A Spatial Decision Support System for climate-adapted agriculture designed with and for stakeholders in West Africa

This paper presents a Spatial Decision Support System (SDSS) designed to assist stakeholders in West Africa in analysing critical climate and land use indicators for risk management in agriculture and further sectors being affected by extreme precipitation and temperature events. Developed as part of the WASCAL WRAP 2.0 project LANDSURF, the SDSS makes scientific data accessible and comprehensible to non-scientific audiences, facilitating informed decision-making among communities affected by climate change. From the beginning of the development process, the web portal was co-designed with relevant West African stakeholders. Due to the challenging conditions during the COVID-19 pandemic, alternative online communication tools, e.g. ZOOM, online surveys and email, successfully were utilized to interact with stakeholders instead of on-site activities. The co-design process carried out with stakeholders includes several steps such as stakeholder analysis, identification of their information needs using specific climate, crop and remote sensing indicators, and the evaluation of the SDSS in a dedicated workshop. In total, the co-design process involved nine different steps, recorded and described in a stakeholder interaction protocol. The SDSS integrates observational data, including CHIRPS and ERA5-Land datasets, and state-of-the-art high-resolution climate model outputs under two greenhouse gas concentration scenarios (RCP2.6 and RCP8.5) and remote sensing data. It enables the comparison of model outputs with observations and facilitates the assessment of regional climate variability and trends. Two concept studies illustrate the SDSS’s functionality: one focusing on a farmer in Burkina Faso assessing irrigation needs for millet cultivation, and another involving a regional planner analysing drought and heat wave impacts in coastal West Africa. These examples highlight the SDSS’s usability in supporting adaptive strategies and enhancing resilience to climate-related challenges, underscoring the importance of integrating local knowledge with scientific data for effective climate adaptation and mitigation.