Review on the Collaborative Research of Water Resources–Water Environment–Water Ecology in Hulun Lake

Abstract

Managing water resources amidst the pressures of climate change and human activities is a significant challenge, especially in regions experiencing shrinking lakes, deteriorating water quality, and ecological degradation. This review focuses on achieving integrated river basin management by learning from the governance experiences of typical watersheds globally, using the Hulun Lake Basin as a case study. Hulun Lake, China’s fifth-largest lake, experienced severe ecological problems from 2000 to 2009 but saw improvements after comprehensive management efforts from 2012 onward. This review systematically explores methods to address water resource, environment, and ecological challenges through the lenses of data acquisition, mechanism identification, model simulation, and regulation and management. Drawing lessons from successful basins such as the Rhine, Ganges, Mississippi, and Murray–Darling, the review proposes key goals for comprehensive management, including establishing extensive monitoring networks, developing predictive models, and creating contingency plans for routine and emergency management. Leveraging advanced technologies like satellite imagery and IoT sensors, alongside continuous improvement mechanisms, will ensure the sustainable use and protection of river basins. This review provides a detailed roadmap for achieving comprehensive watershed management in Hulun Lake, summarizing effective strategies and outcomes from data acquisition to regulation, thus serving as a model for similar regions globally.

1. Introduction

As a large developing country, China is facing great challenges in managing water resources under the pressure of climate change and human activities [1,2]. With the intensification of climate change and human activities, the uncertainty of water resources in northern rivers has further increased [3]. At present, there are shrinking lakes [4], deteriorating water quality [5], and ecological degradation in northern China [6]. Therefore, it is necessary to put forward the core issue that this review wants to try to solve—how to achieve integrated river basin management? It is a positive way to learn the governance experience of typical watersheds. This review will take the Hulun Lake Basin as a typical case. On the one hand, Hulun Lake (117°00′–117°41′ E, 48°30′–49°20′ N) is the fifth largest lake in China and the largest lake in Inner Mongolia. It is located at the intersection of Manzhouli City, Xibaerhu Right Banner, Xinbaerhu Left Banner, and Zalenoer District in the west of Hulun Buir Grassland [7]. Near the borders of China, Mongolia, and Russia [8]. It is not a cross-border lake, but the main recharge runoff is through the international rivers of China and Mogolia. Therefore, to solve the problem of Hulun Lake, it needs to be considered from the basin scale. The geographical location of Hulun Lake Basin (107°31′–122°52′ E, 45°45′–53°26′ N) is shown in Figure 1. The degradation of Hulun Lake is not only serious to the local ecological security, but also has a certain international influence. On the other hand, Hulun Lake had serious water ecological environment problems from 2000 to 2009, but relying on the comprehensive management of the basin, the trend in water resources, water environment, and water ecological deterioration was reversed after 2012, and it has been slowly restored so far. In 2024, the Department of Ecological Environment organized the mid-term assessment of the implementation of the ‘14th Five-Year Plan’ ecological environment protection plan in the autonomous region; 76.9% of the surface water quality in the assessment section of the whole region reached or was better than Class III water body. The proportion of water quality inferior to Class V was 2.5%. Therefore, this review will discuss how the Hulun Lake Basin can identify the water resources, water environment, and water ecological response mechanism of the basin under the conditions of climate change and human activities from the four perspectives of data acquisition, mechanism identification, model simulation, regulation, and management. Using relevant models, the complete simulation results from data acquisition, process simulation, basin evaluation, etc., support the development of regulation and control work, and complete the comprehensive management of the basin. In the subsequent paragraphs, we try to answer the following questions: (1) Hulun Lake Basin in the process of comprehensive management of the basin, what methods have been used, what results have been obtained, and what role has been played in the comprehensive management of water environment and water ecology; (2) How to integrate domestic and international research on comprehensive management of river basins and put forward the goals that need to be achieved for comprehensive management of river basins ? (3) On the basis of clarifying the governance objectives and the current situation of the basin, how to use the governance experience of Hulun Lake to propose a practical research plan? Because the review content is rich, the structure of the article is explained here, as shown in Figure 2.

2. Research Progress

This chapter is to answer what methods are used in the process of comprehensive management of the Hulun Lake Basin, what results are obtained, and what role does the comprehensive management of water environment–water ecology play in it. In this chapter, through a large number of literature and data, it will be explained that the comprehensive management of water resources, water environment, and water ecology is the key to watershed management and focus on the analysis of its related mechanisms.

2.1. Methods and Results of Data Acquisition

First, we introduce the Basic Data of Hulun Lake. The shape of the lake is an irregular rectangle with a long northeast-southwest axis. The length, average width, average water depth, and maximum water depth of the lake at the maximum water level are 93 km, 25 km, 6 m, and 8 m, respectively. When the lake area reaches the maximum of 2339 m², the volume reaches the maximum of 138 × 10⁸ m³ [9]. As an important lake in China, it has an important impact on the local ecology. Hulun Lake is located in the semi-arid continental temperate monsoon region. The main climatic characteristics are as follows: winter is cold and long, and there is an ice age; summer is warm and short, dry and windy, less precipitation; in autumn, the temperature decreases rapidly, the frost-free period is short, and there are 180 days of freezing in a year [9]. The average annual temperature in the lake area is −0.2 °C, and the maximum and minimum temperatures can reach 20.8 °C and −23.3 °C, respectively [7]. The average annual precipitation is 233 mm, and the average annual evaporation is 1446 mm. It is a typical cold and arid lake [10]. The Hulun Lake water system is an integral part of the Erguna water system, including the Halaha River, Bell Lake, Ulson River, Hailar River, Krum River, and Xinkai River. The basin includes three rivers with lengths greater than 1000 km. The whole basin is 2374.9 km long, and the area in China is 37,214 km² [9]. It accounts for about 15% of the total basin area, only a small part of the runoff area. Nearly 28.3 × 10⁸ m³ of river water recharges Hulun Lake every year, but the runoff into Hulun Lake is far less than the evaporation. Surface runoff and groundwater are the main sources of lake recharge. The Kulun River and the Ulson River are the main inflow rivers of Hulun Lake. Most of their runoff areas are located in Mongolia and belong to international rivers [9]. Hulun Lake is an inland river most of the time. Only when the water level reaches 545.33 m can it be discharged to the Erguna River through the Xinkai River. Since 2000, due to the decline in the water level of Hulun Lake, the lake water cannot be leaked, and the water quality has deteriorated. The local government launched the project of diverting water from the river to the lake, introducing the water of the Hailar River into Hulun Lake. It is planned to deliver 7.5 × 10⁸ m³ of water to Hulun Lake every year [11]. After the water delivery, the water level of the lake gradually recovered, and the water environment, and water ecology also improved. After understanding the basic situation, it is necessary to index the concepts of climate change, human activities, water resources, water environment and water ecology, and support the follow-up research through quantifiable indicators. This section focuses on the methods and results of obtaining relevant indicators. As shown in Figure 3. There are four main observation methods: (1) field observation generally refers to the use of instrument field observation data, including weather stations, hydrological stations, field sampling, population economic information statistics, etc., generally considered the highest accuracy. In the continuous observation of the site, its time resolution can reach the hour level. There are seven weather stations around Hulun Lake (Eerguna City, Turi River, Manzhouli, Hailar, New Barag Right Banner, New Barag Left Banner, Zhalantun, Aershan, Solon) [12] and three hydrological stations [10]: Kundu Leng Station (Ulson River), Alatan Emole Station (Kherlen River), and Dalai Station (abandoned in 2011) around Hulun Lake. A large amount of meteorological and water resources data were collected, as well as some data on water environment and water ecology. However, due to the fixed sample points, most of the research will choose to sample on the lake to supplement. The statistics collect population (Pop), gross national product (GDP), agricultural arable land area (CA), livestock product output (LPO), and industrial output value (IO) in the study area, etc., generally used to indicate the intensity of human activities; (2) Obtain data through remote sensing satellites. It is usually divided into two types. One is to obtain the water area through the optical remote sensing images of Landsat, MODIS, Sentinel, and other optical remote sensing satellites and then convert it into water level (water resources) through the corresponding empirical formula; or through the inversion method to calculate the relevant photosensitive water quality parameters, such as chlorophyll a (water environment), and then calculate the algal blooms (water ecology). The other is a laser altimeter using altimetry satellite to directly measure the water level, including GF-7 [13], ICESat-2 [14], Jason [15,16], etc. The advantage of remote sensing data is that its observation range is large and the cost of obtaining data is low, but there is a revisit period, and its relatively low spatial and temporal resolution needs to be taken into account; (3) Model simulation Through equation calculation or model simulation, basic data, process data, evaluation data, etc. Some studies will use them as input data to calculate again, such as component calculation of water balance and iterative calculation of continuous prediction. This part will be more detailed in Section 2.3; (4) Multi-source fusion reanalysis data. Although the accuracy and resolution are improved by mutual verification of multiple data sources, the reanalysis data of multi-source fusion are generally global-scale data, and their resolution generally cannot meet the requirements of regional scale. CRUNCEP7 reanalysis data, ERA5, and other multi-source reanalysis data, land surface model, etc. However, some data can be achieved, such as water level data on Hydroweb, which combine the results of multiple altimetry satellites and actual observations. Many scholars have proved that the data set is a reliable data set [17,18].

