Assessing Temperature Change Impact in the Wake of Ongoing Land Use Change: A Case Study at Lake Dianshan

Abstract

Climate change exerts both direct and indirect influences on the eutrophication of surface water ecosystems in various ways. This study aimed to evaluate the impact of temperature fluctuations on trophic levels through various interdisciplinary coupling analysis methods after land use change, which including water and sediment sample analysis, hydraulic model, remote sensing, and historic data analysis. For the historical analysis, six satellite images of Lake Dianshan were examined to assess algal bloom occurrences and the coverage of Eichhornia crassipes from 2013 to 2023. The correlation between trophic indicators and temperature was analyzed using statistical methods. For the monthly analysis, a total of 27 sediment samples and 54 water samples collected from Lake Dianshan were assessed to determine how seasonal temperature variations influence eutrophication status. The trophic indicators have higher concentration at inlet sampling sites compared to outlet sites, which validated the potential external pollution source. The trophic level of Lake Dianshan is influenced not only by climate change but also significantly by urban human activities. The management of eutrophication has substantially improved the water quality of Lake Dianshan over the past few decades. Furthermore, increasing temperatures demonstrate a positive correlation with the proliferation of cyanobacteria, particularly in urban areas.

1. Introduction

The intensification of climate change is expected to result in global warming. Both climate variations and anthropogenic activities have the potential to significantly impact nutrient dynamics within lake ecosystems [1]. Freshwater ecosystems and their associated biodiversity are under considerable threat from global development and population growth, which contribute to increased nutrient inputs and intensification, particularly in lacustrine environments [2]. Climate change can influence hydrology, water temperature, ecosystem structure, and nutrient concentrations through both direct and indirect mechanisms. Elevated temperatures are likely to increase concentrations of total nitrogen (TN), total phosphorus (TP), water conductivity, and plant biomass, while simultaneously reducing phytoplankton populations due to shading effects at the water surface [3]. In light of a warming climate and ongoing eutrophication, it is anticipated that future generations will face more frequent and severe cyanobacterial blooms [4]. Excessive algal proliferation in eutrophic conditions can lead to the degradation of aquatic environments [5]. However, there is limited understanding of the responses to algal bloom occurrences [6]. Aquatic ecosystems are particularly vulnerable to biodiversity loss [7]. Contrary to common expectations, the ecosystem has remained undisturbed and has not transitioned to a cloudy-water, phytoplankton-dominated state as a result of global warming and eutrophication [3]. The immediate effects of nutrient inputs are closely associated with the composition of food webs, which are further influenced by climatic conditions [8]. Elevated levels of inorganic nitrogen (NO₃⁻-N and NH₄⁺-N) can directly impede the growth and survival of vegetation by diminishing light availability due to overgrowth of phytoplankton, macroalgae, and epiphytic algae. Additionally, nickel enrichment can adversely affect plant growth by altering cellular functions and inducing negative physiological responses [9].

Eutrophication is more likely to occur in small, stagnant bodies of water, such as lakes, fishponds, or reservoirs, and is primarily driven by an excess of nutrients. This phenomenon poses a significant threat to inland aquatic ecosystems [10]. While lakes naturally accumulate nutrients and become enriched over time, eutrophication is predominantly associated with human activities [11]. Previous research has demonstrated that lakes serve as effective indicators of global climate change, with certain climate-related signals being highly visible and measurable through trophic indicators [12]. Numerous studies employ hydrological models and climate scenarios to forecast potential cyanobacterial blooms in regional aquatic ecosystems. The associated impacts on water quality, including but not limited to the occurrence of harmful algal blooms and hypoxia, have been extensively documented and are on the rise. In addition to ecosystem dynamics and anthropogenic activities, population growth and alterations in land use are anticipated to further increase greenhouse gas emissions on a global scale [13].

