Why does the Water in a Natural Pool from Transparent Turn into Pumpkin Soup Color?

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Donglin Li, Mingyang Zhao, Qi Liu, Lizeng Duan, Huayu Li, Yun zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6499228/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lakes, reservoirs, and ponds are crucial inland water bodies that provide essential water resources and deliver significant ecological, social, and cultural services. As a key indicator of water quality, identifying the causes of water color changes is of paramount importance. The periodic reddish-brown "pumpkin soup" phenomenon observed in the Clean Pool (CP) of Heilong Pool (HP) in southwestern China has raised concerns about water quality and ecosystem health. We used analytical approaches including, nutrient elements, heavy metal concentrations, dissolved substances, algal community composition, and δD-δ¹⁸O isotope analytical models to investigate the ecological and geochemical mechanisms underlying this phenomenon. The results indicate that, despite Bacillariophyta dominating the algal community in HP, they are not the deciding factor of water color changes. Instead, Fe(OH)₃ colloidal particles, which originate from groundwater-surface water interactions and are influenced by redox environment fluctuations, are identified as the key factor causing the reddish-brown discoloration. Hydrological analysis reveals that atmospheric precipitation and groundwater dynamics significantly affect the formation and transport of Fe(OH)₃ particles. The distinct physical and biological characteristics of the Clear and Turbid Pools further accentuate the landscape contrast between the two water column. This study challenges the conventional assumption that algal blooms are the sole cause of water color anomalies, emphasizing the critical role of hydrogeochemical processes in karst landscapes. Simultaneously, this findings provide new insights into the evolution and regulation of special habitats in karst regions and offer a theoretical framework for managing similar water bodies. Additionally, the study underscores the importance of integrating hydrological, geochemical, and ecological perspectives to address complex environmental phenomena in extreme terrain condition. Water color change Karst landscape hydrological cycle ecological mechanism Heilong Pool (HP) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Lakes, ponds, reservoirs, and rivers, as crucial components of inland water bodies, not only provide essential water resources for human survival but also play irreplaceable ecological roles in maintaining biodiversity and delivering cultural services (Adrian et al. 2009 ; Klein et al. 2017 ; Lehmann et al. 2023 ; Zhang et al. 2023 ). However, the combined stresses of industrialization and urbanization, including land-use changes, pollutant inputs, and climate warming, are accelerating the degradation of aquatic ecosystems (Breitburg et al. 2018 ). Water quality monitoring and the investigation of the causes of water quality decline are prerequisites for understanding these changes and formulating relevant policies (Adrian et al. 2009 ). "Water color" refers to the apparent color of a water body. Under standard conditions, water is a colorless and transparent liquid, whereas its true color is caused by the chromaticity produced by dissolved substances (< 0.45 µm), which is determined by the quantity of suspended particles such as clay, phytoplankton, and colloidal particles (Wang et al. 2021 ). The color of water, determined by the scattering and absorption of various components in the water, represents the comprehensive result of the interaction between sunlight and substances in the water. Water color is considered a core parameter reflecting the health diagnosis of aquatic environments (Xia et al. 2024 ). Based on the Forel-Ule color scale, water body colors are classified into 21 levels, ranging from deep blue to yellow-brown, known as the Forel-Ule Index (FUI). FUI is an important indicator of water quality in lakes, reservoirs, rivers, and oceans, showing a significant negative correlation with water body cleanliness and eutrophication status. Existing studies indicate that the spatiotemporal variability of FUI is primarily controlled by multiple mechanisms, including mineral particle sedimentation (Zhao et al. 2023 ), atmospheric shortwave scattering (Shi et al. 2014 ), humic substance concentration gradients (Shen et al. 2025 ), and phytoplankton community succession (Kessouri et al. 2021 ). Leveraging visible and near-infrared spectral information captured by satellite or aerial sensors, scientists can retrieve key water quality parameters such as chlorophyll concentration, suspended particle content, and colored dissolved organic matter. This non-contact, large-scale monitoring approach provides unprecedented technological support for tracking water environment dynamics at scales ranging from coastal areas and lakes to the global level. For example, Shen et al. ( 2025 ) analyzed the color changes of 67,579 lakes globally over a 40-year time series using Landsat-5, 7, and 8 datasets, identifying factors such as basin NDVI, population, water volume changes, and lake area that may influence lake color variations. Ying et al. ( 2024 ) conducted an FUI study on Chinese lakes, revealing a spatial pattern of "lower in the west and higher in the east, lower in the south and higher in the north." The variation in FUI across different lake regions is driven by various factors, responding to seasonal changes in temperature, wind speed, and runoff (Topp et al. 2021 ). Although satellite remote sensing technology has achieved global-scale water color dynamic monitoring through multi-source spectral fusion (Shi et al. 2014 ), significant knowledge gaps remain in understanding the driving mechanisms of water color anomalies in special geological units (e.g., karst landscapes) and micro water bodies (e.g., ponds and wetlands). In particular, the color response differences between urban artificial water bodies and natural water systems, as well as the coupling effects of human activities and natural hydrological processes (Topp et al. 2021 ) urgently require interdisciplinary research for resolution. In a typical karst landscape area in Southwest China, two adjacent pools, namely, the Clean Pool (CP, connected to groundwater, with a depth exceeding 9 m) and Turbid Pool (TP, shallower, with an average depth of approximately 1.5 m), have been observed (Fig. 1 ). Since 2010, CP has abnormally exhibited a reddish-brown "pumpkin soup" phenomenon, while the adjacent TP has consistently maintained a stable light yellow hue. Moreover, the abnormal color changes of the water bodies do not follow a clear annual pattern but are concentrated in the early to mid-rainy season (May to August) on a monthly scale. This unique landscape contrast has sparked public attention and cognitive conflicts, even leading to unscientific speculations such as earthquake precursors. Numerous water environment experts have attempted to analyze the phenomenon from the perspectives of water quality, tectonics, and hydrological patterns, but a systematic explanatory framework for the cause of water coloration has yet to be established. This study focuses on HP as a case study, aiming to reveal the key factors of water color changes through water quality monitoring, the analysis of algal community structure succession, δD-δ¹⁸O isotope tracing to elucidate hydrological connectivity mechanisms, and the construction of a hydrodynamic-solute transport model to simulate groundwater-surface water interactions. The research outcomes aim to elucidate the biogeochemical mechanisms underlying water color anomalies under the unique karst geological conditions, establish a coupled model linking micro water bodies with regional water cycles, and provide a theoretical paradigm for safeguarding water security in human settlements in karst areas. This study also provides innovative perspectives for the protection and management of similar water bodies. Furthermore, it not only expands the theoretical framework for interpreting water color remote sensing under extreme terrain conditions but also provides technological support for achieving the United Nations Sustainable Development Goal 6 (Clean Water and Sanitation) outlined in the 2030 Agenda for Sustainable Development. 2. Study area and Methods 2.1. Overview of the Study Area HP (25°8′26′′ N, 102°44′46′′ E; 1914 m a.s.l.) is located at the foot of Wulao Mountain in Panlong District, Kunming City, Yunnan Province, southwestern China (Fig. 1 ). It lies within a subtropical plateau monsoon climate zone characteristic of low-latitude regions. This region is predominantly influenced by the warm and moist southwest monsoon originating from the Indian Ocean. Climatic features include ample solar radiation, a brief frost period, and a mean annual temperature of 15°C. HP, a karst spring, comprises two distinct water bodies: CP and TP. The eastern fault zone of HP features an extensive and deep geological structure that interconnects Carboniferous and Permian aquifers, forming a multi-stratigraphic aquifer complex where groundwater emerges to create CP. This spring-fed pool exhibits exceptional water clarity, covering an area of approximately 600 m² with a maximum depth of 15 m, demonstrating discharge rates ranging between 82.78 and 365.5 L/s. In contrast, TP originates from northeast basalt mountain fissures, receiving southwestward surface runoff that converges in the foothill depression. Characterized by a shallower phreatic system than CP, TP displays yellowish-tinged waters spanning 2,600 m² with an average depth of 0.5 m. Its discharge exhibits significant seasonal variation, diminishing to 3.75 L/s during drought periods. 2.2. Sample collection and identification Algal sampling collection was conducted at HP in response to observed water color variations. Two sampling campaigns were carried out in July 2021 (during the abnormal water color period) and July 2022 (during the normal water color period) at both CP and TP. In CP, sampling points were set at depths of 0.5 m below the water surface, at the 1/2 light penetration layer (with transparency depth measured as three times the transparency depth), and at the bottom of the light penetration layer, from which mixed samples were collected. In TP, sampling was only conducted at the surface water depth of 0.5 m. For phytoplankton community analysis, 1000 mL aqueous samples were fixed with Lugol's iodine solution (1% final concentration) and gravimetrically concentrated to a standardized 30 mL volume following established sedimentation protocols (Zhou et al. 2022 ). Taxonomic enumeration was performed using calibrated Sedgewick-Rafter counting chambers under 400× magnification with an Olympus CX31 compound microscope (Zhang et al. 2022 ). The phytoplankton was identified to the species or genus level according to the book of Freshwater Algae of China–Systematics, Taxonomy and Ecology (Hu and Wei 2006 ). Based on the collection of relevant data from the study area and previous research, a total of four spring water samples were collected from the study area in July 2021 and July 2022. A multi-parameter water quality monitor (YSI-EXO2) was used to measure water quality parameters such as water temperature, pH, etc. Nutrient analysis followed standardized spectrophotometric protocols: Total nitrogen was quantified via alkaline potassium persulfate digestion (120°C, 30 min) followed by UV-Vis determination at 220 nm and 275 nm wavelength pair (GB11894-89). Total phosphorus determination employed persulfate-assisted acid digestion (0.15 MPa, 120°C, 30 min) with subsequent molybdenum blue reaction measured at 700 nm (GB 11893-89). Chlorophyll-a (Chl-a) extraction utilized cold acetone (90% v/v, 4°C, 24 h) with centrifugation (4000 ×g, 10 min), quantified through tetrachromatic equations with absorbance measurements at 664, 647, 630, and 750 nm using a Shimadzu UV-2600 spectrophotometer (HJ 897–2017). Meanwhile, the hydrogen and oxygen stable isotopes in the water samples were measured via cavity ring-down spectroscopy, using an isotopic water analyzer (Picarro L2140, Ltd., Picaro Inc., USA). To ensure data accuracy, one isotopic standard material was measured simultaneously for every ten samples. Each sample and standard was analyzed seven times, with the first three measurements discarded to avoid the memory effect. Subsequently, the last four measurements were averaged to obtain the final value for each sample or standard. Data for the δD and δ¹⁸O composition of the local meteoric water line (LMWL)(Fig. 4 ) were sourced from the Chinese isotope online database, as compiled by Wang et al. ( 2022 ). Using the Vienna Standard Mean Ocean Water (VSMOW) as the reference: \(\:\delta\:\left(\text{‰}\right)=\frac{{R}_{s}-{R}_{VSMOW}}{{R}_{VSMOW}}\) (1) Where \(\:{R}_{s}\) represents the 18 O/ 16 O or 2 H/ 1 H ratio of the sample, and \(\:{R}_{VSMOW}\) refers to the Vienna Standard Mean Ocean Water. The δ 18 O and δD test accuracies were less than 0.8‰ and 0.1‰, respectively, within 24 hours. 