Utilization Of Water Resources Research Articles

The difference in hydrochemical characteristics of geothermal water between the eastern and western parts of the Wugongshan area and its genetic mechanism

The hydrogeochemical signature of the discharged water can reveal significant information on the circulation and evolution of geothermal water, which can further guide the exploration and utilization of geothermal water resources. In this study, the source of major ions, reservoir temperature, and cycle time of geothermal fluids were clarified by the Ion relationship analysis, integrated multicomponent solute geothermometry method, and 14C isotope analysis, respectively, in the Wugongshan area of South China. Results show that the eastern and western parts of the Wugongshan area have distinct types of geothermal fluids, i.e. HCO3-Na and SO4HCO3-Na, respectively. The major source of HCO3− and Na+ is the hydrolysis of silicate minerals, partially accompanied by cation exchange. While gypsum hydrolysis and sulfide oxidation are the primary producers of SO42−. Moreover, higher TDS, PH, and degree of cation exchange of geothermal fluids were found in the western part than that in the eastern part. The reservoir temperatures in the eastern and western portions are comparable (115–150 °C). However, the cycle time of the geothermal fluids in the western part (15,743 years on average) is much greater than in the eastern part (2160 years on average), which is considered to be the main reason for the difference in hydrogeochemical characteristics. This study can provide theoretical support for the rational development and usage of geothermal water resources.

An Attribution Analysis of Runoff Alterations in the Danjiang River Watershed for Sustainable Water Resource Management by Different Methods

Determining the relative roles of climatic versus anthropogenic factors in runoff alterations is important for sustainable water resource utilization and basin management. The Danjiang River watershed is a crucial water resource area of the middle route of the South-to-North Water Transfer Project. In this study, four widely used quantitative methods, including the simple linear regression, the double mass curve, the paired year with similar climate conditions, and an elasticity method based on the Budyko framework were applied to detect the relative contribution of climatic and anthropogenic factors to runoff variation in the Danjiang River watershed. The calculation processes of each method were systematically explained, and their characteristics and applications were summarized. The results showed that runoff decreased significantly (p < 0.05) with an average change rate of −3.88 mm year−1 during the period of 1960–2017, and a significant change year was detected in 1989 (p < 0.05). Generally, consistent estimates could be derived from different methods that human activity was the dominant driving force of significant runoff reduction. Although the impacts of human activity estimated by the paired year with similar climate conditions method varied among paired years, the other three methods demonstrated that human activity accounted for 80.22–92.88% (mean 86.33%) of the total reduction in the annual runoff, whereas climate change only contributed 7.12–19.78% (mean 13.67%). The results of this study provide a good reference for estimating the effects of climate change and human activities on runoff variation via different methods.

Carbon dioxide emission equivalent calculation and inter-sectoral transfer pattern of different water use terminals in China

In the context of global climate change, all industries have put forward the requirements for carbon emission reduction. The effective use of water resources is the key to achieving carbon emission reduction, so it is particularly important to review the carbon dioxide emission equivalents of water resource utilization behaviors (CEE-WRUBs) of water users. However, existing research on CEE-WRUBs across various water sectors remains inadequate. Therefore, this study integrates the enhanced CEEA method, LMDI decomposition model, and IOA method to analyze CEE-WRUBs across diverse water use terminals. Then seeks their main driving factors and their transfer pattern among different industries, which are crucial to the realization of the global carbon neutrality objective. The results showed that: (1) China's CEE-WRUBs show a significant downward trend. Industrial water use emerges as the primary source of CEE, but the CEE-WRUBs of most industrial water terminals show a fluctuating downward trend. Grains' water use behavior (WRUBs) absorbed the largest CEE, reaching 14,698 Mt in 2020. (2) the water efficiency effect emerges as the predominant driver behind the increase in CEE-WRUBs most of the time, and holding a prominent position. The carbon emission intensity effect primarily steers the reduction of CEE-WRUBs. (3) the largest net outflow sector of WRUBs embodied carbon in 2007–2020 was transformed from basic material heavy industry (−153.54 Mt in 2007) to agriculture (−128.26 Mt in 2020). Most of the WRUBs embodied carbon of agriculture flows into light industry, while most of the WRUBs embodied carbon of basic material heavy industry flows into finishing heavy industry and construction. The methods and results of this study provide a potential reference for investigating the regional water-carbon relationship and advancing the global carbon neutrality objective.

