Influences of landscape pattern on water quality at multiple scales in an agricultural basin of western China

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.

Seasonal management of multiple stressors: Interactive effects of dams and urbanization on pollution loads in the Shaying River Basin, eastern China

With population growth and accelerating urbanization in developing regions, numerous dams have been built to support industrial activities and residential water supply. These developments have exposed rivers to the multiple stressors of dams and urbanization, complicating river restoration and water quality predictions. Understanding of how these stressors interact to influence water quality is crucial for effective river management. Therefore, during the wet and dry seasons, we investigated pollutant concentrations (including EC, COD, TN, NH3-N, and TP) and habitat quality in four river systems across different stressor combinations of dam presence/absence and high- or low-intensity urbanization. The findings suggested that the interaction between dams and urbanization exerted additive effect on pollution load (PL) in the wet season and synergistic effect in the dry season. The generalized linear model (GLM) and structural equation modeling (SEM) results revealed that in the wet season, PL was directly influenced by both dams and urbanization, whereas in the dry season, they were driven indirectly by habitat degradation and directly by urbanization. The results of distance-based redundancy analysis (db-RDA) and variation partitioning analysis (VPA) revealed that the interactive effects contributed more to the variation in the structure of water quality parameters (WQPs) in the dry season (27.9 %) than in the wet season (11.3 %). Moreover, in the wet season, PL in the dam group increased gradually from upstream to downstream, whereas in the dry season, an increase occurred in the urban group. Dam elements (flood control and power generation) explained most of variance (29.7 %) in the WQPs in the wet season, whereas urbanization elements (nightlight intensity and land use index) explained most of the variance (33.8 %) in the dry season. It is recommended that in the wet season, dams should be collectively regulated to prevent pollutant migration via flood discharge, whereas in the dry season, efforts should focus on restoring riparian habitats and reducing urban point source pollution.

Long-term dynamics and driving mechanisms of plant communities in a temperate estuary in eastern China based on pollen analysis: a case study of the Liaohe Estuary

Introduction

The Liaohe Estuary, a representative estuarine ecosystem in eastern China, has experienced significant shifts in plant community characteristics due to climate change and anthropogenic influences in recent decades.

Methods

This study employed sediment 210Pb dating, pollen analysis, and environmental factor indicators to comprehensively assess the composition, trends, and drivers of plant communities in the Liaohe Estuary from 1944 to 2022.

Results

The findings revealed that herbaceous plants dominated the estuary's vegetation under a cool and humid climate, though humidity exhibited a declining trend over time. Between 2001 and 2022, pollen concentration and herbaceous plant prevalence increased significantly. Key environmental drivers-mean annual temperature (MAT), salinity, grain size, pH, and agricultural production-were strongly correlated (p < 0.001) with plant community dynamics. Natural factors (grain size, salinity) enhanced the dominance of key species but reduced overall pollen concentration. Conversely, agricultural activities diminished dominant species proportions while increasing pollen concentration.

Discussion

These results highlight the dual influence of climatic and anthropogenic factors on estuarine vegetation. The study provides a theoretical basis for restoring degraded estuarine ecosystems.

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.

Advancing methodologies for assessing the impact of land use changes on water quality: a comprehensive review and recommendations

With increasing scholarly focus on the ramifications of land use changes on water quality, although substantial research has been undertaken, the findings demonstrate pronounced spatial variability and the heterogeneity of research methodologies. To address this critical gap, this review offers a rigorous evaluation of the strengths and limitations of current research methodologies, providing targeted recommendations for refinement. It systematically assesses the existing body of literature concerning the influence of land use changes on water quality, with particular emphasis on the spatial heterogeneity of research results and the uniformity of employed methodologies. Despite variations in geographical contexts and research subjects, the methodological paradigms remain largely consistent, typically encompassing the acquisition and analysis of water quality and land use data, the delineation of buffer zones, and the application of correlation and regression analyses. However, these approaches encounter limitations in addressing regional disparities, nonlinear interactions, and real-time monitoring complexities. The review advocates for methodological advancements, such as the integration of automated monitoring systems and IoT technologies, alongside the fusion of deep learning algorithms with remote sensing techniques, to enhance both the precision and efficiency of data collection. Furthermore, it recommends the standardization of buffer zone delineation, the reinforcement of foundational water quality assessments, and the utilization of catchment-scale analyses to more accurately capture the influence of land use changes on water quality. Future inquiries should prioritize the development of interdisciplinary ecological models to elucidate the interaction and feedback mechanisms between land use, water quality, and climate change.

Monitoring and modelling landscape structure, land use intensity and landscape change as drivers of water quality using remote sensing

The interactions between landscape structure, land use intensity (LUI), climate change, and ecological processes significantly impact hydrological processes, affecting water quality. Monitoring these factors is crucial for understanding their influence on water quality. Remote sensing (RS) provides a continuous, standardized approach to capture landscape structures, LUI, and landscape changes over long-term time series. In this study, RS-based indicators from Landsat data (2018-2021) were used to assess landscape structure, LUI, and land use change for a study area in northern Germany, applying the ESIS/Imalys tool. These indicators were then used to model and predict water quality (Chla) in 119 standing waters. Various machine learning methods, including Generalised Linear Models, Support Vector Machines, Deep Learning, Decision Trees, Random Forest, and Gradient Boosted Trees, were tested. The Random Forest model performed best, with a correlation of 0.744 ± 0.11. Indicators related to landscape structure, such as diversity_mean (0.376) and relation_mean (0.292), had the highest global correlation weights, while LUI and land use change indicators like NirV2_mean (0.369) and NirV_regme (0.284) were also significant. All indicators and their effects on water quality (Chla) are discussed in detail. The study highlights the potential of the ESIS/Imalys tool for quantifying landscape structure, LUI, and land use change with RS to model and predict water quality and suggests directions for future model improvements by incorporating additional influencing factors.