In shallow lakes and littoral zones, the presence of underwater vegetation in warm water is a significant characteristic, rendering these ecosystems more vulnerable to environmental degradation compared to deeper water systems. This vulnerability arises from their limited capacity to manage pollutants and nutrient concentrations [14]. Shallow lakes exhibit heightened sensitivity to climatic changes, as the influence of temperature variations on lake stratification is closely associated with the average water temperature, which may restrict the adaptability of lake stratification to warming trends [15]. These lakes can exist in two distinct stable states that depend on the proliferation of aquatic macrophytes: one characterized by clear water, where macrophytes serve as the primary oxygen producers, and another marked by turbid water, dominated by phytoplankton [16]. Lake ecosystems function as indicators of environmental change, as the effects of climate change manifest directly or indirectly through various ecological indicators [17]. Fluctuations in temperature can influence a lake’s nutrient cycles and thermal equilibrium, thereby impacting its biota, including diatoms [18]. In both warm and cold aquatic environments, an increase in phytoplankton generally suppresses submerged plant growth; however, in warmer waters, submerged plants are often outcompeted by floating species [8]. For instance, in Lake Dianshan, the predominant vegetation is Eichhornia crassipes. Nutrient enrichment frequently leads to the replacement of submerged plants by phytoplankton, resulting in alterations not only to biological communities but also to biogeochemical processes [19].

Global warming not only exerts a direct influence on the growth rates of cyanobacteria but also enhances microbial activity within the sediments and soils at the bottoms of lakes and rivers. The ramifications of global warming further accelerate the release of internal phosphorus (P) loading, which may be contributing to the recent proliferation of algal blooms in large lakes and rivers [10]. An increase in temperature is likely to facilitate the diffusion of P from deeper sediments while simultaneously promoting P sedimentation; these two processes may exhibit compensatory effects, akin to the relationship observed between temperature and wind [20]. Consequently, even if external nutrient sources are meticulously regulated, sediment microbial activity and resuspension induced by frequent precipitation can still precipitate eutrophication due to the release of nutrient loading from internal sources [12].

Sediment serves as a significant source and sink of nutrients in lakes and rivers. Pollutants from external sources, along with the accumulation of aquatic organisms, gradually increase the concentration of pollutants within lake sediments. The enrichment of nutrients in sediments exhibits a notable purification effect, albeit with a delayed response to the input of exogenous nutrients. Previous research has indicated that up to 90% of TP, TN, and chemical oxygen demand (COD) from external sources are sequestered at the bottom of lakes [21]. Even when exogenous nutrient inputs are effectively managed, seasonal resuspension can still lead to eutrophication of the water column. The ongoing debate regarding the efficacy of reducing P versus the strategy of controlling both P and N reflects a conflict in application strategies for deep versus shallow lakes. This discourse highlights the contrasting perspectives on the reduction of algal bloom intensity and the persistence of blooms, which differ between small and large lakes [22].

Consequently, numerous researchers have identified TP as a variable indicative of water quality, which is essential for examining the phenomenon of eutrophication in shallow lakes and reservoirs globally [23]. For instance, despite a significant reduction in nutrient concentration in Lake Rostherne following the implementation of interception measures, the concentration of chlorophyll-a (Chl-a) in the water has not exhibited a corresponding decline [24]. Therefore, some scholars argue that it is imperative to further mitigate nutrient loads, particularly internal loads, in conjunction with rising temperatures [25]. In prior studies conducted on Lake Hongze, sediment variables were found to be largely unaffected by climate change. However, factors such as domestic sewage discharge, population density, and the area dedicated to crop cultivation emerged as the primary contributors to variations in sediment variables [26]. Understanding the mechanisms and historical trends of lake eutrophication is essential for the conservation of lake ecosystems and the promotion of regional sustainability [27]. Additionally, the prevalence of harmful algal blooms and the presence of Eichhornia crassipes were also examined.