A dataset of monthly potential evapotranspiration at ecological stations in arid central Asia from 2005 to 2020

The potential evapotranspiration represents the atmospheric evaporation capacity, which is an important indicator to measure the regional evaporation capacity, and also a key parameter to evaluate drought degree, the balance between supply and demand of water resources, and the water consumption of vegetation. Based on the meteorological data collected by 12 ecological field stations of Central Asia Ecosystem Monitoring Network and 11 ecological field stations of China Ecosystem Research Network in the arid region of Northwest China, and by using Penman-Monteith method recommended by FAO, the potential evapotranspiration was calculated from 2005 to 2020. This dataset has a long time series and covers a variety of ecosystem types. It can be used as basic data, model input data, simulation result verification data for drought research in Central Asia, and also provide data support for the research on the rational development and utilization of water resources, ecological environmental protection and other aspects in this region.

Improved Variable Scale Optimisation Algorithm in Water Saving Landscape Design and Its Application

Abstract With the acceleration of urbanisation, the problem of water resource shortage is becoming increasingly serious. Currently, the water use situation in Chinese cities is severe and the contradiction between the supply and demand of water resources is prominent, which has become one of the main factors restricting sustainable development of cities. Traditional research on water-saving landscape design focuses mainly on the analysis of the efficiency of water resource utilisation within a single area or city. The scope of the research is limited and the research methods are individual, which cannot fully meet the current needs of sustainable urban water-saving landscape design. The improved variable scale optimisation algorithm has become an indispensable tool in modern landscape design and would play a more important role in landscape design and ecological environment protection in the future. In response to the shortcomings of traditional variable-scale optimisation algorithms in solving the optimisation problem of water-saving landscape design for ecological sustainable development, this article would use an improved variable-scale optimisation algorithm to study the water-saving landscape design for ecological sustainable development in Community X, City B, Province A. The research results indicated that there were three experts who rated the effect of the water saving landscape design of the first group of designers as good or above, accounting for 37.5 %. The number of people who rated the effectiveness of water-saving landscape design in the second group as good or higher was 8, representing 100 %. The improved variable-scale optimisation algorithm could effectively improve the application of water-saving landscape design in ecological sustainable development.

A comparative study on aquatic environment of two mega reservoirs in the mainstream of Yangtze River

With the mega dams being built in large rivers around the world for water resources utilization and flood prevention, their environmental impacts such as algae bloom are increasingly concerned by the professionals and the publics, but the mechanism controlling the occurrence of algae are still unclear. Along the mainstream of the Yangtze River, world-class reservoirs have been put into operation successively. Among these reservoirs, the Three Gorges Reservoir (TGR) and the cascade reservoirs (CRs) are adjacent but the conditions of algae bloom in the tributary bays of them are totally different. To explore the reasons of the distinguished situations and factors relating to the high frequent algae bloom in the tributary bays of the TGR, a comparative study was carried out between the TGR and its neighbored reservoir out of the CRs based on field survey investigation, water quality records and literature review. The result reveals the considerable influences of geological and climatic factors on the hydrodynamic processes and environmental problems happening in the tributary bays of mega reservoirs. The two reservoirs exhibit significant differences in physical properties such as the hydrodynamic condition, terrain characteristics and climate. The field-investigation suggests that the water columns in the tributary bays of the CRs tend to experience weak vertical stratification. The analysis shows that this is the result of the combining result of the local terrain characteristics and the cooler, rainier and cloudier climate in the CRs region which significantly affect the hydro-thermal dynamic processes in the tributary bays. With the low level of nutrient and strong vertical mixing, the growth of the algae is constrained and these should be the significant reasons of the low frequency of algae bloom in the tributary bays of the CRs.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 milliliters of deionized water to prepare a single salt solution (0.02% - 1.00%) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86% and 99.66%, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Real-Time Optimal Scheduling of a Water Diversion System Using an Improved Wolf-Pack Algorithm and Scheme Library

