considered in main journals focused on water management in agriculture. However, there is abundant bibliography covering traditional edible, spicy and medicinal herbs. Following the partition adopted in recently published articles on Kc values, such crops are grouped according to the main part of the plant purposefully harvested: roots, rhizomes and tubers; leaves and flowers; fruits, pods and seeds. Since we focused on Kc and not on the products, and because many crops have simultaneously two usages such as spicy and medicinal, main uses are presented to characterize the crop. Interestingly, the reviewed crop coefficients have values quite similar to those of the more common and popular vegetable crops. The single crop coefficients reviewed, and referring to the initial, mid-season and end-season periods, are tabulated according to the referred groups. In addition, aiming at their use in water management and modelling, crop heights, root depths, fractions of ground cover and fractions of soil water extraction without stress are also tabulated. In conclusion, this review aimed at successfully provide Kc values to be used for computing crop evapotranspiration for irrigation scheduling and crop water management of the referred herbs and vegetables. [ABSTRACT FROM AUTHOR]

The FAO-56 two-step approach (Kc-ETo) is commonly used to estimate crop evapotranspiration (ETc) for various crops and climate conditions. This approach consists of the product of a specific crop coefficient (Kc) and the grass-reference evapotranspiration (ETo). Crop coefficients were adjusted to regional climate conditions aiming at incorporating crop-specific impacts of relative humidity and wind speed on Kc in addition to climate impacts already represented in ETo. In the current study, crop water use and actual crop evapotranspiration and transpiration (ETc act and Tc act) were studied for four soybean maturity groups under irrigated and rainfed conditions. Field experiments were conducted in Southern Brazil along two seasons (2018/19 and 2019/20). Observations included measurements of the fraction of ground cover (fc), plant height (h), and soil water content (SWC) to calculate the soil water balance and estimate the soybean yields. Using the soil water balance model SIMDualKc with field collected data allowed estimating the actual and standard Kcb. The model adopts the dual Kc approach and was calibrated and validated using the field observations from, respectively, 2018/19 and 2019/20. Thus, the study provided for accurately estimating and partitioning soybean ETc act into soil evaporation and crop transpiration. Results of fitting observed SWC by model simulated data provided for a set of excellent goodness-of-fit indicators. Therefore, the actual Kcb estimated with SIMDualKc are assumed to have excellent accuracy for computing actual transpiration to be used for supporting soybean irrigation scheduling. This study allowed to evaluate the main water use and yield responses to water stress of diverse soybean maturity groups and to assess the impacts of related water management strategies. [ABSTRACT FROM AUTHOR]

Natural and planted grasslands play a very important role in agriculture as source of various ecosystem services, including carbon sequestration and biodiversity, and are responsible for a large fraction of agricultural water use in rainfed and irrigated fields. It is, therefore, relevant to precisely know their water use and vegetation requirements with consideration of relevant climate, from extremely cold, dry, with long winter seasons, to tropical humid and hot climates, thus with a large variability of vegetation. Semi-natural grasslands are basically used for grazing and mainly refer to highland pastures and meadows, steppes, savannas, pampas, and mixed forest systems. The FAO method to compute crop (vegetation) evapotranspiration (ETc) through the product of a crop coefficient (Kc) by the reference evapotranspiration (ETo) is adopted. The selected papers were those where actual ETc (ETc act) was derived from field observations and ETo was computed with the FAO56 definition, or with another method that could be referred to the former. Field derived ETc act methods included soil water balance, Bowen ratio and eddy covariance measurements, as well as remote sensing vegetation indices or surface energy balance models, thus reviewed Kc act (ETc act/ETo) values were obtained from field data. These Kc act refer to initial, mid-season and end season (Kc act ini, Kc act mid, Kc act end) when reported values were daily or monthly; otherwise, only average values (Kc act avg) were collected. For cases relative to cold or freezing winters, data refer to the warm season only. For grasses cut for hay, Kc act ini, Kc act mid, and Kc act end refer to a cut cycle. Kc act values rarely exceeded 1.25, thus indicating that field measurements reported did respect the available energy for evaporation. Overall, Kc actmid for semi-natural grasslands in cold climates were lower than those in hot climates except when available water was high, with Kc act mid for meadows and mountain pastures generally high. Steppes have Kc act mid values lower than savannas. Grasses commonly planted for hay and for landscape generally showed high Kc act mid values, while a larger variability was observed with grasses for grazing. The collected Kc act values were used to define standard Kc values for all grassland and grasses. Nevertheless, the tabulated Kc act are indicative values of Kc to be used for actual water management purposes and/or irrigation scheduling of planted grasslands. It is expected that a better knowledge of the standard and/or indicative Kc values for a wide variety of grasslands and grasses will support better management aimed to improve grass productivity and ecosystem services, including biodiversity and carbon sequestration. [ABSTRACT FROM AUTHOR]

