Changes In Water Management Research Articles

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing-informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Urban water footprint in Mashhad, Iran’s second largest city

Summary The water footprint (WF) is a metric used to quantify the total volume of fresh water consumed directly and indirectly by individuals and communities; it helps to inform responses to global challenges such as water scarcity, climate change and sustainable water management. This study assessed virtual (VWF), direct (DWF) and total (TWF) water footprints in Mashhad, Iran’s second largest city. Data collected from 382 households showed that the average individual VWF was 1314 m3 per month, the average individual DWF was 228 m3 per month and the average individual TWF was 1538 m3 per month. Additionally, key consumption patterns were identified, with rice and bread emerging as the most consumed items and vegetables as the least consumed. Over a 5-year period, direct water use declined. Significant correlations emerged between family size, annual cost and VWF and TWF, yet no association was found between age and the WFs. There were income-based disparities in VWF and TWF but no differences across education levels in terms of DWF, VWF and TWF. These findings offer crucial insights for policymakers and water authorities in formulating effective water-saving policies to address pressing environmental concerns.

Building Resilience through Territorial Planning: Water Management Infrastructure and Settlement Design in the Coastal Wetlands of Northern Apulia (Salpia vetus-Salapia) from the Hellenistic Period to Late Antiquity

This Gulf of Manfredonia has, for millennia, been the primary water feature of the coastal wetland of Northern Apulia, Italy, although modern reclamation works make writing its long-term history challenging. Our recent paleoenvironmental research has reconstructed the evolution of the southern half of this lagoon since the Neolithic period. Here, we write a history of water management and environmental change in this landscape from the perspective of two key urban sites: pre-Roman Salpia vetus and Roman Salapia. The Roman architectural historian Vitruvius recounts the abandonment of Salpia vetus and the refoundation of Salapia. We employ his narrative as a frame for a more complex environmental history, starting from a historiography of this landscape’s study and a summary of our interdisciplinary research agenda, which unifies environmental, topographical, remote sensing, and archaeological approaches. Resilience in this changeable wetland environment was only possible through an integrated and intentional management of water among rivers, the lagoon, and the Adriatic Sea. While Salpia vetus exploited this wetland and thrived for centuries, the settlement eventually collapsed due to human and environmentally impelled factors. Roman Salapia subsequently emerged with a different approach, new infrastructure, and a new location. This blueprint would sustain urban life in this wetland for six centuries and lay the groundwork for the Medieval town.

Seasonal and annual variations of sediment trapping and particulate organic carbon burial in Yellow River reservoirs

The Yellow River is distinguished by the highest sediment load in mainland China and significant siltation in artificial reservoirs along its main channel, reducing water availability, sediment trapping, and carbon burial in the hydrological project. Since 2002, the Water Sediment Regulation Scheme (WSRS) has been progressively implemented as a hydraulic management strategy to mitigate reservoir sedimentation in the middle-lower basin reservoirs. However, this substantial release of sediment and water has also affected river morphology, carbon burial, and sediment trapping.This research assesses the evolution of particulate organic carbon burial and sediment trapping in eight Yellow River reservoirs from 2002 to 2018, covering the upper and middle-lower basins and two reservoirs in the Yiluo River sub-basin. We calculated the annual and seasonal variations using hydrological data from 11 stations along the Yellow River. A hydrological framework was developed to calculate sediment trapping, and a Monte Carlo analysis was performed to estimate particulate organic carbon burial across all the evaluated reservoirs.We found that sediment trapping and carbon burial in upper basin reservoirs are most influenced by shifts in the precipitation regime, particularly by natural events such as severe droughts or heavy rainfall. A strong correlation was observed between annual precipitation variations and sediment load. In middle-lower basin reservoirs, the major artificial sediment regulation is strongly linked to a significant reduction in sediment trapping and particulate organic carbon burial. This impact is especially notable in the Yiluo River Sub-basin reservoirs, which have experienced a >90 % reduction compared to levels before the WSRS implementation.Finally, we highlight the consequences of climate change and artificial water management strategies in reservoirs, demonstrating how both affect their capacity for sediment trapping and impact their role as significant carbon sinks.

