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Review

A Review of the Characteristics and Mechanisms of Water Environment Evolution in Hulun Lake Under the Dual Drivers of Climate Warming-Drying and Human Activities

by
Bingtao Hu
1,
Yuhong Liu
1,2,*,
Cheng Chen
3,
Yipeng Yao
4,
Yixue Chen
1,
Lixin Wang
5 and
Zhongsheng Wang
4,*
1
College of Environment, Hohai University, Nanjing 210098, China
2
Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake, Ministry of Education, Hohai University, Nanjing 210098, China
3
Nanjing Institute of Environmental Sciences, MEE, Nanjing 210042, China
4
School of Life Sciences, Nanjing University, Nanjing 210098, China
5
College of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(22), 10395; https://doi.org/10.3390/su172210395
Submission received: 2 October 2025 / Revised: 12 November 2025 / Accepted: 16 November 2025 / Published: 20 November 2025
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Abstract

Hulun Lake, the largest freshwater lake in the Eurasian steppe, is a critically climate-sensitive water body facing severe ecological threats. This systematic review synthesizes multidisciplinary evidence from 1961 to 2025 to examine the characteristics and drivers of its water environment and quality evolution. The findings reveal that the primary driver of the lake’s hydrological degradation shifted from natural climate variability to anthropogenic land-use change around 1998. While ecological water diversion has partially alleviated water scarcity, it also introduces a significant external nutrient load, creating a paradox where increased water volume coincides with aggravated eutrophication. Furthermore, overgrazing in the catchment not only enhances conventional runoff pollution but also facilitates a unique “tumbleweed-mediated cross-media pollution” pathway. This review concludes that the restoration of Hulun Lake necessitates a shift from singular water quantity regulation to an integrated management strategy that concurrently addresses water quantity, quality, and aquatic ecosystem health. The insights gained are crucial for informing the sustainable management of Hulun Lake and other inland lakes in cold, arid regions under global change.

1. Introduction

Hulun Lake, situated in the semi-arid steppe of the Mongolian Plateau at the tri-national junction of China, Mongolia, and Russia (48°30′–49°20′ N, 117°00′–117°41′ E), is the largest freshwater lake in the Eurasian steppe zone and the fifth-largest inland freshwater lake in China [1]. The lake basin covers an area of approximately 38,600 km2, characterized by flat terrain in the west and low mountains and hills in the east, with an average altitude of around 700 m. This topographical configuration creates a distinct inland watershed, concentrating all surface runoff within the basin towards the central lake.
The region experiences a typical mid-temperate continental monsoon climate, marked by strong seasonality, low precipitation, and high potential evaporation. Spatially, the climate exhibits a gradient from a semi-humid forest-steppe in the eastern mountainous areas to a semi-arid typical steppe in the central and western parts. The stark contrast between mean annual precipitation (256–352 mm) and evaporation (1140–2203 mm yr−1) establishes a fundamental and persistent water deficit, defining Hulun Lake as a quintessential lake system in a cold and arid region. This spatial representativeness makes it a critical sentinel for studying the ecological and environmental responses of similar lake ecosystems across the Mongolian Plateau and broader cold, arid regions under global change. Formed within a tectonic fault basin on the Hulun Buir grassland, it lies at the northeastern extremity of the East Asian climate-sensitive belt, rendering it highly responsive to global changes and thus of particular indicative value in global change research [2,3,4,5,6,7].
The water balance of Hulun Lake is primarily governed by surface runoff, direct precipitation, evaporation, and groundwater exchange. At a water level of 545.33 m (Yellow Sea datum), the lake covers an area of approximately 2339 km2, with an average depth of 5.7 m and a storage capacity of about 13.85 billion m3. The river system around the lake exhibits a radial pattern, with major inflows including the Kherlen, Urxun, Xinkai, and Hailar Rivers. The Kherlen River alone supplies over 60% of the total inflow [8,9]. The sole outflow, the Xinkai River, is an artificial canal constructed in 1971. This semi-closed hydrological configuration significantly amplifies the lake’s sensitivity to both climate change and human activities [10,11].
Over the past six decades, Hulun Lake has experienced pronounced hydrological fluctuations and ecological degradation under the combined pressures of a warming-drying climate (mean annual precipitation of 256–352 mm versus evaporation rates of 1140–2203 mm yr−1) and intensified human interventions [12,13,14]. During an extreme drought from 1999 to 2011, the lake level dropped sharply from 544.52 m to 540.97 m, the surface area shrank from 2247 km2 to 1760 km2, and water storage decreased by more than 60% [15,16]. This ecological crisis prompted the launch of the “Diverting Rivers to Supplement the Lake” ecological water diversion project in 2009, establishing Hulun Lake as a representative case for studying water environment evolution in inland lakes under global change in cold and arid regions [12,17].
While numerous studies have investigated specific aspects of Hulun Lake, such as its hydrological changes, water quality parameters, or response to particular climate indices, a comprehensive and systematic synthesis is lacking. Existing reviews often focus on singular dimensions (e.g., hydrology or eutrophication in isolation) and fail to fully integrate the complex, nonlinear interactions between the water quantity-quality-ecology nexus under concurrent climate and anthropogenic pressures. This systematic review is therefore warranted to consolidate the fragmented knowledge, identify consistent patterns and critical knowledge gaps across disciplines, and elucidate the cascading effects and feedback loops within the watershed-lake system.
Drawing on multidisciplinary literature from 1961 to 2025, this review systematically examines the characteristics and driving mechanisms of the water environment and water quality evolution in Hulun Lake. In contrast to previous compartmentalized approaches, this study uniquely integrates perspectives from hydrology, biogeochemistry, and ecology, with a particular focus on the interplay between hydrological processes, water quality dynamics, and ecological effects under the dual influences of climate warming-drying and anthropogenic activities. Key differentiators of this review include its systematic analysis of the “ecological paradox” associated with water diversion projects and the identification of unique pollution pathways such as “tumbleweed-mediated cross-media pollution”, which are characteristic of grassland lakes in arid regions. By integrating multi-source data from remote sensing, geochemical analyses, and numerical modeling, the study seeks to elucidate the coupling mechanisms between natural variability and human impacts on the lake’s water environment, thereby providing a theoretical foundation and practical insights for ecological restoration and sustainable management of similar lakes in cold, arid regions.

2. Search Process Description

A systematic literature search was conducted to identify all relevant studies concerning the water environment and quality of Hulun Lake. This section delineates the review protocol, including the review questions, search strategy, data sources, eligibility criteria, and data extraction and synthesis processes.

2.1. Review Questions

This systematic review was guided by the following overarching question: “What are the characteristics, driving mechanisms, and ecological effects of the water environment and quality evolution in Hulun Lake under the combined pressures of climate change and human activities?” This question was further broken down into several sub-questions:
(1)
How have the hydrological dynamics (e.g., water level, water balance) of Hulun Lake evolved, and what are the relative roles of climate variability and anthropogenic factors in driving these changes?
(2)
What are the spatiotemporal patterns and trends of water quality degradation, particularly eutrophication, in Hulun Lake?
(3)
What are the key pathways and magnitudes of external nutrient loading and internal nutrient release from sediments?
(4)
How do specific human activities (e.g., overgrazing, land use change, water diversion) impact the lake’s water environment and ecosystem health?

2.2. Search Strategy and Keywords

A comprehensive literature search was performed using a combination of keywords and Boolean operators. The search keywords were grouped into two core concepts:
(1)
Location: “Hulun Lake” OR “Hu Lun Hu” OR “Dalai Lake” OR “Hulun Nur”.
(2)
Theme: “water quality” OR “eutrophication” OR “hydrolog*” OR “water level” OR “nutrient” OR “pollution” OR “sediment” OR “climate change” OR “human activity” OR “grazing” OR “ecological effect”.
These keywords were used to construct the search string: (“Hulun Lake” OR “Hu Lun Hu”) AND (“water quality” OR “eutrophication” OR “hydrolog*” OR “nutrient” OR “climate change”).

2.3. Information Sources and Search String

The search was executed in several major academic databases and search platforms to ensure extensive coverage of both international and Chinese literature. The primary databases consulted were:
(1)
Web of Science Core Collection
(2)
China National Knowledge Infrastructure (CNKI)
The specific search string was adapted to the syntax requirements of each database. The search was confined to documents published between January 1961 and March 2025, covering the period of modern scientific observation of the lake.

