[EN] Climate Impact Response Functions (CIRFs) can be useful for exploring potential risks of system failure under climate change. The performance of a water resource system can be synthesized through a CIRF that relates climate conditions to system behavior in terms of a specified threshold of deliveries to demands or environmental flow requirements. However, in highly regulated water resource systems this relationship may be quite complex, depending on storage capacity and system operation. In this paper we define a CIRF for these types of systems through a multivariable logistic regression (LR) model where a binary variable (system response) is explained by two continuous variables or predictors (precipitation and temperature). The approach involves generating multivariate synthetic inflow time series and relating them to specific climate conditions. Next, these inflows are used as inputs in a water management model, and the outcome is coded as a binary variable (failure or its absence) depending on selected vulnerability criteria. To identify the time span before the failure event in which climate variables are relevant, we characterized drought development stages through relative standardized indices. Mean values of precipitation and temperature for the selected time span are computed and used as explanatory variables through a LR model, which is validated using data from several climate models and scenarios. Results show that the predictive capacity of LR models is acceptable, so that they could be used as screening tools to detect challenging climate conditions for the system which would require adaption actions.

This study has been supported by the IMPADAPT project (CGL2013-48424-C2-1-R), funded with Spanish MINECO (Ministerio de Economia y Competitividad) and European FEDER funds, and for the earlier ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Ciencia, Innovacion y Universidades (MICINN) of Spain. Patricia Marcos-Garcia has been also supported by a FPI grant from the PhD Training Program (BES-2014-070490) of the former MINECO. The authors thank AEMET (Spanish Meteorological Office) and University of Cantabria for the data provided for this work (dataset Spain02).