Regional Water Resources Research Articles

Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction

Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to capture its features, resulting in inadequate prediction accuracy. In response to the difficulties in leveraging the advantages of machine learning models and limited prediction accuracy in annual runoff forecasting, firstly, the variational mode decomposition (VMD) method is adopted to decompose the annual runoff series into multiple intrinsic mode function (IMF) components and residual sequences, and the spectral clustering (SC) algorithm is applied to classify and reconstruct each IMF. Secondly, an annual runoff prediction model based on the adaptive particle swarm optimization–long short-term memory network (APSO-LSTM) model is constructed. Finally, with the basis of the APSO-LSTM model, the decomposed and clustered IMFs are predicted separately, and the predicted results are integrated to obtain the ultimate annual runoff forecast results. By decomposing and clustering the annual runoff series, the non-stationarity and complexity of the series have been reduced effectively, and the endpoint effect of modal decomposition has been effectively suppressed. Ultimately, the expected improvement in the prediction accuracy of the annual runoff series based on machine learning models is achieved. Four hydrological stations along the upper reaches of the Fen River in Shanxi Province, China, are studied utilizing the method proposed in this paper, and the results are compared with those obtained from other methods. The results show that the method proposed in this article is significantly superior to other methods. Compared with the APSO-LSTM model and the APSO-LSTM model based on processed annual runoff sequences by single VMD or Wavelet Packet Decomposition (WPD), the method proposed in this paper reduces the RMSE by 40.95–80.28%, 25.26–57.04%, and 15.49–40.14%, and the MAE by 24.46–80.53%, 16.50–59.30%, and 16.58–41.80%, in annual runoff prediction, respectively. The research has important reference significance for annual runoff prediction and hydrological prediction in areas with data scarcity.

Connectivity of evapotranspiration processes in a Brazilian dryland reservoir using remote sensing

Understanding the combined open water reservoir evaporation and riparian vegetation transpiration (evapotranspiration) is important for water resource management in semiarid regions like the Brazilian Dryland region. Existing research has explored reductions in open water evaporation due to riparian vegetation transpiration, but evidence for this interdependence in this region is not well understood. The aim of this study was to estimate the connectivity (impact and maximum distance of action) between riparian vegetation and its transpiration and reservoir evaporation, as well as the ratio of the volume losses by transpiration and evaporation, using Landsat 5 and 8 in a Brazilian Dryland environment. Evaporation and transpiration were estimated using the Surface Energy Balance System for Water and the Surface Energy Balance System models, respectively, from 1985 to 2020. A Class A Pan and the Penman-Monteith equation were used, for reference reservoir evaporation and vegetation transpiration, respectively. The occurrence of riparian vegetation was associated with the Normalized Difference Vegetation Index. Both models performed well, with root mean square error ≤ 0.80 mm/day. The riparian vegetation transpiration demonstrated a high negative correlation (R²>0.7) with evaporation, suggesting the vegetation acts to reduce evaporation (average of 30 %) up to a maximum distance of 90 m from the reservoir shoreline. This is explained by the adjacent riparian vegetation-induced reduction in radiation inputs due to shading at the reservoir margin, a lowering of wind speeds and increases in air humidity from transpiration. The ratio of riparian vegetation transpiration to reservoir evaporation within a buffer of 150 m is approximately equal. Combined riparian vegetation and reservoir evapotranspiration equates to the daily water supply for ∼275,000 people. The paper explains and quantifies the connectivity between riparian vegetation transpiration and reservoir evaporation, thereby providing a model for improved reservoir water balance estimates, which can aid regional water resources planning, development, and management.

