Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods
read more
Citations
Investigating the Accuracies in Short-Term Weather Forecasts and Its Impact on Irrigation Practices
An improved statistical bias correction method that also corrects dry climate models
Variations in Projections of Precipitations of CMIP6 Global Climate Models under SSP 2–45 and SSP 5–85
Evaluation of Regional Climate Models (RCMs) Using Precipitation and Temperature-Based Climatic Indices: A Case Study of Florida, USA
Modelling urban stormwater management changes using SWMM and convection-permitting climate simulations in cold areas
References
Numerical Recipes in C: The Art of Scientific Computing
Climate change and water.
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling
Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs
Related Papers (5)
Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods
Statistical bias correction for daily precipitation in regional climate models over Europe.
Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user
Frequently Asked Questions (17)
Q2. What is the key challenge in assessing the vulnerability of hydrological systems to climate change?
Quantifying the effects of future changes in the frequency of daily precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change.
Q3. What are the main downscaling techniques used in the literature?
Downscaling techniques that have been reviewed in the literature include statistical downscaling, which uses empirical relations between climate model outputs and historical observed data, and dynamical downscaling, which involves the use of a Regional Climate Model (RCM) (see Fowler et al., 2007 for a detailed review).
Q4. What is the importance of correcting for biases in climate model output?
When correcting for biases in climate model output, it is also important that changes in the frequency distribution of climatic variables are correctly represented.
Q5. How was the comprehensive correction achieved?
The most comprehensive correction was achieved by using the empirical quantile-mapping methods, which incorporate information from the frequency distributions of modelled and observed precipitation.
Q6. Why do RCMs provide a more physically-realistic approach to downscaling?
RCMs offer a more physically-realistic approach to GCM downscaling than statistical downscaling because they provide an explicit representation of the mesoscale atmospheric processes that produce heavy precipitation.
Q7. Why is the RCM important in producing realistic forcing data for hydrological models?
This property of RCM simulations is important in producing realistic forcing data for hydrological models because many floods and droughts are caused by spatially- and temporally-persistent precipitation patterns.
Q8. What is the effect of the calibration period on the results?
This suggests that, while the greatest accuracy is achieved by an empirical distribution method defined by at least 25 quantiles (i.e., the overall error from the same calibration-evaluation period is smallest), results are also most sensitive to the chosen calibration period.
Q9. What is the potential to over-calibrate the bias correction procedure to aparticular set?
At the same time, the potential to over-calibrate the bias-correction procedure to aparticular set of reference data increases as more and more observed data are used to calculate the correction parameters.
Q10. What are the techniques to correct the biases in the climate model outputs?
Techniques to correct the biases in the climate model outputs are therefore used to improve the realism of GCM/RCM precipitation time series, based on statistical properties obtained from observed data taken from the same baseline period.
Q11. What is the highest frequency of error reduction achieved by the linear method?
For the skewness and kurtosis, the highest frequency of error reduction is achieved by the linear method (61% and 62%, respectively), while the lowest frequency is obtained using the empirical distribution method with 50 quantiles (24% and 18%, respectively).
Q12. How is the accuracy of the bias correction technique determined?
the effectiveness of bias-correction was found to be sensitive to the time-period for which the bias-correction procedures have been calibrated.
Q13. How often does the frequency of error reduction decrease for skewness and kurtos?
For the higher moments, the frequency of error reduction further decreases to 11% (gamma distribution and linear methods) and 6% (empirical distribution method with 25 quantiles) for skewness, and to 11% (linear method and empirical distribution method with 75 and 100 quantiles) methods to 7% (non-linear method) for kurtosis.
Q14. What is the combination of accuracy and robustness of a method?
the correction method based on a gamma distribution offers the best combination of accuracy and robustness, but it is valid only when the observed and modelled precipitation data are gamma distributed.
Q15. What is the common approach to generate synthetic precipitation time series?
Another approach is to generate synthetic precipitation time series using a stochastic weather generator, where the parameters in the generator are changed according to estimated changes in the climate from the GCM/RCM outputs (e.g. Kilsby et al., 2007, Fatichi et al., 2011).
Q16. How many quantile divisions are used to evaluate the robustness of the correction procedure?
To evaluate the robustness of the correction procedure, the authors calculated the average of the absolute value of the relative differences (ARD, defined as | | ⁄ , where X and X’ are statistics from observed and bias-corrected precipitation, respectively) between the N-m+1 sets of corrected and observed precipitation data over the m-year period that was not used to calibrate the bias-correction method.
Q17. How does the robustness of the methods for the six remaining catchments be assessed?
The robustness of the methods for the six remaining catchments is assessed by considering how the performance of each correction method varies with location and climatic characteristics using the methodology described above.