Human activities significantly influence the patterns of urban land use and cover change (LUCC), which in turn affect trophic indicators [28]. Effective land management is crucial for promoting urban economic development and environmental health, as it can directly impact the diversity of biological species [29]. Changes in biological species subsequently influence the self-purification processes of shallow lakes and the trophic state of regional water systems. Remote sensing images (RSIs) are among the most prevalent methodologies for examining LUCC. They play a vital role in monitoring eutrophication, assessing natural disasters, and detecting wildfire damage. However, the transformation of land use types over time complicates the utilization of spatial contextual information [30]. Currently, satellite datasets typically categorize land use into broad categories such as buildings, ground, vegetation, and water. Specific types of buildings, such as schools, shopping centers, and residential areas, fall under the building category. However, detailed categorical information cannot be solely obtained through remote sensing; field investigations and interviews are essential for acquiring more comprehensive data.

To conduct a comprehensive analysis of the impacts of climate change on trophic indicators in shallow lakes, the results will be examined through four key aspects: (1) water and sediment samples collected from Dianshan Lake; (2) a 30-year historical record of trophic levels obtained from the Baoshan meteorological monitoring station, which is situated near Lake Dianshan; (3) interpretation of remote sensing imagery from the period of 2013 to 2023; and (4) hydraulic modeling of Lake Dianshan. We believe that through interdisciplinary and multidimensional analytical methods, we can more clearly reflect the impact of climate change on the eutrophication of inland lakes in urban areas.

2. Materials and Methods

2.1. Study Area

In this study, we assessed the impact of climate change on the trophic levels of water and sediment in Lake Dianshan, located near Shanghai, from an interdisciplinary perspective. Given that the sediment component is expected to remain relatively stable over short timeframes, a total of 27 sediment samples (three samples from each site, collected exclusively in August) and 54 water samples (two samples from each site, collected in August September and November) were obtained from nine strategically selected sites. These sites were chosen to represent the main inlets and outlets of Lake Dianshan. For the historical analysis, we gathered data on TN, TP, and temperature variations from 2000 to 2020 to elucidate the effects of climate change. The study area, Lake Dianshan, is the largest lake within the Shanghai district, as illustrated schematically in Figure 1. The average depth of the lake is approximately 2.63 m. The catchment area comprises eight rivers and canals that are connected to the lake and do not experience winter ice cover [31]. The primary anthropogenic pollutants discharged into the lake include domestic sewage and excessive fertilizer application. This watershed encompasses an area of 63 km² and is characterized by a north subtropical monsoon climate, which is associated with relatively low water temperatures. Eutrophication of Lake Dianshan commenced in the 1990s, coinciding with the widespread adoption of chemical fertilizers in the surrounding areas. The predominant type of pollution is classified as non-point source pollution, with TN and TP being the main contaminants. The annual precipitation in the region is recorded at 1278.6 mm (as of 2023). This area functions as a satellite town of the Shanghai megalopolis, which has undergone significant development in terms of both population and economic growth over the past few decades. The primary sources of gross domestic product (GDP) have shifted from agriculture and livestock to tourism and commerce. Anthropogenic activities have exerted considerable influence on the state of eutrophication, including land use changes, livestock migration, and increases in household waste. Currently, recreational activities predominate in the vicinity of the lake, while the broader catchment area, from which water flows into Lake Dianshan, is primarily utilized for the cultivation of grains and root vegetables. The lake has fluctuated between a clear oligotrophic state and a turbid eutrophic state.

2.2. Data Resource and Processing

According to monitoring data from local authorities, the first large-scale algal bloom was documented in September 1985, with over 90% of the lake’s surface covered by a dense algal layer lasting approximately 15 days. Following a lake-wide phosphorus precipitation treatment using aluminum sulfate, the trophic level was temporarily controlled. In 2007, another significant outbreak of cyanobacterial blooms occurred in Lake Dianshan, with an average concentration of chlorophyll-a reaching 172.7 mg/m³. To comprehensively assess the trophic level, it is insufficient to solely examine historical changes over the past 10 to 20 years. From an interdisciplinary perspective, this study integrates satellite observations with concurrent field investigations to elucidate the relationship between climate change and trophic state. Regional precipitation and temperature data, concentrated from June to October, are presented in Figure 2. The primary inlets are designated as S1 and S3, while the main outlets are S6, S7, and S8. Temperature levels peak between July and August, and precipitation exhibits a positive correlation with temperature, albeit with a time lag. Preliminary investigations have also indicated that Eichhornia crassipes proliferates vigorously after August. To achieve a comprehensive evaluation of the eutrophication state, field experiments were conducted in August, September, and November.

