<p>Streamflow predictions are a vital tool for detecting flood and drought events. Such predictions are even more critical to Sub-Saraharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gauged, with few available gauging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources.&#160;</p> <p>This work presents a novel workflow for predicting streamflow in the presence of missing gauge observations. We leverage bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts for missing data imputation and predict future streamflow using the state-of-the-art Temporal Fusion transformers at ten river gauging stations in the Benin Republic.</p> <p>We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in poor imputation performance over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior performance relative to traditional imputation by established methods such as Random Forest, k-Nearest Neighbour, and GESS lookup. We also show that the Temporal Fusion Transformer yields high predictive skill and further provides explanations for predictions through the weights of its attention mechanism. The findings of this work provide a basis for integrating Global streamflow prediction model data and state-of-the-art machine learning models into operational early-warning decision-making systems (e.g., flood/ drought alerts) in resource-constrained countries vulnerable to drought and flooding due to extreme weather events.</p>
Streamflow predictions are vital for detecting flood and drought events. Such predictions are even more critical to Sub-Saharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gaged, with few available gaging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources. This work presents a novel workflow for predicting streamflow in the presence of missing gage observations. We leverage bias correction of the Group on Earth Observations Global Water and Sustainability Initiative ECMWF streamflow service (GESS) forecasts for missing data imputation and predict future streamflow using the state-of-the-art temporal fusion transformers (TFTs) at 10 river gaging stations in the Benin Republic. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in poor imputation performance over the 10 Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior performance relative to traditional imputation by established methods. We also show that the TFT yields high predictive skill and further provides explanations for predictions through the weights of its attention mechanism. The findings of this work provide a basis for integrating Global streamflow prediction model data and the state-of-the-art machine learning models into operational early-warning decision-making systems in resource-constrained countries vulnerable to drought and flooding due to extreme weather events.
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.
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