A water diversion system (WDS) with cascade pumping stations (CPSs) plays an important role in the application of water resources. However, high energy consumption is reported due to unreasonable scheduling schemes and long decision times. Herein, this paper presents a new method to achieve optimal scheduling schemes effectively, including the head allocation of CPSs, the number of running pumps, and pump blade angles. A double-layer mathematical model for a WDS was established with the goal of achieving minimal energy consumption, considering the constraints of flow rate, water level, and the characteristics of pump units. The inner-layer model was used to obtain scheduling schemes of single-stage pumping stations, as well as the water levels and flow rates of water channels, while the outer-layer model was used to optimize inter-stage head allocation. An improved wolf-pack algorithm (IWPA) was proposed to solve the model, using a Halton sequence to obtain the uniform initial population distribution and introducing simulated annealing (SA) to improve the global searchability. Moreover, an idea for a pre-established scheme library was suggested for inner-layer models to obtain the solutions in real time with less calculation workload. Taking an actual project as a case, in contrast with the actual schemes, the optimal scheduling method could result in energy savings of 14.37–20.39%, a CO2 emission reduction of 13–32 tons per day, and water savings of 0.14–18.34%. Moreover, the time complexity decreased to square order, and the CPU time of the optimal method was about 1% that of the traditional method. This study provides an efficient method for the high-value utilization of energy and water resources for a WDS.

Drought prediction in Jilin Province based on deep learning and spatio-temporal sequence modeling

Jilin Province, a key agricultural hub in Northeast China, has long been impacted by climate change, with drought disasters significantly affecting its agricultural output and ecological environment. Accurate drought prediction is essential for the effective utilization of water resources and agricultural production. This study proposes a novel drought prediction model that utilizes the SSA-VMD (Sparrow Search Algorithm Optimized Variational Mode Decomposition) technique to decompose meteorological data, followed by the reconstruction of the decomposed components using four entropy algorithms, including approximate entropy. The model integrates ARIMA (AutoRegressive Integrated Moving Average) and BiLSTM (Bidirectional Long Short-Term Memory network) for forecasting the reconstructed components, subsequently combining their outputs. In terms of spatio-temporal data, the study employs prediction models including the spatio-temporal cube, spatio-temporal hotspot analysis integrated with empirical kriging, and local outlier analysis to examine spatial distribution. The model’s predictive performance is validated from three perspectives: statistical characteristics of the indicators, comparison between predicted and observed values through prediction curve plots, and box plots. The results demonstrate that the combined SSA-VMD-ARIMA-BiLSTM model significantly enhances prediction accuracy compared to single models, as exemplified by its application in Changchun City. The model achieved an R2 of 0.938 and a root mean square error (RMSE) of 0.047 in drought prediction, outperforming the single ARIMA model (R2: 0.636, RMSE: 0.709) and the BiLSTM model (R2: 0.514, RMSE: 0.901). Additionally, across the entire province, the model’s R2, MAE, and RMSE are 0.82, 0.15, and 0.083, respectively, suggesting that the model exhibits not only high prediction accuracy but also a degree of generalizability. Furthermore, the results from the spatio-temporal cube, spatio-temporal hotspot analysis, and local outlier analysis demonstrate the method’s high accuracy and stability in predicting both short-term and long-term droughts. Particularly in short-term drought prediction, the model effectively captures the spatio-temporal distribution characteristics of short-term meteorological droughts. This study offers new methodological support for enhancing the early warning capabilities of drought risk in Jilin Province, providing a robust foundation for addressing the challenges posed by climate change. The findings not only address certain shortcomings in current drought prediction research but also introduce new methodologies and perspectives for future studies.

Enhancing Water and Soil Resources Utilization via Wolfberry-Alfalfa Intercropping.

