Lake Surface Water Temperatures Research Articles

Effects of Long-Term Increases in Water Temperature and Stratification on Large Artificial Water-Source Lakes in South Korea

Long-term changes in air and water temperatures and the resulted stratification phenomena were observed for Soyang Lake (SY), Paldang Lake (PD), Chungju Lake (CJ), and Daecheong Lake (DC) in South Korea. Non-parametric seasonal Kendall and Mann-Kendall tests, Sen slope estimator, and potential energy anomaly (PEA) were applied. The lake surface water temperatures (LSWTs) of SY and DC increased at the same rate (0.125 °C/y), followed by those of CJ (0.071 °C/y) and PD (0.06 °C/y). Seasonally, the LSWT increase rates for all lakes, except PD, were 2–3 times higher than the air temperature increase rates. The lake stratification intensity order was similar to those of the LSWT increases and correlations. SY and DC displayed significant correlations between LSWT (0.99) and PEA (0.91). Thus, the LSWT significantly affected stratification when the water temperature increased. PD demonstrated the lowest correlation between LSWT and PEA. Inflow, outflow, rainfall, wind speed, and retention time were significantly correlated, which varied within and between lakes depending on lake topographical, hydraulic, and hydrological factors. Thus, hydraulic problems and nutrients should be managed to minimize their effects on lake water quality and aquatic ecosystems because lake cyanobacteria can increase as localized water temperatures increase.

Rapidly expanding lake heatwaves under climate change

Lake heatwaves—prolonged periods of hot surface water temperature in lakes—have recently been shown to increase in intensity and duration, with numerous potential implications for aquatic ecosystems. However, an important physical attribute of lake heatwaves that has not yet been investigated is their spatial extent, and how it varies within a warming world. Here, we show that the spatial extent of lake heatwaves, defined as contiguous regions within a lake that simultaneously experience extreme warm conditions, is increasing in the largest group of freshwater lakes on Earth, The Laurentian Great Lakes. We show that the maximum spatial extent of lake heatwaves is sensitive to inter-annual variations in winter ice cover and the timing of stratification onset in spring. Notably, we find that a lengthening of the warm summer season and, in turn, an overall increase in surface water temperature, stimulates the development of larger lake heatwaves. On average, our results suggest that the mean spatial extent of lake heatwaves has increased two-fold since 1995. We anticipate this rapid expansion of lake heatwaves to have widespread implications for heat-related impacts on aquatic species.

Maximum lake surface water temperatures changing characteristics under climate change.

Lake surface water temperature (LSWT) plays an important role in the metabolism of aquatic organisms, and is also an important indicator in lake ecosystems which affects the ecology and biogeochemical processes of lakes. The current research mainly focuses on the long-term trends of LSWT and the impact of climate warming on LSWT. Researchers have not paid enough attention to the study of extreme changes in trend of LSWT. An ice-free lake in China called Dianchi Lake was selected as our research area. We carried out a quantitative analysis of and provided a discussion on the changes in the maximum lake surface water temperature (MLSWT) from 2001 to 2018 at two timescales (month and year) based on MODIS 11A2 composite product data and water temperature environment of cyanobacteria outbreaks. The results showed that the MLSWT of Dianchi Lake increased between 2001 and 2018 and continued to exceed the temperature threshold (17.6°C) for cyanobacterial outbreaks during some timeframes and that the duration of high temperatures also increased. This phenomenon will extend the suitable growth period of cyanobacteria and will have a complex and long-term impact on water quality, the lake ecological environment, and the growth of aquatic organisms.

Optimizing Lake Surface Water Temperature Simulations Over Large Lakes in China With FLake Model

AbstractLake surface water temperature (LSWT) is sensitive to climate change; however, simulated LSWT and its response to climate change remain uncertain. In this study, FLake, a one‐dimensional freshwater lake model, is optimized to simulate the LSWTs of 94 large lakes with surface areas greater than 100 km2 in China. While most of these lakes are seasonally ice‐covered over the Tibetan Plateau, FLake with default parameters significantly underestimated LSWT in spring and winter and slightly overestimated LSWT in summer and autumn in seasonally ice‐covered lakes. We performed sensitivity experiments and calibration in the trial lake (Qinghai Lake). Then, parameter calibrations of three lake‐specific properties (albedo, lake mean depth and light extinction coefficient) were performed in all the studied lakes. The optimized FLake substantially improved the simulations of seasonal and interannual variations in LSWT. The root mean square error decreased from 3.64 ± 1.54°C to 1.97 ± 0.72°C, and the mean bias of 96% of the lakes decreased to less than 1°C. Our study showed that the optimized FLake can reproduce the temporal variations in LSWT across China with optimized parameters, providing the possibility to simulate and project the response of LSWT to rapid climate change.

