Study on the threshold relationship between landscape pattern and water quality considering spatial scale effect-a case study of Dianchi Lake Basin in China

Estimating thresholds of natural vegetation for the protection of water quality in South African catchments

Many of South Africa's current water quality problems have been attributed to diffuse pollution derived from poorly regulated land use/land cover (LULC) transformations. To mitigate these impacts, the preservation of an adequate amount of natural vegetation within catchment areas is an important management strategy. However, it is not clear how much natural vegetation cover is required to provide adequate levels of protection, nor at which scale(s) this strategy would be most effective. To investigate the possibility of estimating minimum thresholds of natural vegetation required to protect water resources, regression analysis was used to model relationships between water quality (measured using Nemerow's Pollution Index) and metrics of natural vegetation at multiple scales across a sample of sub-catchments located along the western, southern, and south-eastern coast of South Africa. With conspicuous outliers removed, the models were able to explain up to 82 % of the variability in the relationship between land use and water quality. Moreover, a statistically significant, nonlinear, and inverse relationship was found between proportions of natural vegetation cover and pollution levels. This relationship was strongest when measured (1) across the whole catchment and (2) within a 200 m riparian buffer zone. The models further indicated that approximately 80 to 90 % natural vegetation cover was necessary at these scales to maintain water quality at ecologically acceptable levels. Additional nonlinear thresholds estimated using breakpoint analysis suggested that if proportions of natural vegetation fall below 45 % (across the whole catchment) and 60 % (within a 200 m riparian buffer zone) a dramatic increase in pollution levels can be expected. The estimated thresholds are recommended as guidelines that can be used to inform integrated land and water resources management strategies aimed at protecting water quality in the study area. Likewise, the methods described are recommended for the estimation of similar thresholds in other regions.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

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.

Response of stream water quality to the vegetation patterns on arid slope: a case study of Huangshui River basin

Vegetation patterns on slopes strongly affect the water cycle processes in a basin, especially the water yield and confluence in arid areas. Quantifying and evaluating the effects of hydrological change on the migration and transformation of pollutants are challenging. Based on 4-year stream water quality data of 13 monitoring sites in the Huangshui River basin, a typical arid watershed of the Chinese Loess Plateau, the redundancy analysis (RDA) and structural equation modeling (SEM) analysis tools were used to quantify its relationship with vegetation patterns. In the study, land use and the enhanced vegetation index (EVI) were used as a metric of vegetation patterns; accordingly, the 13 catchments were divided into three groups via the cluster analysis, including large (over 80%), medium (70 ~ 80%), and small (below 70%) proportion vegetation patterns (LVP, MVP, SVP). The results of the LVP group showed that vegetation patterns negatively affected the contamination of total phosphorus (TP), ammonia nitrogen (NH3-N), permanganate index (CODMn), and biochemical oxygen demand (BOD5) in the stream water, and the contribution rates were - 0.57. While the proportion of urban area positively correlated with stream water quality in the groups of MVP and SVP, the contribution rates were 0.46 and 0.36, respectively. Moreover, the precipitation in the groups of MVP and SVP negatively correlated with pollutants (- 0.24 and - 0.26). Those results revealed the response of stream water quality to vegetation patterns on the slope with the consideration of precipitation, land use, and socio-economic factors for the regional water and land resource allocation. This study has important management implications for vegetation patterns on slope of fragile ecosystems in arid areas.

Buffer zone-based trace elements indicating the impact of human activities on karst urban groundwater

The tanglesome allocation of landscape types at various spatial dimensions is an important component influencing the quality of groundwater environment in karst cities. Trace elements can be used as indicators of the extent of impact on groundwater which is an effective means of tracing groundwater contamination. In this study, we studied the influence of landscape patterns on trace elements in groundwater of typical karst cities in Southwest China (Guiyang City) on a multi-spatial scale by using multivariate statistical analysis. According to the sampling points, buffer zone scales with different radii (500 m, 1000 m, 1500 m, and 4000 m) were established to quantify the land use model. There are suburban and urban differences in trace element content. The city center has higher levels of trace elements compared to suburban areas, especially Li, Ni, Tl, Cu, Sr, Co, As, and Mn. In addition, the outcomes of the multiple linear regression had shown that the size effect of the association from landscape pattern to trace elements varies with different indicators and parameters. The results of redundancy analysis showed an overall change in trace elements was better interpreted by the landscape pattern of the 1500 m-scale buffer. At the same time, at the 1500 m scale, Ni, Tl, Cu, Co, As, Cr, Sr, Li, and Mn were positively correlated with the urban landscape index (4LPI, 4LSI), influenced by urban anthropogenic activities, while Cd, Zn, and Pb were positively correlated with the cropland landscape index (1AI, 1LPI), influenced by agricultural activities. This study indicates that trace elements are a reliable indicator for tracing groundwater contamination. The buffer zone can reflect the extent of urban impacts on groundwater and provide a new and effective analytical tool for groundwater management.