2.4. Eligibility Criteria

Studies identified through the database search were screened for eligibility based on the following pre-defined criteria:
Inclusion criteria:
(1)
Studies focused on Hulun Lake or its basin.
(2)
Research articles, review articles, and conference proceedings that provided original data, analysis, or synthesis on the lake’s hydrology, water quality, sediment, biogeochemistry, ecology, or related driving mechanisms.
(3)
Publications within the timeframe of 1949–2025.
Exclusion criteria:
(1)
Studies not primarily focused on Hulun Lake.
(2)
Duplicate publications.
(3)
Articles for which the full text could not be retrieved.

2.5. Study Selection and Data Extraction

The process of selecting and processing the documents is detailed in Table 1. The study selection followed a two-stage screening process: first based on title and abstract, and then on full-text assessment.
The main characteristics and findings were systematically extracted from the 63 included studies into a standardized data extraction form. The extracted information included the following:
(1)
Bibliographic information (authors, year, title).
(2)
Study focus (e.g., hydrology, water quality, sediment, ecology).
(3)
Key methods used (e.g., field monitoring, remote sensing, modeling, geochemical analysis).
(4)
Temporal and spatial scale of the study.
(5)
Main findings related to the review questions (e.g., trends in water level, nutrient concentrations, identified driving factors, ecological impacts).

3. Evolution and Integration of Research Methods

Research methodologies for investigating the water environment of Hulun Lake have evolved considerably, shifting from traditional single-point monitoring toward the integration of multi-source information technologies, which has substantially improved the understanding of multi-scale lake processes [8,18] (Table 2). Early studies depended mainly on ground-based observations from four hydrological stations, such as Alatan Emel and Kundulun. However, the decommissioning of several stations after the 1980s resulted in data discontinuities, hindering a comprehensive depiction of its dynamic hydrological behavior [8,19]. In recent years, the broad adoption of remote sensing, geochemical tracing, numerical modeling, and machine learning has progressively enabled more integrated, dynamic, and mechanistic research approaches, offering robust support for unraveling the complex drivers behind the evolution of the lake’s water environment [12,20].

3.1. Remote Sensing and Machine Learning Technologies

Remote sensing technology has enabled significant advances in the large-scale, long-term monitoring of key parameters in Hulun Lake [27]. For instance, Cai, Jin [8] integrated satellite altimetry data from TOPEX/Poseidon and Jason series to develop a monthly water balance model (Nash–Sutcliffe efficiency coefficient = 0.93), effectively overcoming the limitations of ground-based observations. In water quality retrieval, Song, Yinglan [10] employed Landsat 8 OLI and Sentinel-3 OLCI imagery along with machine learning algorithms such as Random Forest and XGBoost to achieve high-precision estimates of chlorophyll-a (Chl-a), total nitrogen (TN), and total phosphorus (TP) (R2 > 0.7), thereby revealing pronounced spatial heterogeneity of pollutants. Further advancing this line of research, Yan, Fang [28] applied Sentinel-3 OLCI data and machine learning models to accurately estimate the biomass proportion of different phytoplankton groups (R2 = 0.92), offering a novel technical approach for analyzing shifts in dominant algal bloom species.

3.2. Geochemical and Sediment Analysis

Geochemical methods have provided critical insights into water cycle pathways and biogeochemical processes in the lake. Zhang, Wang [13] used stable isotopes (δD, δ18O) to demonstrate that lake water exhibits significant deviation from the Global Meteoric Water Line, with enrichment in heavy isotopes, confirming the dominant role of strong evaporation. Fluorescence spectroscopy (EEM-PARAFAC) of dissolved organic matter (DOM) further revealed that DOM concentrations in the lake were substantially higher than those in riverine and groundwater inputs, with a predominance of autochthonous protein-like components, indicating high biological activity and eutrophic conditions [24]. Sediment core analyses, employing isotopic dating (e.g., 210Pb, 137Cs) and high-resolution sampling, have helped reconstruct the eutrophication history of Hulun Lake, corroborating a notable increase in external nutrient inputs over the past four decades [24]. Additionally, Liu, Zhang [15] quantified nutrient diffusion fluxes across the sediment–water interface, highlighting the considerable contribution of internal nutrient release.

3.3. Numerical Models and Driving Mechanism Analysis

The integration of statistical methods with process-based numerical models has emerged as a dominant paradigm for unraveling the driving mechanisms behind hydrological and ecological changes in the lake [29]. This combined approach enables a more nuanced attribution of natural and anthropogenic influences across different time periods. For instance, by applying structural equation modeling (SEM), Huang, Yao [16] demonstrated a pivotal shift in primary drivers: climate-induced increases in potential evapotranspiration were the main cause of the gradual water level decline from 1961 to 1997, whereas anthropogenic factors—particularly land-use change—became dominant during the period of sharp decline from 1998 to 2020. Looking forward, the application of such models has expanded to future projections, as exemplified by Pan, Tang [2], who coupled CMIP5/CMIP6 climate models with the SWAT hydrological model to produce ensemble runoff forecasts for the Kherlen River basin. Beyond hydrological drivers, a suite of specialized models addresses specific ecological responses. Ecological models like ECOPATH and PCLake simulate food web dynamics under varying hydrological regimes [25], while hydrodynamic-water quality models such as GOTM-WET help identify and quantify the key physical and chemical drivers of algal bloom outbreaks [26]. Collectively, these multi-method and multi-scale approaches do not merely coexist; they complement each other, forming a cohesive and systematic framework that significantly advances the mechanistic understanding of water environment evolution in Hulun Lake.

3.4. Limitations and Challenges of Existing Research Methods

Despite significant advancements in research methodologies, a review of the existing literature reveals several important limitations. First, the spatiotemporal discontinuity of monitoring data, particularly the historical gaps in ground-based hydrological station records, poses challenges for the precise analysis of long-term hydrological processes. Second, the effective integration of multi-source data (e.g., remote sensing, ground observations, and model simulations) remains insufficient, leading to uncertainties in quantifying key interface processes, such as groundwater-lake water exchange. Third, the parameterization and validation of hydrological and ecological models in localized applications still rely on limited data, affecting the reliability of their predictions under future scenarios. Furthermore, in situ observations of key internal mechanisms, such as biogeochemical processes during the ice-covered period, remain inadequate, limiting a deeper understanding of the “legacy effect” of internal pollution. Finally, effectively quantifying and integrating socio-economic and other human activity drivers into natural process models represents a cross-disciplinary challenge for current research. Acknowledging these limitations helps to chart the course for future methodological development.

3.5. The Impact of Data and Artificial Intelligence on Research Paradigms

In response to these challenges, the integration of multi-source data and artificial intelligence (AI) is emerging as a transformative pathway, enabling a shift from descriptive studies to mechanistic prediction and proactive management. The fusion of satellite remote sensing with intensive field surveys is effectively compensating for historical data gaps, facilitating dynamic, system-level analysis of hydrological and ecological processes [8,20]. More profoundly, machine learning algorithms excel at deciphering complex, nonlinear relationships within high-dimensional environmental datasets. This capability not only enhances the retrieval of water quality parameters [10] but also provides unprecedented insights into ecological dynamics, such as forecasting shifts in phytoplankton communities [22] and identifying tipping points in ecosystem status [25]. By quantifying the intricate coupling between climate change and anthropogenic pressures, this data-driven paradigm is becoming indispensable for the sustainable management of Hulun Lake.

4. Hydrological Dynamics and Driving Mechanisms

4.1. Phased Water Level Changes and Shift in Driving Mechanisms

Over the past six decades, the hydrological processes of Hulun Lake have displayed distinct phased characteristics, marked by a fundamental shift in the primary driving mechanism from natural climate variability to anthropogenic intervention (Figure 1). Between 1961 and 1997, the lake level experienced a phase of slow decline (average rate: 7.4 mm yr−1), primarily controlled by natural precipitation variability, with reduced runoff serving as the key contributing factor (r = 0.41). Although short-term water-level rebounds occurred in response to ENSO-induced precipitation increases, the overall trend remained one of gradual decrease [16,30].
After 1998, the dominant driver changed significantly. Human activities—particularly land-use changes such as grassland degradation, deforestation, and the expansion of cropland and urban areas—superseded climate factors as the main cause of a rapid water-level decline (average rate: 37.6 mm yr−1) [12,16]. Structural equation modeling (SEM) quantified the total impact coefficient of land-use change during this period as −0.86. By substantially increasing basin-wide evapotranspiration (ET0, impact coefficient: 0.78), these activities reduced runoff generation and lake inflows, becoming the principal reason for the sharp water-level drop [16]. In response, the ecological water diversion project initiated in 2009 played a significant role in mitigating the declining trend. A strong correlation (r = 0.75) was observed between diversion volume and water-level change, underscoring the potential of human interventions to also restore hydrological balance [8].

