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Significant energy transition would be needed in China under the carbon neutrality target. Since it would have important impacts on water resource through increased usage, understanding the water demand under specific climate change targets could enable better policy-making for the energy transition. In order to quantitatively analyze the impacts, this study proposed a methodology that combines CO2 emissions, energy transition, and water demand. An energy-water integrated assessment model was built, to simulate the future energy and CO2 emissions pathways up to 2050 in China. The water demand of energy systems under both policy scenario (PS) and two mitigation scenarios (2C and 1.5C) are calculated. The key influencing factors of water demand are analysed in two low-water-demand mitigation scenarios (2C-LW and 1.5C-LW). The results showed that China's future energy system and CO2 emissions pathways change significantly under mitigation scenarios. The timing of the CO2 emissions peak advances from around 2030 under the PS scenario to between 2020 and 2025 in the mitigation scenarios. Near-zero emissions are achieved by 2050 under the 1.5C scenario. However, with no further water-saving measures, water consumption in energy sector would continue to increase under both the policy scenario and mitigation scenarios. This pressure is compounded by certain mitigation technologies, such as inland nuclear power, biomass energy, and CCS technologies. As such, the potential for water conservation in energy system under climate mitigation targets is studied. The results showed that water-saving measures can significantly reduce long-term water demand in the energy system. [ABSTRACT FROM AUTHOR]

Different types of droughts, with a great number of indices which try to explain/categories them, exist in literature. Certain regions do not necessarily have the same drought pattern changes for different types of droughts. The socio-economic drought index WEI + of one country/region or catchment area is defined as a quotient of water abstraction and water sources availability (after deducting ecological demand), both with calculated returns. Water demand (or water use/abstraction) can be expressed as the total water demand (usually) or water demand for a certain purpose (water supply, irrigation, or industry). Both water demand and water availability can be examined at different time levels: mean annual, maximum (minimum) monthly, max (min) daily, etc. The reasons why these indices differ over time and across one region are different. Four larger and twenty smaller catchment areas in central Serbia were analyzed, the results of which are presented. River flows on hydrological stations (monthly basis data) have been used as water source availability. Available data for the 60-year period (1957–2016) has been included in the analyses. They have been split into two 30-year periods and differences between them have been considered. The adopted time levels are: 1) mean annual, and 2) 97%monthly flow which corresponds with maximum monthly demand. It has been shown that annual WEI + is still low, while 97%monthly is often higher than 0.5. An excessively high WEI + points to potential Environmental hazards, so Ecological demand must be considered. Due to climate change, and the increase in water demand, Ecological demand issues have become very debatable in dry areas worldwide. After presenting the methodology and results, the paper discusses all these issues. [ABSTRACT FROM AUTHOR]

As a result of climate change and human activities, the ecological water demand of wetlands cannot be met and is significantly degraded on a global scale. In order to prevent the deterioration of this situation, a number of wetland ecological recharge projects are being constructed. However, the lack of a foundation for the necessary research is preventing them from operating at their full potential. Therefore, the western part of Jilin Province, a concentrated wetland area, was selected as a typical study area. The Chinese government invested ￥3.3 billion yuan in ecological water recharge projects to restore the degraded wetlands in this region. In this study, the suitable ecological water demand (SEWD) for wetlands restored to different historical periods and their response to changing environments is calculated based on the ecological function method and GeoDetector. The results showed that the SEWD for wetlands is 73.14 × 108m3 if the 2018 scenario is maintained. In addition, if restored to the 2010's, 2000's, 1990's and 1980's scenario, the SEWD for wetlands is 80.35 × 108m3, 85.32 × 108m3, 95.77 × 108m3 and 93.66 × 108m3, respectively. Spatially, SEWD for wetlands is greater in the north than in the south. The order of contribution of different factors in different historical periods was as follows: wetland area > precipitation > paddy field area > evapotranspiration > saline land area > construction land area > dry land area > grassland area > forest land area. Compared with the single factor, the contribution of the interaction factors to the water demand is obviously improved, and the combination of wetland area and most factors can produce a higher q value. The research results provide effective technical support for the formulation of scientific and reasonable water replenishment schemes for wetlands in western Jilin Province and lay a good foundation for the restoration of wetland ecosystems. [ABSTRACT FROM AUTHOR]

Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources. [ABSTRACT FROM AUTHOR]

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands. [ABSTRACT FROM AUTHOR]

