Analysis of conceptual rainfall–runoff modelling performance in different climates

Lidén, Rikard; Harlin, Joakim

doi:10.1016/s0022-1694(00)00330-9

Cited by 143 publications

(106 citation statements)

References 22 publications

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“…The results of several authors (Sorooshian et al, 1983;Yapo et al, 1996;Lidén & Harlin, 2000;Anctil et al, 2004) agree that when short continuous periods are used (say one year), the calibration of model parameters provides more robust results with wet years (i.e. years with high flow values).…”

Section: Discussionmentioning

confidence: 58%

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

Perrin¹,

Oudin²,

Andréassian³

et al. 2007

Hydrological Sciences Journal

171

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: 58%

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

Perrin¹,

Oudin²,

Andréassian³

et al. 2007

Hydrological Sciences Journal

171

149

View full text Add to dashboard Cite

show abstract

“…The actual crop evapotranspiration (ET a , mm/day) depends on climate parameters (which determine potential evapotranspiration), crop characteristics and soil water availability (Allen et al, 1998): Following the approach as in the HBV model (Bergström, 1995;Lidén and Harlin, 2000) the amount of rainfall lost through runoff is computed as:…”

Section: Methodsmentioning

confidence: 99%

A global and high-resolution assessment of the green, blue and grey water footprint of wheat

Mekonnen

Hoekstra

2010

Hydrol. Earth Syst. Sci.

336

206

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Abstract. The aim of this study is to estimate the green, blue and grey water footprint of wheat in a spatially-explicit way, both from a production and consumption perspective. The assessment is global and improves upon earlier research by taking a high-resolution approach, estimating the water footprint of the crop at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in wheat production is estimated for each grid cell. We have used the water footprint and virtual water flow assessment framework as in the guideline of the Water Footprint Network.The global wheat production in the period 1996-2005 required about 108 billion cubic meters of water per year. The major portion of this water (70%) comes from green water, about 19% comes from blue water, and the remaining 11% is grey water. The global average water footprint of wheat per ton of crop was 1830 m 3 /ton. About 18% of the water footprint related to the production of wheat is meant not for domestic consumption but for export. About 55% of the virtual water export comes from the USA, Canada and Australia alone. For the period 1996-2005, the global average water saving from international trade in wheat products was 65 Gm 3 /yr.A relatively large total blue water footprint as a result of wheat production is observed in the Ganges and Indus river basins, which are known for their water stress problems. The two basins alone account for about 47% of the blue water footprint related to global wheat production. About 93% of the water footprint of wheat consumption in Japan lies in other countries, particularly the USA, Australia and Canada.Correspondence to: M. M. Mekonnen (m.m.mekonnen@ctw.utwente.nl) In Italy, with an average wheat consumption of 150 kg/yr per person, more than two times the word average, about 44% of the total water footprint related to this wheat consumption lies outside Italy. The major part of this external water footprint of Italy lies in France and the USA.

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“…Lidén and Harlin, 2000;Olsson and Lindstrom, 2008;van Pelt et al, 2009;Arheimer et al 2011). There are many versions of the HBV model, and the one implemented in this paper is based on HBV light (Seibert, 2003).…”

Section: Hydrological Modelling and Parameter Uncertaintymentioning

confidence: 99%

Modelling climate impact on floods with ensemble climate projections

Cloke

Wetterhall

et al. 2012

Quart J Royal Meteoro Soc

109

104

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The evidence provided by modelled assessments of future climate impact on flooding is fundamental to water resources and flood risk decision making. Impact models usually rely on climate projections from global and regional climate models (GCM/RCMs). However, challenges in representing precipitation events at catchment-scale resolution mean that decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs. Here the impacts on projected high flows of differing ensemble approaches and application of Model Output Statistics to RCM precipitation are evaluated while assessing climate change impact on flood hazard in the Upper Severn catchment in the UK. Various ensemble projections are used together with the HBV hydrological model with direct forcing and also compared to a response surface technique. We consider an ensemble of single-model RCM projections from the current UK Climate Projections (UKCP09); multi-model ensemble RCM projections from the European Union's FP6 'ENSEMBLES' project; and a joint probability distribution of precipitation and temperature from a GCM-based perturbed physics ensemble.The ensemble distribution of results show that flood hazard in the Upper Severn is likely to increase compared to present conditions, but the study highlights the differences between the results from different ensemble methods and the strong assumptions made in using Model Output Statistics to produce the estimates of future river discharge. The results underline the challenges in using the current generation of RCMs for local climate impact studies on flooding.

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Analysis of conceptual rainfall–runoff modelling performance in different climates

Cited by 143 publications

References 22 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

A global and high-resolution assessment of the green, blue and grey water footprint of wheat

Modelling climate impact on floods with ensemble climate projections

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