The impact of the intercropping system on the soil-plant-atmosphere continuum (SPAC), encompassing soil evaporation, soil moisture dynamics, and crop transpiration, remains an area of uncertainty. Field experiments were conducted for two years in conjunction with the SIMDualKc (Simulation Dual Crop Coefficient) model to simulate two planting configurations: sole-cropped wolfberry (Lycium barbarum L.) (D) and wolfberry intercropped with alfalfa (Medicago sativa L.) (J). These configurations were subjected to different irrigation levels: full irrigation (W1, 75-85% θfc), mild deficit irrigation (W2, 65-75% θfc), moderate deficit irrigation (W3, 55-65% θfc), and severe deficit irrigation (W4, 45-55% θfc). The findings revealed that the JW1 treatment reduced the annual average soil evaporation by 32% compared with that of DW1. Additionally, mild, moderate, and severe deficit irrigation reduced soil evaporation by 17, 24, and 36%, respectively, compared with full irrigation. The intercropping system exhibited a more efficient canopy structure, resulting in reduced soil evaporation and alleviation of water stress to a certain extent. In terms of temporal dynamics, monocropping resulted in soil moisture levels from 1% to 15% higher than intercropping, with the most significant differences manifesting in the mid to late stages, whereas differences in the early stages were not statistically significant. Spatially, the intercropping system exhibited 7-19% lower soil water contents (SWCs) than sole cropping, primarily within the root water uptake zone within the 0-60 cm soil layer. The intercropping system showed an enhanced water absorption capacity for plant transpiration, resulting in a 29% increase in transpiration compared with sole cropping, thereby achieving water-saving benefits. These findings contribute to our understanding of the agronomic and environmental implications of intercropping wolfberry and alfalfa in arid regions and provide insights into optimizing water and soil resource management for sustainable agricultural practices.

Effects of Paddy Rain-Flood Storage on Rice Growth Physiological Indices and Nitrogen Leaching under Organic Planting in Erhai Lake Basin.

In order to address the increasingly prominent issues of water resource protection and agricultural non-point source pollution in the Erhai Lake Basin, this study conducted a two-year field experiment in Gusheng Village, located in the Erhai Lake Basin. In 2022, two irrigation treatments were set up: conventional flooding irrigation (CK) and controlled irrigation (C), with three replicates for each treatment. In 2023, aiming to enhance the utilization rate of rainwater resources and reduce the direct discharge of dry-farming tailwater from upstream into Erhai Lake. The paddy field was used as an ecological storage basin, and the water storage depth of the paddy field was increased compared to the depth of 2022. Combined with the deep storage of rainwater, the dry-farming tailwater was recharged into the paddy field to reduce the drainage. In 2023, two water treatments, flooding irrigation with deep storage and controlled drainage (CKCD) and water-saving irrigation with deep storage and controlled drainage (CCD) were set up, and each treatment was set up with three replicates. The growth and physiological index of rice at various stages were observed. Nitrogen leaching of paddy field in surface water, soil water, and groundwater under different water treatments after tillering fertilizer were observed. The research results show that the combined application of organic and inorganic fertilizers under organic planting can provide more reasonable nutrient supply for rice, promote dry matter accumulation and other indices, and also reduce the concentration of NH4+-N in surface water. Compared with CK, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of C are increased by 4.8%, 4.1%, 20.9%, and 9.7%, respectively. Compared with CKCD, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of CCD are increased by 6.5%, 3.8%, 19.6%, and 21.9%, respectively. The yield in 2023 is 19% higher than that in 2022. Treatment C can increase the growth indicators and reduce the net photosynthetic rate to a certain extent, while CCD rain-flood storage can alleviate the inhibition of low irrigation lower limit on the net photosynthetic rate of rice. Both C and CCD can reduce nitrogen loss and irrigation amount in paddy fields. CCD can reduce the tailwater in the Gusheng area of the Erhai Lake Basin to Erhai Lake, and also can make full use of N, P, and other nutrients in the tailwater to promote the formation and development of rice. In conclusion, the paddy field rain-flood storage methodology in the Erhai Lake Basin can promote various growth and physiological indicators of rice, improve water resource utilization efficiency, reduce direct discharge of tailwater into Erhai Lake, and decrease the risk of agricultural non-point source pollution.