Rivers are cradles of human civilisations and continual innovations in water technologies are the key to sustainable human development. Yet, limited research has explored the interactive dynamics among different technologies, hindering our ability to effectively manage technological transition. This paper presents a framework that conceptualizes water technology as a complex adaptive system containing three interrelated sub-systems: water demand, water supply, and water management. The interactions among these subsystems were measured using three network-based metrics: intensity, brokerage, and efficiency. Patents registered in the World Intellectual Property Organization from 1863 to 2020 were used as the data source. It was found that 40,304 patents from 44 countries were registered, with 40% of them belonging to the water management sub-system, followed by water supply (35%), and water demand (25%). Technological development in the three sub-systems presented linear growth over the past 160 years, focusing on water treatment, hydroelectric power, hydraulic engineering and water monitoring. The water-demand subsystem was identified as the structural "bottleneck" with the highest brokerage value, which was considered crucial for knowledge transfer between the other two sub-systems. Overall, the water technology system is characterized by slow development, skewed spatial coverage, categorical homogeneity, and structural imbalances, limiting our ability to address the escalating global water threats. [ABSTRACT FROM AUTHOR]

Universities, as hubs of economic, technological, and social knowledge development, have increasingly adopted metric-based strategies to guide resource management and monitor their growth. The Sustainable University World Ranking, UI GreenMetric, is widely applied for this purpose, measuring performance across six categories aligned with the United Nations Agenda 2030—Sustainable Development Goals (SDGs). This study focused on assessing information concerning the water category of this ranking, or the five water management indicators, at the School of Arts, Sciences, and Humanities of the University of São Paulo, to estimate its classification. The methodology involves assessing the current situation of the university in terms of each indicator, and classifying it according to the ranking guidelines. The information obtained is treated as evidence for posterior validation with the ranking. The findings indicate satisfactory performance in the indicators of water 1, 3, and 5. Notably, the implementation of rainwater collection and storage systems has been successful, alongside maintaining potable water parameters for consumption within the campus, as well as the use of efficient water-saving devices. Indicators 2 and 4, related to effluent treatment and water reuse, are expected to achieve higher classifications with the reactivation of the wastewater treatment system's operation. Over the period from May 2023 to June 2024, the average daily water consumption was measured at 52.89 ± 25.23 m3 day−1, with a per capita consumption rate of 10.28 L consumer agent−1 day−1. An anticipated 20% reduction in water consumption is expected upon the incorporation of water reuse initiatives. The use of the UI GreenMetric framework has been found strategic and useful as a diagnostic tool, facilitating the identification of areas requiring improvement and guiding efforts toward enhancing the sustainability of the institution. [ABSTRACT FROM AUTHOR]

The Ghrib Basin is currently encountering water-related challenges due to population growth and growing competition among water users. Therefore, assessing the current water situation is essential for the anticipation of future needs in the region. This paper assesses water demand and allocation strategies in the Ghrib Basin, Algeria, using the Water Evaluation and Planning (WEAP) system. The simulation is based on five scenarios: "Increase in population and agricultural activity", "Improving agricultural activity", "Minimum domestic consumption", "Enhancing Water Resources", and "Best practice", (which is a combination of two scenarios, i.e., "Improving agricultural activity" and "Minimum domestic consumption"). The simulation outcomes indicate that the "Best practice" scenario represents the most advantageous and beneficial scenario by which the problem of the unmet demand can be solved. The resulting simulations indicated the need for the employment of water-efficient irrigation systems as well as the encouragement of sustainable water use, such as drip irrigation, which necessitates coordinated efforts and particular infrastructural investments. The derived outcomes are highly convincing and have the potential to serve as a decision support system for the effective governance of water resources in the Ghrib Basin. The methodology utilized in this study has the potential to be implemented in any basin across the globe. [ABSTRACT FROM AUTHOR]

Abstract In China, natural snowfall is insufficient, and ski resorts often require artificial snowmaking in winter and turf management in summer, which results in high overall water consumption and considered as one of the high water-consuming service industries, with an urgent need for theoretical foundations related to water management. Based on life cycle theory, we examined the characteristics of the water systems of ski resorts in winter and summer. A Monte Carlo simulation was used to construct a stochastic model to quantify the theoretical water consumption for snowmaking at ski resorts and to investigate the mechanisms by which various factors influence this consumption. We summarize the necessity of summer turf management and irrigation requirements at ski resorts, explore the interaction between ski resorts and agricultural irrigation. The results show that the theoretical water demand of ski resorts is basically consistent with the actual water consumption of ski resorts, that the constructed model is feasible, and that snowmaking water is the most important water use of ski resorts. Exploring the collection and reuse of meltwater or rainwater from ski resorts for agricultural irrigation in the spring and summer could provide more possibilities for the sustainable management of regional water resources. The research findings can provide a theoretical basis for scientifically and reasonably setting the norm of water intake for ski resorts and for developing comprehensive water resource management plans for ski resorts and agricultural irrigation.

