Analyzing the influence of changes in land use and management practices on the lag time of peak flows for tropical watersheds of Ethiopia

Abstract

Changes in peak flow response time governed by rainfall and physical configurations of watersheds have been the topic of many studies, but other factors are also important. We aimed to analyze the dynamics in the lag time (T L ) of peak flow for a tropical watershed (Kecha) and its sub-watersheds (Dokmit, Zenjero Maderia, and Wotit Minch) in Ethiopia, as influenced by land-use changes from 1982 to 2017 and the implementation of soil and water conservation (SWC) practices since 2011. Layered maps of land use, soil type, and SWC practices were used to determine the curve number, and the Natural Resource Conservation Service hydrologic model was used to estimate T L . We compared the estimated values against measured median values of T L for 30 rainfall-runoff events. The estimated T L of 1982, 2005, and 2017 varied from 9 to 19 min, 8 to 18 min, and 10 to 22 min, respectively, in the Dokmit, Zenjero Maderia, and Wotit Minch subwatersheds. The smallest (8 min) and greatest (22 min) values of T L were observed during 2005 and 2017, respectively. These results are likely attributable to the increased amount of cultivated area at the expense of bushland and forest in 2005 as compared to 1982, and the implementation of SWC practices in 2011-2017. Dokmit had the lowest T L values of the three sub-watersheds, most likely because of its greater coverage of grazing and cultivated lands and degraded Nitic Luvisol. The variation in T L values among the sub-watersheds was related to changes in both land use and SWC practices but also to the presence of degraded areas inherited from past human activities. Overall, such spatiotemporal flow response time analysis can provide useful information for the proper design of sustainable development strategies for particular niches in tropical regions of Ethiopia and elsewhere.

Research on Production and Sales Collaborative Management System Based on Big Data

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