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Article

Lake Water Ecological Simulation for a Typical Alpine Lake on the Tibetan Plateau

by
Qunhui Zhang
1,*,
Liang Chang
1,2,3,
Xiaofan Gu
1,2,3,
Rui Duan
1 and
Maonan Ma
4
1
Xi’an Geological Survey Center, China Geological Survey, Xi’an 710119, China
2
Shaanxi Water Resources and Environment Engineering Technology Research Center, Xi’an 710119, China
3
Key Laboratory of Groundwater and Ecology in Arid and Semi-Arid Areas, China Geological Survey, Xi’an 710119, China
4
The Institute of Geological Survey, University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(14), 1982; https://doi.org/10.3390/w16141982
Submission received: 7 June 2024 / Revised: 10 July 2024 / Accepted: 11 July 2024 / Published: 12 July 2024
(This article belongs to the Special Issue Water Environment Pollution and Control, Volume III)
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Abstract

:
Lakes on the Tibetan Plateau (TP) serve as both indicators of and safeguards against climate change, playing a crucial role in the aquatic ecosystems of the TP. While considerable attention has been devoted to studying the thermal and dynamic processes of TP lakes, research focusing on their ecological variations has been limited. In this study, we selected Namco, a representative lake on the TP, to investigate its water ecological processes using the AQUATOX lake ecological model. Long-term ecological variations spanning from 1980 to 2020 were analyzed based on lake observations. Our results revealed a consistent increase in water nutrients, particularly total nitrogen (WTN), and total phosphorus (WTP), over the study period. Additionally, the concentrations of chlorophyll-a (Chl-a) and water gross and net primary production (WGPP and WNPP) exhibited a significant upward trend. Despite the persistent state of poor nutrition in the lake, the ecological conditions improved. Multiple linear regression analysis indicated that the concentrations of WGPP, WNPP, and Chl-a were more sensitive to the local climate and hydrology compared to WTN and WTP. A continuously warming climate would heat up the lake water body, further enhancing primary production and improving water quality in the future. This study provides insights for lake limnological and ecological research and can be used to inform water management strategies in high-altitude alpine regions.

1. Introduction

Lakes are invaluable natural resources and comprise approximately 1.8% of the global land surface area [1,2]. Distinguished by their unique characteristics, including their substantial heat capacity, robust evaporation rates, and expansive level surfaces, lakes play a pivotal role in shaping local climates. These attributes foster increased precipitation in surrounding areas, mitigate temperature fluctuations, and elevate wind speed and humidity levels [3,4,5]. Furthermore, lakes offer a multitude of benefits, including the provision of aquatic resources and freshwater provision, facilitation of power generation, support for transportation networks, and the promotion of tourism. Consequently, they significantly contribute to the sustainable development of both the economy and the environment in their respective regions [6,7].
In the context of global warming, lakes assume a critical role as indicators of and safeguards against climate change, and exhibit sensitive responses [8,9]. The Tibetan Plateau (TP) stands out as one of the world’s most vulnerable regions to climate change [10]. This plateau harbors a cluster of lakes, boasting the highest, most numerous and largest inland lake area globally, encompassing 57.2% of China’s total lake area and exhibiting a high distribution density [11]. TP lakes have a profound influence on local hydrology [12], climate [13] and ecosystems [14]. Amidst climate change on the TP, the thermal and dynamic processes of these lakes have undergone substantial transformations [15]. Utilizing in situ data, remote sensing data, and numerical modeling, extensive research has revealed a consistent trend of increasing lake water temperatures [16], heightened evaporation rates [12], diminished lake ice coverage and shortened ice duration [17], and expansions in water levels [18] with spatial heterogeneity. These alterations in lake processes consequently impact local climate patterns and ecosystems [19].
In addition to their thermal and dynamic aspects, the ecological status of lakes plays a significant role [20,21,22]. Lake ecological processes have garnered increasing attention, particularly for lakes in low-altitude and plain areas, where observation monitoring is easily carried out [23,24,25,26]. The rapidly warming climate has triggered widespread changes in lake ecological processes, as demonstrated by ecological indicators such as water clarity [27], methane production [28], lake water gross primary production [29], and lake thermal habitat [30]. However, due to the TP’s harsh environmental conditions and high altitude of the TP, studies focusing on long-term lake ecological processes are scarce. In situ water ecological observations within lake interiors are insufficient to support long-term research on lake ecological processes. Additionally, remote sensing observations suffer from discontinuity and require validation. Consequently, the current variations in key water ecological indicators for TP lakes, especially those with minimal human activities, remain unclear.
Lake water ecology models serve as valuable tools for investigating lake ecosystems. Presently, there are several lake water ecological models available, each with distinct advantages [31,32,33]. These models help assess changes in water nutrients and pollutants [34], eutrophication and risk assessments [35], and the identification of direct or indirect impact factors on lake ecology [36]. Among these models, AQUATOX, which was developed by the United States Environmental Protection Agency (US EPA), stands out as highly effective and features a flexible structure and user-friendly interface [37]. The AQUATOX model has been widely utilized for nutrient content analysis [38], studying the effects of climate change on water ecosystems [39], and conducting natural resource damage assessments [40].
In this study, we employed this lake ecological model, AQUATOX, to simulate long-term variations in the lake water ecology in Tibetan Plateau (TP) lakes, focusing on Lake Namco, a representative alpine lake in this cold region. We extracted water nutrient data, including total nitrogen (WTN) and total phosphorus (WTP), as well as chlorophyll-a content (Chl-a), and water gross and net primary production (WGPP and WNPP) for analysis. Our objective was to examine variations in these water ecological indicators and identify the main influencing factors, particularly those related to local climate and hydrology, over the past 40 years, i.e., from 1980 to 2020. This paper is structured as follows: Section 2 introduces Lake Namco, the lake ecological model, and the data sources used in this study. Section 3 presents the simulation results and analysis. The discussion and conclusions are provided in Section 4 and Section 5, respectively.