The results of data acquisition can be preliminarily viewed through its time distribution. The average annual temperature of Hulun Lake from 1960 to 2018 was 0.33 °C/10a, the evaporation increased significantly at 50.44 mm/10a, and the relative humidity decreased significantly at −10.7%/10a, while the precipitation showed a non-significant decreasing trend, reflecting the climate change trend in warming and drying in the Hulun Lake area [19]. For the data on human activities, 2009 was used as the time point for the implementation of control. Before that, the primary and secondary industries developed rapidly, and urban land occupied vegetation land. There were a large number of water consumption gaps and pollution, which brought a lot of pressure to the lake ecology and even caused the degradation of the surrounding wetland vegetation. After that, through water diversion and grazing prohibition, the establishment of protected areas restricted the uncontrolled development of the industry and put forward corresponding requirements for industrial sewage discharge, so that Hulun Lake gradually recovered. For water resources, As an intuitive phenomenon of imbalance between supply and demand, from 1991 to 2009, the number of lakes larger than 1 km in Inner Mongolia decreased by 159 [20]. The period from 2000 to 2009 was also a period of obvious decline in the water level of Hulun Lake, and even the phenomenon of Kelulun River cutoff occurred. It was not until the introduction of strict policies and water diversion from the Xinkai River that the water level of Hulun Lake gradually recovered after 2009 [10]. Most of the studies on the change trend in water level data point to a consensus of 1962–1982. The water level decreased slowly, fluctuated from 1982 to 1990, remained high from 1990 to 2000, decreased rapidly from 2000 to 2009, remained low from 2009 to 2012, recovered rapidly from 2012 to 2015, and changed slightly from 2015 to 2019 [10,21]. According to the water storage–water level curve [18], from 1961 to 2019, the water storage can reach up to 14 billion tons of water, and the water level is at 545 m; the lowest is only 6 billion tons of water, and the lowest water level is 541 m. This recovery process can be quantitatively observed in the way of water balance. The evaporation of the lake is converted to 1140–2203 mm, about 2.5 × 10⁹ m³. The average annual precipitation is about 233 mm, and the total annual precipitation is about 5 × 10⁸ m³. After the 21st century, the runoff was significantly less. From 2003 to 2012, the multi-year average runoff of the Ulson River and the Kherlen River supplying the Hulun Lake was 1.30 × 10⁸ m³ and 1.41 × 10⁸ m³, respectively, only 21% and 24% of the multi-year average runoff before 1991 [11]. Groundwater is similar to runoff, and it is also an important source of recharge for northern lakes. The main problem of data acquisition is that there is no long-term observation data, and only sampling data are difficult to obtain. In general, the amount of groundwater entering Hulun Lake is inversely derived through the water balance formula [21]; in addition, through the isotope method, through the stable isotope of hydrogen and oxygen, it is analyzed that the groundwater in this area is mainly infiltrated by atmospheric precipitation [22], and part of the recharge comes from faults [23]. Hulun Lake is recharged by groundwater during the non-freezing period [24]. The results show that in the past 55 years, the water cycle of Hulun Lake is related to groundwater. After deducting seepage and surface drainage, the annual recharge of groundwater is calculated to be about 7.92 × 10⁸ m³ [9]. Some studies have also mentioned the phenomenon of slope confluence [10] and snow melting [25]. From the lake, consider it as the lake’s water resources input, but from the basin scale, consider it only as a process. There have been a number of studies on the water balance of the Hulun Lake Basin. The main controversy lies in the amount of evaporation and runoff data. It takes about 5 × 10⁸ m³ of water to complete the water balance. In recent years, the water level of Hulun Lake has been gradually restored through water diversion. However, because it is an important condition for the survival and development of the northern region, it is necessary to study the water resources of Hulun Lake and the international rivers entering the lake to predict and respond to the future water resources of the lake. For the water environment, Hulun Lake is a typical northern cold and arid area lake. Most of the time, it is an inland lake. A large number of evaporation environmental conditions enrich pollutants and easily lead to excessive water quality indicators, mainly from whether a single indicator exceeds the standard and comprehensive evaluation. There are also various types of single indicators. Nutrient indicators (total nitrogen, TN; total phosphorus, TP) originate from the decomposition of plants entering the lake or from agricultural fertilizer inputs. Their excessive accumulation can cause eutrophication, leading to the rapid growth of phytoplankton and the formation of algal blooms. Heavy metals (chromium ions, Cr³⁺; arsenic ions, As³⁺; cadmium ions, Cd²⁺; copper ions, Cu²⁺; zinc ions, Zn²⁺) mainly come from industrial wastewater discharge and can accumulate in organisms through the food chain. Hulun Lake is also a local fishery, so heavy metal indicators significantly impact it. Photosensitive indicators (chlorophyll-a, Chl-a) are typically related to plant indicators. A rapid increase in Chl-a usually indicates algal bloom occurrence. Organic matter indicators (dissolved organic matter, DOM; chemical oxygen demand, COD) generally evaluate the biological activity in the lake. Environmental indicators (dissolved oxygen, DO; pH) are essential for biological survival and significantly impact aquatic life. Based on single indicators, comprehensive evaluation indicators are generally used to describe the water quality status. For Hulun Lake, this typically involves assessing whether it is eutrophic. Eutrophication refers to the excessive concentration of nutrients like nitrogen and phosphorus, leading to unnecessary water pollution caused by massive algal growth. Lake eutrophication occurs due to natural factors and human activities. The main water quality eutrophication status of Hulun Lake is assessed using indices such as the trophic state index (TSI), water quality index (WQI), and comprehensive trophic level index (TLI). Previous assessments of lake eutrophication in Hulun Lake have shown it to be at a moderate eutrophic level. The WQI value of Hulun Lake increased from 2012 to 2016, indicating deteriorating water quality. However, from 2017 to 2020, water quality began to improve gradually. The main variables affecting lake water quality changed from COD and TN to COD and TP. The change in primary pollutants is a crucial factor leading to water quality deterioration. The results of the TLI indicate values between 49.14 and 71.77, suggesting varying degrees of eutrophication in the lake, with phosphorus being the primary driver of eutrophication. Based on water quality monitoring data from 2012 to 2014, the spatial and temporal distribution of TN, TP, and Chl-a concentrations in the water body were analyzed, and the TLI method was used to evaluate water body eutrophication. The results show that Hulun Lake’s water quality meets the Class IV and V standards for surface water environmental quality, indicating it is a phosphorus-limited lake. Regarding aquatic ecology, it mainly focuses on the situation of aquatic organisms, including microorganisms, phytoplankton, zooplankton, and fish individuals, populations, and food webs. Bacteria have the metabolic potential to produce a variety of secondary metabolites, which play an important role in the biogeochemical cycle [26]. Phytoplankton, zooplankton, and fish build the food chain and food web of the lake. Normal water ecology has a stable food chain and food web structure, and the number of organisms in the food chain and food web will not exceed a certain range. A total of 64 phyla, 165 classes, 218 orders, 386 families and 740 genera were identified in microorganisms [26]. There are also studies showing significant seasonal differences in bacterial distribution [27]. Phytoplankton is generally obtained by field sampling and laboratory observation. The dominant species in Hulun Lake are cyanobacteria and green algae. A total of 76 species of phytoplankton belonging to 6 phyla were investigated in 2012 [28]. Compared with the previous survey results, the phytoplankton in the lakes of Inner Mongolia showed a single dominant species, and the total density and cyanobacteria density increased [29]. In 2022, 112 species of phytoplankton belonging to 8 phyla were investigated [30]. From 2011 to 2022, the survey results showed that compared with the phytoplankton in Inner Mongolia lakes, cyanobacteria were the single dominant species, and the types of phytoplankton were reduced. The acquisition of zooplankton is similar to that of phytoplankton [31]. There were 41 species of zooplankton in 2011 [32]. There are 38 species of zooplankton in Hulun Lake in 2014. Compared with the survey results in the 1980 s, the population density of pollution indicator species in the protozoa of Hulun Lake increased significantly, while the population density of non-pollution indicator species decreased sharply. The results of the zooplankton survey in 2022 were 38 species in 4 categories. Through previous studies, it can also be found that the number of zooplankton population types in Hulun Lake is decreasing. Fish collection has methods of field fishing and environmental DNA identification [30]. The results of fish survey in 2016 showed that 21 species of fish were collected in this survey, belonging to 4 orders, 6 families and 21 genera [33]. The richness index, Shannon–Wiener diversity index and evenness index of fish community were low. Compared with historical data, the number of fish species in Hulun Lake decreased, the composition of dominant species changed greatly, and the trend of miniaturization of fish individuals was obvious. The survey in 2022 recorded 4 orders, 6 families and 21 species of fish. The fish showed a trend in individual miniaturization. Some fish species have disappeared in Hulun Lake [30]. The diversity level of aquatic animal and plant species in Hulun Lake is low, and the ecology of Hulun Lake is at a sub-healthy level. Some studies collected fish samples from lakes and rivers in the Hulun Lake water system [6]. The results showed that a total of 31 fish species belonging to 5 orders and 7 families were recorded by species identification of the collected fish samples. The Shannon–Wiener diversity index, Margalef richness index and Pielou evenness index of the fish community in Hulun Lake were all small, and the trend in fish individual miniaturization was obvious.

