Pub Date : 2023-06-26DOI: 10.1080/02626667.2023.2219397
M. Solans, H. Macian-Sorribes, F. Martínez‐Capel, M. Pulido‐Velazquez
{"title":"Vulnerability assessment for climate adaptation planning in a Mediterranean basin","authors":"M. Solans, H. Macian-Sorribes, F. Martínez‐Capel, M. Pulido‐Velazquez","doi":"10.1080/02626667.2023.2219397","DOIUrl":"https://doi.org/10.1080/02626667.2023.2219397","url":null,"abstract":"","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46933030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-21DOI: 10.1080/02626667.2023.2221791
Imad Janbain, J. Deloffre, A. Jardani, M. Vu, N. Massei
ABSTRACT This paper aims to fill in the missing time series of hourly surface water levels of some stations installed along the River Seine, using the long short-term memory (LSTM) algorithm. In our study, only the water level data from the same station, containing many missing parts, were used as input and output variables, in contrast to other works where several features are available to take advantage of e.g. other station data/physical variables. A sensitive analysis is presented on both the network properties and how the input and output data are reentered to better determine the appropriate strategy. Numerous scenarios are presented, each an updated version of the previous one. Ultimately, the final version of the model can impute missing values of up to one year of hourly data with great flexibility (one-year Root-Mean-Square Error (RMSE) = 0.14 m) regardless of the location of the missing gaps in the series or their size. Graphical abstract
{"title":"Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine","authors":"Imad Janbain, J. Deloffre, A. Jardani, M. Vu, N. Massei","doi":"10.1080/02626667.2023.2221791","DOIUrl":"https://doi.org/10.1080/02626667.2023.2221791","url":null,"abstract":"ABSTRACT This paper aims to fill in the missing time series of hourly surface water levels of some stations installed along the River Seine, using the long short-term memory (LSTM) algorithm. In our study, only the water level data from the same station, containing many missing parts, were used as input and output variables, in contrast to other works where several features are available to take advantage of e.g. other station data/physical variables. A sensitive analysis is presented on both the network properties and how the input and output data are reentered to better determine the appropriate strategy. Numerous scenarios are presented, each an updated version of the previous one. Ultimately, the final version of the model can impute missing values of up to one year of hourly data with great flexibility (one-year Root-Mean-Square Error (RMSE) = 0.14 m) regardless of the location of the missing gaps in the series or their size. Graphical abstract","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1372 - 1390"},"PeriodicalIF":3.5,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43637464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-20DOI: 10.1080/02626667.2023.2218552
P. Nasta, D. Todini-Zicavo, G. Zuecco, C. Marchina, D. Penna, J. McDonnell, Anam Amin, C. Allocca, F. Marzaioli, L. Stellato, M. Borga, N. Romano
ABSTRACT An isotope-enabled module of Hydrus-1D was applied to a potted olive tree to trace water parcels originating from 26 irrigation events in a glasshouse experiment. The soil hydraulic parameters were optimized via inverse modelling by minimizing the discrepancies between observed and simulated soil water content and soil water isotope (18O) values at three soil depths. The model’s performance was validated with observed sap flow z-scores and xylem water 18O. We quantified the source and transit time of irrigation water by analysing the mass breakthrough curves derived from a virtual tracer injection experiment. On average, 26% of irrigation water was removed by plant transpiration with a mean transit time of 94 hours. Our proof of concept work suggests that transit time may represent a functional indicator for the uptake of irrigation water in agricultural ecosystems.