Climate Change and Vegetation Greening Jointly Promote the Increase in Evapotranspiration in the Jing River Basin

Within the Earth’s terrestrial environment, evapotranspiration significantly contributes to the hydrological cycle, accounting for around 80% of the precipitation on landmasses to be reintroduced into the atmosphere. This mechanism profoundly affects the distribution and availability of surface water resources throughout the ecosystem. Gaining insight into the factors influencing local evapotranspiration fluctuations in response to varying climatic and vegetative scenarios is crucial for effective water management strategies and rehabilitating ecosystem resilience. To this end, our study focuses on the Jing River Basin in the Loess Plateau, utilizing multi-source remote sensing data and climatic information to investigate the spatiotemporal dynamics of evapotranspiration from 1984 to 2018 through the application of the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model. Our research results indicate a general ascending tendency in evapotranspiration across the investigated region, demonstrating a notably discernible escalation at a pace of approximately 3.11 mm/year (p < 0.01), with an annual vegetation ET volume reaching 533.88 mm. Across different vegetation types in the Jing River Basin between 1984 and 2018, the mean yearly ET was observed to be highest in forests (572.88 mm), followed by croplands (564.74 mm), shrublands (536.43 mm), and grasslands (503.42 mm). The leaf area index (LAI) demonstrated the strongest partial correlation with ET (r = 0.35) and contributed the most significantly to the variation in ET within the Jing River Basin (0.41 mm/year). Additionally, LAI indirectly influences ET through its impact on vapor pressure deficit (VPD), precipitation (Pre), and temperature (Temp). Radiation is found to govern most ET changes across the region, while radiation and precipitation notably affected ET by modulating air temperature. In summary, these radiant energy changes directly affect the evaporation rate and total evapotranspiration of surface water. It provides important support for understanding how evapotranspiration in the Jing River Basin is adjusting to climate change and increased vegetation cover. These findings serve as a theoretical foundation for devising sustainable vegetation restoration strategies to optimize water resource utilization within the region.

Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.

Long-Term Variation Patterns of Precipitations Driven by Climate Change in China from 1901 to 2022

Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over a century. This study utilized monthly precipitation data from 1901 to 2022 (at a century scale) to analyze and explore the spatiotemporal variability in precipitation across different time scales and regions with a trend analysis, an abrupt change analysis, and gravity center models. The findings indicate that (1) from 1901 to 2022, the precipitation in China generally decreased from the southeast coastal areas toward the northwest inland regions. (2) There were significant differences in the migration of precipitation gravity centers among the different study regions, with the least dispersion being observed in the Liao River basin, while the Hai River basin, various river basins in the northwest, and the Pearl River basin exhibited certain regularities in gravity center movement, and other regions showed periodic variations. (3) Over the period from 1901 to 2022, there was a trend of transitioning from lower to higher precipitation levels. (4) According to continuous long-term abrupt change tests, the timing of precipitation shifts varied across different basins. Precipitation, as a crucial component of natural resources, directly impacts various aspects of socio-economic life. Research findings provide decision support for regional flood control and disaster reduction and offer scientific decisions for ecological security.

Strategies of water resource utilization and agricultural water management in the coastal saline zone of Bangladesh

Strategies of water resource utilization and agricultural water management in the coastal saline zone of Bangladesh

A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Accurate river streamflow forecasting is pivotal for effective water resource planning, infrastructure design, utilization, optimization, and flood planning and warning. Streamflow prediction remains a difficult task due to several factors such as climate change, topography, and lack of observed data in some cases. This paper investigates and evaluates the individual performances of the seasonal auto-regressive integrated moving average (SARIMA) and Prophet models in forecasting the streamflow of the Sobat River and proposes a hybrid SARIMA-Prophet model to leverage the strengths of both approaches. Using the augmented Dickey-Fuller (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, the flow of the Sobat River was found to be stationary. The performance of the models was then assessed based on their residual errors and predictive accuracy using the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Residual analysis and prediction capabilities revealed that Prophet slightly edged SARIMA in terms of prediction efficacy; however, both models struggled to effectively capture extreme values, resulting in significant overestimations and slight underestimations. The hybrid SARIMA-Prophet model significantly reduced residual variability, achieving a lower MAE of 4.047 m3/s, RMSE of 6.17 m3/s, and a higher R2 of 0.92 than did the SARIMA (MAE: 5.39 m3/s, RMSE: 8.70 m3/s, R2: 0.85) and Prophet (MAE: 5.35 m3/s, RMSE: 8.32 m3/s, and R2: 0.86) models. This indicates that the hybrid model handles both long-term patterns and short-term fluctuations more effectively than the individual models. The findings of the present study highlight the potential of hybrid SARIMA-Prophet models for streamflow forecasting in terms of accuracy and reliability, thus contributing to more effective water resource management and planning, particularly in the Sobat River.

