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Long-term data on surface water temperature (SWT) from 9 lakes larger than 10 km2 located in different climatic regions in Austria were analysed for June–September 1965–2009. The lakes are situated north and south of the Alps, in the east bordering Hungary and in the west bordering Germany. Time series of air temperature (AT) and SWT were smoothed...
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... lakes larger than 10 km 2 in or bordering Austria were analysed in this study. The geographical posi- tions and morphometric data are shown in Fig. 1 and summarised in Table 1. The two largest lakes, Bodensee (Lake Constance) and Neusiedler See (Fertö), border Germany and Hungary, respectively. Whilst the greater part of Neusiedler See is in Austria, the share of Bodensee is 14 km 2 only. These two lakes mark the western and eastern end of Austria and are about 800 km apart. The ...
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... is the deepest lake, strongly flushed by the river Traun (retention time B1 year). Attersee, Traunsee, Mondsee and Wolfgangsee represent an essential part of the lake district 'Salzkammergut' north of the Alps. The remaining three lakes, Millstätter See, Ossicher See and Wörther See, are the largest lakes of the lake region south of the Alps (Fig. ...
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... Surrounded by mountains on three sides and bordered by Dianchi Pond on the south, it belongs to the low-latitude plateau mountain monsoon climate and is a typical low-latitude plateau lakeside city. Lake water ecosystems in cities not only determine the environmental quality of lake watersheds, but also the sustainable development of the cities in which the watersheds are located (i.e., lakeside cities) (Dokulil, 2014;Huang et al., 2017), which is important for promoting coordinated sustainable development in the region (Reynaud and Lanzanova, 2017;Veldkamp et al., 2017;Chung et al., 2021). Due to the characteristics of lakeside cities, living around lakes for a long time, most of human production and life occurs within the lake watershed. ...
Introduction
Sustainable Development Goals (SDGs) is another binding target indicator system proposed by the United Nations after the Millennium Development Goals (MDGs). Scientific monitoring of the state of sustainable development of cities can provide a reference for the progress of the implementation of the SDGs, and also provide research support for the successful achievement of the sustainable development of cities around the world.
Methods
Although the indicator system proposed by the United Nations has drawn a vision of global sustainable development and given an evaluation framework and methodology, it fails to and is unlikely to encompass the rich connotations of China's new development philosophy. Therefore, this study takes the new development philosophy as the fundamental guideline, refers to the SDGs assessment system, and constructs a theoretical and methodological framework for assessing the sustainable development of plateau lakeside cities from the five dimensions of "innovation, coordination, greenness, openness, and sharing", and carries out empirical investigation in the research area of Kunming City, a typical plateau lakeside city.
Methods
It is found that the sustainable development level of Kunming is at a medium level, with a relatively fast development speed, but the growth momentum has slowed down; the development level of each dimension is relatively low, with a slow development speed, showing a fluctuating upward trend; the change in the level of coordinated development shows two states of low and medium coordination, showing a steady increase; the contribution of the five philosophies of relevance is sorted as: "Openness>Sharing>Green>Coordination>Innovation", and openness is the most important factor affecting the sustainable development level of plateau lakeside cities.
Discussion
This study demonstrates the need to enhance the level of sustainable development of cities by exploring their internal trade-offs and potential internal contributions.
... It strongly influences the concentration of dissolved oxygen, nutrient conversion rates, metabolic activities of aquatic organisms, phytoplankton productivity, and biochemical reactions. Notably, deviations from critical water temperature values can significantly impact fish populations, leading to increased mortality rates [71][72][73]. Additionally, accurate prediction of water temperature by depth in deep reservoirs is essential for managing selective discharge facilities and controlling downstream water temperature and quality [74][75][76]. ...
