A Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting
For more than a century, the study of streamflow recession has been dominated by seemingly physically based parametric methods that make assumptions on the nonlinear nature of the hydrograph recession. In practice, several studies have shown that various degrees of nonlinearity occur in the same tim...
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description | For more than a century, the study of streamflow recession has been dominated by seemingly physically based parametric methods that make assumptions on the nonlinear nature of the hydrograph recession. In practice, several studies have shown that various degrees of nonlinearity occur in the same time series and that parametric methods can underfit nonlinear recession patterns. As a result, these methods are often applied empirically to each recession segment. We propose a parsimonious data‐driven model, EDM‐Simplex, with two objectives: forecasting recession and characterizing its nonlinear behavior. We evaluate the new model through a global sensitivity analysis applied to three distinctive hydrograph series from a heterogeneous karstic catchment. The results show excellent 1‐day‐ahead forecasting performance (median Nash and Sutcliffe efficiency > 0.99) for all time series with four recession extraction methods. The sensitivity analysis also showed that empirical nonlinearity, that is, sensitivity to initial conditions, is best estimated through the absolute forecast performance and its decline over time. This indicator leads to different interpretations of nonlinearity compared to previous methods but is just as sensitive to the choice of recession extraction method. In particular, when forecasts were made for recession segments containing early stages of recession or flow anomalies, the upstream recession was significantly more linear than the downstream recession hydrographs affected by the karst. Consequently, our results support future research to interpret observed nonlinearities as a function of the catchment hydrological states for better integration of empirical, physical‐based, and operational approaches to recession analysis.
Key Points
EDM‐Simplex, a parsimonious empirical model, is developed to forecast hydrograph recession and assess nonlinearities
One‐day‐ahead forecasts proved highly efficient on three distinctive hydrograph time series with different recession extraction methods
EDM‐Simplex can assess the recession nonlinearities comprehensively, but it is sensitive to the recession extraction method |
doi_str_mv | 10.1029/2019WR025771 |
format | Article |
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Key Points
EDM‐Simplex, a parsimonious empirical model, is developed to forecast hydrograph recession and assess nonlinearities
One‐day‐ahead forecasts proved highly efficient on three distinctive hydrograph time series with different recession extraction methods
EDM‐Simplex can assess the recession nonlinearities comprehensively, but it is sensitive to the recession extraction method</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2019WR025771</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Analysis ; Anomalies ; Catchment area ; data‐driven models ; Downstream effects ; Empirical analysis ; Forecasting ; Hydrographs ; Hydrology ; Initial conditions ; Karst ; Mathematical models ; nonlinear dynamics ; Nonlinear systems ; Nonlinearity ; Parametric methods ; Recession ; Recessions ; Sensitivity analysis ; Stream discharge ; Stream flow ; streamflow recession analysis ; Time series</subject><ispartof>Water resources research, 2020-02, Vol.56 (2), p.n/a</ispartof><rights>2020. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3302-17596690c75d066a20f50a584d2ac021aa116303ff7bca0e0c9c54c9efb37f763</citedby><cites>FETCH-LOGICAL-a3302-17596690c75d066a20f50a584d2ac021aa116303ff7bca0e0c9c54c9efb37f763</cites><orcidid>0000-0003-2838-1514 ; 0000-0003-1358-8723 ; 0000-0002-3552-9444 ; 0000-0002-2859-3122</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2019WR025771$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2019WR025771$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,11494,27903,27904,45553,45554,46446,46870</link.rule.ids></links><search><creatorcontrib>Delforge, Damien</creatorcontrib><creatorcontrib>Muñoz‐Carpena, Rafael</creatorcontrib><creatorcontrib>Van Camp, Michel</creatorcontrib><creatorcontrib>Vanclooster, Marnik</creatorcontrib><title>A Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting</title><title>Water resources research</title><description>For more than a century, the study of streamflow recession has been dominated by seemingly physically based parametric methods that make assumptions on the nonlinear nature of the hydrograph recession. In practice, several studies have shown that various degrees of nonlinearity occur in the same time series and that parametric methods can underfit nonlinear recession patterns. As a result, these methods are often applied empirically to each recession segment. We propose a parsimonious data‐driven model, EDM‐Simplex, with two objectives: forecasting recession and characterizing its nonlinear behavior. We evaluate the new model through a global sensitivity analysis applied to three distinctive hydrograph series from a heterogeneous karstic catchment. The results show excellent 1‐day‐ahead forecasting performance (median Nash and Sutcliffe efficiency > 0.99) for all time series with four recession extraction methods. The sensitivity analysis also showed that empirical nonlinearity, that is, sensitivity to initial conditions, is best estimated through the absolute forecast performance and its decline over time. This indicator leads to different interpretations of nonlinearity compared to previous methods but is just as sensitive to the choice of recession extraction method. In particular, when forecasts were made for recession segments containing early stages of recession or flow anomalies, the upstream recession was significantly more linear than the downstream recession hydrographs affected by the karst. Consequently, our results support future research to interpret observed nonlinearities as a function of the catchment hydrological states for better integration of empirical, physical‐based, and operational approaches to recession analysis.
