Statistical seasonal streamflow forecasting using probabilistic approach over West African Sahel

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• 作者：Abdouramane Gado Djibo ; Harouna Karambiri ; Ousmane Seidou…
• 关键词：Streamflow forecast ; Bayes factor ; SWAT ; Posterior probability ; Sirba watershed ; Sahel
• 刊名：Natural Hazards
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：79
• 期：2
• 页码：699-722
• 全文大小：2,592 KB
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• 作者单位：Abdouramane Gado Djibo (1) (2)
  Harouna Karambiri (1)
  Ousmane Seidou (2)
  Ketevera Sittichok (2)
  Jean Emmanuel Paturel (3)
  Hadiza Moussa Saley (4)
  
  1. International Institute for Water and Environmental Engineering (2iE), 01 BP 594, Ouagadougou, Burkina Faso
  2. Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada
  3. Institut de Recherche pour le D茅veloppement (IRD), Abidjan, C么te d鈥橧voire
  4. Centre Africain d鈥櫭塼udes Sup茅rieures en Gestion, Dakar, S茅n茅gal
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geophysics and Geodesy
  Geotechnical Engineering
  Civil Engineering
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-0840

文摘

Runoff changes are tightly connected to precipitation in West African Sahel in such a way that any impact on precipitation would result in potential changes in runoff. Unfortunately, climate change and variability impacts induced changes in streamflow which directly disturb water availability for socioeconomic activities particularly agricultural sector which constitutes the main survival issue of West African population. Thus, available streamflow information a few months in advance prior to a rainy season with an acceptable forecasting skill will immensely benefit for water users to make an operational planning for water management decision. Streamflow is usually either forecasted directly by linking streamflow to predictors through a multiple linear regression or indirectly using a rainfall-runoff model to transform predicted rainfall into streamflow. Seasonal annual mean streamflow and maximum monthly streamflow were forecasted in this study by using two statistical methods based on change point detection using Normalized Bayes Factors. Each method uses one of the following predictors: Sea level pressure, air temperature and relative humidity (RHUM). Models M1 and M2 respectively allow for change in model parameters according to rainfall amplitude (M1), or along time (M2). They were compared to forecasting models where precipitation is obtained using the classical linear model with constant parameters (M3) and the climatology (M4). The obtained results revealed that model M3 using RHUM as predictor at a lag time of 8 months was the best method for seasonal annual streamflow forecast. Whereas, model M1 using air temperature as predictor at a lag time of 4 months is the best model to predict maximum monthly streamflow in the Sirba watershed. Keywords Streamflow forecast Bayes factor SWAT Posterior probability Sirba watershed Sahel