2. Study Area, Lake Model, and Data

2.1. Study Area

Lake Namco, a brackish lake, is situated in the central region of the Tibetan Plateau, spanning approximately 30.5~30.95° N, 90.2~91.05° E. It is the largest and highest lake in the central plateau. Characterized by a semi-arid cold climate, Namco has an elevation of around 4718 m and covered an area of approximately 1998 km2 in 2019. The lake’s maximum and mean depths are 90 and 40 m, respectively [41]. The primary water sources for Namco are precipitation and glacier melting, with evaporation being the primary mode of water loss [42]. Typically, the lake begins freezing in late November and thaws in late March or early April, with the ice cover lasting for approximately 5 to 6 months [43]. Thermal and dynamic processes within Namco are predominantly influenced by the local climate and environmental factors. With a high altitude and cold climate, Lake Namco is often regarded as an oligotrophic lake. With minimal human activities, Namco serves as an ideal and representative alpine lake for exploring natural lake processes [44,45,46].

2.2. Lake Model

In our study, we utilized AQUATOX, an open-source and widely utilized aquatic system model, to simulate the ecological processes of Lake Namco. AQUATOX has been extensively employed in research pertaining to various water bodies, including lakes, estuaries, and reservoirs. This model operates by simulating changes in the concentrations of organisms, nutrients, chemicals, and sediments within the aquatic ecosystem. AQUATOX employs differential equations to represent the changing values of state variables, which necessitate initial conditions at the onset of the simulation. The time resolution for simulations can be tailored to any desired length, offering flexibility in modeling. It can be utilized as a simple model or configured as a complex food-web model, accommodating diverse research needs. The main variables outputs by this model are water nutrients, CO2 and O2 content, water clarity, primary productivity, and biomass within the lake.
In our study, simulations were conducted for the period from 1979 to 2020 for Lake Namco, with a daily time resolution. Initial conditions were set based on lake observations, ensuring the accuracy and reliability of the model outputs. The first year, 1979, was set as the spin-up time. The model output for the period from 1980 to 2020 was analyzed in this study.

2.3. Data

In this study, in situ observation data were utilized for both model input and evaluation, as presented in Table 1 and Figure 1. The lake characteristics, including depth, length, and area, were incorporated into the model. Additionally, datasets concerning various lake ecological processes, such as water nutrients, phytoplankton, and water quality, were gathered from the academic literature [16,41,47,48,49,50,51,52,53,54], as shown in Table S1.
The meteorological data used in our study encompassed downward shortwave radiation and wind speed, which exhibited a slight decreasing trend over the study period. Furthermore, lake thermodynamic data, comprising lake water temperature (WT), evaporation, lake water volume (WL), and lake water area (WA), were considered. Notably, both the lake volume and area of the lake exhibited rapid increases after the 2000s, as depicted in Figure 2.
In our study, simulations were conducted for the period from 1980 to 2020, with a daily temporal resolution. To mitigate the effects of interannual fluctuations, the simulation results for each decade (1981–1990, 1991–2000, 2001–2010, and 2011–2020) were analyzed. In this paper, the main variables used to represent lake ecology are WTP, WTN, and Chl-a), and primary production (WGPP and WNPP).
To elucidate the factors influencing lake ecological processes, long-term series data for meteorological and lake thermodynamic elements were employed to assess their respective impacts on lake ecological variations. Meteorological data were sourced from the multi-circle comprehensive observation and research station around Lake Namco [48,49] and included downward shortwave radiation (DSR), air temperature (AT), specific humidity (SH), wind speed (WD), air pressure (AP), and precipitation (PC).
To delineate the influence of meteorological elements on lake ecological indicators, including WTN, WTP, Chl-a, WGPP, and WNPP, a multiple linear regression model was employed using the SPSS software version 25. Generally, the relationship between the dependent and independent variables is expressed as shown in Equation (1):
y = a1x1 + a2x2 + a3x3+ ⋯ + aixi + b
where y is the dependent variable, xi (i = 0, 1,…, n) is the independent variable, ai (i = 0, 1,…, n) is the regression coefficient, and b is the constant term. The relative contribution of the changes in lake water ecological indicators was estimated using multiple regression coefficients as shown in Equation (2):
ni = ai/(|a1| + |a2| + |a3| + ⋯ + |ai|)
where ni is the relative contribution coefficient. The larger this value, the greater the contribution from the independent variable to the dependent variable.

3. Results

3.1. Model Calibration and Validation

To ensure the accurate simulation of lake ecological processes and obtain reasonable results, uncertainty and sensitivity analyses were conducted in this study using the lake ecology model. By combining uncertainty and sensitivity analyses, we identified and controlled the parameters affecting lake water ecological processes. To evaluate the performance of the parameter settings in the model, we compared the simulated results with limited observed data, which included water clarity (SD), WTP, and WTN. Water clarity, i.e., water transparency, serves as a relatively intuitive indicator for monitoring and assessing lake water quality, which plays a crucial role in water thermodynamic and ecological processes.
Through calibration processes, the mineralization parameters were found to be crucial for simulating lake water ecological processes. The optimal temperature was set to 15 °C. The extinction coefficient for water and sediment were set to 0.12 and 0.17 m−1, respectively. The maximum decomposition rate was 0.14 g (g d)−1. The maximum rate of denitrification was set to 0.005 d−1. In addition, the physiological parameters of phytoplankton were the most critical in terms of reflecting the reasonable ecological conditions of the lake. The main parameters, with adjustment, included the optimal temperature, photorespiration coefficient, mortality coefficient, sedimentation rate, and saturation intensity. On the basis of the default settings in the model and the literature, the final values used in the model are shown in Table 2.
With the well-configured model, we assessed the simulated relative errors of these three variables and found them to be within 20%, as shown in Table 3. Consequently, despite the limited water ecological observations for Namco, the lake ecology model was deemed capable of capturing the variations in key indicators in the lake ecosystems.