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis. [ABSTRACT FROM AUTHOR]

The Yellow River, China's second-largest river, is renowned for its high sediment content. In response to the potential impacts of climate change on Yellow River water resources and water environmental management, an advanced monitoring and forecasting system for water and sediment throughout the entire Yellow River basin—from its source to the sea—is urgently needed. In this paper, based on the current status of water and sediment monitoring technologies, we proposed an integrated remote sensing monitoring network that combines satellite remote sensing, drone remote sensing, and ground-based wireless automatic monitoring networks, aiming to achieve the digital monitoring of water and sediment across the entire Yellow River basin, from its upper reaches to its estuary in the Bohai Sea. By utilizing ground-based in situ hyperspectral stations for sediment source areas in the upper reaches, such as the Qingshui River basin in Ningxia, and satellite remote sensing for midstream processes in the Xiaolangdi reservoir before the flood season in 2023, as well as downstream monitoring at the Yellow River estuary, this paper demonstrates the novelty and efficiency of the space–air–ground integrated remote sensing monitoring technology. [ABSTRACT FROM AUTHOR]

Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land-use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation. The intensity of land development decreased, and only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000–2010 and 2010–2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for comprehensive ES (CES) functions. By considering the trade-off–synergy relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia. [ABSTRACT FROM AUTHOR]

Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 105 hectares, distributed over 853,404 parcels with an average size of 6202 m2. Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 108 m3 to 30.0 × 108 m3, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 108 m3 according to the water balance approach, resulting in a minimal water deficit of 0.64 × 108 m3, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions. [ABSTRACT FROM AUTHOR]

The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3's processing baseline, listed as follows: Baseline Collection 003 (BC003), including "CHL_NN", "TSM_NN", and "ADG443_NN". These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for "conc_tsm"; 118% for "conc_chl"; 33% for "iop_agelb"; 50% for "iop_adg"; and 10% for "kd_z90max" using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region. [ABSTRACT FROM AUTHOR]

This opinion article presents arguments to sustain the inclusion of local actors in the governance and management of water resource restoration projects. There is a lack of knowledge regarding this issue, and we propose that governance and management must be adaptive because of the complexity and constant modification of the local systems. Our proposal has theoretical, methodological, and management impacts on the network governance of local projects. We present a matrix of variables to facilitate research and management plans. [ABSTRACT FROM AUTHOR]

Operational water management is a critical global challenge, and decision making can be improved by using mathematical optimization. This paper provides an overview of optimization techniques, both exact and heuristic, used in water management. It focuses on the use of optimization techniques in the short term: operational planning in reservoir management, control of open channels, hydropower scheduling, and operation of polder drainage pumps. Principles of model predictive control, methods for optimization under forecast uncertainty, and approaches for conflict resolution are explained with the help of educational examples and practical cases. Challenges and research questions to be addressed in the future are presented as an outlook. This article is categorized under:Engineering Water > MethodsScience of Water > Water and Environmental ChangeWater and Life > Conservation, Management, and Awareness [ABSTRACT FROM AUTHOR]

After Tunisia's independence from France in 1956, the country was engaged in what has been termed the "supply management policy", that is, a maximum mobilization of water resources through large‐scale modern hydraulic infrastructure. Towards the beginning of the 1980s, the country entered a crisis and had to adopt in 1986, under pressure from the International Monetary Fund and the World Bank, a "Structural Adjustment Program" (SAP) that prioritized measures to improve economic efficiency and liberalize the economy. Since then, Tunisian water policy has progressively shifted towards "Integrated Water Resources Management" (IWRM). This review critically analyzes the water governance discourses that have framed these policy interventions. I will draw on academic research and official and civil society reports to suggest that these discourses have been "rendered technical", stripping issues of any political (and therefore conflicting) character. When rendered technical discourses raise development projects and policies above the terrain of political contestation, the possibilities of action available to those opposed to them become limited. This depoliticization of development interventions through technical discourses narrows the public space of democratic debate. I will also argue that the "technification" (rendering technical) process is reversible. Opposition groups can challenge technical discourses with a critical approach that can reconfigure and alter the trajectory of governmental programs. However, critical discourses can, in turn, be rendered technical. This article is categorized under:Human Water > Water GovernanceHuman Water > Rights to Water [ABSTRACT FROM AUTHOR]

An urban water demand with high accuracy and reliability plays fundamental role in creating a predictive water supply system. This is necessary to implement mechanisms and systems that can be used to estimate water demands. The paper aims to forecast the amounts of clean water given to metropolis with single multiplicative neuron model artificial neural network. For this purpose, a regular data set of Istanbul metropolis was selected as a model and utilised in the application process. Single multiplicative neuron model artificial neural network is a neural network that does not have the hidden layer unit number problem that many shallow and deep artificial neural networks have. In this study, the black hole optimization algorithm is used for the first time in the literature for the training of the single multiplicative neuron model artificial neural network. In line with the analysis results, it is concluded that the proposed new approach achieved better prediction results than the other compared methods for the time series of amounts of clean water given to metropolis. [ABSTRACT FROM AUTHOR]

