Managing landscape patterns at the riparian zone and sub-basin scale is equally important for water quality protection

Significant differences in optimal riparian buffer zone on water quality between different segments within the same river

Determining the optimal riparian buffer zone based on the relationship between landscape metrics and water quality is a widely used method for water quality management. However, failing to account for the differences between various segments of the same river can lead to inaccurate identification of riparian buffer zones, thereby affecting the effectiveness of water quality improvement. Here, based on water quality monitoring data from January 2023 to December 2023 in different segments (JuRong segment and XieXi segment) of a typical traditional-intensive agricultural watershed (Qinhuai River watershed), we identified the differences in the optimal riparian buffer zone on water quality between different segments within the same river through redundancy analysis (RDA) and variance partitioning analysis (VPA). Subsequently, utilizing the nonparametric change-point analysis (nCPA), we further identified the critical landscape thresholds causing abrupt changes in water quality within the optimal riparian buffer zone. Results showed that in the JuRong segment, the optimal width for riparian buffer zones was 100 m, with landscape metrics explaining 96.7% of the water quality variation. In the XieXi segment, the optimal riparian buffer zone width was 600 m, with landscape metrics explaining 82.6% of the water quality variation. Interspersion and Juxtaposition index of water (IJI_Water) and Interspersion and Juxtaposition index of Garden (IJI_Garden) were found to be the most influential landscape metrics on water quality in the JuRong and XieXi segments, respectively. Furthermore, the landscape thresholds of IJI_Garden and IJI_Water resulting in abrupt changes in water quality were 68.50 and 39.88 in the JuRong segment, and 76.07 and 56.39 in the XieXi segment, respectively. This study highlights the importance of considering the varying effects in optimal riparian buffer zones and developing distinctive water quality management strategies between different segments within the same river.

Response of river water quality to landscape features in a subtropical hilly region

Landscape features have a profound impact on river water quality. However, its impact in subtropical hilly region is unclear. Here, water quality data from 15 catchments were obtained based on a typical subtropical hilly area, the upper Ganjiang River basin. The landscape features in the catchment and buffer zone were calculated, and its effects on river water quality were investigated using redundancy analysis (RDA) and multiple linear regression (MLR) model. Catchment landscape features were found to better explain overall water quality changes compared to buffer zone, and landscape features were found to explain water quality changes more in winter than in summer. Moreover, within the buffer zone, the percentage of grassland had the greatest impact on winter water quality (72.8%), while at the catchment scale, the aggregation index (AI) of grassland contributed the most to changes in winter water quality (31.6%). Nonparametric change-point analysis (nCPA) was used to identify thresholds of landscape features that lead to abrupt changes in water quality. It was found that river water quality can be improved when the percentage of grassland > 0.193%, the largest patch index (LPI) of forest > 7.48% at the buffer zone or the percentage of impervious surfaces < 2.92%, the AI of forest > 98.6% at the catchment scale. This study demonstrated the pivotal role in enhancing river water quality by implementing informed and effective landscape planning for conservation implementation.

Enhancing river water quality in different seasons through management of landscape patterns at various spatial scales

Landscape patterns have a great effect on river water quality. However, the strategies for enhancing water quality through landscape pattern management remain unclear. In this study, we aimed to provide effective guidance for water quality management by quantifying the key spatial scales and landscape metrics that influence the seasonal variations in water quality and establishing threshold relationships between these metrics and abrupt variations in water quality in the Chaohu Lake basin, China. Results discovered that water quality was poorer in summer and better in spring, with degraded water conditions primarily concentrated in the middle and lower reaches of the watershed. The 100 m riparian zone buffer scale landscape pattern was identified as the key scale affecting water quality in the summer, which accounted for 51.3% of the overall water quality variation. Furthermore, abrupt threshold analysis indicated that summer water quality could be effectively improved by maintaining the proportion and largest patch index of construction land within the 100 m riparian buffer below 22.0%. At the sub-basin scale, landscape pattern-based water quality management was most effective in spring, explaining 43.6% of the variation in water quality. Setting the largest patch index of construction land at the sub-basin scale below 43.0% and increasing the proportion of forest cover above 36.0% can also alleviate water pollution issues. These findings emphasize the importance of incorporating landscape patterns across scales into environment management decisions, providing a scientific basis for effective watershed water quality management.