4.2. Dual Pressures of Water Imbalance

The water imbalance in Hulun Lake arises mainly from the dual pressures of decreased precipitation-runoff and increased evaporation [21,31]. During the extreme drought period (2000–2010), runoff into the primary inflow rivers—the Kherlen and Urxun—decreased by approximately 70% compared to the long-term average. This was attributed to both a reduction in precipitation (−49 ± 45 mm yr−1) and diminished runoff generation capacity due to declining soil moisture [8,17]. Hydrological simulations indicate that runoff exerts the strongest influence on water level changes over multi-decadal timescales (R2 = 0.32–0.44), significantly greater than the contributions of direct precipitation (R2 = 0.16) or lake surface evaporation (R2 < 0.045) [30]. Simulations using the Global Land Data Assimilation System (GLDAS/Noah) further revealed that subsurface runoff nearly ceased in the basin during the drought, emphasizing the critical role of soil water storage in sustaining streamflow. Although unmapped areas around the lake provide a net annual recharge of approximately 210 million m3 through surface water–groundwater interactions, this is insufficient to offset losses from intensified evaporation. Lake surface evaporation increased by 6.2% after the 2000s relative to the multi-year average, maintaining Hulun Lake in a prolonged negative water balance [30].

4.3. Multi-Period Coupling Mechanism of Water Level Fluctuations

Water-level fluctuations in Hulun Lake exhibit coupling with climate cycles across multiple time scales, showing significant teleconnections with large-scale climate patterns such as the North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) [16,32,33]. Wavelet analysis has identified an 11–12 year cycle that aligns with the NAO cycle: a positive NAO phase weakens the Siberian High, favoring the northward transport of warm, moist air masses and increasing basin precipitation, whereas a negative phase leads to precipitation reduction, though the lake level response lags by 1–2 years [16]. On an interannual scale, a 4–7 year cycle is closely linked to ENSO events. For example, the strong ENSO event in the mid-1980s triggered extreme summer rainfall, causing a short-term water-level rise between 1984 and 1986. Furthermore, the PDO modulates regional dry-wet patterns on decadal scales, with its positive phases (e.g., 1920s–1940s) often corresponding to drought and low-water periods [30]. These natural oscillations across different time scales, superimposed on a long-term warming-drying trend, collectively shape the multi-period fluctuation characteristics of Hulun Lake’s water level.

4.4. Spatially Heterogeneous Response

Spatially, Hulun Lake’s response to climate change and human activities shows pronounced heterogeneity, shaped by basin morphology, wind-field dynamics, and the ecological water diversion project. Basin morphology provides the fundamental template for spatial differentiation: the northeastern sector is shallow with gentle slopes, making it highly sensitive to hydrological changes, whereas the southern part is deeper and possesses greater buffering capacity [11]. Persistent northwesterly winds reinforce a “northwest erosion–southeast accumulation” hydrodynamic pattern, governing the transport and distribution of sediments and pollutants [11]. Remote-sensing observations from 1986 to 2020 indicate that the northeastern shallow zone experienced the most pronounced shrinkage during low-water periods, while the southern area, directly replenished by water diversions, showed more notable recovery [10].
Hydrochemical and isotopic data further reveal spatial differentiation in lake processes. Although lake water, river water, and groundwater are all of the Na-HCO3 type, indicating good hydrological connectivity across the basin, total ion salinity (TIS) differs markedly (groundwater: 1750.42 mg L−1 > lake water: 1250.40 mg L−1 > river water: 352.89 mg L−1), reflecting strong evaporative concentration under arid conditions [13]. Analyses of hydrogen and oxygen stable isotopes (δD and δ18O) show the highest enrichment in the central lake area (strongest evaporation) and gradual depletion toward river inflows (enhanced freshwater dilution), clearly outlining a spatial gradient of evaporation versus runoff recharge [13].
The water diversion project, as a strong anthropogenic intervention, has not only altered the spatial configuration of the lake’s hydrodynamic structure but also reconfigured the water-balance pattern. This results in sharp water-level fluctuations and shorter water residence times near the inflow area, whereas the central and northern parts of the lake remain more influenced by natural processes [34].

5. Water Quality Evolution and Eutrophication Process

5.1. Eutrophication Process

Water quality degradation in Hulun Lake is centrally reflected in its continuously intensifying eutrophication. Driven by both climatic warming-drying and human activities, this process exhibits significant fluctuations and complex evolutionary dynamics [35,36]. Starting from a mesotrophic state in the 1980s (TLI ≈ 44.19), the lake’s trophic level gradually rose, fluctuating between moderate and severe eutrophication after 1994 [37]. Extreme hydrological events markedly accelerated this trend: sharply reduced inflow from 2006 to 2009 led to falling water levels and pollutant concentration, pushing the TLI above 70 in 2008–2009, indicating severe eutrophication [38]. Another extreme drought in 2016 (annual precipitation only 8.3 mm) drove the TLI to 71.82 [39]. Although ecological water diversion and management measures contributed to partial water quality recovery between 2017 and 2020, remote sensing inversions in 2021 revealed that average total nitrogen (TN) and total phosphorus (TP) concentrations reached their highest levels of the study period, exceeding Class III water quality standards by 56% and 140%, respectively [10]. Notably, the ecological water diversion project contributes up to 46.30% of the TP load in Hulun Lake, underscoring an “ecological paradox” in which increased water volume coincides with deteriorating water quality [40]. This recurring pattern indicates that controlling eutrophication in Hulun Lake requires not only external source reduction but also management of complex feedbacks from internal nutrient release and hydrological regulation [12,15].

5.2. Spatiotemporal Heterogeneity of Water Quality

The water quality of Hulun Lake displays pronounced spatiotemporal heterogeneity, shaped by riverine inputs, wind-field dynamics, and water diversion operations. Pollutant concentrations generally decrease along a gradient from river estuaries toward the lake center, forming distinct pollution input zones near the estuaries of the main inflow rivers (the Kherlen River and Urxun River) [9,39]. Rainfall events significantly alter the flux and speciation of nutrient outputs from these estuaries, further complicating spatial water quality patterns [41]. Wind field dynamics also play a key role in structuring pollutant distributions: prevailing northwesterly winds drive algae and suspended solids toward southeastern bays, resulting in significantly higher chlorophyll-a concentrations in these areas compared to other regions [42]. The ecological water diversion project has substantially reconfigured the spatial structure of water quality. The southern inflow area, characterized by frequent water exchange, exhibits a greater capacity for pollutant dilution and diffusion, with TP concentrations varying between 0.05 and 0.18 mg L−1. However, Getis-Ord Gi hotspot analysis indicates that the northwestern shore has become a persistently sensitive zone for water quality, accounting for 22.7% of the lake area from 2012 to 2016 [34].
Temporally, water quality variations follow distinct seasonal rhythms. During the wet season (June–September), external inputs dominate; heavy rainfall events flush accumulated pollutants from the watershed into the lake via surface runoff, causing acute water quality deterioration in estuary areas. In the ice-covered period (November–April), limited re-aeration and vertical mixing under the ice promote an anaerobic environment in lower layers, enhancing internal nutrient release from sediments—the phosphate release rate under such conditions is 2.75 times that under aerobic conditions [15]. Additionally, solute exclusion during ice formation concentrates substances such as fluorides in the underlying water, creating high-fluoride zones in littoral shallow-water areas [43]. These unique ice-covered biogeochemical processes accumulate sufficient nutrients to support algal blooms after spring ice melt.

