Soil erosion in agricultural landscapes in the context of dynamic changes of land use patterns and structures (DYLAMUST)

土地利用模式和结构动态变化背景下农业景观的土壤侵蚀（DYLAMUST）

• 批准号: 509809226
• 负责人: Professor Dr. Peter Fiener
• 金额: --
• 依托单位: Soil Science and Geomorphology work group
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/509809226?language=en
• 关键词: Soil; erosion; agricultural; landscapes; context

Today's landscapes are largely shaped by human activities. Since the Holocene human-land interactions are driven by environmental attributes that are relevant for the location of settle-ments. In the recent past, it has been especially the type of ownership and inheritance of land as well as the land consolidation and expansion of the size of single fields due to mechanization that control land use patterns and structures largely. Today agricultural landscapes face tremendous challenges including sustaining biodiversity, soil and water conservation, sustainability in general, and mitigation of and adaption to climate change. Soil erosion in agricultural landscapes is direct-ly related to dynamic changes in land use patterns and structures. Key soil erosion control variables such as slope length, vegetation cover, erosion control, type of cultivation, and sediment transport pathways depend on it. Soil erosion models have been used for decades to better un-derstand the transport processes and the interrelationships between land use and soil erosion. However, the effects of the above-described dynamics of landscape patterns and structures on soil erosion processes are so far poorly understood or only conditionally represented by soil erosion models. Direct measurements of soil erosion for larger catchments considering landscape patterns and structures are lacking. Thus, it remains largely unclear whether, why, and to what extent changing landscape patterns and structures affect the hydrological and sedimentological connectivity of agricultural landscapes. The objective of the proposed project is to quantify and better understand these effects on erosion and deposition patterns and sediment input to water bodies. To this end, a classical erosion model will be compared with a data driven model from the field of machine learning and merged into a hybrid modeling approach. Therefore, the innovation potential of machine learning could be applied in soil erosion research by merging the ca-pabilities of a conceptual erosion model with the capability of data driven machine learning mod-eling. Finally, scenarios accounting for climate change and different pathways of future agricultural development will be defined and used for modeling to estimate impacts of changing land use patterns and structures on soil erosion and sediment discharge and lateral pollutant transport. Such landscape scale modeling also opens the possibility of identifying optimal land use patterns that reduce on-site and off-site damage from soil erosion.

今天的景观在很大程度上是由人类活动塑造的。全新世以来，人地相互作用是由与聚落位置相关的环境属性驱动的。近年来，土地所有权和继承权的类型以及机械化导致的土地合并和单田规模的扩大在很大程度上控制了土地利用模式和结构。今天，农业景观面临着巨大的挑战，包括维持生物多样性，水土保持，总体可持续性以及减缓和适应气候变化。农业景观土壤侵蚀与土地利用方式和结构的动态变化有直接关系。土壤侵蚀控制的关键变量，如坡长，植被覆盖，侵蚀控制，耕作类型和泥沙输运途径取决于它。土壤侵蚀模型已经使用了几十年，以更好地了解运输过程和土地利用与土壤侵蚀之间的相互关系。然而，上述景观格局和结构的动态对土壤侵蚀过程的影响到目前为止知之甚少，或只有有条件的土壤侵蚀模型表示。考虑到景观格局和结构，缺乏对较大集水区土壤侵蚀的直接测量。因此，很大程度上仍然不清楚，为什么，以及在何种程度上不断变化的景观格局和结构影响农业景观的水文和沉积连通性。拟议项目的目标是量化和更好地了解这些对侵蚀和沉积模式以及沉积物进入水体的影响。为此，将经典侵蚀模型与机器学习领域的数据驱动模型进行比较，并将其合并为混合建模方法。因此，通过将概念侵蚀模型的能力与数据驱动的机器学习建模能力相结合，可以将机器学习的创新潜力应用于土壤侵蚀研究。最后，气候变化和未来农业发展的不同途径的假设情景将被定义和用于建模，以估计不断变化的土地利用模式和结构对土壤侵蚀和沉积物排放和污染物横向迁移的影响。这种景观规模的建模也打开了确定最佳的土地利用模式，减少现场和场外的土壤侵蚀损害的可能性。