Perspectives of integrated climate change and water policies in Hungary

The paper aims to outline the climate impacts induced and transferred by water and the socio-economic factors that exacerbate them. It also seeks to provide a common and integrated policy framework for climate change adaptation and water management. The climatic and non-climate drivers, the risk chains and options for policy integration are assessed in four complex thematic areas: (i) ecosystem services, (ii) food supply chain: agriculture, food processing industry, food safety; (iii) industrial and service activities, energy and water supply, infrastructure, built environment, and (iv) human health and well-being, social justice. In these complex thematic areas, we examine the risk cascade chain, the current state of impacts and expected future challenges within a coherent framework and then make concrete proposals for possible specific areas of policy integration. Finally, in the conclusions, we present municipal-level good practices in water-resilient climate adaptation and formulate the horizontal actions needed to implement integrated climate-water policy.

Time‐varying copula‐based compound flood risk assessment of extreme rainfall and high water level under a non‐stationary environment

AbstractQuantifying flood risk depends on accurate probability estimation, which is challenging due to non‐stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time‐varying copulas was introduced for the treatment of compound flood risk and bivariate design in non‐stationary environments. Archimedean copulas were developed to diagnose the non‐stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non‐stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non‐stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non‐stationarity may lead to risk over or underestimation. It showed the limitations of the 1‐day scale and quantified the uncertainty of non‐stationary models. This study provided a flood risk assessment framework in a changing environment and a risk‐based design technique, which is essential for climate change adaptation and water management.

Improving high uncertainty of evapotranspiration products under extreme climatic conditions based on deep learning and ERA5 reanalysis data

Accurate quantification of Terrestrial Evapotranspiration (ET) is crucial for understanding global water resource management amid climate change. However, against the backdrop of frequent global extreme climate events, existing ET products don’t accurately reflect ground data under extreme climate conditions (high temperature and high vapor pressure deficit (VPD)) and no studies have addressed this uncertainty. This study, based on data from 150 flux towers (FLUXNET ET), utilizes four daily-scale ET product datasets (REA, GLEAMv3.6b, REA, ERA5) and ERA5 reanalysis meteorological data as inputs for Convolutional Neural Network (CNN), to explore ways to improve the accuracy of ET products under extreme climatic conditions, while three Machine Learning (ML) models were selected as comparisons. An additional 35 nearby sites provide independent validation for model applicability. The results show that the estimation accuracy of ET products under extreme climatic conditions has an average decrease of 37.5 % in the correlation coefficient (R2) and an average increase of 40 % in the root-mean-square-error (RMSE), whereas the CNN significantly improves this high uncertainty and outperforms the other ML models. The accuracy was significantly improved with the addition of ET products (R2 = 0.924, RMSE=0.323 mm/d) compared to having only meteorological data as input (R2 = 0.888, RMSE=0.369 mm/d). Under extreme climatic conditions, the R2 and RMSE of CNN are 0.786 (171.6 % average improvement) and 0.448 (65.4 % average reduction) compared to ET products, with PBias ranging from −9% to 9 % and the most significant improvement in the America (region), EBF (land cover type), and Cwa (climate type). Furthermore, using ET products as inputs significantly enhances the CNN’s estimation accuracy, yielding R2 and RMSE improvements between 10 % and 30 %. At the 35 independent validation sites, the CNN still performs well, with R2 and RMSE around 0.720 and 0.510 mm/d under extreme climatic conditions, and the accuracy within 500 km is even higher. This study enhances model precision, providing a valuable tool for optimizing water management and tackling climate change. These advancements help formulate precise irrigation strategies and water policies, particularly in extreme weather-prone regions, supporting ecological stability and mitigating water scarcity.

Evaluating machine learning models for precipitation prediction in Casablanca City

Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2 ) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.

Evolving pathways towards water security in the Vietnamese Mekong Delta: An adaptive management perspective.

The interplay of climate change, upstream hydropower development, and local water engineering interventions for agricultural production contributes substantially to the transformation of waterscapes and water scarcity in the Vietnamese Mekong Delta. This paper aims to examine how these dynamics are linked to the paradigm shift in water management in An Giang and Ben Tre, the two ecologically distinct provinces that face serious water scarcity in the delta. We used the adaptive management concept to examine how state-led policy directions from food security towards water security enable change in water management that gives priority to water retention. While policy learning is evident, questions remain about how this ad-hoc solution could help address the presently acute water scarcity and water security over the long term. The paper advocates achieving water security should focus not only on diplomatic interventions into upstream climate-development complexities but also local water-livelihood politics.