Warm Arctic Proglacial Lakes in the ASTER Surface Temperature Product

Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is important to test, given the rising air temperatures in the region (reported in this study) and potential to increase water temperatures, thus increasing subaqueous melting and the retreat of glacier termini from where they are in contact with lakes. Through analysis of ASTER surface temperature product data, we report warm (>4 °C) proglacial lake surface water temperatures (LSWT) for both ice-contact and non-ice-contact lakes, as well as substantial spatial heterogeneity. We present in situ validation data (from problematic maritime areas) and a workflow that facilitates the extraction of robust LSWT data from the high-resolution (90 m) ASTER surface temperature product (AST08). This enables spatial patterns to be analysed in conjunction with surrounding thermal influences, such as parent glaciers and topographies. This workflow can be utilised for the analysis of the LSWT data of other small lakes and crucially allows high spatial resolution study of how they have responded to changes in climate. Further study of the LSWT is essential in the Arctic given the amplification of climate change across the region.

An integrated dataset of daily lake surface water temperature over Tibetan Plateau

Abstract. 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 modelling, we have produced an integrated daily lake surface water temperature for 160 lakes across the Tibetan Plateau where in-situ observation is limited. The satellite-based lake-wide mean LSWT in the integrated dataset includes that for the daytime, night-time and for the daily mean for the period 2000–2017. The dataset is comparable with other satellite-based LSWT products (e.g., LSWT from AVHRR and ARC-Lake) and unique for its tempo-spatial span and resolution. Calibrated and validated against the satellite-based LSWT, complete and consistent daily LSWT dataset have been reconstructed and extended to the period 1978–2017 basing on the modified air2water model. According to the reconstructed LSWT dataset, it is found that annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with increase rate ranging at 0.01 to 0.4 °C 10 a−1. The warming trends of the lakes are more evident in winter than in summer. The integrated dataset together with the methods introduced herein can contribute to the research community to explore water and heat balance changes and the consequent ecological effects at the Tibetan Plateau in the future researches. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.5111400 (Guo et al., 2021).

Relative impacts of increases of solar radiation and air temperature on the temperature of surface water in a shallow, eutrophic lake

Abstract We monitored lake surface water temperatures from 1992 to 2019 in Lake Kasumigaura, a shallow lake in Japan. We hypothesized that increases of shortwave radiation had increased surface water temperatures and heat fluxes more than had the increases of air temperature. We used the heat flux analyses and the sensitivity analyses to test the hypothesis. The fluxes of solar radiation gradually increased during the study period in a manner consistent with the phenomenon of global brightening. The increase was especially apparent in the spring. The rate of increase of surface water temperature was especially significant in May. Air temperature did not significantly increase in May, but it increased significantly in June (0.40 °C decade−1). A sensitivity analysis of the heat fluxes at the lake surface (shortwave radiation, longwave radiation, latent heat flux, and sensible heat flux) revealed that surface water temperature was more sensitive to changes of shortwave radiation than to air temperature during the spring. Although other factors such as inflows of groundwater and river water may also have impacted surface water temperatures, the increase of solar radiation appeared to be the major factor responsible for the increase of surface water temperature during the spring in Lake Kasumigaura.

Using the “mean temperature of the catch” to assess fish community responses to warming in a temperate lake

This contribution applies the “mean temperature of the catch” (MTC) concept of Cheung et al. (Nature 497:365–368, 2013) to fish catch data for Lake Peipsi, Estonia/Russia, covering the years 1931 to 2019. The preferred temperature of each of the ten target fish species was obtained from the literature, and combined with the species-specific catch data to obtain MTC values for each year. The analysis of the MTC time series thus obtained with a segmented regression yielded two trend lines, one horizontal at 14.5 °C (1931–1986), and the other (1987–2019) ascending with a slope 0.85 °C·decade−1. Overall, the segmented regression model explains over half of the variance of the MTC data set (multiple R2 = 0.53; adjusted R2 = 0.51). Lake surface water temperatures correlate with MTC, even though weakly (r = 0.30), when considering a 2-year time lag. The fish community of the shallow Lake Peipsi reacts more strongly to temperature changes than marine ecosystems so far studied using the MTC.