Unveiling the Dynamics and Influence of Water Footprints in Arid Areas: A Case Study of Xinjiang, China

A prerequisite for the rational development and utilization of regional water resources is the measurement of water stress. In this study, from the perspective of water footprints, we took the proportion of the agricultural water footprint within the total water resource usage of Xinjiang (hereafter referred to as XJ) as an example to measure its water stress index and explore the state of water stress in the region and its corresponding driving factors. The ESDA method was applied to characterize the spatial patterns of and correlations with water stress. The effects of different factors on the spatial differentiation between the water footprint and water stress were quantified using the LMDI and geoprobes, respectively. The results showed that (1) both the agricultural water footprint and the water stress index in XJ showed an upward trend, the spatial distribution of water stress was uneven, and the regional pressure difference between the east and the west was greater than that between the north and the south; (2) the water stress index has an obvious negative spatial correlation, fluctuations in its discrete nature have been enhanced, and the number of spatially correlated prefectures is decreasing; (3) water consumption of CNY 10,000 GDP, GDP per capita, and total CO2 emissions have the most significant impact on the evolution of the agricultural water footprint in XJ. Meanwhile, spatial variations in water stress are mainly determined by the area of cultivation, the area of natural oasis, and the proportion of water used in agriculture. Analysis of the characteristics of and factors influencing water stress in XJ from the perspective of its agricultural water footprint provides a new perspective for further analyzing the actual state of the water footprint and water stress in XJ and supplies a reference basis for the decision-makers of the XJ government.

Improving a hydrological model by coupling it with an LSTM water use forecasting model

Long short-term memory (LSTM) networks have demonstrated excellent capabilities in identifying nonlinear relationships between variables and have proven to be effective in water use forecast modeling. However, current LSTM water use forecasting models do not consider the influence of regional water resources, which limits their ability to make full use of hydrological data. To address this issue, a distributed hydrological model (WetSpa-WW) was integrated in this study with a deep learning model (LSTM) based on water use activities. The water resources results simulated by WetSpa-WW were considered as an additional training input in the LSTM for water use forecasting. The coupled WetSpa-LSTM model was tested on the upper-middle of the Weihe River Basin, and the simulations were compared with those obtained from an experiment that did not consider water resources factors. The results indicated that the WetSpa-LSTM model performed better than the original model in simulating the streamflow. During calibration, logarithmic form of Nash-Sutcliffe efficiency (NSEln) increased from 0.33 to 0.54 and the relative error (RE) decreased from 28 % to 13 %. Moreover, we observed that water resources factors can improve the accuracy of agricultural water use forecasting (NSE increases by 0.10) more than that of non-agricultural water use forecasting (NSE increases by 0.04). Overall, this study demonstrates the value of considering regional water resources in water use forecasting, and provides a perspective for integrating a distributed hydrological model (WetSpa-WW) and a deep learning model (LSTM) based on water use activities.

Research progress on structure and hydrological processes in the karst critical zone of southwest China

The southwestern region of China is the largest exposed karst area in the world and serves as an important ecological security barrier for the upstream of Yangtze River and Pearl River. Different from the critical zone of non-karst areas, the epikarst, formed by an interwoven network of denudation pores, is the core area of karst critical zone. Water is the most active component that participates in internal material cycle and energy flow within the critical zone. We reviewed relevant research conducted in the southwestern region from three aspects: the characte-rization of critical zone structure, the hydrological processes of soil-epikarst system, and their model simulations. We further proposed potential research hotpots. The main approach involved multi-scale and multi-method integrated observations, as well as interdisciplinary collaboration. Precisely characterizing the eco-hydrological processes of the vegetation-soil-epikarst coupling system was a new trend in the future research. This review would provide scientific reference for further studies on hydrological processes in critical zones and regional hydrological water resource management in karst areas.

