Performance of conceptual rainfall‐runoff models in low‐yielding ephemeral catchments

Ye, W.; Bates, Bryson C.; Viney, Neil R.; Sivapalan, Murugesu; Jakeman, Anthony J.

doi:10.1029/96wr02840

Cited by 247 publications

(204 citation statements)

References 30 publications

(4 reference statements)

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“…This may be due to the fact that the model structures tested here are not the best suited for these catchments. Michaud & Sorooshian (1994) and Ye et al (1997) had already noted the specificity of semi-arid catchments.…”

Section: Discussionmentioning

confidence: 97%

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Perrin¹,

Oudin²,

Andréassian³

et al. 2007

Hydrological Sciences Journal

166

149

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Streamflow data are essential for the calibration of continuous rainfall-runoff (RR) models. The quantity and quality of streamflow data can significantly influence parameter calibration and thus model robustness. Most existing sensitivity analysis studies on the role of streamflow data have used continuous periods to calibrate model parameters, with a minimum of one year, though ideally much longer periods are generally advised. However, in practical model applications, streamflow data series available for model calibration may be rather short or non-continuous. This study aims at assessing the sensitivity of continuous RR models to the quantity of information used during model calibration when it is randomly sampled in the observed hydrograph, i.e. using non-continuous calibration periods. This sampling provides less auto-correlated streamflow information for model calibration than continuous records. Two daily RR models with four and six free parameters were tested on a sample of 12 basins in the USA to obtain more general conclusions. The results showed that, in general, 350 calibration days sampled out of a longer data set including dry and wet conditions are sufficient to obtain robust estimates of model parameters. The more parsimonious model requires fewer calibration data to obtain stable and robust parameter values. Stable parameter values prove more difficult to reach in the driest catchments.Key words rainfall-runoff modelling; calibration; sampling; sensitivity analysis; streamflow data Impact d'une limitation de données de débit sur l'efficacité et les paramètres de modèles pluie-débit Résumé Les données de débit sont essentielles pour caler les modèles pluie-débit continus. La quantité et la qualité des données peuvent influencer de manière significative le calage des paramètres et donc la robustesse du modèle. La plupart des analyses de sensibilité ayant abordé le rôle des données de débit ont utilisé des périodes continues pour le calage des paramètres des modèles, avec un minimum d'une année, bien qu'idéalement des chroniques beaucoup plus longues soient généralement conseillées. Cependant, dans des applications pratiques de modélisation, les séries de données disponibles pour le calage des modèles sont courtes ou non-continues. L'objectif de cette étude est d'évaluer la sensibilité des modèles pluie-débit continus à la quantité d'information utilisée pour leur calage lorsqu'elle est échantillonnée aléatoirement dans l'hydrogramme observé, c'est-à-dire en utilisant des périodes de calage non-continues. Cet échantillonnage fournit une information de débit moins corrélée qu'une série continue pour le calage. Nous avons utilisé ici deux modèles pluie-débit avec quatre et six paramètres, et nous les avons testés sur un échantillon de 12 bassins aux Etats-Unis, pour obtenir des conclusions plus générales. Les résultats montrent qu'en général, 350 jours de calage tirés dans une série plus longue comprenant des conditions sèches et humides sont suffisants pour obtenir des valeurs robustes des...

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Section: Discussionmentioning

confidence: 97%

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Perrin¹,

Oudin²,

Andréassian³

et al. 2007

Hydrological Sciences Journal

166

149

View full text Add to dashboard Cite

show abstract

“…Runoff generation in such regions is a complex process, dominated by infiltration excess and saturated excess mechanisms (Wheater, 2002;Greenbaum et al, 2006). Thus, rainfall-runoff modeling of semiarid catchments is a challenging task (Pilgrim et al, 1988;Ye et al, 1997;McIntyre et al, 2007;Wheater et al, 2008). We applied a simple hydrological model to simulate streamflow in a semiarid catchment.…”

Section: Discussionmentioning

confidence: 99%

“…Rainfall-runoff modeling of semiarid catchments is a challenging task (Ye et al, 1997;Wheater et al, 2008), owing to a delicate hydrologic balance and different mix of hydrologic processes (Pilgrim et al, 1988). Runoff generation in such zones may involve two different mechanisms: infiltration excess and saturation excess (Scoging, 1979;Gallart et al, 1994;Taha et al, 1997;Fitzjohn et al, 1998;MartõÂnez-Mena et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Jiang

Liu

et al. 2015

Environmental Modelling & Software

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a b s t r a c tWe present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE.

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“…In particular, a model taking into account spatial variability is required for applications which intend to assess the effect of changing boundary conditions or of disturbances, like land cover or climate change. A lumped catchment model, although it may well capture the overall catchment dynamics in terms of the hydrograph at the outlet (Chiew et al, 1993;Ye et al, 1997), will hardly be able to incorporate such changes which affect individual processes or parts of the total catchment area only, due to the loss of physical foundation of basin-average model parameters. Additionally, a spatially distributed model representation of the catchment is obviously required where distributed results are to be given as one objective of the model application, for example when soil moisture patterns have to be linked to a crop or vegetation model.…”

Section: Model Representation Of Landscape Variability and Lateral Flmentioning

confidence: 99%

Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas

Güntner

Bronstert

2004

Journal of Hydrology

114

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The spatial variability of landscape features such as topography, soils and vegetation defines the spatial pattern of hydrological state variables like soil moisture. Spatial variability thereby controls the functional behaviour of the landscape in terms of its runoff response. A consequence of spatial variability is that exchange processes between landscape patches can occur at various spatial scales ranging from the plot to the basin scale. In semi-arid areas, the lateral redistribution of surface runoff between adjacent landscape patches is an important process. For applications to large river basins of 10 4 -10 5 km 2 in size, a multi-scale landscape discretization scheme is presented in this paper. The landscape is sub-divided into modelling units within a hierarchy of spatial scale levels. By delineating areas characterized by a typical toposequence, organised and random variability of landscape characteristics is captured in the model. Using runoff -runon relationships with transition frequencies based on areal fractions of modelling units, lateral surface and subsurface water fluxes between modelling units at the hillslope scale are represented. Thus, the new approach allows for a manageable description of interactions between fine-scale landscape features for inclusion in coarse-scale models. Model applications for the State of Ceará (150,000 km 2 ) in the north-east of Brazil demonstrate the importance of taking into account landscape variability and interactions between landscape patches in a semiarid environment. Using mean landscape characteristics leads to a considerable underestimation of infiltration-excess surface runoff and total simulated runoff. Re-infiltration of surface runoff and lateral redistribution processes between landscape patches cause a reduction of runoff volumes at the basin scale and contribute to the amplification of variations in runoff volumes relative to variations in rainfall volumes for semi-arid areas. q 2004 Published by Elsevier B.V.

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Performance of conceptual rainfall‐runoff models in low‐yielding ephemeral catchments

Abstract: Abstract. Low-yielding catchments with ephemeral streams involve highly nonlinear relationships between rainfall and runoff, and there is much less documentation and appreciation of the ability to predict streamflow in these very difficult cases than in humid

Cited by 247 publications

References 30 publications

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas

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