Abstract It is widely predicted that climate change’s adverse effects will intensify in the future, and along with inadequate agricultural practices, settlement development, and other anthropic activities, could contribute to rapid wetland degradation and thus exert significant negative effects on local communities. This study sought to develop an approach based on the Integrated Water Resource Management (IWRM) in the Ruzizi Plain, eastern Democratic Republic of Congo (DRC), where adverse effects of the climate change are increasingly recurrent. Initially, we analyzed the trends of climate data for the last three decades (1990–2022). Subsequently, the Water Evaluation and Planning (WEAP) approach was employed on two contrasting watersheds to estimate current and future water demands in the region and how local wetlands could serve as reservoirs to meeting water demands. Results indicate that the Ruzizi Plain is facing escalating water challenges owing to climate change, rapid population growth, and evolving land-use patterns. These factors are expected to affect water quality and quantity, and thus, increase pressure on wetland ecosystems. The analysis of past data shows recurrence of dry years (SPI ≤ − 1.5), reduced daily low-intensity rainfall (Pmm

Abstract Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley—the source of most tourists to the Oregon coast—was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to—if not greater than—the contribution from resident population change alone over the next 50 years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand.

Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley—the source of most tourists to the Oregon coast—was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to—if not greater than—the contribution from resident population change alone over the next 50 years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand. [ABSTRACT FROM AUTHOR]

It is widely predicted that climate change's adverse effects will intensify in the future, and along with inadequate agricultural practices, settlement development, and other anthropic activities, could contribute to rapid wetland degradation and thus exert significant negative effects on local communities. This study sought to develop an approach based on the Integrated Water Resource Management (IWRM) in the Ruzizi Plain, eastern Democratic Republic of Congo (DRC), where adverse effects of the climate change are increasingly recurrent. Initially, we analyzed the trends of climate data for the last three decades (1990–2022). Subsequently, the Water Evaluation and Planning (WEAP) approach was employed on two contrasting watersheds to estimate current and future water demands in the region and how local wetlands could serve as reservoirs to meeting water demands. Results indicate that the Ruzizi Plain is facing escalating water challenges owing to climate change, rapid population growth, and evolving land-use patterns. These factors are expected to affect water quality and quantity, and thus, increase pressure on wetland ecosystems. The analysis of past data shows recurrence of dry years (SPI ≤ − 1.5), reduced daily low-intensity rainfall (Pmm < 10 mm), and a significant increase in extreme rainfall events (Pmm ≥ 25 mm). The WEAP outcomes revealed significant variations in future water availability, demand, and potential stressors across watersheds. Cropland and livestock are the main water consumers in rural wetlands, while households, cropland (at a lesser extent), and other urban uses exert significant water demands on wetlands located in urban environments. Of three test scenarios, the one presenting wetlands as water reservoirs seemed promising than those considered optimal (based on policies regulating water use) and rational (stationary inputs but with a decrease in daily allocation). These findings highlight the impact of climate change in the Ruzizi plain, emphasizing the urgency of implementing adaptive measures. This study advocates for the necessity of the IWRM approach to enhance water resilience, fostering sustainable development and wetland preservation under changing climate. [ABSTRACT FROM AUTHOR]

Water scarcity and climate change led to changes in water management, especially in urban areas. RainWater Harvesting (RWH) is a promising technique that allows the collection and reuse of rainwater, as well as protecting sewage systems from overload. This article reviews the current state of RWH in Europe, including advantages, implementation, potential efficiency, usage requirements, quality, and treatment processes. The main findings include the importance of RWH as a sustainable water management technique, the historical background and renewed interest in RWH systems in recent years, the positive impact of RWH on reducing energy consumption and greenhouse gas emissions, the versatility of rainwater usage, and the potential cost savings and benefits in various regions. RWH systems are gaining popularity in Europe, particularly in Germany, Austria, and Switzerland. Climate change and precipitation patterns affect rainwater availability and quality. RWH can be used for various purposes, including drinking, but requires proper purification for health safety. It is also being implemented in new locations like airports and large buildings. RWH systems have a high potential to overcome undesired results of climate change. Among that, numerous aspects still need to be considered in the future that allow the application of RWH systems on a larger scale. [ABSTRACT FROM AUTHOR]

