Impact of robustness of hydrological model parameters on flood prediction uncertainty

Qi, Wei1,2,3,4,5; Zhang, Chi1; Fu, Guangtao2; Sweetapple, Chris2; Liu, Yanli6,7

2019-10

10.1111/jfr3.12488

Journal of Flood Risk Management   影响因子和分区

1753-318X

The robustness of hydrological model parameter values in flood predictions is a known area of concern, but there is a lack of a comprehensive approach to the handling of model parameter robustness, model simulation uncertainty and multiobjective model calibration when calibrating multiple flood data sets. For investigation of the impact of robustness of hydrological model parameters on flood simulation uncertainty, this paper develops a Minimax-Regret robust multiobjective optimisation framework for robust hydrological model parameter calibration and uncertainty analysis. The robustness is considered as an objective function in this study instead of a constraint as in previous research. A physically based semi-distributed hydrological model is employed to illustrate the proposed framework in a midscale catchment. Results show that the proposed framework can effectively explore robust hydrological model parameter values and quantify flood simulation uncertainty. A trade-off between robustness and Nash-Sutcliffe efficiency is found, implying that the better the Nash-Sutcliffe efficiency, the less robust the non-dominated parameter values and that improving robustness alone cannot guarantee narrower uncertainty intervals and greater containing ratios. These results reveal that robustness should not be used alone to select behavioural parameter sets, and a balance has to be made between robustness and Nash-Sutcliffe efficiency.

flash flood hydrological modelling robustness TOPMODEL uncertainty analysis

SCI

英语

其他

UK Engineering & Physical Sciences Research Council[EP/N010329/1]

Environmental Sciences & Ecology ; Water Resources

Environmental Sciences ; Water Resources

WOS:000485980500021

WILEY

Web of Science

1.Dalian Univ Technol, Sch Hydraul Engn, Dalian 116024, Peoples R China
2.Univ Exeter, Coll Engn Math & Phys Sci, Ctr Water Syst, Exeter, Devon, England
3.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China
4.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Guangdong Prov Key Lab Soil & Groundwater Pollut, Shenzhen, Peoples R China
5.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Peoples R China
6.Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Jiangsu, Peoples R China
7.Minist Water Resources, Res Ctr Climate Change, Nanjing, Jiangsu, Peoples R China

Qi, Wei,Zhang, Chi,Fu, Guangtao,et al. Impact of robustness of hydrological model parameters on flood prediction uncertainty[J]. Journal of Flood Risk Management,2019,12.

Qi, Wei,Zhang, Chi,Fu, Guangtao,Sweetapple, Chris,&Liu, Yanli.(2019).Impact of robustness of hydrological model parameters on flood prediction uncertainty.Journal of Flood Risk Management,12.

Qi, Wei,et al."Impact of robustness of hydrological model parameters on flood prediction uncertainty".Journal of Flood Risk Management 12(2019).