Long-term trends in human-induced water storage changes for China detected from GRACE data

Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging ±10 mm. For the future period (2024–2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources.

Does artificial ecosystem recharge make sense? based on the coupled water orbit research framework

By formulating an innovative Coupled Water Orbit Research Framework (CWORF) in cohesion with the modified Hypothesis Extraction Method (HEM), this study undertakes a comprehensive examination and assessment of the Autonomous Water Orbit (AWO) linkages within each sector. Building upon this foundation, the CWROF also integrates Structure Path Analysis (SPA) to scrutinize the critical paths of the AWO in each sector. Additionally, the framework quantifies specific water consumption at different layers of the production chain, enabling a more precise assessment of water consumption and inter-sectoral water usage linkages compared to existing studies. By applying this framework, existing water conservation policies can be refined, thereby achieving enhanced environmental sustainability. The study utilizes six Input-Output (IO) tables and water resource bulletins spanning from 2002 to 2020, as published by the China Bureau of Statistics. It is instrumental in dissecting the current interplay between water resource utilization and economic development. This research aims to analyze the structural nuances of water consumption and the dependency characteristics across diverse sectors within the production chain. It strategically identifies pivotal paths and sectors in the production chain where water consumption is most pronounced, and highlights critical chains contributing to water stress. The results indicate that sectors such as “Farming, Forestry, Animal Husbandry, Fishery, and Water Conservancy” exhibit high water consumption. Furthermore, light industries, represented by “Food and Tobacco” and “Textile Product” sectors, significantly impact indirect water usage across sectors. The study also reveals that the strength of the water feedback loop in the economic system diminishes as the number of layers increases, with the aforementioned sectors frequently appearing in transmission paths and serving as key interface nodes. This research underscores the critical interconnections between water usage and economic activities providing a foundational framework for refining water conservation policies and enhancing water allocation and management capabilities. The introduced CWORF extends beyond conventional methods, offering a more precise and nuanced approach to measuring inter-sectoral water utilization.

Zero-dimensional quantum dot-modified membrane materials for the effective treatment of industrial wastewater: A comprehensive review

The efficient treatment and recycling of industrial wastewater is an important way to improve water resource utilization. Membrane separation technology has been widely used in the field of industrial wastewater treatment due to its high separation efficiency, low energy consumption, good scalability, and other advantages. A recent breakthrough in membrane technologies is the emergence of various nanocomposite membranes, which not only breaks the trade-off between membrane selectivity and permeability, but also enhances the anti-fouling ability and multi-scale separation effects of membranes. Among the nanomaterials used for membrane fabrication, zero-dimensional quantum dots (QDs) with size-dependent optical properties and excellent photocatalytic potential have become promising candidates for novel nanocomposite membrane construction. Significant improvements in membrane surface hydrophilicity, anti-fouling property, and permeability can be achieved by incorporating QDs during the membrane formation process or through membrane post-modification. This review focuses on recent advances in QD-modified membranes for industrial wastewater treatment. The synthesis methods of QDs were summarized firstly, especially for carbon-based and semiconductor QDs. Based on the blending modification and surface modification, the research progress in the modification strategies of QD-modified membranes is presented. Subsequently, the state-of-the-art studies of QD-modified membranes in the application of industrial wastewater treatment are reviewed. Finally, the current challenges and opportunities in QD-modified membranes are discussed.