Supporting risk assessment and adaptation at multiple spatial scales: Co-development of methods to utilize uncertain multi-model based information on freshwater-related hazards of climate change
It is state of the art that multi-model ensembles (MME) of future freshwater-related hazards of climate change (CC) (e.g. derived by driving a number of global hydrological models by the output of a number of climate models) are optimal for informing CC risk management. However, studies on how to best utilize MMEs (e.g. ISIMIP MMEs) in risk management are lacking. The main project goal is to co-develop methods for providing and utilizing MME data on freshwater-related hazards for risk and adaptation assessments at various spatial scales, and to provide data in a suitable way, in order to increase availability and applicability of information for different types of end-users, with a focus on how to address uncertainties. Furthermore, future research in the framework of ISIMIP is to be co-designed, focusing on uncertainty and water. Co-development of PUNI (Providing and Utilizing eNsemble Information) methods will be done jointly by global hydrological modelers, scientists investigating co-development methods and societal information needs, boundary organizations and stakeholders (end-users). They will all participate in three stakeholder dialogues at the global scale (end-user industries), transboundary scale (Morocco, Algeria, Tunisia), and river basin scale (Ebro). PUNI methods will be co-developed by testing alternative ways of presenting MME data in support of exemplary (participatory) CC risk assessments in each stakeholder dialogue in an iterative manner, based on MME data that are either available or generated specifically in the project in response to end-user input. Expected results include a handbook on PUNI methods and a web portal at UNESCO’s International Centre for Water Resources and Global Change where end-users from around the world will be able to access hydrological MME data for their region of interest for free and in a way that suits their needs, e.g. by selecting a hazard indicator for low flows as well as its spatial and temporal aggregation.
It is state of the art that multi-model ensembles (MME) of future freshwater-related hazards of climate change (CC) (e.g. derived by driving a number of global hydrological models by the output of a number of climate models) are optimal for informing CC risk management. However, studies on how to best utilize MMEs (e.g. ISIMIP MMEs) in risk management are lacking. The main project goal is to co-develop methods for providing and utilizing MME data on freshwater-related hazards for risk and adaptation assessments at various spatial scales, and to provide data in a suitable way, in order to increase availability and applicability of information for different types of end-users, with a focus on how to address uncertainties. Furthermore, future research in the framework of ISIMIP is to be co-designed, focusing on uncertainty and water. Co-development of PUNI (Providing and Utilizing eNsemble Information) methods will be done jointly by global hydrological modelers, scientists investigating co-development methods and societal information needs, boundary organizations and stakeholders (end-users). They will all participate in three stakeholder dialogues at the global scale (end-user industries), transboundary scale (Morocco, Algeria, Tunisia), and river basin scale (Ebro). PUNI methods will be co-developed by testing alternative ways of presenting MME data in support of exemplary (participatory) CC risk assessments in each stakeholder dialogue in an iterative manner, based on MME data that are either available or generated specifically in the project in response to end-user input. Expected results include a handbook on PUNI methods and a web portal at UNESCO’s International Centre for Water Resources and Global Change where end-users from around the world will be able to access hydrological MME data for their region of interest for free and in a way that suits their needs, e.g. by selecting a hazard indicator for low flows as well as its spatial and temporal aggregation.

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