According to the above results, the results of a single variable can be recognized from the time series, and this change trend is not independent. In order to intuitively illustrate this point, through the obtained data, through the line chart or hot spot chart, the change trend and simple correlation can be roughly observed, as can be seen in Figure 4. Its discount uses some representative data, such as climate data, which is an important source of regional water resources supplement. The human activity intensity data uses the grazing density of sheep. The increase in sheep population will lead to an increase in plant demand and water resource demand. On the other hand, grazing will also bring some pollution, which will affect the environment. The water resources data adapt to the area of Hulun Lake. As the end point of runoff generation and confluence, the change trend in its area directly illustrates the final response of water resources in the basin. The water environment data use TP. The eutrophication of Hulun Lake has always been a concern, and the TP concentration can reflect its eutrophication status. The water ecological data use the Shannon index of fish, which reflects the species richness. As the highest layer of the food chain in the lake, it reflects whether the lake state is stable. Figure 4 shows that climate change and the water environment are relatively stable. Although they show fluctuating changes, there is no obvious trend that can be directly identified. Human activities, water resources, and water environment indicators have changed significantly in 2000–2010. The intensity of human activities has increased significantly, while water resources have decreased significantly, while the water environment has deteriorated, and the time of change is relatively the same. Therefore, it is considered that there may be a link between the three. In order to simply explore this connection, the correlation is analyzed by the heat map of Figure 5. From the diagram, it can be seen that the water environment has a strong correlation with other elements, while for other elements, this correlation is weak. Hulun Lake experienced climate change and human activities from 1960 to 2023, and there were phenomena such as supply river disconnection and eutrophication [10,34]. The study of these phenomena has accumulated a lot of achievements, which can be used as the basis for future research. Obviously, many papers have shown that climate change and human activities have a certain impact on any element of water resources–water environment–water ecology. However, there are many studies on individual factors, which have not yet formed a system. In fact, we should also pay attention to its internal interaction, which will be systematically discussed in the next section.

2.2. Methods and Results of Mechanism Identification

The mechanism reflects the complex relationship of reality and establishes the relationship between data. The mechanism is the cause of the phenomenon. The understanding of the mechanism requires a large amount of basic data and process identification. In the comprehensive management of river basins, it is necessary to consider the impact of external drivers such as climate change and human activities on water resources, water environment, and water ecology. However, in fact, not only external drivers should be considered, but also the interaction process of water resources–water environment–water ecology should be considered. In this section, it mainly answers what kind of external drive and internal interaction process exists in Hulun Lake and what kind of unique response mechanism exists in Hulun Lake compared with rivers with similar surrounding environmental conditions. This review divides the response into seven categories, as shown in Figure 6.

2.2.1. Impacts of External Drivers of Climate Change on Water Resources

The climate factors that affect the local water resources are mainly the wind speed, relative humidity, temperature, wetland conditions, as well as rainfall and evapotranspiration data, which are not only the driving factors of the climate, but also an important component of the lake’s water balance. It has an important direct impact on the water resources of the lake. The average annual rainfall in the Hulun Lake Basin is 200–400 mm, and the evapotranspiration is more than 800 mm, which constitutes the main contradiction of water resources in the basin, that is, dependence on runoff and groundwater supply. Using hydrogen and oxygen isotopes in precipitation in the Hulun Lake Basin [24], it is confirmed that the main source of water supply in the study area is precipitation. Through the analysis of the main ion water chemistry and hydrogen and oxygen isotopes of river water and groundwater in the lower reaches of the Kherlen River Basin, it can be seen that groundwater and surface water in the basin mainly come from precipitation recharge, and groundwater is also the main recharge source of the Kherlen River [35]. The warming and drying of the climate leads to a decrease in rainfall, an increase in temperature, an increase in evapotranspiration, and a decrease in water area and water level. Reduce river area and increase drought disasters. The freezing time is delayed and the thawing time is advanced. Wetland plants are short of water and groundwater levels are falling. The results showed that the annual temperature increased significantly [36], the precipitation did not change significantly, the evaporation decreased significantly, the relative humidity decreased significantly, the climate developed in the direction of warming and drying, and the water area of Hulun Lake Basin decreased by 22.4%. The increase in temperature and evaporation is the main factor affecting the decrease in water area and water level [37]. The water storage changes in the Hulun Lake area are mainly caused by changes in precipitation and temperature [38]; showed that the Hulun Lake wetland area showed a warming and drying trend, and drought disasters occurred in the region [39]. Climatic factors also affect water conservation from wetland conditions. When the annual evaporation and annual precipitation are constant, the change in runoff is the same as that of wetland water area and water level elevation [40]. Due to the semi-arid and cold conditions in the study area, precipitation cannot meet the needs of vegetation, and the water shortage stress of vegetation in the northern reaches of Hulun Lake is more prominent [41,42].

2.2.2. Impacts of External Drivers of Climate Change on Water Environment

It is necessary to analyze the impact of climate factors on natural phenomena related to the water environment. For example, wind speed will affect the number of withered grasses entering the lake, which in turn affects the natural elements such as N and P entering the lake. Most of the organic matter in Hulun Lake comes from the decomposition of tumbleweed in the lower reaches of a natural river [43]. The Pearson correlation analysis of algal bloom area and driving factors showed that wind speed was the main influencing factor of algal bloom [44]. In recent years, the temperature in the Hulun Lake basin has increased [45], resulting in an enhanced release effect of organic matter in the sediments of the Hulun Lake. It has also been suggested that the time of identification is also important. Due to the rapid migration of phytoplankton under wind or hydrodynamic conditions, this may lead to a considerable deviation in the monitoring of phytoplankton bloom areas. Linear fitting of the observation results of Terra and Aqua was proposed to reduce the monitoring error of phytoplankton blooms in inland lakes [46]. Some study carried out spatio-temporal analysis and statistical analysis on the color of cyanobacteria blooms in Hulun Lake from 2019 to 2022. The results showed that cyanobacteria blooms in Hulun Lake increased year by year and became the most serious lake of cyanobacteria blooms in 2022 [47].