2.3. Species diversity analysis The following indicators are used to characterize species diversity: (1) Margalef Richness Index \(\:d=\frac{S-1}{\text{ln}\left(N\right)}\) (2) (2) Shannon-Wiener Diversity Index: \(\:{H}^{{\prime\:}}=-\sum\:_{i=1}^{s}\frac{{N}_{i}}{N}\text{ln}\frac{{N}_{i}}{N}\) (3) (3) Simpson dominance index \(\:\lambda\:=1-\sum\:_{i=1}^{s}\frac{{N}_{i}\left({N}_{i}-1\right)}{N\left(N-1\right)}\) (4) (4) Pielou evenness index \(\:J=\frac{{H}^{{\prime\:}}}{\text{ln}S}\) (5) Where, S is the total number of species in the sample plot, N is the total number of individuals of all species in the sample plot, and \(\:{N}_{i}\:\) is the total number of individuals of the i species in the sample plot. 3. Results and discussion 3.1. Phytoplankton community structure and dominant species The phytoplankton assemblage in HP demonstrates moderate biodiversity, encompassing 53 morphospecies distributed across 39 genera from 7 phyla. Taxonomic composition analysis revealed the following phylogenetic distribution: Cyanophyta (3 genera), Bacillariophyta (7 genera, dominance contributors), Chlorophyta (19 genera), Euglenophyta (2 genera), Chrysophyta (5 genera), Cryptophyta (2 genera), and Dinophyta (1 genus). Community structure exhibited pronounced phylum-level dominance, with Bacillariophyta constituting 79.3% of total biomass, followed by Chlorophyta at 14.4% (Fig. 2 ). Comparative interannual analysis showed significantly elevated algal cellular densities in July 2021 (8.17×10⁶ ± 1.2×10⁶ cells L − 1 ) versus July 2022 (2.95×10⁶ cells L − 1 ). The 2021 bloom event featured extraordinary proliferation of Aulacoseira spp. (Bacillariophyta), reaching peak densities of 72.2×10⁶ cells L − 1 , which have exceeded the contemporary co-occurring taxa by two orders of magnitude (Table 1 ). Subsequent monitoring revealed a 96.3% reduction in Aulacoseira biomass by 2022 (2.69×10⁶ cells L − 1 ), though maintaining its ecological dominance within restructured communities (Table 1 ). Table 1 Algal community structure in HP (cells L -1 ) No Species 2021 2022 CP 1 TP 1 CP 2 TP 2 Cyanophyta 1 Limnothrix sp. 600000 0 0 0 2 Rhabdogloea sp. 0 250000 0 0 3 Pseudanabaena sp. 0 0 15000 75000 Bacillariophyta 1 Aulacoseira sp. 5950000 66250000 367500 2325000 2 Cyclotella sp. 25000 7500000 27500 70000 3 Synedra sp. 25000 0 0 0 4 Attheya sp. 0 250000 0 0 5 Nitzschia sp. 0 500000 0 0 6 Rhizosolenia sp. 0 250000 0 0 7 Achnanthes sp. 0 0 0 12500 Chlorophyta 1 Chlorogonium sp. 25000 500000 0 0 2 Chlorella sp. 75000 500000 0 0 3 Tetraedron sp. 25000 0 0 0 4 Planctonema sp. 150000 500000 0 50000 5 Pectodictyon sp. 500000 3000000 0 0 6 Scenedesmus sp. 100000 0 15000 75000 7 Micractinium sp. 0 1000000 0 0 8 Kirchneriella sp. 0 1000000 0 50000 9 Chodatella sp. 0 750000 0 0 10 Actinastrum sp. 0 500000 2500 0 11 Tetrastrum sp. 0 1000000 0 0 12 Crucigenia sp. 0 5000000 10000 0 13 Ankistrodesmus sp. 0 0 7500 37500 14 Characium sp. 0 0 0 12500 15 Cosmarium sp. 0 0 0 12500 16 Pediastrum sp. 0 0 0 200000 17 Treubaria sp. 0 0 2500 0 18 Spermatozopsis sp. 0 0 5000 37500 19 Monoraphidium sp. 0 0 2500 0 Euglenophyta 1 Euglena sp. 0 500000 0 0 2 Phacus sp. 0 250000 0 0 Chrysophyta 1 Ochromonas sp. 25000 1250000 0 0 2 Mallomonas sp. 25000 250000 0 12500 3 Chrysochromulina sp. 0 500000 0 0 4 Dinobryon sp. 0 500000 7500 100000 5 Synura sp. 0 0 7500 87500 Cryptophyta 1 Chroomonas sp. 150000 1250000 22500 75000 2 Cryptomonas sp. 25000 500000 17500 112500 Dinophyta 1 Glenodinium sp. 0 0 0 12500 Quantitative analysis of biodiversity indices demonstrates consistent superiority in TP's ecological metrics across both sampling years (Table 2 ). The Margalef richness index (Turbid 1 is 1.25 vs Clean 1 is 0.82; Turbid 2 is 1.13 vs Clean 2 is 0.99), Shannon-Wiener diversity (Turbid 1 is 1.34 vs 0.96; Turbid 2:1.40 vs 1.24), and Pielou evenness (Turbid 1:0.42 vs 0.36; Turbid 2:0.48 vs 0.47) all indicate enhanced niche partitioning in TP. Paradoxically, higher Simpson dominance indices (TP: 0.49–0.51 vs CP: 0.39–0.47) coexist with increased diversity, reflecting dual selection pressures from anthropogenic forcing: sustained Aulacoseira dominance (present in all samples) coupled with periodic proliferation of supplemental dominants (Cyclotella in 2021, Pediastrum in 2022). Table 2 Algal dominance and diversity in HP Dominant species Margalef Shannon.Wiener Simpson Pielou 2021 CP 1 Aulacoseira、Limnothrix 0.82 0.96 0.39 0.36 TP 1 Aulacoseira、Cyclotella 1.25 1.34 0.49 0.42 2022 CP 2 Aulacoseira、Cyclotella 0.99 1.24 0.47 0.47 TP 2 Aulacoseira、Pediastrum 1.13 1.40 0.51 0.48 Diversity index analysis reveals that the species richness, diversity index, dominance, and evenness in the TP are all higher than in the CP, indicating that the ecological environment of the TP is more suitable for algal growth, with a more pronounced dominance of certain species. There are several reasons that could contribute to the high diversity index in TP. TP has a larger surface water area and a shallower depth compared to CP. A large number of ornamental fish and turtles are kept in the TP, and tourists feed them bait all year round. As a result, the primary productivity in the water body is relatively high. Human intervention in the form of frequent feeding by tourists significantly boosts the nutrient load in the TP. The respiration of fish and the decomposition of organic matter, in turn, accelerate the consumption of dissolved oxygen (DO). Moreover, the TP's lower water exchange rate limits its self-purification capacity. Consequently, it experiences a much higher level of eutrophication than the CP, accompanied by increased water turbidity. Generally, the water in the TP is cloudier in color and has lower transparency compared to that in the CP. To determine whether the change in water color was caused by excessive algal growth, the algal species composition in the two pools was analyzed (Table 2 ). The genus Aulacoseira is a major dominant species in both of CP and TP (Table 2 ). It is widely distributed in rivers and lakes worldwide, particularly flourishing in eutrophic waters, thereby demonstrating strong ecological adaptability (Kociolek 2018 ). This algal belongs to the phylum Bacillariophyta, class Coscinodiscophyceae , order Coscinodiscales , and family Thalassiosiraceae . The cells of Aulacoseira are cylindrical in shape, with a siliceous cell wall, and the cell ends are connected by small spines to form a chain-like structure(Wang et al. 2020 ). In aquatic ecosystems, dominant algal species often serve as crucial environmental indicators. They not only function as sensitive indicators of environmental changes but also play a pivotal role in influencing ecosystem functions and the human living environment (El Semary 2022 ; Thomas et al. 2013 ). For example, Bacillariophyta such as Synedra can indicate clean water conditions while facilitating global carbon cycling through carbon fixation. Dinoflagellates like Ceratium , conversely, reflect the ecosystem's response to stress by reproducing in extreme environments. Additionally, certain algae, including dinoflagellates and cyanobacteria, form toxic blooms or red tides in global freshwater ecosystems, causing widespread impacts on both ecosystems and human activities (Dick et al. 2021 ). Particularly in the context of global climate change, the large-scale proliferation of dominant algal species poses an even greater challenge to the stability of aquatic ecosystems. Harmful algal blooms threaten drinking water supplies, fisheries, and recreational resources (Paerl and Paul 2012 ). These dominant algal species, through their adaptability and competitiveness, shape the structure of ecological communities, drive energy flow, and maintain ecological balance. This makes them indispensable for aquatic ecosystem monitoring, environmental management, and ecological restoration. From a landscape perspective, when the density of dominant algae reaches a certain threshold, their characteristic pigments selectively absorb and diffract light, causing the water to exhibit specific colors (Duppeti et al. 2017 ). For instance, waters dominated by cyanobacteria often appear blue-green, while dinoflagellates or Euglenophytes may result in reddish-brown waters. However, it is important to note that existing studies have not found significant effects of Aulacoseira on water color. In contrast, algae commonly associated with significant reddening phenomena, such as Euglena , Alexandrium , Gymnodinium , and Peridinium , were either absent or present in extremely low quantities in HP. This makes it unlikely that they play a significant role in the water color change. Therefore, we can infer the possibility that the dominant algal species in HP contributed to the "pumpkin soup" phenomenon. 3.2. Comparison of Water Quality Physicochemical Parameters Algal-induced color changes are generally a significant cause of water color variation, making the in-depth analysis of the water quality environment in HP crucial for verifying the impact of algae on water color changes. Previous studies have demonstrated that external pollution, leading to nutrient overloads of Nitrogen and Phosphorus, can significantly increase the primary productivity of aquatic ecosystems, resulting in eutrophication and abnormal algal blooms(Xu et al. 2010 ). However, our research found that during the period of abnormal reddening in the CP, the concentrations of Total nitrogen, Total phosphorus, dissolved total nitrogen, and dissolved total phosphorus were all lower than in the TP (Table 3 ). Furthermore, TP exhibited relatively stable water color. In addition, there were higher Chl-a concentrations in TP in both observation periods compared to the CP. Those suggested higher phytoplankton biomass and a more stable eutrophic state in the TP. In contrast, the lower nutrient levels during the period of water color change in the CP further indicated that algal biomass is not the dominant factor driving the water color change in CP. In general, the reasons for the pumpkin soup color in CP ecosystem were the external inputs influenced by short-term disturbances rather than abnormal algal proliferation. Table 3 Water Quality Parameters of HP Parameters 2021 2022 CP 1 TP 1 CP 2 TP 2 TN (mg L − 1 ) 1.99 4.27 2.61 1.85 TP (mg L − 1 ) 0.10 0.05 0.10 0.15 DTN (mg L − 1 ) 1.53 3.71 2.60 1.71 DTP (mg L − 1 ) 0.01 0.02 0.02 0.01 PO 4 3− (mg L − 1 ) 0.00 0.01 0.02 0.01 NO 3 -N (mg L − 1 ) 0.83 2.79 2.27 1.16 Chl-a (µg/L) 5.76 57.61 8.86 65.76 DOC (mg L − 1 ) 3.52 5.36 4.75 8.45 δ 18 O(‰) -11.16 -11.24 -11.51 -11.31 δD(‰) -81.32 -80.97 -83.09 -82.41 Algae are commonly considered as a key driver of water color changes (Fowler et al. 2022 ), but water color variation is influenced by a variety of factors (Ma et al. 2006 ; Pitblado 1992 ). Environmental factors such as groundwater recharge, fluctuations in mineral content, and redox reactions can significantly impact the color of spring waters (Chen et al. 2023 ; Sun et al. 2023 ), especially under the karst topography region. Numerous previous studies have demonstrated that dissolved substances, including mineral ions, humic substances, and Chl-a, can lead to changes in water color (Arvola et al. 2025 ; Škerlep et al. 2020 ; Spiegel et al. 2024 ). To identify whether sediment particles in the water, such as minerals like iron oxide and manganese oxide, cause water discoloration, we analyzed the dissolved heavy metal ions in the water. The results are shown in Table 4 . Among them, iron minerals, which are most likely to cause discoloration, had concentrations of 4.85E + 02 µg L − 1 in the CP and 6.01E + 02 µg L − 1 in the TP. The results indicated that the concentration of dissolved Fe in the CP, which turned pumpkin soup-colored, was lower than that in the TP. There were no significant differences in other dissolved heavy metal ions between the CP and the