Hierarchy of climate and hydrological uncertainties in transient low-flow projections

Abstract

International audienceThis 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 stochas-tic 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 evap-otranspiration 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

Mapping (dis)agreement in hydrologic projections

“…Several other studies also identified snow processes as critical in hydrologic projections (e.g. Dobler et al, 2012;Vidal et al, 2016). These results provide a strong motivation to carefully test the snow conceptualization in hydrologic models before applying them in climate change impact studies.…”

“…The representation of underlying (physical) principles of hydrological processes in the hydrologic model can thus have a profound effect on the results and conclusion of a study. Although uncertainty in hydrologic projections has already been discussed and investigated in the literature, studies usually focus on one source of uncertainty (Gutmann et al, 2014) or a limited number of catchments (Vidal et al, 2016;Addor et al, 2014;Dobler et al, 2012). Here, we investigate three sources of uncertainty (GCM forcing, hydrologic parameters, hydrologic model structure) in hydrologic projections for 605 basins throughout the contiguous US over a wide range of climates, in order to reveal spatial patterns in uncertainty.…”

Assessing the relative effects of emissions, climate means, and variability on large water supply systems

“…Streamflow sequences were then used as input into water supply systems models to assess performance over time. Hydrologic model error is an additional source of uncertainty, with possible errors associated with the hydrologic model parameters, model structure, and prediction errors [ Steinschneider et al , ; Renard et al , ; Khan and Coulibaly , ; Hingray and Saïd , ; Vidal et al , ]. However, we do not address hydrologic model uncertainty because it is not expected to be significant in this case per conclusions of several earlier studies, particularly for nonextreme runoff quantiles [ Steinschneider et al , ; Dobler et al , ; Bosshard et al , ].…”

Climate Change Impacts and Water Management Adaptation in Two Mediterranean-Climate Watersheds: Learning from the Durance and Sacramento Rivers

“…However, dispersion within hydrological models is perceptible on the graphs, e.g., the CLSM model projects more substantial decrease in summer flows than the other models. This scattering stems from the difference in the evapotranspiration and snow pack components of the selected hydrological models [18]. The sign and the magnitude of the seasonal changes depend on the current river flow regime.…”