5.3. Sediment Pollution and Internal Release Mechanisms

Sediment serves as both a sink and a source of pollutants, recording the historical trajectory of watershed environmental pollution. Internal nutrient release from sediment is a key mechanism sustaining the eutrophic state of Hulun Lake [44,45]. Analysis of sediment cores from the northern part of the lake reveals a sharp increase in pollutant accumulation over the past century. Surface sediments (0–10 cm) contain organic matter (OM), total nitrogen (TN), and total phosphorus (TP) as high as 5.61%, 3.07 g kg−1, and 1.15 g kg−1, respectively, representing increases of 141%, 201%, and 248% compared to background values in the bottom layer (90–100 cm) [24]. The peak pollution level occurs in the 10–20 cm layer, corresponding to the period of intensified grazing and agricultural reclamation in the watershed during the 1980s [46,47]. Assessments using the geo-accumulation index (Igeo) and organic pollution index (OPI) indicate that modern sediments in Hulun Lake are already in a high-risk state [24]. Stable carbon isotope (δ13C) and C/N ratio analyses reveal an evolution in pollutant sources: modern sediments exhibit higher δ13C values and lower C/N ratios, suggesting a declining contribution of terrestrial organic matter and a significant increase in autochthonous aquatic sources. This shift potentially reflects a transition in the lake ecosystem from macrophyte-dominated to phytoplankton-dominated [24]. Phosphorus fractionation indicates that iron-bound phosphorus (Fe-P) and organic phosphorus (Org-P) are major components of the potentially releasable phosphorus pool, their stability highly dependent on the redox conditions of the overlying water [46]. Quantitative studies show that the bioavailable phosphorus (BAP) content in surface sediments reaches 555 mg kg−1, accounting for 52% of TP, indicating a high release potential. Annual releases from surface sediments are estimated at approximately 2992 tons of ammonium nitrogen (NH4+-N) and 144.9 tons of soluble reactive phosphorus (SRP), equivalent to 127% and 24% of external nitrogen and phosphorus inputs, respectively [15]. This substantial internal load means that even after effective external source control, sediments can sustain eutrophic conditions for an extended period, creating a significant “legacy effect” [25].

5.4. External Drivers of Eutrophication

External nutrient input is unequivocally the primary driver of eutrophication in Hulun Lake [35,48], with rivers, engineered water transfers, and atmospheric deposition constituting the principal pathways. Among these, riverine transport is the most significant conduit. The Kherlen River, as the largest inflow, not only contributes over 60% of the total lake water volume [8] but also delivers the highest nutrient loads. Its estuary water quality fluctuates dramatically, consistently rated as “poor” with key pollutant concentrations (TN, TP, COD) far exceeding the worst national water quality standards [9]. Critically, the pollution from this basin is predominantly non-point source in nature. Model estimates (DPeRS) indicate that 87.6% of TN and 88.9% of TP loads originate from surface runoff [9], a consequence of severe grassland degradation and soil erosion driven by regional overgrazing [49]. This translates into substantial seasonal fluxes, with wet-season inputs often pushing the equivalent pollution load hundreds of tons beyond water quality standards [50,51].
The role of other inflows is also shaped by their distinct watershed characteristics. The Urxun River, for instance, carries heavy loads of sediments and colored dissolved organic matter (CDOM) from its pastoral surroundings, creating a clear spatial imprint of elevated CDOM in the eastern and southern nearshore zones [23]. Furthermore, human intervention has created a new, potent pollution pathway. The ecological water diversion project, while designed to alleviate water scarcity, can paradoxically function as an engineered pollution conduit when source waters (e.g., from the Hailar River) are contaminated [10,26,52].
Atmospheric dry and wet deposition provides a supplementary nutrient input pathway. Grassland degradation and desertification in the basin enhance wind erosion, increasing atmospheric dust and aerosol loads, thereby elevating pollution from atmospheric deposition [53,54]. Coupled with the regional climate pattern where evaporation vastly exceeds precipitation, the impact of atmospheric deposition on the lake may be further amplified [12].

6. Watershed Human Activities and Water Environment Response

6.1. Hydrological and Ecological Effects of Land Use Change

Human activities in the Hulun Lake basin have profoundly impacted the lake’s water environment by altering underlying surface properties and ecological processes [55]. Over recent decades, the land use pattern in the watershed has changed significantly. From 1992 to 2020, cropland expanded by 3919 km2 and urban built-up areas increased continuously, while forest area decreased correspondingly [16,17,56]. These changes influence the water environment through two primary mechanisms: modifying watershed hydrological processes and increasing pollution loads. Natural vegetation (e.g., forests and grasslands) exhibits strong water conservation capacity, regulating runoff through canopy interception, litter layer retention, and enhanced infiltration. In contrast, impervious or low-coverage surfaces such as cropland and construction land reduce infiltration, accelerate surface runoff, and intensify soil erosion [57].
A study by Song, Yinglan [10] using Principal Component Analysis (PCA) also identified human activities as the main driver of lake surface shrinkage from 2000 to 2016 (explaining 48% of the variation), highlighting key roles of coal mining (through aquifer disruption), agricultural irrigation, and urbanization. These activities not only reduce the volume of clean water entering the lake but also introduce large quantities of nutrients and organic matter. Essentially, land use changes reshape the underlying surface properties and hydro-ecological processes of the watershed, exerting systemic and cascading impacts on the water environment [16,57].

6.2. Pollution Pathways and Ecological Impacts of Overgrazing

Overgrazing represents a characteristic anthropogenic pressure in the Hulun Lake basin, affecting the lake’s water environment through multiple pathways [58]. Long-term overstocking has led to severe degradation of the grassland ecosystem. From 1980 to 2015, livestock numbers increased significantly (average annual increase of 80.5 thousand heads, p < 0.05), considerably promoting wind erosion and desertification [53]. Studies indicate that within the Kherlen and Urxun River corridors, grazing is the most important factor for habitat degradation, with a contribution rate exceeding 80% [57]. Overgrazing not only causes a notable decline in vegetation cover (65.01% of the area showed decreasing vegetation coverage from 1986 to 2017) [59] but also disrupts soil structure and intensifies erosion. As a result, large amounts of sediment and adsorbed nitrogen and phosphorus nutrients are transported by rainfall runoff into the river network and ultimately into Hulun Lake [9,60]. Zhao, Yang [47] demonstrated through simulated rainfall experiments that sediment yield modulus and total phosphorus loss under heavy rainfall were significantly higher in degraded grasslands than in non-degraded areas. Traditional grazing pastures exhibited TP losses 2.2 times greater than those in fenced pastures. Source tracing by Lu, Guo [46] further revealed that 70.7% of the particulate phosphorus (PP) in Hulun Lake’s sediments originates from degraded grassland.
Notably, overgrazing also gives rise to a unique “tumbleweed-driven cross-media pollution” pathway: intensive grazing leads to a 65% decline in high-quality forage biomass, replaced by annual tumbleweed species. These plants are blown onto the lake surface by wind in winter and decompose after spring ice melt, directly increasing the chemical oxygen demand (COD) in Hulun Lake by 62–119 mg L−1 (r = 0.635). This creates a clear chain reaction: overgrazing → grassland degradation → desertification → tumbleweed proliferation → wind-driven transport into the lake → decomposition and pollution release [61].

6.3. Ecological Security Assessment

Under the multiple pressures facing the Hulun Lake ecosystem, a scientifically grounded assessment of ecological security provides a critical basis for establishing sound conservation objectives and management strategies. In terms of water quantity security, studies indicate that to prevent lake salinization (with water salinity below 1000 mg L−1), the water level must be maintained no lower than 544.33 m, corresponding to a lake area of 2075.76 km2 and a storage capacity of 11.272 billion m3. This volume represents the minimum requirement for sustaining the lake’s basic ecological functions [17]. Under current conditions, however, the average annual inflow from the Kherlen and Urxun Rivers is only about 1.06 billion m3, falling far below the minimum ecological water demand. This shortfall underscores both the urgency and long-term necessity of ecological water supplementation projects.
Water quality security remains challenging, with the lake fluctuating between moderate and severe eutrophication over the long term [38,39]. Notably, the water diversion project has significantly altered the hydrodynamic pattern of Hulun Lake [10], thereby reshaping the spatial distribution of pollutants. As a result, water quality-sensitive areas have continued to expand toward the northwestern shore [34].
Ecosystem health assessments reveal severe degradation: phytoplankton diversity has declined, with cyanobacteria becoming a single dominant group; zooplankton communities show an increase in pollution-tolerant species and a decrease in clean-water indicators; and the fish community structure has simplified, accompanied by a reduction in species richness [12]. An ecological security assessment based on the Pressure-State-Response (PSR) model showed that the ecological security index of the Hulun Lake watershed dropped sharply by 13.9% between 1990 and 2000. Spatially, a distinct gradient pattern emerged: “eastern forest area (safe) → central grassland (warning) → western lake area (dangerous)” [54]. Studies using the ECOPATH model further demonstrated that the food web structure of Hulun Lake had shifted from a pyramid shape in 1982 to a truncated form between 2009 and 2014. Energy flows exhibit “short-circuiting”, while system maturity and cycling efficiency have significantly declined—evinced by a drop in Finn’s cycling index from 15.5% to 5.1% [25]. These findings collectively indicate that the ecosystem is in a generally “sub-healthy” to “unhealthy” state.

