Unravelling Landscape Evolution and Soil Erosion Dynamics in the Xynias Drained Lake Catchment, Central Greece: A GIS and RUSLE Modelling Approach

Nikos Charizopoulos (), Simoni Alexiou, Nikolaos Efthimiou, Emmanouil Psomiadis and Panagiotis Arvanitis
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Nikos Charizopoulos: Department of Natural Resources & Agricultural Engineering, Laboratory of Mineralogy-Geology, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
Simoni Alexiou: Department of Natural Resources & Agricultural Engineering, Laboratory of Mineralogy-Geology, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
Nikolaos Efthimiou: Faculty of Environmental Sciences, Czech University Life Sciences Prague, 165 00 Prague, Czech Republic
Emmanouil Psomiadis: Department of Natural Resources & Agricultural Engineering, Laboratory of Mineralogy-Geology, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
Panagiotis Arvanitis: Geological and Research Services, Panagiotis Arvanitis, Ypsilantou 55, 351 31 Lamia, Greece

Sustainability, 2025, vol. 17, issue 12, 1-21

Abstract: Understanding a catchment’s geomorphological and erosion processes is essential for sustainable land management and soil conservation. This study investigates the Xynias drained lake catchment in Central Greece using a twofold geospatial modelling approach that combines morphometric analysis with the Revised Universal Soil Loss Equation (RUSLE) to evaluate the area’s landscape evolution, surface drainage features, and soil erosion processes. The catchment exhibits a sixth-order drainage network with a dendritic and imperfect pattern, shaped by historical lacustrine conditions and the carbonate formations. The basin has an elongated shape with steep slopes, high total relief, and a mean hypsometric integral value of 26.3%, indicating the area is at an advanced stage of geomorphic maturity. The drainage density and frequency are medium to high, reflecting the influence of the catchment’s relatively flat terrain and carbonate formations. RUSLE simulations also revealed mean annual soil loss to be 1.16 t ha −1 y −1 from 2002 to 2022, along with increased erosion susceptibility in hilly and mountainous areas dominated by natural vegetation. In comparison to these areas, agricultural regions displayed less erosion risk. These findings demonstrate the effectiveness of combining GIS with remote sensing for detecting erosion-prone areas, informing conservation initiatives. Along with the previously stated results, more substantial conservation efforts and active land management are required to meet the Sustainable Development Goals (SDGs) while considering the monitored land use changes and climate parameters for future catchment management.

Keywords: hydrographic network; geomorphology; geospatial analysis; soil erosion model; morphometry; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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