000004878 001__ 4878
000004878 005__ 20200428082845.0
000004878 0247_ $$2DOI$$a10.1029/2019WR025771
000004878 037__ $$aSCART-2020-0106
000004878 100__ $$aDelforge, D
000004878 245__ $$aA Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting
000004878 260__ $$c2020
000004878 520__ $$aFor more than a century, the study of streamflow recession has been dominated by seemingly physically-based parametric methods that make assumptions on the nonlinear nature of the hydrograph recession. In practice, several studies have shown that various degrees of nonlinearity occur in the same time series and that parametric methods can underfit nonlinear recession patterns. As a result, these methods are often applied empirically to each recession segment. We propose a parsimonious data‐driven model, EDM‐Simplex, with two objectives: forecasting recession and characterizing its nonlinear behavior. We evaluate the new model through a global sensitivity analysis applied to three distinctive hydrograph series from a heterogeneous karstic catchment. The results show excellent 1‐day‐ahead forecasting performance (median Nash and Sutcliffe efficiency > 0.99) for all time series with four recession extraction methods. The sensitivity analysis also showed that empirical nonlinearity, that is, sensitivity to initial conditions, is best estimated through the absolute forecast performance and its decline over time. This indicator leads to different interpretations of nonlinearity compared to previous methods but is just as sensitive to the choice of recession extraction method. In particular, when forecasts were made for recession segments containing early stages of recession or flow anomalies, the upstream recession was significantly more linear than the downstream recession hydrographs affected by the karst. Consequently, our results support future research to interpret observed nonlinearities as a function of the catchment hydrological states for better integration of empirical, physical‐based, and operational approaches to recession analysis.
000004878 536__ $$aF.R.S. ‐ FNRS | Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA)/$$cF.R.S. ‐ FNRS | Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA)/$$fF.R.S. ‐ FNRS | Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA)
000004878 594__ $$aNO
000004878 6531_ $$aRochefort
000004878 6531_ $$aLhomme
000004878 6531_ $$aKarst
000004878 6531_ $$aMIGRADAKH
000004878 6531_ $$aKARAG
000004878 6531_ $$aStreamflow recession analysis
000004878 6531_ $$aData-driven models
000004878 6531_ $$aNon-linear dynamics
000004878 700__ $$aVanclooster, M
000004878 700__ $$aVan Camp, M
000004878 700__ $$aMuñoz-Carpena, R
000004878 773__ $$n2$$pWater Resources Research$$v56$$y2020
000004878 8560_ $$fmichel.vancamp@observatoire.be
000004878 85642 $$ahttps://doi.org/10.1029/2019WR025771
000004878 905__ $$apublished in
000004878 980__ $$aREFERD