A hybrid deterministic–fuzzy rule based model for catchment scale nitrate dynamics

Shrestha, Rajesh R.; Bàrdossy, András; Rode, Michael

doi:10.1016/j.jhydrol.2007.05.020

Cited by 28 publications

(14 citation statements)

References 33 publications

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“…For example, runoff from rainfall and snowmelt events is known to drive nitrate transport processes (e.g., Creed et al, 1996;Shrestha et al, 2007), and snowmelt in combination with frozen soils has been found to be especially important in the transport of phosphorus in the Canadian Prairies (Salvano et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Modelling of climate-induced hydrologic changes in the Lake Winnipeg watershed

Shrestha

Dibike

Prowse

2012

Journal of Great Lakes Research

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Section: Introductionmentioning

confidence: 99%

Modelling of climate-induced hydrologic changes in the Lake Winnipeg watershed

Shrestha

Dibike

Prowse

2012

Journal of Great Lakes Research

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“…Because of the size of the study catchment and the mixed land use within the catchment, infiltration excess surface runoff can only be produced in parts of the catchment and during the spring and summer season with sufficient high rainfall intensities. Former studies [Shrestha et al, 2007] showed that this infiltration excess surface runoff component led only to small runoff events during the vegetation period. Larger events are restricted to situations with high soil moisture conditions and associated saturated surface runoff generation especially during the cold seasons.…”

Section: Uncertainty Of Simulated Dischargesmentioning

confidence: 99%

Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?

Balin

Lee

Rode

2010

Water Resources Research

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[1] Uncertainty analysis has become an important topic in environmental research. Uncertainty in hydrological modeling, in general, has been studied by investigating mainly the influence of the parameter uncertainty on the uncertainty of the simulated outputs. This paper focuses essentially on the impact of point input uncertainty on fully distributed hydrological modeling and proposes an integrated approach to cope with input and parameter uncertainty. The approach uses Bayesian theory in two steps: first, to compute the uncertainty in input forcing data, and second, to compute the hydrological parameter uncertainty and the uncertainties of a lumped-error term that include other sources of errors: response errors and model error. The method is applied with a fully distributed model: WaSiM-ETH. The results show that the methodology proposed here is a valuable tool to assess different sources of uncertainty in hydrological modeling and show the effects of uncertainty in the input forcing when a fully distributed, physically based hydrological model is used.Citation: Balin, D., H. Lee, and M. Rode (2010), Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?, Water Resour. Res., 46, W11520,

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“…The model was applied to an agricultural watershed in central Illinois for predicting daily runoff and nitrate-nitrogen concentration. Shrestha et al (2007) developed a fuzzyrule based model for simulating watershed scale nitrate transport. The model uses simulated flows generated by WaSiM-ETH (Schulla and Jasper, 2001) upon which a fuzzy-rule based nitrate transport model was built using simulated annealing.…”

Section: Real Time Flood Forecastingmentioning

confidence: 99%