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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Veröffentlicht in:Water resources research 2020-02, Vol.56 (2), p.n/a
Hauptverfasser: Delforge, Damien, Muñoz‐Carpena, Rafael, Van Camp, Michel, Vanclooster, Marnik
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Muñoz‐Carpena, Rafael
Van Camp, Michel
Vanclooster, Marnik
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
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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 &gt; 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><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2019WR025771</identifier><language>eng</language><publisher>Washington: John Wiley &amp; 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. 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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. 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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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