DOI: 10.5194/hess-21-1611-2017
Scopus记录号: 2-s2.0-85015725584
论文题名: Seasonal forecasting of hydrological drought in the Limpopo Basin: A comparison of statistical methods
作者: Seibert M ; , Merz B ; , Apel H
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 3 起始页码: 1611
结束页码: 1629
语种: 英语
Scopus关键词: Atmospheric temperature
; Catchments
; Decision trees
; Deep neural networks
; Drought
; Forestry
; Neural networks
; Oceanography
; Runoff
; Stream flow
; Submarine geophysics
; Surface properties
; Surface waters
; Coefficient of determination
; Hydrological droughts
; Multiple linear models
; Non-linear relationships
; Receiver operating characteristics
; Sea surface temperature (SST)
; Seasonal forecasting
; Teleconnection indices
; Forecasting
; catchment
; comparative study
; drought
; early warning system
; hydrological modeling
; model validation
; runoff
; sea surface temperature
; statistical analysis
; streamflow
; teleconnection
; weather forecasting
; Limpopo Basin
; South Africa
英文摘要: The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Ninõ and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42% explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts. © 2017 The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79220
Appears in Collections: 气候变化事实与影响
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作者单位: GFZ-German Centre for Geosciences, Section 5.4: Hydrology, Potsdam, Germany; University of Potsdam, Institute of Earth and Environmental Science, Potsdam, Germany
Recommended Citation:
Seibert M,, Merz B,, Apel H. Seasonal forecasting of hydrological drought in the Limpopo Basin: A comparison of statistical methods[J]. Hydrology and Earth System Sciences,2017-01-01,21(3)