2.2.3. Impacts of External Drivers of Human Activities on Water Resources

It is necessary to analyze the influence of agricultural irrigation and drainage, industry, animal husbandry, domestic water demand and drainage on the water quantity of each water component in the basin and to identify the consumption situation and the need to identify the input of human water diversion. Generally, the economic and population data on the statistical yearbook will be used. The advantage is that as official data, their accuracy and credibility will generally be recognized. However, at the same time, the years that have been disclosed are short, and the data explain the overall situation of an administrative region, and the spatial and temporal accuracy is general. Human activities can be divided into three categories: water diversion, socio-economic conditions, and land use change. About water diversion: A study found that increased grazing and agricultural production, industrialization, population growth, and subsequent land use and land cover changes have greatly increased water use [48]. The results show that the water surface area of Hulun Lake decreased significantly from 1961 to 2018, and the water surface area gradually recovered and stabilized after the implementation of artificial water diversion in 2009 [19]. The driving force of human activities on water resources–water environment–water ecology is not only that [49], The recharge of artificial water injection into lake water makes the lake water level rise, and the trend in water level rise is obvious during the water injection period in 2021 [50]. The study also shows that by the end of 2012, the water diversion project prevented the water level from further falling. On socio-economic conditions: Some studies used the GDP intensity and population intensity of the Hulun Lake Basin to assess the risk of human activities on the ecological health of the basin [51]. The drainage of groundwater by grassland mining activities has led to a decline in regional groundwater levels and serious vegetation degradation [52]. The most suitable groundwater depth for vegetation growth in the study area is about 1 m; 130 m is the threshold groundwater depth to maintain the normal growth of vegetation in the study area. Land use change: From 1999 to 2007, the land use types of Hulun Lake Nature Reserve changed greatly [53]. The population pressure and economic development on the upstream water use may be related to the increase in sand area caused by the decrease in water level in Hulun Lake. Using Landsat satellite data to identify the land use of the Hulun Lake Basin, it was found that the lakes in the Hulunbeier grassland area shrank significantly in the past 30 years from 1986 to 2017, and the lake area change was mainly affected by human activities. The impact of climate change is relatively small [4], and studies have shown that during the period from 1999 to 2007, the land use type of the Hulun Lake Nature Reserve has undergone major changes, manifested as grassland degradation, shrinking water area, and increasing land desertification area [54].

2.2.4. Impacts of External Drivers of Human Activities on Water Environment

The process of introducing pollutants into the lake is mainly divided into two categories. One is a stable way to enter the lake, which can be directly discharged into the lake or have a fixed channel to discharge into the groundwater and runoff into the lake and other parts of the water exchange with the lake. Point source and line source pollution are represented by the discharge of domestic sewage and industrial wastewater; the second type is non-point source pollution that converges to the surface of the soil, only seeps into the water body when the runoff or the water body is submerged, and finally sinks into the lake [55]. The sources of human activities include agriculture. The use of agricultural products, including phosphate fertilizers, pesticides, and organic fertilizers. It is related to the accumulation of N³⁻, P³⁻, Cr³⁺, As³⁺, Cd²⁺ and other heavy metals in the lake water [56]. Animal husbandry is one of the causes of pollution in the two rivers [57]. Animal manure emissions from animal husbandry will increase N and P emissions and may increase organic matter in lakes [58]; lake Hulun is also an important local fishing ground, and the bait used for aquaculture in the fishing ground will increase the content of N in the lake [59]. Industrial pollution is also an important source of lake pollution. The upper reaches of the Kherlen River are densely populated, and there are industrial parks for leather and nitric acid production, which will discharge some industrial wastewater; the mining industry in the Zhalainuoer area is developed. During the coal mining process, a large amount of nitrogen and phosphorus pollutants enter the water body through sedimentation and surface runoff [43]; metal ores have a great impact on the water quality of the Kherlen River, and wastewater discharged during mining and processing contains heavy metals such as Cu and Zn [56].The daily water used in the adjacent urban areas of the basin will be discharged into the river water, especially the TN and ammonia nitrogen contents in the sewage, such as kitchen waste water and laundry wastewater, which are very high [59].

2.2.5. Impacts of Internal Interaction between Water Resources and Water Environment

In the interaction of water resources and the water environment, through the coupling of quantity and quality, the available water resources are identified for the water quality conditions of different water quantity components. Under the premise of not exceeding the goal of water quality control, the water diversion and reasonable water allocation into the lake are calculated.

After 2000, due to the gradual concentration of nutrients in the lake water, the content of organic matter from endogenous lake sources began to increase significantly, indicating that the decrease in river flow and the decrease in lake water level were the direct causes of lake environment deterioration during this period [58]. The annual average TSM of total suspended solids (TSM) was negatively correlated with water level and water area [60]. The lakes in Inner Mongolia generally show the characteristics of water level decline, small area shrinkage, water volume reduction, and water salinization [61]. There is a clear correspondence between the change in lake water quality and the change in lake water volume. The lake water volume increases water desalination, and vice versa. The water salinity of Hulun Lake increased rapidly. Results show that preventing wind-induced sediment resuspension while minimizing tributary inflows is the main measure to mitigate eutrophication [34]. There is a positive correlation between water quality and water quantity at the lake inlet [62]. Water quantity is the key factor affecting the flux of pollutants into the lake, while water quality is the main factor affecting the flux into the lake. Controlling the flux of total nitrogen and chemical oxygen demand into the lake is the primary task at present. Reasonable control of grazing and discharge of sewage wastewater from villages and towns to meet the standard after treatment are the urgent tasks to reduce the pollution of river and lake water.

2.2.6. Impacts of Internal Interaction between Water Resources and Water Ecology

In the interaction of water resources and water ecology, it is mainly to determine the minimum water threshold that meets the local aquatic biological ecology. The current research is mainly about the impact of aquatic organisms on aquatic organisms. It was found that the microbial community composition showed a consistent pattern of water depth attenuation, indicating that changes in water depth may significantly change the structure and function of ecosystems in arid areas [63,64]. Research showed that there was an intrinsic relationship between the distribution of diatoms and water depth, and deep water led to a high proportion of planktonic diatoms and benthic diatoms [65].

2.2.7. Impacts of Internal Interaction between Water Ecology and Water Environment

In the interaction of water ecology and water environment, it is necessary to consider that this is a two-way effect. Water quality conditions affect water ecological conditions. For example, eutrophication has a significant correlation with the outbreak of cyanobacteria and the reduction in fish, and the poor ecological conditions lead to the reduction in dissolved oxygen and the death of aquatic organisms, which will aggravate this situation and lead to further deterioration of water quality. Some studies showed that cyanobacteria were the most dominant and participated in the whole nitrogen metabolism, but mainly participated in the reduction in nitrate assimilation and nitrogen fixation, which aggravated the eutrophication of lakes [66]. Some studies showed that exogenous input and endogenous release increased the relative abundance of genes with nitrogen fixation and nitrification potential nitrogen metabolism functions in surface and bottom water, respectively [67]. Since Planktobacteria and Proteobacteria are potential key groups regulating nitrogen metabolism, Proteobacteria may contribute to the consumption of nitrate in surface water and bottom water, and the close contact between surface water and atmosphere accelerates the nitrogen fixation dominated by Planktobacteria. The impact of water environment on water ecology. There are also some studies that showed that the change in environmental variables was significantly correlated with the change in the microbial community [27]. The rapid changes in temperature, pH, and dissolved oxygen may be the main factors affecting the trend of seasonal bacterial diversity. The results showed that water temperature (WT), total phosphorus (TP), total nitrogen (TN), conductivity (Cond), water transparency (SD), and dissolved oxygen (DO) significantly affected the biomass of phytoplankton functional groups [68]. The study result showed that DO, TP, and TN were the most important environmental factors controlling the distribution of phytoplankton functional groups [69]. Heavy metals have an impact on the survival of lake organisms [70]. Freshwater organisms have the highest sensitivity to heavy metal Cu and the lowest sensitivity to heavy metal Zn. High concentrations of dissolved organic carbon (DOC) and cations (such as Na⁺, K⁺ and Mg²⁺) in lake water may lead to decreased bioavailability of metals to fish in Hulun Lake [71]. Both water and sediment have very high connectivity, dominated by positive interactions, and have similar interaction patterns. The fungal community structure was affected by water quality indicators such as temperature, chemical oxygen demand, conductivity, total phosphorus, and pH [72]. Redundancy analysis (RDA) showed that water temperature, chlorophyll a, pH, and nutrient concentration were the main environmental factors affecting the distribution of phytoplankton communities in Hulun Lake [73]. On this basis, the water quality of Hulun Lake was evaluated by the model with the ecological index of phytoplankton species, and the feasibility of the model for eutrophication evaluation was verified. The results showed that Hulun Lake was in a eutrophic state [74].

