Article Linear and Non-Linear Approaches for Statistical Seasonal Rainfall Forecast in the Sirba Watershed Region (SAHEL) (original) (raw)
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Climate, 2015
Since the 90s, several studies were conducted to evaluate the predictability of the Sahelian rainy season and propose seasonal rainfall forecasts to help stakeholders to take the adequate decisions to adapt with the predicted situation. Unfortunately, two decades later, the forecasting skills remains low and forecasts have a limited value for decision making while the population is still suffering from rainfall interannual variability: this shows the limit of commonly used predictors and forecast approaches for this region. Thus, this paper developed and tested new predictors and new approaches to predict the upcoming seasonal rainfall amount over the Sirba watershed. Predictors selected through a linear correlation analysis were further processed using combined linear methods to identify those having high predictive power. Seasonal rainfall was forecasted using a set of linear and non-linear models. An average lag time up to eight months was obtained for all models. It is found that the combined linear methods performed better than non-linear, possibly because non-linear models require larger and better datasets for calibration. The R 2 , Nash and Hit rate score are
Journal of Hydrology: Regional Studies, 2015
a b s t r a c t Study region: The Sirba watershed, Niger and Burkina Faso countries, West Africa. Study focus: Water resources management in the Sahel region, West Africa, is extremely difficult because of high inter-annual rainfall variability. Unexpected floods and droughts often lead to severe humanitarian crises. Seasonal rainfall forecasting is one possible way to increase resilience to climate variability by providing information in advance about the amount of rainfall expected in each upcoming rainy season. Rainfall forecasting models often arbitrarily assume that rainfall is linked to predictors by a multiple linear regression with parameters that are independent of time and of predictor magnitude. Two probabilistic methods based on change point detection that allow the relationship to change according to time or rainfall magnitude were developed in this paper using normalized Bayes factors. Each method uses one of the following predictors: sea level pressure, air temperature and relative humidity. Method M1 allows for change in model parameters according to annual rainfall magnitude, while M2 allows for changes in model parameters with time. M1 and M2 were compared to the classical linear model with constant parameters (M3) and to the climatology (M4). New hydrological insights for the region: The model that allows a change in the predictor-predictand relationship according to rainfall amplitude (M1) and uses air temperature as predictor is the best model for seasonal rainfall forecasting in the study area.
2016
The Sahel region located in Western Africa is well known for its high rainfall variability. Severe and recurring droughts have plagued the region during the last three decades of the 20 century, while heavy precipitation events (with return periods of up to 1,200 years) were reported between 2007 and 2014. Vulnerability to extreme events is partly due to the fact that people are not prepared to cope with them. It would be of great benefit to farmers if information about the magnitudes of precipitation and streamflow in the upcoming rainy season were available a few months before; they could then switch to more adapted crops and farm management systems if required. Such information would also be useful for other sectors of the economy, such as hydropower production, domestic/industrial water consumption, fishing and navigation. A logical solution to the above problem would be seasonal rainfall and streamflow forecasting, which would allow to generate knowledge about the upcoming rain...
bilmes En 2021 ISBN: 978-605-74786-5-8, 2021
Forecasting of rainfall extremes is still an eminent challenge, especially in developing regions where the difficulty in prediction of these rainfall extremes is partly due to lack of advanced scientific tools and reliable data sets. The economy of Uganda being heavily dependent on agriculture becomes vulnerable due to lack of adequate irrigation facilities. In this paper a statistical approach is used over the historical data to predict the rainfall and establish its relationship to various atmospheric variables. The multiple linear regression (MLR) methodology is applied on the data collected over 9 years of Kasese district, Uganda. The model forecasts precipitation for a year considering monthly precipitation data and building model was used for years 2010-2013. The model testing and validation were performed using years 2014-2018 dataset of monthly precipitation. The equation developed from the model thereby displayed a superb result. The model predictions showed an excellent association with the actual data. The coefficient of determination (R 2) and adjusted R 2 value was obtained to be 0.804 and 0.721 respectively. This understanding validates the application of the developed model over the study area to prognosis rainfall, thereby helping in proper planning and management.
Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables
Theoretical and Applied Climatology, 2013
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MAUSAM
Statistical predictive models were developed to investigate how global rainfall predictors relate to the March-May (MAM) rainfall over Southern Nations, Nationalities and People's Region (SNNPR) of Ethiopia. Data utilized in this study include station rainfall data, oceanic and atmospheric indices. Because of the spatial variations in the interannual variability and the annual cycle of rainfall, an agglomerative hierarchical cluster analyses were used to delineate a network of 20 stations over study area into three homogeneous rainfall regions in order to derive rainfall indices. Time series generated from the delineated regions were later used in the rainfall/teleconnection indices analyses. The methods employed were correlation analysis and multiple linear regressions. The regression modes were based on the training period from 1987-2007 and the models were validated against observation for the independent verification period of 2008-2012. Results obtained from the analysis revealed that sea surface temperature (SST) variations were the main drivers of seasonal rainfall variability. Although SSTs account for the majority of variance in seasonal rainfall, a moderate improvement of rainfall prediction was achieved with the inclusion of atmospheric indices in prediction models. The techniques clearly indicate that the models were reproducing and describing the pattern of the rainfall for the sites of interest. For the forecast to become useful at an operational level, further development of the model will be necessary to improve skill and to determine the error bounds of the forecast.
Seasonal prediction of East African rainfall
International Journal of Climatology, 2014
Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to overestimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales.
Seasonal Rainfall Prediction in Kenya Using Empirical Methods
Prediction schemes for forecasting of onset and cessation dates as well as seasonal amount of rainfall in the well known Kenyan "long rains" that extends from March to May (MAM) using Nairobi as the case study are proposed. In order to obtain onset and cessation dates that will be of practical usage for agriculture and water resources management, weekly crop water requirement (CWR) are utilized. The models proposed for the onset and cessation dates as well as seasonal rainfall amount are based on the anomalies of equivalent potential temperature, θ' e and their saturated components, θ' es . These parameters are capable of monitoring the daily, monthly and annual variations in the moisture content of the air over any station. The models are developed using 10 years of surface synoptic and upper air data. The predicted onset dates are generally within ±15 days of actual onsets on at least 80 % of the occasions while correlation coefficient between actual and predicted seasonal rainfall amounts for the March-May period is greater than 0.5. This study, in particular, investigates the applicability of the empirical methods of seasonal rainfall prediction proposed and which are in use in Nigeria, West Africa since the year 2000 to the East African region. Results indicate the applicability of these schemes to Kenya in particular and East Africa in general.
Hydrological Sciences Journal, 2014
The ability of various statistical techniques to forecast the Jul-August-September (JAS) total rainfall and monthly streamflow in the Sirba watershed (in West Africa) was tested in this paper. First, multiple linear regression was used to link predictors derived from the Atlantic and Pacific sea surface temperature (SST) to JAS rainfall in the watershed up to 18 months ahead; then, daily precipitation was generated using temporal disaggregation; and finally, a rainfall-runoff model was used to generate future hydrographs. Different combinations of lag times and time windows on which SSTs were averaged were considered. Model performance was assessed using the Nash-Sutcliffe coefficient (E f ), the coefficient of determination (R 2 ) and a three-category hit score (H). The best results were achieved using the Pacific Ocean SST averaged over the March-June period of the year before the rainy season and led to a performance of R 2 =0.458, E f = 0.387 and H = 66.67% for JAS total rainfall and R 2 = 0.552, E f = 0.487 and H = 73.28% for monthly streamflow.