Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble

Niel, Jan De; Uytven, Els Van; Willems, Patrick

doi:10.1007/s11269-019-02370-0

Cited by 25 publications

(18 citation statements)

References 79 publications

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“…Although this study mainly concentrated on investigating how the uncertainty of climate projections is propagated in streamflow projections, there are other sources of uncertainty, like uncertainties originating from downscaling methods, hydrological model structures and hydrological parameters, etc. Chen et al (2011) and Meaurio et al (2017) found that downscaling methods might also have a large contribution to the uncertainty in peak-flow projections, as different types of downscaling methods might lead to significantly different extreme high flows, and uncertainty in simulated extreme low flows is also critically impacted by hydrological model structures as well as calibration strategies (De Niel et al, 2019;Vansteenkiste et al, 2014;Velá zquez et al, 2013). Therefore, to obtain a comprehensive insight into projected changes of high flows and low flows and the uncertainty therein, all sources of uncertainty arising from scenarios, climate models, internal climate variability, downscaling methods, hydrological models and hydrological parameters can be considered in future studies.…”

Section: Discussionmentioning

confidence: 99%

“…A question arising is how these uncertainty sources in climate projections will affect future streamflow projections? In recent years, different sources of uncertainty in streamflow projections have also been investigated (Bosshard et al, 2013;De Niel et al, 2019). Vetter et al (2016) assessed different uncertainty sources in projections of hydrological changes using four RCPs, five GCMs and nine hydrological models (HMs), and concluded that GCMs generally resulted in the largest uncertainty contribution, followed by RCPs and HMs.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

Gao

Booij

2020

Preprint

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Abstract. Projections of streamflow, particularly of extreme flows under climate change are essential for future water resources management and development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explore the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, East China. ANOVA (Analysis of Variance) is employed to quantify the contribution of different uncertainty sources from RCPs (Representative Concentration Pathways), GCMs (Global Climate Models) and internal climate variability, using an ensemble of four RCP scenarios, nine GCMs and 1,000 simulated realizations of each model-scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty is becoming more pronounced, particularly for low flows. The findings in this study can help water managers to get a better knowledge and understanding of streamflow projections and support decision making on adaptions to changing climate under uncertainty in the Qu River basin.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

Gao

Booij

2020

Preprint

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“…is cropland, 23% forest, 19% grass land, 19% urban and built-up area. Recently, (De Niel et al, 2018b) investigated the climate change impact on the catchments' hydrological extremes. They identified a minor uncertainty contribution by the hydrological models in the peak flow changes and therefore solely NAM, a lumped conceptual hydrological model for rainfall runoff simulation, is applied in this study (DHI, 2009;Nielsen and Hansen, 1973).…”

Section: Catchment Characteristics and Rainfall Runoff Modellingmentioning

confidence: 99%

“…To minimize that potential impact, our society opts for two complementary strategies: climate mitigation and adaptation (IPCC, 2014). Consequently, vulnerability, impact and adaptation studies find ground in our society (Alfieri et al, 2016;Åström et al, 2016;Brekke et al, 2009;De Niel et al, 2018b;Termonia et al, 2018;Vansteenkiste et al, 2014a;Vermuyten et al, 2018;Willems, 2013). These studies require projected hydro-meteorological time series, using the output of global climate models as primary information.…”

Section: Introductionmentioning

confidence: 99%

Uncovering the shortcomings of a weather typing based statistical downscaling method

Uytven

Niel

Willems

2019

Preprint

Self Cite

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Abstract. In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology. The skill is evaluated by the verification of the general statistical downscaling assumptions, and by the perfect predictor experiment that includes hydrological impact analysis. The approach has been tested for an advanced weather typing based SDM and for impact analysis on river peak flows in a Belgian river catchment. Significant shortcomings of the selected SDM were uncovered such as biases in the frequency of weather types and non-stationarities in the extreme precipitation distribution per weather type. Such evaluation of SDMs becomes of use for future tailoring of SDM ensembles to end user needs.

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“…CC BY 4.0 License. Van Uytven, 2019;De Niel et al, 2019;Hosseinzadehtalaei et al, 2020). The results of SDMs are, nevertheless, often compromised with bias and limitations due to assumptions and approximations made within each method (Trzaska and Schnarr, 2014;Maraun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Comparison of statistical downscaling methods for climate change impact analysis on drought

Tabari

Buekenhout

Willems

2020

Preprint

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Abstract. General circulation models (GCMs) are the primary tools to evaluate the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Besides the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small and large scale variables. This study compares four statistical downscaling methods (SDMs) of bias correction (BC), change factor of mean (CFM), quantile perturbation (QP) and event based weather generator (EBWG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000. A set of drought related aspects is analysed: dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 14-member ensemble of CMIP6 GCMs, each powered by four future scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability of the climate. While all methods show a good agreement on downscaling total precipitation, the CFM method fails to downscale dry day frequency well. The QP method outperforms the others in downscaling dry spells. Using this method, dry day frequency is projected to increase significantly in the summer months, with relative changes of up to 20.4 % in the worst-case climate change scenario. At the same time, total precipitation is projected to decrease significantly by up to 41.9 % in these months. Lastly, extreme dry spells are projected to increase in length by up to 7.4 %.

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Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble

Cited by 25 publications

References 79 publications

Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

Uncovering the shortcomings of a weather typing based statistical downscaling method

Comparison of statistical downscaling methods for climate change impact analysis on drought

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