Most of the above response mechanisms are common. On this basis, the characteristics of some lakes in the northern cold and arid regions are proposed. Hulun Lake, Wuiangsuhai, and Daihai are located in the cold and arid region of northern China, facing similar issues due to climate change and human activities, yet each has unique characteristics that require targeted solutions. Hulun Lake’s primary concern is its aquatic ecosystem. Overfishing and pollution have led to decreased biodiversity and ecosystem degradation. To address these issues, measures such as implementing fishing bans, ecological water replenishment, wetland restoration, and promoting eco-tourism have been taken to gradually restore the lake’s ecological balance. Wuliangsuhai, on the other hand, is primarily concerned with water environment issues. Agricultural and industrial discharges have severely polluted the lake, causing reduced water transparency and algal blooms. To combat these problems, pollution source control, wetland restoration, water quality monitoring, and ecological purification projects have been implemented to improve water quality and enhance the lake’s self-purification capacity. Daihai faces significant challenges related to water resources. Reduced precipitation and increased evaporation, exacerbated by climate change, have led to a reduction in the lake’s water volume and over-extraction of groundwater. Measures to address these issues include promoting efficient irrigation technologies, implementing water diversion projects, strictly managing groundwater extraction, and enhancing water conservation awareness, which collectively help to increase the water supply to the lake and reduce water resource wastage. Through these comprehensive measures, Hulun Lake, Wuliangsuhai, and Daihai have made significant progress under the “three-water governance” strategy, focusing on aquatic ecology, water environment, and water resources, respectively. This approach has led to substantial improvements in their water systems and ensures sustainable development. Despite facing similar overarching challenges, the unique characteristics of each lake necessitate tailored solutions to effectively address their specific issues.

2.3. Methods and Results of Model Simulation

After having a certain understanding of the mechanism, the relevant model can be established by using the two driving methods of mechanism and data, and the mechanism or data relationship can be described by a quantitative formula group to obtain relevant data, simulate the process, or evaluate the lake state, as shown in Figure 7.

The mechanism model is a model that uses accurate mathematical formulas to describe the interaction between things. The model parameters have obvious physical significance, and they are adapted to different regions by adjusting the parameters. The disadvantage is that the understanding of the interaction relationship needs to summarize the existing phenomena and practices. When used, a large number of parameters need to be determined. The accuracy of the parameters has a great influence on the accuracy of the model simulation. A study [22] estimated the average annual evaporation of Hulun Lake by using the hydrogen and oxygen stable isotope mass balance method. The difference between the estimated lake evaporation and the measured value is only 5.4%. Compared with the study of precipitation, there is a simulation formula for lake evaporation. A study has simplified the Penman equation and proposed a formula for approximate calculation results (1) [75]. There is also the use of the Manning formula to calculate the flow (2) [76]. In the actual situation, the water level, area, and water storage can be converted to each other through the water level–area–water volume curve [16] or through linear or nonlinear empirical formulas. Lake water level-area-water quantity is the transformation of lake data from one-dimensional to three-dimensional data, which is the manifestation of lake state. Therefore, it can be obtained by the transformation of water level–area–water quantity curve or empirical formula. Two kinds of regression equations are listed below. A linear regression equation of water level and lake area is as follows (3) [49]. The nonlinear regression equation of water level and area of Hulun Lake is as follows (4) [23].

$$E_{\mathrm{PEN}}\approx 0.047R_{\mathrm{S}}\sqrt{T+9.5}-2.4{({\displaystyle \frac{R_S}{R_{\mathrm{A}}}})}^2+0.9(T+20)(1-{\displaystyle \frac{RH}{100}})$$

(1)

where R_S and R_A are the parameter, T is the temperature (°C), RH is the relative humidity (%).

$$n=R^{2/3}{S}_f^{1/2}/v$$

(2)

where v is the velocity of the flow (m/s), n is Manning’s roughness coefficient, R is the hydraulic radius (m) and Sf is the friction slope.

$$y=0.0116x+519.684$$

(3)

where y is the water level (m) of the corresponding period; x is the lake area (km²).

$$H=-5\times {10}^{-12}{S}^5+5\times {10}^{-8}{S}^4-2\times {10}^{-4}{S}^3+0.35S^2-332.35S$$

(4)

where H represents the water level of Hulun Lake (m); s represents the surface water area of Hulun Lake (km²).

Its simulation of the process is generally called the hydrological model, which reflects the mechanism through a number of equations with physical significance, such as canopy interception, infiltration and filling, runoff and confluence, etc. SWAT model, CRUNCEP7 reanalysis data and sparse records of lake water level from 1900 to 1950 were used to simulate the hydrological process from 1904 to 2016, and the water level data were obtained [75].Evaluation models such as: trophic state index (TSI) [57], water quality index (WQI) [5]; comprehensive nutrition level index (TLI) [77], risk identification [78], and multi-factor comprehensive evaluation model [51]

$$TSI(Chl-a)={9.81}^{*}{\log }_{10}(Chl-a)+30.6$$

(5)

where Chl-a represents the Chlorophyll a concentration (μg/L).

$$WQI={\displaystyle \sum _{i=1}^n{q}_i}{w}_i$$

(6)

where q_i represents the mass fraction of water quality parameters, w_i represents the corresponding weight.

$$TLI={\displaystyle \sum _{i=1}^n{\mathrm{a}}_i}{P}_i$$

(7)

where a_i represents the weight of each individual nutritional status index, P_i represents each individual nutritional status index except Lake energy balance model [79];Water balance is used to simulate the water level in Hulun Lake area [25]. Based on the measured data of water level from 1963 to 1980, according to the principle of water balance, the monthly water level change in the lake from 1981 to 2008 is calculated.

The data model relies on the computing power of the computer, through a large amount of data input, as well as the establishment of machine learning, deep learning and other algorithms, through continuous learning to independently establish the relationship between data, to achieve data association and clustering analysis such as algal blooms were identified by image recognition [80] and exponential modeling [59], the advantage is to continuously learn and update the model, the disadvantage is that it depends on the accuracy of the data. When using the data model to obtain data, some studies have obtained water level by extrapolating lake shoreline and water level line [10], and some studies have obtained water level by water index such as MNDVI [81], and deep learning such as convolutional neural network [11]. Machine learning methods, such as the extreme gradient boosting tree (XGBoost) [18], were used to extract the area of Hulun Lake. single parameter identification such as MNDWI, multi-parameter identification, and decision tree identification of permanent water bodies [21]; based on the principle of the Otsu method, the dynamic threshold extraction is realized, the boundary contour of the river is obtained by vectorization, and the lake shoreline is identified. The neural network classification algorithm extraction and other methods are used to realize the identification and extraction of the lake surface; for water volume data, it is necessary to integrate the area and water level or water depth [18]. In the process simulation, although there is no description of the mechanism, with the help of a large number of observation data, it may obtain better results, such as the use of algal bloom prediction. The data model in the evaluation will be taking the Pressure-State-Response (PSR) model [82] of lake water resources–water environment–water ecological status as an example; the influence of the overall elements of the basin is taken into account. In addition to the water quality index, there are also studies to identify the eutrophication status of lakes through models and correlations. Established a nonlinear probabilistic Probit model for the influencing factors of eutrophication in Hulun Lake [83]. The results showed that the main water environmental factors affecting eutrophication were TP, pH, S, H, TN, and DO, among which TP had the greatest impact on the degree of eutrophication in Hulun Lake, indicating that phosphorus was an important limiting factor for eutrophication in Hulun Lake.

2.4. Methods and Results of Management Regulation

Through the model, we can solve what happened and why the problem, but in fact, we not only need to talk about these problems; more important is to solve them. Regulation is an important step from theory to practice, but the actual research is out of reach or unable to start. The comprehensive management of the basin has brought a new direction to the regulation. Compared with the previous regulation, the comprehensive management of the river basin is mainly due to the change in the main body of the governance. For the lake, as the final link of the production and confluence, it undertakes the water consumption and pollution in the process, presenting the final state of the river basin. The change of its own can change the state for a while, but if it does not involve the regulation and guarantee of the mechanism, it will eventually be assimilated. The comprehensive management of the basin is only the goal. To achieve this goal, the problem needs to be decomposed. Taking the management of Hulun Lake as an example, from 2000 to 2009, the water level of Hulun Lake decreased significantly, and the water quality deteriorated significantly. On the one hand, the measures to limit pollution and reduce pollution were actively implemented, including the closure of some highly polluting mining industries and the prohibition of sewage discharge around the lake. On the other hand, the strategy of inter-basin water transfer was adopted in time to ensure the water level of Hulun Lake, which together guaranteed the local ecological security.

