Analysis of the Effect of Uncertainty in Rainfall-Runoff Models on Simulation Results Using a Simple Uncertainty-Screening Method

被引:3
|
作者
Shin, Mun-Ju [1 ]
Kim, Chung-Soo [2 ]
机构
[1] Jeju Prov Dev Corp, Water Resources Res Team, 1717-35 Namjo Ro, Jeju Si 63345, Jeju Do, South Korea
[2] Korea Inst Civil Engn & Bldg Technol, Dept Land Water & Environm Res, 283 Goyangdae Ro, Goyang Si 10223, Gyeonggi Do, South Korea
来源
WATER | 2019年 / 11卷 / 07期
关键词
uncertainty analysis; rainfall-runoff model; DREAM algorithm; indicators of hydrologic alterations; equifinality; SENSITIVITY; EQUIFINALITY; CALIBRATION; EVOLUTION;
D O I
10.3390/w11071361
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Various uncertainty analysis methods have been used in various studies to analyze the uncertainty of rainfall-runoff models; however, these methods are difficult to apply immediately as they require a long learning time. In this study, we propose a simple uncertainty-screening method that allows modelers to investigate relatively easily the uncertainty of rainfall-runoff models. The 100 best parameter values of three rainfall-runoff models were extracted using the efficient sampler DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, and the distribution of the parameter values was investigated. Additionally, the ranges of the values of a model performance evaluation statistic and indicators of hydrologic alteration corresponding to the 100 parameter values for the calibration and validation periods was analyzed. The results showed that the Sacramento model, which has the largest number of parameters, had uncertainties in parameters, and the uncertainty of one parameter influenced all other parameters. Furthermore, the uncertainty in the prediction results of the Sacramento model was larger than those of other models. The IHACRES model had uncertainty in one parameter related to the slow flow simulation. On the other hand, the GR4J model had the lowest uncertainty compared to the other two models. The uncertainty-screening method presented in this study can be easily used when the modelers select rainfall-runoff models with lower uncertainty.
引用
收藏
页数:24
相关论文
共 39 条
  • [1] A conceptual grey rainfall-runoff model for simulation with uncertainty
    Alvisi, Stefano
    Bernini, Anna
    Franchini, Marco
    JOURNAL OF HYDROINFORMATICS, 2013, 15 (01) : 1 - 20
  • [2] Effect of rainfall uncertainty on the performance of physically based rainfall-runoff models
    Fraga, Ignacio
    Cea, Luis
    Puertas, Jeronimo
    HYDROLOGICAL PROCESSES, 2019, 33 (01) : 160 - 173
  • [3] Assessing the Uncertainty of the Xinanjiang Rainfall-Runoff Model: Effect of the Likelihood Function Choice on the GLUE Method
    Alazzy, Alaa Alden
    Lue, Haishen
    Zhu, Yonghua
    JOURNAL OF HYDROLOGIC ENGINEERING, 2015, 20 (10)
  • [4] Comparison of uncertainty analysis methods for a distributed rainfall-runoff model
    Yu, PS
    Yang, TC
    Chen, SJ
    JOURNAL OF HYDROLOGY, 2001, 244 (1-2) : 43 - 59
  • [5] Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models
    Sikorska, A. E.
    Scheidegger, A.
    Banasik, K.
    Rieckermann, J.
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2012, 16 (04) : 1221 - 1236
  • [6] Application of a Sampling Based on the Combined Objectives of Parameter Identification and Uncertainty Analysis of an Urban Rainfall-Runoff Model
    Zhao, Dongquan
    Chen, Jining
    Wang, Haozheng
    Tong, Qingyuan
    JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2013, 139 (01) : 66 - 74
  • [7] Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates
    Yao, Lei
    Zhang, Jiangjiang
    Cao, Chenglong
    Zheng, Feifei
    ENVIRONMENTAL MODELLING & SOFTWARE, 2025, 185
  • [8] Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study
    Shafii, Mahyar
    Tolson, Bryan
    Matott, Loren Shawn
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2014, 28 (06) : 1493 - 1510
  • [9] Quantifying uncertainty in rainfall-runoff models due to design losses using Monte Carlo simulation: a case study in New South Wales, Australia
    Loveridge, Melanie
    Rahman, Ataur
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2014, 28 (08) : 2149 - 2159
  • [10] Parameter calibration and uncertainty estimation of a simple rainfall-runoff model in two case studies
    Zhang, X.
    Hoermann, G.
    Fohrer, N.
    Gao, J.
    JOURNAL OF HYDROINFORMATICS, 2012, 14 (04) : 1061 - 1074