Evaluation Of Water Resources Research Articles

Evaluation and Prediction of Water Resources Carrying Capacity in Dongting Lake Area Based on County Unit and DWM-BP Neural Network Model

Evaluation of water resource carrying capacity is one of the methods used to measure the degree of realization of resource and environmental planning. This study takes 26 county-level units in the Dongting Lake area of Hunan Province as the evaluation object and screens 22 indicators for the evaluation of water resource carrying capacity. The comprehensive weight of the water resources carrying capacity of the Dongting Lake area is calculated using double weight methods (DWM) based on the analytical hierarchy process (AHP) and the entropy weighting method. And then, the back propagation (BP) neural network is used to predict the evolution of the alarm situation of the water resources carrying capacity. The results show the following: (1) In terms of time trend, the water resources carrying capacity of Dongting Lake area is generally stable and is between overload and critical state, but the change trends of water resources carrying capacity of each county are very different; (2) in terms of spatial distribution, the water resources carrying capacity in the western and southern regions of the Dongting Lake area is relatively high, while that in the Yueyang urban area in the eastern region is relatively low, showing a pattern of “high in the west and low in the east”; (3) among the criterion layers, the water resources support force has the highest weight and the smallest change, and the economic support force has the largest change in weight. The decline of water resources carrying capacity is closely related to the speed of economic development; (4) by 2025, the water resources carrying capacity of Nanxian County, Hanshou County, and Xiangyin County will be significantly increased from overload to critical state, and Dingcheng District will gradually turn from critical to overloaded status. This study is the first attempt to evaluate and predict the water resources carrying capacity of the Dongting Lake area at the county level so that the evaluation results are more applicable to grass-roots administrative units with decision-making power. The double-weighting method is used to determine the weights of the indicators so that the evaluation results can be more accurate.

Water-use Efficiency with Machine Learning: A Global Perspective

Water scarcity is a pressing social, economic, and environmental issue with significant corporate sustainability and planetary health implications. Therefore, improving the efficiency of water resources use is an important research issue. With the rapid development of machine learning technology, relying on the learning ability of learning algorithms and the support of computer computing power, it is possible to optimize the global allocation of water resources through machine learning technology. This paper investigates the progress made by machine learning methods in water use efficiency. Specifically, this paper introduces the basic principle of the machine learning method for evaluating water resources efficiency, including classification and regression tasks. Then, several mainstream evaluation indexes of water resource utilization efficiency are introduced. Finally, this paper analyzes the main challenges and future development direction of machine learning to improve the efficiency of water resources utilization. This paper aims to provide a complete research view for researchers using machine learning methods to improve water efficiency.

WaterGPT: Training a Large Language Model to Become a Hydrology Expert

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4.

Dual Method for Comprehensive Evaluation of Sustainable Water Resources’ Utilization Capacity in Huangshui River in Yellow River Basin, China

The evaluation of sustainable water resources’ utilization capacity in the Huangshui Basin is essential for effective management and development in the water-scarce regions of northwest China. This research provides valuable insights into the basin’s potential capacity for sustainable water resource use by developing a comprehensive evaluation index that addresses the following three critical aspects: climate factors, water resource systems, and socio-economic conditions. The evaluation is conducted using a dual method, as follows: the fuzzy comprehensive evaluation model and the ELECTRE III evaluation method. The results indicate that the Huangshui Basin’s water resources, as a whole, exhibit a medium sustainable utilization capacity. Climatic factors and socio-economic characteristics are the main factors affecting the sustainable utilization of water resources in the Huangshui Basin. Remarkably, both methods yield consistent results, indicating that the overall sustainable utilization capacity of the Huangshui Basin’s water resources is medium. Climatic factors and socio-economic characteristics are identified as the primary aspects influencing the sustainable utilization of water resources in the basin. Based on these findings, recommendations such as enhancing the introduction of external water resources, improving water resources’ management, and implementing comprehensive remediation efforts can help to elevate the level of sustainable water use. This research not only contributes to a deeper understanding of the basin’s water resources’ dynamics, but also serves as an important reference for informed decision making regarding sustainable utilization in the Huangshui Basin.