A major data gap in tropical river ecology is the limited documentation of natural water purification of upstream rivers. This study documented the improvement of water quality of downstream Balili River as affected by environmental factors and macrophyte diversity using a mixed-method approach (water quality assessment, plant inventory, field observation, canonical correspondence analysis, community interview). Results showed that the distance from pollution plays a significant role in the self-purification of the river while the floral diversity maintains the riparian from further contaminating the water and at the same time absorbing air pollutants. Generally, the pollution reduction and %change is exponential at 2 and 3 km from point source then taper at 4 and 5 km. At 3 km from the point source, 30% improvement in TDS, BOD and salinity, 25% for EC, 20% in TSS, 35% in WQI, 36% in CPI, and 50% for DO were noted. Key factors that influence the self-purification process in the river were distance from pollution source, elevation, human disturbance (as indicated by %tree canopy) and temperature. These findings highlight the importance of distance from pollution sources and floral diversity in driving water quality improvements, with implications for sustainable water resource management in tropical regions. NOVELTY STATEMENT: This study documented the improvement of water quality of downstream Balili River as affected by environmental factors and macrophyte diversity – as contribution to the major data gap in tropical river ecology which currently has limited documentation on natural water purification of upstream rivers. The study also shows the mosaic interplay of water quality, plant diversity, distance from pollution, human disturbance, and air quality in understudied tropical river. Further, this study is unique in documenting the downstream of Balili River since majority of the previous studies were concentrated in the upstream portion of the river. [ABSTRACT FROM AUTHOR]

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments. [ABSTRACT FROM AUTHOR]

This study explores hydrological and meteorological drought in the Coastal Constantine basin, the Cheffia and Mexa dams in northeast Algeria in particular, using a time series of monthly rainfall and dam inflow data over 54 years (1965–2018). Recently, these dams have experienced recurring droughts, significantly reducing water storage and severely impacting the population's drinking water supply. To better assess the severity and trends of these droughts, we applied a range of methods (Mann-Kendall test, Sen's slope estimator, Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Innovative Trend Analysis (ITA) method). SPI and SSI were chosen because they can be applied across various timescales, easy to calculate, and have demonstrated accuracy. Additionally, the trend analysis techniques were preferred because they have few assumptions, are resistant to incomplete data, offer the advantage of visual interpretation, and have the ability to capture changes within data subgroups. The findings analysis shows an autumn rainfall increase (1.18 mm/year) and a significant spring decrease (-2.23 mm/year). In addition, an annual rainfall trend magnitude ranging from − 5.26 mm/year to 1.71 mm/year. The Cheffia Dam experiences a summer inflow decrease (-0.05 hm³/year) and an overall annual decline (-0.35 hm³/year), while the Mexa Dam shows a significant spring inflow decrease (-0.17 hm³/year) with a slight annual decline (-0.07 hm³/year). The ITA method revealed severe and prolonged droughts with increasing severity over time, as shown by the SPI and SSI drought trend analysis. These trends affirm the growing impact of reduced precipitation on water resources and emphasize the urgent need for proactive water management strategies in the region. The results highlight the extremely difficult conditions and the severe drought, particularly in central parts of the basin, over the past 54 years, and underscore ongoing challenges in managing water resources amid changing climatic conditions. This situation negatively impacts the Boulatane dam project and the under-construction Boukhroufa dam. Thus, it is necessary to implement necessary measures to mitigate the effects of prolonged droughts on water availability and infrastructure development. The study's findings provide a foundation for spatial and temporal analysis and management of droughts in water-stressed regions globally, in the context of climate change with innovative techniques. The results emphasize the need for policymakers to prepare for droughts and adopt early warning systems. It is recommended that water conservation efforts be strengthened, new water structures be built, and climate change policies be adapted. [ABSTRACT FROM AUTHOR]

The impact of the Madden-Julian Oscillation (MJO) on India's intraseasonal and interannual variability of rainfall has been investigated in this study. Anomalous MJO amplitude points from 1979 to 2021 are classified into post-, pre- and normal onset years of the Indian Summer Monsoon (ISM). High MJO amplitudes, particularly those exceeding three standard deviations from the mean, correlate with significant rainfall anomalies, affecting both the timing and intensity of the monsoon. Specific MJO phases contribute differently to rainfall patterns, with phases 3, 4, and 5 generally suppressing rainfall, while phases 1 and 8 enhance it. Pre-onset years show significant rainfall anomalies driven by strong MJO activity, particularly during phases 3, 4, and 5. Normal onset years exhibit notable suppression and enhancement patterns, with significant fluctuations observed in 1996 and 2020. Post-onset years have the highest frequency of anomalies, with phases 3 and 4 prominently influencing rainfall variability. Overall, this research underscores the critical role of MJO dynamics in modulating rainfall variability in India. The findings suggest that understanding MJO phases can improve rainfall predictability and monsoon onset forecasting, aiding in better climate prediction and water resource management. This study contributes to the broader climatological discourse by elucidating the interplay between MJO activity and regional precipitation patterns. [ABSTRACT FROM AUTHOR]