3.2. Simulation Analysis

3.2.1. Water Nutrients

Water nutrients, particularly WTN and WTP, serve as valuable indicators that reflect lake ecological processes within lake water ecosystems. Variations in WTP and WTN concentrations can significantly influence lake nutrient levels. The WTN and WTP simulations were analyzed, as depicted in Figure 3. Both the WTN and WTP concentrations exhibited a noticeable increasing trend over the past 40 years. WTN levels rose from 0.135 mg L−1 during the 1980s to 0.245 mg L−1 during the 2010s, with an increasing trend of 0.0369 mg L−1 per decade. Similarly, WTP concentrations increased from 0.0321 mg L−1 during the 1980s to 0.0395 mg L−1 during the 2010s with an increasing trend of 0.00245 mg L−1 per decade. The regression coefficients R2 for WTN and WTP were found to be 0.99 and 0.97, respectively. Despite Namco Lake on the Tibetan Plateau being characterized as an oligotrophic alpine lake [55,56], it demonstrated an improving trend in terms of its nutritional condition. However, it is worth noting that the lake remains in a state of lower nutrient levels.

3.2.2. Concentrations of WGPP, WNPP, and Chl-a

Primary productivity and Chl-a concentrations are the fundamental attributes of inland aquatic ecosystems and are influenced by complex environmental factors. These properties of lake water ecosystems are also often used to evaluate water quality. Simulations of WGPP, WNPP, and Chl-a were conducted (Figure 4). WGPP, WNPP, and Chl-a concentrations all exhibited a significant increasing trend over the past 40 years. WGPP increased from 0.00186 g O2 m−2 d−1 during the 1980s to 0.208 g O2 m−2 d−1 during the 2010s with an increasing trend of 0.0651 g O2 m−2 d−1 per decade. WNPP increased from 0.0147 g O2 m−2 d−1 during the 1980s to 0.162 g O2 m−2 d−1 during the 2010s with an increasing trend of 0.0509 g O2 m−2 d−1 per decade. Chl-a increased from 0.00569 µg L−1 during the 1980s to 0.643 µg L−1 during the 2010s with an increasing trend of 0.2 µg L−1 per decade. The regression coefficients R2 for GPP, NPP, and Chl-a were found to be 0.97, 0.97, and 0.96, respectively. These results indicated that aquatic phytoplankton in the lakes have flourished, and the productivity of lake water ecosystems has increased over the years.

3.2.3. The Impact of Meteorological and Hydrological Factors

The relative contributions of climatic and hydrological elements to variations in lake ecological variables were extracted, as shown in Figure 5. Lake water temperature exhibited a significant effect on WGPP, WNPP, and Chl-a, with the relative contributions of WT for WGPP and WNPP surpassing 0.4. Increases in WT have the potential to enhance the water environment and stimulate the growth of aquatic vegetation growth. Furthermore, specific humidity and water volume also contributed to these three lake ecological indicators. The combined contribution from the local climate and hydrology on these indicators exceeded 0.5, indicating the substantial influence of local climate and lake hydrology on lake ecological changes. In contrast, WTN and WTP exhibited relatively small contributions from climatic and hydrological elements, with all relative contributions being less than 0.1. This suggests that WTP and WTN may be more sensitive to lake ecological interaction processes. Additionally, concentrations of CO2 and O2 within the lake were found to correlate with WTN and WTP. Biological processes (e.g., nutrient uptake by phytoplankton) and sediment nutrient release play crucial roles in regulating water nutrient concentrations within the lake. Changes in these processes would influence WTP and WTN dynamics over time. In summary, WGPP, WNPP, and Chl-a appear to be more susceptible to changes in the local climate and hydrology compared to water nutrients.

4. Discussion

4.1. The Ecological Status of This Lake

In this study, lake ecology simulations were conducted with limited water ecological observations. Through these simulations, we aimed to deepen our preliminary understanding of the ecological status and changes of lakes on the TP. Despite experiencing minimal human activities and being oligotrophic over the long term, the results indicated that the lake exhibited a clear trend toward improvement in terms of its ecological condition. With WTN and WTP concentrations less than 0.4 and 0.02, respectively, Lake Namco was generally classified as an oligotrophic lake, according to the trophic type classification standards for Chinese lakes [56,57]. In addition, the content of Chl-a in this lake was lower than the threshold of Chl-a content for eutrophication according to most standards for eutrophication. The ecological condition of Lake Namco derived from this study was consistent with the conclusions drawn in previous research [56].
In the simulation, Lake Namco, i.e., a typical alpine lake on the TP, was in a state of poor nutrient content and developed towards improvement. For the other TP lakes, the mean Chl-a content was in the range 4.46 ± 3.64 µg L−1 with heterogeneity [29]. The spatial Chl-a distribution in this lake was found to be 0~1 µg L−1 in 1995 and 0~2 µg L−1 in 1999 [58]. In addition, the Chl-a content in most lakes on the southern TP increased significantly [59]. In summary, the results from this study were consistent with those from previous research. In addition, geographical features, lake characteristics, and the local climate should be considered when carrying out similar research on water ecological processes for the other TP lakes [60].

4.2. The Factors Influencing Changes in Water Ecology

Primary productivity is affected by phytoplankton growth, which, in turn, affects the Chl-a content and water quality. In this study, lake water temperature was found to be the most critical factor influencing WGPP, WNPP, and Chl-a. Due to the harsh environment, high altitude, and low air temperature surrounding this lake, WT was one of the most important factors restricting the phytoplankton growth in Namco. According to the linear regression model analysis, with a 1 °C increase in water temperature in the epilimnion, WGPP and WNPP would increase 0.047 and 0.034 g O2 m−2 d−1, respectively. With increasing air temperatures and melting glaciers, this lake expanded as a result of the dilution effect. Lake volume had a negative effect on the increase in the WGPP, WNPP, and Chl-a. It is worth noting that atmospheric humidity also played an adverse role in the variations in primary productivity. This may be because a wetter climate leads to more precipitation and, thus, an increased lake water volume. Using the regression equation based on meteorological and hydrological factors, the regression coefficients were 0.58, 0.57, and 0.58 for WGPP, WNPP, and Chl-a, respectively. Therefore, primary productivity in such an alpine lake was shown to be sensitive to changes in local climate and lake evolution.
Water nutrients were not closely related to meteorological and hydrological factors. Even though the concentrations of water nutrients within this lake exhibited a significant upward trend, WTN and WTP exhibited no significant correlations with local climatic or hydrological elements, all producing a correlation coefficient R2 lower than 0.1. WTN and WTP may be easily affected by initial conditions, source loadings from the surrounding environment, and biochemical processes within the lake. Since this paper focuses on long-term variations in water quality and primary productivity, local climatic and hydrological elements were considered principally as factors influencing lake ecosystems. Thus, attention should also be paid to the biochemical processes affecting water nutrients and the differences between TP lakes and other lakes on the plain.

