Quantifying different sources of uncertainty in hydrological projections in an Alpine watershed

C. Dobler,R. L. Wilby,S. Hagemann,J. Stötter

doi:10.5194/hess-16-4343-2012

C. Dobler, R. L. Wilby + Show 2 more

Open Access

https://doi.org/10.5194/hess-16-4343-2012

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Abstract

Abstract. Many studies have investigated potential climate change impacts on regional hydrology; less attention has been given to the components of uncertainty that affect these scenarios. This study quantifies uncertainties resulting from (i) General Circulation Models (GCMs), (ii) Regional Climate Models (RCMs), (iii) bias-correction of RCMs, and (iv) hydrological model parameterization using a multi-model framework. This consists of three GCMs, three RCMs, three bias-correction techniques, and sets of hydrological model parameters. The study is performed for the Lech watershed (~ 1000 km2), located in the Northern Limestone Alps, Austria. Bias-corrected climate data are used to drive the hydrological model HQsim to simulate runoff under present (1971–2000) and future (2070–2099) climate conditions. Hydrological model parameter uncertainty is assessed by Monte Carlo sampling. The model chain is found to perform well under present climate conditions. However, hydrological projections are associated with high uncertainty, mainly due to the choice of GCM and RCM. Uncertainty due to bias-correction is found to have greatest influence on projections of extreme river flows, and the choice of method(s) is an important consideration in snowmelt systems. Overall, hydrological model parameterization is least important. The study also demonstrates how an improved understanding of the physical processes governing future river flows can help focus attention on the scientifically tractable elements of the uncertainty.

Highlights

The global climate has changed during recent decades and there is high confidence that this is partly due to human activity (Oreskes, 2004; Solomon et al, 2007; Jones et al, 2008; Rosenzweig et al, 2008)
Most climate change impact studies are based on a modelling chain consisting of (i) General Circulation Models (GCMs), (ii) Regional Climate Models (RCMs), (iii) bias-correction techniques, and (iv) an impact model such as a hydrological model
A large number of studies are based on this kind of approach, relatively little attention has been given to assessing uncertainty in the hydrological projections

Summary

Introduction

The global climate has changed during recent decades and there is high confidence that this is partly due to human activity (Oreskes, 2004; Solomon et al, 2007; Jones et al, 2008; Rosenzweig et al, 2008). General Circulation Models (GCMs) are the most favoured tools for assessing climate change. These models represent major Earth system components including atmosphere, oceans, land surface and sea ice. GCMs operate on a global to continental scale and, are unable to resolve regional climate effects. Dynamical and statistical downscaling is used to generate climate information at finer spatial resolutions. Dynamical downscaling includes Regional Climate Models (RCMs) which are nested within the domain of a GCM over a region of interest (Giorgi et al, 1990; Giorgi and Mearns, 1999). RCMs use GCM output as initial and lateral boundary conditions and can generate climate information at resolutions as fine as 7 km (Pavlik et al, 2012). Comprehensive reviews of downscaling methods are provided elsewhere (e.g. Fowler et al, 2007; Maraun et al, 2010; Wilby et al, 2009)

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