Abstract

Catchment response as consequence of changes in vegetation cover and land use management could not be well explained by statistical methods alone. At the same time, long range periodic and trend components of time series are not adequately predicted by watershed modeling. Therefore, joint application of statistical time series analysis and watershed modeling better help to understand the underlying climate variability and catchment dynamics. In this paper, an attempt has been made to examine the effects of climate variability and catchment dynamics at two agricultural watersheds situated in Rift Valley lakes basin of Ethiopia. Distributed hydrologic modeling is used to characterize catchment dynamics whereas statistical methods (time-trend, double mass curve, flow duration curve analysis) are applied to explain the accompanying climate variability. The simulated surface runoff component increased progressively since 1970s. Percentage annual surface runoff varies from 10 to 23% at Bilate, and 16% to over twofold at Hare watersheds. Statistical time-trend analysis reveals that annual streamflow do not show significant monotonic trend, whereas, extreme daily streamflow at Alaba Kulito of Bilate catchment is characterized by increasing trend during the analysis period. Recurrent yet statistically weaker change point years are found and are independent of each other in two watersheds and hence they are governed by land use attributes unique to respective watersheds that influence overland flow. A rising slope of rainfall-runoff double mass curve during post-1992 and 1994 period at Bilate and Hare watersheds respectively supports increasing trend of streamflow that is not fully explained by time-trend analysis. Time-segmented FDCs of monthly streamflow at Bilate shows increased quantile estimates of high flows for similar level of exceedance probability for recent years. The resulting runoff variability over the analysis period is attributed to climate variability and altered land use/cover conditions, the latter being dominant in the watersheds.

Key words: Land use dynamics, runoff, watershed modeling, trend analysis, climate change.