Trends and Spatial Patterns of Drought Incidence in the Omo‐Ghibe River Basin,Ethiopia          下载免费PDF全文

Mekonnen A. Degefu  Woldeamlak Bewket 《Geografiska Annaler: Series A, Physical Geography》2015,97(2):395-414

The objective of this paper is to evaluate trends and spatial patterns of drought incidence across the Omo‐Ghibe River Basin using monthly rainfall data from eight stations for the period 1972–2007. It also aims to estimate the probability of drought episodes for a 100‐year period. Drought indices were generated using the Standard Precipitation Index (SPI) computed at 3‐, 6‐, 12‐ and 24‐month time‐steps for three intensity classes: moderate, severe and extreme drought events. The Mann–Kendall's trend test and Sen's slope estimator were employed to detect temporal changes. The results show complex spatial patterns on the frequency and magnitude of drought events across the study area for all timescales and intensity classes. However, the total number of drought events for the three intensity classes for all timescales were larger in the southern lowlands, where there exists a serious water scarcity for the rain‐fed pastoral system, than in the northeastern part (around Wolaita Sodo area). In contrast to this, the longest and most extreme (SPI < ?4.0) drought events for all timescales were observed at Wolaita Sodo station. In a 100‐year period one could expect 57–69 drought events with 3 months' duration, 19–34 events with 6 months' duration, 9–16 events with 12 months' duration and 5–9 events with 24 months' duration. The SPI values show negative rainfall anomalies in the 1980s while positive anomalies have occurred in the 1990s and 2000s, which implies tendency towards decreasing drought events. The Mann–Kendall's trend test for the 12‐ and 24‐month timescales and for seasonal events also confirms this general trend.  相似文献   

Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models     

Balew A. Mekonnen  Alireza Nazemi  Kerry A. Mazurek  Amin Elshorbagy  Gordon Putz 《水文科学杂志》2013,58(9):1473-1489

Abstract

Much of the prairie region in North America is characterized by relatively flat terrain with many depressions on the landscape. The hydrological response (runoff) is a combination of the conventional runoff from the contributing areas and the occasional overflow from the non-contributing areas (depressions). In this study, we promote the use of a hybrid modelling structure to predict runoff generation from prairie landscapes. More specifically, the Soil and Water Assessment Tool (SWAT) is fused with artificial neural networks (ANNs), so that SWAT and the ANN module deal with the contributing and non-contributing areas, respectively. A detailed experimental study is performed to select the best set of inputs, training algorithms and hidden neurons. The results obtained in this study suggest that the fusion of process-based and data-driven models can provide improved modelling capabilities for representing the highly nonlinear nature of the hydrological processes in prairie landscapes.