7. Conclusions

Warming-drying climate and intensive human activities. Research indicates that climate change dominates the hydrological imbalance primarily through two key pathways: “reduced precipitation → soil drought → runoff attenuation” and “rising temperature → increased evapotranspiration”. Human activities further amplify this systemic crisis through multi-pathway, multi-scale interference, among which overgrazing not only diminishes the water conservation capacity of the watershed via vegetation degradation and soil erosion but also introduces substantial nutrient inputs through pathways such as livestock manure discharge and cross-media transport of tumbleweed-associated pollutants. Based on the above mechanisms, this study proposes the following specific management decisions:
  • Implement Smart Water Resource Management Centered on Ecological Flow:
Optimize Ecological Water Diversion Projects: Establish dynamic water supplementation plans linked to source water quality. Consider constructing pre-reservoirs or constructed wetlands for purification to prevent the project from becoming a new pollution conduit [40]. The timing of water diversion should be coordinated with the lake’s natural hydrological cycle to maximize ecological benefits [10].
Safeguard Ecological Water Level: Maintaining the lake level above 544.33 m should be a core management objective, representing the minimum requirement to prevent salinization and sustain basic ecological functions [17]. This requires integrated basin management to ensure that inflow meets this minimum ecological water demand.
2.
Enforce Precision Grazing Regulation Based on Watershed Carrying Capacity:
Implement the Grass-Livestock Balance System: Scientifically determine the carrying capacity for different grassland types based on remote sensing and ground surveys [57]. Promote scientific grazing models to facilitate grassland recovery, thereby controlling soil erosion and non-point source pollution [47].
Manage Tumbleweed Pollution: Employ measures to remove tumbleweed plants before their seeds mature and are blown into the lake, cutting off this unique “cross-media pollution” pathway [61].
3.
Construct an Integrated Watershed Management System Synergizing Water Quantity, Quality, and Ecology:
Control Internal Pollution Load: Explore engineering techniques to target and reduce the release flux of active phosphorus from sediments [15,25].
Restore Ecological Buffer Zones: Establish and rehabilitate ecological buffer zones to effectively intercept and purify sediments and nutrients in surface runoff [10].
The proposed integrated “water quantity-water quality-aquatic ecology” framework offers a comprehensive pathway for addressing the complex challenges facing Hulun Lake. However, the implementation of such an integrated framework faces several challenges, including the significant data requirements for monitoring and modeling complex watershed processes [8], and the need for cross-sectoral coordination. These limitations highlight the importance of adaptive management—applying the framework principles in a flexible, phased approach that can be refined as new data and resources become available.
Existing management projects, particularly ecological water diversion, have demonstrated preliminary success in restoring water levels, but their ecological impacts are highly complex [12]. Relying solely on water quantity regulation cannot resolve the systemic ecological crisis. It is essential to establish an ecological restoration and integrated management framework based on watershed integrity, aiming for the synergistic enhancement of “water quantity-water quality-aquatic ecology,” to achieve fundamental recovery of the Hulun Lake ecosystem.

8. Outlook

Despite significant research progress on the evolution of the Hulun Lake water environment, considerable knowledge gaps and methodological challenges remain, hindering accurate prediction and effective management of the lake ecosystem behavior. Building on a systematic review of existing studies, future research should prioritize the following five directions to address the scientific and practical challenges in restoring Hulun Lake and similar inland lakes in cold and arid regions.
First, there is a need to enhance the precise characterization and quantification of key internal processes within the system. Current understanding of groundwater exchange fluxes, pathways, and their influence on lake water quality remains inadequate. The spatiotemporal dynamics of groundwater-lake water interactions and their role in pollutant transport and transformation require urgent clarification [2]. Simultaneously, biogeochemical processes at the sediment–water and water–air interfaces need deeper investigation, particularly nutrient release mechanisms under anaerobic conditions during ice-covered periods and their potential triggering effect on spring algal blooms [15]. Research on these interface processes will benefit from developing high-resolution in situ monitoring technologies and multi-scale model coupling approaches to elucidate the dynamics of critical mechanisms.
Second, it is crucial to elucidate the nonlinear coupling effects and identify tipping points among multiple stressors. Climate warming-drying, human activities, and hydrological changes jointly influence the lake ecosystem through complex feedback loops, yet their synergistic or antagonistic interactions remain unquantified [16]. Developing coupled watershed–lake models can help quantify the cascading effects of nutrient-hydrology-climate interactions. Special attention should be paid to how warming-drying amplifies pollution risks by prolonging water residence time, increasing storm runoff scouring, and accelerating organic matter mineralization [17]. Furthermore, the feedback mechanisms between grazing activities and hydrological processes warrant an in-depth study to assess how overgrazing may amplify the risk of abrupt water quality deterioration. The ultimate aim is to identify critical tipping points and ecological thresholds for management [25].
Third, future work should focus on analyzing the ecological effects of human interventions and identifying optimized regulation pathways. The ecological water diversion project presents an “ecological paradox” in practice—alleviating water scarcity while potentially exacerbating pollution—and its long-term ecological consequences require urgent assessment [40]. Developing dynamic optimal water allocation models that incorporate water quality objectives is necessary. Integrating engineering measures such as pre-reservoirs for water quality purification and constructed wetlands into diversion systems could provide pathways for synergistically ensuring both water quantity and quality [10]. Additionally, exploring ecological restoration models based on Nature-based Solutions (NbS), such as restoring riparian vegetation and establishing ecological buffer zones to reduce non-point source pollution, could enhance ecosystem services synergistically.
Fourth, efforts should be directed toward improving data integration, simulation capabilities, and early-warning systems. Significant gaps persist in the spatiotemporal coverage of monitoring data, particularly for key interfaces like groundwater and the sediment-water interface [8]. Establishing an integrated “space-air-ground” stereoscopic monitoring network, combining remote sensing, automated sensors, and traditional sampling, is vital to bridge these data gaps. Simultaneously, enhancing water environment simulation and early-warning systems using multi-source data and artificial intelligence, including machine learning to uncover nonlinear relationships among driving factors, will improve the prediction of ecological risks such as algal blooms and sudden water quality changes [22,62].
Finally, establishing a comprehensive evaluation system that links ecosystem services to sustainable management is imperative. Current research often focuses on single-element assessments of the water environment, lacking a holistic perspective that connects environmental changes to ecosystem services (e.g., water supply, fisheries, carbon sequestration, tourism) and human well-being [12]. Developing coupled social-ecological system models to assess changes in ecosystem services and their socio-economic impacts under different management scenarios can provide a theoretical basis for resilient watershed management and NbS implementation. Particular attention should be paid to cost-benefit analyses of ecological and economic impacts associated with human interventions like water diversion projects and grazing management, thereby providing a scientific foundation for sustainable development decision-making.

Author Contributions

B.H.: Conceptualization, Writing—Original Draft. Writing—review and editing. C.C., Y.C., L.W. and Y.Y.: Investigation and Data collation. Y.L. and Z.W.: Funding acquisition and Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work is financially supported by Science and Technology Major Project of Inner Mongolia (Nos. ZDZX2018054 and 2022YFHH0017), China and by the National Natural Science Funds of China (31370474 and 32160279).