ZONATION OF SUBALPINE LAKES BASED ON REMOTELY SENSED WATER QUALITY PARAMETERS

Abstract. SIMILE is an INTERREG Italy-Switzerland project that aims to preserve water quality of the subalpine lakes Como, Lugano and Maggiore (Northern Italy), through an integrated innovative monitoring system. For this purpose, satellite images are processed to map and monitor Chlorophyll-a (CHL-a), Total Suspended Solids (TSM) and Lake Water Surface Temperature (LWST). This study combines these remotely sensed water quality parameters (WPQs) maps, produced for the SIMILE project during 2019–2020, to propose and discuss a zonation approach that can support the monitoring of the study lakes through the analysis of spatial and temporal dynamics of the selected parameters. The approach consists in performing a cluster analysis on a combined sample of WQPs maps, on a monthly basis, for each lake; then the different lake clusters are compared over time, through time series analysis of the WQPs patterns. Finally, the clusters patterns are aggregated over time to map the lakes’ areas that have experienced higher or lower WQPs values during 2019–2020. The results show a high spatial variability for the lakes under study, both during the different seasons and years; a North-South gradient has been identified for all WQPs pattern, requiring for further investigation.

INVESTIGATION OF THE CHANGES OF LAKE SURFACE TEMPERATURES AND AREAS: CASE STUDY OF BURDUR AND EGIRDIR LAKES, TURKEY

Abstract. The world's average surface temperature has been increasing in recent decades. This situation is expected to affect aquatic systems and lakes are one of the most important aquatic systems. The main aims of this study are to examine Lake Surface Water Temperature (LSWT) and area changes of Burdur and Egirdir lakes located in the West Mediterranean Region (TR61) of Turkey for the years 1998, 2008 and 2018 using Landsat satellite images. For this purpose, initially, Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) images were generated and the lake shorelines were extracted by thresholding these images. Then, the LSWT values were obtained by using Landsat thermal images. Finally, the area and LSWT changes of Burdur and Egirdir lakes between the years 1998-2008-2018 and the relationships of these parameters with each other were analysed. The obtained results showed that the lake boundaries could be semi-automatically extracted with overall accuracy values higher than 95%. In 20-year time period it was also observed that the Burdur Lake area decreased significantly, while the Egirdir Lake area decreased slightly. When the LSWT values were analysed, it can be stated that the LSWT values increased in both lakes during this time period. The amount of increase in LSWT values was about 2.2 °C for Burdur Lake, while about 1.3 °C for Egirdir Lake.

Evaluation of Global Surface Water Temperature Data Sets for Use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Variance Based Sensitivity Analysis of FLake Lake Model for Global Land Surface Modeling

AbstractGiven the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the IPSL (Institut Pierre Simon Laplace) climate model and its land surface component, ORganizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE), with the Freshwater Lake model, FLake. To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the FLake parameters. As a result, three different climates and four lake depth configurations were selected as test cases. The Sobol method as sensitivity analysis based on variance decomposition was chosen to rank parameters impact on the model output, that is, lake surface water temperature, latent and sensible heat fluxes. We focus on the 11 parameters of the FLake model, which are the lake depth, the albedo and light extinction coefficient of water, snow, and ice respectively, the fetch, and the relaxation coefficient of the thermocline shape factor. The results show different sensitivity features according to the lake type and climate. The dominant role and time varying contribution of the lake depth, radiative parameters (albedo, light extinction coefficient) and thermocline relaxation coefficient linked to the atmospheric conditions, were clearly highlighted. These findings will lead us to distinguish between different lake categories in each grid cell of ORCHIDEE in the future implementation.

Impact of climate warming on the surface water temperature of plateau lake

Lake surface water temperature (LSWT) is a vital indicator in evaluating the ecological environment and has a direct or indirect impact on regulating physical, chemical, and ecological processes of lakes. Changes in LSWT have a huge impact on the water quality, ecosystem function, and biome composition of the entire lake. In this study, we selected Dianchi Lake as the research area, the monthly average LSWT data collected at the monitoring station from 1998 to 2009 were used as the calibration dataset. The error analysis dataset is the average annual temperatures of the Dianchi Lake extracted from MODIS remote sensing images from 2001 to 2017. The daily average LSWT of Dianchi Lake was estimated by the air–water model and the historical changes from 1980 to 2017 of the LSWT were reproduced. The research draws a conclusion that the LSWT of Dianchi Lake has been greatly affected by air temperature over the past 37 years. LSWT and the annual average air temperature correlation coefficient were R > 0.9. Both the air temperature and LSWT assumed an increasing tendency. The annual average LSWT increased with the rate of 0.36 °C/decade, and the annual average warming rate of the air temperature was 0.47 °C/decade. The monthly average LSWT and air temperature showed a significant correlation at a confidence interval of α = 0.001. The LSWT of Dianchi Lake increased significantly in February, March, and December. The periodicity of the seasonal mean air temperature change was consistent with that of the LSWT in Dianchi Lake. The relative lag of the thermal response of the LSWT to climate warming was also observed. This study fills in the gaps in the long-term sequence data on the LSWT of Dianchi Lake and reveals the effect of climate warming on the LSWT of low-latitude plateau lakes.

