Abstract

Abstract. This paper proposes a methodology for estimating the transient probability distribution of yearly hydrological variables conditional to an ensemble of projections built from multiple general circulation models (GCMs), multiple statistical downscaling methods (SDMs), and multiple hydrological models (HMs). The methodology is based on the quasi-ergodic analysis of variance (QE-ANOVA) framework that allows quantifying the contributions of the different sources of total uncertainty, by critically taking account of large-scale internal variability stemming from the transient evolution of multiple GCM runs, and of small-scale internal variability derived from multiple realizations of stochastic SDMs. This framework thus allows deriving a hierarchy of climate and hydrological uncertainties, which depends on the time horizon considered. It was initially developed for long-term climate averages and is here extended jointly to (1) yearly anomalies and (2) low-flow variables. It is applied to better understand possible transient futures of both winter and summer low flows for two snow-influenced catchments in the southern French Alps. The analysis takes advantage of a very large data set of transient hydrological projections that combines in a comprehensive way 11 runs from four different GCMs, three SDMs with 10 stochastic realizations each, as well as six diverse HMs. The change signal is a decrease in yearly low flows of around −20 % in 2065, except for the more elevated catchment in winter where low flows barely decrease. This signal is largely masked by both large- and small-scale internal variability, even in 2065. The time of emergence of the change signal is however detected for low-flow averages over 30-year time slices starting as early as 2020. The most striking result is that a large part of the total uncertainty – and a higher one than that due to the GCMs – stems from the difference in HM responses. An analysis of the origin of this substantial divergence in HM responses for both catchments and in both seasons suggests that both evapotranspiration and snowpack components of HMs should be carefully checked for their robustness in a changed climate in order to provide reliable outputs for informing water resource adaptation strategies.

Highlights

  • Incorporating global change in long-term water resource planning, water management, and water governance is a major issue water managers currently have to face

  • Looking first at this grand ensemble mean, low flows are projected to decrease in both catchments and in both seasons

  • The dispersion between general circulation models (GCMs) effects around the grand ensemble mean is quite large in winter, leading to changes ranging, for example, from −20 to +2 % for the Durance in 2065

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Summary

Introduction

Incorporating global change in long-term water resource planning, water management, and water governance is a major issue water managers currently have to face (see e.g. Clarvis et al, 2014; Bréthaut and Hill Clarvis, 2015). Christierson et al, 2012; Chauveau et al, 2013) In this context, a water manager with some degree of awareness in potential climate change impact studies is entitled to ask the following question, relevant for long-term planning: for a given year in the future, what will be the probability of having a low-flow value lower than a given baseline? In order to answer the water manager question, one should address four different scientific issues: (1) computing future hydrological changes, (2) generating a transient evolution of those changes, (3) disentangling the hydrological change signal from effects of natural/internal climate variability, and (4) focusing on the lower part of the streamflow distribution. The following paragraphs propose a brief review of how the issues listed above have been tackled in the literature

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