TP. The result suggested that the dissolved particulate matter in the water body (< 0.45 µm) is not responsible for the observed color change. Table 4 Heavy metal concentration of water bodies in HP (µg L -1 ) Elements CP 1 TP 1 Elements CP 1 TP 1 As 5.90E-01 4.11E-01 Mo 7.31E-01 2.95E-01 Be 5.56E-04 2.50E-03 Ni 5.16E + 00 6.20E + 00 Ca 4.08E + 01 4.81E + 01 Pb 4.53E-02 5.50E-02 Cd 5.37E-03 7.48E-03 Sb 1.26E-01 7.98E-02 Co 1.96E-01 2.29E-01 Se 2.72E-01 1.99E-01 Cr 1.15E + 00 1.81E + 00 Sr 1.62E + 02 9.50E + 01 Cu 6.26E-01 5.07E-01 Ti 3.96E + 01 4.98E + 01 Fe 4.85E + 02 6.01E + 02 Tl 7.02E-03 6.53E-03 Li 7.13E-01 3.65E-01 V 1.24E + 00 1.46E + 00 Mg 1.13E + 04 9.49E + 03 Zn 3.27E + 00 5.00E + 00 Mn -6.38E-05 1.77E-05 Solid particulate matter in the water body could potentially cause changes in the water color. In 2021, water samples collected from the CP and the TP precipitated within 24 hours, with the water color changing from pumpkin soup-colored to transparent. When the water samples were shaken or stirred, the solid particulate matter was resuspended, making the water turbid and reddish-brown, consistent with the pumpkin soup color. Previous studies had shown that Fe and Mn ions could lead to the accumulation of sediments in water supply systems, thereby affecting the color and turbidity of groundwater(Zhang et al. 2020 ). In summary, the reason the CP turned pumpkin soup-colored was that a large amount of solid particulate matter was generated in the water body, which might be Fe(OH)₃ colloidal precipitate. According by the phenomenon, the reason why the CP turned pumpkin soup-colored could be that a large amount of solid particulate matter was generated in the water body, which might be Fe(OH)₃ colloidal precipitate. To verify the above hypothesis, we simulated this process in the laboratory. That is, we injected FeCl₂ solution into the uncolored water body in a reducing environment and found no obvious changes. On the contrary, in an oxidizing environment, we observed reddish - brown colloidal precipitates similar to the pumpkin color. Based on this finding, we can infer that the groundwater in HP, which interacts with the underlying rock layers through cracks or channels, may have undergone redox changes, which affect the species and migration of Fe in the groundwater. During this process, Fe²⁺ is oxidized to Fe³⁺ in an oxidizing environment, forming Fe(OH)₃ precipitates suspended in the water, resulting in a reddish-brown color change. Therefore, this is also the reason why the dissolved Fe in the CP and the TP is abnormally low. This phenomenon is closely related to the temporal and spatial heterogeneity of water color changes, providing a reasonable explanation for the observed abnormal water color changes. 3.3. Hydrological Cycle Process Based on the foregoing analysis, we have ascertained that Fe(OH)₃ precipitation is the principal cause of the "pumpkin soup" phenomenon. However, the formation of this phenomenon is not solely contingent upon redox conditions but is also substantially influenced by groundwater dynamics. The unique karst topographical characteristics of HP facilitate the intricate exchange of substances between groundwater and surface water via fractures and conduits. This hydrological cycle constitutes the primary pathway for the transport of minerals into the water body (Fonollá et al. 2020 ) and is a crucial factor driving the landscape disparities between the CP and the TP. We have further analyzed the chemical dynamics of HP's groundwater with the aim of elucidating the groundwater recharge patterns, mineral migration pathways, and their interaction mechanisms with surface water. This comprehensive understanding will afford deeper insights into the formation mechanisms underlying the abnormal water color changes in HP. By analyzing the stable isotopic values of hydrogen and oxygen in atmospheric precipitation within the study area and combining the δO-δD relationship, we established the Local Meteoric Water Line (LMWL: δ D = 6.46 δ ¹⁸O-4.44). We also calculated the corresponding evaporation line ( δ D = 5.64 δ ¹⁸O-18.16) for HP water samples based on hydrogen and oxygen isotopes and projected it onto the δO-δD plot (Fig. 4 ). The results indicate that the isotopic values of the spring samples are located to the lower right of the LMWL, with significant deviations. However, the slope of the evaporation line (5.64) bears resemblance to that of the LMWL (6.46), suggesting that the principal water source for HP is atmospheric precipitation. This water has undergone significant evaporative fractionation or water-rock interactions, thereby resulting in isotopic variations. By calculating the deuterium excess (d value), we discovered that the d value for Clean 1 was the lowest (7.94 ‰ -8.02 ‰ ), while the d value for CP 2 was the highest (9.02 ‰ -9.35 ‰ ). This indicates that the groundwater-rock interactions in the CP vary in intensity over time, with the most pronounced interactions occurring during periods of abnormal water color changes, leading to longer groundwater residence times (Liu et al. 2016 ). In contrast, the d value for the TP remained relatively stable across both periods, suggesting that its hydrogen and oxygen isotopic values are primarily influenced by evaporative fractionation, owing to its shallower water depth. These differences reflect substantial hydrological and geological process variations between the CP and the TP in HP, underscoring the distinct processes governing water sources and isotopic behavior in these two water bodies. Based on the above results, we can comprehensively deduce the hydrological cycle process behind the abnormal water color changes in HP (Fig. 5 ). It is widely accepted that oxygen enters shallow groundwater through the four main pathways: vertical infiltration of oxygenated precipitation, infiltration of oxygenated surface water, diffusion of air in the seepage zone, and gas capture resulting from fluctuations in the groundwater level caused by intermittent well operations (Kohfahl et al. 2009 ; Williams and Oostrom 2000 ). The dynamics of HP's groundwater are primarily influenced by precipitation and human activities. Oxygenated precipitation entering the groundwater system can trigger fluctuations in the groundwater level. When the water table rises, the groundwater may inundate larger areas, covering soils and rocks that were previously exposed to the air, thus reducing their contact with atmospheric oxygen. This results in a decrease in DO levels and an increase in dissolved carbon dioxide (DIC) concentrations, shifting the environment towards a more reducing state (Bao et al. 2023 ; Grenthe et al. 1992 ). Under these conditions, iron-containing minerals are more likely to dissolve, releasing Fe²⁺. When Fe²⁺ comes into contact with higher oxygen levels in the groundwater or is introduced into the CP via an upward spring, it oxidizes to Fe³⁺. The Fe³⁺ then undergoes hydrolysis reactions in the water, forming hydroxyl complexes, which eventually lead to the formation of Fe(OH)₃ (Kazak and Pozdniakov 2021 ; Smith et al. 2017 ; Xia et al. 2022 ). These Fe(OH)₃ particles remain suspended in the CP, temporarily causing the water to exhibit a reddish-brown "pumpkin soup" phenomenon. Additionally, before the water turned red, a large number of aquatic animals were found dead. This was due to the formation of ascending springs under the influence of water pressure, as long-sealed stagnant anoxic water bodies in the karst region were forced to the surface. This led to a decrease in DO levels within the water body, subsequently causing the death of aquatic organisms. This further validates that changes in the redox conditions of the water body not only lead to a transformation in water color but also have a detrimental impact on aquatic life (Linnik et al. 2023 ; Mahler and Bourgeais 2013 ). CP, as a deeper area, has stronger vertical mixing capabilities, which are conducive to the vertical distribution of DO, the oxidation of Fe²⁺, and the suspension and diffusion of Fe(OH)₃ colloids, resulting in significant water color changes. In contrast, TP, with its shallower depth and less significant underground channels and water level fluctuations, has a poor water exchange capacity and mainly retains the original water storage. Furthermore, due to the narrow communication channel and the large depth difference between CP and TP, the water exchange capacity between the two pools is limited, leading to a weak response of TP's water color to the hydrological processes of CP, thus making water color changes barely noticeable. These differences in hydrological and geochemical processes ultimately result in the significant contrast in water color between CP and TP. 3.4. Environmental Significance and Limitations This study found that the algal community in HP is dominated by Bacillariophyta and Chlorophyta, with high biomass significantly positively correlated with eutrophic conditions (high Total phosphorus, Total nitrogen, and Chl-a), consistent with the mechanisms of algal response to nutrient input in classical eutrophication theory (Carpenter et al. 1998 ). As the dominant species in HP's eutrophic state, Bacillariophyta are favored due to the special ecological adaptation of Aulacoseira to the conditions in HP. Notably, although the CP exhibits oligotrophic conditions, its anomalous color changes highlight the limitations of the conventional paradigm that attributes water coloration primarily to algal activity. (Li et al. 2020 ) employed redundancy analysis and correlation analysis to demonstrate that travertine deposition is the key driver behind the bluish-green coloration of karst lakes in Jiu Zhai Gou. This finding broadens the conceptual framework of abiotic controls on water color and underscores the importance of integrating hydrogeochemical processes into the interpretation of water color dynamics in karst environments. The water color changes caused by Fe(OH)₃ precipitation in CP illustrated the dynamic characteristics of the redox interface in the karst groundwater system. The sensitivity of iron form transformations to the DO gradient makes it an effective tracer for groundwater-surface water interactions (Grünenbaum et al. 2024 ; Osorio-Leon et al. 2023 ). In this study, the temporal and spatial differentiation of the d-value differences, reflecting the intensity of rock-water interactions, and groundwater residence time jointly control the oxidation state and form distribution of iron (Song et al. 2024 ; Xia et al. 2023 ). However, the study did not quantitatively characterize the microbial-mediated iron oxidation process (e.g., the catalytic role of bacteria such as Gallionella , Acidithiobacillus ), which may have led to an underestimation of the contribution of biogeochemical coupling mechanisms (Karimian et al. 2018 ; Melton et al. 2014 ). The differentiation of water color between the two pools is essentially an apparent manifestation of differences in hydrological connectivity. In TP, the shallow water retention characteristics form a closed material circulation, maintaining the current eutrophic state through the sediment-water interface feedback, which reflects historical nutrient loads (Smith et al. 2011 ). In contrast, CP is controlled by active groundwater exchange, and its rapid redox state switching is similar to the "pulsed material transport" mode seen in karst spring systems (Ford and Williams 2007 ). In addition, algal biology plays a key role in the water color differentiation between the two pools. The high eutrophic degree of TP results in low DO levels, while the narrow communication channel limits the high-DO water from CP from entering TP, thereby inhibiting the oxidation of iron in TP. Furthermore, algae require iron for chlorophyll synthesis and the electron transport chain (Raven 1988 ; Sunda and Huntsman 1995 ), and in TP, the high algal density may lead to the significant absorption of Fe²⁺, reducing the concentration of free Fe²⁺ and inhibiting the formation of Fe(OH)₃. Additionally, algal metabolism may release dissolved organic matter (DOM), which can form complexes with Fe²⁺ or Fe³⁺, affecting the migration and transformation of iron (Barbeau et al. 2001 ; Rose and Waite 2003 ). These complex hydrological-biological coupling mechanisms dynamically influence the water color differentiation between the two pools. This heterogeneity suggests water body classification and management strategies in karst areas: TP should focus on controlling internal pollution (e.g., sediment dredging), while CP should address external input blockage (e.g., sealing fracture channels). This study extends traditional algal ecology into the field of hydro-geochemistry, conducting a cross-scale mechanism analysis and investigating the complexity of multi-interface processes (water-rock-gas-biological) in shaping the apparent characteristics of water bodies. It challenges the conventional attribution model that relies solely on biological or chemical factors. Moreover, by revealing the formation mechanism of iron oxide precipitation, this research provides new insights into pollution tracing and ecological restoration in groundwater systems. However, in the context of global environmental change, the "pumpkin soup" phenomenon will continue to emerge. In response to these abnormal natural landscapes, this study offers a framework for cross-scale mechanism analysis. Future efforts should focus on further improving the accuracy and data support of the "hydrological-geochemical-ecological" coupling model, providing a more comprehensive understanding of the evolutionary mechanisms and regulatory pathways of unique habitats, and offering more precise scientific foundations for environmental protection and ecological restoration. 