Increasing human activity shifts the key spatial scale of landscape patterns on water quality from sub-basins to riparian zones

The relationship between landscape patterns and water quality has been extensively studied, yet the understanding of how human activity modulates the spatial scale effects of landscape patterns on water quality remains limited. Here, we investigated the water quality and landscape patterns of three rivers in the Poyang Lake Basin, China, subjected to different intensities of human activity, and analyzed the extent to which water quality parameters were influenced by human activity to unravel the spatial scale effects and identify critical landscape metrics that significantly influence water quality. The results showed that the influence of riparian zone landscape patterns on water quality progressively exceeded that of sub-basin landscape patterns as the intensity of human activity increased. For water quality parameters that were minimally affected by human activity, the influence of sub-basin landscape patterns slightly exceeded that of riparian zone landscape patterns at different intensities of human activity (differences were 0.63 %, 4.25 % and 7.65 %, respectively). Conversely, for water quality parameters significantly affected by human activity, the landscape patterns of the riparian zone had a significantly greater influence than the sub-basin landscape patterns (differences were 5.90 %, 13.00 % and 17.86 %, respectively). Furthermore, the discrepancy between the influence of riparian zone and sub-basin landscape patterns on water quality increased with increasing intensity of human activity, while the overall influence of landscape patterns on water quality showed a decreasing trend (decreasing from 60.35 % to 39.10 %). In addition, the proportions of construction land, farmland, and forestland, and the fragmentation of grassland, were identified as critical landscape metrics that significantly influenced water quality at different intensities of human activity. This study revealed that different intensities of human activity were key factors influencing the spatial scale effects of landscape patterns on water quality.

Influence of construction works on urban streamflow water quality variations

Construction activities can have long-lasting impacts on receiving water bodies, especially when they receive polluted urban runoff. Therefore, it is essential to minimize these impacts on water quality and consider the long-term environmental effects of development activities. This study aims to provide insights into the assessment, temporal variations, and key variables associated with the impact of construction works on streamflow water quality. However, current assessment methods relating to construction works and streamflow water quality may lead to spurious correlations. A spurious correlation refers to a connection between two variables that appears to be causal but is not. This study proposes a novel approach to avoid spurious correlations between construction work signatures and water quality, ensuring causality and correlation between water quality parameters. The approach was applied to a developing urban catchment in Hong Kong. Compared to existing assessment models, the proposed approach advances in ensuring true correlations between construction works and streamflow water quality. It is also the first to develop a new indicator to represent the key variable of construction works. In this study, salinity, turbidity, and suspended solids were used as substitutes for construction activity parameters, such as the number of construction works, to correlate with water quality parameters. Additionally, principal component analysis and the construction work signature index were both adopted to calculate the key variables of water quality on behalf of construction works. Results demonstrate that the new approach has significantly improved causality by 45 % compared to previous assessment methods. However, the method has limitations as it does not consider the impact of rainfall on construction works.

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3--N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Landscape fragmentation of built-up land significantly impact on water quality in the Yellow River Basin

Urbanization development often leads to significant changes in the extent in area and fragmentation of built-up land landscape pattern (BLLP) in river basins, which greatly impact the processes of rainfall runoff and pollutant migration. Understanding the spatial scale effects and driving mechanisms of BLLP changes on water quality in large river basins is a challenging research topic and an international frontier in the interdisciplinary fields of geography and environment. This study analyzes the spatial variations of BLLP and water quality throughout the Yellow River Basin (YRB) during the rainy seasons from 2019 to 2021 (4 h scale). Utilized the random forest model to quantitatively separates the contributions of rainfall processes to surface runoff and water pollution, revealing the scale effects and non-linear driving mechanisms of BLLP impacts on water environment changes. The results indicate that: 1) The YRB exhibits great spatial heterogeneity in terms of both BLLP and water quality, with places with lower water quality displaying bigger areas and higher degrees of BLLP fragmentation. 2) The patch density and built-up land area (PD.B and CA.B) have a major impact on changes in water quality in the YRB, with notable impacts noted in circular buffer zones with radii of 20 km and 5 km, respectively. 3) PD.B is sensitive to water quality in the YRB, explaining 39.1%-49.5% of the variance under different rainfall conditions, and exhibits a significant non-linear relationship, with an impact threshold of 0.38 (n/100 ha). The study suggests that for large-scale regions like the YRB, the degree of BLLP fragmentation is more likely to lead to degradation of water environmental quality compared to its area. BLLP fragmentation due to higher PD.B and CA.B disrupts the original ecosystem and hydrological connectivity, resulting in poorer retention and filtration of pollutants carried by rainfall runoff, while increasing the export of other pollutants. However, once urbanization surpasses a certain threshold, the BLLP fragmentation can enhance water quality by reducing the impermeable surface connectivity, as they are no longer impacted by expanding areas. To achieve ecologically sustainable development, it is necessary to apply rational landscape management and water resource management policies that consider the dual process of how BLLP fragmentation affects the water environment.

