LRFP-2024-9191883- Flood Modelling, forecasting, and mapping for improved early warning -Repub

Terms of Reference

Flood Modelling, forecasting and mapping for improved early warning IN NIGER

1. 1. bACKGROUND and objectives

   Niger is highly vulnerable to the impacts of extreme weather events given its location Sahelian strip, rapid population growth, unsustainable urbanization, climate variability and change, and environmental degradation. The most common weather-related shocks affecting Niger include floods, drought, stormy rains. These reoccurring natural shocks have made Niger a flood-prone country, which ultimately prevents from sustainable socio-economic development of the country.

   Resilient communities, facilities, and infrastructure, early warning systems, and strategic disaster preparedness are key to mitigate harms and overall minimize the impact and possible damages of natural shocks. For each of these key components and practices in Niger, much of the evidence used to make informed decisions is scattered, not consolidated or optimized, and not even hosted on interoperable (geographic information) systems. That makes the early warning and disaster preparedness process less effective, which ultimately leads to vulnerable communities not being evacuated prior to disasters, key infrastructures and sectors not being secured and resilient.

   Therefore, a need has been identified to consolidate the existing knowledge, data and expertise and incorporate newly gathered data into an impact-based flood module for the Lower Tarka Valley and Komadougou Yobe Basin, which could also be easily scalable nationally and which could be used to influence and inform regular programming (resilience building and development) as well as emergency preparedness work in Niger.

   Accurate flood modelling and risk forecasting aligns with UNICEF Niger’s 2023-2027 Country Programme to support climate-resilient communities, by developing and building effective risk mitigation measures for a range of essential social services, including Health, Nutrition, Education, WASH and Social Protection. Moreover, these services determine the scale and scope of vulnerable assets, facilities, infrastructure or households, which then inform programmatic and operational decisions for:

2. Regular programming (e.g. construction of durable and environmentally friendly infrastructure and livelihood)
3. Emergency response and logistics planning

Predictive insights through data intelligence will facilitate effective emergency preparedness plans as well as shock-responsive infrastructures, ultimately contributing to cost and operational efficiencies on development programming and emergency response.