How much evaporation occurs in Brazilian reservoirs? A multi-model perspective

Estimating evaporative losses from reservoirs is essential for water resource management. While remote sensing provides great opportunities for better constrain this process, there are still large uncertainties related to the behavior of evaporative losses across large domains. Here, we present a multi-model approach combined with remote sensing data to estimate evaporation losses at a national scale for dozens of reservoirs in Brazil. Open water evaporation (Ew) rates were obtained using two surface energy balance (SEB) models (the Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL) and the Simplified Surface Energy Balance (SSEBop)). Due to the lack of in situ evaporation data, the estimations were cross-validated with results from the validated product Moderate-Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 Global (MOD28C3) and the widely used open water evaporation equation described by Penman. Ew estimates were obtained for 74 large reservoirs, representing almost 70% of the area occupied by artificial water bodies in Brazil. Monthly estimates of Ew showed satisfactory agreement among the models, especially for geeSEBAL, SSEBop, and MOD28C3. Reservoirs with higher Ew were located in the semi-arid Caatinga Biome. The total annual evaporation and net evaporation (difference between lake Ew and surrounding evapotranspiration) for the assessed reservoirs were 46.6 ± 1.0 km³ and 16.4 ± 0.5 km³, respectively, for the period 2003–2021, based on an average of geeSEBAL and SSEBop. We also found that net evaporation is very high in dry climate zones and lower in tropical and humid subtropical climate zones. Our results show the advantage of integrating remote sensing datasets and evaporation models with cloud computing to obtain evaporation estimates in a tropical region and over a large domain. These multi-model evaporation estimates can improve our understanding of water resources in ungauged regions at a large spatial scale.

Ecological risk assessment and spatial–temporal differentiation of soil and water resources in the Hefei metropolitan area

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.

Environmental impacts of geothermal fields on water resources in the Central Anatolia region, Turkey

Hot and mineral waters in thermal areas may interact with other water resources in the region. When such a situation occurs, it is possible for hot water to negatively affect the chemical structure and quality of cold-water resources. This limits the use of drinking and/or irrigation water in water resources. In the scope of this study, geothermal fields in the Central Anatolia region of Turkey were examined in detail and the quality and usability characteristics of the water resources in these fields were examined. Thus, the environmental effects of geothermal in terms of water resources were tried to be revealed. The geological, hydrogeological and hydro geochemical properties of 18 different geothermal fields located in 5 different regions were examined and their similarities and differences with each other were revealed. In addition, the quality and usability characteristics of hot and cold-water resources in each field were examined and the possible negative effects of these geothermal fields on water resources were investigated. According to the results obtained, thermal energy causes pollution of both freshwater resources and soil, especially due to their high ion content. In addition, considering the major ion and heavy metal contents of thermal waters, it is seen that health problems may occur if the water is used as drinking water for a long time.

The changes in the annual distribution of mountain runoff during the period of 1965–2018 in Hexi Corridor, Northwest China

The annual distribution characteristics of river runoff in arid regions have significant implications for water resource stability and management. Based on the mountain runoff data from 1965 to 2018, this study examines the annual change characteristics of monthly runoff of the Shiyang River Basin, Heihe River Basin, and Shule River Basin in the Hexi Corridor, Northwest China. Many indexes are used and analyzed, including the coefficient of variance, the complete regulation coefficient, the concentration degree and concentration period, the magnitude of change, the skewness coefficient, and the kurtosis coefficient of the annual distribution curves. The results reveal the following: (1) The inhomogeneity of annual runoff distribution in the Taolai River and the rivers to the west of it, except the Shiyou River, show an increasing trend. Conversely, the inhomogeneity of the rivers to the east of the Taolai River generally show a downward trend, but the coefficient of variance value is still very high. (2) In the Shiyang River Basin, the annual distribution of the concentration period is characterized by a relatively discrete pattern. Conversely, the Heihe River Basin exhibits a relatively concentrated pattern, and the distribution pattern of the Shule River Basin is quite different. Notably, all concentration periods in the three basins have shifted backward after the 2000s. (3) The Shiyang River Basin exhibits disordered annual distribution curves of runoff in different years. In contrast, the Heihe River Basin presents a typical ‘single-peak’ pattern with a prominent right-skewed. The Shule River Basin has regular distribution curves, with a gradually significant ‘double-peak’ pattern from east to west. Overall, there has been a slight change in runoff in the Shiyang River Basin, while the Heihe River Basin and Shule River Basin have experienced significant increases in runoff. The annual distribution curves of runoff in the Liyuan River and the rivers to the east of it exhibit a gentle peak pattern, and the appearance probability of extreme runoff during the year is low. Conversely, the rivers to the west of the Liyuan River, excluding the Danghe River, display a sharp peak and thick tail pattern, indicating that the appearance probability of extreme runoff during the year is high. These findings have practical implications for the planning and management of water resources in the Hexi Corridor. Moreover, they provide a solid foundation for predicting future changes in regional water resources.