Increasing maximum lake surface temperature under climate change

Annual maximum lake surface temperature influences ecosystem structure and function and, in particular, the rates of metabolic activities, species survival and biogeography. Here, we evaluated 50 years of observational data, from 1966 to 2015, for ten European lakes to quantify changes in the annual maximum surface temperature and the duration above a potentially critical temperature of 20 °C. Our results show that annual maximum lake surface temperature has increased at an average rate of +0.58 °C decade−1 (95% confidence interval 0.18), which is similar to the observed increase in annual maximum air temperature of +0.42 °C decade−1 (95% confidence interval 0.28) over the same period. Increments in lake maximum temperature among the ten lakes range from +0.1 in the west to +1.9 °C decade−1 in the east. Absolute maximum lake surface water temperatures were reached in Wörthersee, 27.5 °C, and Neusiedler See, 31.7 °C. Periods exceeding a critical temperature of 20 °C each year became two to six times longer than the respective average (6 to 93). The depth at which water temperature exceeded 20 °C increased from less than 1 to more than 6 m in Mondsee, Austria, over the 50 years studied. As a consequence, the habitable environment became increasingly restricted for many organisms that are adapted to historic conditions.

On the role of local depth and latitude on surface warming heterogeneity in the Laurentian Great Lakes

ABSTRACT Lake surface water temperature (LSWT) responds rapidly to changes in climatic variables. This response is heterogeneous in space and its spatial distribution is primarily influenced by lake bathymetry and latitude. Such heterogeneity is not captured by one-dimensional water temperature models, which can accurately predict only the average LSWT. We performed a spatially distributed application of the hybrid physically based/data-driven model air2water to predict the LSWT variability in the 5 Laurentian Great Lakes and to deepen our understanding of the role of local depth and latitude in shaping this heterogeneous response. Daily remotely sensed LSWT data were used to calibrate and validate the model during 1995–2018, and additional simulations considering a synthetic warmer climate scenario in which air temperature was increased by 2 °C were run to assess the inter- and intra-lake differences in LSWT warming rates. The model reproduces the observed spatial distribution of LSWT with an average root mean squared error of 1.2 °C and suggests that, under the warmer scenario, the LSWT of the 5 lakes could increase heterogeneously, with the deepest zones showing the maximum warming rates. Summer stratification lengthening is expected to increase with higher local depth; this behaviour attenuates with increasing latitude, whereas the LSWT warming is essentially dependent on the local depth, irrespective of latitude. We highlight the importance of accounting for LSWT spatial heterogeneity to adequately assess the thermal response of the Great Lakes to a warming climate.

How does the calibration method impact the performance of the air2water model for the forecasting of lake surface water temperatures?

The air2water model is a widely used tool for the forecasting of lake water temperatures and projection of climate change. It has been applied in thousands of lakes worldwide. However, it employed the Particle Swarm Optimization (PSO) as the calibration method, which was proposed more than 20 years ago. In this study, 12 advanced optimization algorithms proposed during the recent few years were implemented to calibrate and validate the air2water model. Daily observed water temperature in temperate lakes (22 lowland lakes in Poland) were used to evaluate the model performance. Each optimizer was run 30 times with 3 different settings of the maximum number of function calls, 5000, 20,000, and 100,000. We have found that the PSO method used in the original air2water model performs relatively poor compared with most of the recent algorithms, and the majority of recently proposed algorithms reach the similar best solutions on many lakes when the highest number of function calls is allowed. However, only the HARD-DE (hierarchical archive-based mutation strategy with depth information of differential evolution) algorithm is never outperformed by any competitor, irrespective which lake and maximum number of function calls is considered. As a result, we highly recommend using HARD-DE as the calibration method for the air2water model in the future studies.