Lake Dianshan is locked at the intersection of Shanghai and Jiangsu Province. It is the source of the Huangpu River. It is connected with the Jinze Reservoir, which is the potable water source in Shanghai. The water quality of Lake Dianshan directly affects the daily security of inhabitants. The water quality monitoring is managed by the Jishuigang Bridge Monitoring station upstream. Figure 1 revealed that 3 out of 4 major inlets are concentrated on the southwest side of the lake, and the outlets are on the east side of the lake. The sampling points are located at each inlet and outlet. The S9 is the geographic center of Lake Dianshan Catchment. Thus, the majority flow direction is from west to east in most regions. On the north side, there is an aquaculture area near the S4 inlet which caused serious algal bloom at downstream as shown in Figure 1. This aquaculture has been closed for rectification since 2018.

2.3. Methods

Before the sampling process commenced, a 4-m nylon rope was affixed to the grab dredge. Upon arrival at the designated sampling location, the grab dredge was hoisted vertically above the water’s surface, utilizing a gravity snap hook, and subsequently released to descend naturally until it reached the riverbed. Following this, the grab dredge was retrieved and brought back onto the boat. Sediment samples were collected in 250 mL borosilicate glass reagent bottles, which had been pre-wrapped with aluminum foil to prevent adhesion. A total of three parallel samples were obtained from nine distinct locations, resulting in 27 sediment samples overall. These sediment samples were transported to the laboratory and analyzed within 48 h. Given the stability of sediment samples over a short duration, this study aimed to investigate the spatial differences in trophic levels of sediment samples collected in August 2023. The methodologies employed for analyzing heavy metal concentrations (Cadmium, Lead, Chromium), Total phosphorus (TP), Total Nitrogen (TN), pH, moisture content, Ammonia Nitrogen (NH₄-N), and Antimony (Sb) adhered to the following standards: GB/T 17141-1997, HJ 632-2011, HJ 711-2014, HJ 962-2018, NY/T 52-1987, HJ 634-2012, and HJ 803-2016 [32,33,34,35,36,37,38]. Water samples were collected concurrently at the same locations as the sediment samples, with each water sample requiring a minimum volume of 2 L. Three parallel tests were conducted in August (temperature = 33 °C), September (temperature = 29 °C), and November (temperature = 18 °C) of 2023, with two parallel samples taken at each site, culminating in a total of 54 water samples collected.

3. Results

By analyzing the water samples collected from Lake Dianshan, we found that TN (R² = 0.79 for S1) and Chl-a (R² = 0.61 for S1) exhibit greater sensitivity to temperature fluctuations compared to other indicators. The concentrations of TN and Chl-a demonstrated a gradual decline in relation to water temperature during the months of August, September, and November. Furthermore, the findings revealed that the majority of trophic indicators, including TP, TN, and Chl-a, in the inlet water samples collected from sites S1, S2, S3, and S4 were consistently higher than those observed in the outlet samples. The concentration of TP (Coefficient of Variation = 35%) was significantly influenced by the location of the sampling sites, suggesting that the primary source of TP is external. Additionally, the concentrations of NO₃⁻-N and ammonium nitrogen (NH₄⁺-N) were primarily influenced by the activities of nitrogen-fixing cyanobacteria. The results presented in

Table 1. Water Sampling analysis result at August 2023 in Lake Dianshan (T = 33 °C).

