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Summary: The overall goal of the present study is to show through a simulation experiment the value of adaptive management under climatic variability and change. The Northern California water resources management system is used as an example of a multi-agency, multi-site, multi-objective water resources management system that supports a trillion dollar economy to demonstrate the utility of the methods and to exemplify the assessment studies. Simulations are performed using an adaptation of the Integrated Forecast and Reservoir Management (INFORM) system of Northern California with 6-hourly 1.4° input from the CCSM3.0 climate model for historical/control (1970–2019) and future periods (2050–2099). The present paper examines the assessment of the hydroclimatological sensitivities and a companion paper () examines the assessment of reservoir management sensitivities. The future period was based on the moderate CCSM3.0 A1B scenario. The INFORM system includes an intermediate complexity regional model for dynamic downscaling of the CCSM3.0 scenario output to produce surface precipitation and temperature fields with a 10×10km2 resolution, and snow–soil-channel modeling of all the watersheds upstream of the major reservoirs in Northern California. The flow results indicate a shift in the monthly average flow volume toward earlier times in the year and higher flow variability for the future period. Higher average temperatures in the future period and, consequently, earlier snow pack melt are mainly responsible for these flow changes. Both daytime and nighttime average monthly temperatures are higher in the simulation of the future period for the entire domain with the northern catchments experiencing higher temperature increases. Simulated monthly average precipitation for the future period is higher in the southern high Sierras and lower in the northern drainage basins than the historical period precipitation. The uncertainty in the assessments for Northern California mentioned may be estimated with additional simulations similar to those shown in this work using high spatial and temporal resolution output from different climate models. [ABSTRACT FROM AUTHOR]

Summary: Water companies in the United Kingdom have considered climate change in their water resources plans for more than a decade through studies funded by UK Water Industry Research (UKWIR). This paper presents an initial assessment of the impact of the UK Climate Projections 2009 (UKCP09) on river flows at a national scale for the 2020s under the A1B scenario and the implications for water resource planning. A daily hydrological modelling framework based on two conceptual model structures and the Generalized Likelihood Uncertainty Estimation (GLUE) methodology has been applied to 70 catchments across the UK. A Latin Hypercube Sampling approach was adopted to handle the probabilistic nature of UKCP09. Results show a decrease in mean annual flow over most of the UK, with negative median values of all monthly changes except in winter over the western and northern mountainous areas. Furthermore the results indicate a high likelihood of a significant decline in summer flows. An analysis of variance shows that the main uncertainty in river flow changes comes from the spread in climate projections. Finally results are found to be consistent with a previous UKWIR assessment based on individual projections from 6 Global Climate Models (GCMs) under the A2 scenario. The reduction in summer low-flows, critical for water resources, appears however more limited with UKCP09. Although most expected changes are within natural variability, the drier conditions overall and the greater spread of results with respect to previous assessments indicates a need for testing the robustness of water resource plans. [Copyright &y& Elsevier]

• CIRFs are useful for exploring risks of system failure under climate change. • Synthetic streamflow series are generated using SA and linked to climate conditions. • Logistic regression relates system responses to climate drivers. • System failure defined according to performance (reliability) and threshold. • LR model detects climate conditions which would require adaptation. 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. [ABSTRACT FROM AUTHOR]