Journal ArticleDOI
Catchment properties, function, and conceptual model representation: Is there a correspondence?
Fabrizio Fenicia,Dmitri Kavetski,Hubert H. G. Savenije,Martyn P. Clark,Gerrit Schoups,Laurent Pfister,Jim Freer +6 more
Reads0
Chats0
TLDR
In this paper, a set of conceptual model structures are proposed and implemented using the SUPERFLEX framework to investigate the possible correspondence between catchment structure, as represented by perceptual hydrological models developed from fieldwork investigations, and mathematical model structures selected on the basis of reproducing observed catchment hydrographs.Abstract:
This study investigates the possible correspondence between catchment structure, as represented by perceptual hydrological models developed from fieldwork investigations, and mathematical model structures, selected on the basis of reproducing observed catchment hydrographs. Three Luxembourgish headwater catchments are considered, where previous fieldwork suggested distinct flow-generating mechanisms and hydrological dynamics. A set of lumped conceptual model structures are hypothesized and implemented using the SUPERFLEX framework. Following parameter calibration, the model performance is examined in terms of predictive accuracy, quantification of uncertainty, and the ability to reproduce the flow–duration curve signature. Our key research question is whether differences in the performance of the conceptual model structures can be interpreted based on the dominant catchment processes suggested from fieldwork investigations. For example, we propose that the permeable bedrock and the presence of multiple aquifers in the Huewelerbach catchment may explain the superior performance of model structures with storage elements connected in parallel. Conversely, model structures with serial connections perform better in the Weierbach and Wollefsbach catchments, which are characterized by impermeable bedrock and dominated by lateral flow. The presence of threshold dynamics in the Weierbach and Wollefsbach catchments may favour nonlinear models, while the smoother dynamics of the larger Huewelerbach catchment were suitably reproduced by linear models. It is also shown how hydrologically distinct processes can be effectively described by the same mathematical model components. Major research questions are reviewed, including the correspondence between hydrological processes at different levels of scale and how best to synthesize the experimentalist's and modeller's perspectives. Copyright © 2013 John Wiley & Sons, Ltd.read more
Citations
More filters
Journal ArticleDOI
A decade of Predictions in Ungauged Basins (PUB)—a review
Markus Hrachowitz,Hubert H. G. Savenije,Günter Blöschl,Jeffrey J. McDonnell,Murugesu Sivapalan,John W. Pomeroy,Berit Arheimer,Theresa Blume,Martyn P. Clark,Uwe Ehret,Fabrizio Fenicia,Jim Freer,Alexander Gelfan,Hoshin V. Gupta,Denis A. Hughes,Rolf Hut,Alberto Montanari,Saket Pande,Doerthe Tetzlaff,Peter Troch,Stefan Uhlenbrook,Thibaut Wagener,Hessel Winsemius,Ross Woods,Erwin Zehe,Christophe Cudennec +25 more
TL;DR: The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS) launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23-25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power as discussed by the authors.
Journal ArticleDOI
Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
TL;DR: In this article, the authors proposed an ensemble weight-of-evidence (WoE) and support vector machine (SVM) model to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis.
Journal ArticleDOI
Improving the representation of hydrologic processes in Earth System Models
Martyn P. Clark,Ying Fan,David M. Lawrence,Jennifer C. Adam,Diogo Bolster,David Gochis,Richard P. Hooper,Mukesh Kumar,L. Ruby Leung,D. Scott Mackay,Reed M. Maxwell,Chaopeng Shen,Sean Swenson,Xubin Zeng +13 more
TL;DR: In this paper, the authors present a review of the current representation of hydrologic processes in Earth System Models and identify the key opportunities for improvement, and suggest that the development of ESMs has not kept pace with modeling advances in hydrology.
Journal ArticleDOI
A unified approach for process-based hydrologic modeling: 1. Modeling concept
Martyn P. Clark,Bart Nijssen,Jessica D. Lundquist,Dmitri Kavetski,David E. Rupp,Ross Woods,Jim Freer,Ethan Gutmann,Andrew W. Wood,Levi D. Brekke,Jeffrey R. Arnold,David Gochis,Roy Rasmussen +12 more
TL;DR: The Structure for Unifying Multiple Modeling Alternatives (SUMMA) as mentioned in this paper is a unified approach to process-based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) and scaling behavior.
Journal ArticleDOI
The CAMELS data set: Catchment attributes and meteorology for large-sample studies
TL;DR: The CAMELS data set as mentioned in this paper is a large-scale data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities.
References
More filters
A physically based, variable contributing area model of basin hydrology
Mike Kirkby,Keith Beven +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple lumped parameter basin models.
Journal ArticleDOI
Bayesian data analysis.
TL;DR: A fatal flaw of NHST is reviewed and some benefits of Bayesian data analysis are introduced and illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power are presented.
Journal ArticleDOI
A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant
Keith Beven,Mike Kirkby +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that combines the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple luminescence.
Journal ArticleDOI
Bayesian Inference in Statistical Analysis
BookDOI
Rainfall-runoff modelling : the primer
TL;DR: Rainfall Runoff Modelling: The Primer Second Edition as discussed by the authors provides a comprehensive overview of available techniques based on established practices and recent research and offers a thorough and accessible overview of the area.
Related Papers (5)
Pursuing the method of multiple working hypotheses for hydrological modeling
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more