Jie Chen, R. Arsenault, F. Brissette, Shaobo Zhang
{"title":"气候变化影响研究:我们应该偏向正确的气候模型输出还是过程后影响模型输出?","authors":"Jie Chen, R. Arsenault, F. Brissette, Shaobo Zhang","doi":"10.1029/2020WR028638","DOIUrl":null,"url":null,"abstract":"The inter‐variable dependence of climate variables is usually not considered in many bias correction methods, even though it has been deemed important for various impact studies. Another possible approach is to forgo the bias correction of climate model outputs, and instead, post‐process the outputs of the impact model. This has the advantage of circumventing the difficulties associated with correcting the inter‐variable dependence of climate variables. Using a hydrological impact study as an example, this study investigates the feasibility of bias correcting impact model outputs by comparing the performance of the pre‐processing and post‐processing of hydrological model simulations when using bias correction methods. The performance over calibration and validation periods was used to assess the transferability of both approaches. The results show that both the pre‐processing and post‐processing procedures are capable of significantly reducing the bias of simulated streamflow time series for most global climate models (GCMs), even though their performances depend on GCM simulations, hydrological models, streamflow metrics and watersheds. Both approaches were likely to perform badly over the validation period when bias correction factors have a strong seasonal variability and are therefore sensitive to bias nonstationarity of climate model outputs and/or streamflow between the calibration and validation periods. This problem is found to be more acute for the post‐processing method because streamflows often have a seasonal pattern with more abrupt changes than precipitation and temperature. For this reason, pre‐processing is recommended as it is less likely to suffer from this problem.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post‐Process Impact Model Outputs?\",\"authors\":\"Jie Chen, R. Arsenault, F. Brissette, Shaobo Zhang\",\"doi\":\"10.1029/2020WR028638\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The inter‐variable dependence of climate variables is usually not considered in many bias correction methods, even though it has been deemed important for various impact studies. Another possible approach is to forgo the bias correction of climate model outputs, and instead, post‐process the outputs of the impact model. This has the advantage of circumventing the difficulties associated with correcting the inter‐variable dependence of climate variables. Using a hydrological impact study as an example, this study investigates the feasibility of bias correcting impact model outputs by comparing the performance of the pre‐processing and post‐processing of hydrological model simulations when using bias correction methods. The performance over calibration and validation periods was used to assess the transferability of both approaches. The results show that both the pre‐processing and post‐processing procedures are capable of significantly reducing the bias of simulated streamflow time series for most global climate models (GCMs), even though their performances depend on GCM simulations, hydrological models, streamflow metrics and watersheds. Both approaches were likely to perform badly over the validation period when bias correction factors have a strong seasonal variability and are therefore sensitive to bias nonstationarity of climate model outputs and/or streamflow between the calibration and validation periods. This problem is found to be more acute for the post‐processing method because streamflows often have a seasonal pattern with more abrupt changes than precipitation and temperature. For this reason, pre‐processing is recommended as it is less likely to suffer from this problem.\",\"PeriodicalId\":23799,\"journal\":{\"name\":\"Water Resources Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Resources Research\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1029/2020WR028638\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Resources Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2020WR028638","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post‐Process Impact Model Outputs?
The inter‐variable dependence of climate variables is usually not considered in many bias correction methods, even though it has been deemed important for various impact studies. Another possible approach is to forgo the bias correction of climate model outputs, and instead, post‐process the outputs of the impact model. This has the advantage of circumventing the difficulties associated with correcting the inter‐variable dependence of climate variables. Using a hydrological impact study as an example, this study investigates the feasibility of bias correcting impact model outputs by comparing the performance of the pre‐processing and post‐processing of hydrological model simulations when using bias correction methods. The performance over calibration and validation periods was used to assess the transferability of both approaches. The results show that both the pre‐processing and post‐processing procedures are capable of significantly reducing the bias of simulated streamflow time series for most global climate models (GCMs), even though their performances depend on GCM simulations, hydrological models, streamflow metrics and watersheds. Both approaches were likely to perform badly over the validation period when bias correction factors have a strong seasonal variability and are therefore sensitive to bias nonstationarity of climate model outputs and/or streamflow between the calibration and validation periods. This problem is found to be more acute for the post‐processing method because streamflows often have a seasonal pattern with more abrupt changes than precipitation and temperature. For this reason, pre‐processing is recommended as it is less likely to suffer from this problem.
期刊介绍:
Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.