Application of the Kalman filter to the Nash model

Lee, Y. H.; Singh, V. P.

doi:10.1002/(sici)1099-1085(19980430)12:5<755::aid-hyp623>3.0.co;2-#

Cited by 22 publications

(17 citation statements)

References 26 publications

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“…The possibility of including various types of criteria into the likelihood measure makes the concept attractive for evaluating reliability in flood extent modeling, as demonstrated in several examples (Aronica et al, 1998(Aronica et al, , 2002Romanowicz and Beven, 2003;Werner, 2004). Well known performance criterion functions have been used to build a criterion evaluating the performance of the MARINE model: it consists in a linear combination of the efficiency coefficient (Nash and Sutcliffe, 1970) and the error of peak time and runoff (Lee and Singh, 1998):…”

Section: Model Performance Criterion and Sensitivity Analysismentioning

confidence: 99%

A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

Roux

Labat

Garambois

et al. 2011

Nat. Hazards Earth Syst. Sci.

102

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Abstract.A spatially distributed hydrological model, dedicated to flood simulation, is developed on the basis of physical process representation (infiltration, overland flow, channel routing). Estimation of model parameters requires data concerning topography, soil properties, vegetation and land use. Four parameters are calibrated for the entire catchment using one flood event. Model sensitivity to individual parameters is assessed using Monte-Carlo simulations. Results of this sensitivity analysis with a criterion based on the Nash efficiency coefficient and the error of peak time and runoff are used to calibrate the model. This procedure is tested on the Gardon d'Anduze catchment, located in the Mediterranean zone of southern France. A first validation is conducted using three flood events with different hydrometeorological characteristics. This sensitivity analysis along with validation tests illustrates the predictive capability of the model and points out the possible improvements on the model's structure and parameterization for flash flood forecasting, especially in ungauged basins. Concerning the model structure, results show that water transfer through the subsurface zone also contributes to the hydrograph response to an extreme event, especially during the recession period. Maps of soil saturation emphasize the impact of rainfall and soil properties variability on these dynamics. Adding a subsurface flow component in the simulation also greatly impacts the spatial distribution of soil saturation and shows the importance of the drainage network. Measures of such distributed variables would help discriminating between different possible model structures.

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Section: Model Performance Criterion and Sensitivity Analysismentioning

confidence: 99%

A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

Roux

Labat

Garambois

et al. 2011

Nat. Hazards Earth Syst. Sci.

102

View full text Add to dashboard Cite

show abstract

“…The inadequacy of the model itself, parameter uncertainty, errors in the data used for parameter estimation and inadequate understanding of the rainfall-runoff process, owing in part to randomness, may cause errors in a rainfall-runoff model. Incorporating the Kalman filter in a rainfall-runoff model may reduce the error in the runoff prediction arising from the uncertainty caused by the physical process, the model and the input data (Lee and Singh, 1998). Lee and Singh also reviewed in detail several applications of the Kalman filter to hydrology.…”

Section: Introductionmentioning

confidence: 99%

Efficient nonlinear modeling of rainfall-runoff process using wavelet compression

Chou

2007

Journal of Hydrology

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“…The NASH model [18] [19] [20] [21] is a conceptual hydrological concentration model developed by Nash, J.E., and it is widely used in the watershed concentration simulation [22] [23]. In the model, the research basin is divided into a series of identical reservoirs, and the reallocation of the net rainfall in the catchment is assimilated to be an adjustment of the reservoirs.…”

Section: Nash Modelmentioning

confidence: 99%

Parameter Uncertainty Estimation by Using the Concept of Ideal Data in GLUE Approach

Zhu¹,

Du²

2017

JWARP

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The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through "ideal data" concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Yanduhe research catchment in Yangtze River, China. And a suitable likelihood measure is assured here to reduce the uncertainty coming from the parameters relationship. "Ideal data" is assumed to be no error for the input-output data and model structure. The relationship between parameters k and n of NASH model is clearly quantitatively demonstrated based on the real data and it shows the existence of uncertainty factors different from the parameter one. Ideal data research results show that the accuracy of data and model structure are the two important preconditions for parameter estimation. And with suitable likelihood measure, the parameter uncertainty could be decreased or even disappeared. Moreover it is shown how distributions of predicted discharge errors are non-Gaussian and vary in shape with time and discharge under the single existence of parameter uncertainty or under the existence of all uncertainties.

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Application of the Kalman filter to the Nash model

Cited by 22 publications

References 26 publications

A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

Efficient nonlinear modeling of rainfall-runoff process using wavelet compression

Parameter Uncertainty Estimation by Using the Concept of Ideal Data in GLUE Approach

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