The Effects of Lake Representation on the Regional Hydroclimate in the ECMWF Reanalyses

AbstractLakes are an integral part of the geosphere, but they are challenging to represent in Earth system models which either exclude lakes or prescribe properties without simulating lake dynamics. In ECMWF Interim reanalysis (ERA-Interim), lakes are represented by prescribing lake surface water temperatures (LSWT) from external data sources, while the newer generation ERA5 introduces the FLake parameterization scheme to the modelling system with different LSWT assimilation data sources. This study assesses the performance of these two reanalyses over three regions with the largest lakes in the world (Laurentian Great Lakes, African Great Lakes, and Lake Baikal) to understand the effects of their simulation differences on hydrometeorological variables. We find that differences in lake representation alter the associated hydrological and atmospheric processes and can affect regional hydroclimatic assessments. There are prominent differences in LSWT between the two datasets which influence the simulation of lake-effect snowstorms in the Laurentian winters and lake-land breeze circulation patterns in the African region. Generally, ERA5 has warmer LSWT in all three regions for most months (by 2-12 K) and its evaporation rates are up to twice the magnitudes in ERA-Interim. In the Laurentian lakes, ERA5 has strong biases in LSWT and evaporation magnitudes. Over Lake Baikal and the African Great Lakes, ERA5 LSWT magnitudes are closer to satellite-based datasets, albeit with warm bias (1-4 K), while ERA-Interim underestimates the magnitudes. ERA5 also simulates intense precipitation hotspots in lake proximity that are not visible in ERA-Interim and other observation-based datasets. Despite these limitations, ERA5 improves the simulation of lake-land circulation patterns across the African Great Lakes.

Seasonal overturn and stratification changes drive deep-water warming in one of Earth\u2019s largest lakes

Most of Earth’s fresh surface water is consolidated in just a few of its largest lakes, and because of their unique response to environmental conditions, lakes have been identified as climate change sentinels. While the response of lake surface water temperatures to climate change is well documented from satellite and summer in situ measurements, our understanding of how water temperatures in large lakes are responding at depth is limited, as few large lakes have detailed long-term subsurface observations. We present an analysis of three decades of high frequency (3-hourly and hourly) subsurface water temperature data from Lake Michigan. This unique data set reveals that deep water temperatures are rising in the winter and provides precise measurements of the timing of fall overturn, the point of minimum temperature, and the duration of the winter cooling period. Relationships from the data show a shortened winter season results in higher subsurface temperatures and earlier onset of summer stratification. Shifts in the thermal regimes of large lakes will have profound impacts on the ecosystems of the world’s surface freshwater.

Warming of lowland Polish lakes under future climate change scenarios and consequences for ice cover and mixing dynamics

Study regionThe study region comprises 25 lowland Polish lakes in the northern part of the country. The studied lakes provide domestic, industrial and agricultural water supply, and are major attractions for tourism, thus playing a significant role in the Polish economy. Study focusThe expected impact of future climate change on lake surface water temperature (LSWT) was predicted using the air2water model, which relies solely on daily air temperature (AT) as model input. LSWT and AT observations for the period 1987–2016 were used for model calibration and validation. Then, historical (1987–2005) and future (2006–2100) AT time series from nine EURO-CORDEX climate models were used to project future LSWT under emission scenarios RCP4.5 and RCP8.5. New hydrological insights for the regionThe results showed that air2water can well reproduce daily LWST with root mean square errors lower than 1 °C on average. The warming trends of both AT and LSWT are expected to be lower than those observed in the past decades (after the 1980s), and ice cover and weak stratification conditions are expected to partially buffer the LSWT response to the high AT warming expected in future winters. However, the overall enduring warming will substantially alter future thermal dynamics, leading to a shortening of ice cover and inverse stratification periods, possibly leading to serious consequences for lake water quality and ecosystem health.

Analysis of Temporal and Spatial Variation Process of Dianchi Lake Surface Water Temperature Based on MODIS Remote Sensing Images

Lake surface water temperature (LSWT) is an important physical attribute that affects the ecological environment of lakes. It plays an important role in the ecological process of the water environment and directly affects human production and life, the ecological environment system of the water area, and the diversity of organisms. In order to adjust and improve the ecological conditions of the watershed, it is necessary to obtain a large amount of effective lake surface water temperature data, and use this as a basis to analyze the temporal and spatial change process of the LSWT. This study uses MODIS (MOD11A2) surface temperature product data to obtain the LSWT of Dianchi Lake, and then analyzes the temporal and spatial variation characteristics of the LSWT of Dianchi Lake on different time scales (year, season, month) during the day and night. The research results show that the annual, seasonal and monthly average values of the LSWT during the day and night are all increasing from 2001 to 2017. The average annual temperature rise rate of Dianchi LSWT during the day and night is 0.09°Cyr−1 and 0.05°Cyr−1, respectively. The spatial analysis results show that the LSWT increase area of Dianchi Lake is expanding.