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Tao, S.L.; Fang, J.Y.; Zhao, X.; Zhao, S.Q.; Shen, H.H.; Hu, H.F.; Tang, Z.Y.; Wang, Z.H.; Guo, Q.H. Rapid loss of lakes on the Mongolian Plateau. Proc. Natl. Acad. Sci. USA 2015, 112, 2281–2286. [Google Scholar] [CrossRef]
  2. Pan, H.; Tang, J.P.; Cheng, L.; Li, M.C. Ensemble projections of climate and streamflow in a typical basin of semi-arid steppes in Mongolian Plateau of 2021–2100. Adv. Clim. Change Res. 2024, 15, 230–243. [Google Scholar] [CrossRef]
  3. Williamson, C.E.; Saros, J.E.; Vincent, W.F.; Smol, J.P. Lakes and reservoirs as sentinels, integrators, and regulators of climate change. Limnol. Oceanogr. 2009, 54, 2273–2282. [Google Scholar] [CrossRef]
  4. Adrian, R.; O’Reilly, C.M.; Zagarese, H.; Baines, S.B.; Hessen, D.O.; Keller, W.; Livingstone, D.M.; Sommaruga, R.; Straile, D.; Donk, E.V. Lakes as sentinels of climate change. Limnol. Oceanogr. 2009, 54, 2283–2297. [Google Scholar] [CrossRef] [PubMed]
  5. Maberly, S.C.; O’Donnell, R.A.; Woolway, R.I.; Cutler, M.E.J.; Tyler, A.N. Global lake thermal regions shift under climate change. Nat. Commun. 2020, 11, 1232. [Google Scholar] [CrossRef] [PubMed]
  6. Cui, X.Y.; Yang, J.; Hao, J.X.; Bu, T.G.; Liu, Z.G. The formation history and changes of Hulun Lake. Inn. Mong. Sci. Technol. Econ. 2015, 1, 43–47. [Google Scholar]
  7. Zhang, Z.K.; Wang, S.M. Comparison of Hulun Lake surface fluctuation, peat development, eolian sand paleosol sequence and its paleoclimatic significance since 13 ka. J. Arid Land Res. Environ. 2000, 3, 56–59. [Google Scholar]
  8. Cai, Z.S.; Jin, T.Y.; Li, C.Y.; Ofterdinger, U.; Zhang, S.; Ding, A.Z.; Li, J.C. Is China’s fifth-largest inland lake to dry-up? Incorporated hydrological and satellite-based methods for forecasting Hulun lake water levels. Adv. Water Resour. 2016, 94, 185–199. [Google Scholar] [CrossRef]
  9. Zhao, Y.L.; Sun, B.; Shi, X.H.; Tao, Y.L.; Wang, Z.L.; Wang, S.H.; Ye, B.W. The Relationship Between Riparian Soil Nutrients and Water Quality in Inlet Sections of Lakes: A Case Study of the Kherlen River. Sustainability 2025, 17, 1367. [Google Scholar] [CrossRef]
  10. Song, W.; Yinglan, A.; Wang, Y.T.; Fang, Q.Q.; Tang, R. Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning. J. Contam. Hydrol. 2024, 260, 104282. [Google Scholar] [CrossRef]
  11. Wang, L.; Schuster, M.; Xin, S.W.; Zainescu, F.; Xue, X.Y.; Storms, J.; May, J.H.; Nutz, A.; van der Vegt, H.; Bozetti, G.; et al. Littoral landforms of Lake Hulun and Lake Buir (China and Mongolia): Wind-driven hydrosedimentary dynamics and resulting clastics distribution. J. Palaeogeogr. 2024, 13, 309–326. [Google Scholar] [CrossRef]
  12. Dai, X.L.; A, Y.L.; Wang, L.B.; Xue, B.L.; Wang, Y.T.; Zhou, X.Y.; Ma, G.W.; Li, H.; Chen, H.; Liao, T.K.; et al. Review on the Collaborative Research of Water Resources-Water Environment-Water Ecology in Hulun Lake. Water 2024, 16, 2508. [Google Scholar] [CrossRef]
  13. Zhang, Y.; Wang, S.Y.; Xu, W.J.; Zhang, B.; Yi, L.X.; Lu, X.Q. Geochemical Characteristics and Their Environmental Implications for the Water Regime of Hulun Lake, Inner Mongolia, China. Water 2022, 14, 3696. [Google Scholar] [CrossRef]
  14. Wu, Y.N.; Zhang, F.J.; Ran, C.Q. Analysis of Climate Change in the Krulun River Basin in the Eastern Mongolian Plateau over the Past 50 Years. J. Dalian Natl. Univ. 2009, 11, 193–195. [Google Scholar]
  15. Liu, B.; Zhang, X.F.; Tong, Y.; Ao, W.; Wang, Z.L.; Zhu, S.L.; Wang, Y.P. Quantification of Nutrient Fluxes from Sediments of Lake Hulun, China: Implications for Plateau Lake Management. Sustainability 2023, 15, 8680. [Google Scholar] [CrossRef]
  16. Huang, Y.Q.; Yao, B.; Li, Y.; Zhang, H.; Wang, S.R. Deciphering Hulun lake level dynamics and periodical response to climate change during 1961–2020. J. Hydrol.-Reg. Stud. 2023, 46, 101352. [Google Scholar] [CrossRef]
  17. Guo, J.; Zhang, Y.L.; Shi, X.H.; Sun, B.; Wu, L.J.; Wang, W. Driving Mechanisms of the Evolution and Ecological Water Demand of Hulun Lake in Inner Mongolia. Water 2022, 14, 3415. [Google Scholar] [CrossRef]
  18. Kravitz, J.; Matthews, M.; Bernard, S.; Griffith, D. Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges. Remote Sens. Environ. 2019, 237, 111562. [Google Scholar] [CrossRef]
  19. Wang, Z.J.; Li, C.Y.; Li, W.P.; Zhang, S. Calculation and Analysis of Water Balance in Hulun Lake, Inner Mongolia. J. Lake Sci. 2012, 24, 273–281. [Google Scholar]
  20. Sagan, V.; Peterson, K.T.; Maimaitijiang, M.; Sidike, P.; Sloan, J.; Greeling, B.A.; Maalouf, S.; Adams, C. Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Sci. Rev. 2020, 205, 103187. [Google Scholar] [CrossRef]
  21. Hu, Y.L.; Zhou, Q.; Li, T.; Wang, H.S.; Jiang, L.M.; Shen, X. Accurate estimation of lake levels by the spatio-temporal modeling of satellite altimetry data. Remote Sens. Environ. 2023, 295, 113681. [Google Scholar]
  22. Yan, H.; Fu, H.Y.; Chen, Z.; Liao, A.R.; Shen, M.Y.; Tao, Y.; Wu, Y.H.; Hu, H.Y. A multi-task deep neural network reveals inflowing river impacts for predictive lake management. Environ. Sci. Ecotechnol. 2025, 26, 100592. [Google Scholar] [CrossRef] [PubMed]
  23. Zhang, H.; Yao, B.; Wang, S.R.; Huang, Y.Q. Understanding the changes of optically active substances (OACs) in Hulun Lake in the past 35 years and its indication to the degradation of aquatic ecology. J. Clean. Prod. 2022, 377, 134286. [Google Scholar] [CrossRef]
  24. Wang, W.N.; Zhao, L.; Li, W.; Chen, J.Y.; Wang, S.H. Response mechanism of sediment organic matter of plateau lakes in cold and arid regions to climate change: A case study of Hulun Lake, China. Environ. Sci. Pollut. Res. 2023, 30, 26778–26790. [Google Scholar]
  25. Xue, Y.F.; Kong, X.Z.; Mao, Z.G.; Zhang, C.; Xue, B.; Shi, X.H.; Gu, X.H. Hydrological variation drives changes in food web structure and ecosystem function with potential hysteresis in a large temperate shallow lake. J. Hydrol. 2025, 650, 132463. [Google Scholar] [CrossRef]
  26. Tao, Y.L.; Zhang, Y.R.; Kong, X.Z.; Zhang, S.; Xue, Y.F.; Ao, W.; Pang, B.; Dou, H.S.; Xue, B. Record-setting cyanobacterial bloom in the largest freshwater lake in northern China caused by joint effects of hydrological variations and nutrient enrichment. Environ. Res. 2025, 268, 120813. [Google Scholar] [CrossRef] [PubMed]
  27. Sayers, M.J.; Fahnenstiel, G.L.; Shuchman, R.A.; Bosse, K.R. A new method to estimate global freshwater phytoplankton carbon fixation using satellite remote sensing: Initial results. Int. J. Remote Sens. 2021, 42, 3708–3730. [Google Scholar] [CrossRef]
  28. Yan, Z.; Fang, C.; Song, K.; Wang, X.; Wen, Z.; Shang, Y.; Tao, H.; Lyu, Y. Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images. Sci. Rep. 2025, 15, 2739. [Google Scholar] [CrossRef]
  29. Janssen, A.B.G.; Teurlincx, S.; Beusen, A.H.W.; Huijbregts, M.A.J.; Rost, J.; Schipper, A.M.; Seelen, L.M.S.; Mooij, W.M.; Janse, J.H. PCLake plus: A process-based ecological model to assess the trophic state of stratified and non-stratified freshwater lakes worldwide. Ecol. Model. 2019, 396, 23–32. [Google Scholar]
  30. Cao, Y.; Fu, C.S.; Wang, X.; Dong, L.Y.; Yao, S.C.; Xue, B.; Wu, H.W.; Wu, H.H. Decoding the dramatic hundred-year water level variations of a typical great lake in semi-arid region of northeastern Asia. Sci. Total Environ. 2021, 770, 145353. [Google Scholar]
  31. Li, C.L.; Leal, W.; Nagy, G.J.; Wang, J.; Ciani, A.; Sidsaph, H.; Fedoruk, M.; Yin, S.; Bao, Y.H.; Ayal, D.Y.; et al. Satellite imagery evidence for a multiannual water level decline in Hulun Lake, China, with suggestions to future policy making responses. Erde 2019, 150, 31–39. [Google Scholar]
  32. Fu, C.S.; Wu, H.W.; Zhu, Z.C.; Song, C.Q.; Xue, B.; Wu, H.H.; Ji, Z.M.; Dong, L.Y. Exploring the potential factors on the striking water level variation of the two largest semi-arid-region lakes in northeastern Asia. Catena 2021, 198, 105037. [Google Scholar] [CrossRef]
  33. Linderholm, H.W.; Ou, T.H.; Jeong, J.H.; Folland, C.K.; Gong, D.Y.; Liu, H.B.; Liu, Y.; Chen, D.L. Interannual teleconnections between the summer North Atlantic Oscillation and the East Asian summer monsoon. J. Geophys. Res.-Atmos. 2011, 116, 13107. [Google Scholar] [CrossRef]
  34. Wu, R.; Zhang, S.; Liu, Y.; Shi, X.H.; Zhao, S.N.; Kang, X.E.; Quan, D.; Sun, B.; Arvola, L.; Li, G.H. Spatiotemporal variation in water quality and identification and quantification of areas sensitive to water quality in Hulun lake, China. Ecol. Indic. 2023, 149, 110176. [Google Scholar] [CrossRef]
  35. Smith, V.H. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res. 2003, 10, 126–139. [Google Scholar] [CrossRef]
  36. Carpenter, S.R.; Correll, D.L.; Howarth, R.W.; Sharpley, A.N.; Smith, V.H. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 1998, 8, 559–568. [Google Scholar] [CrossRef]
  37. Wang, R.; Wang, F.; Ma, Q.; Xiong, W.H. Water quality assessment and impact factor driving analysis of Hulun Lake in recent 40 years. Latit. Longit. Sky 2025, 1, 76–81. [Google Scholar]
  38. Liang, L.E.; Li, C.Y.; Shi, X.H.; Zhao, S.N.; Tian, Y.; Zhang, L.J. Eutrophication trend and analysis of Hulun Lake, Inner Mongolia, 2006–2015. J. Lake Sci. 2016, 28, 1265–1273. [Google Scholar]
  39. Yu, H.F.; Shi, X.H.; Sun, B.; Zhao, S.N.; Liu, Y.; Zhao, M.L. Analysis on water quality and eutrophication change of Hulun Lake from 2011 to 2020. Arid Zone Res. 2021, 38, 1534–1545. [Google Scholar]
  40. Zhang, H.; Li, Y.; Yao, B.; Huang, Y.Q.; Wang, S.R.; Ni, S.Q. Untangling the coupling effect of water quality and quantity on lake algal blooms in Lake Hulun from a dual perspective of remote sensing and sediment cores. J. Hydrol. 2024, 645, 132141. [Google Scholar] [CrossRef]
