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The Iberian Peninsula is a climate change hotspot, where the temperature is increasing faster than the global annual mean surface temperature, with the largest reduction of precipitation. Consequently, freshwater availability is expected to decrease substantially. In this context, freshwater systems are especially vulnerable in terms of meeting the water demands and ecosystem requirements we know today. In this paper, we present an extension of the eco-engineering decision scaling (EEDS) method to explore trade-offs in agricultural and ecologic metrics at the catchment scale across a range of unknown future hydrological and climate states. The extended EEDS method evaluates current water resource management rules focusing on agricultural and ecologic objectives, identifies climate hazards that make the system fail and assesses climate risk in three time horizons for the design of adaptation measures. The case study is the Serpis River basin, Spain, where 72% of available water is used for agricultural purposes. [ABSTRACT FROM AUTHOR]

This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a "proof of concept" that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework. [ABSTRACT FROM AUTHOR]