The use of spatial empirical models to estimate soil erosion in arid ecosystems
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- 作者：Meshal Abdullah ; Rusty Feagin ; Layla Musawi
- 关键词：Soil erosion ; GIS ; Remote sensing ; MPSIAC model ; EPM model ; USLE ; Arid ecosystem
- 刊名：Environmental Monitoring and Assessment
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：189
- 期：2
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Monitoring/Environmental Analysis; Environmental Management; Ecotoxicology; Atmospheric Protection/Air Quality Control/Air Pollution; Ecology;
- 出版者：Springer International Publishing
- ISSN：1573-2959
- 卷排序：189
文摘
The central objective of this project was to utilize geographical information systems and remote sensing to compare soil erosion models, including Modified Pacific South-west Inter Agency Committee (MPSIAC), Erosion Potential Method (EPM), and Revised Universal Soil Loss Equation (RUSLE), and to determine their applicability for arid regions such as Kuwait. The northern portion of Umm Nigga, containing both coastal and desert ecosystems, falls within the boundaries of the de-militarized zone (DMZ) adjacent to Iraq and has been fenced off to restrict public access since 1994. Results showed that the MPSIAC and EPM models were similar in spatial distribution of erosion, though the MPSIAC had a more realistic spatial distribution of erosion and presented finer level details. The RUSLE presented unrealistic results. We then predicted the amount of soil loss between coastal and desert areas and fenced and unfenced sites for each model. In the MPSIAC and EPM models, soil loss was different between fenced and unfenced sites at the desert areas, which was higher at the unfenced due to the low vegetation cover. The overall results implied that vegetation cover played an important role in reducing soil erosion and that fencing is much more important in the desert ecosystems to protect against human activities such as overgrazing. We conclude that the MPSIAC model is best for predicting soil erosion for arid regions such as Kuwait. We also recommend the integration of field-based experiments with lab-based spatial analysis and modeling in future research.