Background and Objectives Evapotranspiration is considered the water requirement of plants. Therefore, its measurement is necessary for all agricultural and irrigation projects. Reference evapotranspiration (ET0) is one of the main components of the hydrological cycle associated with agricultural systems. Accurate estimation and prediction of ET0 is essential in managing water resources, planning irrigation, and determining the water requirement of plants. Prediction of the ET0 by providing information about the future state in different time scales can help to make appropriate decisions, plan, and apply water resources management methods. Also, assessing agricultural drought conditions by well-known indices such as the Standardized Precipitation-Evaporation Index (SPEI) and Palmer Drought Severity Index (PDSI) directly requires ET0 of the region. The sharp decrease in the level of Lake Urmia and the threat to the region's ecosystem have also made the need for accurate calculation of ET0 more significant than in the past. One of the solutions to calculate ET0 is to use the FAO-56 Penman- Mantis equation (FAO-56 PM), an acceptable alternative for the scarce lysimeter data. However, the Penman- Mantis equation is highly dependent on the wind speed parameter, so a small error in the wind speed measurement causes a significant error. Therefore, this study aims to provide an innovative and reliable model for estimating ET0 without the need for wind speed parameters in Tabriz and Urmia stations. Methodology In this study, to predict daily ET0 from intelligent models including multi-layer perceptron neural network (ANN-MLP), support vector regression (SVR), and support vector regression combined with firefly algorithm (SVR-FFA) in Urmia and Tabriz stations during the period 2002-2022 was used. The input parameters of the models include minimum relative humidity (RHmin), maximum relative humidity (RHmax), average relative humidity (RHavg), sunshine hours (SSH), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg), and average soil temperature (Tsoil) was obtained from Iran Meteorological Organization (IRIMO). Also, four different scenarios were used to run the models. The selection of different input combinations was based on the correlation coefficient, so the first combination had the lowest correlation and the last combination had the highest correlation concerning ET0. Also, data from 2002-2015 for 14 years were considered for model training and from 2022-2016 for 6 years for model testing. Correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and normalized root mean square error (NRMSE) indices were used to evaluate the used models. Findings The comparison and evaluation of the models used in Tabriz station showed that the SVR-FFA-4 model was the best model in this station with the RMSE of 1.23 mm day-1. Among the SVR models, the SVR-4 model showed a good performance with the RMSE of 1.95 mm day-1 after the combined model. Finally, the ANN-4 model also obtained an acceptable accuracy compared to other ANN combinations by having the RMSE of 1.99 mm day-1. Finally, the evaluation of the results used for the Urmia station shows that the SVR-FFA-3 model has made the best predictions compared to other models with a RMSE of 1.16mm day-1. The SVR-3 and SVR-4 models had a higher accuracy than other SVR combinations with a RMSE of 1.78 mm day-1, but the third scenario was chosen as the appropriate model in the SVR model due to having less input. Among the ANN combinations, the ANN-3 model has a good performance compared to the other combinations of this model with the RMSE of 1.81 mm day-1. Conclusion The results of this study revealed that in both stations, the hybrid model showed higher accuracy than the individual models. Therefore, in Tabriz station, the SVR-FFA-4 model had the best performance with an error rate of 1.23 mm day-1. In the Urmia station, the SVR-FFA-3 model showed good accuracy with an error rate of 1.16 mm day-1. Finally, it is suggested to use the hybrid model to predict the daily reference evapotranspiration in the northwest of the country. One of the limitations of this research is the lack of access to the parameters of dew point temperature and solar radiation. Therefore, it is suggested to use these parameters in the subsequent studies. [ABSTRACT FROM AUTHOR]

Copyright of Teras Jurnal: Jurnal Teknik Sipil is the property of Teras Jurnal: Jurnal Teknik Sipil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau's one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models' high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability. [ABSTRACT FROM AUTHOR]

