Identifying drivers of streamflow extremes in West Africa to inform a nonstationary prediction model

Kwok Pan Chun, Bastien Dieppois, Qing He, Moussa Sidibe, Jonathan Eden, Jean Emmanuel Paturel, Gil Mahé, Nathalie Rouché, Julian Klaus, Declan Conway

    Research output: Contribution to journalArticlepeer-review

    Abstract

    West Africa exhibits decadal patterns in the behaviour of droughts and floods, creating challenges for effective water resources management. Proposed drivers of prolonged shifts in hydrological extremes include the impacts of land-cover change and climate variability in the region. However, while future land-degradation or land-use are highly unpredictable, recent studies suggest that prolonged periods of high-flows or increasing flood occurrences could be predicted by monitoring sea-surface temperature (SST) anomalies in the different ocean basins. In this study, we thus examine: i) what ocean basins would be the most suitable for future seamless flood-prediction systems; ii) how these ocean basins affect high-flow extremes (hereafter referred as extreme streamflow); and iii) how to integrate such nonstationary information in flood risk modelling. We first use relative importance analysis to identify the main SST drivers modulating hydrological conditions at both interannual and decadal timescales. At interannual timescales, Pacific Niño (ENSO), tropical Indian Ocean (TIO) and eastern Mediterranean (EMED) constitute the main climatic controls of extreme streamflow over West Africa, while the SST variability in the North and tropical Atlantic, as well as decadal variations of TIO and EMED are the main climatic controls at decadal timescales. Using regression analysis, we then suggest that these SST drivers impact hydrological extremes through shifts in the latitudinal location and the strength of the Intertropical Convergence Zone (ITCZ) and the Walker circulation, impacting the West African Monsoon, especially the zonal and meridional atmospheric water budget. Finally, a nonstationary extreme model, with climate information capturing regional circulation patterns, reveals that EMED SST is the best predictor for nonstationary streamflow extremes, particularly across the Sahel. Predictability skill is, however, much higher at the decadal timescale, and over the Senegal than the Niger catchment. This might be due to stronger impacts of land-use (-cover) and/or catchment properties (e.g. the Inner Delta) on the Niger River flow. Overall, a nonstationary framework for floods can also be applied to drought risk assessment, contributing to water regulation plans and hazard prevention, over West Africa and potentially other parts of the world.

    Original languageEnglish
    Article number100346
    JournalWeather and Climate Extremes
    Volume33
    Early online date30 Jun 2021
    DOIs
    Publication statusPublished - Sep 2021

    Bibliographical note

    Funding Information:
    This research was conducted using the resources of the High Performance Cluster Computing Centre, Hong Kong Baptist University, which receives funding from Research Grant Council, University Grant Committee of the HKSAR and Hong Kong Baptist University. The extreme approach in the paper was developed from the PROCORE-France/Hong Kong Joint Research Scheme 2020/21 (F-HKBU201/20).

    Funding Information:
    This research was conducted using the resources of the High Performance Cluster Computing Centre, Hong Kong Baptist University , which receives funding from Research Grant Council, University Grant Committee of the HKSAR and Hong Kong Baptist University . The extreme approach in the paper was developed from the PROCORE-France/Hong Kong Joint Research Scheme 2020/21 (F-HKBU201/20).

    Publisher Copyright:
    © 2021 The Authors

    Keywords

    • Eastern mediterranean (EMED)
    • Floods
    • Nonstationary extreme model
    • Streamflow extremes
    • Tropical indian ocean (TIO)
    • West africa

    ASJC Scopus subject areas

    • Geography, Planning and Development
    • Atmospheric Science
    • Management, Monitoring, Policy and Law

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