Stochastic disaggregation of seasonal precipitation forecasts of the West African Regional Climate Outlook Forum.

Abstract

Seasonal rainfall forecasts from the West African Regional Climate Outlook Forum (RCOF) are essential for adapting to climate variability. However, their temporal aggregated nature is a strong limitation, especially when used with impact models requiring daily resolution, such as hydrological or crop models. To address this issue, this study proposes a temporal disaggregation method for these forecasts using in situ data from two districts (Kandi and Parakou) in northern Benin, spanning from 1971 to 2020. A resampling technique was used to construct a daily historical record that aligns with seasonal rainfall forecasts. Three stochastic disaggregation models for rainfall (SRGs) were developed, including two parametric models (SRG1 and SRG2) and one semiparametric (SRG3). Their parameters were estimated from the resampled record to generate daily synthetic data replicating the forecasts. Evaluation of the SRGs revealed that SRG2, which combined a first-order Markov chain with a mixed exponential distribution, performs well in simulating various characteristics of the rainy season, including dry spells, wet spells and daily precipitations. Furthermore, SRG2 maintained the trends of the initial forecasts and outperformed SRG1 and SRG3, as confirmed by the chi-square test. Indeed, a good agreement was observed between the probabilities of the initial prediction and those calculated from the temporal disaggregation with the SRG2 method. Also, for the forecasts expressed by probabilities 15–35–50 and 20–50–30, the cumulative distribution function curves (CDF) of the SRGs exhibited appropriate shifts compared to climatology. These forecasts were specific to the Kandi area in 2008 and 2003, respectively, during the West African RCOFs. Although this study focused specifically on the Kandi and Parakou districts, the temporal disaggregation methodology used can be applied to other locations within West Africa or other RCOFs worldwide. This study offers valuable guidance for generating sector-specific seasonal forecasts for the West African region.