For the northern inland areas, water resources have obvious restrictions on local development, and the rational allocation of water resources is an important condition for achieving local sustainable development. The study of the scale of the river basin often involves problems beyond the administrative boundary, and there are cross-city, cross-province, and even cross-border rivers. Due to the large difference in the location of the runoff-producing area and the confluence area, the previous discussion of the rivers within the administrative area is not enough to solve the water resources–water environment–water ecological problems that are prominent due to the lack of water resources in the river basin. The large consumption of water resources in the upper reaches will lead to the deterioration of water resources–water environment–water ecology in the lower reaches. Now it is necessary to take the whole basin as the control target, and realize the comprehensive management of regional water resources–water environment–water ecology under the condition of identifying the external conditions of the whole basin. The water level of Hulun Lake gradually recovered after water diversion through the Xinkai River. The water quality of the lake has been poor, and the improvement effect is not good. It is found that the main cause is the high background value of upstream water and lake water. Considering that the quality of water cannot fully reflect the level of environmental quality, poor water quality is not suitable for human drinking, but it can still meet the survival of fish and other organisms. In 2021, the Ministry of Ecology and Environment agreed to no longer carry out water quality assessments of Hulun Lake during the 14th Five-Year Plan period. Hulun Lake has also become the first pilot lake in the country to carry out water ecological environment evaluation and assessment. On the basis of “stable quantity and quality”, Hulun Lake governance pays more attention to water ecological construction. China has carried out corresponding lake management work. Multi-sectoral cooperation: In order to eliminate the management drawbacks of overlapping functions and multi-departments, since 2013, Hulunbuir City has integrated the three institutions of Hulun Lake Nature Reserve Fishery Administration and Fishing Port Supervision and Management Bureau, Hulun Lake Water Resources Allocation Project Management Bureau, and Wulannuoer Reservoir Management Station into the Hulun Lake Nature Reserve Management Bureau, straightened out the management system, established a number of working mechanisms such as joint law enforcement of Hulun Lake Nature Reserve, water resources allocation and operation of Hulun Lake, and solved the problem of ‘water control in Kherlen’. At the moment, when water resources are limited due to the characteristics of transnational rivers supplying water in Hulun Lake, it is necessary to carry out international cooperation between China and Mongolia, rate the use of rivers, and rationally classify the rivers of the two countries to protect the common interests of both sides. In 1994, China and Mongolia signed the Agreement on the Protection and Utilization of Boundary Water between China and Mongolia. The agreement stipulates that in order to protect and use the boundary water fairly and reasonably, the parties should jointly protect the ecosystem of the boundary water and develop and use the boundary water in a way that does not cause damage to the other party. Any development and utilization of boundary water must abide by the principles of fairness and rationality and shall not cause damage to the reasonable use of boundary water. The eighth meeting of the China–Mongolia Boundary Water Joint Committee was held in Beijing in December 2018. There are few data and studies on the rational allocation of water resources in Kherlen River. The purpose of regulation is to realize the comprehensive management of the basin. The results are used to guide practice. In the actual work, the comprehensive management of water resources in the basin is realized while taking into account the development. Taking the governance of Hulun Lake after 2009 as an example, after the rapid decline of water level from 2000 to 2009, on the one hand, measures to limit pollution and reduce pollution were actively implemented, including shutting down some high-pollution mining industries and prohibiting sewage discharge around the lake. On the other hand, it also pays attention to the excessive concentration of some water environmental parameters in Hulun Lake caused by natural conditions, and designs appropriate local standards for these factors in the evaluation. As Hulun Lake involves multiple administrative units and is even one of the main sources of runoff, the Kherulun River is an international river spanning China and Mongolia. To achieve comprehensive river basin management, it is necessary to cooperate with multiple departments, set up reasonable control principles, implement unified planning, realize rational allocation of water resources, and meet the standards of water environment and water ecology in the whole basin.

3. Research Framework

The methods and results of Hulun Lake basin management were introduced in detail in the previous article. Now, based on the results, combined with the management of multiple basins at home and abroad, through the experience of the basin that has achieved comprehensive management, the goal of comprehensive management needs of the basin is summarized, and the existing gap is faced squarely.

3.1. Integrated Monitoring

The Rhine River Basin in Europe stands as a prime example of how integrated monitoring can lead to effective, comprehensive basin management. By employing a multi-source monitoring approach, the Rhine River Basin has successfully managed its water resources, water environment, and water ecology amidst the pressures of climate change and human activities [84]. By leveraging multi-source monitoring capabilities such as remote sensing—which includes continuous and large-scale data collection through satellite imagery and high-resolution, real-time data acquisition via aerial drones—and surface-based monitoring—which encompasses continuous measurement of water quality parameters like temperature, pH, dissolved oxygen, and pollutant levels through automated sensors, as well as periodic manual sampling of water, soil, and biological samples for laboratory analysis—alongside comprehensive data collection and integration through real-time data aggregation and advanced data integration platforms that facilitate comprehensive analysis and decision-making, the Rhine River Basin has effectively managed water resources, water environment, and water ecology [76]. This integrated approach enables adaptive water allocation and sustainable use across agricultural, industrial, and domestic sectors, aids in predicting and mitigating the impacts of extreme weather events through proactive water management strategies, ensures timely interventions and enforcement of environmental regulations to control pollution, prevents eutrophication by informing better agricultural practices and wastewater treatment improvements [85], tracks the health of aquatic ecosystems to inform conservation efforts, and enhances biodiversity and ecological resilience through data-driven habitat restoration projects, thereby demonstrating a model of comprehensive basin management that integrates water resource management, environmental protection, and ecological conservation [86].

By studying the advanced experience of the Rhine River management, we propose future management goals aimed at establishing an integrated sky-ground monitoring network. This network will achieve watershed-scale monitoring by combining long-term and short-term observations and sensitively identifying anomalies. The indicators are detailed in

Table 1. Identification of water ecological indicators.

Domain	Indicators	Current Spatial and Temporal Resolution	Future Needs
Climate change	Wind speed, Relative humidity, Temperature, Wetland situation, Precipitation, Evapotranspiration	Weather stations, long time series	Watershed scale, forecast prediction
Human activities	Artificial water injection, Social and economic data, Changes in land cover	Statistical data, long time series	Process reduction, real-time simulation
Water resources	Surface water, groundwater, soil–water	Data are multi-source and difficult to couple.	monitoring on water circulation
Water environment	Chl-a, TP/TN, COD, DO, DOM, SOM, SPOM, Heavy metals, Salinity	Mainly based on sampling data, the time series is short.	Multi-index observation, long time series
Water ecology	Microorganisms, Phytoplankton, Zooplankton, Fish	Mainly based on sampling data, the time series is short.	Multi-means rapid monitoring and identification; regular observation

.

In long-term monitoring, a variety of observation methods can be used in combination. Through remote sensing technology, we can conduct extensive, high-precision long-term monitoring of the entire watershed. Remote sensing provides information on surface temperature, vegetation cover, water body area, and water quality. By utilizing satellite imagery, we can periodically monitor the state of water resources, the water environment, and the water ecology, detecting environmental changes and anomalies through temporal data comparisons. In terrestrial monitoring, we will set up multiple weather stations within the watershed to monitor temperature, precipitation, humidity, and other meteorological data in real time. Additionally, establishing observation areas for typical vegetation types allows us to monitor the health of various vegetation types, assessing the overall health of the ecosystem. Soil condition identification helps us understand soil moisture, fertility, and pollution status, enabling the formulation of soil improvement measures. Analyzing grazing intensity will help us understand the impact of human activities on the watershed ecosystem and develop reasonable grazing management strategies. In aquatic monitoring, the focus is on the status identification of water resources, water environment, and water ecology. Monitoring whether water resources are deficient, whether the water environment is polluted, and whether the water ecology meets standards are crucial. By installing water quality monitoring instruments in rivers, lakes, and reservoirs, we can obtain real-time data on water temperature, pH, dissolved oxygen, and turbidity, allowing us to promptly detect and address water pollution incidents. To enhance the accuracy of anomaly detection, drones will be employed for secondary short-term monitoring. When remote sensing or terrestrial monitoring identifies anomalies, drones can quickly respond, capturing detailed images and data of the abnormal areas. Drones offer flexibility and high-resolution imaging capabilities, providing detailed information that ground-based monitoring methods might miss. Establishing a mechanism that combines long-term and short-term observations is essential. Long-term monitoring helps us understand the overall trends in the watershed’s ecological environment, while short-term monitoring enables timely responses to sudden events. By integrating long-term and short-term observation data, we can gain a comprehensive understanding of the watershed’s ecological status and formulate more scientific and reasonable management strategies. An important goal of the integrated sky-ground monitoring network is the sensitive identification of anomalies. Through multi-source data fusion and big data analysis technology, we can quickly detect anomalies in large datasets. For example, by analyzing combined meteorological, vegetation, and water quality data, we can promptly identify and warn of potential ecological problems, allowing us to take appropriate measures to prevent deterioration.