Site	TP [mg/L]	TN [mg/L]	Chl-a [μg/L]	NO3-N [mg/L]	Sb [mg/L]	CODMn [mg/L]
S1	0.16	2.25	106.04	0.114	0.0020	3.44
S2	0.07	1.32	35.42	0.651	0.0018	3.96
S3	0.12	0.66	28.83	0.041	0.0016	3.72
S4	0.10	1.31	67.36	0.442	0.0015	4.07
S5	0.15	1.74	56.94	0.056	0.0019	4.98
S6	0.16	0.95	41.47	0.019	0.0018	3.88
S7	0.09	0.93	6.03	0.342	0.0019	3.62
S8	0.05	1.11	6.22	0.533	0.0019	3.81
S9	0.14	1.04	70.20	0.008	0.0018	3.99

(TP represents Total Phosphorus, TN represents Total Nitrogen, Chl-a represents Chlorophyll-a, NO₃-N represents Nitrate Nitrogen, Sb represents Antimony concentration, COD_Mn represents Chemical Oxygen demand tested by KMnO₄).

,

Table 2. Water Sampling analysis result at September 2023 in Lake Dianshan (T = 29 °C).

Site	TP [mg/L]	TN [mg/L]	Chl-a [μg/L]	NO3-N [mg/L]	Sb [mg/L]	CODMn [mg/L]
S1	0.14	1.34	14.95	0.342	0.0017	3.32
S2	0.13	2.18	11.34	1.270	0.0015	2.06
S3	0.15	1.28	10.22	0.486	0.0015	2.09
S4	0.20	1.34	9.67	0.697	0.0012	2.22
S5	0.16	1.31	12.96	0.101	0.0013	2.60
S6	0.14	0.82	11.04	0.216	0.0015	2.34
S7	0.13	1.37	7.05	0.628	0.0016	2.57
S8	0.17	2.07	25.11	0.568	0.0017	3.52
S9	0.14	1.34	15.33	0.342	0.0017	3.32

and

Table 3. Water Sampling analysis result at November 2023 in Lake Dianshan (T = 18 °C).

Site	TP [mg/L]	TN [mg/L]	Chl-a [μg/L]	NO3-N [mg/L]	Sb [mg/L]	CODMn [mg/L]
S1	0.23	0.92	2.01	1.440	0.0021	3.24
S2	0.18	0.76	2.21	1.760	0.0023	3.31
S3	0.17	0.71	1.67	0.549	0.0008	3.58
S4	0.17	0.81	4.41	1.600	0.0019	3.51
S5	0.15	0.92	3.28	0.852	0.0019	3.92
S6	0.14	0.82	3.16	0.470	0.0021	3.74
S7	0.16	0.81	3.03	0.913	0.0021	3.86
S8	0.16	0.86	2.44	1.190	0.0024	3.30
S9	0.23	0.92	1.98	1.440	0.0021	3.23

indicated that the variations in water temperature is not the primary impact factors of activity of cyanobacteria in shallow lakes.

4. Discussion

The analysis of the water sample results presented in Table 1, Table 2 and Table 3 illustrates the response of trophic indicators to variations in temperature. The concentrations of TN and TP in Lake Dianshan during the months of August, September, and November are as follows: 0.66–2.25 mg/L and 0.05–0.16 mg/L for August; 0.82–1.34 mg/L and 0.13–0.20 mg/L for September; and 0.81–0.92 mg/L and 0.14–0.23 mg/L for November, respectively. The concentration of chemical oxygen demand (COD_Mn) is observed to be higher on the northern side (S3, S4, S5) of Lake Dianshan compared to the southern side, which can be attributed to variations in velocity magnitude, as depicted in Figure 3. The primary inlet and outlet are predominantly located on the southern side of the lake, resulting in a relatively high-velocity area. Generally, lower velocities lead to extended residence times in urban lakes, thereby contributing to elevated levels of eutrophication.

This study also examines the spatial distribution of nutrient concentrations in sediment, which remain relatively constant over short periods. The analysis of sediment samples, as presented in

Table 4. Sediment Sampling analysis result at November 2023 in Lake Dianshan (T = 18 °C).

