Study of the Xinanjiang Model Parameter Calibration

The Xinanjiang rainfall–runoff model has been successfully applied in many humid and sub-humid areas in China since 1973. The wide application is due to the simple model structure, the clear physical meaning of the parameters and the well-defined model calibration procedure. However, due to a data scarcity problem and short runoff concentration time, its applications to small drainage basins are difficult. Therefore, we investigate the model application in Lianghui, a small drainage basin of Zhejiang province in China. By using generalized likelihood uncertainty estimation (GLUE) methodology, the sensitivity of parameters of Xinanjiang model was investigated. The data clearly showed that equifinality phenomenon was evident in both water balance parameter calibration and runoff routing parameter calibration procedures. The results showed that K (evapotranspiration conversion coefficient), Cs (recession constant in channel system) and Sm (areal free water storage capacity of surface soil) are the most sensitive parameters for the water balance parameter calibration while Cs, Sm and Wm (mean area tension water capacity) are the most sensitive parameters for runoff routing parameter calibration. The conclusion is favourable for understanding parameters of Xinanjiang model in order to provide valuable scientific information for simulating hydrological processes in small drainage basins.

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Application of extended Kalman filter techniques for dynamic model parameter calibration

2009 IEEE Power & Energy Society General Meeting ◽

10.1109/pes.2009.5275423 ◽

2009 ◽

Cited By ~ 46

Author(s):

Zhenyu Huang ◽

Pengwei Du ◽

Dmitry Kosterev ◽

Bo Yang

Keyword(s):

Kalman Filter ◽

Dynamic Model ◽

Extended Kalman Filter ◽

Parameter Calibration ◽

Model Parameter

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Model parameter calibration method of SiC power MOSFETs behavioural model

IET Power Electronics ◽

10.1049/iet-pel.2019.0400 ◽

2020 ◽

Vol 13 (3) ◽

pp. 426-435

Author(s):

Yang Wen ◽

Yuan Yang ◽

Yong Gao

Keyword(s):

Calibration Method ◽

Parameter Calibration ◽

Power Mosfets ◽

Model Parameter ◽

Behavioural Model

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Machine learning approaches to parameter calibration and uncertainty propagation for seasonal flood risk prediction

10.5194/egusphere-egu21-13262 ◽

2021 ◽

Author(s):

Oliver Bent ◽

Julian Kuehnert ◽

Sekou Remy ◽

Anne Jones ◽

Blair Edwards

Keyword(s):

Climate Change ◽

Machine Learning ◽

Uncertainty Quantification ◽

Supervised Learning ◽

Risk Prediction ◽

Flood Risk ◽

Parameter Calibration ◽

Model Parameter ◽

Seasonal Flood ◽

Node Type

<div data-node-type="line"> <div data-node-type="line">The increase in extreme weather associated with acute climate change is leading to more frequent and severe flood events.&#160; In the window of months and years, climate change adaption is critical to mitigate risk on socio-economic systems. Mathematical and computational models have become widely used tools to quantify the impact of catastrophic flooding and to predict future flood risks. For decision makers to plan ahead and to select informed policies and interventions, it is vital that the uncertainties of these models are well estimated. Besides the inherent uncertainty of the mathematical model, uncertainties arise from parameter calibration and the driving observational climate data.</div> <div data-node-type="line">Here we focus on the uncertainty of seasonal flood risk prediction for which we treat uncertainty propagation as a two step process. Firstly through calibration of model parameter distributions based on observational data. In order to propagate parameter uncertainties, the posed calibration framework is required to infer model parameter posterior distributions, as opposed to a single best-fit estimate. While secondly uncertainty is propagated by the seasonal weather forecasts driving the flood risk prediction models, such model drivers have their own inherent uncertainty as predictions. Through handling both sources of uncertainty and its propagation we investigate the impacts of combined uncertainty quantification methods for flooding predictions. The first step focussing on the flooding models own characterisation of uncertainty and the second characterising how uncertain model drivers impact our future predictions.</div> <div data-node-type="line">In order to achieve the above features of a calibration framework for flood models we leverage concepts from machine learning. At the core we assume a minimisation of a loss function by the methods based on the supervised learning task in order to achieve calibration of the flood model. Uncertainty quantification is equally a growing field in machine learning or AI with regards the interpretability of parametric models. For this purpose we have adopted a Bayesian framework which contains natural descriptions of model expectation and variance. Through combining uncertainty quantification with the steps of supervised learning for parameter calibrations we propose a novel approach for seasonal flood risk prediction.</div> </div><div data-node-type="line"></div>

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