Assessment of Crop Water Footprint and Actual Agricultural Water Consumption in Arid Inland Regions: A Case Study of Aksu Region

Water scarcity is a major issue in arid regions, and it is crucial to have an accurate understanding of water resource utilization for informed decision-making regarding water-related issues. However, due to various reasons such as inadequate measuring facilities, the actual agricultural water usage is often underestimated. The concept and methodology of the water footprint, based on a life-cycle perspective, provide a powerful tool for studying the actual water usage in agriculture. By utilizing the theories and models of water footprints, a method for reviewing the actual agricultural water usage in arid regions is proposed. Taking the Aksu region as an example, the evolution patterns and water consumption of 15 major crops over a 31-year period from 1990 to 2020 were calculated and analyzed. The research shows that the total water footprint of crops in the Aksu region has increased by nearly 3.13 times over the 31-year period, with significant accelerations in 2003 and 2016. The green water footprint accounts for an average of about 8% of the major crops, while the average water footprint of cotton accounts for over 57.2% of the total water footprint of major crops in the study area. Based on the calculation of the blue water footprint, the actual water usage in the Aksu region in 2020 was estimated to be 11.128 billion cubic meters, which is 1.30 times higher than the reported water usage, with groundwater extraction being 2.46 times higher than reported. This method of water footprint analysis for reviewing actual water usage and its application examples provide a methodological foundation and technical support for regional water resource management and policymaking.

COMPREHENSIVE ASSESSMENT OF WATER SUPPLY OF THE TURKESTAN REGION FOR THE DEVELOPMENT OF ECONOMIC SECTORS AND RECREATIONAL TOURISM

The purpose of this study was a comprehensive assessment of water supply of the population, territory and economic sectors in the context of water-resource regions of Turkestan region of the Republic of Kazakhstan based on integrated specific water supply index, taking into account the environmental flows of river basins, providing data on the current state of water resources, level of water supply and their changes under influence of natural and anthropogenic factors. The research methodology is based on statistical analysis, long-term information-analytical materials on the hydrological condition of rivers, territorial organization of water consumption and the population of Turkestan region. For a comprehensive assessment of water supply of the population, territory and economy, a methodology was developed that allowed to assess the current state of water supply in the territory of river basins in the region and their changes under the influence of natural and anthropogenic factors. A comparative assessment of water supply in the Turkestan region in terms of water-resource areas based on the proposed methodological approach and the created research base covering the period of 2002-2020 showed that, in general, there is a high level of water shortage, which is associated with water shortage problems in the region. The obtained results of study are of fundamental environmental and economic importance when assessing water supply and can be used as an effective tool for the assessment of the influence of water factors during socio-economic forecasting of the region’s development, especially recreational activities.

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.

Unveiling Water Allocation Dynamics: A Text Analysis of 25 Years of Stakeholder Meetings