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.

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.

Response of water quality in major tributaries to the difference of multi-scale landscape indicators in Dongting Lake basin, China

River water quality has been increasingly deteriorated because of the influence of natural process and anthropogenic activities. Quantifying the influence of landscape metrics, namely topography and land use pattern, which encompass land use composition and landscape configuration, across different spatial and seasonal scales that reflect natural process and anthropogenic activities, is highly beneficial for water quality protection. In this study, we focused on investigating the effects of topography, landscape configuration and land use composition on water quality at different spatial scales, including 1-km buffer and sub-watershed, and seasonal scales, including wet and dry season, based on the monthly water quality data in 2016 of Dongting Lake in China. Multivariate statistical analysis of redundancy analysis and partial redundancy analysis was used to quantify the contributions of these factors under different scales. Our results showed that among the three environmental groups, topography made the greatest pure contribution to water quality, accounting for 11.4 to 30.9% of the variation. This was followed by landscape configuration, which accounted for 9.4 to 23.0%, and land use composition, which accounted for 5.9 to 15.7%. More specifically, water body made the greatest contribution to the water quality variation during dry season at both spatial scales, contributing 16.6 to 17.2% of the variation. In contrast, edge density was the primary interpreter of the variability in water quality during wet season at both spatial scales, accounting for 9.9 to 11.1% of the variation. The spatial variability in the influence of landscape metrics on water quality was not markedly distinct. However, these metrics have a minimal impact difference on water quality at the buffer scale and the sub-watershed scale. Moreover, the contribution of landscape configuration varied the most from the buffer to sub-watershed scales, indicating its importance for the spatial scale difference in water quality. The findings of this study offer useful insights into enhancing water quality through improved handling of landscape metrics.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.

Fertilization intensities at the buffer zones of ponds regulate nitrogen and phosphorus pollution in an agricultural watershed

The sudden increase in water nutrients caused by environmental factors have always been a focus of attention for ecologists. Fertilizer inputs with spatio-temporal characteristics are the main contributors to water pollution in agricultural watersheds. However, there are few studies on the thresholds of nitrogen (N) and phosphorus (P) fertilization rates that affect the abrupt deterioration of water quality. This study aims to investigate 28 ponds in Central China in 2019 to reveal the relationships of basal and topdressing fertilization intensities in surrounding agricultural land with pond water N and P concentrations, including total N (TN), nitrate (NO3--N), ammonium (NH4+-N), total P (TP), and dissolved P (DP). Abrupt change analysis was used to determine the thresholds of fertilization intensities causing sharp increases in the pond water N and P concentrations. Generally, the observed pond water N and P concentrations during the high-runoff period were higher than those during the low-runoff period. The TN, NO3--N, TP, DP concentrations showed stronger positive correlations with topdressing intensities, while the NH4+-N concentrations exhibited a higher positive correlation with basal intensities. On the other hand, the NO3--N concentrations had a significant positive correlation with the topdressing N, basal N, and catchment slope interactions. Significant negative correlations were observed between all water quality parameters and pond area. Spatial scale analysis indicated that fertilization practices at the 50 m and 100 m buffer zone scales exhibited greater independent effects on the variations in the N and P concentrations than those at the catchment scale. The thresholds analysis results of fertilization intensities indicated that pond water N concentrations increased sharply when topdressing and basal N intensities exceeded 163 and 115 kg/ha at the 100 and 50 m buffer zone scales, respectively. Similarly, pond water P concentrations rose significantly when topdressing and basal P intensities exceeded 117 and 78 kg/ha at the 50 m buffer zone scale, respectively. These findings suggest that fertilization management should incorporate thresholds and spatio-temporal scales to effectively mitigate pond water pollution.

Response of riparian plant community to landscape matrix differs by taxonomic and functional diversity: Implications for the planning of riparian landscapes regulated by dams

Local plant communities are embedded within the landscape matrix, and thus, diversity of these communities is largely determined by the spatial pattern of surrounding land-use. However, it is not yet clear how taxonomic and functional diversity within riparian plant communities respond differently to the landscape matrix at different scales, and whether and how these landscape-scale responses can be shaped by flooding conditions in the riparian landscape. This study is based upon field surveys of riparian plant communities in a total of 136 quadrats exposed to two different levels of flooding intensity along the reservoir formed by the Three Gorges Dam (TGD). We investigated the relationship between plant diversity of riparian communities and the surrounding landscape at different scales, which examined how the diversity, in terms of both plant taxonomy and functional traits, was affected by the landscape matrix at 15 different spatial scales ranging from 200 m to 3000 m in increments of 200 m around each sampling site. Furthermore, the community diversity in response to the landscape matrix under different levels of flooding intensity was also examined. Our findings suggested that trait-based functional diversity was more closely related to the landscape matrix at all 15 scales examined, compared to taxon-based diversity. The dispersal trait was found to be more susceptible to landscape connectivity across the matrix. Further, both types of diversity responded more strongly to landscape configurational metrics than compositional metrics. In addition, we observed that, in the communities exposed to high flooding intensity, the two types of diversity tended to have more consistent responses to the landscape matrix compared to those exposed to low flooding intensity. We concluded that while the landscape matrix had different effects on taxonomic and functional diversity, the high flooding intensity could lessen their differences in the riparian landscape. Our findings revealed that the robust local environmental filtering process, which affects the diversity of riparian communities, could lead to both diversity components exhibiting similar responses to the landscape matrix in riparian landscapes.