4.3. Possible Changes in the Ecological Conditions for This Lake

A decrease in CO2 emissions was noted in the TP lakes, which was likely induced by lake expansion due to climate change [61]. Such a reduction in CO2 emissions may cause continuous lake water warming and accelerated phytoplankton growth. Primary productivity in the inland water bodies is an important indicator of phytoplankton growth, which is closely related to the terrestrial carbon cycle.
As informed from above analysis, lake water temperature had the most prominent impact on primary productivity within the lake body. According to the WT projections from ISIMIP2b simulations conducted with the Community Land Model version 4.5 (CLM4.5) [62], the bias-corrected WT output from four global climate models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) for future periods indicated that, from 2021 to 2099, lake temperatures in the epilimnion and hypolimnion will exhibit almost no obvious changing trends under RCP2.6. However, with the high CO2 emissions under RCP6.0 and RCP8.5, lake temperatures increased at 0.41 and 0.70 °C per decade in epilimnion and at 0.17 and 0.32 °C per decade in hypolimnion, respectively. Such a warming water environment as a result of the warmer climate in the future would benefit the phytoplankton growth status and further drive primary productivity.
Water nutrients (WTN and WTP) could be the limiting factors of phytoplankton photosynthesis and growth [29]. To better understand changes in water nutrients under different climate change scenarios, the external inputs from the surrounding rivers and possible human impacts need to be incorporated. Thereafter, we can attempt to predict possible changes in primary productivity and water quality in the lake water bodies under different climate change scenarios for fragile ecological lake ecosystems in high-altitude regions.

4.4. Limitations of This Study

There exist certain limitations in our study. Firstly, the parameters related to plant growth were established based on the existing research literature alongside uncertainty and sensitivity tests. In the future, more refined observational data will be necessary to verify and calibrate this model. Secondly, the contribution of water nutrients from surrounding rivers to the lake was not considered in this study, potentially leading to an underestimation of water nutrient concentrations. Additionally, lake ecological interaction processes were not thoroughly explored; however, they were not the focus of this paper. Our attention was primarily directed towards the construction and application of an ecological modeling of TP lakes by aggregating observations and analyzing interannual variations in key ecological indicators of lakes. Possible factors influencing these indicators were also discussed to explore the effects of climate and hydrology on lake ecosystems on the TP.
To thoroughly understand the lake water ecological processes on the TP as related to global climate change, obtaining long-term variations in lake ecological parameters, such as WTP, WTN, and WGPP, is crucial [29,63,64]. With the availability of satellite remote sensing data, some important parameters for lake water ecology, such as water clarity, can be obtained in areas with abundant observations [65,66]. However, in regions with minimal human activity, particularly in high-altitude and high-latitude areas, actual observations are still necessary to validate remote sensing inversion datasets. Thus, establishing a long-term observation network for lake ecosystems in these regions is imperative.

5. Conclusions

This study employed the AQUATOX lake ecological model to simulate the ecological variations in a typical alpine lake, Namco, on the TP, using the available lake observation data. Through uncertainty and sensitivity modules, the key parameters affecting the ecological indicator simulations were tested and calibrated. The model demonstrated acceptable performance in modeling the concentrations of water nutrients, specifically WTP and WTN, within the lake. Long-term simulations revealed an upward trend in the concentrations of water nutrients, WGPP, WNPP, and Chl-a over the period from 1980 to 2020, even though this lake was in an oligotrophic state. The WTN and WTP concentrations increased to 0.0369 mg L−1 and 0.00245 mg L−1 per decade, respectively. The regression coefficients R2 for WTN and WTP of lake ecology were found to be 0.99 and 0.97, respectively. Moreover, the WGPP, WNPP, and Chl-a concentrations increased at 0.0651 g O2 m−2 d−1, 0.0509 g O2 m−2 d−1, and 0.2 µg L−1 per decade, respectively. For WGPP, WNPP, and Chl-a, the regression coefficients R2 were 0.97, 0.97, and 0.96, respectively. Furthermore, utilizing multiple regression analysis, it was determined that WGPP, WNPP, and Chl-a were largely influenced by the local climate and hydrology. The lake’s thermodynamic status, including lake water temperature and volume, was found to be an important factor. In addition, water nutrients, i.e., WTP and WTN, exhibited insensitivity to local environmental changes. With continuous lake warming in the future, water primary productivity would maintain an increasing trend, especially under high-emission scenarios. Future research should focus on integrating datasets for lake water ecology and enhancing the accuracy of lake ecological simulations, particularly for lakes situated in high-altitude and high-latitude regions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16141982/s1, Table S1: Data used for initialization and modeling the ecological indicators in the AQUATOX model.

Author Contributions

Conceptualization—Q.Z.; formal analysis—Q.Z., L.C. and X.G.; methodology—Q.Z., L.C., X.G., R.D. and M.M.; writing—original draft, Q.Z.; review and editing—Q.Z., L.C., X.G., R.D. and M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Geological Survey Project of China (grant no. DD20230301), the Technology project of Qinghai Bureau of Environmental Geology Exploration of China (grant no. 2023-ZK-01), and the Natural Science Basic Research Program of Shaanxi (2022JQ-238). Qunhui was also supported by the foundation of Director of the Xi’an Center of Geological Survey, CGS (Grant XACGS-2023-05).

Data Availability Statement

Data are contained within the article and Supplementary Material.