Abstract Managing water resources in regions with high climate variability and frequent extreme weather events poses challenges for policymakers. To facilitate water allocation in these cases, participatory and collaborative decision-making approaches have become common. However, the evaluation of these approaches is hindered by the lack of structured methods and data to understand them. To address this knowledge gap, we propose a novel methodology that leverages text data to identify key topics, conflicts, and influential actors that shape water allocation dynamics. Our methodology is tested using records of 1020 water basin committee meetings held between 1997 and 2021 across twelve basin committees in Ceará, Brazil – a region known for its extensive history of droughts that have impacted water governance. To uncover key water management issues discussed during these meetings, we employed a three-step topic modeling framework: (1) sentence embedding, (2) dimensionality reduction, and (3) sentence clustering. Furthermore, we used entity recognition, dependency parsing, and network graphs to identify powerful actors influencing these meetings and, ultimately, the decisions taken. Our findings revealed stakeholders’ heightened concern for urban water supply over agricultural demand during droughts. We found that “reservoir operation” was the most recurring topic, especially in basins where the strategic reservoirs are located. Discussions related to “climate information” became significantly more important over time, which indicates that water allocation decisions are increasingly based on the seasonal forecast and data on oceanic indices provided by the meteorology agency. Despite the presence of local users in the committees, governmental representatives dominated the discussions and were central in all river basins. In conclusion, our proposed approach harnesses existing text data to uncover spatiotemporal patterns related to participatory water allocation. This study opens new avenues for investigating water governance using text-based analysis.

Editorial Wetlands for human well-being

Editorial Wetlands for human well-being

Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China

Accurate forecasting of water demand plays a crucial role in decision makers to effectively allocate regional water resources and promotes the rational development and utilization of water resources. To address the challenges posed by limited raw data availability and the difficulty in selecting parameters for water demand forecasting models, this study proposes a coupled model called MF-HHO-GRNN (Multi-Fidelity Data, Hybrid Harris Hawks Optimization, and Generalized Regression Neural Network). Initially, multi-fidelity data was constructed employing water demand factor data for Heilongjiang Province, China (1999–2020) as high-fidelity data, alongside data from Liaoning and Jilin Provinces of China as low-fidelity data. Adaptive boosting algorithms, data concatenation, and data dimensionality reduction techniques were employed to construct multi-fidelity data, thereby improving the availability of the original data. Subsequently, Harris hawk optimization algorithms were implemented to optimize the smoothing factor of the GRNN to resolve the challenge of selecting suitable parameters for water demand forecasting models. Finally, the MF-HHO-GRNN model was compared with other prediction models to analyze the prediction accuracy. The optimized MF-HHO-GRNN model meeting accuracy requirements was utilized to forecast agricultural irrigation, industrial, domestic, ecological water demand, and total water demand in Heilongjiang Province for the next 5 years. Results demonstrated that the proposed MF-HHO-GRNN model attained predictive accuracy of 98 %, 98 %, and 97 % for total water demand under three distinct time horizons (1 year, 3 years, and 5 years), proving excellent predictive capabilities. Furthermore, a comprehensive comparison was made between the predicted total water demand and user-specific water demands for the next 5 years in Heilongjiang Province and the regional development plans. The results showed that the predictions of the coupled model aligned with the future development trends of Heilongjiang Province, validating the practicality of the MF-HHO-GRNN model. This study provides decision-makers with an effective method for forecasting water demand, contributing to the future realization of more intelligent and accurate water resource management and decision-making.

WATER SECURITY IN THE KYRGYZ REPUBLIC: POST-SOVIET CHALLENGES AND REGIONAL DYNAMICS

The article describes the features of the use of water resources of the Kyrgyz Republic after the collapse of the USSR in 1991. Water security, stocks and potential of water resources are considered. At the same time, examples show the importance of water resources in the economy and politics of the Kyrgyz Republic, relations with the states of Central Asia. An analysis of tensions with neighboring countries on water resources was carried out. The importance, benefits and potential problems of the CASA-1000 project are also analyzed. The article describes the legal regulation of water resources in the Kyrgyz Republic, economic benefits and role in international relations.

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.