Riverine bacterial communities are more shaped by species sorting in intensive urban and agricultural watersheds

Bacterial communities play a crucial role in maintaining the stability of river ecosystems and driving biogeochemical cycling, exhibiting high sensitivity to environmental change. However, understanding the spatial scale effects and assembly mechanisms of riverine bacterial communities under distinct anthropogenic disturbances remains a challenge. Here, we investigated bacterial communities across three distinct watersheds [i.e., intensive urban (UW), intensive agricultural (AW), and natural (NW)] in both dry and wet seasons. We explored biogeographic patterns of bacterial communities and the influence of landscape patterns at multi-spatial scales and water chemistry on bacterial communities. Results showed that α diversity was significantly lower in UW and AW compared to NW, particularly in the dry season. A gradient of β diversity with NW > UW > AW was observed across both seasons (p < 0.05). Pseudomonadota, Bacteroidota, and Actinobacteriota were the most abundant phyla across all watersheds, with specific taxa enriched in each watershed (i.e., the class Actinobacteria was significant enrichment in UW and AW, and Clostridia in NW). The influence of landscape patterns on bacterial communities was significantly lower in human-disturbed watersheds, particularly in UW, where this influence also varied slightly from near riparian buffers to sub-watershed. Homogeneous selection and drift jointly dominated the bacterial community assembly across all watersheds, with homogeneous selection exhibiting a greater influence in UW and AW. Landscape patterns explained less variance in bacterial communities in UW and AW than in NW, and more variance was explained by water chemistry (particularly in UW). These suggest that the stronger influence of species sorting in UW and AW was driven by more allochthonous inputs of water chemistry (greater environmental stress). These findings provide a theoretical foundation for a deeper understanding of riverine bacterial community structure, spatial scale effects, and ecological management under different anthropogenic activities.

The spatial scale and threshold effects of the relationship between landscape metrics and water quality in the Hutuo River Basin

The impact of landscape patterns on river water quality has been widely studied; however, it remains unclear which spatial scale has the greatest impact on water quality. Here, we analyzed the spatial scale and threshold impacts of the link between landscape metrics and water quality in a large-scale basin using the random forest (RF) model and nonparametric change point analysis (nCPA) method. The concentrations of nitrate nitrogen (NO3--N) and total nitrogen (TN) were comparatively high in winter and relatively low during spring and summer, whereas the total phosphorus (TP) concentrations were comparatively low during winter and summer and relatively high during spring. The R2 values of the RF models at the sub-basin scale were generally higher than those at the riparian zone scale. Moreover, the R2 of water quality modelling at the riparian zone scale demonstrated a declining tendency from a riparian zone 30 m-210 m wide in the majority of seasons. This shows that landscape metrics at the subbasin scale provide a better explanation for the variability in water quality than those at the riparian zone scale in the Hutuo River Basin. The results of the RF model indicated that landscape metrics of landscape configuration were more important in determining water quality during winter, whereas landscape metrics of landscape composition or physiography were more important in determining water quality during summer. Furthermore, several abrupt thresholds were estimated by nCPA; for example, the summertime slope abrupt threshold was 10.79° in the relationship between the slope and NO3--N. This study contributes to the understanding of the debate regarding the scale effects of landscape patterns on water quality, emphasizing the significance of the basin area and offering managers valuable insights into the control of non-point source pollution.

Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China

Reservoir nearshore areas are influenced by both terrestrial and aquatic ecosystems, making them sensitive regions to water quality changes. The analysis of basin landscape hydrological features provides limited insight into the spatial heterogeneity of eutrophication in these areas. The complex characteristics of shoreline morphology and their impact on eutrophication are often overlooked. To comprehensively analyze the complex relationships between shoreline morphology and landscape hydrological features, with eutrophication, this study uses Danjiangkou Reservoir as a case study. Utilizing Landsat 8 OLI remote sensing data from 2013 to 2022, combined with a semi-analytical approach, the spatial distribution of the Trophic State Index (TSI) during flood discharge periods (FDPs) and water storage periods (WSPs) was obtained. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), explained the relationships between landscape composition, landscape configuration, hydrological topography, shoreline morphology, and TSI, identified key factors at different spatial scales and validated their reliability. The results showed that: (1) There is significant spatial heterogeneity in the TSI distribution of Danjiangkou Reservoir. The eutrophication levels are significant in the shoreline and bay areas, with a tendency to extend inward only during the WSPs. (2) The importance of landscape composition, landscape configuration, hydrological topography, and shoreline morphology to TSI variations during the FDPs are 25.12 %, 29.6 %, 23.09 %, and 22.19 % respectively. Besides shoreline distance, the Landscape Shape Index (LSI) and Hypsometric Integral (HI) are the two most significant environmental variables overall during the FDPs. Forest and grassland areas become the most influential factors during the WSPs. The influence of landscape patterns and hydrological topography on TSI varies at different spatial scales. At the 200 m riparian buffer zone, the increase in cropland and impervious areas significantly elevates eutrophication levels. (3) Morphology complexity, shows a noticeable threshold effect on TSI, with complex shoreline morphology increasing the risk of eutrophication.

