Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL?

Blažková, Šárka; Beven, Keith; Tachecí, Pavel; Kulasova, Alena

doi:10.1029/2001wr000912

Cited by 49 publications

(48 citation statements)

References 38 publications

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“…Blažková et al, 2002;Lamb et al, 1998), the MIKE-SHE model (Christiaens and Feyen, 2002), and the HYDRUS-1D model (Hansson and Lundin, 2006). To our knowledge no comparable numerical study has been conducted to analyze prediction uncertainties associated with the parameterization of preferential flow in dual-continuum modeling of subsurface runoff.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of a Dual-Continuum Model Used to Simulate Subsurface Hillslope Runoff Involving Oxygen-18 as Natural Tracer

Dohnal¹,

Vogel²,

Šanda³

et al. 2012

Journal of Hydrology and Hydromechanics

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A one-dimensional dual-continuum model (also known as dual-permeability model) was used to simulate the lateral component of subsurface runoff and variations in the natural 18 O content in hillslope discharge. Model predictions were analyzed using the GLUE generalized likelihood uncertainty estimation procedure. Model sensitivity was evaluated by varying two separate triplets of parameters. The first triplet consisted of key parameters determining the preferential flow regime, i.e., the volumetric proportion of the preferential flow domain, a first-order transfer coefficient characterizing soil water exchange between the two flow domains of the dual-continuum system, and the saturated hydraulic conductivity of the preferential flow domain. The second triplet involved parameters controlling exclusively the soil hydraulic properties of the preferential flow domain, i.e., its retention curve and hydraulic conductivity function. Results of the analysis suggest high sensitivity to all parameters of the first triplet, and large differences in sensitivity to the parameters of the second triplet. The sensitivity analysis also confirmed a significant improvement in the identifiability of preferential flow parameters when 18 O content was added to the objective function. K simulacím laterální složky podpovrchového proudění a změn koncentrace izotopu kyslíku 18 O ve vodě vytékající ze svahu byl použit jednorozměrný model využívající přístupu duálního kontinua. Nejistota modelových předpovědí byla odhadnuta s využitím metody zobecněné věrohodnosti (GLUE). Citlivost modelu byla zjišťována pomocí variací dvou samostatných trojic parametrů. První trojice sestávala z klíčových parametrů pro určení režimu preferenčního proudění, tj. objemového podílu preferenční domény proudění, přenosového koeficientu charakterizujícího výměnu vody mezi oběma doménami duálního systému a nasycené hydraulické vodivosti preferenční domény. Druhá trojice zahrnovala výhradně parametry určující hydraulické charakteristiky preferenční domény proudění, tj. retenční křivku a funkci hydraulické vodivosti. Z výsledků analýzy vyplývá vysoká citlivost modelu na všechny parametry z první trojice a velké rozdíly v citlivostech parametrů druhé trojice. Analýza dále potvrdila významné zlepšení zjistitelnosti parametrů preferenční domény v případě, kdy je do cílové funkce přidána koncentrace izotopu kyslíku 18 O.KLÍČOVÁ SLOVA: odtok ze svahu, preferenční proudění, model duální permeability, analýza citlivosti, GLUE, koncentrace 18 O.

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Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of a Dual-Continuum Model Used to Simulate Subsurface Hillslope Runoff Involving Oxygen-18 as Natural Tracer

Dohnal¹,

Vogel²,

Šanda³

et al. 2012

Journal of Hydrology and Hydromechanics

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show abstract

“…So far, there have been a number of rainfall-runoff model applications using GLUE to assess model performance (Freer & Beven, 1996;Nandakumar & Mein, 1997;Lamb et al, 1998;Franks & Beven, 1999;Cameron et al, 2000;Yu et al, 2001, Blazkova et al, 2002. However, few are based on distributed physical-based models (though see Christiaens & Feyen, 2001).…”