Data-driven models (DDMs) are extensively used in environmental modeling yet encounter obstacles stemming from limited training data and potential discrepancies with physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL model incorporates an energy constraint term derived from W2′s thermal energy equilibrium into the LSTM’s cost function, alongside the mean square error term. Through this mechanism, PGDL optimizes parameters while penalizing deviations from the energy law, thereby ensuring adherence to crucial physical constraints. In comparison to LSTM’s root mean square error (RMSE) of 0.062 °C, PGDL exhibits a noteworthy 1.5-fold enhancement in water temperature prediction (RMSE of 0.042 °C), coupled with improved satisfaction in maintaining energy balance. Intriguingly, even with training on just 20% of field data, PGDL (RMSE of 0.078 °C) outperforms both LSTM (RMSE of 0.131 °C) and calibrated W2 (RMSE of 1.781 °C) following pre-training with 80% of the data generated by the uncalibrated W2 model. The successful integration of the PBM and DDM in the PGDL validates a novel technique that capitalizes on the strengths of multidimensional mathematical models and data-based deep learning models. Furthermore, the pre-training of PGDL with PBM data demonstrates a highly effective strategy for mitigating bias and variance arising from insufficient field measurement data.
... Although these results were incomplete, they adhered to the energy conservation law and accurately captured the physical characteristics and meteorological conditions of the reservoir. By leveraging the mechanical principles embedded in the W2 model, the LSTM EC,p model generated water temperature predictions that reflected these principles [56]. Specifically, the spatiotemporal predictions of water temperature over time and depth from the W2 model were utilized as training data for the LSTM EC,p model. ...
... It strongly influences the concentration of dissolved oxygen, nutrient conversion rates, metabolic activities of aquatic organisms, phytoplankton productivity, and biochemical reactions. Notably, deviations from critical water temperature values can significantly impact fish populations, leading to increased mortality rates [54][55][56][57]. Additionally, accurate prediction of water temperature by depth in deep reservoirs is essential for managing selective discharge facilities and controlling downstream water temperature and quality [58,59]. ...
Data-driven models (DDMs) are extensively used in environmental modeling but face challenges due to limited training data and potential results not adhering to physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL included an energy constraint term from W2's thermal energy equilibrium into the cost function of the LSTM, besides the mean square error term. In PGDL, parameters were optimized by penalizing deviations from the energy law, ensuring adherence to physical constraints. Compared to LSTM, PGDL demonstrated enhanced satisfaction with the energy balance and superior performance in water temperature prediction. Even with less field data for training, PGDL outperformed both LSTM and calibrated W2 after pre-training with data generated using the uncalibrated W2. Therefore, integration of DDM with a PBM ensured physical consistency in water temperature prediction for complex stratified reservoirs with limited data. Moreover, pre-training the PGDL with PBM proved highly effective in mitigating bias and variance due to insufficient field measurement data.
... For this, the average temperature was decreased or increased by 1°C and 2°C (T adj = ±1°C, ±2°C), whereas the average irradiance was decreased or increased by 25 Wm −2 or 50 Wm −2 (I adj = ±25 Wm −2 , ±50 Wm −2 ). To test for potential interacting effects of increased temperature and decreased light availability associated with predicted impacts of ongoing global warming and brownification of freshwater ecosystems(Blanchet et al., 2022;Woolway et al., 2020), we run the ATN model after increasing T by 2°C-which is a conservative estimate of the average temperature increase of summer surface waters in large Austrian lakes by 2050(Dokulil, 2014)-and decreasing I by 50 Wm −2 . We also evaluated how a gradual 0.037°C year −1 increase in water temperature as observed in LC(Adrian et al., 2009) influences productivity of primary producers and consumption gains of consumers (see "gain from resources (j)" in equation 2), thereby simulating the effects of warming climate on food-web productivity. ...
Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect, for example, consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity, and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance food web indicated marked effects of temporally changing abiotic parameters on seasonal bio-mass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had minor effect, increasing metabolic rate associated with 1-2°C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2-and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch (Perca fluviatilis). However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to, for example, assess potential future community-level responses to ongoing environmental changes.