4. Conclusion This study takes the abnormal water color changes in HP, Southwest China as an example. By employing a variety of analytical methods, including geochemical element analysis and biological community identification, it reveals the complex interrelationships among algal community structure, water quality physicochemical parameters, and hydrological processes. The results showed that during all observation periods, the algal density of diatoms (mainly the genus Fragilaria ) was significantly higher than that of other algae, making them a key dominant species in the algal community of HP. However, diatoms are not the main driving force behind the abnormal water color changes. Further analysis indicates that there is no significant correlation between water nutrient status, dissolved metal content, and abnormal water color changes. Instead, Fe(OH)3 particles are identified as the core driving factor for the abnormal water color changes. In addition, during the period of abnormal water color, significant groundwater-rock interactions occurred in HP. Atmospheric precipitation affected the redox environment of the groundwater system (including caves and channels), leading to the formation of Fe(OH) 3 particles, which eventually entered the pond water. The differences in physical properties and biological environment between the clear and turbid ponds jointly drive the differentiated water color characteristics of the two ponds, reflecting the comprehensive impact of water exchange properties, groundwater dynamics, and local ecological processes on water color changes. This study innovatively elucidates the water color change pattern, providing a new theoretical paradigm for the analysis of ecological environment effects of special hydrological landscapes in karst areas. Future research can employ in-situ micro-interface sensing and isotope tracing techniques to further quantitatively explore the coupling mechanisms of material fluxes across multiple spheres, deepening the understanding of the ecological environment effects of similar hydrological landscapes. Declarations Conflicts of Interest The authors declare no conflicts of interest. Funding This research was funded by the Scientific Research Projects of the Yunnan Education Department (Grant No. 2025J0900), the Yunnan Fundamental Research Projects (Grant No.202501AU070173 and 202401AT070458) and the College Students Innovation Training Program (Grant No. 202410684005). Author Contribution D.L. conceived the study, developed the methodology, and secured funding; B.Q. contributed to writing, reviewing, editing, formal analysis, and software; Q.L. provided supervision, software, and validation; Y.Z. and Q.G. was responsible for data curation and formal analysis; L.D., H.Z. and H.L. handled data curation, visualization, and investigation. All authors have read and agreed to the published version of the manuscript. Data Availability Statement The data and code used are listed in the manuscript. References Adrian R, O'Reilly CM, Zagarese H, Baines SB, Hessen DO, Keller W, Livingstone DM, Sommaruga R, Straile D, Van Donk E et al (2009) Lakes as sentinels of climate change. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6499228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449060650,"identity":"d421b1df-da74-4b76-a2ba-e73d7cdea9e3","order_by":0,"name":"Donglin Li","email":"","orcid":"","institution":"Qujing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Donglin","middleName":"","lastName":"Li","suffix":""},{"id":449060651,"identity":"b5763195-5357-4bb2-8f0b-02eae53b26c5","order_by":1,"name":"Mingyang Zhao","email":"","orcid":"","institution":"Qujing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Zhao","suffix":""},{"id":449060652,"identity":"573e7071-1922-40d4-9314-422abb03db31","order_by":2,"name":"Qi Liu","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Liu","suffix":""},{"id":449060653,"identity":"9a145a8e-cb40-44d9-b277-bb9c28581faf","order_by":3,"name":"Lizeng Duan","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Lizeng","middleName":"","lastName":"Duan","suffix":""},{"id":449060654,"identity":"f5bed2d6-b907-4cf0-8ac5-cc673128edd1","order_by":4,"name":"Huayu Li","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Huayu","middleName":"","lastName":"Li","suffix":""},{"id":449060655,"identity":"a12544fb-3542-4172-9533-864cfe004f65","order_by":5,"name":"Yun zhang","email":"","orcid":"","institution":"Hubei Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"zhang","suffix":""},{"id":449060656,"identity":"1f6a9ef7-cc6b-4bf6-968d-f190edc05ae8","order_by":6,"name":"Qingyan Gao","email":"","orcid":"","institution":"Qujing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qingyan","middleName":"","lastName":"Gao","suffix":""},{"id":449060657,"identity":"0f67f273-43cb-4593-9490-dee80ccbf916","order_by":7,"name":"Bofeng Qiu","email":"data:image/png;base64,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","orcid":"","institution":"Qujing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Bofeng","middleName":"","lastName":"Qiu","suffix":""}],"badges":[],"createdAt":"2025-04-22 01:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6499228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6499228/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81613676,"identity":"f819bda3-af46-4daa-bdd9-f3bd9823c968","added_by":"auto","created_at":"2025-04-29 07:52:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9356591,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and remote sensing imagery of the study area. (a) Provincial boundaries of China, (b) municipal boundaries of Yunnan Province, and (c) remote sensing map of Kunming, while (d) aerial view of HP during water discoloration.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499228/v1/6bbd081427f87a919d819f85.jpg"},{"id":81612403,"identity":"94d44382-f616-4827-8826-a4fe106dbdb5","added_by":"auto","created_at":"2025-04-29 07:36:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1107760,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Distribution of Major Algae in HP\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499228/v1/8c548f5035430f95099b919d.jpg"},{"id":81613129,"identity":"dfae849a-5957-48e8-92e8-db4fd0d23be9","added_by":"auto","created_at":"2025-04-29 07:44:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2524264,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of water samples after standing and after shaking (TP on the left and CP on the right. a. After settling, b. After shaking.)\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499228/v1/99d7a5f427a50d2d9f60b3ce.jpg"},{"id":81612413,"identity":"9d87a91f-4109-4963-af7a-809c1154614a","added_by":"auto","created_at":"2025-04-29 07:36:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":950961,"visible":true,"origin":"","legend":"\u003cp\u003eAtmospheric waterline (LMWL: Local Meteoric Water Line, GMWL: Global Meteoric Water Line)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499228/v1/9a544a21c8af773cde8c9467.jpg"},{"id":81612406,"identity":"5b00bc6a-b283-4d2f-a1fa-a9d8142fdeca","added_by":"auto","created_at":"2025-04-29 07:36:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3596730,"visible":true,"origin":"","legend":"\u003cp\u003eHydrological cycle model of HP\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499228/v1/a35fedddfee14ebfdc168e4f.jpg"},{"id":81859646,"identity":"00cec29f-5d49-4a9b-9d48-15585a85649c","added_by":"auto","created_at":"2025-05-03 02:01:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18668750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6499228/v1/5bf73ac5-f950-48eb-b424-991e140b493f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Why does the Water in a Natural Pool from Transparent Turn into Pumpkin Soup Color?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLakes, ponds, reservoirs, and rivers, as crucial components of inland water bodies, not only provide essential water resources for human survival but also play irreplaceable ecological roles in maintaining biodiversity and delivering cultural services (Adrian et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Klein et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lehmann et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the combined stresses of industrialization and urbanization, including land-use changes, pollutant inputs, and climate warming, are accelerating the degradation of aquatic ecosystems (Breitburg et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Water quality monitoring and the investigation of the causes of water quality decline are prerequisites for understanding these changes and formulating relevant policies (Adrian et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). \"Water color\" refers to the apparent color of a water body. Under standard conditions, water is a colorless and transparent liquid, whereas its true color is caused by the chromaticity produced by dissolved substances (\u0026lt;\u0026thinsp;0.45 \u0026micro;m), which is determined by the quantity of suspended particles such as clay, phytoplankton, and colloidal particles (Wang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The color of water, determined by the scattering and absorption of various components in the water, represents the comprehensive result of the interaction between sunlight and substances in the water. Water color is considered a core parameter reflecting the health diagnosis of aquatic environments (Xia et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the Forel-Ule color scale, water body colors are classified into 21 levels, ranging from deep blue to yellow-brown, known as the Forel-Ule Index (FUI). FUI is an important indicator of water quality in lakes, reservoirs, rivers, and oceans, showing a significant negative correlation with water body cleanliness and eutrophication status. Existing studies indicate that the spatiotemporal variability of FUI is primarily controlled by multiple mechanisms, including mineral particle sedimentation (Zhao et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), atmospheric shortwave scattering (Shi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), humic substance concentration gradients (Shen et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and phytoplankton community succession (Kessouri et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Leveraging visible and near-infrared spectral information captured by satellite or aerial sensors, scientists can retrieve key water quality parameters such as chlorophyll concentration, suspended particle content, and colored dissolved organic matter. This non-contact, large-scale monitoring approach provides unprecedented technological support for tracking water environment dynamics at scales ranging from coastal areas and lakes to the global level. For example, Shen et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) analyzed the color changes of 67,579 lakes globally over a 40-year time series using Landsat-5, 7, and 8 datasets, identifying factors such as basin NDVI, population, water volume changes, and lake area that may influence lake color variations. Ying et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted an FUI study on Chinese lakes, revealing a spatial pattern of \"lower in the west and higher in the east, lower in the south and higher in the north.