Significance of land use as a flood control measure: Unveiling the historical and contemporary strategies in the unique case of Kofu basin alluvial fan, Japan

Vulnerability to flood damage is increasing due to the drastic increase in external forces caused by the climate change and high populations in urban areas. As there are limitations to structural flood control measures, measures that include land use optimization are essential for the resilience of humankind. This study investigated the relationship between water management, settlement, and land-use changes from the Paleolithic to the present day in the Kofu basin alluvial fan, Japan, which has a high flood frequency. The results of a chronological analysis of 191 archaeological sites showed that the highest elevation was 352 m in the Paleolithic period and the lowest elevation was 290 m in the Middle Ages, which later indicated the expansion of settlements into lowlands as time passed. Analysis of the relationship between land use and microtopography in the post-modern period also revealed that approximately until 1929, most of the settlement was located on natural levees. However, the construction of continuous levees since the modern era has led to settlements in areas with a high risk of flooding, which is known as the levee effect. While the importance of land use in flood control has been highlighted, here, by examining the relationship between the distribution of settlements, the change of river channels, and flood risk since the recorded history, we highlight the heterogeneity of the current practice of prioritizing development and the timeless universality of land use as a countermeasure for flood inundation.

Strong declines of waders between 1991 and 2021 related to changes in water management in the Syvash wetlands

Strong declines of waders between 1991 and 2021 related to changes in water management in the Syvash wetlands

Social, behavioral and environmental determinants of vector-borne diseases: A narrative review of evidence and implications for integrated control approaches.

Vector-borne diseases exert immense health burdens worldwide. Malaria alone causes over 200 million cases and 600,000 deaths annually. Transmission involves complex drivers requiring examination beyond entomological factors. A systematic literature search across databases identified relevant studies on vector-borne disease determinants published in the last two decades, with priority given to rigorous designs like longitudinal analyses, reviews, and meta-analyses from diverse epidemic regions, allowing narrative synthesis of key determinants, relationships, and gaps. This narrative review synthesizes evidence on social, behavioral, and environmental determinants of major vector-borne disease risks over the past decade. Lower education, poverty, unplanned urbanization, gender inequities, inadequate water and waste management, climate variations, and land-use changes stood out as key determinants. However, significant knowledge gaps remain around quantifying precise threshold effects and impacts of tailored interventions across contexts. A social-ecological perspective recognizing interdependencies between determinants should frame integrated control programs. Multicomponent strategies addressing environmental modifications, protective measures, behavioral motivators, and infrastructure alongside governance and community engagement require implementation research and impact evaluation. Overall, this review highlights priority areas for advancing evidence-based vector control through contextualized, determinant-targeted policies and interventions. Further research incorporating modeling, trials, and cost-effectiveness analyses is critical to validate approaches that address influential drivers, leverage motivators, and reduce the global burden.

Implications of the transition towards water-wise approaches in urban areas: Elucidating the risk from micropollutants release

The transition towards circular and sustainable urban water systems, embracing a water-wise city approach, results in highly interconnected systems, introducing complexities in managing water quality due to the intricate interplay of pollutant sources, pathways, and receptors in urban environments. This study focuses on assessing the impact of transitioning to a water-wise city approach on water quality. The analysis employs an integrated model-based approach to evaluate the release of micropollutants (PFOA, PFOS, pyrene) in the urban water system, considering potential future changes. We considered Milan (Italy) as a case study and explored sustainable urban water management strategies related to the hydraulic reconnection of canals, including (i) the use of groundwater for energy purposes, (ii) the establishment of a separate stormwater sewer system, and (iii) the indirect reuse of reclaimed water for crop irrigation. Acute and chronic environmental risks were assessed at various locations of the recipients, under different water management and climate change scenarios. Uncertainties on projections related to rainfall and runoff concentrations were also considered. The model accurately predicted average micropollutants concentrations in the current situation. PFOA and PFOS exhibit chronic, but not acute risks in all scenarios; high removal efficiency is required to reduce the risk at acceptable levels. Pyrene poses higher risks for increasing separation fractions of the sewer system, with further increase for climate change, but concentration uncertainty has more influence than precipitation uncertainty on the risk extent. This study provides a water management framework, identifying critical sources and locations under current, future, and climate change scenarios.