Site	TP [g/kg]	TN [g/kg]	pH	Moisture Content	Sb [mg/kg]	Cd [mg/kg]	Pb [mg/kg]	Cr [mg/kg]
S1	2.46	1.75	7.32	58.4	1.4	0.11	27.0	58.0
S2	2.11	1.21	7.63	48.4	0.9	0.20	34.1	62.0
S3	2.37	1.41	7.73	42.6	0.8	0.18	32.8	48.0
S4	2.22	1.30	7.62	51.7	0.9	0.15	34.3	53.0
S5	2.46	1.42	7.84	43.8	1.1	0.19	27.6	42.0
S6	2.73	0.78	7.81	38.9	0.9	0.17	19.6	32.0
S7	2.31	0.87	7.86	35.3	0.7	0.13	28.0	44.0
S8	2.54	0.91	7.77	40.1	1.3	0.12	26.8	39.0
S9	2.57	1.31	7.45	49.7	1.2	0.14	34.7	50.0

, indicates a positive correlation (R² for TP = 0.61; R² for TN = 0.60) between the concentrations of TN and TP in sediment and those of water samples shown in Table 1. The utilization of the data presented in Table 1 is predicated on the fact that this dataset was collected in August, a period characterized by the most severe conditions of eutrophication. Consequently, this dataset is particularly effective in illustrating the variations among different sampling locations. Notably, TN concentrations in sediment are significantly elevated at inlet locations. Furthermore, the pH value at the outlet is higher than that at the inlet locations, particularly at site S1. The concentrations of trophic indicators in sediment are primarily attributed to historical accumulation, which varies by location. Additionally, the coefficient of variation of antimony (Sb), lead (Pb), and chromium (Cr) are respectively 23.34%, 20.76, 16.97, and 19.84%, which represented heavy mental concentration are not related to internal indicators. The distribution patterns of several heavy metal pollutants, align with those of the trophic indicators, suggesting a common origin from external pollution sources. In contrast, the concentration of cadmium (Cd) does not exhibit a relationship with either location or temperature, likely indicating its sole origin from historical accumulations.

The historical analysis of trophic level revealed that the annual average TN concentration in Lake Dianshan has demonstrated significant fluctuations and a declining trend over the past decade. The annual TN concentration ranged from a minimum of 2.103 mg/L in 2020 to a maximum of 5.325 mg/L in 2005, with a mean concentration of 3.720 mg/L, as illustrated in Figure 4. The total phosphorus (TP) concentration in Lake Dianshan, depicted in Figure 5, varied from 0.228 mg/L in 2003 to 0.075 mg/L in 2020, resulting in a mean concentration of 0.165 mg/L. Between 2005 and 2010, the concentration of TP gradually decreased in correlation with declining temperatures (R² = 0.7). Following 2010, temperatures increased, exhibiting considerable fluctuations attributed to the effects of global warming, with the exception of the years 2016 and 2017. Nonetheless, trophic indicators have consistently declined since the implementation of governmental interventions in land use change. According to the land use satellite interpretation results presented in Figure 6, the building coverage in the vicinity of Lake Dianshan peaked at 46.35 km². It is evident that the trophic level has shown minimal correlation with temperature since that time. The eutrophication issue in the Lake Dianshan region has been significantly influenced by administrative interventions and changes in land use. The water ecosystem of Lake Dianshan has been effectively protected through appropriate development policies, which have substantially mitigated eutrophication levels in light of the impacts of global warming.

The primary factors contributing to changes in eutrophication levels can be categorized into natural environmental influences, anthropogenic activities, and the effects of global warming, among others. Anthropogenic activities have been particularly significant in this context. The principal sources of pollution affecting Lake Dianshan include upstream pollution, local emissions, atmospheric deposition (both dry and wet), and the resuspension of lake sediments. The observed inconsistencies in temporal trends related to temperature, precipitation, and nutrient concentrations, coupled with a combination of positive and negative trends across individual lakes, underscore the varied responses of these aquatic systems to global change. This heterogeneity further highlights the necessity for long-term regional satellite observations in ecological assessments. The Landsat data collected from 2013 to 2023 is illustrated in Figure 6. In the course of our field trip, we examined the primary determinants influencing changes in building structures and vegetation, identifying tourism-related land use and agricultural land use as the most significant factors, respectively. As illustrated in

Table 5. The interpretation results of satellite imagery during 2013–2023.