3.2. Multi-Process Identification

Multi-Process Identification in the comprehensive management of the Ganges River Basin ensures scientific and effective governance by meticulously identifying and analyzing key driving mechanisms and response processes [87]. Firstly, the identification of driving mechanisms includes aspects of human activities and climate change: for human activities, it identifies the water demand and pollution discharge mechanisms of agricultural irrigation, industrial water use, and urban development [84]; for climate change, through meteorological data and model analysis, it identifies the impacts of precipitation changes, evaporation and transpiration, and extreme weather events on water resources [88]. Secondly, the identification of response mechanisms encompasses the response processes of water resources, water environment, and water ecology: in terms of water resource response, through hydrological models and monitoring data, it analyzes the dynamic changes in surface water and groundwater and their recharge and consumption balance; in terms of water environment response, through water quality monitoring and pollutant diffusion models, it assesses the sources of pollutants, diffusion mechanisms, and changes in water quality; in terms of water ecology response, through ecological models and biological monitoring, it identifies changes in habitats and biodiversity. Multi-Process Identification ensures that the basin management measures are scientific, systematic, and efficient through scientific data support [89], comprehensive model construction, decision support, dynamic adjustment, and public participation. Continuous monitoring and evaluation allow for timely identification of new driving mechanisms and response processes, dynamically adjusting management measures to ensure the sustainable development and long-term benefits of the Ganges River Basin governance.

To achieve comprehensive management of river basins, it is essential to absorb the experiences from the Ganges River Basin. This involves using historical data and model simulations to identify critical thresholds for key variables such as water quality, flow rates, and pollutant levels. By determining the impact thresholds for single-element extreme events like heavy rainfall and drought and analyzing multi-element synergistic drivers combining factors such as industrial discharge and agricultural runoff, we can evaluate how these elements interact and identify thresholds beyond which they disrupt water resources and ecological balance. Internally, it is crucial to monitor and model water availability, assess ecological flow requirements, and determine water quality parameters essential for fish habitats. Developing models to balance human water use with ecological needs, implementing measures to improve water quality, and maintaining conditions that support ecosystem health are key to establishing optimal interaction relationships. These efforts ensure sustainable water use, pollution control, and ecosystem restoration, which are vital for the successful comprehensive management of any river basin.

3.3. Integrated Model

The Mississippi River Basin uses Integrated Models, which encompass hydrological, environmental, and ecological aspects, to achieve comprehensive watershed management [90]. Hydrological models simulate water flow, storage, and distribution, considering both surface [91] and groundwater interactions [92]. Environmental models assess water quality, including pollutant levels, nutrient loading, and sediment transport. Ecological models evaluate the health of aquatic ecosystems, focusing on habitat conditions, biodiversity, and species populations [93]. Climate impact analysis uses climate models to predict the effects of changing precipitation patterns, temperature variations, and extreme weather events on water resources, while human activity impact analysis examines how agricultural runoff, industrial discharges, urbanization, and water withdrawals affect water quality and availability. Critical thresholds are identified for water quality (pollutants like nitrogen, phosphorus, and heavy metals), flow rates (necessary for ecological health and erosion prevention), and ecological health (parameters like temperature, dissolved oxygen, and habitat conditions). Scenario analysis and predictive modeling involve creating scenarios to simulate future conditions based on climate change, land use, and policy interventions and running simulations to identify the most effective management strategies. Decision support and policy formulation include developing management strategies for water conservation, pollution control, and habitat restoration and providing policy recommendations for sustainable basin management [94]. Continuous monitoring programs regularly collect data on water quality, flow rates, and ecological health, ensuring model accuracy, while adaptive management adjusts practices based on new data to address emerging challenges. By using Integrated Models, the Mississippi River Basin can improve water quality, sustain water resources, enhance ecological health, and inform policy, ensuring a comprehensive approach to watershed management that addresses the interconnectedness of water resources, water environment, and water ecology, and promotes sustainable management and long-term ecological health.

To achieve comprehensive watershed management by learning from the advanced experience of the Mississippi River Basin, we should set the following goals: full basin simulation, multi-process integration and mechanism understanding, and long-term applicability and learning (data-driven). Developing a holistic simulation of the entire river basin involves using integrated models encompassing hydrological, environmental, and ecological aspects to simulate water flow, storage, distribution, water quality, and ecosystem health. This requires extensive data collection on precipitation, river flow, groundwater levels, temperature, land use, and socioeconomic factors to ensure comprehensive model accuracy. Combining multiple processes to understand the underlying mechanisms entails assessing the impacts of agricultural practices, industrial activities, urban development, and climate change on water resources, quality, and ecosystem health. Identifying critical thresholds for key variables such as pollutant levels, flow rates, and ecological parameters is essential, as is analyzing synergistic interactions between various factors to determine compound thresholds and their impacts on the basin. Ensuring that management practices are adaptable and based on long-term data-driven insights involves developing predictive models to simulate future conditions under varying levels of climate change, land use practices, and policy interventions.

3.4. Event Prevention

The Murray–Darling Basin in Australia employs Event Prevention strategies for comprehensive watershed management, encompassing both routine management and emergency response [95]. Routine management includes water allocation and regulation [96] through water rights and licensing to balance agricultural, industrial, and urban demands with environmental needs and water trading schemes to promote efficient use and adapt to varying availability. It also involves maintaining and upgrading irrigation systems to enhance efficiency and ensure flood control structures are in good condition. Environmental flows are allocated to maintain the health of rivers, wetlands, and floodplains, supporting biodiversity and ecosystem services, while habitat restoration projects improve ecological resilience. Emergency response includes drought management through preparedness plans and drought monitoring using climate and hydrological models. Flood management employs advanced forecasting models and early warning systems, alongside regularly updated emergency evacuation plans. Contingency plans for pollution incidents and rapid response teams ensure quick and effective handling of environmental emergencies. Implementation and coordination enhance water security, protect ecosystems, improve resilience to extreme weather events and environmental hazards, and foster collaborative governance and stakeholder engagement [95]. These strategies demonstrate a comprehensive approach to watershed management, integrating routine management and emergency response to address both everyday challenges and unexpected events, ensuring the long-term health and sustainability of the Murray–Darling Basin [97].

To achieve comprehensive watershed management by learning from the Murray–Darling Basin’s experiences, we should focus on the following goals: prediction and early warning for risk prevention (routine prevention), emergency response plans for unexpected events (emergency handling), and goal setting and intelligent planning (long-term goals). Here is how these goals can be implemented: In terms of long-term goals, developing strategies for sustainable water use that balance human needs with ecological preservation, including setting targets for water conservation, pollution reduction, and habitat restoration, is vital. Implementing integrated management practices that consider the interconnections between water resources, land use, and ecological health involves coordinated planning across sectors and regions within the basin. Leveraging technology and innovation to enhance water management practices, using satellite imagery, IoT sensors, and data analytics to monitor and manage water resources more effectively, will further support these goals. For emergency response, developing comprehensive preparedness plans that outline specific actions to be taken before, during, and after droughts and floods, including water restrictions, alternative water sources, and evacuation procedures, is crucial. Establishing detailed contingency plans for handling pollution incidents, such as chemical spills or algal blooms, with immediate response actions, containment measures, and long-term remediation strategies, ensures effective emergency handling. Creating a comprehensive management framework that integrates prediction, prevention, emergency response, and long-term planning, investing in building the capacity of institutions, communities, and individuals to implement and sustain these management practices, and establishing mechanisms for regular review and improvement based on new data, technological advancements, and feedback ensures continuous improvement. By focusing on these goals and implementing the outlined strategies, comprehensive watershed management can be achieved, ensuring the sustainable use and protection of river basins.

4. Research Direction

On the premise of clarifying the gap between the goals and current situation of comprehensive watershed management, propose practical research directions, answer how to apply the experience of comprehensive watershed management to practice, and propose specific questions in each field to achieve the full process of governance from theory to practice.

