Multi-objective autocalibration of SWAT model for improved low flow performance for a small snowfed catchment

A reliable modelling framework needs to ensure that the model is simulating reality with limited uncertainty, thus enhancing its predictive ability. In the literature, hydrological model assessment using one or more metrics is reported to be inadequate when the river flow regime is required to be re...

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Vydané v:Hydrological sciences journal Ročník 63; číslo 10; s. 1482 - 1501
Hlavní autori: Chilkoti, Vinod, Bolisetti, Tirupati, Balachandar, Ram
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 27.07.2018
Taylor & Francis Ltd
Predmet:
algorithms
Borg multi-objective evolutionary algorithm (MOEA)
Computer simulation
Evaluation
Evolutionary algorithms
Frameworks
Hydrologic models
hydrological signatures
Hydrology
Low flow
Modelling
multi-objective calibration
Multiple objective analysis
River flow
Rivers
Signatures
Soil
Soil and Water Assessment Tool model
Soil water
SWAT
time series analysis
uncertainty
Watersheds
ISSN:0262-6667, 2150-3435, 2150-3435
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Shrnutí:A reliable modelling framework needs to ensure that the model is simulating reality with limited uncertainty, thus enhancing its predictive ability. In the literature, hydrological model assessment using one or more metrics is reported to be inadequate when the river flow regime is required to be reproduced comprehensively. This research is aimed to: (a) calibrate the Soil and Water Assessment Tool (SWAT) based on the concept of multi-objective optimization by applying the Borg multi-objective evolutionary algorithm (MOEA); (b) apply hydrological signatures as objective functions; and (c) adopt a multi-metric approach for model evaluation. The SWAT model was coupled with a relatively newer and powerful Borg MOEA. The inclusion of hydrological signatures as objective functions along with the conventional statistical functions assisted in improving the performance for low flows by 135% in terms of volume efficiency and 65% for flow time series simulation.
Bibliografia:ObjectType-Article-1
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ISSN:0262-6667
2150-3435
2150-3435
DOI:10.1080/02626667.2018.1505047