Quantifying uncertainty sources in an ensemble of hydrological climate‐impact projections

Abstract

[1] The quantification of uncertainties in projections of climate impacts on river streamflow is highly important for climate adaptation purposes. In this study, we present a methodology to separate uncertainties arising from the climate model (CM), the statistical postprocessing (PP) scheme, and the hydrological model (HM). We analyzed ensemble projections of hydrological changes in the Alpine Rhine (Eastern Switzerland) for the near-term and farterm scenario periods 2024-2050 and 2073-2099 with respect to 1964-1990. For the latter scenario period, the model ensemble projects a decrease of daily mean runoff in summer (À32.2%, range [À45.5% to À8.1%]) and an increase in winter (þ41.8%, range [þ4.8% to þ81.7%]). We applied an analysis of variance model combined with a subsampling procedure to assess the importance of different uncertainty sources. The CMs generally are the dominant source in summer and autumn, whereas, in winter and spring, the uncertainties due to the HMs and the statistical PP gain importance and even partly dominate. In addition, results show that the individual uncertainties from the three components are not additive. Rather, the associated interactions among the CM, the statistical PP scheme, and the HM account for about 5%-40% of the total ensemble uncertainty. The results indicate, in distinction to some previous studies, that none of the investigated uncertainty sources are negligible, and some of the uncertainty is not attributable to individual modeling chain components but rather depends upon interactions.Citation: Bosshard, T., M. Carambia, K. Goergen, S. Kotlarski, P. Krahe, M. Zappa, and C. Sch€ ar (2013), Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections, Water Resour. Res., 49, 1523Res., 49, -1536

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

“…Major uncertainties associated with estimating future floods are related to the effects and interactions of the following components in the model chain (e.g., Bosshard et al, 2013): (1) climate model projections; (2) downscaling/bias correction techniques; and (3) hydrological model uncertainties in the region studied.…”

“…Dankers and Feyen (2008) and Hirabayashi et al (2008) indicated a decrease in water flow, whereas Lehner et al (2006) suggested an increase, and Arheimer et al (2012) projected very little overall change in water discharge to the Baltic Sea. Discrepancies between conclusions regarding the future can arise due to uncertainties in GCMs, downscaling methods, or hydrological models (e.g., Bosshard et al, 2013;Donnelly et al, 2014;Hall et al, 2014).…”

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

“…ANOVA could underestimate variance when the sample size is small (Bosshard et al, 2013). To reduce the effect of the sample size, Bosshard et al (2013) proposed a subsampling method, which was used in this paper.…”

“…To reduce the effect of the sample size, Bosshard et al (2013) proposed a subsampling method, which was used in this paper. In the subsampling method, the superscript j in Eq.…”

“…(7) is replaced with g(h, i). According to Bosshard et al (2013), in each subsampling iteration i, data from two products should be selected out of all the six products, and thus 15 combinations can be obtained. Therefore, the superscript g becomes a 2 × 15 matrix:…”