Extreme precipitation events have substantial implications for water resources, ecosystems, and human populations in Central Africa (CA). Consequently, understanding the spatial variability of these events is crucial for effective climate change adaptation and water management strategies. In this study, we assess the performance of the state-of-the-art global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme precipitation events over CA. By considering three observational datasets, we evaluated the ability of sixteen CMIP6 models as well their multi-model ensemble (MME), to capture the patterns of extreme precipitation events. We then focus on key metrics such as duration and intensity, based on a total of ten indices of extreme precipitation events, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that the individual models as well as the MME exhibited acceptable performance in reproducing the patterns of extreme precipitation events, especially when dealing with wet-day amount, frequency, dry spell duration and persistence of extreme precipitation over the two northern CA subregions (i.e., Sudano-Sahelian and Northern Equatorial). Moreover, the analyses revealed that CMIP6 models generally lack the ability of in simulating not only the wet spell duration, but also heavy precipitation indices, particularly over the central and southeastern CA subregions (i.e., Equatorial East and Southern Equatorial, respectively). These results highlight the remaining challenges in reproducing local-scale processes governing precipitation variability and extreme precipitation events over the region. The results of this study provide an understanding of both the strengths and limitations of the CMIP6 models over CA, which would help to improve regional climate projections and strengthen the ability of decision makers to assess the future risks associated with extreme precipitation events in the region. [ABSTRACT FROM AUTHOR]

Agricultural water consumption security is crucial for maintaining food production stability. Aiming to investigate water consumption efficiency and objectives in agricultural and non-agricultural sectors, we adopted a Budyko-analogous framework to establish an optimal water allocation model based on the maximum water utilization benefits principle. The proposed Budyko framework was verified through its application in Anhui Province, China, and the difference in water utilization efficiencies among different regions was discussed. The research findings can be concluded as follows: (1) during 2011–2020, the provincial agricultural productive loss caused by per-unit water shortage was lower compared to non-agricultural sectors, and this trend was adaptive in the south but totally opposite in the north; (2) the guaranteed level of agricultural water consumption was higher compared to non-agricultural sectors in the north due to the predominance of agricultural production but totally contrary in the south. Overall, the above findings are consistent with the actual industrial structure of Anhui Province. [ABSTRACT FROM AUTHOR]

The prediction of tap water quality serves as a pivotal component in enhancing water resource management. The intricate nonlinearity and inherent instability in water quality data make this task challenging. In this paper, we present a Tap-Water Quality Temporal Prediction Network (TWQ-TPN) to accurately predict tap-water quality by focusing on the impact of temporal nonlinear patterns and long-term seasonal fluctuations. To achieve this, we design two modules, namely the Temporal Feature Extraction Module (TFEM) and the Feature Transformation and Prediction Module (FTPM). The TFEM learns complex dynamic nonlinear features in the temporal domain. The FTPM is to realize feature transformation in the high-dimensional features for long-term seasonal fluctuations. Thus, our TWQ-TPN can accurately predict tap water quality trends to help improve water management. We validate TWQ-TPN's superiority using 5 years' data from 33 major water facilities in South Korea, demonstrating excellence in pH, turbidity, and residual chlorine. Ablation experiments support TWQ-TPN's rationale. [ABSTRACT FROM AUTHOR]

This research aims to cost-effectively identify inundation due to combined rainfall with tidal surges by evaluating the existing urban drainage system. The Storm Water Management Model (SWMM) applied two scenarios based on different Digital Elevation Model (DEM) sources. These are scenario A, i.e. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and scenario B, i.e. Shuttle Radar Topography Mission (SRTM). Then, catchment delineation and SWMM parameterization in the low-lying urban region of Chattogram City were executed. In contrast to scenario A, i.e. ASTER DEM, identified inundated nodes in the northwest part of the city, scenario B, i.e. SRTM DEM, showed more inundated nodes. In both scenarios, drainage capacity was observed in the field at two outlet points of Ispahani Khal (R2 = 0.91 and 0.85) and Chaktai Khal (R2 = 0.90 and 0.92), which reasonably mimics the model outcome. With an intensive field dataset, this model could provide relevant information for betterment. [ABSTRACT FROM AUTHOR]