Acknowledgments

The data used in this study were provided by the National Tibetan Plateau/Third Pole Environment Data Center at http://data.tpdc.ac.cn (last accessed on 5 June 2024). Lake temperature projections are available at https://data.isimip.org/ (last accessed on 5 June 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Verpoorter, C.; Kutser, T.; Seekell, D.A.; Tranvik, L.J. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 2014, 41, 6396–6402. [Google Scholar] [CrossRef]
  2. Messager, M.L.; Lehner, B.; Grill, G.; Nedeva, I.; Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 2016, 7, 13603. [Google Scholar] [CrossRef] [PubMed]
  3. Wu, Y.; Huang, A.; Yang, B.; Dong, G.; Wen, L.; Lazhu; Zhang, Z.; Fu, Z.; Zhu, X.; Zhang, X.; et al. Numerical study on the climatic effect of the lake clusters over Tibetan Plateau in summer. Clim. Dynam. 2019, 53, 5215–5236. [Google Scholar] [CrossRef]
  4. Dai, Y.; Wang, L.; Yao, T.; Li, X.; Zhu, L.; Zhang, X. Observed and Simulated Lake Effect Precipitation Over the Tibetan Plateau: An Initial Study at Nam Co Lake. J. Geophys. Res. Atmos. 2018, 123, 6746–6759. [Google Scholar] [CrossRef]
  5. Dai, Y.; Yao, T.; Li, X.; Ping, F. The impact of lake effects on the temporal and spatial distribution of precipitation in the Nam Co basin, Tibetan Plateau. Quatern. Int. 2018, 475, 63–69. [Google Scholar] [CrossRef]
  6. Sterner, R.W.; Reinl, K.L.; Lafrancois, B.M.; Brovold, S.; Miller, T.R. A first assessment of cyanobacterial blooms in oligotrophic Lake Superior. Limnol. Oceanogr. 2020, 65, 2984–2998. [Google Scholar] [CrossRef]
  7. Carpenter, S.R.; Stanley, E.H.; Vander Zanden, M.J. State of the World’s Freshwater Ecosystems: Physical, Chemical, and Biological Changes. Annu Rev. Env. Resour. 2011, 36, 75–99. [Google Scholar] [CrossRef]
  8. Woolway, R.I.; Jennings, E.; Shatwell, T.; Golub, M.; Pierson, D.C.; Maberly, S.C. Lake heatwaves under climate change. Nature 2021, 589, 402–407. [Google Scholar] [CrossRef] [PubMed]
  9. Woolway, R.I.; Merchant, C.J. Worldwide alteration of lake mixing regimes in response to climate change. Nat. Geosci. 2019, 12, 271–276. [Google Scholar] [CrossRef]
  10. Yang, K.; Wu, H.; Qin, J.; Lin, C.; Tang, W.; Chen, Y. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review. Glob. Planet Change 2014, 112, 79–91. [Google Scholar] [CrossRef]
  11. Ma, R.; Duan, H.; Hu, C.; Feng, X.; Li, A.; Ju, W.; Jiang, J.; Yang, G. A half-century of changes in China’s lakes: Global warming or human influence? Geophys. Res. Lett. 2010, 37, L24106. [Google Scholar] [CrossRef]
  12. Wang, B.; Ma, Y.; Su, Z.; Wang, Y.; Ma, W. Quantifying the evaporation amounts of 75 high-elevation large dimictic lakes on the Tibetan Plateau. Sci. Adv. 2020, 6, eaay8558. [Google Scholar] [CrossRef] [PubMed]
  13. Zhu, L.; Jin, J.; Liu, Y. Modeling the Effects of Lakes in the Tibetan Plateau on Diurnal Variations of Regional Climate and Their Seasonality. J. Hydrometeorol. 2020, 21, 2523–2536. [Google Scholar] [CrossRef]
  14. Wang, Q.; Wang, R.; Yang, X.; Anderson, N.J.; Kong, L. Interactive effects of climate-atmospheric cycling on aquatic communities and ecosystem shifts in mountain lakes of southeastern Tibetan Plateau. Sci. Total Environ. 2024, 914, 169825. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, G.; Yao, T.; Xie, H.; Yang, K.; Zhu, L.; Shum, C.K.; Bolch, T.; Yi, S.; Allen, S.; Jiang, L.; et al. Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms. Earth-Sci. Rev. 2020, 208, 103269. [Google Scholar] [CrossRef]
  16. Lazhu; Yang, K.; Wang, J.; Lei, Y.; Chen, Y.; Zhu, L.; Ding, B.; Qin, J. Quantifying evaporation and its decadal change for Lake Nam Co, central Tibetan Plateau. J. Geophys. Res. Atmos. 2016, 121, 7578–7591. [Google Scholar] [CrossRef]
  17. Guo, L.; Zheng, H.; Wu, Y.; Zhang, T.; Wen, M.; Fan, L.; Zhang, B. Responses of Lake Ice Phenology to Climate Change at Tibetan Plateau. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 3856–3861. [Google Scholar] [CrossRef]
  18. Lei, Y.; Yao, T.; Sheng, Y.; Yang, K.; Yang, W.; Li, S.; Zhou, J.; Jiang, Y.; Yu, Y. Unprecedented lake expansion in 2017–2018 on the Tibetan Plateau: Processes and environmental impacts. J. Hydrol. 2023, 619, 129333. [Google Scholar] [CrossRef]
  19. Zhang, G.; Duan, S. Lakes as sentinels of climate change on the Tibetan Plateau. All Earth 2021, 33, 161–165. [Google Scholar] [CrossRef]
  20. Flaim, G.; Eccel, E.; Zeileis, A.; Toller, G.; Cerasino, L.; Obertegger, U. Effects of re-oligotrophication and climate change on lake thermal structure. Freshw. Biol. 2016, 61, 1802–1814. [Google Scholar] [CrossRef]
  21. Seelen, L.M.S.; Flaim, G.; Jennings, E.; De Senerpont Domis, L.N. Saving water for the future: Public awareness of water usage and water quality. J. Environ. Manag. 2019, 242, 246–257. [Google Scholar] [CrossRef]