Application and Evaluation of Stage–Storage–Discharge Methodology in Hydrological Study of the Southern Phosphate Mining Model Domain in Southwest Florida

This hydrological study investigated a combined rating methodology tested on a 14,090 km2 area in Southwest Florida. The approach applied the Hydrological Simulation Program-Fortran (HSPF) over a 23-year period and was validated by 28 stream gauging stations. The regional hydrological complexity includes natural and agricultural areas, as well as extensive phosphate mining and urbanizing areas. This application is a novel and efficient methodology for generating stage–storage–discharge relationships using a geographic information system (GIS), empirical equations, and spreadsheets for 148,000 isolated and connected alluvial wetlands within the model domain. The validation metrics used to evaluate the applied methodology for populating the stage–storage–discharge relationship demonstrated the model effectiveness in simulating a range of hydrological events across various regions. For discharge prediction, the Nash–Sutcliffe efficiency values surpassed 0.7 at most stations, with an average of 0.67, and the average R squared was 0.74. This methodology, when applied, achieved a root-mean-square error of 4 m3/s for discharge prediction and 0.47 m for stage prediction. However, limitations emerged in simulating baseflow (low flows), highlighting the need for integrated modeling approaches to accurately capture groundwater–surface water interactions. The research provides an improved means for modeling regional water resources and lays the groundwork for enhanced hydrological modeling in watersheds with complex alluvial and isolated wetland systems.

Response of blue-green water to climate and vegetation changes in the water source region of China's South-North water Diversion Project

The characteristics of water fluctuations within the basin have remained ambiguous in recent years due to climate and vegetation changes, posing a challenge to the planning and utilization of regional water resources. This study coupled the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, which can consider vegetation dynamics information and evapotranspiration physical mechanisms, into the Distributed Time Variant Gain Model (DTVGM) to improve the hydrological simulation accuracy under changing environments. Based on different simulation scenarios of the coupled model, the effects of climate and vegetation changes on hydrological processes were fully investigated using blue and green water (BW and GW) as assessment metrics. The upper Han River Basin (UHRB), as water source for the Central Line Project of China’s South-North Water Diversion (CSNWD), was selected as a typical basin for the study, results indicated that: (1) The DTVGM coupled with PT-JPL could better reflect the spatiotemporal patterns of major hydrological processes (runoff, evapotranspiration, and soil moisture) in UHRB from 2001 to 2019; (2) The BW in UHRB exhibited decreasing trend from 2001 to 2019, while the GW on the contrary. Surface runoff (Rs), vegetation transpiration (Et), and soil moisture (W) contributed most in the components of BW and GW; (3) Vegetation change dominated by greening in UHRB, which facilitated the transformation of BW to GW in the basin, and it increased the proportion of GW mainly by promoting Et. BW was mainly influenced by precipitation, but vegetation change altered the original correlation between climatic factors and hydrological processes. This study can support the management of water resources in the basin under changing environments.

Vegetation restoration strategies based on plant water use patterns

The study on the water source of plants in alpine mountainous is of great significance to optimize the allocation and management of water resources, and can also provide important reference for ecological restoration and protection. However, the controls of water sources for different plants in alpine mountainous region remain poorly understood. Based on the advantages of stable isotope tracer and Bayesian (MixSIAR) model, the water source of plants in Qilian Mountains was quantitatively analyzed. The results showed that the water sources of plants in Qilian Mountain mainly included two parts: direct source and indirect source. The direct source is soil water, which provides most of the water that plants need. The highest contribution of soil water to shrubs was 80 %, followed by trees (73 %) and herbs (72 %). It is worth mentioning that trees mainly use deeper soil water (below 60 cm), shrubs mainly use surface and intermediate soil water (0-60 cm), and herbs mainly use surface soil water (0-40 cm). What is more noteworthy is that indirect sources, such as precipitation, glacier and snow meltwater, and groundwater, are also water sources that cannot be ignored for plant growth in study area. Shrubs and Herbs use more soil water in the range of 40-60 cm, which leads to the possibility of water competition between these two planting types. Therefore, attention should be paid to this phenomenon in the process of vegetation restoration and water resources management. Especially when planting or restoring artificial plants, it is necessary to consider the water use strategy of the two plants to avoid unnecessary water competition and water waste. This is of great significance for ecological stability and sustainable utilization of water resources in the study region.