{"title":"Quantifying irrigation uptake in olive trees: a proof-of-concept approach combining isotope tracing and Hydrus-1D","authors":"P. Nasta, D. Todini-Zicavo, G. Zuecco, C. Marchina, D. Penna, J. McDonnell, Anam Amin, C. Allocca, F. Marzaioli, L. Stellato, M. Borga, N. Romano","doi":"10.1080/02626667.2023.2218552","DOIUrl":"https://doi.org/10.1080/02626667.2023.2218552","url":null,"abstract":"ABSTRACT An isotope-enabled module of Hydrus-1D was applied to a potted olive tree to trace water parcels originating from 26 irrigation events in a glasshouse experiment. The soil hydraulic parameters were optimized via inverse modelling by minimizing the discrepancies between observed and simulated soil water content and soil water isotope (18O) values at three soil depths. The model’s performance was validated with observed sap flow z-scores and xylem water 18O. We quantified the source and transit time of irrigation water by analysing the mass breakthrough curves derived from a virtual tracer injection experiment. On average, 26% of irrigation water was removed by plant transpiration with a mean transit time of 94 hours. Our proof of concept work suggests that transit time may represent a functional indicator for the uptake of irrigation water in agricultural ecosystems.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1479 - 1486"},"PeriodicalIF":3.5,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41952436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-19DOI: 10.1080/02626667.2023.2224922
G. Bürger
ABSTRACT Trend significance of time series that are serially correlated is once more addressed. Most conventional techniques to “pre-whiten” the series prior to calculating trends rely on the assumption of autoregressive residual noise, AR(1). Monthly recordings of 40 water level stations in Germany are investigated, revealing strong memory up to lag 2. A new scheme (PW(p) ) is introduced that extends pre-whitening to AR(p) with p > 1. It performs well on surrogate series with prescribed trend and memory. For seven series the estimated trends are unrealistically off, raising doubts about the validity of the basic assumptions of short-memory noise. The series are characterized by a hockey stick pattern from which any pre-whitening produces trends that are all but plausible. The pattern also reveals that pre-whitening is not invariant under time reversal. Regardless of the validity of the noise model, these special cases serve as a warning for using pre-whitening in general.
{"title":"Trends? Complicated answers to a simple question","authors":"G. Bürger","doi":"10.1080/02626667.2023.2224922","DOIUrl":"https://doi.org/10.1080/02626667.2023.2224922","url":null,"abstract":"ABSTRACT Trend significance of time series that are serially correlated is once more addressed. Most conventional techniques to “pre-whiten” the series prior to calculating trends rely on the assumption of autoregressive residual noise, AR(1). Monthly recordings of 40 water level stations in Germany are investigated, revealing strong memory up to lag 2. A new scheme (PW(p) ) is introduced that extends pre-whitening to AR(p) with p > 1. It performs well on surrogate series with prescribed trend and memory. For seven series the estimated trends are unrealistically off, raising doubts about the validity of the basic assumptions of short-memory noise. The series are characterized by a hockey stick pattern from which any pre-whitening produces trends that are all but plausible. The pattern also reveals that pre-whitening is not invariant under time reversal. Regardless of the validity of the noise model, these special cases serve as a warning for using pre-whitening in general.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1680 - 1692"},"PeriodicalIF":3.5,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44065184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Understanding the impact of climate change and human activities on runoff is crucial for water resources management. However, an evaluation of available methods for analysing this impact is lacking. In this study, we comprehensively reviewed four commonly used quantitative methods: the Soil and Water Assessment Tool (SWAT) model, Budyko-based approach, and two empirical methods, i.e., Double mass curve (DMC) and Modified DMC (MDMC). Using the Wei River basin as a case study, we assessed the runoff reduction influenced by climate change and human activities from 1970 to 2017. The results show that human activities are the primary driver for runoff reduction. The highest contribution of human activities was estimated by the DMC (93.2%–99.9%), followed by MDMC and SWAT (65.6%–87.1%), while the Budyko-based had the smallest estimates (55.3%–61.2%). Each method has advantages and limitations, so the appropriate method should be selected based on research objectives and data availability/quality.