Statistical evaluation and quality analysis of water resources around quarry site in Abuja Suburban Nigeria

Water resources occupies vital position regarding the lifespan and general wellbeing humans while significant shortfalls on its reserves hinders almost all sustainable development globally. Most host communities to quarry companies without a water treatment plant are greatly affected by the use of water resources within its area. The present study highlights statistical evaluation and quality analysis of water resources around a quarry site in FCT Nigeria. The research engaged biological assay, physiological and chemical analysis to quantify contamination level of the water resources from obtained data which were also subjected to statistical analysis. The statistical tools employed are Pearson's Correlation, descriptive statistics, Levene's test for homogeneity and one–way analysis. The physiochemical analysis revealed the water resources to have high turbidity value in 58.3 % of sampled population while nitrate concentration and electrical conductivity value were very low. The bioassay revealed presence of coliform bacteria in 100 % of sampled population while thermotolerance count discovered disease-causing-pathogens in 41.7 % of sampled population. Chemical analysis revealed that Pb, Cr, Ni and As were 91.7 %, 83.3 % 100 % and 100 % above recommended permissible limits for sampled population. These results suggests that water resources in this study is unsafe and holds a health-treat with regards to the contaminations inherent in them. Statistical evaluation revealed average positive linear relationship between heavy metal concentration suggesting close relationship of contamination source. Observed strong positive linear relationship between lead and copper, nickel and chromium as well as nickel and arsenic in the scatterplot depicts same source of contamination.

Spatial-Temporal Evaluation and Prediction of Water Resources Carrying Capacity in the Xiangjiang River Basin Using County Units and Entropy Weight TOPSIS-BP Neural Network

Spatial-Temporal Evaluation and Prediction of Water Resources Carrying Capacity in the Xiangjiang River Basin Using County Units and Entropy Weight TOPSIS-BP Neural Network

Retraction Note: Comprehensive evaluation of water resources carrying capacity and analysis of obstacle factors in Weifang City based on hierarchical cluster analysis-VIKOR method.

Retraction Note: Comprehensive evaluation of water resources carrying capacity and analysis of obstacle factors in Weifang City based on hierarchical cluster analysis-VIKOR method.

Robust multi-objective optimization under multiple uncertainties using the CM-ROPAR approach: case study of water resources allocation in the Huaihe River basin

Abstract. Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources are uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinders the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated in a case study of water resources allocation in the Huaihe River basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.

Evaluation of Water Resources Carrying Capacity in Shandong Province Based on Entropy Weight TOPSIS Model

In order to scientifically evaluate the water resources carrying capacity of Shandong Province, this paper constructs an evaluation framework of nine key indicators, including water supply coefficient and per capita GDP, from three dimensions: hydrological resource status, economic and social development level and ecological environmental protection. Then, the entropy weight method combined with TOPSIS model was used to carry out in-depth analysis and quantitative evaluation of Shandong's water resources carrying capacity. The results show that: from 2015 to 2020, the water resources carrying capacity of Shandong Province first increased steadily, then decreased slightly, and then continued to rise sharply. From 2015 to 2020, the water resources carrying capacity of Shandong Province was always in a state of shortage; In the future work, we should pay more attention to the balanced development of economic and social development level and carrying capacity, and continue to strengthen the management of water-saving and planned water use, so as to achieve sustainable use of water resources.

Coordination analysis and evaluation of population, water resources, economy, and ecosystem coupling in the Tuha region of China

The non-coordination between the socio-economic systems and ecosystems of a region is a crucial obstacle to sustainable development. To reveal the relationships between complex urban systems and achieve the goal of sustainable and coordinated urban development, we constructed a coupling coordination degree model (CCDM) and coupling angle model (CAM) and analyzed the degree of coupling coordination and evolution process among the population, water resources, economy, and ecology (PWEE) system of the Tuha region for 2005–2020. The results indicated that: (1) During 2005–2020, the comprehensive development index (CDI) of the population, water resources and economy subsystems was 0.21–0.65, with the three subsystems portraying an overall increase; the average values of the RSEI at five-year intervals were 0.29, 0.28, 0.28, and 0.26, indicating a downward trend in the environmental quality. (2) The coupling coordination effect of the PWEE system portrayed a low level; the coupling coordination degree (CCD) values were 0.28–0.58, portraying a fluctuating upward trend. The level of CCD increased from low disorder to marginal coordination. (3) The PWEE system’s scissor difference reflects large evolutionary characteristics. The ecological support capacity was not observed until the late stage. We conclude that the PWEE composite system of the region is in a stage of disordered development. These findings significantly bolster the theoretical underpinnings of sustainable development studies, offering essential scientific theories and methodological frameworks for crafting sustainable development policies tailored to urban systems in the Tuha region.