Nyaen Weir is one of the weirs in Sukoharjo Regency. This weir has an irrigation area called the Nyaen irrigation area. The Nyaen irrigation area experiences water shortages in the third planting period. Therefore, further analysis is needed to overcome the water shortage appropriately. This study aims to determine the water balance in the Nyaen Irrigation Area. The method used is direct observation along with a quantitative descriptive method. The analysis stage begins with a site survey to discover the problems in the irrigation network and then collect secondary data at the research location. The water balance analysis step is to compare the availability of water with irrigation water needs. According to the water balance analysis in the Nyaen Irrigation Area, it is not balanced because the right intake is 25% sufficient, and the left intake is 75% acceptable. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Advances in farming technologies and practices have helped farmers to increase crop yields, but continued production increases - while needed to feed a growing global population - are being hindered by climate change and other environmental challenges. Kansas, a globally important region for wheat production, is already experiencing threats to staple crop production. This study explores one possible alternative future for Kansas crop production in which farmers could, by crop switching, continue to grow nutrient-rich crops while responding to hotter temperatures and increasing water demand. Methods: We used a combination of climate and crop modeling with simple water budgets to identify optimal crop mixes under anticipated climatic and water constraints. Interviews with Kansas farmers helped identify feasible crop switching options: sorghum instead of corn (maize), winter rye and winter oats instead of winter wheat, and millet instead of soybeans. Results: Our analysis suggests that a sizeable proportion of current Kansas cropland would need to shift to these alternative crops by 2050 to meet anticipated water constraints and produce equivalent nutritional value under projected climate conditions. Alternative crops could increase from 16% of Kansas' crop area in 2021 to 43% of the area in 2050, resulting in a reduction in 2050 crop water demand of 12% relative to that of the current cropmix. This crop water demand reduction would be concentrated in parts of the state that will experience the greatest change in water needs between today and mid-century due to changing climate conditions. Discussion: Our analysis shows that, by changing (diversifying) the mix of crops grown, it is biophysically possible for crop production in Kansas to be both sustainable and resilient under future climate conditions. However, achieving a more climate-resilient crop mix on the ground, in Kansas and elsewhere, will require major shifts in the broader agricultural system. Food companies, agricultural lenders, and policymakers can play a key role in enabling farmers to adapt cropping systems in the face of climate and environmental challenges. [ABSTRACT FROM AUTHOR]

This study, titled "Assessing Water Demand and Access: A Comparative Study of Rural and Urban Communities," investigates the disparities in water access and consumption patterns across different demographic groups. Utilizing a mixed-methods approach, we conducted structured surveys and in-depth interviews with households in both rural and urban settings. Key results indicate that total monthly expenditure on water is significantly high, averaging -20,640, particularly impacting low-income families reliant on purchased water. The analysis reveals that larger family sizes correlate with increased water demand, exacerbating the challenges faced by communities with inadequate infrastructure. Implications of these findings underscore the urgent need for targeted interventions, such as subsidies for low-income households and community-led water management initiatives. Future directions include further exploration of the relationship between socio-economic factors and water access, as well as the implementation of sustainable practices like rainwater harvesting. This research aims to inform policymakers and stakeholders in developing equitable water management strategies that address the unique needs of diverse communities, ultimately promoting public health and well-being. [ABSTRACT FROM AUTHOR]

Various methodologies are used to estimate the impact of changing climatic factors, such as precipitation, temperature, and solar radiation, on crop production and water demand. In this study, the changes in rice yield, water demand, and crop phenology were estimated with varying CO2 concentration and an ensemble of general circulation models (GCMs), using a decision support system for agrotechnology transfer (DSSAT), a crop growth model. The measured CO2 concentration of 400 ppm from the Keeling curve, was used as the default CO2 concentration to estimate yield, water demand, and phenology. These outputs, obtained with the default concentration, were compared with the results from climate change scenarios' concentrations. Further, the outputs corresponding to the ensembled GCMs' climate data were obtained, and the results were compared with the ensembled crop model outputs simulated with each GCM. The yield was found to increase with the increase in CO2 concentration up to a certain threshold, whereas water demand and phenology were observed to decrease with the increase in CO2 concentration. The two approaches of the ensemble technique to obtain final outputs from DSSAT results did not show a large difference in the predictions. [ABSTRACT FROM AUTHOR]

More than half of global water use can be attributed to crop irrigation, and as the human population grows, so will the water requirements of agriculture. Improved irrigation will be critical to mitigating the impact of increased requirements. An ideal irrigation system is informed by measurements of water demand—a combination of water use and water status signals—and delivers water to plants based on this demand. In this review, examples of methods for monitoring water status are reviewed, along with details on stem and trunk water potential measurements. Then, methods for monitoring evapotranspiration (ET), or water use, are described. These methods are broken into coarse- and fine-scale categories, with a 10 m spatial resolution threshold between them. Fourteen crop ET technologies are presented, including examples of a successful estimation of ET in research and field settings, as well as limitations. The focus then shifts to water distribution technologies, with an emphasis on the challenges associated with the development of systems that achieve dynamic single plant resolution. Some attention is given to the process of choosing ET and water status sensing methods as well as water delivery system design given site characteristics and agronomic goals. This review concludes with a short discussion on the future directions of ET research and the importance of translating findings into useful tools for growers. [ABSTRACT FROM AUTHOR]