... For this, the average temperature was decreased or increased by 1°C and 2°C (T adj = ±1°C, ±2°C), whereas the average irradiance was decreased or increased by 25 Wm −2 or 50 Wm −2 (I adj = ±25 Wm −2 , ±50 Wm −2 ). To test for potential interacting effects of increased temperature and decreased light availability associated with predicted impacts of ongoing global warming and brownification of freshwater ecosystems(Blanchet et al., 2022;Woolway et al., 2020), we run the ATN model after increasing T by 2°C-which is a conservative estimate of the average temperature increase of summer surface waters in large Austrian lakes by 2050(Dokulil, 2014)-and decreasing I by 50 Wm −2 . We also evaluated how a gradual 0.037°C year −1 increase in water temperature as observed in LC(Adrian et al., 2009) influences productivity of primary producers and consumption gains of consumers (see "gain from resources (j)" in equation 2), thereby simulating the effects of warming climate on food-web productivity. ...
Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models provide an attractive framework to study consumer-resource interactions from organisms to ecosystems, but they rarely consider changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here we investigate how seasonal changes in carrying capacity and light-dependent growth rate of producers and temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the complex Lake Constance (LC) food web indicated marked effects of seasonal abiotic drivers on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (autotrophs and invertebrates). While the adjustment of irradiance level had minor effect, increasing metabolic rate associated with 1–2˚C temperature increase lead to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old perch. A gradual temperature increase of 0.037˚C year observed in LC increased the productivity of highest trophic levels (i.e., juvenile and adult fish) by ca. 40–50% over the 100-year simulation period. However, when looking at biomass distribution and transfer between trophic guilds in the LC food web, inclusion of seasonal abiotic drivers caused only minor changes in average standing stock biomasses and productivity of different trophic guild groups. Our results demonstrate the potential of introducing seasonal variation in abiotic ATN model parameters to simulate within-year fluctuations in community dynamics, as well as to assess potential future community-level responses to ongoing environmental changes.
... Lakes are an important part of surface water resources, and the health of their water ecological environment is closely related to natural factors and human activities in the basin (Zhang et al., 2015;Dokulil, 2014). Water temperature is one of the most basic physical properties of lakes, and its changes lead to differences in biochemical processes, hydrodynamic conditions and heat exchange between upper and lower water bodies, which affect the eutrophication intensity of the lake and the surrounding ecological environment (Sharma et al., 2007;Xu et al., 2018;Woolway et al., 2014;Li et al., 2014). ...
Currently, lake surface water temperature (LSWT), as one of the most important indicators for evaluating lake health, is rapidly rising due to the influence of large-scale climate change and regional human activities in rapidly developing urban areas. The variety of LSWT will affect water environment problems such as lake water quality, aquatic organism growth and reproduction. Therefore, this study selected six lakes that play a crucial role in China’s economic and social development, and explored the causes and driving mechanisms of LSWT changes, which would provide support for the governance and protection of the ecological environment. Meanwhile, according to the fluctuation of lake boundary, the lakes were divided into two types, A and B. Based on the classification, the changing characteristics of LSWT from 2001 to 2018 were analyzed, and the stepwise polynomial regression analysis was used as new method to quantify the contribution from each driving factor to LSWT. Then, the driving mechanisms of natural and anthropogenic factors to LSWT was discussed. Results show that (1) the mean comprehensive change rates of LSWT-day and LSWT-night showed an upward trend in the past 18 years. The correlation between near surface air temperature (NSAT) and the annual average LSWT of the 6 lakes was higher than that of other factors, and this feature was most significant in spring, autumn and winter. The correlation between anthropogenic factors and annual average LSWT was affected by lake type, NSAT and precipitation in the basin. (2) Natural factors (especially NSAT) had higher contribution rates to LSWT. The contribution from anthropogenic factors to LSWT-night was higher than that in the daytime. For lakes classified in type B, the effect intensity of anthropogenic factors on LSWT-day was affected by NSAT, precipitation and lake area. The contribution rate to LSWT-night was related to the growth rate of the impervious surface area in the basin.
... Lake areas account for about 2.8% of the global land surface area and are the hub of the various layers of the earth circle [1], responding to climate change; regulating the climate, water, and land systems [2]; and, with the interaction of the surrounding environment, maintaining the material and energy balance of the ecosystem. They also provide services such as water resources and electricity supply for humans [3] and play an important role in the geosphere [4]. ...