A new inventory and future projections of thermokarst lakes in the permafrost regions of the Qilian Mountains, northeastern Qinghai-Tibet Plateau, China

Thermokarst lakes—lacustrine basins formed by thermokarst processes—are key components of permafrost ecosystems that significantly influence the geomorphology, hydrology, ecology, and the stability of infrastructure, while also contributing to greenhouse gas emissions. High-resolution mapping of thermokarst lakes' current and future distribution presents a considerable challenge. In this study, we leverage a deep learning approach to delineate the boundaries of thermokarst lakes in the permafrost regions of the Qilian Mountains at the northeastern margin of the Qinghai-Tibet Plateau (QTP) using Sentinel-2 imagery, followed by an estimation of future susceptibility to thermokarst lake formation by employing a machine learning method. We identified a total of 2795 thermokarst lakes covering an area of approximately 51.6 km2. Based on future climate scenarios, there will be a significant reduction in the proportion of high-susceptibility areas. Specifically, under the most extreme SSP5–8.5 scenario, the proportion of areas highly susceptible to thermokarst lake formation decreases from the current 6.7 % to 0.01 % in the period 2081–2100. Our comprehensive inventory provides essential data for research on permafrost hydrology and the evaluation and management of water resources, and enhances our understanding of thermokarst lake formation in relation to environmental factors.

Heterogeneous changes in global glacial lakes under coupled climate warming and glacier thinning

Global glacial lakes have expanded markedly in recent decades, accompanied by many catastrophic glacial lake outburst floods. However, our understanding of glacial lake evolution and connections with global warming and glacier thinning remains insufficient due to fragmented studies and a lack of standardized mapping. Here we evaluated the glacial lake changes and their drivers, taking care to accurately map glacial lakes while excluding non-glacier fed lakes in the same geographic area. In 2020, a total of 71,508 glacial lakes were identified worldwide, covering an area of 21,770.9±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document}544.2 km2. The total volume of 1280.6±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document}354.1 km3 is equivalent to a sea-level rise of 3.45 mm. Since the 1990s, glacial lake numbers, areas, and volumes increased by 54%, 11%, and 9%, respectively. The spatial evolution patterns align well with resolved large-scale, decadal glacier mass changes. Glacier-fed lakes extensively expanded due to glacier retreat and melting, while ice-dammed lakes were primarily affected by thinning ice dams, resulting in heterogeneous changes and underestimated outburst potential. Under the sustained glacier degradation scenario, the results of this study have implications for further evaluation of glacial lake water resources and outburst risks on a global scale.

Groundwater modelling in SWAT+: Considerations for a realistic baseflow simulation

Hydrological models are valuable tools that support the evaluation and management of water resources. Among catchment scale models, SWAT has been widely used, and a revision of this model, SWAT+, was recently released. Groundwater simulation has been pointed out as one of the main weaknesses of these models, and despite the introduction of some improvements in the new version, an accurate simulation of groundwater flow is still a pending task. One of the most common issues is the inability of the model to maintain baseflow during long dry periods. Baseflow is common in aquifer areas and has great relevance for water quality and ecosystems. Although some authors proposed different solutions to its inaccurate simulation (e.g., editing aquifers configuration, coupling with other models, etc.) this work aimed to determine if a realistic groundwater simulation is possible using SWAT+ and determine the reasons why this is not being achieved. For this purpose, a groundwater dominated catchment was modelled, and different calibration approaches were carried out and compared, including strategies such as hard calibration, soft calibration, or sensitivity analysis. A comprehensive analysis of model outputs was crucial to achieve a model with a realistic simulation of groundwater contribution to the streamflow, both in amount and timing. The properly parameterization of some parameters (perco, latq_co, lat_ttime, and especially alpha) was key. Limitations of hard calibration were evidenced, such as the necessity of complementing this strategy with an evaluation of the simulation of hydrological processes. For those parameters governing streamflow components simulation, a detailed description of its functioning is included in this manuscript, together with some guidelines to achieve a statistically accurate model with a realistic groundwater flow simulation. Follow these recommendations will be useful for SWAT users, and will serve to create more reliable models.

Climate dynamics and the effect of topography on snow cover variation in the Indus-Ganges-Brahmaputra river basins