Colomadu District is an area on the outskirts of or around a city that is experiencing rapid development due to the local population’s interest in moving, causing an increase in population. The aim is to determine the projected use of groundwater in Colomadu District, Karanganyar Regency, to determine the use of groundwater needs in each village in Colomadu District, Karanganyar Regency in 2021, and to determine when the balance between supply (availability) and demand of groundwater in Colomadu District will be exceeded or in deficit. The research method used was data collection in the form of sampling, measuring water quality, and interviews. Sampling was carried out by collecting water from the residents' wells and taking as many as two samples per village, which represented the water quality in each village. The samples were collected by first drawing water from the well, then the prepared sample bottles were rinsed three times using sample water (well water), and the rinsed bottles were then filled with sample water. The projection of groundwater use in Colomadu District in 2025 is 3,345,311.68 m³ year-1 ; in 2030 is 3,716,727.52 m³ year-1 ; in 2035 is 4,129,380.08 m³ year-1 ; and in 2040 is 4,587,847.71 m3 year-1 . Projections are made until demand exceeds supply, namely, in 2085, when demand can reach 11,833,854.16 m³ year-1 with a population of 272,244 people. [ABSTRACT FROM AUTHOR]

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The 204-million-hectare Brazilian tropical savanna (Cerrado biome), located in the central part of Brazil, constitutes the main region of food and natural fiber production in the country. An important part of this production is based on center pivot irrigation. Existing studies evaluating the spatio-temporal dynamics of center pivots in Brazil do not consider their retraction. This study aimed to evaluate the expansion and retraction of center pivots in the Cerrado biome in the period 1985–2020. We relied on the data produced by the MapBiomas Irriga project. In this period, the area occupied by center pivots increased from 47 thousand hectares in 1985 to 1.2 million hectares in 2020, mostly concentrated in the states of Minas Gerais, Goiás, São Paulo, and Bahia, confirming previous reports available in the literature. Among the 13 irrigation poles recognized by the National Water Agency (ANA), the Oeste Baiano (Bahia State) and the São Marcos (Goiás State) presented the largest areas of center pivots (173,048 ha and 101,725 ha, respectively). We also found that 76% of the center pivots are concentrated in the regions with low water availability (0.01–0.45 mm day−1). Within this 16-year period (2005–2020), more than 10% of center pivots found in 2005 were either abandoned or converted into rain-fed crop production. The results of this study can provide an important foundation for public policies directed toward the sustainable use of water resources by different consumers. [ABSTRACT FROM AUTHOR]

Significant energy transition would be needed in China under the carbon neutrality target. Since it would have important impacts on water resource through increased usage, understanding the water demand under specific climate change targets could enable better policy-making for the energy transition. In order to quantitatively analyze the impacts, this study proposed a methodology that combines CO2 emissions, energy transition, and water demand. An energy-water integrated assessment model was built, to simulate the future energy and CO2 emissions pathways up to 2050 in China. The water demand of energy systems under both policy scenario (PS) and two mitigation scenarios (2C and 1.5C) are calculated. The key influencing factors of water demand are analysed in two low-water-demand mitigation scenarios (2C-LW and 1.5C-LW). The results showed that China’s future energy system and CO2 emissions pathways change significantly under mitigation scenarios. The timing of the CO2 emissions peak advances from around 2030 under the PS scenario to between 2020 and 2025 in the mitigation scenarios. Near-zero emissions are achieved by 2050 under the 1.5C scenario. However, with no further water-saving measures, water consumption in energy sector would continue to increase under both the policy scenario and mitigation scenarios. This pressure is compounded by certain mitigation technologies, such as inland nuclear power, biomass energy, and CCS technologies. As such, the potential for water conservation in energy system under climate mitigation targets is studied. The results showed that water-saving measures can significantly reduce long-term water demand in the energy system.

AbstractThe experiment was conducted to determine the irrigation scheduling effect on hot pepper green pods, yield attributes, and irrigation water productivity (WP) in the Arba Minch area. Field trials comprised five levels of treatments: 140 % MAD, 120% MAD, 100% MAD, 80% MAD and 60% MAD. The results revealed that different depletion levels had significantly affected hot pepper’s yield and related attributes. The maximum yield of hot pepper was observed under 100% MAD without significant variation of 120% MAD whereas the minimum was under 140% MAD in both years of experimentation. WP was also highly influenced by depletion, and the maximum water productivity of (24.67 kg/ha-mm, 24.72 kg/ha-mm was observed under 120% MAD and minimum (19.26 kg/ha-mm, 16.49 kg/ha-mm) under 60% MAD in the year 2019 and 2021, respectively. The results revealed that as the level of depletion increased, irrigation frequency (event) increased, yield and water productivity decreased of hot peppers. 120% MAD offered the highest economic return (11,795.96 US$) and saved water, wage, and irrigation events compared to more frequent applications. The current findings showed that applying 120% MAD is better for hot pepper production in the Arba Minch areas and similar ecology.