Section: Introductionmentioning

confidence: 99%

Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model

Pappenberger

Beven

et al. 2006

Hydrological Sciences Journal

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Distributed hydrological models are considered to be a promising tool for predicting the impacts of global change on the hydrological processes at the basin scale. However, distributed models typically require values of many parameters to be specified or calibrated, which exacerbates model prediction uncertainty. This study uses the generalized likelihood uncertainty estimation (GLUE) technique to analyse the parameter sensitivities of a distributed hydrological model, LISFLOOD-WB. Discharge time series and event volume data of the Luo River at upstream and downstream sites, Lingkou and Lushi, are used to analyse parameter uncertainty. Eight key parameters in the model are selected for conditioning and sampled using the Monte Carlo method under assumed prior distributions. The results show that maximum efficiency of model performance is lower and the number of behavioural parameter sets giving acceptable performance is fewer in the Lingkou sub-basin than in the Lushi sub-basin with the same criteria of acceptability. For both sub-basins the distribution shape parameter B in the fast runoff generation scheme is the most sensitive in predicting both discharge time series and event volume at the outlet. It is also shown that the value of parameter B at which the highest efficiency is derived is shifted from a high value for Lushi to a low value for Lingkou, consistent with past experience of model calibration that the larger the basin, the larger the B value is. The channel Manning coefficient N c shows some sensitivity in the prediction of discharge time series, but less in the prediction of event volumes. The other key parameters show little sensitivity and good simulations are found across the full range of parameter values sampled. The uncertainty bounds of predicted discharges at the Lushi sub-basin are broad in the peak and narrow in the recession. The normalized difference between the upper and lower uncertainty bounds for both discharge and evapotranspiration are broad in summer and narrow in winter and that of recharge is the opposite.Key words distributed hydrological model; GLUE; parameter calibration; uncertainty Conditionnement de paramétrage et incertitudes de prévision du modèle hydrologique distribué LISFLOOD-WBRésumé Les modèles hydrologiques distribués sont considérés comme un outil prometteur pour la prévision des impacts du changement global sur les processus hydrologiques à l'échelle du bassin versant. Cependant, les modèles distribués nécessitent généralement la spécification ou le calage des valeurs de nombreux paramètres, ce qui aggrave l'incertitude de prévision de la modélisation. Cette étude utilise la technique GLUE (generalized likelihood uncertainty estimation) pour analyser les sensibilités de paramétrage du modèle hydrologique distribué LISFLOOD-WB. Des séries temporelles de débit et des données de volume événementiel de la Rivière Luo, au niveau des sites amont et aval de Lingkou et Lushi, sont utilisées pour analyser l'incertitude de paramétrage. Huit paramètres clefs du m...

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“…All studies reveal that Uhlirska catchment represents a catchment type with a significant effect of valley aquifer (i.e., so-called groundwater supplied wetland). Blazkova et al (2002) applied simple hydrological model TOPMODEL to predict water table levels at Uhlirska. Major runoff mechanism contributing to a rapid surface runoff in the model were variable source areas in the valley.…”

Section: Introductionmentioning

confidence: 99%

Episodic runoff generation at Central European headwater catchments studied using water isotope concentration signals

Votrubová

Dohnal

Vogel

et al. 2017

Journal of Hydrology and Hydromechanics

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Hydrological monitoring in small headwater catchments provides the basis for examining complex interrelating hydraulic processes that govern the runoff generation. Contributions of different subsurface runoff mechanisms to the catchment discharge formation at two small forested headwater catchments are studied with the help of their natural isotopic signatures. The Uhlirska catchment (Jizera Mts., Czech Republic) is situated in headwater area of the Lusatian Neisse River. The catchment includes wetlands at the valley bottom developed over deluviofluvial granitic sediments surrounded by gentle hillslopes with shallow soils underlain by weathered granite. The Liz catchment (Bohemian Forest, Czech Republic) is situated in headwater area of the Otava River. It belongs to hillslope-type catchments with narrow riparian zones. The soil at Liz is developed on biotite paragneiss bedrock. The basic comparison of hydrological time series reveals that the event-related stream discharge variations at the Uhlirska catchment are bigger and significantly more frequent than at Liz. The analysis of isotope concentration data revealed different behavior of the two catchments during the major rainfall-runoff events. At Uhlirska, the percentage of the direct runoff formed by the event water reaches its maximum on the falling limb of the hydrograph. At Liz, the event water related fraction of the direct outflow is maximal on the rising limb of the hydrograph and then lowers. The hydraulic functioning of the Uhlirska catchment is determined by communication between hillslope and riparian zone compartments.

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Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL?

Cited by 49 publications

References 38 publications

Uncertainty Analysis of a Dual-Continuum Model Used to Simulate Subsurface Hillslope Runoff Involving Oxygen-18 as Natural Tracer

Uncertainty Analysis of a Dual-Continuum Model Used to Simulate Subsurface Hillslope Runoff Involving Oxygen-18 as Natural Tracer

Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model

Episodic runoff generation at Central European headwater catchments studied using water isotope concentration signals

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