{"title":"Quantifying climate and anthropogenic impacts on runoff using the SWAT model, a Budyko-based approach and empirical methods","authors":"Ruirui Xu, Dexun Qiu, Chang-wen Wu, Xingmin Mu, Guangju Zhao, Wenyi Sun, P. Gao","doi":"10.1080/02626667.2023.2218551","DOIUrl":"https://doi.org/10.1080/02626667.2023.2218551","url":null,"abstract":"ABSTRACT Understanding the impact of climate change and human activities on runoff is crucial for water resources management. However, an evaluation of available methods for analysing this impact is lacking. In this study, we comprehensively reviewed four commonly used quantitative methods: the Soil and Water Assessment Tool (SWAT) model, Budyko-based approach, and two empirical methods, i.e., Double mass curve (DMC) and Modified DMC (MDMC). Using the Wei River basin as a case study, we assessed the runoff reduction influenced by climate change and human activities from 1970 to 2017. The results show that human activities are the primary driver for runoff reduction. The highest contribution of human activities was estimated by the DMC (93.2%–99.9%), followed by MDMC and SWAT (65.6%–87.1%), while the Budyko-based had the smallest estimates (55.3%–61.2%). Each method has advantages and limitations, so the appropriate method should be selected based on research objectives and data availability/quality.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1358 - 1371"},"PeriodicalIF":3.5,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48215087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.1080/02626667.2023.2208754
Carles Beneyto, José Ángel Aranda, F. Francés
ABSTRACT Stochastic weather generators are powerful tools capable of extending the available precipitation records to the desired length. These, however, rely upon the amount of information available, which often is scarce, especially in arid and semi-arid regions. No studies can be found dealing with the uncertainty associated with these estimates related to the amount of information used in the weather generation calibration process, which is precisely the aim of the present study. A Monte Carlo simulation from a synthetic population was performed, evaluating the uncertainty of the simulated quantiles in different practical available information scenarios. The results showed that incorporating a regional study of annual maximum daily precipitation in the model parameterization clearly reduced the uncertainty of all quantile estimates. In addition, it has been proved that the uncertainty of these estimates increases with the population extremality, thus marking the importance of integrating additional information in regions with extreme precipitation patterns.
{"title":"Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios","authors":"Carles Beneyto, José Ángel Aranda, F. Francés","doi":"10.1080/02626667.2023.2208754","DOIUrl":"https://doi.org/10.1080/02626667.2023.2208754","url":null,"abstract":"ABSTRACT Stochastic weather generators are powerful tools capable of extending the available precipitation records to the desired length. These, however, rely upon the amount of information available, which often is scarce, especially in arid and semi-arid regions. No studies can be found dealing with the uncertainty associated with these estimates related to the amount of information used in the weather generation calibration process, which is precisely the aim of the present study. A Monte Carlo simulation from a synthetic population was performed, evaluating the uncertainty of the simulated quantiles in different practical available information scenarios. The results showed that incorporating a regional study of annual maximum daily precipitation in the model parameterization clearly reduced the uncertainty of all quantile estimates. In addition, it has been proved that the uncertainty of these estimates increases with the population extremality, thus marking the importance of integrating additional information in regions with extreme precipitation patterns.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1203 - 1212"},"PeriodicalIF":3.5,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47129108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1080/02626667.2023.2215932
Andrés Mauricio Munar, Nelly Mendez, Gabriel Narváez, Fernando Campo Zambrano, David Motta-Marques, João Paulo Lyra Fialho Brêda, Ayan Santos Fleischmann, H. Angarita
ABSTRACT Climate change may have significant impacts on water balance and may considerably influence flooding dynamics of river systems by increasing extreme precipitation. This study evaluates the potential effects of climate change on river discharge and inundation in the Magdalena River basin, the main river in Colombia, using the synergy between the MGB (Modelo de Grandes Bacias) hydrological–hydrodynamic model and downscaled Eta-regional climate model (RCM) projections based on four global climate models (GCMs): BESM (Brazilian Earth System Model), CanESM2 (Canadian Earth System Model), MIROC5 (Model for Interdisciplinary Research on Climate Version Five), and HadGEM2-ES (Hadley Centre Global Environment Model version 2). We used two different greenhouse gas scenarios (RCP4.5 and RCP8.5 (Representative Concentration Pathway)) for the “historical” (1986–2005) and “mid-term prospective” (2046–2065) periods. Model results for the “mid-term prospective” period under scenarios RCP4.5 and RCP8.5 indicate increase in mean river discharges in the east portion of the basin, decreased river discharges (mainly in the dry season) in the upper Magdalena basin, and increased inundation extent. By coupling hydrological–hydrodynamic and GCMs/RCMs models, modelling frameworks like the one used in this study provide an effective management tool for stakeholders interested in potential climate change impacts on tropical river basins.
