Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

Chen, Jie; Ran, Xiangbin; Brissette, François; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

doi:10.1016/j.jhydrol.2018.03.040

Cited by 65 publications

(57 citation statements)

References 44 publications

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“…So far, few studies have evaluated multivariate-corrected GCM-RCM data in hydrological modelling. Chen et al (2018) found that the joint bias correction of precipitation and air temperature led to a much better performance in terms of hydrological modelling for all their study basins located in various climates except for the coldest Canadian basin. In contrast, an overall additional benefit of using bivariate bias correction methods for hydrological impact projections was not evident in results by 15 Räty et al (2018) when compared to using a univariate quantile mapping applied as a delta change method, i.e.…”

Section: Discussionmentioning

confidence: 96%

“…So far, there have been only few studies (Räty et al, 2018;Chen et al, 2018) that investigated the effect of using a multivariate bias correction technique on 30 hydrological projections. Chen et al (2018) found that jointly corrected precipitation and air temperature data better modelled eleven out of twelve catchments in the calibration period than the meteorological data that was corrected based on a univariate method. An advantage of using a bivariate bias correction approach was not evident for the coldest snow- dominated catchment of the sample though.…”

Section: Introductionmentioning

confidence: 99%

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Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Meyer¹,

Kohn²,

Stahl³

et al. 2018

Preprint

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Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing 10 meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of individual variables. However, univariate correction neglects the relationships that exist between climate variables. In this study glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2100. These simulations were compared to those obtained by using the common univariate quantile 15 mapping for bias correction. As both methods correct each climate variable's distribution in the same way, the marginal distributions of the individual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach lead to more precipitation below air temperatures of 0 °C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the 20 catchments. The multivariate bias correction forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates in the future. For the historical period the fraction of precipitation above and below 0 °C, the simulated snow water equivalents, glacier volumes, and the streamflow regime resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches 25 may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow concluding definitively that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies. 30Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Meyer¹,

Kohn²,

Stahl³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Cannon (2016Cannon ( , 2018a) also demonstrated better results for multivariate-corrected data in other examples, including fire weather indices and atmospheric river detection. In practice, some kind of bias correction is needed for many impact studies, although it is known that recent literature is rich in controversial debate of its use and major limitations of the application of empirical-statistical bias correction methods (e.g., Ehret et al, 2012;Maraun, 2013Maraun, , 2016Clark et al, 2016;Maraun et al, 2017;Casanueva et al, 2018;Zscheischler et al, 2019). Some of the fundamental issues, the details of which are beyond the scope of this study, are shared with univariate bias correction, for example, the question of stationarity (regarding biases in marginal distributions).…”

Section: Discussionmentioning

confidence: 99%

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Meyer

Kohn

Stahl

et al. 2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of individual variables, neglecting the relationships that exist between climate variables. In this study we test the hypothesis that the explicit consideration of the relation between air temperature and precipitation will affect hydrological impact modelling in a snow-dominated mountain environment. Glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2099. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable's distribution in the same way, the marginal distributions of the individual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach led to more precipitation below air temperatures of 0 ∘C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias-correction-forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates. In all aspects – the fraction of precipitation above and below 0 ∘C, the simulated snow water equivalents, glacier volumes, and the streamflow regime – simulations resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow conclusive evidence that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies in snow-dominated environments.

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“…Correlation values are 0.51, 0.58 and 0.50 respectively for May, June, and October. Note that, we present the result for the validation period when the performance of the model depends more on the watershed information than climate inputs [35]. Figure 3a shows the time series of model-simulated and observed streamflow.…”

Section: Performance Of Training Datasetmentioning

confidence: 99%

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

Bhowmik

Seo

Sahoo³

2018

Water

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Statistical models for hydrologic simulation are a common choice among researchers particularly when catchment information is limited. In this study, we adopt a new statistical approach, namely Bayesian regression with multivariate linear spline (BMLS) for long-term simulation of streamflow on a Hydroclimate Data Network (HCDN) site in the United States. The study aims to: (i) evaluate the performance of the BMLS model; (ii) compare the performance of climate model outputs as predictors in hydrologic simulation; and (iii) estimate the changes in streamflow caused by anthropogenic climate change which is defined as the projected change in precipitation and temperature under different greenhouse gas emission scenarios. Performance of the BMLS model is compared with climatology for the validation period. Results suggest that the BMLS model forced with observed monthly precipitation and average temperature exhibits information that is not presented in the climatology of the validation period. Later, we consider Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and hindcast runs to simulate streamflow at the HCDN site. The study found that sea-surface temperature-initialized decadal hindcast runs are performing no better than 20th century historical runs regarding hydrologic simulation. Finally, the changes in mean and variability in streamflow at the HCDN site are estimated by forcing the model with CMIP5 future projections for the period 2000-2049.

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Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

Cited by 65 publications

References 44 publications

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

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