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Defining future climate conditions for a river basin in northeast Brazil: combining GCMs predictions, promoting scenario planning and embracing uncertainty

IWRA World Water Congress 2017 - Cancun Mexico
A. Bridging science and policy
Author(s): Layla Lambiasi
Alexandre Gross
Mário Monzoni

Layla Lambiasi
Center for Sustainability Studies (GVces) of the Business Administration School of Fundação Getulio Vargas (FGV-EAESP)
layla.lambiasi@fgv.br
Alexandre Gross
Center for Sustainability Studies (GVces) of the Business Administration School of Fundação Getulio Vargas (FGV-EAESP)
alexandre.gross@fgv.br
Mário Monzoni
Center for Sustainability Studies (GVces) of the Business Administration School of Fundação Getulio Vargas (FGV-EAESP)
mario.monzoni@fgv.br


Keyword(s): Northeast Brazli, GCMs, climate change, scenario planning, uncertainty
Article: Oral:

Abstract

The aim of the project was to define future climate scenarios considering possible changes in patterns and physical behaviors of a river basin located in Northeast Brazil. Although the intention seems common, its methodology has potential to represent a new understanding over scenario planning under the context of a changing climate. Once introducing a counterpoint to GCMs (General Circulation Models) when engaged in a practical application, the goal is to embrace uncertainty and promote its incorporation on climate risk management.

Far from the ambition of predicting the future, the challenge is to find an efficient set of scenarios that can represent possible and plausible situations and therefore perceive science in a more comprehensive way, placing the habitual predictive modeling closer to the decision-making process.

The region of interest is recognized as one of the most vulnerable in the country, being constantly threatened by rough droughts.  Besides that, the area itself already has to deal with its peculiar climate dynamics: all the rain drops between February and May in a way that the water supplies for the rest of the year depends primarily on this period. Therefore, the basin is mainly characterized as a system of 51 integrated reservoirs operating in order to maintain the water flow and storage.

Being of high national interest, the region is the focus of different researches. A previous study compiled IPCC’s 21 GCMs results of precipitation, evaporation and flow data. Such results were than analyzed using statistics methods to understand the main hydroclimate parameters to be replicated in order to achieve a contextualized and representative ensemble of future flow rates scenarios.

In a simplified way the methodology establishes, in this order, the following steps:

  1. Assume each simulation as an independent prediction,
  2. Mapping hydroclimate parameters (magnitude, frequency and distribution) according to five classes of events defined by percentiles ranges to overcome bias due to extreme values characteristic of the local flow series.
  3. Apart from absolute values, deal only with the predicted variation rates by each models
  4. Triage the models according to the majoritarian trend observed in each class of event among all models
  5. Chose the appropriate model that will be established as representative of each scenario of interest  
  6. Apply the variation rates predicted by the chosen models on historic flow data producing the estimated future values

Three scenarios came out from the process, each one representing a different possible future state, gathering a range of interesting contexts and comprehending many potential analyses. In a changing climate, relying upon just one GCM might be inefficient in the public policy sphere. Thus, to embrace uncertainty means not trying to have a better prediction of the future, but to be better prepared for whatever the future might be.

The process involving data acquisition, processing and application of GCMs is still unclear and inaccessible to most decision-makers. Trying another approach and use in a consistent way the results of GCMs can open a whole new perspective for this field in the public policy dimension.  

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