Climate services are attracting growing attention and interest as instruments to promote climate change adaptation. The transparent assessment of the potential value brought by the services can play a major role. It can foster the commitment of the user towards a co-generation process increasingly central to climate services creation, can provide developers important information to better tailor the service to the user needs, and can finally increase recognition of the value of the service boosting confidence and trust in the tool. This study presents and then demonstrates the applicability of an evaluation methodology based on the Bayesian framework derived from the information value theory. The specific case study is the Smart Climate Hydropower Tool (SCHT), a climate service designed to support management decisions in hydropower generation. The service uses freely available seasonal forecasts and machine learning algorithms to predict incoming discharge to hydropower reservoirs. The user is ENEL Green Power Italy, and the testing environments are two water basins in Colombia. The study defines the expected value of perfect information, the expected value of the information currently used by the hydropower producer and the expected value of the service information. It then discusses pros and cons of the applicability of the method.

Co-evaluation of climate services. A case study for hydropower generation

F. Bosello;
2022

Abstract

Climate services are attracting growing attention and interest as instruments to promote climate change adaptation. The transparent assessment of the potential value brought by the services can play a major role. It can foster the commitment of the user towards a co-generation process increasingly central to climate services creation, can provide developers important information to better tailor the service to the user needs, and can finally increase recognition of the value of the service boosting confidence and trust in the tool. This study presents and then demonstrates the applicability of an evaluation methodology based on the Bayesian framework derived from the information value theory. The specific case study is the Smart Climate Hydropower Tool (SCHT), a climate service designed to support management decisions in hydropower generation. The service uses freely available seasonal forecasts and machine learning algorithms to predict incoming discharge to hydropower reservoirs. The user is ENEL Green Power Italy, and the testing environments are two water basins in Colombia. The study defines the expected value of perfect information, the expected value of the information currently used by the hydropower producer and the expected value of the service information. It then discusses pros and cons of the applicability of the method.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5007142
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