\" The variation in FUI across different lake regions is driven by various factors, responding to seasonal changes in temperature, wind speed, and runoff (Topp et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although satellite remote sensing technology has achieved global-scale water color dynamic monitoring through multi-source spectral fusion (Shi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), significant knowledge gaps remain in understanding the driving mechanisms of water color anomalies in special geological units (e.g., karst landscapes) and micro water bodies (e.g., ponds and wetlands). In particular, the color response differences between urban artificial water bodies and natural water systems, as well as the coupling effects of human activities and natural hydrological processes (Topp et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) urgently require interdisciplinary research for resolution.\u003c/p\u003e \u003cp\u003eIn a typical karst landscape area in Southwest China, two adjacent pools, namely, the Clean Pool (CP, connected to groundwater, with a depth exceeding 9 m) and Turbid Pool (TP, shallower, with an average depth of approximately 1.5 m), have been observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Since 2010, CP has abnormally exhibited a reddish-brown \"pumpkin soup\" phenomenon, while the adjacent TP has consistently maintained a stable light yellow hue. Moreover, the abnormal color changes of the water bodies do not follow a clear annual pattern but are concentrated in the early to mid-rainy season (May to August) on a monthly scale. This unique landscape contrast has sparked public attention and cognitive conflicts, even leading to unscientific speculations such as earthquake precursors. Numerous water environment experts have attempted to analyze the phenomenon from the perspectives of water quality, tectonics, and hydrological patterns, but a systematic explanatory framework for the cause of water coloration has yet to be established.\u003c/p\u003e \u003cp\u003eThis study focuses on HP as a case study, aiming to reveal the key factors of water color changes through water quality monitoring, the analysis of algal community structure succession, δD-δ\u0026sup1;⁸O isotope tracing to elucidate hydrological connectivity mechanisms, and the construction of a hydrodynamic-solute transport model to simulate groundwater-surface water interactions. The research outcomes aim to elucidate the biogeochemical mechanisms underlying water color anomalies under the unique karst geological conditions, establish a coupled model linking micro water bodies with regional water cycles, and provide a theoretical paradigm for safeguarding water security in human settlements in karst areas. This study also provides innovative perspectives for the protection and management of similar water bodies. Furthermore, it not only expands the theoretical framework for interpreting water color remote sensing under extreme terrain conditions but also provides technological support for achieving the United Nations Sustainable Development Goal 6 (Clean Water and Sanitation) outlined in the 2030 Agenda for Sustainable Development.\u003c/p\u003e"},{"header":"2. Study area and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Overview of the Study Area\u003c/h2\u003e\n\u003cp\u003eHP (25\u0026deg;8\u0026prime;26\u0026prime;\u0026prime; N, 102\u0026deg;44\u0026prime;46\u0026prime;\u0026prime; E; 1914 m a.s.l.) is located at the foot of Wulao Mountain in Panlong District, Kunming City, Yunnan Province, southwestern China (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It lies within a subtropical plateau monsoon climate zone characteristic of low-latitude regions. This region is predominantly influenced by the warm and moist southwest monsoon originating from the Indian Ocean. Climatic features include ample solar radiation, a brief frost period, and a mean annual temperature of 15\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eHP, a karst spring, comprises two distinct water bodies: CP and TP. The eastern fault zone of HP features an extensive and deep geological structure that interconnects Carboniferous and Permian aquifers, forming a multi-stratigraphic aquifer complex where groundwater emerges to create CP. This spring-fed pool exhibits exceptional water clarity, covering an area of approximately 600 m\u0026sup2; with a maximum depth of 15 m, demonstrating discharge rates ranging between 82.78 and 365.5 L/s. In contrast, TP originates from northeast basalt mountain fissures, receiving southwestward surface runoff that converges in the foothill depression. Characterized by a shallower phreatic system than CP, TP displays yellowish-tinged waters spanning 2,600 m\u0026sup2; with an average depth of 0.5 m. Its discharge exhibits significant seasonal variation, diminishing to 3.75 L/s during drought periods.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Sample collection and identification\u003c/h2\u003e\n\u003cp\u003eAlgal sampling collection was conducted at HP in response to observed water color variations. Two sampling campaigns were carried out in July 2021 (during the abnormal water color period) and July 2022 (during the normal water color period) at both CP and TP. In CP, sampling points were set at depths of 0.5 m below the water surface, at the 1/2 light penetration layer (with transparency depth measured as three times the transparency depth), and at the bottom of the light penetration layer, from which mixed samples were collected. In TP, sampling was only conducted at the surface water depth of 0.5 m.\u003c/p\u003e\n\u003cp\u003eFor phytoplankton community analysis, 1000 mL aqueous samples were fixed with Lugol's iodine solution (1% final concentration) and gravimetrically concentrated to a standardized 30 mL volume following established sedimentation protocols (Zhou et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Taxonomic enumeration was performed using calibrated Sedgewick-Rafter counting chambers under 400\u0026times; magnification with an Olympus CX31 compound microscope (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The phytoplankton was identified to the species or genus level according to the book of Freshwater Algae of China\u0026ndash;Systematics, Taxonomy and Ecology (Hu and Wei \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBased on the collection of relevant data from the study area and previous research, a total of four spring water samples were collected from the study area in July 2021 and July 2022. A multi-parameter water quality monitor (YSI-EXO2) was used to measure water quality parameters such as water temperature, pH, etc. Nutrient analysis followed standardized spectrophotometric protocols: Total nitrogen was quantified via alkaline potassium persulfate digestion (120\u0026deg;C, 30 min) followed by UV-Vis determination at 220 nm and 275 nm wavelength pair (GB11894-89). Total phosphorus determination employed persulfate-assisted acid digestion (0.15 MPa, 120\u0026deg;C, 30 min) with subsequent molybdenum blue reaction measured at 700 nm (GB 11893-89). Chlorophyll-a (Chl-a) extraction utilized cold acetone (90% v/v, 4\u0026deg;C, 24 h) with centrifugation (4000 \u0026times;g, 10 min), quantified through tetrachromatic equations with absorbance measurements at 664, 647, 630, and 750 nm using a Shimadzu UV-2600 spectrophotometer (HJ 897\u0026ndash;2017).\u003c/p\u003e\n\u003cp\u003eMeanwhile, the hydrogen and oxygen stable isotopes in the water samples were measured via cavity ring-down spectroscopy, using an isotopic water analyzer (Picarro L2140, Ltd., Picaro Inc., USA). To ensure data accuracy, one isotopic standard material was measured simultaneously for every ten samples. Each sample and standard was analyzed seven times, with the first three measurements discarded to avoid the memory effect. Subsequently, the last four measurements were averaged to obtain the final value for each sample or standard. Data for the \u0026delta;D and \u0026delta;\u0026sup1;⁸O composition of the local meteoric water line (LMWL)(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) were sourced from the Chinese isotope online database, as compiled by Wang et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Using the Vienna Standard Mean Ocean Water (VSMOW) as the reference:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:\\left(\\text{\u0026permil;}\\right)=\\frac{{R}_{s}-{R}_{VSMOW}}{{R}_{VSMOW}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{s}\\)\u003c/span\u003e\u003c/span\u003e represents the \u003csup\u003e18\u003c/sup\u003eO/\u003csup\u003e16\u003c/sup\u003eO or \u003csup\u003e2\u003c/sup\u003eH/\u003csup\u003e1\u003c/sup\u003eH ratio of the sample, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{VSMOW}\\)\u003c/span\u003e\u003c/span\u003e refers to the Vienna Standard Mean Ocean Water. The \u0026delta;\u003csup\u003e18\u003c/sup\u003eO and \u0026delta;D test accuracies were less than 0.8\u0026permil; and 0.1\u0026permil;, respectively, within 24 hours.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Species diversity analysis\u003c/h2\u003e\n\u003cp\u003eThe following indicators are used to characterize species diversity:\u003c/p\u003e\n\u003cp\u003e(1) Margalef Richness Index\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d=\\frac{S-1}{\\text{ln}\\left(N\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e(2) Shannon-Wiener Diversity Index:\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}^{{\\prime\\:}}=-\\sum\\:_{i=1}^{s}\\frac{{N}_{i}}{N}\\text{ln}\\frac{{N}_{i}}{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e(3) Simpson dominance index\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabd\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:=1-\\sum\\:_{i=1}^{s}\\frac{{N}_{i}\\left({N}_{i}-1\\right)}{N\\left(N-1\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e(4) Pielou evenness index\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabe\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:J=\\frac{{H}^{{\\prime\\:}}}{\\text{ln}S}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere, \u003cem\u003eS\u003c/em\u003e is the total number of species in the sample plot, \u003cem\u003eN\u003c/em\u003e is the total number of individuals of all species in the sample plot, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the total number of individuals of the \u003cem\u003ei\u003c/em\u003e species in the sample plot.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Phytoplankton community structure and dominant species\u003c/h2\u003e \u003cp\u003eThe phytoplankton assemblage in HP demonstrates moderate biodiversity, encompassing 53 morphospecies distributed across 39 genera from 7 phyla. Taxonomic composition analysis revealed the following phylogenetic distribution: Cyanophyta (3 genera), Bacillariophyta (7 genera, dominance contributors), Chlorophyta (19 genera), Euglenophyta (2 genera), Chrysophyta (5 genera), Cryptophyta (2 genera), and Dinophyta (1 genus). Community structure exhibited pronounced phylum-level dominance, with Bacillariophyta constituting 79.3% of total biomass, followed by Chlorophyta at 14.4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Comparative interannual analysis showed significantly elevated algal cellular densities in July 2021 (8.17\u0026times;10⁶ \u0026plusmn; 1.2\u0026times;10⁶ cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) versus July 2022 (2.95\u0026times;10⁶ cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The 2021 bloom event featured extraordinary proliferation of \u003cem\u003eAulacoseira\u003c/em\u003e spp. (Bacillariophyta), reaching peak densities of 72.2\u0026times;10⁶ cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which have exceeded the contemporary co-occurring taxa by two orders of magnitude (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequent monitoring revealed a 96.3% reduction in \u003cem\u003eAulacoseira\u003c/em\u003e biomass by 2022 (2.69\u0026times;10⁶ cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), though maintaining its ecological dominance within restructured communities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlgal community structure in HP (cells L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCP 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTP 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyanophyta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimnothrix sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e600000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRhabdogloea sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudanabaena sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBacillariophyta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAulacoseira sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5950000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e367500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2325000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyclotella sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSynedra sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAttheya sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNitzschia sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRhizosolenia sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAchnanthes sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChlorophyta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChlorogonium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChlorella sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTetraedron sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlanctonema sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePectodictyon sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eScenedesmus sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicractinium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKirchneriella sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChodatella sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e750000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eActinastrum sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTetrastrum sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCrucigenia sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnkistrodesmus sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCharacium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCosmarium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePediastrum sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e200000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTreubaria sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSpermatozopsis sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMonoraphidium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEuglenophyta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEuglena sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhacus sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChrysophyta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOchromonas sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMallomonas sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChrysochromulina sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDinobryon sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSynura sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCryptophyta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChroomonas sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1250000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCryptomonas sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e112500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDinophyta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGlenodinium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eQuantitative analysis of biodiversity indices demonstrates consistent superiority in TP's ecological metrics across both sampling years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Margalef richness index (Turbid 1 is 1.25 vs Clean 1 is 0.82; Turbid 2 is 1.13 vs Clean 2 is 0.99), Shannon-Wiener diversity (Turbid 1 is 1.34 vs 0.96; Turbid 2:1.40 vs 1.24), and Pielou evenness (Turbid 1:0.42 vs 0.36; Turbid 2:0.48 vs 0.47) all indicate enhanced niche partitioning in TP. Paradoxically, higher Simpson dominance indices (TP: 0.49\u0026ndash;0.51 vs CP: 0.39\u0026ndash;0.47) coexist with increased diversity, reflecting dual selection pressures from anthropogenic forcing: sustained \u003cem\u003eAulacoseira\u003c/em\u003e dominance (present in all samples) coupled with periodic proliferation of supplemental dominants (Cyclotella in 2021, Pediastrum in 2022).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlgal dominance and diversity in HP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDominant species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMargalef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShannon.Wiener\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePielou\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCP 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAulacoseira、Limnothrix\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTP 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAulacoseira、Cyclotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCP 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAulacoseira、Cyclotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTP 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAulacoseira、Pediastrum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiversity index analysis reveals that the species richness, diversity index, dominance, and evenness in the TP are all higher than in the CP, indicating that the ecological environment of the TP is more suitable for algal growth, with a more pronounced dominance of certain species. There are several reasons that could contribute to the high diversity index in TP. TP has a larger surface water area and a shallower depth compared to CP. A large number of ornamental fish and turtles are kept in the TP, and tourists feed them bait all year round. As a result, the primary productivity in the water body is relatively high. Human intervention in the form of frequent feeding by tourists significantly boosts the nutrient load in the TP. The respiration of fish and the decomposition of organic matter, in turn, accelerate the consumption of dissolved oxygen (DO). Moreover, the TP's lower water exchange rate limits its self-purification capacity. Consequently, it experiences a much higher level of eutrophication than the CP, accompanied by increased water turbidity. Generally, the water in the TP is cloudier in color and has lower transparency compared to that in the CP.\u003c/p\u003e \u003cp\u003eTo determine whether the change in water color was caused by excessive algal growth, the algal species composition in the two pools was analyzed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The genus \u003cem\u003eAulacoseira\u003c/em\u003e is a major dominant species in both of CP and TP (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It is widely distributed in rivers and lakes worldwide, particularly flourishing in eutrophic waters, thereby demonstrating strong ecological adaptability (Kociolek \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This algal belongs to the phylum Bacillariophyta, class \u003cem\u003eCoscinodiscophyceae\u003c/em\u003e, order \u003cem\u003eCoscinodiscales\u003c/em\u003e, and family \u003cem\u003eThalassiosiraceae\u003c/em\u003e. The cells of \u003cem\u003eAulacoseira\u003c/em\u003e are cylindrical in shape, with a siliceous cell wall, and the cell ends are connected by small spines to form a chain-like structure(Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn aquatic ecosystems, dominant algal species often serve as crucial environmental indicators. They not only function as sensitive indicators of environmental changes but also play a pivotal role in influencing ecosystem functions and the human living environment (El Semary \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For example, Bacillariophyta such as \u003cem\u003eSynedra\u003c/em\u003e can indicate clean water conditions while facilitating global carbon cycling through carbon fixation. Dinoflagellates like \u003cem\u003eCeratium\u003c/em\u003e, conversely, reflect the ecosystem's response to stress by reproducing in extreme environments. Additionally, certain algae, including dinoflagellates and cyanobacteria, form toxic blooms or red tides in global freshwater ecosystems, causing widespread impacts on both ecosystems and human activities (Dick et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticularly in the context of global climate change, the large-scale proliferation of dominant algal species poses an even greater challenge to the stability of aquatic ecosystems. Harmful algal blooms threaten drinking water supplies, fisheries, and recreational resources (Paerl and Paul \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These dominant algal species, through their adaptability and competitiveness, shape the structure of ecological communities, drive energy flow, and maintain ecological balance. This makes them indispensable for aquatic ecosystem monitoring, environmental management, and ecological restoration.\u003c/p\u003e \u003cp\u003eFrom a landscape perspective, when the density of dominant algae reaches a certain threshold, their characteristic pigments selectively absorb and diffract light, causing the water to exhibit specific colors (Duppeti et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, waters dominated by cyanobacteria often appear blue-green, while dinoflagellates or Euglenophytes may result in reddish-brown waters. However, it is important to note that existing studies have not found significant effects of \u003cem\u003eAulacoseira\u003c/em\u003e on water color. In contrast, algae commonly associated with significant reddening phenomena, such as \u003cem\u003eEuglena\u003c/em\u003e, \u003cem\u003eAlexandrium\u003c/em\u003e, \u003cem\u003eGymnodinium\u003c/em\u003e, and \u003cem\u003ePeridinium\u003c/em\u003e, were either absent or present in extremely low quantities in HP. This makes it unlikely that they play a significant role in the water color change. Therefore, we can infer the possibility that the dominant algal species in HP contributed to the \"pumpkin soup\" phenomenon.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Comparison of Water Quality Physicochemical Parameters\u003c/h2\u003e \u003cp\u003eAlgal-induced color changes are generally a significant cause of water color variation, making the in-depth analysis of the water quality environment in HP crucial for verifying the impact of algae on water color changes. Previous studies have demonstrated that external pollution, leading to nutrient overloads of Nitrogen and Phosphorus, can significantly increase the primary productivity of aquatic ecosystems, resulting in eutrophication and abnormal algal blooms(Xu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, our research found that during the period of abnormal reddening in the CP, the concentrations of Total nitrogen, Total phosphorus, dissolved total nitrogen, and dissolved total phosphorus were all lower than in the TP (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, TP exhibited relatively stable water color. In addition, there were higher Chl-a concentrations in TP in both observation periods compared to the CP. Those suggested higher phytoplankton biomass and a more stable eutrophic state in the TP. In contrast, the lower nutrient levels during the period of water color change in the CP further indicated that algal biomass is not the dominant factor driving the water color change in CP. In general, the reasons for the pumpkin soup color in CP ecosystem were the external inputs influenced by short-term disturbances rather than abnormal algal proliferation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater Quality Parameters of HP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTN (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDTN (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDTP (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePO\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e3\u0026minus;\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e(mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-N (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChl-a (\u0026micro;g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDOC (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e18\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eO(\u0026permil;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eδD(\u0026permil;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-81.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-80.