2020
DOI: 10.3390/cli8100108
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Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios

Abstract: Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction metho… Show more

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Cited by 25 publications
(13 citation statements)
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“…For this reason, runoff derived from RCM results and from PCR-GLOBWB was corrected for bias in each location. The chosen method for bias correction was linear scaling [45]. This method is justified by data availability, because GRDC only provides monthly long-term means of runoff.…”
Section: Current and Future Runoff Scenariosmentioning
confidence: 99%
“…For this reason, runoff derived from RCM results and from PCR-GLOBWB was corrected for bias in each location. The chosen method for bias correction was linear scaling [45]. This method is justified by data availability, because GRDC only provides monthly long-term means of runoff.…”
Section: Current and Future Runoff Scenariosmentioning
confidence: 99%
“…Generally, GCM or RCM modeled data are not directly used because of their biasness as natural phenomena cannot be predicted accurately. Furthermore, this uncertainty and biasness were developed during the advancement of circulation models by scientists due to lack of an absolute and concrete idea about the nature of atmospheric circulation (Tan et al 2020). All these facts deduce that GCM models may not estimate climate variables in the necessary way to reduce the differences, and there is always a deviation between observed and simulated climate variables even if the difference is insignificant.…”
Section: Introductionmentioning
confidence: 99%
“…All these facts deduce that GCM models may not estimate climate variables in the necessary way to reduce the differences, and there is always a deviation between observed and simulated climate variables even if the difference is insignificant. Therefore, it is very important to count on bias correction methods to remove biasness from GCM/RCM outputs for predicting climate change impacts over the world's climate regions (Luo et al 2018;Tan et al 2020). The application of appropriate and suitable bias correction methods to the climate model enables researchers to be more confident of the hydrological models' outcome as large errors are expected to be removed.…”
Section: Introductionmentioning
confidence: 99%
“…To pave the way for further study and realization of the impact of climate change on water resources, it is very important to incorporate climate models by considering their performance in simulating Precipitation and Temperatures (Worku et al, 2018). This assessment of the climate model's performance will encourage a researcher to prioritize which climate model is more relevant in simulating climate variables during climate change impact assessment subjectively (Tan et al, 2020). The hydrologic effect of each set of meteorological data of inputs was determined by comparing the resultant simulations to those created by using the same hydrologic model in different various ways to recognize and estimate its diverse impacts (Mekonnen et al, 2011).…”
Section: Introductionmentioning
confidence: 99%