Analysis, Variability and Rainfall Prediction in Sub-Saharan Africa: The Case of the Lake Guiers in Senegal  

作　　者：Abdou Arame Fall Saidou Ndao Aba Diop Abdou Arame Fall;Saidou Ndao;Aba Diop(UFR Sciences et Technologies (SET), University Iba Der Thiam of Thies (UIDT), Thies, Senegal;Laboratory of Water and Environment Sciences and Technologies (LaSTEE), Polytechnic School of Thies (EPT), Thies, Senegal;Statistics and Random Models Research Team (ERESMA), University Alione Diop (UAD), Bambey, Senegal)

机构地区：[1]UFR Sciences et Technologies (SET), University Iba Der Thiam of Thies (UIDT), Thies, Senegal [2]Laboratory of Water and Environment Sciences and Technologies (LaSTEE), Polytechnic School of Thies (EPT), Thies, Senegal [3]Statistics and Random Models Research Team (ERESMA), University Alione Diop (UAD), Bambey, Senegal

出　　处：《Open Journal of Ecology》2023年第11期806-819,共14页生态学期刊（英文）

摘　　要：The aim of this article is to predict the rainfall evolution of a sub-Saharan area in which one of the most important freshwater resources is located: Lake Guiers. Characterized by short seasonal rains of three months, it experienced a long period of drought in the 1970s. We begin by analyzing the temporal distribution of the rainfall including the variability of the data, with a view to predicting a possible return. For this reason, we present here univariate modeling results of rainfall series collected on three stations in the area. The challenge lies in the adequacy of the parameters for the monthly rainfall series, which generates more or less significant forecast errors on the learning bases because of the missing data. This later motivated their conversion to moving average series. On the other hand, the normality of the latter seems to be rejected by the D’Agostino test. Student’s and Mann-Whitney’s tests confirmed the homogeneity. The autocorlograms show the presence of autoregressive terms in the data. Dickey-Fuller and Mann-Kendall tests reveal both trend and seasonality. The stationarity tests of Dickey-Fuller, Phillips-Perron and KPSS have shown that they are non-stationary. As a result, we did an ARIMA modeling method using the Box-Jenkins [1] method with the R software, which involves estimating model parameters, tests of significance, analysis of residualss, selection according to information criteria and forecasts. The results obtained during the learning-test phase showed a quasi-similarity of the base-tests in all the series except for that of Louga.The aim of this article is to predict the rainfall evolution of a sub-Saharan area in which one of the most important freshwater resources is located: Lake Guiers. Characterized by short seasonal rains of three months, it experienced a long period of drought in the 1970s. We begin by analyzing the temporal distribution of the rainfall including the variability of the data, with a view to predicting a possible return. For this reason, we present here univariate modeling results of rainfall series collected on three stations in the area. The challenge lies in the adequacy of the parameters for the monthly rainfall series, which generates more or less significant forecast errors on the learning bases because of the missing data. This later motivated their conversion to moving average series. On the other hand, the normality of the latter seems to be rejected by the D’Agostino test. Student’s and Mann-Whitney’s tests confirmed the homogeneity. The autocorlograms show the presence of autoregressive terms in the data. Dickey-Fuller and Mann-Kendall tests reveal both trend and seasonality. The stationarity tests of Dickey-Fuller, Phillips-Perron and KPSS have shown that they are non-stationary. As a result, we did an ARIMA modeling method using the Box-Jenkins [1] method with the R software, which involves estimating model parameters, tests of significance, analysis of residualss, selection according to information criteria and forecasts. The results obtained during the learning-test phase showed a quasi-similarity of the base-tests in all the series except for that of Louga.

关 键 词：Time Series RAIN TREND Stationarity RESIDUALS Model FORECAST 

分 类 号：O17[理学—数学]