Regional Water Resources Allocation Research Articles

Medium and long-term regional water demand prediction using Harris hawks optimisation–backpropagation neural network model

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation–Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.

Research on a cloud model intelligent computing platform for water resource management

ABSTRACT As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references for intelligent water management and refined allocation of water resources in the Weihe River Basin.

Seeking a pathway towards a more sustainable human-water relationship by coupled model – From a perspective of socio-hydrology

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010–2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

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.

Analysis of regional water resources spatial pattern and allocation equity

Water scarcity is a worldwide concern, and with the impact of climate change and human activities, the imbalance in the distribution of water resources has further intensified. The equitable allocation of water resources has become a thorny issue for water resources management authorities. It is challenging to find a relatively equitable method of water resources allocation. This study aims to propose a method for evaluating the fairness of water resources allocation, which can be used both to evaluate the fairness of water resources allocation and as a basis for water resources allocation decisions. The specific approach is to first analyze the spatial pattern of regional water resources by using water resources abundance and water resources intensity, and then to conduct a comprehensive analysis of regional water resources allocation fairness by combining Lorenz curve, Gini coefficient and Theil index to form an index system that can systematically analyze the spatial pattern of water resources, allocation fairness and its changes. A real-world example study was conducted in the Yangtze River Delta region of China, and the results showed that the index system can well reflect the allocation of water resources among regions and cities within the region. The results obtained using this method can provide some reference for promoting coordinated regional development.

Joint optimal allocation of regional water and land resources considering their mutual feed relationship

Water and land resources are indispensable prerequisites for the sustenance and advancement of human civilization. In recent decades, China has faced significant challenges in managing its water and land resources due to the intensifying competition resulting from rapid urbanization, industrialization, and population growth. The joint optimal allocation of water and land resources can effectively address these issues. However, there have been limited accomplishments in investigating the integrated water and land resources allocation system’s interaction with social, economic, and environmental development. This paper develops a novel joint optimal allocation model of regional water and land resources (JOAMRWLS). The model integrates water and land resources for integrated allocation, taking into account the mutual feed relationship between them, and thereby achieving comprehensive utilization and coordination of water and land resources within the region. Then a two-layer nesting algorithm based on successive approximation and nonlinear programming (SA-NLP) is proposed to solve the model, obtaining the regional optimal land use pattern and optimal water allocation scheme. Subsequently, this paper uses Luoyang City in Henan Province, China, as a case study to verify the proposed JOAMRWLS. The results indicate that the water volume and area of each land use type tend to stabilize after the fifth iteration of the calculation with a consistent water volume of 18.65 × 108 m3. Compared with the conventional optimization model of water resources (COMWR), the JOAMRWLS encompasses five cities experiencing water scarcity, whereas the latter includes twelve such cities. Furthermore, the economic benefit of the JOAMRWLS is 2.65 × 1011 CNY, surpassing that of the former at 2.56 × 1011 CNY, highlighting its superiority.

Optimal allocation planning of regional water resources with multiple objectives using improved firefly algorithm

Abstract Population growth and economic development, coupled with water pollution and the frequent occurrence of extreme weather, have led to a growing contradiction between water supply and demand in some regions. To address this challenge, rational and optimal allocation of regional water resources has emerged as a crucial approach. This study focuses on creating a comprehensive model for optimizing regional water resource allocation, taking into account social, economic, and ecological factors. In addition, three innovative modifications are introduced to the firefly algorithm (FA), resulting in the development of the improved firefly algorithm (IFA). The effectiveness of IFA is validated through experiments involving nine benchmark functions. The results highlight the improved search efficiency and convergence achieved by IFA compared to other intelligent algorithms. Moreover, the application of IFA in solving the water resource allocation challenge in Shannxi Province, China, for 2020 and 2021 demonstrates a reduction in the overall water shortage rate to 4.69 and 1.72%, at a 75% guarantee rate. This reduction in water shortages contributes to addressing future scarcities. The proposed allocation scheme offers comprehensive benefits and provides crucial technical support for water resource management. Ultimately, this study offers valuable insights and guidance for addressing the issue of water supply–demand disparities.