The uncertainty in climate change and high water demand pose pressure on the natural water resources supply. Not only does this require better understanding but also a call for immediate interventions, mitigation and adaptive measures. This study evaluates catchment water resources in the Luwombwa sub-catchment in Zambia through statistical analysis in the downscaling of past, present and future climatic variables from the CMIP6 climatic model. These variables are then integrated into WEAP - a semi-distributed hydrological and water evaluation model - to perform water demand and allocation scenario modelling. Mult-site calibration and validation were conducted on five selected micro-catchments within Luwombwa sub-catchment. The model performance was assessed usng the R2, NSE and PBIAS as the objective functions. Satisfactory values of 92 % for R2, 82 % for NSE and 6.9 % for PBIAS were achieved. This allowed for scenario modelling on water demand and allocation among competing users. Three future scenarios (2022–2050) were developed from the historical to baseline (1988–2022) and included state of water resources availability under climate change, expansion of irrigation area and impact of dam construction in the sub-catchment. The study reveals a decrease of 20 % in sub-catchment's water availability resulting from 9.3 % (equivalent to 4oC) rise in maximum temperature and 4.5 % reduction in rainfall within the entire sub-catchment. This is especially under the persistence of SSP370 climate variability scenario projections downscaled from four GCM models by the year 2050. The study further revealed that the change point for anticipated future climate extremes is likely to occur between 2027 and 2030. The results are indicative of downward trends in streamflow under climate change and socioeconomic development leading to increase in water value and water scarcity. The insights from the study are critical to inform formulation of effective catchment water resources management strategies such as the development of management plans and adapation measures in the face of climate change and the needs for different stakeholders involvement.

The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 meters) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth embracing a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Introduction Effective approaches and policies including identifying priorities and optimal water allocation techniques, especially in basins with different users are considered essential for sustainable development in each region. With 1100 m3 of renewable water per person per year, Iran is considered to be the most critical region in the world in terms of water resources. Unfortunately, most plans in the water sector of such countries are based on local economic growth, and no attention is paid to the amount of available water resources. Considering the issue of a water crisis and the droughts of the last few years, the issue of water resources management has gained high importance. To overcome the mentioned problems, it is inevitably essential to use newly developed water management techniques based on advanced approaches. Although optimization techniques are well-known tools in these issues, the simulation method is utilized as a helpful approach. To simulate water management in the basin, there are various available models. RIBASIM, MIKE BASIN, WEAP, and MODSIM models are famous and user-friendly ones in this collection. WEAP software is a comprehensive and advanced water resource system simulation tool widely used in watershed management and can consider physical and hydrological processes. The scenarios that can be investigated with this software include population growth, economic development, changing the policy of operating reservoirs, extracting more from underground water resources, saving water, allocating ecosystem needs, integrated use of surface and underground water, reuse of water, etc. Materials and Methods This study was conducted in the Nahand catchment area which is located in East Azerbaijan province. Nahand river is the main draining course of this catchment, on which a dam has been built to supply a part of Tabriz's drinking water. To control the performance indicators of the reservoir, several management and exploitation scenarios were developed and evaluated in the WEAP model. The WEAP model was presented in 1990 by the Stockholm Environment Institute (SEI). It is a comprehensive and advanced model for simulating water resource systems, which is extensively used in the management of water resources in watersheds. This model has provided a practical tool for water resource planning and policy analysis to put all the issues related to water resources and uses in a single environment. The WEAP model is capable of simulating issues related to consumption such as water consumption patterns, water reuse strategies, costs, and water allocation patterns, as well as issues related to resources such as river flow, groundwater resources, reservoirs, and water transmission lines. The inputs of the WEAP model include data on the population of Tabriz City, per capita consumption of drinking water per person, the amount of water wastage in the distribution network, the inlet discharge of the Nahand reservoir, the information of the Nahand dam, the amount of cultivated area, etc., and to evaluate the model R^2, RMSE, and MAE statistical indicators were used in two periods of calibration and validation. Then, various operating conditions were investigated by compiling the Reference (continuation of the status quo), SC1 (increase of input flow by 10%), and SC2 (decrease of input flow by 10%) scenarios. Besides, Reservoir performance indicators are used to measure its performance under different operating circumstances. Results and Discussion The simulation results of the studied area indicated that the WEAP model with evaluation criteria including R2, RMSE, and MAE in the calibration stage was 0.89, 1.16, and 1.01 MCM, respectively, and in the validation stage were 0.88, 6.22, and 6.01 MCM, respectively. The results also showed that the amount of water demand for the near future period (2021-2040) will increase due to the increase in population, and therefore, the resources in the basin will not be able to meet all assumed needs. The findings showed that the studied system for the near future period (2021-2040) under the reference (continuation of the status quo), SC1 (increase in flow by 10 %) and SC2 (decrease in flow by 10 %) scenarios from the drinking water supply point of view, will result in a shortage of 28.1, 7.3 and 44.3%, respectively, and from the supply of agricultural needs point of view will result in 31.4, 18.3 and 44.4%, respectively. Also, by evaluating the reservoir's performance indicators, it was found that under all assumed scenarios, the system will fail under the condition of supplying 100% and 80% of the needs, whereas the reservoir will be more sustainable by applying the SC1 scenario in comparison with the other two scenarios. Conclusion To choose the best management and exploitation scenarios, due to existing circumstances and limitations such as time limitation, cost, possible risks to the environment, etc., it is not possible to apply all scenarios in the basins and, thence, it is logical to choose the most suitable scenario. Therefore, software tools can help experts to make decisions by considering all limitations. By examining the results of the reservoir performance indicators, it can be seen that the reservoir will encounter failure in supplying 100 and 80% of the needs in the future period under all scenarios and the sustainability index of the reservoir (remedial stability) in supplying 100%. The needs under the Reference, SC1, and SC2 scenarios will reach 31, 49, and 22%, respectively, and in meeting 80% of the needs, the sustainability index will be slightly higher.

Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case. HIGHLIGHTS Machine learning algorithms are appropriate for water demand estimation.; Preprocessing allows for better data quality and final predictive results.; The addition of historical data showed positive results in the case study.; A case study in a coastal touristic city, Guaratuba – Paraná – Brazil is presented.; Artificial neural networks provided the best predictions for the case study.;

Knowledge of how certain crops respond to water stress is one of the prerequisites for choosing the best variety and best management practices to maximize crop water productivity (WPc). The selection of a more efficient protocol for managing irrigation depths throughout the cultivation cycle and in the maturation process at the end of the growth period for each sugarcane variety can maximize bioethanol productivity and WPc for bioethanol, sugar and biomass, in addition to the total energy captured by the sugarcane canopy in the form of dry biomass. This study aimed to evaluate the effect of four irrigation depths and four water deficit intensities on the maturation phase for eight sugarcane varieties under drip irrigation, analyzing the responses related to WPc for bioethanol, sugar and biomass. These experiments were conducted at the University of São Paulo. The plots were positioned in three randomized blocks, and the treatments were distributed in a factorial scheme (4 × 8 × 4). The treatments involved eight commercial varieties of sugarcane and included four water replacement levels and four water deficits of increasing intensity in the final phase of the crop season. It was found that for each variety of sugarcane, there was an optimal combination of irrigation management strategies throughout the cycle and during the maturation process. The RB966928 variety resulted in the best industrial bioethanol yield (68.7 L·Mg−1), WPc for bioethanol (0.97 L·m−3) and WPc for sugar (1.71 kg·m−3). The energy of the aerial parts partitioned as sugar had a direct positive correlation with the availability of water in the soil for all varieties. The RB931011 variety showed the greatest potential for converting water into shoots with an energy of 1.58 GJ·ha−1·mm−1, while the NCo376 variety had the lowest potential at 1.32 GJ·ha−1·mm−1. The productivity of first-generation bioethanol had the highest values per unit of planted area for the greatest water volumes applied and transpired by each variety; this justifies keeping soil moisture at field capacity until harvesting time only for WR100 water replacement level with a maximum ethanol potential of 13.27 m3·ha−1.

Implementation of different strategies on the demand and supply side to deal with potential water scarcity is based on a comparison of future water demand and availability of water resources based on different scenarios of climate change and population growth. Especially, the Alpine region is characterised by many small and medium water supply systems (WSSs) having neither human resources nor time for advanced planning, requiring simple methods for estimating future development. Therefore, the aim of this work is to provide future projections of water demand, resource availability, and drinking water quality for an Alpine area based on simple approaches with minimal data requirements. As the results of the case study show, linear and polynomial regression with precipitation and temperature data can illustrate the temporal variation of system input and drinking water temperature with sufficient accuracy and is suitable for an estimation of future development. The groundwater modelling, however, requires the consideration of a non-linear term depending on the depth to obtain reasonable results. Due to the usage of open-access data and the easy approaches developed and applied, a good transferability to other case studies is expected which can provide stakeholders a first assessment of the future need for action. HIGHLIGHTS Simple regression analyses are used for future projections for water resources.; Precipitation and temperature are utilised as input parameters.; Water demand increases by 25–125% due to climate change and population growth.; Increased availability of open-access data can help future planning.;