Assessment of water shortage in underground aquifer in Abuja during dry season

Water shortage is a complex issue with severe implications for human well-being, agriculture, and ecosystems. Climate change, population growth, and inadequate water management practices contribute to this global problem. This study is to identify the causes of ground water scarcity and determine groundwater potential zones in the Abuja community. The research area's borehole coordinates, Landsat 8 satellite imagery, AsterDEM 30m resolution DEM data, and vertical electrical sounding (VES) data were all used to do this. Unsupervised classification of Landsat imagery was used to create a layer of land use and cover, image filtering techniques were used to improve the visualization of lineament extraction, and density analysis was used to quantify lineament density. AsterDEM data was subjected to surface analysis to acquire elevation data, while VES data were interpolated to produce spatial distributions of the resistivity and thickness parameters. To identify groundwater potential zones, multiple criterion analysis using the analytical hierarchical process (AHP) was utilized to reclassify variables such as land use, elevation, lineament density, resistivity, and thickness. To create the zones, these characteristics were weighted and layered. According to the findings, resistivity levels varied from 6.25 m to 676 m, and the geologic ground structure's thickness ranged from 0.27 m to 92.37 m. Elevation varied from 41 meters to 720 meters, while lineament density ranged from 0 to 0.04. 74% of the land was categorized as vegetated, 21% as built up, 2.7% as water bodies, and 2.3% as rock formations. Within Abuja, zones of groundwater potential were identified as being very low (5%), low (26.7%), marginally high (26.3%), moderately high (28.5%), and very high (13.4%)

Biodesalination of saline aquaculture wastewater with simultaneous nutrient removal and biomass production using the microalgae Arthrospira and Dunaliella in a circular economy approach

Freshwater scarcity is escalating due to factors such as climate change, droughts, increased demand, population growth, and poor water management practices. A promising sustainable solution to this challenge is biodesalination using microalgae. We demonstrate that Arthrospira platensis and Dunaliella salina can thrive in saline aquaculture wastewater, reducing both its salinity and the concentration of nutrients. The salinity removal ability was quantified through measurements of electrical conductivity (EC) and ICP-OES, revealing reductions in EC by up to 45 % (from 31.5 to 17.2 ms/cm) for A. platensis and 35 % (from 31.5 to 20.5 ms/cm) for D. salina. FESEM indicated the formation of a salt layer on the surface of both microalgae, suggesting biosorption and bioaccumulation as likely mechanisms. FTIR spectroscopy analysis demonstrated the binding of functional groups within the cell wall of A. platensis and the cell membrane of D. salina with the ions present in the medium. Scaling up the cultivation of A. platensis in a photobioreactor under non-sterile conditions validated the processes' potential for industrial-scale biodesalination. Although the treated water did not reach the standards for irrigation or potable use, this approach provides a preliminary desalination step, reducing burden on subsequent treatments and simultaneously providing nutrient removal and biomass production.

Climate change water management planning based on hydrological models

The current conventional water resources management planning method realizes the optimal allocation of water resources by constructing a function aiming at economic benefits; it causes poor model planning repercussions as a result of the disregard of comprehensive benefits. In this regard, a hydrological model-based water resource management planning method for climate change is proposed. By combining geological conditions, hydrological conditions and other climate change factors, a hydrological model is constructed to calculate watershed flows, and the hydrological model is used to divide the watershed scale and hydrological response units. A multi-objective function planning model is constructed with economic and ecological benefits as the objective functions. The proposed approach is tested in trials and shown to provide advantages for thorough planning. The results of the study demonstrate that the algorithm has a high value of extensive benefit when the recommended strategy is utilized for the optimum allocation of water resources, and has a more preferable optimal allocation consequence.

A practice-oriented framework for stationary and nonstationary flood frequency analysis

In flood frequency analysis (FFA), choices of distribution and methods can hinder the reproducibility of results. Besides, changes in climate, land use/cover, and water management can induce nonstationarity. Frameworks to select between stationary FFA (S-FFA) and nonstationary FFA (NS-FFA) are lacking, and NS-FFA tools are limited. Therefore, this paper introduces a systematic and software-supported framework enabling repeatable workflows for both S-FFA and NS-FFA. The framework has three modules to a) process flood series for exploratory data analysis (EDA) and NS-FFA model determination (if needed), b) select the S-FFA or NS-FFA approach underpinned by the EDA, and c) perform FFA including model determination, parameter estimation, uncertainty quantification, and model performance assessment. The framework incorporates various distributions, methods, and metrics, and recent advancements in NS-FFA for model determination and uncertainty quantification and allows for the modeller's intervention while ensuring reproducibility. The software is freely available to the public.

