Intercomparison of Univariate and Joint Bias Correction Methods in Changing Climate From a Hydrological Perspective

Räty, Olle; Räisänen, Jouni; Bosshard, Thomas; Donnelly, Chantal

doi:10.3390/cli6020033

Cited by 35 publications

(43 citation statements)

References 40 publications

(70 reference statements)

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“…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. retaining present-day correlation structures.…”

Section: Discussionmentioning

confidence: 88%

“…An advantage of using a bivariate bias correction approach was not evident for the coldest snow- dominated catchment of the sample though. According to Räty et al (2018) their hydrological simulations did not substantially benefit from bivariate bias correction approaches, whereas simulations of high flows and snow water equivalents in snow-influenced catchments improved slightly in comparison to simulations using univariate-corrected climate model data.…”

Section: Introductionmentioning

confidence: 93%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: 88%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…In addition, dOTC proposes a definition of nonstationarity, and explicitly gives what the correction corresponds to (the evolution of the model applied to observations). In the particular case of the temperatures/precipitation correction, compared to, e.g., Piani and Haerter (2012) and Räty et al (2018), the correction is at least as good during the calibration period, although the comparison is not done over the projection period, because the indicators are different.…”

Section: Discussionmentioning

confidence: 99%

Multivariate stochastic bias corrections with optimal transport

Robin

Vrac

Naveau

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. Bias correction methods are used to calibrate climate model outputs with respect to observational records. The goal is to ensure that statistical features (such as means and variances) of climate simulations are coherent with observations. In this article, a multivariate stochastic bias correction method is developed based on optimal transport. Bias correction methods are usually defined as transfer functions between random variables. We show that such transfer functions induce a joint probability distribution between the biased random variable and its correction. The optimal transport theory allows us to construct a joint distribution that minimizes an energy spent in bias correction. This extends the classical univariate quantile mapping techniques in the multivariate case. We also propose a definition of non-stationary bias correction as a transfer of the model to the observational world, and we extend our method in this context. Those methodologies are first tested on an idealized chaotic system with three variables. In those controlled experiments, the correlations between variables appear almost perfectly corrected by our method, as opposed to a univariate correction. Our methodology is also tested on daily precipitation and temperatures over 12 locations in southern France. The correction of the inter-variable and inter-site structures of temperatures and precipitation appears in agreement with the multi-dimensional evolution of the model, hence satisfying our suggested definition of non-stationarity.

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“…To make an initial assessment, one multivariate method is chosen to be combined with the different occurrence-bias-adjusting techniques, namely the Multivariate Bias Correction in n dimensions (MBCn) method (Cannon, 2018). This method is chosen 75 as it is one of the more commonly implemented multivariate bias-adjusting methods (Räty et al, 2018;Meyer et al, 2019;Zscheischler et al, 2019). Additionally, it only differs from the QDM method in the final step, where a rank-based shuffling is applied.…”

Section: Introductionmentioning

confidence: 99%

Comparison of occurrence-bias-adjusting methods for hydrological impact modelling

Velde

Baets

Demuzere

et al. 2020

Preprint

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Abstract. Over the past decade, various methods for bias adjustment of precipitation occurrence or intensity have been proposed. However, the performance of combined methods has not yet been thoroughly evaluated, especially in a hydrological and climate change context. In this study, four occurrence-bias-adjusting methods are combined with one univariate and one multivariate intensity-bias-adjusting method. The occurrence-bias-adjusting methods include thresholding, Stochastic Singularity Removal, Triangular Distribution Adjustment, and are compared with the intensity-bias-adjusting methods without specific adjustment as a baseline. These combined methods are compared with respect to precipitation amount, precipitation occurrence and discharge. This comparison, summarized in terms of the residual bias relative to both the observations and the model bias,shows significant differences in performance. Occurrence-bias-adjusting methods that add stochasticity perform worse, an effect that is reinforced by multivariate intensity-bias-adjusting methods. The use of simpler methods is thus advised until the uncertainty caused by combining methods is better understood.

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Intercomparison of Univariate and Joint Bias Correction Methods in Changing Climate From a Hydrological Perspective

Cited by 35 publications

References 40 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

Multivariate stochastic bias corrections with optimal transport

Comparison of occurrence-bias-adjusting methods for hydrological impact modelling

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