Determining reclaimed water quality thresholds and farming practices to improve food crop yield: A meta-analysis combined with random forest model

Irrigated agricultural systems with reclaimed water (RW) play a crucial role in alleviating global water scarcity and increased food demand. However, appropriate reclaimed water quality thresholds and farming practices to improve food crop yield is virtually unclear. Therefore, for the first time, this study made a large compilation of previous studies using meta-analysis combined with a random forest (RF) model and analyzed the impact of RW versus freshwater (FW) on the yield of food crops (cereals, vegetables, and fruits). It was found that magnesium ion (Mg²⁺), calcium ion (Ca²⁺), electrical conductivity (EC), total nitrogen (TN), and potential of hydrogen (pH) were the most important factors for RW quality indicators. Based on the results, water managers should establish more conservative RW quality thresholds to promote food crop production, especially for salts and pollutants in RW. Compared to international water quality standards, it could be slightly relaxed the restrictions of TN in RW. The optimal farming practices obtained that irrigation amount of the mixed RW and FW (RW + FW) was from 1000 m³ ha^-1 to 5000 m³ ha^-1, and the cultivation period was no more than three years. Flood irrigation (FI) and drip irrigation (DI) for cereals were also recommended. Finally, a comparison of the determined results from this method with other scenarios published, finding a good agreement.

The effects of landscape change on habitat quality in arid desert areas based on future scenarios: Tarim River Basin as a case study

Human activities have caused spatiotemporal patterns of land use and land cover (LULC) change. The LULC change has directly affected habitat quality (HQ) and ecosystem functions. Assessing, simulating, and predicting spatiotemporal changes and future trends under different scenarios of LULC-influenced HQ is beneficial to land use planners and decision-makers, helping them to formulate plans in a sustainable and responsible way. This study assesses and simulates the HQ of the Tarim River Basin (TRB) using the future land use simulation model (FLUS), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and partial least squares regression (PLSR). Since 2000, the TRB has experienced a declining trend in HQ from 0.449 to 0.444, especially in the lower elevations (740-2000m) and on sloped land (<10°). The decline will continue unless effective and sustainable plans are implemented to halt it. Agricultural and settlement areas have a lower HQ and a higher degree of habitat degradation than native habitats. This shows that the expansion of oasis agriculture (with an annual growth rate of 372.17 km²) and settlements (with an annual growth rate of 23.50 km²) has caused a decline in native habitat and subsequent habitat fragmentation. In other words, changes in LULC have caused a decline in the HQ. Moreover, there is a significant negative correlation between HQ and urbanization rate (p<0.01), and the PLSR also indicate that number of patches (NP), area-weighted mean fractal dimension index (FRAC_AM), percentage of landscape (PLAND), and largest patch index (LPI) were also important contributors to worsening the HQ. Therefore, the TRB urgently needs appropriate strategies to preserve its natural habitats into the future, based on the ecological priority scenario (EPS) and harmonious development scenario (HDS), which can help to maintain a high-quality habitat.

Spatiotemporal Evolution of Landscape Patterns and Their Driving Forces Under Optimal Granularity and the Extent at the County and the Environmental Functional Regional Scales

Research on the evolution and driving forces of landscape patterns can provide important support for ecological governance decision-making. However, the heterogeneity of landscape patterns at the microscale (grain size and extent) and the enforceability of the zoning scale at the macroscale deserve more attention. The optimal grain size (30 ×30 m) and the extent (500 m) for landscape pattern research were obtained by analyzing the fluctuation of landscape metrics and semivariogram models in this study. The research area was divided into environmental functional regions (EFRs), which were defined according to the main ecological functions and protection objectives of each region. The analysis results of land use and land cover changes (LUCCs) showed that land use transfer in the past 20 years occurred mainly between woodland and cultivated land at the county scale, but this was not always the case in EFRs. The results of the landscape pattern analysis showed that landscape fragmentation, aggregation, and heterogeneity increased at the county scale during 1999–2020. Moreover, except within agricultural environmental protection areas (AEP) and living environment guaranteed areas (LEG), the degree and the speed of landscape damage decreased by 2020, and the turning point occurred in 2006–2013. The analysis results of geographical detectors showed that the digital elevation mode (DEM) and GDP were the main driving factors in most regions. At the county scale, the average explanatory power of the selected factors increased by 13.27% and 16.16% in 2006–2013 and 2013–2020, respectively. Furthermore, the study area was divided into three categories according to the intensity of human disturbance. The areas with high human disturbance need to focus on increasing land-use intensification and strengthening the development in low-slope hill regions. The areas of moderate human disturbance need to focus on improving the connectivity of ecological patches and optimizing industrial structures. Attention should be given to the monitoring of natural drivers and policy support for ecological governance in low human disturbance areas. The methods and findings in this study can provide a reference for decision-makers to formulate land-use policies, especially for integration into relevant urban planning, such as the spatial planning of national land that is being widely implemented in China.