气候变化可能对水平衡产生重大影响，并可能通过增加极端降水而显著影响河流系统的洪水动态。本研究利用MGB (Modelo de Grandes Bacias)水文-水动力模型和基于四种全球气候模式(GCMs)的缩小尺度eta -区域气候模式(RCM)预测之间的协同作用，评估了气候变化对哥伦比亚主要河流马格达莱纳河流域河流流量和淹没的潜在影响:我们使用了两种不同的温室气体情景(RCP4.5和RCP8.5(代表性浓度路径))对“历史”时期(1986-2005)和“中期展望”时期(2046-2065)进行了模拟。RCP4.5和RCP8.5情景下的“中期预测”期模型结果表明，流域东部平均径流量增加，马格达莱纳流域上游径流量减少(主要在旱季)，淹没程度增加。通过耦合水文-水动力和GCMs/RCMs模型，本研究中使用的建模框架为关注气候变化对热带河流流域潜在影响的利益相关者提供了有效的管理工具。
{"title":"Modelling the climate change impacts on river discharge and inundation extent in the Magdalena River basin – Colombia","authors":"Andrés Mauricio Munar, Nelly Mendez, Gabriel Narváez, Fernando Campo Zambrano, David Motta-Marques, João Paulo Lyra Fialho Brêda, Ayan Santos Fleischmann, H. Angarita","doi":"10.1080/02626667.2023.2215932","DOIUrl":"https://doi.org/10.1080/02626667.2023.2215932","url":null,"abstract":"ABSTRACT Climate change may have significant impacts on water balance and may considerably influence flooding dynamics of river systems by increasing extreme precipitation. This study evaluates the potential effects of climate change on river discharge and inundation in the Magdalena River basin, the main river in Colombia, using the synergy between the MGB (Modelo de Grandes Bacias) hydrological–hydrodynamic model and downscaled Eta-regional climate model (RCM) projections based on four global climate models (GCMs): BESM (Brazilian Earth System Model), CanESM2 (Canadian Earth System Model), MIROC5 (Model for Interdisciplinary Research on Climate Version Five), and HadGEM2-ES (Hadley Centre Global Environment Model version 2). We used two different greenhouse gas scenarios (RCP4.5 and RCP8.5 (Representative Concentration Pathway)) for the “historical” (1986–2005) and “mid-term prospective” (2046–2065) periods. Model results for the “mid-term prospective” period under scenarios RCP4.5 and RCP8.5 indicate increase in mean river discharges in the east portion of the basin, decreased river discharges (mainly in the dry season) in the upper Magdalena basin, and increased inundation extent. By coupling hydrological–hydrodynamic and GCMs/RCMs models, modelling frameworks like the one used in this study provide an effective management tool for stakeholders interested in potential climate change impacts on tropical river basins.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"8 4","pages":"1286 - 1300"},"PeriodicalIF":3.5,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41243517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1080/02626667.2023.2217332
D. J. Peres, B. Bonaccorso, Nunziarita Palazzolo, A. Cancelliere, G. Mendicino, A. Senatore
ABSTRACT Drought is often monitored through standardized indices. However, while enabling comparisons across different climatic regions, standardization poses an issue when using indices to assess future climate change impacts on drought, since they have a null average by definition. To address this issue, in this study we introduce a dynamic approach where future changes are assessed by computing climate normals using moving time windows. The approach is applied to Sicily and Calabria (Southern Italy) using the standardized precipitation index (SPI) and the standardized precipitation–evapotranspiration index (SPEI), and considering climate change scenarios RCP4.5 and RCP8.5. An optimized ensemble weighted average (OEWA) of climate models is introduced to reduce model biases. The results indicate that the region is likely to experience an increase in drought events due to climate change. The findings highlight the need for revised drought identification strategies that account for non-stationarity in climate.