Ағынды суларды отандық көміртек құрамды адсорбенттермен тазарту және су құрамына физика-химиялық талдау жүргізу

Various strategies are being implemented to improve water use efficiency in the face of increasing world population and increasing demand for depleting fresh water sources. Wastewater treatment and reuse are effective strategies, especially if the treated water can be reused. Water scarcity is a growing global problem caused by a combination of factors such as population growth, climate change and unsustainable water management practices.
 This article presents the results of a study in the accredited analytical laboratory of the treatment facilities of the State Industrial Complex "Astana Su Arnasy", where wastewater was treated with domestic carbon-containing adsorbents (Shubarkol, Shoptykol, humic acid, carbon molecular sieves, carbon black) and were carried out with chemical water analysis. The results of this study showed that samples obtained on the basis of carbon materials can be used as adsorbents for purifying liquid media (from suspended solids, heavy metal salts, surfactants, petroleum products, etc.).

Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance

Abstract. The presence of a ski resort modifies the snow cover at the local scale, due to snow management practices on ski pistes, especially grooming and snowmaking. Snow management exerts 2-fold effects on the local hydrological cycle, through (i) abstraction and transfer of water used for snowmaking, and (ii) changes in water runoff due to added snow mass through snowmaking and/or delayed melting of the snowpack due to snow grooming. This induces a local pressure on water resources, which has seldom been addressed in scientific studies hitherto. Here we introduce a method to compute the hydrological effects of snow management on ski pistes and we apply and illustrate its results for the case study of the La Plagne ski resort in the Northern French Alps. The approach mainly relies on snow cover modelling using the Crocus snow cover driven by SAFRAN reanalysis and climate projections. Model results are evaluated against in-situ hydrological observations and show that the modelling approach, although very simplified for many hydrological processes, provides relevant information and insights in terms of the influence of snow-related processes on water resources. Our study shows a visible impact of grooming, virtually eliminating snowmelt in winter, thus delaying the onset of snowmelt. This results is a lower snowmelt flux during the wintertime, low flow period, on the order of −10 % to −20 %, compensated by higher amounts when snow melts. While about 10 % of the water used for snowmaking is estimated to be lost by evaporation through the ice formation process from the liquid water droplets, we find that, in the case studied, the annual scale alteration of water resources is limited and estimated to be on the order of 1 % to 2 %. This is due to the fact that the amount of water used for snowmaking on ski pistes represents a fraction of 10 % to 20 % of total annual precipitation, that ski pistes cover typically 10 % of the surface area of catchments within which ski resorts are located, and that snowmaking equipment covers, in the case of La Plagne, 40 % of the surface area of ski pistes. Therefore, in this case, snowmaking mainly leads to a moderate shift in snow cover formation and snowmelt processes and plays, for example, a smaller role than the influence of future climate change on mountain hydrology. This study provides an initial overview of the influence of grooming and snowmaking on river flows in a mountain catchment, which can inform future studies on water management and climate change adaptation in areas with ski tourism facilities. This study does not discuss long-term sustainability challenges of ski tourism and other aspects of the local environmental impacts (landscape, biodiversity) of snow management, such as the construction and use of mountain water reservoirs and other earthworks in ski resorts.

Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951–2021

Streamflow is a vital component of the global water cycle. Long-term streamflow observations are required for water resources planning and management, hydroclimatic extremes analysis, and ecological assessment. However, long-term streamflow observations for the Indian-Subcontinental (ISC) river basins are lacking. Using meteorological observations, state-of-the-art hydrological model, and river routing model, we developed hydrological model-simulated monthly streamflow from 1951–2021 for the ISC river basins. We used high-resolution vector-based routing model (mizuRoute) to generate streamflow at 9579 stream reaches in the sub-continental river basins. The model-simulated streamflow showed good performance against the observed flow with coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) above 0.70 for more than 60% of the gauge stations. The dataset was used to examine the variability in low, average, and high flow across the streams. Long-term changes in streamflow showed a significant decline in flow in the Ganga basin while an increase in the semi-arid western India and Indus basin. Long-term streamflow can be used for planning water management and climate change adaptation in the Indian sub-continent.