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-83.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-82.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlgae are commonly considered as a key driver of water color changes (Fowler et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but water color variation is influenced by a variety of factors (Ma et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pitblado \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Environmental factors such as groundwater recharge, fluctuations in mineral content, and redox reactions can significantly impact the color of spring waters (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), especially under the karst topography region.\u003c/p\u003e \u003cp\u003eNumerous previous studies have demonstrated that dissolved substances, including mineral ions, humic substances, and Chl-a, can lead to changes in water color (Arvola et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Škerlep et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Spiegel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To identify whether sediment particles in the water, such as minerals like iron oxide and manganese oxide, cause water discoloration, we analyzed the dissolved heavy metal ions in the water. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Among them, iron minerals, which are most likely to cause discoloration, had concentrations of 4.85E\u0026thinsp;+\u0026thinsp;02 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the CP and 6.01E\u0026thinsp;+\u0026thinsp;02 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ein the TP. The results indicated that the concentration of dissolved Fe in the CP, which turned pumpkin soup-colored, was lower than that in the TP. There were no significant differences in other dissolved heavy metal ions between the CP and the TP. The result suggested that the dissolved particulate matter in the water body (\u0026lt;\u0026thinsp;0.45 \u0026micro;m) is not responsible for the observed color change.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy metal concentration of water bodies in HP (\u0026micro;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.90E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.11E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.31E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.95E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.56E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.20E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.81E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.53E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.50E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.48E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.98E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.72E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.99E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.50E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.26E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.07E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.96E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.98E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.85E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.01E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.02E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.53E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.13E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.49E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.27E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.38E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSolid particulate matter in the water body could potentially cause changes in the water color. In 2021, water samples collected from the CP and the TP precipitated within 24 hours, with the water color changing from pumpkin soup-colored to transparent. When the water samples were shaken or stirred, the solid particulate matter was resuspended, making the water turbid and reddish-brown, consistent with the pumpkin soup color. Previous studies had shown that Fe and Mn ions could lead to the accumulation of sediments in water supply systems, thereby affecting the color and turbidity of groundwater(Zhang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In summary, the reason the CP turned pumpkin soup-colored was that a large amount of solid particulate matter was generated in the water body, which might be Fe(OH)₃ colloidal precipitate. According by the phenomenon, the reason why the CP turned pumpkin soup-colored could be that a large amount of solid particulate matter was generated in the water body, which might be Fe(OH)₃ colloidal precipitate.\u003c/p\u003e \u003cp\u003eTo verify the above hypothesis, we simulated this process in the laboratory. That is, we injected FeCl₂ solution into the uncolored water body in a reducing environment and found no obvious changes. On the contrary, in an oxidizing environment, we observed reddish - brown colloidal precipitates similar to the pumpkin color. Based on this finding, we can infer that the groundwater in HP, which interacts with the underlying rock layers through cracks or channels, may have undergone redox changes, which affect the species and migration of Fe in the groundwater. During this process, Fe\u0026sup2;⁺ is oxidized to Fe\u0026sup3;⁺ in an oxidizing environment, forming Fe(OH)₃ precipitates suspended in the water, resulting in a reddish-brown color change. Therefore, this is also the reason why the dissolved Fe in the CP and the TP is abnormally low. This phenomenon is closely related to the temporal and spatial heterogeneity of water color changes, providing a reasonable explanation for the observed abnormal water color changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Hydrological Cycle Process\u003c/h2\u003e \u003cp\u003eBased on the foregoing analysis, we have ascertained that Fe(OH)₃ precipitation is the principal cause of the \"pumpkin soup\" phenomenon. However, the formation of this phenomenon is not solely contingent upon redox conditions but is also substantially influenced by groundwater dynamics. The unique karst topographical characteristics of HP facilitate the intricate exchange of substances between groundwater and surface water via fractures and conduits. This hydrological cycle constitutes the primary pathway for the transport of minerals into the water body (Fonoll\u0026aacute; et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and is a crucial factor driving the landscape disparities between the CP and the TP.\u003c/p\u003e \u003cp\u003eWe have further analyzed the chemical dynamics of HP's groundwater with the aim of elucidating the groundwater recharge patterns, mineral migration pathways, and their interaction mechanisms with surface water. This comprehensive understanding will afford deeper insights into the formation mechanisms underlying the abnormal water color changes in HP.\u003c/p\u003e \u003cp\u003eBy analyzing the stable isotopic values of hydrogen and oxygen in atmospheric precipitation within the study area and combining the δO-δD relationship, we established the Local Meteoric Water Line (LMWL: \u003cem\u003eδ\u003c/em\u003eD\u0026thinsp;=\u0026thinsp;6.46\u003cem\u003eδ\u003c/em\u003e\u0026sup1;⁸O-4.44). We also calculated the corresponding evaporation line (\u003cem\u003eδ\u003c/em\u003eD\u0026thinsp;=\u0026thinsp;5.64\u003cem\u003eδ\u003c/em\u003e\u0026sup1;⁸O-18.16) for HP water samples based on hydrogen and oxygen isotopes and projected it onto the δO-δD plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results indicate that the isotopic values of the spring samples are located to the lower right of the LMWL, with significant deviations. However, the slope of the evaporation line (5.64) bears resemblance to that of the LMWL (6.46), suggesting that the principal water source for HP is atmospheric precipitation. This water has undergone significant evaporative fractionation or water-rock interactions, thereby resulting in isotopic variations.\u003c/p\u003e \u003cp\u003eBy calculating the deuterium excess (d value), we discovered that the d value for Clean 1 was the lowest (7.94\u003cb\u003e\u0026permil;\u003c/b\u003e-8.02\u003cb\u003e\u0026permil;\u003c/b\u003e), while the d value for CP 2 was the highest (9.02\u003cb\u003e\u0026permil;\u003c/b\u003e-9.35\u003cb\u003e\u0026permil;\u003c/b\u003e). This indicates that the groundwater-rock interactions in the CP vary in intensity over time, with the most pronounced interactions occurring during periods of abnormal water color changes, leading to longer groundwater residence times (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, the d value for the TP remained relatively stable across both periods, suggesting that its hydrogen and oxygen isotopic values are primarily influenced by evaporative fractionation, owing to its shallower water depth. These differences reflect substantial hydrological and geological process variations between the CP and the TP in HP, underscoring the distinct processes governing water sources and isotopic behavior in these two water bodies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the above results, we can comprehensively deduce the hydrological cycle process behind the abnormal water color changes in HP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It is widely accepted that oxygen enters shallow groundwater through the four main pathways: vertical infiltration of oxygenated precipitation, infiltration of oxygenated surface water, diffusion of air in the seepage zone, and gas capture resulting from fluctuations in the groundwater level caused by intermittent well operations (Kohfahl et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Williams and Oostrom \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The dynamics of HP's groundwater are primarily influenced by precipitation and human activities. Oxygenated precipitation entering the groundwater system can trigger fluctuations in the groundwater level. When the water table rises, the groundwater may inundate larger areas, covering soils and rocks that were previously exposed to the air, thus reducing their contact with atmospheric oxygen. This results in a decrease in DO levels and an increase in dissolved carbon dioxide (DIC) concentrations, shifting the environment towards a more reducing state (Bao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Grenthe et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Under these conditions, iron-containing minerals are more likely to dissolve, releasing Fe\u0026sup2;⁺. When Fe\u0026sup2;⁺ comes into contact with higher oxygen levels in the groundwater or is introduced into the CP via an upward spring, it oxidizes to Fe\u0026sup3;⁺. The Fe\u0026sup3;⁺ then undergoes hydrolysis reactions in the water, forming hydroxyl complexes, which eventually lead to the formation of Fe(OH)₃ (Kazak and Pozdniakov \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xia et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These Fe(OH)₃ particles remain suspended in the CP, temporarily causing the water to exhibit a reddish-brown \"pumpkin soup\" phenomenon. Additionally, before the water turned red, a large number of aquatic animals were found dead. This was due to the formation of ascending springs under the influence of water pressure, as long-sealed stagnant anoxic water bodies in the karst region were forced to the surface. This led to a decrease in DO levels within the water body, subsequently causing the death of aquatic organisms. This further validates that changes in the redox conditions of the water body not only lead to a transformation in water color but also have a detrimental impact on aquatic life (Linnik et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mahler and Bourgeais \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCP, as a deeper area, has stronger vertical mixing capabilities, which are conducive to the vertical distribution of DO, the oxidation of Fe\u0026sup2;⁺, and the suspension and diffusion of Fe(OH)₃ colloids, resulting in significant water color changes. In contrast, TP, with its shallower depth and less significant underground channels and water level fluctuations, has a poor water exchange capacity and mainly retains the original water storage. Furthermore, due to the narrow communication channel and the large depth difference between CP and TP, the water exchange capacity between the two pools is limited, leading to a weak response of TP's water color to the hydrological processes of CP, thus making water color changes barely noticeable. These differences in hydrological and geochemical processes ultimately result in the significant contrast in water color between CP and TP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Environmental Significance and Limitations\u003c/h2\u003e \u003cp\u003eThis study found that the algal community in HP is dominated by Bacillariophyta and Chlorophyta, with high biomass significantly positively correlated with eutrophic conditions (high Total phosphorus, Total nitrogen, and Chl-a), consistent with the mechanisms of algal response to nutrient input in classical eutrophication theory (Carpenter et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). As the dominant species in HP's eutrophic state, Bacillariophyta are favored due to the special ecological adaptation of \u003cem\u003eAulacoseira\u003c/em\u003e to the conditions in HP. Notably, although the CP exhibits oligotrophic conditions, its anomalous color changes highlight the limitations of the conventional paradigm that attributes water coloration primarily to algal activity. (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employed redundancy analysis and correlation analysis to demonstrate that travertine deposition is the key driver behind the bluish-green coloration of karst lakes in Jiu Zhai Gou. This finding broadens the conceptual framework of abiotic controls on water color and underscores the importance of integrating hydrogeochemical processes into the interpretation of water color dynamics in karst environments.