Research on the joint adjustment model of regional water resource network based on the network flow theory

Abstract This study uses the network flow theory to optimize regional water resource allocation. In order to solve the problem of inefficient utilization of water resources with decentralized decision-making by different administrative units, a regional water resource networking and joint dispatching model with multi-objective nonlinear characteristics based on the network flow theory (hereinafter referred to as the network flow model) is constructed in the study. The network flow model was simulated and applied in the Xin-Sheng area of the Cao'e River, a tributary of the Qiantang River, and the results of the study showed that the network flow model scheduling increased significantly in efficiency compared with the current conventional scheduling, with an increase of 35.24 and 9.91% in the water resource utilization rate in the two typical years of 2019 and 2022, respectively, and showed that 2022, which has less rainfall, has a better effect than 2019. The study concludes that the network flow model can effectively improve the efficiency of water resource utilization, solve the problem of water resource imbalance between cities in the region, and play a positive role in the construction of the national water network.

Response of stream water quality to the vegetation patterns on arid slope: a case study of Huangshui River basin.

Vegetation patterns on slopes strongly affect the water cycle processes in a basin, especially the water yield and confluence in arid areas. Quantifying and evaluating the effects of hydrological change on the migration and transformation of pollutants are challenging. Based on 4-year stream water quality data of 13 monitoring sites in the Huangshui River basin, a typical arid watershed of the Chinese Loess Plateau, the redundancy analysis (RDA) and structural equation modeling (SEM) analysis tools were used to quantify its relationship with vegetation patterns. In the study, land use and the enhanced vegetation index (EVI) were used as a metric of vegetation patterns; accordingly, the 13 catchments were divided into three groups via the cluster analysis, including large (over 80%), medium (70 ~ 80%), and small (below 70%) proportion vegetation patterns (LVP, MVP, SVP). The results of the LVP group showed that vegetation patterns negatively affected the contamination of total phosphorus (TP), ammonia nitrogen (NH3-N), permanganate index (CODMn), and biochemical oxygen demand (BOD5) in the stream water, and the contribution rates were - 0.57. While the proportion of urban area positively correlated with stream water quality in the groups of MVP and SVP, the contribution rates were 0.46 and 0.36, respectively. Moreover, the precipitation in the groups of MVP and SVP negatively correlated with pollutants (- 0.24 and - 0.26). Those results revealed the response of stream water quality to vegetation patterns on the slope with the consideration of precipitation, land use, and socio-economic factors for the regional water and land resource allocation. This study has important management implications for vegetation patterns on slope of fragile ecosystems in arid areas.

Different types of meteorological drought and their impact on agriculture in Central China

Challenges in drought monitoring hinder social development. In this study, a robust drought monitoring framework composed of ten indicators was established based on the standard precipitation actual evapotranspiration (SPAEI) index calculated using the maximum entropy production (MEP) model. Additionally, the relationship between meteorological drought and agricultural development was analyzed. The results indicated that the seasonal SPAEI performed better than the standardized precipitation evapotranspiration index (SPEI) in terms of drought area, vegetation drought, and drought identification in Central China. The annual drought duration was between 3.4 and 4.5 months, and the annual drought frequency ranged from 1.1 to 1.9 times per year. The main drought pattern was divided into persistent light drought and short-term severe drought, with the latter dominating crop yields in Central China. Although persistent light drought contributes more to drought, short-term severe drought has a greater impact on crop yields. Thoroughly investigating regional drought patterns and assessing their impacts on agriculture will contribute to a better understanding of drought characteristics and provide valuable guidance for regional agricultural water resource allocation and management.

Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework

A novel newsvendor model-based framework for regional industrial water resources allocation that considers uncertainties in water supply and demand was proposed in this study. This framework generates optimal water allocation schemes while minimizing total costs. The total cost of water allocation consists of the allocated water cost, the opportunity loss for not meeting water demand, and the loss of the penalty for exceeding water demand. The uncertainties in water demand and supply are expressed by cumulative distribution functions. The optimal water allocation for each water use sector is determined by the water price, the unit loss of the penalty and opportunity loss, and the cumulative distribution functions. The model was then applied to monthly water allocation for domestic, industrial, and agricultural water use in two counties of Huizhou City, China, whose water supply mainly depends on Baipenzhu Reservoir. The water demand for each water use sector and the monthly reservoir inflow showed good fits with the uniform and P-III distributions, respectively. The water demand satisfied ratio for each water use sector was stable and increased for the optimal water allocation scheme from the newsvendor model-based framework, and the costs were lower compared with the actual water allocation scheme. The novel framework is characterized by less severe water shortages, lower costs, and greater similarity to actual water use compared with the traditional deterministic multi-objective analysis model, and demonstrates strong robustness in the advantages of lower released surplus water and higher water demand satisfied ratio. This novel framework yields the optimal water allocation for each water use sector by integrating the properties of the market (i.e., determining the opportunity loss for not meeting water demand) with the government (i.e., determining the water price and the loss of the penalty for exceeding water demand) under the strictest water resources management systems.

Multi-scenario optimization model for operation of inter-basin water transfer-supply systems considering cost–benefit relationships

Abstract The optimized operation of an inter-basin water transfer system is a widely used approach to achieving optimized regional water resource allocation. The costs of water transfers vary considerably, depending on the composition of the water source, the target audience and the route and distance of the transfer. This study proposes a multi-scenario optimization model for the operation of inter-basin water transfer-supply systems that balances the cost–benefit relationships. A two-objective optimization model is first established including minimizing the sum of squared water supply deficits and the cost of water transfers. Three water transfer scenarios are developed for different cost–benefit relationships. Based on these scenarios, the established optimization model is further converted into a single-objective optimization model to avoid the direct calculation of complex water transfer costs. The model is finally solved to obtain water transfer schemes. The proposed model is applied to the eastern route of the South-to-North Water Diversion Project in China. The results show that with a contracted diversion of 60% of the Yangtze River, the reliability of the water supply is 78.2% and the water shortage index is 2.8%, which is a compromise between balancing the water shortage index and the volume and cost of water transfer.

Research on multi-objective optimal allocation of regional water resources based on improved sparrow search algorithm

Abstract Nowadays, the conflict between the supply and demand of water resources in many regions is becoming increasingly prominent. Scientific allocation of regional water resources has become the key to solving the contradiction. In this study, a regional multi-objective water resources optimization allocation model considering social, economic, and ecological objectives is established, and four improvement spots are introduced to the sparrow search algorithm (SSA) to form an improved sparrow search algorithm (ISSA). By testing nine benchmark functions including monotonic and multi-peaked, the search efficiency and average convergence results of ISSA are significantly enhanced compared with other intelligent algorithms. Meanwhile, this research uses Luanchuan County, Henan Province, China, as an example to solve the water resource allocation scheme for 2025 and 2030 in the region using ISSA. The results show that the overall water shortage rate decreases to 3.49 and 2.79%, respectively, under the 75% guarantee rate, resulting in an effective reduction in future water shortages. Simultaneously, the scheme proposed has sound comprehensive benefits and can provide important technical support for the refined management of water resources, which is a reference and guidance for solving the contradiction between water supply and demand at the current stage.

