Bias correction of climate models for hydrological modelling – are simple methods still useful?

Shrestha, Manish; Acharya, Suwash Chandra; Shrestha, Pallav Kumar

doi:10.1002/met.1655

Cited by 169 publications

(77 citation statements)

References 25 publications

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“…In general, linear scaling (LS) yielded better results for mean monthly flow simulations, followed by parametric (except QMS) and nonparametric quantile mapping techniques. Similar results have been reported over Kaligandaki River basin, Nepal (Shrestha et al ., ). The assessment of these individual bias correction techniques over the independent testing period also confirms an advantage of monthly over annual approach, as presented in Table S1, Supporting Information.…”

Section: Resultsmentioning

confidence: 97%

Assessment of rainfall bias correction techniques for improved hydrological simulation

Ghimire¹,

Srinivasan²,

Agarwal³

2018

Intl Journal of Climatology

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Eight rainfall bias correction techniques were compared over the Chindwin River basin in Myanmar to improve hydrological simulation at multiple timescales using two approaches, viz. monthly and annual. The techniques included linear scaling, parametric quantile mapping using linear, scale, power and exponential assymptotic transfer functions and nonparametric quantile mapping using empirical, robust regression and smoothing splines interpolation methods. Three global climate models (GCMs), wet, near-normal and dry in nature to estimate mean rainfall at the country and the basin scales were selected from a set of 13 GCMs. The rainfall bias correction factors for each GCM were generated from the control period 1981-1999 and verified over 2000-2005. Application of bias correction techniques resulted in reduction of biases and improved the flow simulations. These techniques showed better performance statistics in simulating daily, monthly and seasonal flows under the monthly approach, where correction factors were generated and applied separately for different months. The inconsistencies in magnitude and seasonality of flows were addressed under the monthly approach while only the biases related to magnitude were corrected under the annual approach. Linear scaling followed by parametric (linear and power transformation) and nonparametric empirical quantile mapping methods yielded a very good hydrological performance at all temporal scales when applied under the monthly approach. Parametric quantile mapping with scaling function yielded least efficiency under the annual approach for all temporal scales. These results are expected to be valid for other river basins in the region showing similar strong rainfall seasonality. K E Y W O R D Sbias correction, Chindwin River basin, climate models, ensemble, improved hydrological simulation, Myanmar

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Section: Resultsmentioning

confidence: 97%

Assessment of rainfall bias correction techniques for improved hydrological simulation

Ghimire¹,

Srinivasan²,

Agarwal³

2018

Intl Journal of Climatology

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“…Similar results were obtained by Teutschbein and Seibert [69] in their evaluation of several bias correction methods over five Swedish catchments using 15 different RCMs driven by ERA40, where LS resulted in slightly larger model error growth and deviations compared to more advanced correction methods such as QM, which makes LS the least reliable method under changed conditions. In contrast, Shrestha et al [110] found no significant differences between the performances of LS and EQM bias corrected outputs for the Kaligandaki River Basin in Nepal. Such discrepancies however, are most likely related to substantial climatic differences between the Himalayas, Sweden and the Central-American tropics.…”

Section: Sonmentioning

confidence: 87%

“…The full amount of these complex factors could also explain why DT, being simpler than the remaining BC methods, normally exhibits better performances than more complex methods such as EQM or GPQM for the North and Caribbean regions, which could also be limited to monthly temporal resolution data as suggested by Yang et al [47], Shrestha et al [110] and Hanel et al [111]. Nevertheless, the diminished GCM-RCM model performances in terms of R and PBIAS does not necessarily indicate severe model biases but rather shows that GCM-RCM pairs cannot properly reproduce JJA monthly deviations within the annual precipitation distribution cycle.…”

Section: Jjamentioning

confidence: 99%

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Mendez

Maathuis

Hein-Griggs

et al. 2020

Water

138

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Six bias correction (BC) methods; delta-method (DT), linear scaling (LS), power transformation of precipitation (PTR), empirical quantile mapping (EQM), gamma quantile mapping (GQM) and gamma-pareto quantile mapping (GPQM) were applied to adjust the biases of historical monthly precipitation outputs from five General Circulation Models (GCMs) dynamically downscaled by two Regional Climate Models (RCMs) for a total of seven different GCM-RCM pairs over Costa Rica. High-resolution gridded precipitation observations were used for the control period 1951-1980 and validated over the period 1981-1995. Results show that considerable biases exist between uncorrected GCM-RCM outputs and observations, which largely depend on GCM-RCM pair, seasonality, climatic region and spatial resolution. After the application of bias correction, substantial biases reductions and comparable performances among most BC methods were observed for most GCM-RCM pairs; with EQM and DT marginally outperforming the remaining methods. Consequently, EQM and DT were selectively applied to correct the biases of precipitation projections from each individual GCM-RCM pair for a near-future (2011-2040), mid-future (2041-2070) and far-future (2071-2100) period under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the control period . Results from the bias-corrected future ensemble-mean anticipate a marked decreasing trend in precipitation from near to far-future periods during the dry season (December, January, February (DJF) and March, April, May (MAM)) for RCP4.5 and 8.5; with pronounced drier conditions for those climatic regions draining towards the Pacific Ocean. In contrast, mostly wetter conditions are expected during the dry season under RCP2.6, particularly for the Caribbean region. In most of the country, the greatest decrease in precipitation is projected at the beginning of the rainy season (June, July, August (JJA)) for the far-future period under RCP8.5, except for the Caribbean region where mostly wetter conditions are anticipated. Regardless of future period, slight increases in precipitation with higher radiative forcing are expected for SON excluding the Caribbean region, where precipitation is likely to increase with increasing radiative forcing and future period. This study demonstrates that bias correction should be considered before direct application of GCM-RCM precipitation projections over complex territories such as Costa Rica.

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“…For example, previous studies examining the performance of the Weather Research and Forecasting (WRF) model at high resolution in the HKKH region have typically focused on either one small river catchment or over larger regions that considered only a limited number of in situ measurements for relatively short periods of time of 1 year or less (Bonekamp et al, ; Collier & Immerzeel, ; Li et al, ; Maussion et al, ; Norris et al, ; Orr et al, ), therefore failing to fully assess the model's ability to reproduce the actual detailed patterns of precipitation. Furthermore, though multiple studies have shown the importance of bias correction of precipitation over topographically complex regions (e.g., Bordoy & Burlando, ; Lafon et al, ; Teutschbein & Seibert, ), the usefulness of such methods over the HKKH has yet to be conclusively proven (Shrestha et al, ).…”

Section: Introductionmentioning

confidence: 99%

Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments

et al. 2019

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The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5‐km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.

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Bias correction of climate models for hydrological modelling – are simple methods still useful?

Cited by 169 publications

References 25 publications

Assessment of rainfall bias correction techniques for improved hydrological simulation

Assessment of rainfall bias correction techniques for improved hydrological simulation

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments

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