The Case Studies

Paiva, Rodrigo Cauduro Dias de; Collischonn, Walter; Schettini, Edith Beatriz Camaño; Vidal, Jean‐Philippe; Hendrickx, F.; López, Ana María

doi:10.1002/9781444324921.ch6

Cited by 6 publications

(4 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results are in accordance with findings of other authors who suggest that the choice of the GCM is the largest quantified source of uncertainty in projected impacts of climate change on river flow (Bates et al, 2008;Kay et al, 2009;Blöschl and Montanari, 2010;Paiva and Collischonn, 2010).…”

Section: Discussionsupporting

confidence: 83%

Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil

Nóbrega

Collischonn

Tucci

et al. 2011

Hydrol. Earth Syst. Sci.

101

View full text Add to dashboard Cite

Abstract. We quantify uncertainty in the impacts of climate change on the discharge of Rio Grande, a major tributary of the Paraná River in South America and one of the most important basins in Brazil for water supply and hydroelectric power generation. We consider uncertainty in climate projections associated with the greenhouse-gas emission scenarios (A1b, A2, B1, B2) and increases in global mean air temperature of 1 to 6 • C for the HadCM3 GCM (Global Circulation Model) as well as uncertainties related to GCM structure. For the latter, multimodel runs using 6 GCMs (CCCMA CGCM31, CSIRO Mk30, IPSL CM4, MPI ECHAM5, NCAR CCSM30, UKMO HadGEM1) and HadCM3 as baseline, for a +2 • C increase in global mean temperature. Pattern-scaled GCM-outputs are applied to a large-scale hydrological model (MGB-IPH) of Rio Grande Basin. Based on simulations using HadCM3, mean annual river discharge increases, relative to the baseline or control run period , by +5% to +10% under the SRES emissions scenarios and from +8% to +51% with prescribed increases in global mean air temperature of between 1 and 6 • C. Substantial uncertainty in projected changes to mean river discharge (−28% to +13%) under the 2 • C warming scenario is, however, associated with the choice of GCM. We conclude that, in the case of Rio Grande Basin, the most important source of uncertainty derives from the GCM rather than the emission scenario or the magnitude of rise in mean global temperature.

show abstract

Section: Discussionsupporting

confidence: 83%

Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil

Nóbrega

Collischonn

Tucci

et al. 2011

Hydrol. Earth Syst. Sci.

101

View full text Add to dashboard Cite

show abstract

“…Indeed, the time transferability of HM parameters in a climate change context and its contribution to overall uncertainties has recently been explored by some studies (see e.g. Finger et al, 2012;Dobler et al, 2012;Parajka et al, 2016). One way to incorporate this source of uncertainty into the QE-ANOVA framework and combine it with HM structure uncertainty would be to devise a calibration protocol common to all HMs that would split the calibration period into distinct subperiods showing climatic contrasts, as proposed and applied by Thirel et al (2015).…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

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

Vidal¹,

Hingray

Magand

et al. 2016

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Sources Of Uncertaintymentioning

confidence: 99%

Hierarchy of climate and hydrological uncertainties in transient low flow projections

Vidal¹,

Hingray²,

Sauquet³

et al. 2015

Preprint

View full text Add to dashboard Cite

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. The QE-ANOVA framework 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 dataset of transient hydrological projections that combines in a comprehensive way 11 runs from 4 different GCMs, 3 SDMs with 10 stochastic realizations each, as well as 6 diverse HMs. The change signal is a decrease in yearly low flows of around −20 % in 2065, except for the most 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 on 30 year low-flow averages is however around 2035, i.e. for time slices starting in 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.

show abstract

The Case Studies

Cited by 6 publications

References 72 publications

Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil

Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil

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

Hierarchy of climate and hydrological uncertainties in transient low flow projections

Contact Info

Product

Resources

About