The role of cross-correlation between precipitation and temperature in basin-scale simulations of hydrologic variables

Seo, Seung Beom; Bhowmik, Rajarshi Das; Sankarasubramanian, A.; Mahinthakumar, G.; Kumar, Mukesh

doi:10.1016/j.jhydrol.2018.12.076

Cited by 25 publications

(15 citation statements)

References 36 publications

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“…First, the co-dependency between precipitation and temperature is considered for neither a synthetic probabilistic forecast generator nor the Bivar_update model for the sake of easy applicability. Seo et al [27] discussed that co-dependency between precipitation and temperature needed to be considered in hydrologic simulations. Nonetheless, co-dependency between precipitation and temperature is significant only for several months (e.g., winter and summer in South Korea), and it is not homogenous at a spatial level depending on climate regimes.…”

Section: Discussionmentioning

confidence: 99%

“…Shukla et al [26] also argued that the role of temperature plays an important role especially under drought conditions. It must be noted that cross-correlation between precipitation and temperature can potentially impact streamflow simulations [27]. Scheuerer et al [28] discussed that interdependency between precipitation and temperature must be reconstructed to physically generate realistic forecast trajectories.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

2020

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In order to enhance the streamflow forecast skill, seasonal/sub-seasonal streamflow forecasts can be post-processed by incorporating new information, such as climate signals. This study proposed a simple yet efficient approach, the “Bivar_update” model that utilizes bivariate climate forecast to update individual probabilities of the ensemble streamflow prediction. The Bayesian updating scheme is used to update the joint probability mass function derived from historic precipitation and temperature data sets. Thirty-five dam basins were used for the case study, and the modified Tank model was embedded into the ensemble streamflow prediction framework. The performance of the proposed approach was evaluated through a comparison with a reference streamflow forecast model, the “Univar_update” model, that reflects only precipitation forecast, in terms of deterministic and categorical streamflow forecast accuracy. For this purpose, multiple cases of probabilistic precipitation and temperature forecasts were synthetically generated. As a result, the Bivar_update model was able to decrease the errors in forecast under below-normal conditions. The improvements in forecasting skills were found for both measures; deterministic and categorical streamflow forecasts. Since the proposed Bivar_update model reflects both precipitation and temperature information, it can compensate low predictability especially under dry conditions in which the streamflow’s dependency on temperature increases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

2020

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“…Additionally, simulations/projections of various hydrologic fluxes (e.g., soil moisture and overland flow) rely on cross‐correlations among climate variables for preserving cross‐correlations across land‐surface fluxes. Hydrologic models forced with climate variables that have a bias in their cross‐correlation will result in a biased simulation of land‐surface attributes (Seo et al , ; Chen et al , ). Thus, the emphasis laid here on bias‐correcting GCM outputs could also potentially apply to bias‐correcting land‐surface models and semi‐distributed watershed model outputs (Libera and Sankarasubramanian, ).…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…Thus, the emphasis laid here on bias‐correcting GCM outputs could also potentially apply to bias‐correcting land‐surface models and semi‐distributed watershed model outputs (Libera and Sankarasubramanian, ). A recent study (Seo et al , ) considered two sets of monthly climate forcing to run long‐term simulation of hydrologic fluxes. One set of monthly forcing preserve the observed cross‐correlation, while the other set ignores it.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Limitations of univariate linear bias correction in yielding cross‐correlation between monthly precipitation and temperature

Bhowmik

Sankarasubramanian

2019

Intl Journal of Climatology

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Statistical bias correction techniques are commonly used in climate model projections to reduce systematic biases. Among the several bias correction techniques, univariate linear bias correction (e.g., quantile mapping) is the most popular, given its simplicity. Univariate linear bias correction can accurately reproduce the observed mean of a given climate variable. However, when performed separately on multiple variables, it does not yield the observed multivariate cross-correlation structure. In the current study, we consider the intrinsic properties of two candidate univariate linear bias-correction approaches (simple linear regression and asynchronous regression) in estimating the observed cross-correlation between precipitation and temperature. Two linear regression models are applied separately on both the observed and the projected variables. The analytical solution suggests that two candidate approaches simply reproduce the cross-correlation from the general circulation models (GCMs) in the biascorrected data set because of their linearity. Our study adopts two frameworks, based on the Fisher z-transformation and bootstrapping, to provide 95% lower and upper confidence limits (referred as the permissible bound) for the GCM cross-correlation. Beyond the permissible bound, raw/bias-corrected GCM cross-correlation significantly differs from those observed. Two frameworks are applied on three GCMs from the CMIP5 multimodel ensemble over the coterminous United States. We found that (a) the univariate linear techniques fail to reproduce the observed cross-correlation in the bias-corrected data set over 90% (30-50%) of the grid points where the multivariate skewness coefficient values are substantial (small) and statistically significant (statistically insignificant) from zero; (b) the performance of the univariate linear techniques under bootstrapping (Fisher z-transformation) remains uniform (non-uniform) across climate regions, months, and GCMs; (c) grid points, where the observed crosscorrelation is statistically significant, witness a failure fraction of around 0.2 (0.8) under the Fisher z-transformation (bootstrapping). The importance of reproducing crosscorrelations is also discussed along with an enquiry into the multivariate approaches that can potentially address the bias in yielding cross-correlations. K E Y W O R D S

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“…Räty et al (2018) compared the hydrological simulation of univariate quantile mapping corrected data with two multivariate methods (JBC and MBCn) corrected data over four watersheds and found that the additional benefit of using multivariate bias correction methods is not obvious, and only a slight improvement in simulating snow water equivalents is observed. Seo et al (2019) investigated the impacts of biased P ‐ T correlation on hydrological variables over two watersheds and found that the impacts of P ‐ T correlation are more evident on low flow and subsurface hydrological variables while less remarkable to flow variables with high variability. More recently, Meyer et al (2019) compared univariate quantile mapping and MBCn in simulating hydrological variables over two alpine catchments.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

Guo

Chen

Zhang

et al. 2020

Water Resources Research

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Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state-of-the-art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow-characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.

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The role of cross-correlation between precipitation and temperature in basin-scale simulations of hydrologic variables

Cited by 25 publications

References 36 publications

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

Limitations of univariate linear bias correction in yielding cross‐correlation between monthly precipitation and temperature

Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

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