Document Type : Original Article
Authors
1
Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran
2
Department of the Environment, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran
10.22034/iwm.2025.2067686.1244
Abstract
Extended Abstract
Introduction: Hydrological ecosystem services (HES), such as water yield and sediment retention, are fundamental to ecological balance and sustainable resource management. However, these critical functions are increasingly disrupted by rapid land use and landscape changes driven by human activities. These dynamics are effectively captured by landscape metrics, which quantify the spatial configuration, composition, and structure of land cover patches. This study aims to model and predict key HES in the Talar watershed, Mazandaran Province, Iran, and to quantitatively examine their relationships with landscape metrics and the processes of landscape change.
Materials and methods: The research employed a multi-step methodological framework. First, land use/land cover (LULC) maps for 1989, 2000, and 2014 were generated using the Support Vector Machine (SVM) classifier. A projected LULC map for 2030 was then created using a Markov chain integrated with the Land Change Modeler (LCM). A suite of landscape metrics—including Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), and others—was calculated using FRAGSTATS software. Concurrently, specific landscape change processes (e.g., deformation, shift, creation) were quantified for each LULC class using TerrSet. The InVEST model was applied to simulate and project the HES of water yield and sediment retention for all study years, including the 2030 scenario based on current change trends. Finally, the relationships between the landscape metrics, change processes, and ecosystem service delivery were analyzed using Pearson correlation coefficients in SPSS.
Results and Discussion: The Land Change Modeler demonstrated excellent performance in predicting LULC changes (Klocation = 0.969, Kno = 0.964). Projections for 2014–2030 indicate significant shifts: a decline in forest (–14.86 km²) and rangeland (–19.85 km²) areas, contrasted with an expansion of rainfed agriculture (+21.01 km²) and residential areas (+13.60 km²). Pearson correlation analysis revealed that most spatial pattern indices, except for patch density and edge density, had significant correlations with both water yield and sediment retention. The Perimeter-Area Ratio (SHAPE_AM or a similar PA metric) exhibited the strongest correlation (0.699 for water yield; 0.782 for sediment retention). An inverse relationship was found between the Largest Patch Index (LPI) and these services, suggesting that the dominance of large, consolidated patches can negatively impact hydrological functions. Furthermore, the Landscape Shape Index (LSI) for forest and rangeland patches decreased over the 25-year period, a trend associated with reduced water yield and sediment retention. Analysis of change processes showed that the expansion of rainfed agriculture increased water yield but decreased sediment retention, while the decline in forest cover negatively impacted both services, highlighting the adverse consequences of deforestation.
Conclusion: The findings indicate that ongoing land use changes and landscape fragmentation are likely to degrade hydrological ecosystem services and reduce the ecological resilience of the Talar watershed. This study confirms the significant influence of landscape structure on water yield and sediment retention, with metrics like the Perimeter-Area Ratio serving as strong indicators. The negative correlation with the Largest Patch Index and the detrimental impact of forest loss underscore the need for integrated land use planning and targeted conservation strategies in northern Iran. The combined methodology of spatial modeling, landscape metric analysis, and change process assessment provides a robust and practical framework for evaluating and predicting ecosystem services at the watershed scale, offering valuable insights for sustainable environmental management.
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