Interactive impact of landscape composition and configuration on river water quality under different spatial and seasonal scales

Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

Driving mechanism of land use and landscape pattern to phytoplankton and zooplankton community and their trophic interactions in river ecosystems

The trophic interactions between phytoplankton and zooplankton communities are essential for maintaining river ecosystem integrity and health. However, the driving mechanisms of land use and landscape patterns (LULP) affecting their trophic interactions are not fully understood. Therefore, the research objective of this study was to reveal the driving mechanisms of LULP on the interaction of phytoplankton with zooplankton through remote sensing interpretation of LULP in different buffer scales (500 m, 1000 m, 1500 m, and catchment), combined with water environment factors and plankton community structures analyzed. Results showed that LULP had the most significant effect on the phytoplankton and the zooplankton community structure at 500 and 1500 m buffer scales, respectively. Construction land (CON) and edge density (ED) most influenced phytoplankton and zooplankton community structure and their influence mechanisms were identified, i.e., CON increased the species (S) of phytoplankton by increasing the concentration of NO3-N in river water at the 500 m buffer scale. ED reduced the biological density (BD) of zooplankton by decreasing the concentration of heavy metal (HM) in river water at the 1500 m buffer scale. The water area (WAT) and ED showed the most significant influence on plankton interaction. Three pathways were found to explain their influence mechanisms, i.e., ED decreased the BD or Shannon-Weiner index (H') of zooplankton by increasing the dissolved oxygen (DO) to enhance BD of phytoplankton in river water at the 1500 m buffer scale; the WAT increased the BD of phytoplankton by increasing water temperature to reduce the H' of zooplankton at the 500 m buffer. These findings have implications for effective ecological planning of future human activities in the stream domain and maintaining river ecosystem health.

Balancing river water pollution and agricultural development: A tradeoff threshold approach

Balancing environmental protection and social-economic development in agricultural land use management is a dilemma for decision-makers. Based on the modelling of the impacts of land use changes on river water pollution by SWAT model, the tradeoff between tea plantation expansion and river water quality was detected. SWAT model performs well in simulating the non-point source (NPS) pollution in agricultural watershed. The results showed that the tea plantation area expanded dramatically from 44 km2 in 2000 to 169 km2 in 2020 at the high cost of forest land. Consequently, the mean contents of NO3--N and TN have significantly increased by 100% and 91% respectively in the past 20 years. And the NO3--N in river water accounted for over 80% of TN in the tea plantation area. The NO3--N and TN concentrations were positively related with the proportions of tea plantation area (Tea%) at different periods. The high pollution levels of NO3--N and TN are priority control targets for river water quality management. The results indicated that the proportion of tea plantation thresholds lead to abrupt changes in river water quality. When the Tea% exceeded 3.0% in 2000, the probability of N pollution increased sharply. Whereas in 2020, it is suggested that the Tea% should not exceeds 18% to avoid sudden deterioration of water quality. The critical interval value of the Tea% for sudden change in N pollution showed an obvious increase tendency. The accelerating of nutrient pollution in rivers reduced the sensitivity of water quality to tea plantation expansion. Our results can provide new insights and empirical evidence for balancing the tradeoff between agricultural development and river water quality protection by demonstrating the carrying capacity threshold of river water environment for the expansion tea plantation.

Effects of landscape changes on water quality: A global meta-analysis

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

Impacts of land use on phosphorus and identification of phosphate sources in groundwater and surface water of karst watersheds

The thin soil layer with uneven distribution in karst areas facilitates the migration of phosphorus (P) to groundwater, threatening the safety of water sources seriously. To offer a scientific guidance for water pollution control and land use planning in karst areas, this study examined the relationships between land use and P in groundwater and surface water, and quantified the phosphate sources in Gaoping river basin, a small typical watershed in karst areas. Spatial distribution analysis revealed that the highest mean P concentrations in groundwater and surface water were in farmland and construction-farmland zones, respectively. Land use impact analysis showed that the concentration of P in groundwater was influenced positively by farmland but negatively by forest land. In contrast, the concentration of P in surface water was influenced positively by both farmland and construction land. The mixed end-element and Bayesian-based Stable Isotope Analysis in R (SIAR) model results showed that agricultural fertilizers were the main phosphate source for groundwater in farmland and forest-farmland zones, while urban sewage was the main source in the construction-farmland zone. For surface water, the main phosphate source was agricultural fertilizers in both farmland and construction-farmland zones. This study indicates that controlling P pollution in local water bodies should pay close attention to the management of land use related to human activities, including regulating sewage discharge from construction land and agricultural fertilizer usage.

Modelling relationships between land use and water quality using statistical methods: A critical and applied review

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO3--N, NH4+-N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.