  22. Tao, H.; Song, K.; Liu, G.; Wen, Z.; Lu, Y.; Lyu, L.; Shang, Y.; Li, S.; Hou, J.; Wang, Q.; et al. Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China. Sci. Total Environ. 2022, 846, 157328. [Google Scholar] [CrossRef]
  23. Nyamweya, C.S.; Natugonza, V.; Taabu-Munyaho, A.; Aura, C.M.; Njiru, J.M.; Ongore, C.; Mangeni-Sande, R.; Kashindye, B.B.; Odoli, C.O.; Ogari, Z.; et al. A century of drastic change: Human-induced changes of Lake Victoria fisheries and ecology. Fish Res. 2020, 230, 105564. [Google Scholar] [CrossRef]
  24. Reed, K.M.; Izzo, L.K.; Binder, T.; Hayden, T.; Dembkowski, D.; Hansen, S.; Caroffino, D.; Vandergoot, C.; Krueger, C.C.; Isermann, D. Initial insights on the thermal ecology of lake whitefish in northwestern Lake Michigan. J. Great Lakes Res. 2023, 49, 757–766. [Google Scholar] [CrossRef]
  25. Lin, J.; Ding, W.; Zhou, H.; Wang, H. Mitigating adverse impacts of reservoir impoundment on lake ecology: A case study of the Three Gorges Reservoir and Dongting Lake. J. Clean Prod. 2024, 451, 141835. [Google Scholar] [CrossRef]
  26. Xu, T.; Ma, W.; Chen, J.; Duan, L.; Li, H.; Zhang, H. Water Quality of Lake Erhai in Southwest China and Its Projected Status in the near Future. Water-Sui 2024, 16, 972. [Google Scholar] [CrossRef]
  27. Zhang, Y.; Qin, B.; Shi, K.; Zhang, Y.; Deng, J.; Wild, M.; Li, L.; Zhou, Y.; Yao, X.; Liu, M.; et al. Radiation dimming and decreasing water clarity fuel underwater darkening in lakes. Sci. Bull. 2020, 65, 1675–1684. [Google Scholar] [CrossRef]
  28. Jansen, J.; Woolway, R.I.; Kraemer, B.M.; Albergel, C.; Bastviken, D.; Weyhenmeyer, G.A.; Marcé, R.; Sharma, S.; Sobek, S.; Tranvik, L.J.; et al. Global increase in methane production under future warming of lake bottom waters. Glob. Change Biol. 2022, 28, 5427–5440. [Google Scholar] [CrossRef]
  29. Jia, J.; Wang, Y.; Lu, Y.; Sun, K.; Lyu, S.; Gao, Y. Driving mechanisms of gross primary productivity geographical patterns for Qinghai–Tibet Plateau lake systems. Sci. Total Environ. 2021, 791, 148286. [Google Scholar] [CrossRef] [PubMed]
  30. Kraemer, B.M.; Pilla, R.M.; Woolway, R.I.; Anneville, O.; Ban, S.; Colom-Montero, W.; Devlin, S.P.; Dokulil, M.T.; Gaiser, E.E.; Hambright, K.D.; et al. Climate change drives widespread shifts in lake thermal habitat. Nat. Clim. Change 2021, 11, 521–529. [Google Scholar] [CrossRef]
  31. Gurkan, Z.; Zhang, J.; Jørgensen, S.E. Development of a structurally dynamic model for forecasting the effects of restoration of Lake Fure, Denmark. Ecol. Model. 2006, 197, 89–102. [Google Scholar] [CrossRef]
  32. Arhonditsis, G.B.; Brett, M.T. Eutrophication model for Lake Washington (USA). Ecol. Model. 2005, 187, 140–178. [Google Scholar] [CrossRef]
  33. Jorgensen, S.E. State-of-the-art management models for lakes and reservoirs. Lakes Reserv. Sci. Policy Manag. Sustain. Use 1995, 1, 79–87. [Google Scholar] [CrossRef]
  34. Wu, D.; Cao, M.; Gao, W.; Duan, Z.; Zhang, Y. Simulating critical nutrient loadings of regime shift in the shallow plateau Lake Dianchi. Ecol. Model. 2024, 491, 110689. [Google Scholar] [CrossRef]
  35. Chang, M.; DeAngelis, D.L.; Janse, J.H.; Janssen, A.B.G.; Troost, T.A.; van Wijk, D.; Mooij, W.M.; Teurlincx, S. A generically parameterized model of Lake eutrophication: The impact of Stoichiometric ratios and constraints on the abundance of natural phytoplankton communities (GPLake-S). Ecol. Model. 2022, 473, 110142. [Google Scholar] [CrossRef]
  36. Han, Y.; Zhang, K.; Lin, Q.; Huang, S.; Yang, X. Assessing lake ecosystem health from disturbed anthropogenic landscapes: Spatial patterns and driving mechanisms. Ecol. Indic. 2023, 147, 110007. [Google Scholar] [CrossRef]
  37. Park, R.A.; Clough, J.S.; Wellman, M.C. AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems. Ecol. Model. 2008, 213, 1–15. [Google Scholar] [CrossRef]
  38. Akkoyunlu, A.; Karaaslan, Y. Assessment of improvement scenario for water quality in Mogan Lake by using the AQUATOX Model. Environ. Sci. Pollut. Res. 2015, 22, 14349–14357. [Google Scholar] [CrossRef] [PubMed]
  39. Niu, Z.; Gou, Q.; Wang, X.; Zhang, Y. Simulation of a water ecosystem in a landscape lake in Tianjin with AQUATOX: Sensitivity, calibration, validation and ecosystem prognosis. Ecol. Model. 2016, 335, 54–63. [Google Scholar] [CrossRef]
  40. Zhang, L.; Cui, J.; Song, T.; Liu, Y. Application of an AQUATOX model for direct toxic effects and indirect ecological effects assessment of Polycyclic aromatic hydrocarbons (PAHs) in a plateau eutrophication lake, China. Ecol. Model. 2018, 388, 31–44. [Google Scholar] [CrossRef]
  41. Wang, J.; Zhu, L.; Daut, G.; Ju, J.; Lin, X.; Wang, Y.; Zhen, X. Investigation of bathymetry and water quality of Lake Nam Co, the largest lake on the central Tibetan Plateau, China. Limnology 2009, 10, 149–158. [Google Scholar] [CrossRef]