Estimation and Spatiotemporal Evolution Analysis of Actual Evapotranspiration in Turpan and Hami Cities Based on Multi-Source Data

The accurate inversion of actual evapotranspiration (ETa) at a regional scale is crucial for understanding water circulation, climate change, and drought monitoring. In this study, we produced a 1 km monthly ETa dataset for Turpan and Hami, two typical arid cities in northwest China, using multi-source remote sensing data, reanalysis information, and the ETMonitor model from 1980 to 2021. We analyzed the spatiotemporal variation of ETa using various statistical approaches and discussed the impact of climate and land use and cover changes (LUCC) on ETa. The results show the following: (1) the estimation results correlate well with ETa products on monthly scales (coefficient of determination (R2) > 0.85, root mean square error (RMSE) < 15 mm/month) with high reliability. (2) The ETa values were spatially distributed similarly to precipitation and LUCC, with the multi-year (1980–2021) average of 66.31 mm and a slightly fluctuating downward trend (−0.19 mm/a). (3) During the 42-year period, 63.16% of the study area exhibited an insignificant decrease in ETa, while 86.85% experienced pronounced fluctuations (coefficient of variation (CV) > 0.20), and 78.83% will show an upward trend in the future. (4) ETa was significantly positively correlated with precipitation (94.17%) and insignificantly positively correlated with temperature (55.81%). The impact of human activities showed an insignificant decreasing trend (85.41%). Additionally, the intensity of ETa varied considerably among land types, with the largest for cropland (424.12 mm/a). The results of the study have implications for promoting the rational allocation of regional water resources and improving water use efficiency in arid zones.

A Regional Water Resource Allocation Model Based on the Human–Water Harmony Theory in the Yellow River Basin

Considering the issues of water scarcity, water environment deterioration, and unreasonable allocation of water resources in the urban area of the Yellow River Basin, this paper introduces the human–water harmony theory to the allocation of regional water resources. Based on an analysis of the structural characteristics of the regional water resource system, the harmonious water resource allocation (HWRA) model—which includes three sub-systems (i.e., the water service system, ecological environmental system, and economic and social system)—is established. In addition, considering the uncertain factors in the HWRA model, the inexact fuzzy multi-objective programming (IFMOP) method is used to solve the model, aiming at achieving the minimum amount of regional water scarcity, the minimum amount of sewage discharge, and the maximum total economic benefit. A case study of water resource allocation of Binzhou, a city located in the Yellow River Basin, is conducted to validate the model. The model solution results show that the water resource system in Binzhou in 2025 and 2035 could be optimized after harmonious allocation, especially in terms of the water service and ecological environmental systems. Compared with the optimal water resource allocation (OWRA) model, the HWRA model has a more scientific water supply structure, and a smaller amount of sewage discharge. The HWRA model solves the variables using an interval number, so it can flexibly and scientifically reflect the decision-making process.

Study on the ecological water demand security assessment for the Panjin wetland based on landscape pattern

Abstract The model was used to predict and analyze the future changes in the total ecological water demand of the Panjin wetland, with a view to providing some scientific reference for the optimal allocation of regional water resources and the sustainable development of wetlands. The results showed that (1) the quality of the habitat environment in the Panjin wetland has a great influence on the change of the total water demand, and the total ecological water demand in the Panjin wetland will continue to decrease in the future. (2) There is a clear correlation between landscape pattern and ecological water demand. (3) The distribution characteristics of the landscape pattern are greatly influenced by political factors, thus affecting the total ecological water demand of the Panjin wetland.

Influences of variability of stable isotopes and composition of moisture sources on precipitation at multiple timescales in the Alpine regions of Central Asia

The accurate representation of variability of isotopic composition of modern precipitation based on long-term continuous monitoring is vital for interpreting hydrological and climatic processes. Based on measurements of δ2H and δ18O of 353 precipitation samples from five stations in the Alpine Mountains of Central Asia (ACA) during 2013–2015, the spatiotemporal variability of isotopic composition of precipitation and its controlling factors under multiple timescales were explored. Results showed that (1) the stable isotopes in precipitation at multiple timescales displayed an obviously inconsistent trend, especially in winter. (2) δ18O composition of precipitation (δ18Op) under multiple timescales had a significant correlation with the variability of air temperature, except for in the case of the synoptic scale, while the correlation was weak between precipitation amount and variability in altitude. (3) The westerly wind had a stronger influence on the ACA, the southwest monsoon had an important influence on the transport of water vapor in the Kunlun Mountains region, and Arctic water vapor had a higher contribution to the region of the Tianshan Mountains. (5) The contribution rate of recycled vapor to precipitation ranged from 15.44 to 24.11 %, and the composition of moisture source of precipitation in arid inland areas of Northwestern China exhibited spatial heterogeneity. The results of this study improve our understanding of the regional water cycle and will enable the optimization of the allocation of regional water resources.