... The basic concept of the Air2water model [18] is based on the hypotheses that air temperature is an external integrated effect of water temperature increase, that it controls the surface heat balance of lakes, and that water temperature fluctuations depend directly on the heat flux in the surface water and the surface area of the lake [24], the volume of surface water of the lake involved in heat exchange (reaction volume). The total heat integral heat equation for the reaction volume is shown in Equation (1). The relationship between the air temperature (T a ) and the lake water temperature (T w ) in the lake surface water heat flux was obtained with the linear Taylor expansion (Equation (2)). ...
Lake surface water temperature is a fundamental metabolic indicator of lake ecosystems that affects the exchange of material and energy in lake ecosystems. Estimating and predicting changes in lake surface water temperature is crucial to lake ecosystem research. This study selected Dianchi Lake, a typical urban lake in China, as the research area and used the Air2water model combined with the Mann-Kendall mutation statistical method to analyze the temporal and spatial variation in the surface water temperature of Dianchi Lake under three climate models. The research results show that, under the RCP 5-8.5 scenario model, the surface water temperature change rate for Dianchi Lake from 2015 to 2100 would be 0.28 ℃/10a, which was the largest change rate among the three selected scenarios. The rate of change during 2015-2100 would be 9.33 times higher than that during the historical period (1900-2014) (0.03 °C/10a). Against the background of Niulan River water diversion and rapid urbanization, the surface water temperature of Dianchi Lake experienced abrupt changes in 1992, 2016, 2017, and 2022. Against the background of urbanization, the impact of human activities on the surface water temperature of urban lakes will become greater.
... The duration of ice cover affects the stability and vertical mixing of lakes, as well as the lake-atmosphere matter and energy exchange (Rösner et al., 2012;Efremova et al., 2013;Ramp et al., 2015). Ice cover regulates lake biochemical indicators, such as the concentration of dissolved oxygen, nitrogen, and phosphorus, changing the biochemical reaction rate and affecting the water quality and distribution of aquatic organisms (Weitere et al., 2010;Dokulil, 2013; G. C. Hardenbicker et al., 2016). Shortening of the ice season has been observed worldwide (Sharma et al., 2019;Dauginis and Brown, 2021) and attributed to anthropogenic warming (Grant et al., 2021). ...
The seasonal ice cover in lakes of the Qinghai–Tibet Plateau is a transient and vulnerable part of the cryosphere, whose characteristics depend on the regional climate: strong solar radiation in the context of the dry and cold environment because of the high altitude and relatively low latitude. We use the first under-ice temperature observations from the largest Tibetan freshwater lake, Ngoring Lake, and a one-dimensional lake model to quantify the mechanism of solar thermal accumulation under ice, which relies on the ice optical properties and weather conditions, as well as the effect of the accumulated heat on the land–atmosphere heat exchange after the ice breakup. The model was able to realistically simulate the feature of the Ngoring Lake thermal regime: the “summer-like” temperature stratification with temperatures exceeding the maximum density point of 3.98 ∘C across the bulk of the freshwater column. A series of sensitivity experiments revealed solar radiation was the major source of under-ice warming and demonstrated that the warming phenomenon was highly sensitive to the optical properties of ice. The heat accumulated under ice contributed to the heat release from the lake to the atmosphere for 1–2 months after ice-off, increasing the upward sensible and latent surface heat fluxes on average by ∼ 50 and ∼ 80 W m−2, respectively. Therefore, the delayed effect of heat release on the land–atmosphere interaction requires an adequate representation in regional climate modeling of the Qinghai–Tibet Plateau and other lake-rich alpine areas.
... Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change (Austin and Colman, 2008;Livingstone, 2003;Williamson et al., 2009). A rapid rise in water temperature has been observed in many lakes around the world, which not only reflects the changes in the heat budget of lakes associated with global warming (Bates et al., 2008;Dokulil, 2014) but also has resulted in a succession of changes in physical, chemical, and biological processes within the lake system (Hondzo and Stefan, 1993;Ke and able due to the costs and the geographical restrictions of the region. However, in the TP, where there are more than 1100 alpine lakes with an area larger than 1 km 2 and an elevation above 4000 m, most of the lakes have no in situ water temperature records due to the harsh nature for ground observation (Zhang et al., 2014). ...
Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modeling, we have produced an integrated daily LSWT for 160 lakes across the Tibetan Plateau where in situ observation is limited. The MODIS-based lake-wide mean LSWT in the integrated dataset includes the daytime, nighttime, and daily mean for the period 2000–2017. The MODIS-based daily mean LSWT is used to calibrate a simplified physically based model (i.e., modified air2water model), upon which a complete and consistent daily LSWT dataset is reconstructed for the period 1978–2017. The reconstructed LSWT dataset is validated by comparing it with both the satellite-based and in situ observations. The validation shows that the reconstructed LSWT is in good agreement with the observations. According to the reconstructed LSWT dataset, the annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with an increase rate ranging from 0.01 to 0.47 ∘C per 10 years. The warming rate is higher in winter than in summer. The integrated dataset is unique for its relatively large temporospatial span (1978–2017) and high temporal resolution. The dataset together with the methods developed can contribute to research in exploring water and heat balance changes and the consequent ecological effects at the Tibetan Plateau. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.6637526 (Guo et al., 2022).
... In fact, studies predict a summer surface water temperature increase in alpine lakes of 1.2-2.9 °C until 2050 (Dokulil, 2014). In comparison, deep water temperatures showed on average almost no change (+0.06 °C decade −1 ), "but had high variability across lakes, with trends in individual lakes ranging from −0.68 °C decade −1 to +0.65 °C decade −1 " (Pilla et al., 2020). ...
Macrophytes are key components of freshwater ecosystems because they provide habitat, food, and improve the water quality. Macrophyte are vulnerable to environmental change as their physiological processes depend on changing environmental factors, which themselves vary within a geographical region and along lake depth. Their spatial distribution is not well understood and their importance is publicly little-known. In this thesis, I have investigated the spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes to understand their diversity pattern along different scales and to predict and communicate potential consequences of global change on their richness. In the introduction (Chapter 1), I provide an overview of the current scientific knowledge of the species richness patterns of macrophytes in freshwater lakes, the influences of climate and land-use change on macrophyte growth, and different modelling approaches of macrophytes. The main part of the thesis starts with a study about submerged and emergent macrophyte species richness in natural and artificial lakes of Bavaria (Chapter 2). By analysing publicly available monitoring data, I have found a higher species richness of submerged macrophytes in natural lakes than in artificial lakes. Furthermore, I showed that the richness of submerged species is better explained by physio-chemical lake parameters than the richness of emergent species. In Chapter 3, I considered that submerged macrophytes grow along a depth gradient that provides a sharp environmental gradient on a short spatial scale. This study is the first comparative assessment of the depth diversity gradient (DDG) of macrophytes. I have found a hump-shaped pattern of different diversity components. Generalised additive mixed-effect models indicate that the shape of the DDG is influenced mainly by light quality, light quantity, layering depth, and lake area. I could not identify a general trend of the DDG within recent years, but single lakes show trends leading into different directions. In Chapter 4, I used a mechanistic eco-physiological model to explore changes in the distribution of macrophyte species richness under different scenarios of environmental conditions across lakes and with depths. I could replicate the hump-shaped pattern of potential species richness along depth. Rising temperature leads to increased species richness in all lake types, and depths. The effect of turbidity and nutrient change depends on depth and lake type. Traits that characterise “loser species” under increased turbidity and nutrients are a high light consumption and a high sensibility to disturbances. “Winner species” can be identified by a high biomass production. In Chapter 5, I discuss the image problem of macrophytes. Unawareness, ignorance, and the poor accessibility of macrophytes can lead to conflicts of use. I assumed that an increased engagement and education could counteract this. Because computer games can transfer knowledge interactively while creating an immersive experience, I present in the chapter an interactive single-player game for children. Finally, I discuss the findings of this thesis in the light of their implications for ecological theory, their implications for conservation, and future research ideas (Chapter 6). The findings help to understand the regional distribution and the drivers of macrophyte species richness. By applying eco-physiological models, multiple environmental shaping factors for species richness were tested and scenarios of climate and land-use change were explored.