{"title":"A dynamic approach for assessing climate change impacts on drought: an analysis in Southern Italy","authors":"D. J. Peres, B. Bonaccorso, Nunziarita Palazzolo, A. Cancelliere, G. Mendicino, A. Senatore","doi":"10.1080/02626667.2023.2217332","DOIUrl":"https://doi.org/10.1080/02626667.2023.2217332","url":null,"abstract":"ABSTRACT Drought is often monitored through standardized indices. However, while enabling comparisons across different climatic regions, standardization poses an issue when using indices to assess future climate change impacts on drought, since they have a null average by definition. To address this issue, in this study we introduce a dynamic approach where future changes are assessed by computing climate normals using moving time windows. The approach is applied to Sicily and Calabria (Southern Italy) using the standardized precipitation index (SPI) and the standardized precipitation–evapotranspiration index (SPEI), and considering climate change scenarios RCP4.5 and RCP8.5. An optimized ensemble weighted average (OEWA) of climate models is introduced to reduce model biases. The results indicate that the region is likely to experience an increase in drought events due to climate change. The findings highlight the need for revised drought identification strategies that account for non-stationarity in climate.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1213 - 1228"},"PeriodicalIF":3.5,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42316314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1080/02626667.2023.2218550
Temesgen Zelalem, K. Kasiviswanathan
ABSTRACT Potential changes in hydro-meteorological events have been causing mass damage to the economy and lives. Among several other factors, the progression of climate change over a long time is expected to cause non-stationarity in annual maximum rainfall. Understanding the characteristics of annual maximum rainfall series is crucial for coastal cities as they are highly vulnerable due to the greatly varying weather patterns. In this paper, we propose stationary and non-stationary methods to model the effect of non-stationarity on the differing duration of annual maximum rainfall and demonstrate the impacts on nine coastal cities spread across the Arabian Sea and Bay of Bengal stretch of India. The Bayesian inference parameter estimation technique was used. It was found that while stationary models often fit well for longer-duration rainfall, non-stationary models often best fit the short duration.
{"title":"A Bayesian modelling approach for assessing non-stationarity in annual maximum rainfall under a changing climate","authors":"Temesgen Zelalem, K. Kasiviswanathan","doi":"10.1080/02626667.2023.2218550","DOIUrl":"https://doi.org/10.1080/02626667.2023.2218550","url":null,"abstract":"ABSTRACT Potential changes in hydro-meteorological events have been causing mass damage to the economy and lives. Among several other factors, the progression of climate change over a long time is expected to cause non-stationarity in annual maximum rainfall. Understanding the characteristics of annual maximum rainfall series is crucial for coastal cities as they are highly vulnerable due to the greatly varying weather patterns. In this paper, we propose stationary and non-stationary methods to model the effect of non-stationarity on the differing duration of annual maximum rainfall and demonstrate the impacts on nine coastal cities spread across the Arabian Sea and Bay of Bengal stretch of India. The Bayesian inference parameter estimation technique was used. It was found that while stationary models often fit well for longer-duration rainfall, non-stationary models often best fit the short duration.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1460 - 1478"},"PeriodicalIF":3.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49372537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1080/02626667.2023.2218036
Syed Ataharul, Faisal Anwar, M. Bari
ABSTRACT In this study, a simple polynomial bias correction method is developed to correct the bias in the forecasted streamflow (runoff) derived from a global circulation model (GCM). First, a set of polynomial correction factors was derived comparing observed and GCM-derived runoff for a hindcast period (1961–2000) for each of the 11 selected GCMs. The correction factors are used to correct the GCM-derived streamflow for projected periods (2046–2065 and 2081–2100) for the Intergovernmental Panel on Climate Change scenarios A2 and B1 (CMIP3) for the Murray-Hotham catchment of Western Australia. The assumption is that the correction factors derived for each GCM for the observed period (1961–2000) are valid for the projected periods. Results show the method reduces biases considerably for the projected runoff at a catchment scale. The method developed here uses CMIP3 data but it may be applicable to any GCM data, such as CMIP5/CMIP6.
{"title":"A simple method of bias correction for GCM derived streamflow at catchment scale","authors":"Syed Ataharul, Faisal Anwar, M. Bari","doi":"10.1080/02626667.2023.2218036","DOIUrl":"https://doi.org/10.1080/02626667.2023.2218036","url":null,"abstract":"ABSTRACT In this study, a simple polynomial bias correction method is developed to correct the bias in the forecasted streamflow (runoff) derived from a global circulation model (GCM). First, a set of polynomial correction factors was derived comparing observed and GCM-derived runoff for a hindcast period (1961–2000) for each of the 11 selected GCMs. The correction factors are used to correct the GCM-derived streamflow for projected periods (2046–2065 and 2081–2100) for the Intergovernmental Panel on Climate Change scenarios A2 and B1 (CMIP3) for the Murray-Hotham catchment of Western Australia. The assumption is that the correction factors derived for each GCM for the observed period (1961–2000) are valid for the projected periods. Results show the method reduces biases considerably for the projected runoff at a catchment scale. The method developed here uses CMIP3 data but it may be applicable to any GCM data, such as CMIP5/CMIP6.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1409 - 1425"},"PeriodicalIF":3.5,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45450517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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