Key Points
EDM‐Simplex, a parsimonious empirical model, is developed to forecast hydrograph recession and assess nonlinearities
One‐day‐ahead forecasts proved highly efficient on three distinctive hydrograph time series with different recession extraction methods
EDM‐Simplex can assess the recession nonlinearities comprehensively, but it is sensitive to the recession extraction method</description><subject>Analysis</subject><subject>Anomalies</subject><subject>Catchment area</subject><subject>data‐driven models</subject><subject>Downstream effects</subject><subject>Empirical analysis</subject><subject>Forecasting</subject><subject>Hydrographs</subject><subject>Hydrology</subject><subject>Initial conditions</subject><subject>Karst</subject><subject>Mathematical models</subject><subject>nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Nonlinearity</subject><subject>Parametric methods</subject><subject>Recession</subject><subject>Recessions</subject><subject>Sensitivity analysis</subject><subject>Stream discharge</subject><subject>Stream flow</subject><subject>streamflow recession analysis</subject><subject>Time series</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp90M9LwzAcBfAgCs7pzT8g4NXqN7_NsYxNBUGpyo4lyxLNaJuadIz991bmwZOnd_nweDyELgncEKD6lgLRywqoUIocoQnRnBdKK3aMJgCcFYRpdYrOct4AEC6kmqCqxC8m5dDGLsRtxvO2DylY0-Cy71M09hMPEb8OyZnWN3GHK2ddziF2uOxMs88hY9Ot8SImZ00eQvdxjk68abK7-M0pel_M32YPxdPz_eOsfCoMY0ALooSWUoNVYg1SGgpegBF3fE2NBUqMIUQyYN6rlTXgwGoruNXOr5jySrIpujr0jju_ti4P9SZu0zgq15RpwZjikozq-qBsijkn5-s-hdakfU2g_nmt_vvayNmB70Lj9v_aelnNKsq5pOwbQ-Ntsg</recordid><startdate>202002</startdate><enddate>202002</enddate><creator>Delforge, Damien</creator><creator>Muñoz‐Carpena, Rafael</creator><creator>Van Camp, Michel</creator><creator>Vanclooster, Marnik</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0003-2838-1514</orcidid><orcidid>https://orcid.org/0000-0003-1358-8723</orcidid><orcidid>https://orcid.org/0000-0002-3552-9444</orcidid><orcidid>https://orcid.org/0000-0002-2859-3122</orcidid></search><sort><creationdate>202002</creationdate><title>A Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting</title><author>Delforge, Damien ; Muñoz‐Carpena, Rafael ; Van Camp, Michel ; Vanclooster, Marnik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3302-17596690c75d066a20f50a584d2ac021aa116303ff7bca0e0c9c54c9efb37f763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Analysis</topic><topic>Anomalies</topic><topic>Catchment area</topic><topic>data‐driven models</topic><topic>Downstream effects</topic><topic>Empirical analysis</topic><topic>Forecasting</topic><topic>Hydrographs</topic><topic>Hydrology</topic><topic>Initial conditions</topic><topic>Karst</topic><topic>Mathematical models</topic><topic>nonlinear dynamics</topic><topic>Nonlinear systems</topic><topic>Nonlinearity</topic><topic>Parametric methods</topic><topic>Recession</topic><topic>Recessions</topic><topic>Sensitivity analysis</topic><topic>Stream discharge</topic><topic>Stream flow</topic><topic>streamflow recession analysis</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Delforge, Damien</creatorcontrib><creatorcontrib>Muñoz‐Carpena, Rafael</creatorcontrib><creatorcontrib>Van Camp, Michel</creatorcontrib><creatorcontrib>Vanclooster, Marnik</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Delforge, Damien</au><au>Muñoz‐Carpena, Rafael</au><au>Van Camp, Michel</au><au>Vanclooster, Marnik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting</atitle><jtitle>Water resources research</jtitle><date>2020-02</date><risdate>2020</risdate><volume>56</volume><issue>2</issue><epage>n/a</epage><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>For more than a century, the study of streamflow recession has been dominated by seemingly physically based parametric methods that make assumptions on the nonlinear nature of the hydrograph recession. In practice, several studies have shown that various degrees of nonlinearity occur in the same time series and that parametric methods can underfit nonlinear recession patterns. As a result, these methods are often applied empirically to each recession segment. We propose a parsimonious data‐driven model, EDM‐Simplex, with two objectives: forecasting recession and characterizing its nonlinear behavior. We evaluate the new model through a global sensitivity analysis applied to three distinctive hydrograph series from a heterogeneous karstic catchment. The results show excellent 1‐day‐ahead forecasting performance (median Nash and Sutcliffe efficiency > 0.99) for all time series with four recession extraction methods. The sensitivity analysis also showed that empirical nonlinearity, that is, sensitivity to initial conditions, is best estimated through the absolute forecast performance and its decline over time. This indicator leads to different interpretations of nonlinearity compared to previous methods but is just as sensitive to the choice of recession extraction method. In particular, when forecasts were made for recession segments containing early stages of recession or flow anomalies, the upstream recession was significantly more linear than the downstream recession hydrographs affected by the karst. Consequently, our results support future research to interpret observed nonlinearities as a function of the catchment hydrological states for better integration of empirical, physical‐based, and operational approaches to recession analysis.
Key Points
EDM‐Simplex, a parsimonious empirical model, is developed to forecast hydrograph recession and assess nonlinearities
One‐day‐ahead forecasts proved highly efficient on three distinctive hydrograph time series with different recession extraction methods
EDM‐Simplex can assess the recession nonlinearities comprehensively, but it is sensitive to the recession extraction method</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2019WR025771</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-2838-1514</orcidid><orcidid>https://orcid.org/0000-0003-1358-8723</orcidid><orcidid>https://orcid.org/0000-0002-3552-9444</orcidid><orcidid>https://orcid.org/0000-0002-2859-3122</orcidid></addata></record> |
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subjects | Analysis Anomalies Catchment area data‐driven models Downstream effects Empirical analysis Forecasting Hydrographs Hydrology Initial conditions Karst Mathematical models nonlinear dynamics Nonlinear systems Nonlinearity Parametric methods Recession Recessions Sensitivity analysis Stream discharge Stream flow streamflow recession analysis Time series |
title | A Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting |
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