  42. Zhou, S.; Kang, S.; Chen, F.; Joswiak, D.R. Water balance observations reveal significant subsurface water seepage from Lake Nam Co, south-central Tibetan Plateau. J. Hydrol. 2013, 491, 89–99. [Google Scholar] [CrossRef]
  43. Si, Y.; Li, Z.; Wang, X.; Liu, Y.; Jin, J. Lake Ice Simulation and Evaluation for a Typical Lake on the Tibetan Plateau. Water-Sui 2023, 15, 3088. [Google Scholar] [CrossRef]
  44. Wu, C.; Liu, G.; Cong, L.; Li, X.; Liu, X.; Liu, Y.; Wu, D.; Zhang, Y.; Bai, D. ENSO-driven hydroclimate changes in central Tibetan Plateau since middle Holocene: Evidence from Zhari Namco’s lake sediments. Quat. Sci. Rev. 2024, 330, 108593. [Google Scholar] [CrossRef]
  45. Keil, A.; Berking, J.; Mügler, I.; Schütt, B.; Schwalb, A.; Steeb, P. Hydrological and geomorphological basin and catchment characteristics of Lake Nam Co, South-Central Tibet. Quatern. Int. 2010, 218, 118–130. [Google Scholar] [CrossRef]
  46. Song, C.; Ye, Q.; Cheng, X. Shifts in water-level variation of Namco in the central Tibetan Plateau from ICESat and CryoSat-2 altimetry and station observations. Sci. Bull. 2015, 60, 1287–1297. [Google Scholar] [CrossRef]
  47. Li, M.; Yan, D.; Liu, S.; Qin, T.; Yao, L. Variation characteristics of water surface area and water storage capacity of Namucuo Lake in recent 40 years. Water Resour. Power 2017, 35, 41–43, (In Chinese with English Abstract). [Google Scholar]
  48. Wang, Y.; Wu, G. Meteorological Observation Data from the Integrated Observation and Research Station of Multiple Spheres in Namco (2005–2016). National Tibetan Plateau/Third Pole Environment Data Center. 2018. Available online: https://data.tpdc.ac.cn/en/data/c97bce0f-bf67-4dcc-b864-d7e4d8cff62f (accessed on 5 June 2024).
  49. Wang, J.; Wu, G. Meteorological Observation Data of Namuco Multi Circle Comprehensive Observation and Research Station (2017–2018). National Tibetan Plateau/Third Pole Environment Data Center. 2019. Available online: https://data.tpdc.ac.cn/en/data/aa49fcc6-521f-4027-a481-8e49b32d16b5/ (accessed on 5 June 2024).
  50. Wang, J.; Huang, L.; Ju, J.; Daut, G.; Ma, Q.; Zhu, L.; Haberzettl, T.; Baade, J.; Mäusbacher, R.; Hamilton, A.; et al. Seasonal stratification of a deep, high-altitude, dimictic lake: Nam Co, Tibetan Plateau. J. Hydrol. 2020, 584, 124668. [Google Scholar] [CrossRef]
  51. Wang, J. Water Temperature Observation Data at Nam Co Lake in Tibet (2011–2014). National Tibetan Plateau/Third Pole Environment Data Center. 2020. Available online: https://data.tpdc.ac.cn/en/data/44702bf5-52e4-4a47-ab8a-7ad359ef1a98/ (accessed on 5 June 2024).
  52. Ren, M.; Sun, L. Investigation, development and utilization of fish resources in Namco, Xizang. Freshw. Fish. 1982, 4, 1–10. (In Chinese) [Google Scholar]
  53. Kai, J.; Wang, J.; Huang, L.; Wang, Y.; Ju, J.; Zhu, L. Seasonal variations of dissolved organic carbon and total nitrogen concentrations in Nam Co and inflowing rivers, Tibet Plateau. J. Lake Sci. 2019, 31, 1099–1108, (In Chinese with English Abstract). [Google Scholar]
  54. Guo, J.; Kang, S.; Zhang, Q.; Huang, J.; Wang, K. Temporal and Spatial Variations of Major Ions in Nam Co Lake Water, Tibetan Plateau. Environ. Sci. 2012, 33, 2295–2302. (In Chinese) [Google Scholar]
  55. Wang, S.; Li, J.; Zhang, B.; Spyrakos, E.; Tyler, A.N.; Shen, Q.; Zhang, F.; Kuster, T.; Lehmann, M.K.; Wu, Y.; et al. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index. Remote Sens. Environ. 2018, 217, 444–460. [Google Scholar] [CrossRef]
  56. Li, N.; Jiaxi, L.; Guowen, L.; Ye, L.; Beidou, X.; Yiwen, W.; Caole, L.; Wei, L.; Lieyu, Z. The eutrophication and its regional heterogeneity in typical lakes of China. Acta Hydrobiol. Sin. 2018, 42, 854–864, (In Chinese with English Abstract). [Google Scholar]
  57. Shu, J.; Huang, W.; Wu, Y. Studies on the classification of tropic types of China’s lakes. J. Lake Sci. 1996, 8, 193–200. [Google Scholar]
  58. Chen, F.; Li, S.; Song, K. Remote sensing of lake chlorophyll-a in Qinghai-Tibet Plateau responding to climate factors: Implications for oligotrophic lakes. Ecol. Indic. 2024, 159, 111674. [Google Scholar] [CrossRef]
  59. Pang, S.; Zhu, L.; Liu, C.; Ju, J. Causes and Impacts of Decreasing Chlorophyll-a in Tibet Plateau Lakes during 1986–2021 Based on Landsat Image Inversion. Remote Sens. 2023, 15, 1503. [Google Scholar] [CrossRef]
  60. Sun, K.; Deng, W.; Jia, J.; Gao, Y. Spatiotemporal patterns and drivers of phytoplankton primary productivity in China’s lakes and reservoirs at a national scale. Glob. Planet Change 2023, 228, 104215. [Google Scholar] [CrossRef]
  61. Xiao, Q.; Xu, X.; Qi, T.; Luo, J.; Lee, X.; Duan, H. Lakes shifted from a carbon dioxide source to a sink over past two decades in China. Sci. Bull. 2024, 69, 1857–1861. [Google Scholar] [CrossRef] [PubMed]