  41. Hu, B.T.; Liu, Y.H.; Chen, Y.X.; Yao, Y.P.; Liu, H.Y.; Wang, Z.S. Water quality and pollution source apportionment responses to rainfall in steppe lake estuaries: A case study of Hulun Lake in northern China. Ecol. Indic. 2024, 168, 112791. [Google Scholar] [CrossRef]
  42. Li, X.Y.; Liu, Y.; Zhang, S.; Li, G.H.; Tao, Y.L.; Wang, S.H.; Yu, H.F.; Shi, X.H.; Zhao, S.N. Evolution Characteristics and Driving Factors of Cyanobacterial Blooms in Hulun Lake from 2018 to 2022. Water 2023, 15, 3765. [Google Scholar] [CrossRef]
  43. Jun, S.; Zhang, B.; Wang, P.F.; Li, H.; Jiang, X.; Wang, S.H. Evolution characteristics and influencing factors of fluoride in Hulun Lake. Environ. Sci. Res. 2021, 34, 841–848. [Google Scholar]
  44. Sondergaard, M.; Jensen, J.P.; Jeppesen, E. Role of sediment and internal loading of phosphorus in shallow lakes. Hydrobiologia 2003, 506, 135–145. [Google Scholar] [CrossRef]
  45. Wu, Y.H.; Wen, Y.J.; Zhou, J.X.; Wu, Y.Y. Phosphorus release from lake sediments: Effects of pH, temperature and dissolved oxygen. Ksce J. Civ. Eng. 2014, 18, 323–329. [Google Scholar] [CrossRef]
  46. Lu, X.F.; Guo, Y.N.; Wang, G.X.; Jiang, X.; Wang, K. Distribution of phosphorus forms in Hulun Lake basin and traceability of particulate phosphorus. China Environ. Sci. 2023, 43, 4810–4818. [Google Scholar]
  47. Zhao, W.; Yang, P.L.; Li, H.S.; Hu, G.W.; Liu, X.P. Characteristics of soil and water loss, nitrogen and phosphorus loss in three types of grassland use in Hulun Lake basin. Trans. CSAE 2011, 27, 6. [Google Scholar]
  48. Qin, B.Q.; Gao, G.; Zhu, G.W.; Zhang, Y.L.; Song, Y.Z.; Tang, X.M.; Xu, H.; Deng, J.M. Lake eutrophication and its ecosystem response. Chin. Sci. Bull. 2013, 58, 961–970. [Google Scholar] [CrossRef]
  49. Xie, C.Y.; Wang, C.Y.; Huang, L.; Gao, B.B.; Yin, W.J.; Wang, Q.T.; Chen, H.J.; Feng, Q.L.; Li, S.H.; Feng, A.P. Development of a 2022 Spatial Distribution Dataset for Surface Source Pollution Load in Grassland type Watersheds of the Krulun River Basin Integrated with Multi source Information. J. Agric. Big Data 2025, 7, 31–42. [Google Scholar]
  50. Li, W.P.; Chen, A.H.; Yu, L.H.; Yang, W.H.; Yin, Z.Y.; Yang, P.F.; Jiao, L.Y. Pollutant flux of the main river flowing into Hulun Lake during the flood season (2010–2014). J. Lake Sci. 2016, 28, 281–286. [Google Scholar]
  51. Yang, W.H.; Chen, A.H.; Li, W.P.; Yu, L.H.; Yin, Z.Y.; Han, P.J.; Duan, H.J. Water quality assessment of the Klulun River and its impact on the water environment of Hulun Lake. Environ. Eng. 2015, 33, 113–116. [Google Scholar]
  52. Zuo, J.; Yang, S.Y.; Grossart, H.P.; Xiao, P.; Zhang, H.; Sun, R.; Li, G.Y.; Jiang, H.R.; Zhao, Q.H.; Jiao, M.; et al. Sequential decline in cyanobacterial, total prokaryotic, and eukaryotic responses to backward flow in a river connected to Lake Taihu. Water Res. 2025, 269, 122784. [Google Scholar] [CrossRef]
  53. Na, R.S.; Du, H.B.; Na, L.; Shan, Y.; He, H.S.; Wu, Z.F.; Zong, S.W.; Yang, Y.; Huang, L.R. Spatiotemporal changes in the Aeolian desertification of Hulunbuir Grassland and its driving factors in China during 1980–2015. Catena 2019, 182, 104123. [Google Scholar] [CrossRef]
  54. Cao, B.S.; Shan, N.; Gu, Y.Y.; Ao, W.; Pang, B.; Dou, H.S.; Wang, W.L.; Zou, C.X. Ecological security assessment and spatial-temporal distribution pattern change trend in Hulun Lake basin. Environ. Sci. Res. 2021, 34, 801–811. [Google Scholar]
  55. Foley, J.A. Global Consequences of Land Use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef]
  56. Mei, R.; Han, L.R.; Li, H. Study on the Change of Land Ecosystem Service Value in Hulun Lake National Nature Reserve. J. Hulunbeier Coll. 2023, 31, 86–92. [Google Scholar]
  57. Gu, Y.Y.; Lin, N.F.; Cao, B.S.; Ye, X.; Pang, B.; Du, W.; Dou, H.S.; Zou, C.X.; Xu, C.; Xu, D.L.; et al. Assessing the effectiveness of Ecological Conservation Red Line for mitigating anthropogenic habitat degradation in river corridors. Ecol. Indic. 2023, 154, 110742. [Google Scholar] [CrossRef]
  58. Zhang, M.N.; Delgado-Baquerizo, M.; Li, G.Y.; Isbell, F.; Wang, Y.; Hautier, Y.; Wang, Y.; Xiao, Y.L.; Cai, J.T.; Pan, X.B.; et al. Experimental impacts of grazing on grassland biodiversity and function are explained by aridity. Nat. Commun. 2023, 14, 5040. [Google Scholar] [CrossRef]
  59. Mao, P.P.; Zhang, J.; Li, M.; Liu, Y.L.; Wang, X.; Yan, R.R.; Shen, B.B.; Zhang, X.; Shen, J.; Zhu, X.Y.; et al. Spatial and temporal variations in fractional vegetation cover and its driving factors in the Hulun Lake region. Ecol. Indic. 2022, 135, 108490. [Google Scholar] [CrossRef]
  60. Onda, Y.; Kato, H.; Tanaka, Y.; Tsujimura, M.; Davaa, G.; Oyunbaatar, D. Analysis of runoff generation and soil erosion processes by using environmental radionuclides in semiarid areas of Mongolia. J. Hydrol. 2007, 333, 124–132. [Google Scholar] [CrossRef]
  61. Wang, W.L.; Li, W.J.; Yan, Y.; Liu, B.; Wang, T.J.; Mao, S.C.; Song, L.H.; Dou, H.S.; Ao, W.; Zou, C.X. Organic Matter Pollution During the Spring Thaw in Hulun Lake Basin: Contribution of Multiform Human Activities. Bull. Environ. Contam. Toxicol. 2020, 105, 307–316. [Google Scholar] [CrossRef] [PubMed]
  62. Morain, A.; Nedd, R.; Poole, K.; Hawkins, L.; Jones, M.; Washington, B.; Anandhi, A. Artificial Intelligence Application in Nonpoint Source Pollution Management: A Status Update. Sustainability 2025, 17, 5810. [Google Scholar] [CrossRef]
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Figure 1. Water level changes and key driving factors in Hulun Lake.
Figure 1. Water level changes and key driving factors in Hulun Lake.
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Table 1. Document screening and selection process.
Table 1. Document screening and selection process.
StageProcess DescriptionOutcome
1Initial records identified through database searching (Web of Science, CNKI)n = 348 records
2Duplicates removed using reference management software and manual checkingn = 348 records
3Records based on title and abstract against eligibility criterian = 98 records
4Full-text articles assessed for eligibilityn = 63 records
5Final number of studies included in the qualitative synthesisn = 63 records
Table 2. Summary of primary research methods, research contents, and limitations in Hulun Lake studies.
Table 2. Summary of primary research methods, research contents, and limitations in Hulun Lake studies.
Research Method CategorySpecific TechniquesResearch ContentsMethod Advantages and ApplicabilityLimitations
Remote Sensing & Machine LearningSatellite Altimeters (TOPEX/Poseidon, Jason); Optical Sensors (Landsat, Sentinel-3); Machine Learning Algorithms (Random Forest, XGBoost)
  • Large-scale, long-term monitoring of water level and surface area [8,21].
  • Retrieval of water quality parameters (Chl-a, TN, TP) [10].
  • Estimation of phytoplankton community biomass [22].
  • Advantages: Broad spatial coverage, long time series, relatively low cost, facilitates data acquisition in inaccessible areas.
  • Applicability: Suitable for macroscopic spatiotemporal dynamic monitoring and trend analysis; effectively supplements periods lacking ground-based data.
  • Susceptible to weather conditions (clouds, rain).
  • Retrieval accuracy depends on atmospheric correction and algorithm models, requiring validation with ground data.
  • Limited capability for acquiring vertical water column information.
Geochemical & Sediment AnalysisStable Isotopes (δD, δ18O); Fluorescence Spectroscopy (EEM-PARAFAC); Isotopic Dating (210Pb, 137Cs); High-Resolution Sediment Sampling
  • Tracing water cycle pathways and assessing evaporation effects [23].
  • Identifying sources and composition of dissolved organic matter [24].
  • Reconstructing pollution history [24].
  • Quantifying nutrient diffusion fluxes across the sediment-water interface [15].
  • Advantages: Capable of revealing material sources, transport and transformation processes, and historical evolution; provides strong mechanistic insights.
  • Applicability: Suitable for source apportionment analysis, long-term process reconstruction, and mechanistic studies of interfacial biogeochemical processes.
  • Typically provides point-scale information; spatial representativeness requires multiple sampling points.
  • Complex analytical procedures, demanding high-end equipment and expertise.
  • Dating results may have uncertainties in areas with sediment disturbance.
Numerical Models & Driving Mechanism AnalysisStructural Equation Modeling; Watershed Hydrological Models (SWAT); Ecological Models (ECOPATH, PCLake); Hydrodynamic-Water Quality Models (GOTM-WET)
  • Identifying and quantifying multi-factor driving mechanisms [16].
  • Future scenario forecasting (runoff, climate) [2].
  • Simulating ecosystem dynamics and food webs [25].
  • Identifying key drivers of algal bloom outbreaks [26].
  • Advantages: Capable of integrating multi-source data, quantifying complex causal relationships, and simulating/predicting system behavior.
  • Applicability: Suitable for mechanistic investigation, future prediction, management scenario simulation, and decision support.
  • Model development requires substantial data support and parameter calibration.
  • Models are simplifications of reality, leading to inherent uncertainties in simulations.
  • Requires significant expertise and experience from the user.
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MDPI and ACS Style