	Year
Area (km2)	2013	2015	2017	2020	2022	2023
Eichhornia Crassipes	0.38	0.86	1.54	0.13	1.31	2.16
Building	26.54	30.86	46.35	35.63	30.46	32.64
Algal bloom	10.70	2.66	3.74	0.11	1.39	1.33

, the expansion of building area, predominantly allocated for tourism facilities such as hotels, restaurants, and shopping centers, has had a substantial impact from 2013 to 2017. An analysis of bloom areas reveals that the proliferation of cyanobacterial blooms is influenced by multiple factors, including land use changes, economic development, regional policies, and the impacts of global warming. In 2013, the Lake Dianshan watershed was predominantly agricultural, with fertilizers entering the lake through precipitation, leading to significant overgrowth of vegetation on the northern shore. Beginning in 2015, the surrounding agricultural and residential areas transitioned to tourism and landscape development, with only a small portion of the northern area remaining dedicated to aquaculture. The aquaculture operations were completed by 2017, resulting in the lowest recorded nutrient load in the lake’s history. As of 2023, the primary sources of pollution in Lake Dianshan are domestic sewage and effluents from light industry [39]. These findings indicate that climate change exerts a considerable influence on the spread of algal blooms, provided that land use types remain unchanged.

The urban climate has been significantly influenced by human activities, which, in turn, have altered the trophic indicators of urban lakes as a feedback mechanism. The relationship between surface cyanobacterial coverage and hydraulic models requires further analysis. Additional case studies involving urban lakes will be conducted to identify patterns and regularities. Future research will encompass early summer and winter periods, as well as an exploration of multifunctional remote sensing monitoring and interpretation for long-term observations.

5. Perspective and Outlooks

The strong drivers of anthropogenic activities lead to a significant decrease in trophic levels in Lake Dianshan, which might neglect some minor factors such as animal farm runoff, algal migration, wind speed, and so on. In addition, there is uncertainty in the results due to nutrient coupling. This study examined macro factors such as temperature rising, human activity, local policy shifting, and land-use type change, but did not include micro-level impacts such as fluctuations in sediment trophic levels, due to data constraints. The current remote sensing can roughly identify land use type, future research needed to refine the analysis of land use type with the development of interpretation technology. Strengthening external source monitoring and formulating policies to control nutrient input is essential for future research. Consequently, it is necessary to strengthen external source monitoring and formulate policies to control nutrient input. Effective nutrient reduction requires a comprehensive assessment method that includes all aspects of intended mitigation measures as part of the systemic analysis in future research.

6. Conclusions

The historical analysis of the trophic status and ecological responses of Lake Dianshan over the past two decades indicates that urban climate change and anthropogenic activities are the primary drivers of lake eutrophication. Trophic level in shallow lake is one of the most obvious indicators among all detectable metrics that reflect urban climate change. By analyzing the results and investigate historical record, the following key conclusions were drawn:

(1) The results for one set of samples indicated that the concentrations of most trophic indicators at locations S1, S2, S3, and S4 (inlets) in Lake Dianshan were significantly higher compared to those at other locations. This suggests that the primary sources of pollution are external to the lake.

(2) The hydraulic model’s velocity contour indicates that the primary direction of velocity does not shift in response to varying precipitation levels. Areas characterized by low velocity magnitudes tend to exhibit higher levels of trophic indicators.

(3) The analysis of sediment revealed that only the concentration of nitrogen (N) among all trophic indicators exhibited a significant difference across various locations.

(4) The analysis of historical trophic level records indicates that regional administrative interventions and land use changes have a more significant impact on urban lakes than climate change.

(5) The positive correlation between urban climate change and cyanobacteria proliferation in Lake Dianshan is supported by the results of remote sensing interpretations.