\u003c/p\u003e \u003cp\u003eThe water color changes caused by Fe(OH)₃ precipitation in CP illustrated the dynamic characteristics of the redox interface in the karst groundwater system. The sensitivity of iron form transformations to the DO gradient makes it an effective tracer for groundwater-surface water interactions (Gr\u0026uuml;nenbaum et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Osorio-Leon et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, the temporal and spatial differentiation of the d-value differences, reflecting the intensity of rock-water interactions, and groundwater residence time jointly control the oxidation state and form distribution of iron (Song et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xia et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the study did not quantitatively characterize the microbial-mediated iron oxidation process (e.g., the catalytic role of bacteria such as \u003cem\u003eGallionella\u003c/em\u003e, \u003cem\u003eAcidithiobacillus\u003c/em\u003e), which may have led to an underestimation of the contribution of biogeochemical coupling mechanisms (Karimian et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Melton et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe differentiation of water color between the two pools is essentially an apparent manifestation of differences in hydrological connectivity. In TP, the shallow water retention characteristics form a closed material circulation, maintaining the current eutrophic state through the sediment-water interface feedback, which reflects historical nutrient loads (Smith et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, CP is controlled by active groundwater exchange, and its rapid redox state switching is similar to the \"pulsed material transport\" mode seen in karst spring systems (Ford and Williams \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In addition, algal biology plays a key role in the water color differentiation between the two pools. The high eutrophic degree of TP results in low DO levels, while the narrow communication channel limits the high-DO water from CP from entering TP, thereby inhibiting the oxidation of iron in TP. Furthermore, algae require iron for chlorophyll synthesis and the electron transport chain (Raven \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sunda and Huntsman \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), and in TP, the high algal density may lead to the significant absorption of Fe\u0026sup2;⁺, reducing the concentration of free Fe\u0026sup2;⁺ and inhibiting the formation of Fe(OH)₃. Additionally, algal metabolism may release dissolved organic matter (DOM), which can form complexes with Fe\u0026sup2;⁺ or Fe\u0026sup3;⁺, affecting the migration and transformation of iron (Barbeau et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rose and Waite \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These complex hydrological-biological coupling mechanisms dynamically influence the water color differentiation between the two pools. This heterogeneity suggests water body classification and management strategies in karst areas: TP should focus on controlling internal pollution (e.g., sediment dredging), while CP should address external input blockage (e.g., sealing fracture channels).\u003c/p\u003e \u003cp\u003eThis study extends traditional algal ecology into the field of hydro-geochemistry, conducting a cross-scale mechanism analysis and investigating the complexity of multi-interface processes (water-rock-gas-biological) in shaping the apparent characteristics of water bodies. It challenges the conventional attribution model that relies solely on biological or chemical factors. Moreover, by revealing the formation mechanism of iron oxide precipitation, this research provides new insights into pollution tracing and ecological restoration in groundwater systems. However, in the context of global environmental change, the \"pumpkin soup\" phenomenon will continue to emerge. In response to these abnormal natural landscapes, this study offers a framework for cross-scale mechanism analysis. Future efforts should focus on further improving the accuracy and data support of the \"hydrological-geochemical-ecological\" coupling model, providing a more comprehensive understanding of the evolutionary mechanisms and regulatory pathways of unique habitats, and offering more precise scientific foundations for environmental protection and ecological restoration.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study takes the abnormal water color changes in HP, Southwest China as an example. By employing a variety of analytical methods, including geochemical element analysis and biological community identification, it reveals the complex interrelationships among algal community structure, water quality physicochemical parameters, and hydrological processes. The results showed that during all observation periods, the algal density of diatoms (mainly the genus \u003cem\u003eFragilaria\u003c/em\u003e) was significantly higher than that of other algae, making them a key dominant species in the algal community of HP. However, diatoms are not the main driving force behind the abnormal water color changes. Further analysis indicates that there is no significant correlation between water nutrient status, dissolved metal content, and abnormal water color changes. Instead, Fe(OH)3 particles are identified as the core driving factor for the abnormal water color changes. In addition, during the period of abnormal water color, significant groundwater-rock interactions occurred in HP. Atmospheric precipitation affected the redox environment of the groundwater system (including caves and channels), leading to the formation of Fe(OH)\u003csub\u003e3\u003c/sub\u003e particles, which eventually entered the pond water. The differences in physical properties and biological environment between the clear and turbid ponds jointly drive the differentiated water color characteristics of the two ponds, reflecting the comprehensive impact of water exchange properties, groundwater dynamics, and local ecological processes on water color changes.\u003c/p\u003e \u003cp\u003eThis study innovatively elucidates the water color change pattern, providing a new theoretical paradigm for the analysis of ecological environment effects of special hydrological landscapes in karst areas. Future research can employ \u003cem\u003ein-situ\u003c/em\u003e micro-interface sensing and isotope tracing techniques to further quantitatively explore the coupling mechanisms of material fluxes across multiple spheres, deepening the understanding of the ecological environment effects of similar hydrological landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Scientific Research Projects of the Yunnan Education Department (Grant No. 2025J0900), the Yunnan Fundamental Research Projects (Grant No.202501AU070173 and 202401AT070458) and the College Students Innovation Training Program (Grant No. 202410684005).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.L. conceived the study, developed the methodology, and secured funding; B.Q. contributed to writing, reviewing, editing, formal analysis, and software; Q.L. provided supervision, software, and validation; Y.Z. and Q.G. was responsible for data curation and formal analysis; L.D., H.Z. and H.L. handled data curation, visualization, and investigation. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe data and code used are listed in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdrian R, O'Reilly CM, Zagarese H, Baines SB, Hessen DO, Keller W, Livingstone DM, Sommaruga R, Straile D, Van Donk E et al (2009) Lakes as sentinels of climate change. 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Sci Total Environ 834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.155303\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.155303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water color change, Karst landscape, hydrological cycle, ecological mechanism, Heilong Pool (HP)","lastPublishedDoi":"10.21203/rs.3.rs-6499228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6499228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLakes, reservoirs, and ponds are crucial inland water bodies that provide essential water resources and deliver significant ecological, social, and cultural services. As a key indicator of water quality, identifying the causes of water color changes is of paramount importance. The periodic reddish-brown \"pumpkin soup\" phenomenon observed in the Clean Pool (CP) of Heilong Pool (HP) in southwestern China has raised concerns about water quality and ecosystem health. We used analytical approaches including, nutrient elements, heavy metal concentrations, dissolved substances, algal community composition, and δD-δ\u0026sup1;⁸O isotope analytical models to investigate the ecological and geochemical mechanisms underlying this phenomenon. The results indicate that, despite Bacillariophyta dominating the algal community in HP, they are not the deciding factor of water color changes. Instead, Fe(OH)₃ colloidal particles, which originate from groundwater-surface water interactions and are influenced by redox environment fluctuations, are identified as the key factor causing the reddish-brown discoloration. Hydrological analysis reveals that atmospheric precipitation and groundwater dynamics significantly affect the formation and transport of Fe(OH)₃ particles. The distinct physical and biological characteristics of the Clear and Turbid Pools further accentuate the landscape contrast between the two water column. This study challenges the conventional assumption that algal blooms are the sole cause of water color anomalies, emphasizing the critical role of hydrogeochemical processes in karst landscapes. Simultaneously, this findings provide new insights into the evolution and regulation of special habitats in karst regions and offer a theoretical framework for managing similar water bodies. Additionally, the study underscores the importance of integrating hydrological, geochemical, and ecological perspectives to address complex environmental phenomena in extreme terrain condition.\u003c/p\u003e","manuscriptTitle":"Why does the Water in a Natural Pool from Transparent Turn into Pumpkin Soup Color?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 07:36:35","doi":"10.21203/rs.3.rs-6499228/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c5c99e6-d26b-4bcd-b456-3dedc5bbe7ec","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-03T01:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 07:36:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6499228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6499228","identity":"rs-6499228","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}