{"title":"埃塞俄比亚南部大裂谷盆地matenselect流域土壤侵蚀随土地利用和土地覆盖动态变化","authors":"M. Mathewos, Misgena Tsegaye, N. Wondrade","doi":"10.1111/nrm.12379","DOIUrl":null,"url":null,"abstract":"The global community recognizes land use and land cover change (LULC) as a primary cause of ecological modification that has a considerable impact on natural resources, particularly soil and water resources. The aim of this research was to investigate land use change's influences on soil erosion in the Matenchose watershed of Ethiopia in 1991, 2003, and 2020. The maximum likelihood classification (MLC) method was used in the study for supervised image analysis. Soil erosion was estimated using the geographic information system (GIS), remote sensing, and the Revised Universal Soil Loss Equation (RUSLE) model. According to the LULC data from 1991, the watershed was mostly covered by grassland (35%), while in 2003 and 2020, it was typically enclosed by cultivated land (36%) and (52%), respectively. The watershed's mean annual soil erosion rate grew significantly from 13 t/ha in 1991 to 18 t/ha in 2003 to 21 t/ha in 2020. Based on the current soil loss rate result, the Matenchose watershed was divided into five priority groups for soil management practices. In contrast, the watershed is made up of 2052 ha (21%) of areas with high to very high erosion risk, 3304 ha (33%) of areas with moderate erosion risk, and 2866 ha (29%) of areas with severe erosion risk. Based on the average annual rate of soil erosion, several vital subwatersheds were identified for potential future land management‐related actions. Over the 29 years, the area of grassland and forest decreased while agricultural and settlement areas expanded, and they contributed to the enhanced hazards of soil erosion. Particularly vulnerable to erosion are the watershed's hilly and steeper areas. The identified subwatersheds that are most at risk of erosion should be given priority for upcoming LULC initiatives, proper participatory watershed planning and management, and measures to conserve soil and water to preserve the Matenchose watershed's soil resources.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Soil erosion variations along land use and land cover dynamics in Matenchose watershed, Rift Valley Basin, Southern Ethiopia\",\"authors\":\"M. Mathewos, Misgena Tsegaye, N. Wondrade\",\"doi\":\"10.1111/nrm.12379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The global community recognizes land use and land cover change (LULC) as a primary cause of ecological modification that has a considerable impact on natural resources, particularly soil and water resources. The aim of this research was to investigate land use change's influences on soil erosion in the Matenchose watershed of Ethiopia in 1991, 2003, and 2020. The maximum likelihood classification (MLC) method was used in the study for supervised image analysis. Soil erosion was estimated using the geographic information system (GIS), remote sensing, and the Revised Universal Soil Loss Equation (RUSLE) model. According to the LULC data from 1991, the watershed was mostly covered by grassland (35%), while in 2003 and 2020, it was typically enclosed by cultivated land (36%) and (52%), respectively. The watershed's mean annual soil erosion rate grew significantly from 13 t/ha in 1991 to 18 t/ha in 2003 to 21 t/ha in 2020. Based on the current soil loss rate result, the Matenchose watershed was divided into five priority groups for soil management practices. In contrast, the watershed is made up of 2052 ha (21%) of areas with high to very high erosion risk, 3304 ha (33%) of areas with moderate erosion risk, and 2866 ha (29%) of areas with severe erosion risk. Based on the average annual rate of soil erosion, several vital subwatersheds were identified for potential future land management‐related actions. Over the 29 years, the area of grassland and forest decreased while agricultural and settlement areas expanded, and they contributed to the enhanced hazards of soil erosion. Particularly vulnerable to erosion are the watershed's hilly and steeper areas. The identified subwatersheds that are most at risk of erosion should be given priority for upcoming LULC initiatives, proper participatory watershed planning and management, and measures to conserve soil and water to preserve the Matenchose watershed's soil resources.\",\"PeriodicalId\":49778,\"journal\":{\"name\":\"Natural Resource Modeling\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Natural Resource Modeling\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1111/nrm.12379\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Resource Modeling","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1111/nrm.12379","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Soil erosion variations along land use and land cover dynamics in Matenchose watershed, Rift Valley Basin, Southern Ethiopia
The global community recognizes land use and land cover change (LULC) as a primary cause of ecological modification that has a considerable impact on natural resources, particularly soil and water resources. The aim of this research was to investigate land use change's influences on soil erosion in the Matenchose watershed of Ethiopia in 1991, 2003, and 2020. The maximum likelihood classification (MLC) method was used in the study for supervised image analysis. Soil erosion was estimated using the geographic information system (GIS), remote sensing, and the Revised Universal Soil Loss Equation (RUSLE) model. According to the LULC data from 1991, the watershed was mostly covered by grassland (35%), while in 2003 and 2020, it was typically enclosed by cultivated land (36%) and (52%), respectively. The watershed's mean annual soil erosion rate grew significantly from 13 t/ha in 1991 to 18 t/ha in 2003 to 21 t/ha in 2020. Based on the current soil loss rate result, the Matenchose watershed was divided into five priority groups for soil management practices. In contrast, the watershed is made up of 2052 ha (21%) of areas with high to very high erosion risk, 3304 ha (33%) of areas with moderate erosion risk, and 2866 ha (29%) of areas with severe erosion risk. Based on the average annual rate of soil erosion, several vital subwatersheds were identified for potential future land management‐related actions. Over the 29 years, the area of grassland and forest decreased while agricultural and settlement areas expanded, and they contributed to the enhanced hazards of soil erosion. Particularly vulnerable to erosion are the watershed's hilly and steeper areas. The identified subwatersheds that are most at risk of erosion should be given priority for upcoming LULC initiatives, proper participatory watershed planning and management, and measures to conserve soil and water to preserve the Matenchose watershed's soil resources.
期刊介绍:
Natural Resource Modeling is an international journal devoted to mathematical modeling of natural resource systems. It reflects the conceptual and methodological core that is common to model building throughout disciplines including such fields as forestry, fisheries, economics and ecology. This core draws upon the analytical and methodological apparatus of mathematics, statistics, and scientific computing.