The river basins of the Indus-Ganges-Brahmaputra (IGB) are characterized by rapid population growth, increased water demand, and rapid snow/ice melting, all of which combine to make these catchments especially vulnerable to climate change. Here, we present a comprehensive dataset (2003−2020) for precipitation, temperature and snow cover area (SCA) to investigate the spatio-temporal variability of important climate factors in relation to elevation and across three major sub-basins of the IGB. Improved MODIS (Moderate Resolution Imaging Spectro-radiometer) Terra and Aqua snow cover data are used to estimate regional snow cover, while Climate Hazards Group InfraRed Precipitation with Station Data version 2.0 (CHIRPS-V2.0) is deployed for precipitation, and MODIS Land Surface Temperature (LST) data are used to estimate temperature. The analysis reveals significant changes in precipitation, LST and snow cover throughout the IGB region together with a strong degree of spatial coherence between SCA and regional surface topography (R2 = 0.82).Maximum changes were observed in the Brahmaputra basin at mean annual precipitation (13.9 mm/year) and reduced SCA (155.4 km2/year) over the period studied. Similar trends are also evident, albeit with relatively lower absolute values in the other two basins, whereby the Indus basin evidences a significant reduction in annual SCA, averaging 82.2 km2/year with increased precipitation (3.12 mm/year) and LST. This investigation further highlights the relative influence of climate change factors and regional human activities in determining high-elevation changes in SCA. Most of the middle elevation regions of the Ganges experienced a significant reduction in SCA, with human impact contributing 82% of the observed variation, while climate factors are more prominent in the Indus and Brahmaputra basins. Understanding the dynamics of snow cover and its regional influencing factors is crucial to the more accurate evaluation of water resources and to defining appropriate mitigation and adaptation strategies for the coming decades.

Exploring the ecological security evaluation of water resources in the Yangtze River Basin under the background of ecological sustainable development

The Yangtze River (hereafter referred to as the YZR), the largest river in China, is of paramount importance for ensuring water resource security. The Yangtze River Basin (hereafter referred to as the YRB) is one of the most densely populated areas in China, and complex human activities have a significant impact on the ecological security of water resources. Therefore, this paper employs theories related to ecological population evolution and the Driving Force-Pressure-State-Impact-Response (DPSIR) model to construct an indicator system for the ecological security of water resources in the YRB. The report evaluates the ecological security status of water resources in each province of the YRB from 2010 to 2019, clarifies the development trend of its water resource ecological security, and proposes corresponding strategies for regional ecological security and coordinated economic development. According to the results of the ecological population evolution competition model, the overall indicator of the ecological security of water resources in the YRB continues to improve, with the safety level increasing annually. Maintaining sound management of water resources in the YRB is crucial for sustainable socioeconomic development. To further promote the ecological security of water resources in the YRB and the coordinated development of the regional economy, this paper proposes policy suggestions such as promoting the continuous advancement of sustainable development projects, actively adjusting industrial structure, continuously enhancing public environmental awareness, and actively participating in international ecological construction and seeking cooperation among multiple departments.

Statistical Evaluation and Quality Analysis of Water Resources Around Quarry Site in Abuja Suburban Nigeria

Water resources occupy a vital position regarding the source of human hope for longevity and significant shortfalls on the strive towards sustainable development globally. Host communities to quarry companies without a water treatment plant are greatly affected by use of untreated water. This research highlights statistical evaluation and quality analysis of water resources around a quarry site in FCT Nigeria. The study engaged biological assay, physiological and chemical analysis to quantify contamination levels in the water resources and obtained data were subjected statistically using Pearson’s Correlation, descriptive statistics, Levene’s test for homogeneity and one–way analysis. The physiochemical analysis revealed the water resources to have high turbidity value in 58.3% of sampled population while nitrate concentration and electrical conductivity value were very low. The bioassay revealed presence of coliform bacteria in 100% of sampled population while thermotolerance count discovered disease-causing-pathogens in 41.7% of sampled population. Chemical analysis revealed that Pb, Cr, Ni and As were 91.7%, 83.3% 100% and 100% above recommended permissible limits for sampled population. These results suggests that water resources in this study is unsafe and holds a health-treat with regards to the contaminations inherent in them. Statistical evaluation revealed average positive linear relationship between heavy metal concentration suggesting close relationship of contamination source. Observed strong positive linear relationship between lead and copper, nickel and chromium as well as nickel and arsenic in the scatterplot depicts same source of contamination.

Evaluation of water resources carrying capacity in China based on entropy weight TOPSIS model

Water resources carrying capacity refers to the ability of the water resources ecosystem to continuously carry the coordination relationship between human society and economy in the normal development process of a country or a region. Its self-sustaining ability, self-regulation and self-development potential often hinder sustainable development in water shortage areas. Research on water resources’ carrying capacity is a meaningful way to support regional water resources security and realize harmonious development of society, economy, and ecological environment. Correct assessment of water carrying capacity and response to government policies will contribute to improved water use and sustainable economic and social development. This study first sorts out the relevant questionnaires of water resources carrying capacity level evaluation, proposes the evaluation indicators of water resources carrying capacity level, collects and standardizes the required data, and calculates the weight of each evaluation index by entropy weight method. Then, it calculates the comprehensive evaluation value of China’s water resources carrying capacity from 2012 to 2022 in the TOPSIS model. The results show that the total afforestation area, total investment in environmental pollution control, and total industrial wastewater discharge are the third most important factors in improving the carrying capacity of water resources. From 2003 to 2010, China’s water resources carrying capacity improved year by year. From 2011 to 2021, China’s water resources carrying capacity remained stable year by year. The continuous adjustment of China’s industrial structure and strengthening environmental pollution control are inevitable measures to improve the carrying capacity of China’s water resources. This study provides a scientific basis for exploring the changing trend of China’s water resources carrying capacity and formulating reasonable optimal allocation of water resources. It also has great significance for promoting China’s water resources’ carrying capacity and sustainable development of the social economy and ecological environment.