Hu, B.; Liu, Y.; Chen, C.; Yao, Y.; Chen, Y.; Wang, L.; Wang, Z. A Review of the Characteristics and Mechanisms of Water Environment Evolution in Hulun Lake Under the Dual Drivers of Climate Warming-Drying and Human Activities. Sustainability 2025, 17, 10395. https://doi.org/10.3390/su172210395

AMA Style

Hu B, Liu Y, Chen C, Yao Y, Chen Y, Wang L, Wang Z. A Review of the Characteristics and Mechanisms of Water Environment Evolution in Hulun Lake Under the Dual Drivers of Climate Warming-Drying and Human Activities. Sustainability. 2025; 17(22):10395. https://doi.org/10.3390/su172210395

Chicago/Turabian Style

Hu, Bingtao, Yuhong Liu, Cheng Chen, Yipeng Yao, Yixue Chen, Lixin Wang, and Zhongsheng Wang. 2025. "A Review of the Characteristics and Mechanisms of Water Environment Evolution in Hulun Lake Under the Dual Drivers of Climate Warming-Drying and Human Activities" Sustainability 17, no. 22: 10395. https://doi.org/10.3390/su172210395

APA Style

Hu, B., Liu, Y., Chen, C., Yao, Y., Chen, Y., Wang, L., & Wang, Z. (2025). A Review of the Characteristics and Mechanisms of Water Environment Evolution in Hulun Lake Under the Dual Drivers of Climate Warming-Drying and Human Activities. Sustainability, 17(22), 10395. https://doi.org/10.3390/su172210395

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