  62. Marcé, R.; Pierson, D.; Mercado-Bettin, D.; Thiery, W.; Tan, Z.; Seneviratne, S.; Golub, M.; Debolskiy, A.; Stepanenko, V.; Perroud, M.; et al. ISIMIP2b Simulation Data from the Global Lakes Sector; ISIMIP Repository: 2022. Available online: https://data.isimip.org/10.48364/ISIMIP.931371 (accessed on 5 June 2024).
  63. Huo, S.; Xi, B.; Su, J.; Zan, F.; Chen, Q.; Ji, D.; Ma, C. Determining reference conditions for TN, TP, SD and Chl-a in eastern plain ecoregion lakes, China. J. Environ. Sci. 2013, 25, 1001–1006. [Google Scholar] [CrossRef]
  64. Yang, T.; Zhang, Y.; Zhou, T.; Wang, Y.; Wang, L.; Yang, J.; Shang, Y.; Chen, F.; Hei, P. Phosphorus accumulation during the ice-on season in macrophyte-dominated eutrophic lakes and its implications. J. Environ. Manag. 2024, 360, 121096. [Google Scholar] [CrossRef]
  65. Wang, S.; Li, J.; Zhang, B.; Lee, Z.; Spyrakos, E.; Feng, L.; Liu, C.; Zhao, H.; Wu, Y.; Zhu, L.; et al. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS. Remote Sens. Environ. 2020, 247, 111949. [Google Scholar] [CrossRef]
  66. He, Y.; Lu, Z.; Wang, W.; Zhang, D.; Zhang, Y.; Qin, B.; Shi, K.; Yang, X. Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images. Water Res. 2022, 215, 118241. [Google Scholar] [CrossRef]
Water 16 01982 g001
Figure 1. Geophysical location map of Lake Namco.
Figure 1. Geophysical location map of Lake Namco.
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Water 16 01982 g002
Figure 2. Variations in meteorological and lake thermal and dynamic elements of (a) downward shortwave radiation, (b) wind speed, (c) lake water volume, and (d) lake water temperature for Lake Namco.
Figure 2. Variations in meteorological and lake thermal and dynamic elements of (a) downward shortwave radiation, (b) wind speed, (c) lake water volume, and (d) lake water temperature for Lake Namco.
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Water 16 01982 g003
Figure 3. Simulated concentrations of WTN and WTP for the periods of 1981–1990, 1991–2000, 2001–2010, and 2011–2020.
Figure 3. Simulated concentrations of WTN and WTP for the periods of 1981–1990, 1991–2000, 2001–2010, and 2011–2020.
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Water 16 01982 g004
Figure 4. Simulated concentrations of WGPP, WNPP, and Chl-a for the periods of 1981–1990, 1991–2000, 2001–2010, and 2011–2020.
Figure 4. Simulated concentrations of WGPP, WNPP, and Chl-a for the periods of 1981–1990, 1991–2000, 2001–2010, and 2011–2020.
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Figure 5. Contribution of climate and hydrology elements to variations in the lake ecological indicators.
Figure 5. Contribution of climate and hydrology elements to variations in the lake ecological indicators.
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Table 1. Main input parameters and setting values for the AQUATOX model.
Table 1. Main input parameters and setting values for the AQUATOX model.
TypeParameterValueReference
Lake characteristicsSurface area
Mean depth
Maximum depth		1997.55 km2
40 m
95 m		[47]
[41]
[41]
Local climateShortwave radiation
Wind speed		Figure 2a
Figure 2b 		[48,49]
[48,49]
Lake thermodynamics Water temperature
Water volume
Water evaporation		Figure 2d
Figure 2c
27.52 int		[50,51]
[47]
[16]
Table 2. The physiological parameters of phytoplankton set in the model.
Table 2. The physiological parameters of phytoplankton set in the model.
ParameterValue Set for Diatom AlgaeValue Set for Green Algae
Optimal temperature18 °C20 °C
Photorespiration coefficient Mortality coefficient0.05 d−1
0.001 g (g d)−1		0.08 d−1
0.001 g (g d)−1
Sedimentation rate0.015 m d−10.005 m d−1
Saturation intensity300 Ly d−1110 Ly d−1
Table 3. Simulated relative errors compared to observations.
Table 3. Simulated relative errors compared to observations.
VariableSimulated Relative Error (%)
SD12
WTP
WTN		17
15
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Zhang, Q.; Chang, L.; Gu, X.; Duan, R.; Ma, M. Lake Water Ecological Simulation for a Typical Alpine Lake on the Tibetan Plateau. Water 2024, 16, 1982. https://doi.org/10.3390/w16141982

AMA Style

Zhang Q, Chang L, Gu X, Duan R, Ma M. Lake Water Ecological Simulation for a Typical Alpine Lake on the Tibetan Plateau. Water. 2024; 16(14):1982. https://doi.org/10.3390/w16141982

Chicago/Turabian Style

Zhang, Qunhui, Liang Chang, Xiaofan Gu, Rui Duan, and Maonan Ma. 2024. "Lake Water Ecological Simulation for a Typical Alpine Lake on the Tibetan Plateau" Water 16, no. 14: 1982. https://doi.org/10.3390/w16141982

APA Style

Zhang, Q., Chang, L., Gu, X., Duan, R., & Ma, M. (2024). Lake Water Ecological Simulation for a Typical Alpine Lake on the Tibetan Plateau. Water, 16(14), 1982. https://doi.org/10.3390/w16141982

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