title: Redistribution patterns of soil and soil nutrients : A study using process-oriented and machine-learning approaches, with applications to mountain catchments under monsoon climate
Creators: name: Choi, Kwanghun
Publisher: name: University of Bayreuth
Year: 2021
Subjects: subject: Soil Erosion
subject: Soil Surface Dynamics
subject: Soil Erosion Model
subject: 550

description: Soil erosion is one of the significant environmental problems worldwide, causing surface soil degradation and freshwater deterioration. As the world’s population increases, the problem is worsening because erosion-resistant natural ecosystems are being converted into erosion- prone croplands to meet the increasing demands for food. South Korea is also suffering from recurrent severe soil erosion and consequent water deterioration in the monsoon season because of upland agricultural expansion driven by economic incentives. To define the optimal mitigation measures for soil erosion, we need tools that are suitable for upland agricultural areas. In this thesis, we focused on the spatial patterns of soil and soil organic matter containing nitrogen (N) and phosphorus (P), which are relevant to the soil erosion and water quality of mountainous catchments with complex terrain. Also, we aimed to propose measures to mitigate soil and soil organic matter exports to streams from mountainous catchments in the Soyang watershed by spatial reconfiguration of land use and land cover (LULC) at the landscape level. To investigate the spatial redistribution of soils and soil organic matter from mountain catchments, we developed a soil erosion model termed the Daily based Morgan–Morgan–Finney (DMMF) soil erosion model. The DMMF model was derived from the Modified Morgan–Morgan– Finney (MMMF) model, a variant of the well-known and widely used Morgan–Morgan–Finney (MMF) model, with modifications to make it suitable for complex terrain configurations under seasonal monsoon climates. While the MMMF model improved the physical foundations of the MMF model concerning topography, physical structures of vegetation, and subsurface interflow, several additional aspects needed to be corrected for a better representation of the physical processes. As the MMMF model was originated from an area with a simple terrain configu- ration and comparatively regular rainfall regimes, the model needed modification for a better representation of the study area, which has a complex terrain configuration under a seasonal monsoon climate. We identified and corrected the problematic aspects of the calculation in the effective rainfall, interflow, and transport capacity of the MMMF model by analyzing its entire process (chapter 2). In chapter three, we suggest a new soil erosion model, the DMMF model, based on the MMMF model. In chapter four, we evaluated the effect of a spatial reconfiguration of erosion hotspots on stream sediment load from an upland agricultural catchment utilizing the DMMF model. In this study, we estimated the sediment redistribution pattern and soil erosion risk on the Haean catchment, a well-known upland agricultural catchment belonging to the Soyang watershed. According to the results, the DMMF model can be applied to catchments with complicated terrain configurations affected by a monsoon climate. Results confirmed that a spatial recon- struction of the landscape, complementarily with other best management practices emphasizing the management of dry crop field, can be an effective method to reduce sediment yield from upland agricultural catchments such as Haean. In the final chapter five, we also investigate the environmental drivers that affect the spatial redistribution patterns of soil nutrients such as N and P in mountain forests. To achieve this goal, we used high-resolution light detection and ranging (LiDAR) to derive detailed information regarding the topography and physical structure of vegetation. Then, we predicted the spatial patterns of soil nutrients such as N and P in the organic layer and mineral topsoil. Specifically, we analyzed the relative importance of vegetation and topographical parameters extracted from LiDAR for a better understanding of the spatial patterns of N and P. In addition, we identified areas with critical P contents and tested different validation strategies for N and P.