Quartet trade-off for regional water resources allocation optimization with multiple water sources: A decentralized bi-level multi-objective model under hybrid uncertainty

Multiple uncertainties from water resource allocation have posed new challenges to balanced regional development. On the one hand, in the context of global water shortage crisis, groundwater, as an important water supply module, is facing severe risk of shortage. On the other hand, the regional authorities need to coordinate the contradictory water demand brought by the heterogeneous water use objectives from multiple water use levels, and these demands are also subject to complex uncertainties. How to mitigate the risks posed by uncertainties, ensure regional water security and balance participants, is attracting the keen attention of decision-makers around the world. Therefore, this study establishes a decentralized bi-level multi-objective regional water resources allocation (RWRA) optimization model under hybrid uncertainty, aiming at an optimal quartet trade-off of equity, sustainability, equilibrium, and efficiency. To deal with the hybrid uncertainty in the model, water demand is set as a random fuzzy number, and the groundwater constraint is combined with fuzzy chance-constrained programming (FCCP). Then, the decentralized bi-level multi-objective model is solved by the bi-level interactive method based on the satisfactory solution incorporated with the chaotic-particle swarm optimization (CPSO) algorithm. The proposed model and solution approach are then applied to the Dongjiang River Basin in Guangdong Province of China to verify the validity and utility of the model. The results show that (1) the model can effectively meet the different objectives requirements of regional authority and sub-areas; (2) groundwater constraint parameters has a significant impact on the model results, especially the Gini coefficient decreases by 13.6% when groundwater is used as a water source supplement; (3) from single-level model to decentralized bi-level model, the satisfactory balance of the two levels increases from 0.1457 to 0.793, which indicates that the decentralized bi-level model makes the two levels more balanced than the single-level model. Based on the results, some management recommendations for participants are finally provided.

Variability of precipitation-stable isotopes and moisture sources of two typical landforms in the eastern Loess Plateau, China

Study regionTwo typical landforms in the eastern Loess Plateau, China Study focusHydrogen and oxygen stable isotopes in precipitation are highly efficient tracers for quantitatively identifying the regional water cycle. Based on 237 precipitation event data points from seven precipitation stable isotope monitoring stations in the eastern Loess Plateau (ECLP), this study first presented the spatial-temporal variation of precipitation isotopes. It explored the mainly controlling factor and source of water vapor for regional precipitation. New hydrological insights for the regionThis research found a consistent temporal trend was observed in the ECLP except for the Jiexiu station, characterized by enrichment from December to May and depletion from June to October. Signals of the summer monsoon reaching the ECLP were recorded using precipitation δ18O (δ18Op), especially in July. Relatively smaller slope of the local meteoric water line (LMWL) of summer precipitation reflects that sub-cloud evaporation strongly influences regional precipitation process. δ18Op values did not exhibited significant relationships with air temperature and an apparent "precipitation amount effect" was observed in precipitation samples with precipitation amount less than 10 mm. An apparent "anti-altitude" effect appeared in precipitation samples with the elevation between 400 and 750 m of the ECLP. The sub-cloud evaporation had stronger influence on the precipitation process of the the Fenhe river basin valley (FRV) especially in the Jiexiu station. The water vapor of precipitation in the ECLP mainly originated from the Bohai Sea and the East China Sea in the near-surface and southeast path directions, especially in summer. These studies are essential in regional water resources allocation, especially under the influence of climate change.