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Soil erosion is a critical process that leads to landscape degradation, compromising soil fertility and ecosystem functions. Forest ecosystems, with their intricate characteristics, play a pivotal role in mitigating soil erosion and providing soil retention ecosystem services (SRES). This study explores the impact of forest patch thresholds and critical points on soil erosion rates, focusing on 401 catchments in Poland using generalised additive models to identify thresholds and critical points in forest patches. Landscape metrics were applied to measure landscape structure, including shape, fractal dimension, contiguity, related circumscribing circles, and perimeter-area ratio indexes. These metrics, along with slope, rainfall, organic carbon content, water content, and clay ratio variables, were considered dependent variables in the models. The developed models have demonstrated reliable performance in estimating soil erosion rates, with a significant deviation explained from 80.5 to 81.1 for coniferous forest patches, 79.1 to 80.1 for broad-leave forest patches, and 80.9 to 81.4 for mixed forest patches at p < 0.05. In broad-leaved forests, three thresholds are identified in the shape index, which influence soil erosion rates in a complex manner. For coniferous forests, thresholds in the perimeter area ratio, related circumscribing circles, and contiguity indexes exhibit nonlinear relationships with soil erosion rates. Mixed forests show two thresholds in the related circumscribing circle and one in the fractal dimension index, affecting soil erosion rates differently. This research contributes significantly to understanding the interplay between forest patch shapes and soil erosion rates, providing decision support for land use planning. The identified thresholds and critical points offer valuable tools to enhance sustainable landscape functionality, emphasizing the importance of considering forest landscape structure in preserving soil retention ecosystem services.

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Urban stormwater runoff has posed significant challenges in the face of urbanization and climate change, emphasizing the importance of trees in providing runoff reduction ecosystem services (RRES). However, the sustainability of RRES can be disturbed by urban landscape modification. Understanding the impact of landscape structure on RRES is crucial to manage urban landscapes effectively to sustain supply of RRES. So, this study developed a new approach that analyzes the relationship between the landscape structural pattern and the RRES in Tabriz, Iran. The provision of RRES was estimated using the i-Tree Eco model. Landscape structure-related metrics of land use and cover (LULC) were derived using FRAGSTATS to quantify the landscape structure. Stepwise regression analysis was used to assess the relationship between landscape structure metrics and the provision of RRES. The results indicated that throughout the city, the trees prevented 196854.15 m3 of runoff annually. Regression models (p ≤ 0.05) suggested that the provision of RRES could be predicted using the measures of the related circumscribing circle metric (0.889 ≤ r2 ≤ 0.954) and the shape index (r2 = 0.983) of LULC patches. The findings also revealed that the regularity or regularity of the given LULC patches' shape could impact the patches' functions, which, in turn, affects the provision of RRES. The landscape metrics can serve as proxies to predict the capacity of trees for potential RRES using the obtained regression models. This helps to allocate suitable LULC through optimizing landscape metrics and management guidance to sustain RRES.

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This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998-2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na+, Mg2+, Ca2+, SO42-, Cl-, HCO3-, K+, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson's Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems.

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The maintenance of connectivity is critical to the proper functioning of an ecosystem. The present study was conducted with the aim of comparing graph theory connectivity indices and landscape connectivity metrics for the purpose of modeling river water quality. To conduct this study, a forest layer was extracted from land cover map and 25 large watersheds were selected. River water quality was then assessed from the perspective of 8 landscape connectivity metrics and 12 graph theory indices. We developed predictive models using stepwise linear regression, power, exponential, and logarithmic models to locate the best model form for each water quality parameter (dependent variable) we examined. The results indicated that models developed using graph theory connectivity indices resulted in higher coefficients of determination (R2) than models developed using landscape metrics. Only 5 independent variables from a potential set of 13 were significant in explaining the variation in water quality parameters. Also, the models with the highest R2 attempted to explain variations in CO3 (0.818), water discharge (0.733), and Ca levels (0.702). Therefore, the results of this study showed that graph theory connectivity indices had more significant correlation with water quality parameters compared to landscape connectivity metrics. This work also indicates that there exist nonlinear relationships among connectivity indices and water quality parameters.

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Most studies that address the relationship between socio-economic characteristics and soil erosion focus on the effects of soil erosion on socio-economic conditions at different levels, from global to smallholder. Few, if any, efforts are made to address the influence of socio-economic variables on the soil erosion rate as an indicator of landscape degradation. The present study was carried out using spatial data from 402 catchments that cover Poland, to find out how socio-economic variables, which include area-weighted average income per capita (PLN km-2), area-weighted average gross domestic product (PLN km-2), population density (person km-2), and human development index can drive the soil erosion rate (kg ha-1 yr-1), along with annual precipitation, soil and geomorphological variables that include soil organic carbon content, soil water content, clay ratio, stream gradient, and terrain slope. The results showed that the soil erosion rate is indirectly driven by the socio-economic variables in the study catchments, as it is alleviated by increasing population density, the area-weighted average gross domestic product, and the human development index. Furthermore, analyzing the incremental relationship between soil erosion rate and the area-weighted average of socio-economic variables revealed that no uniform change can be observed in the relationship between the area-weighted average socio-economic variables and soil erosion in the study catchments.

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The aim of this study is to evaluate an alternative approach to indicate how hydrological processes behave in a given watershed, and to test whether this approach can replace traditional calibration, particularly under data deficient conditions. Therefore, a regional calibration method (RC) was adapted to characterize "parameter-based hydrologic processes" as a function of watershed ecologic attributes. The methodological process included (1) temporal phase, (2) correlation analysis and (3) spatial phase. The defined methodology was carried out on a 4160 km2 area containing 21 watersheds laying in the southern coastal line of the Caspian Sea, Iran. By implementing the RC, regional models were specified corresponding to each hydrological process defined in the Tank model. Testing the reliability of the transferring process of hydrological parameters was conducted using multi-level accuracy comparison (MAC) benefiting from descriptive statistics, scatter-plots and T-test. Both temporal and spatial phases have shown acceptable outputs backed by their ecologic significance, but as an alternative approach to traditional calibration, the standalone RC still needs development to achieve a more robust basis covering all the parameters of the hydrologic model. According to the post-processor MAC, the transferability of six out of twelve regional models (height of lower outlet at the first tank, intermediate flow, deep-percolation, infiltration, surface flow, height of outlet at the second tank) was accepted with respect to the given tests. As such, our method outperformed the number of transferable parameters by an outstanding regional model predicting the surface flow in comparison with similar studies. Although the RC could not achieve total perfection, nevertheless it could still help users by providing more information about the contribution of ecologic variables in the prediction of the hydrological processes of a certain watershed.

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Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.

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