{"title":"A GIS-based approach for estimating fallow-season cropland soil erosion based on rainfall erosivity","authors":"C. Day, Q. Long","doi":"10.1080/02723646.2023.2188632","DOIUrl":null,"url":null,"abstract":"ABSTRACT Rainfall erosivity describes the capability of rainfall to cause soil erosion from a surface during a storm. Currently, the average long-term annual and monthly rainfall erosivity for a location may be obtained from isoerodent maps using a sample of climate stations across the United States (US). However, at the state-scale these measures are sporadic, using rainfall data from an increasingly outdated period, which may not reflect possible changing rainfall erosivity. Focusing on the state of Kentucky, US, we developed rainfall erosivity grids from the state Mesonet system to determine if erosivity has changed spatially and temporally between the original erosivity datasets from the latter twentieth-century and 2011–2020. We further used the Mesonet-derived dataset to develop a GIS-based model to estimate potential soil erosion for croplands during the fallow-season when soil is most prone to erosive storms. Results indicated that monthly erosivity increased across the state between the two periods. Larger increases in erosivity during the latter part of the fallow-season resulted in higher estimated soil erosion from croplands, particularly to the west. These findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.","PeriodicalId":54618,"journal":{"name":"Physical Geography","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Geography","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/02723646.2023.2188632","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT Rainfall erosivity describes the capability of rainfall to cause soil erosion from a surface during a storm. Currently, the average long-term annual and monthly rainfall erosivity for a location may be obtained from isoerodent maps using a sample of climate stations across the United States (US). However, at the state-scale these measures are sporadic, using rainfall data from an increasingly outdated period, which may not reflect possible changing rainfall erosivity. Focusing on the state of Kentucky, US, we developed rainfall erosivity grids from the state Mesonet system to determine if erosivity has changed spatially and temporally between the original erosivity datasets from the latter twentieth-century and 2011–2020. We further used the Mesonet-derived dataset to develop a GIS-based model to estimate potential soil erosion for croplands during the fallow-season when soil is most prone to erosive storms. Results indicated that monthly erosivity increased across the state between the two periods. Larger increases in erosivity during the latter part of the fallow-season resulted in higher estimated soil erosion from croplands, particularly to the west. These findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.
期刊介绍:
Physical Geography disseminates significant research in the environmental sciences, including research that integrates environmental processes and human activities. It publishes original papers devoted to research in climatology, geomorphology, hydrology, biogeography, soil science, human-environment interactions, and research methods in physical geography, and welcomes original contributions on topics at the intersection of two or more of these categories.