4.1. Enhancing the Spatiotemporal Availability of Data

In view of the current research trend from the governance of one lake to the governance of the basin and the need to solve the actual problems of Hulun Lake, it is necessary to realize the water resources–water environment–water ecology research of Hulun Lake on the basin scale. Due to the expansion of the observation range, there is a problem of imperfect coordination of multi-scale observation data. Taking the study of water resources–water environment–water ecology of Hulun Lake as an example, on the lake scale, climate factors are represented by precipitation and evapotranspiration. Most of the studies use the monitoring data of meteorological stations to transform into lake surface data through empirical formulas; the previous research used the population in the statistical yearbook and the proportion of industry and land use to represent the intensity of human activities; water resources–water environment–water ecology factors generally only consider the administrative divisions near Hulun Lake and the parts within the national boundaries, which are obtained through field sampling. The data are mainly based on the point-scale data collected by meteorological and hydrological stations. The remote sensing data usually uses Land-sat8 to extract the lake surface. The transformation of the study area from the lake to the basin has put forward new requirements for data sources and quality. In the past, the point data of sampling acquisition and fixed station observation could not be empirically extended to the watershed scale, and the acquisition of sampling data have become difficult due to the limitation of national boundaries. As large-scale and high-precision data, remote sensing data have been the main data source for watershed change monitoring and have been adopted by a large number of research institutes. Combining multi-source remote sensing satellites to improve the accuracy of products and the fusion processing of cross-scale data are a hot and difficult point in current research. For the development of spatial-scale data, it is mainly to achieve multi-source and multi-scale data fusion to the greatest extent while expanding the monitoring range so as to improve the accuracy of products and obtain more accurate information. At present, 10 m resolution ground object recognition has been achieved, but for sub-meter-level recognition targets, sub-meter-level ground object recognition needs to be achieved through high-resolution data and downscaling methods to provide data support for related research. Although the expansion of the study area will not directly affect the data requirements on the time scale, the study is to identify the driving and impact of climate and human activities on the water resources–water environment–water ecology in the study area. For this purpose, the time matching degree of the data should also be considered. On the basis of collecting a large number of historical observation data, on the one hand, the known data are archived and classified to improve the comparability of the data; on the other hand, the spatial resolution of the data is increased to achieve data acquisition on the interannual and intra-annual scales. On the basis of mechanism identification and model construction through historical observation data, the demand for real-time data acquisition is put forward for real-time regulation of river basins. It is recognized that the model in a changing environment should also have the ability to learn. Through the input of real-time data, it can not only quickly obtain the current situation of the river basin and respond to emergencies, but also realize real-time feedback on the current regulation results, which is convenient for the adjustment of relevant measures.

4.2. Integrated Mechanism Identification of Water Resources–Water Environment–Water Ecology

On the basis of a large number of historical data and real-time observation data acquisition, the data are deeply excavated to realize the identification from a single factor to a multi-factor law, and the key factors and thresholds are used to quantify the mechanism driven by the important water resources–water environment–water ecological process in the basin. Previous studies have been devoted to decomposing the research objectives, transforming the complex multi-factor influence problem into a simple single factor’s influence on the target, and constructing the correlation function of the two through correlation, and then as the driving mechanism. Taking Hulun Lake as an example, the attribution of the impact of climate change and human activities on water resources–water environment–water ecology is mostly achieved through the linear relationship integration of a single factor on a single target. It is of certain significance to explore the basic correlation of specific elements, but it ignores the nonlinear law in complex systems. In practice, the change in elements is usually accompanied by the change in multiple elements at the same time. With the help of machine learning and other technologies, the description of the nonlinear process can also be realized. For future research, we should identify the impact of the overall driving factors on the overall water resources–water environment–water ecology on the basis of exploring the correlation.

4.3. Simulation of Key Processes in the River Basin

To recognize the overall impact, it is not to ignore the impact of a single factor. Although the result is caused by the overall impact, there will be one or more key factors of the dominant process under different conditions. The key factors describe the key process, which can simplify the mechanism and realize quantification of the mechanism. The impact of climate on water resources and the water environment has been quantitatively studied. It is mainly necessary to solve the problem of poor connection between representative human activity indicators and water resources and water environment indicators. Quantify the response of statistical indicators of human activities to water resources and the water environment. Taking Hulun Lake as an example, in the interaction of water resources and water environment, it is most necessary to quantify the response of each component of water quantity and quality to ensure that the amount of water entering the lake is an effective input; in the interaction of water resources and water ecology, the main concern is the limitation of water quantity to ecology, that is, the ecological flow to ensure the development of aquatic organisms in rivers and lakes; in the interaction between water environment and water ecology, it is necessary to pay attention to its two-way influence and avoid entering a vicious circle. The process is the main source of endogenous pollution, and it is necessary to quantify the conditions that affect endogenous pollution.

4.4. Providing Concrete Evidence to Support Management

Water resources are scarce resources in the northern region, with structural contradictions and fierce competition. Most of the current research and countermeasures aim at water resources, and strive to ensure that the lake water level reaches a safe water level. In recent years, the rise of lake water level is also the result of joint efforts of research and adjustment, but it is also found that there is a phenomenon of water quantity guarantee, water quality and water ecology are still degraded. In the future, it is necessary to change from the guarantee of a single factor to the comprehensive guarantee of water resources, water environment and water ecology. At the same time, it is also necessary to realize that the coordinated development of water resources–water environment–water ecology is the development path of the cold and arid regions in the north rather than the development obstacle. It is not only necessary to establish the constraint threshold of water resources–water environment–water ecological security, but also to establish the constraint threshold of economy, environment, and security. Through multi-dimensional constraints, the optimal combination of model simulation is used to realize the two-way real-time constraint regulation of Hulun Lake under the changing environment. In particular, Hulun Lake involves the problem of international rivers. Different from the southern international rivers, the upstream water will affect the water resources–water environment–water ecology of Hulun Lake. The management of the lake has increased the local water use cost. A lot of research is needed to obtain data, ensure fair distribution, and foster joint development.

5. Conclusions

This review attempts to answer the question of how to achieve comprehensive watershed management. It systematically explains the management measures and results of the typical watershed, Hulun Lake Basin, from the four parts of data, mechanism, model, and regulation and then analyzes the management experience of other watersheds. The goal of comprehensive watershed management is proposed, and a practical research path is proposed at the end. The conclusions are as follows:

(1) The comprehensive management of the Hulun Lake Basin employs various methods, resulting in significant improvements in water resources, environment, and ecology. Enhanced data acquisition through field observation, remote sensing, model simulation, and multi-source fusion has provided critical insights. Mechanism identification reveals complex interactions driven by climate change and human activities. Simulations offer predictive capabilities and assessment tools, guiding effective regulation and management practices. Notable improvements in water level and quality, driven by targeted interventions and multi-sector cooperation, demonstrate the vital role of comprehensive watershed management in achieving sustainable development and ecological balance in Hulun Lake and similar regions.

(2) To achieve comprehensive watershed management by learning from global examples, this review integrates advanced practices from various successful basin management strategies. Drawing from the Rhine River Basin’s integrated monitoring, which utilizes multi-source approaches like remote sensing and surface-based monitoring, and the Ganges River Basin’s multi-process identification, which analyzes the impacts of human activities and climate change, we can enhance our understanding and management of water resources. The Mississippi River Basin’s use of integrated models, combining hydrological, environmental, and ecological aspects, provides a framework for simulating water flow, assessing water quality, and evaluating ecological health. Additionally, the Murray–Darling Basin’s event prevention strategies, which include routine management and emergency response, highlight the importance of preparedness and adaptability. Key goals for comprehensive management should include establishing extensive monitoring networks and predictive models for routine prevention, developing preparedness and contingency plans for droughts, floods, and pollution incidents, and setting long-term strategies for sustainable water use and integrated basin management. Leveraging technology, such as satellite imagery and IoT sensors, along with continuous improvement mechanisms, will ensure the sustainable use and protection of river basins.

(3) To bridge the gap between the goals and current situation of comprehensive watershed management, practical research directions must be proposed, past experiences applied to practice, and specific questions addressed to achieve effective governance from theory to practice. Enhancing spatiotemporal data availability through advanced remote sensing and multi-source data fusion is critical for basin-scale research. Integrated mechanism identification using technologies like machine learning can reveal complex, nonlinear relationships among water resources, environment, and ecology. Key process simulations will help quantify the impact of dominant factors, ensuring balanced water quantity, quality, and ecological health. Finally, providing concrete evidence through comprehensive, multi-dimensional management strategies will support sustainable development and cooperative international efforts, particularly for shared resources.