Spatiotemporal Evaluation of Water Resources in Citarum Watershed during Weak La Nina and Weak El Nino

This study investigates the dynamics of water resources in the Citarum watershed during periods of weak La Niña, normal, and weak El Niño conditions occurring sequentially. The Citarum watershed serves various purposes, being utilized not only by seven (7) districts and two (2) cities in West Java, Indonesia but also as a source of raw water for drinking in the City of Jakarta. Using a time-series analysis of surface water data, data-driven (machine learning) methods, and statistical analysis methods, spatiotemporal predictions of surface water have been made. The surface water time series data (2017–2021), obtained from in situ instruments, are used to assess water resources, predict groundwater recharge, and analyze seasonal patterns. The results indicate that surface water follows a seasonal pattern, particularly during the monsoon season, corresponding to the groundwater recharge pattern. In upstream areas, water resources exhibit an increasing trend during both weak La Nina and weak El Niño, except for Jatiluhur Dam, where a decline is observed in both seasons. Machine learning predictions suggest that water levels and groundwater recharge tend to decrease in both upstream and downstream areas.

Evaluating water resource carrying capacity in Pearl River-West River economic Belt based on portfolio weights and GRA-TOPSIS-CCDM

Research on the spatial differences in the water resource carrying capacity (WRCC) and their causes can effectively address the imbalance of water resources and enhance the economic, social, and ecological properties of the basin. Sixteen cities in the Pearl River-West River Economic Belt (PR-WR EB) were selected for this study from 2017 to 2021. Based on the theoretical Driving-Pressure-State-Impact-Response model and related research, an evaluation model for WRCC was constructed. The weights obtained from the entropy method and CRITIC method were combined and assigned using the geometric method, after which they were applied to a model that combines the grey relational analysis (GRA), technique for order preference by similarity to ideal solution (TOPSIS), and coupling coordination degree model (CCDM) to calculate the evaluation value of the WRCC subsystem. Meanwhile, the spatial variability and spatial correlation of WRCC were analyzed using the natural breakpoint method, Moran's index, clustering characteristics, and outlier index. The overall WRCC of the PR-WR EB showed stable characteristics from 2017 to 2021. In terms of spatial correlation, significant spatial clustering characteristics were observed. The subsystem coupling degree ranged between 0.6 and 1.0 for all subsystems, thus exhibiting good coupling efficiency; meanwhile, the coordination degree was low and ranged between 0.17 and 0.36.

2018–2022年青海省主要湖泊蓄水量时空变化数据集

Qinghai Province is an important ecological function area, which is located in the inland of Northwest China. Qinghai Province is abundant in the total amount of water resource, but the spatial-temporal distribution of water resource in Qinghai Province is extremely uneven. Moreover, due to the factors such as climate and geographical environment, most lakes lack monitoring for a long time. As a significant carrier of surface water resource, lakes are important indicators for studying the impact of climate change and human activities on the water cycle process. The domestic GF-1 and GF-6 satellites have the ability to observe the earth repeatedly for a period of 2 days by collaborating, thus the surface water area can be obtained at high frequency. A high-precision surface water level can be acquired by ICESat-2, which has centimeter-level height measurement accuracy. The water area derived from GF-1/6 and water level derived from ICESat-2 were adopted to build the models for monitoring spatial-temporal variation of water storage of lakes in Qinghai Province. We developed a dataset of spatial-temporal variation of lake water storage in Qinghai Province during 2018–2022 via the models. The monitoring accuracy of the dataset had been verified with an overall accuracy better than 93%, which meets the needs of remote sensing monitoring of spatial-temporal variation of lake water storage. The dataset contains a total of 60 monthly storage variation of 33 lakes an area of more than 30 km2 in Qinghai Province during 2018–2022. These lakes are mainly distributed in the west, north and southeast of Qinghai Province. The dataset can provide basic data support for lake, hydrological and climate change process monitoring, dynamic evaluation of water resources, and optimization of water resources management and scheduling in Qinghai Province.