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
  • [21] Uncertainty of areal average rainfall and its effect on runoff simulation: A case study for the Chungju Dam Basin, Korea
    Yoo, Chulsang
    Kim, Jungho
    Yoon, Jungsoo
    KSCE JOURNAL OF CIVIL ENGINEERING, 2012, 16 (06) : 1085 - 1092
  • [22] Analysis of Problems Related to the Calculation of Flood Frequency Using Rainfall-Runoff Models: A Case Study in Poland
    Mlynski, Dariusz
    SUSTAINABILITY, 2020, 12 (17)
  • [23] Quantifying uncertainty in rainfall–runoff models due to design losses using Monte Carlo simulation: a case study in New South Wales, Australia
    Melanie Loveridge
    Ataur Rahman
    Stochastic Environmental Research and Risk Assessment, 2014, 28 : 2149 - 2159
  • [24] Model calibration and uncertainty analysis of runoff in the Zayanderood River basin using generalized likelihood uncertainty estimation (GLUE) method
    Mirzaei, Majid
    Galavi, Hadi
    Faghih, Mina
    Huang, Yuk Feng
    Lee, Teang Shui
    El-Shafie, Ahmed
    JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA, 2013, 62 (05): : 309 - 320
  • [25] A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source
    Dutta, Dushmanta
    Welsh, Wendy D.
    Vaze, Jai
    Kim, Shaun S. H.
    Nicholls, David
    WATER RESOURCES MANAGEMENT, 2012, 26 (15) : 4397 - 4415
  • [26] A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source
    Dushmanta Dutta
    Wendy D. Welsh
    Jai Vaze
    Shaun S. H. Kim
    David Nicholls
    Water Resources Management, 2012, 26 : 4397 - 4415
  • [27] Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method
    Dong, Leihua
    Xiong, Lihua
    Yu, Kun-xia
    JOURNAL OF APPLIED MATHEMATICS, 2013,
  • [28] Development and uncertainty analysis of infiltration models using PSO and Monte Carlo method
    Podeh, Hassan Torabi
    Parsaie, Abbas
    Shahinejad, Babak
    Arshia, Azadeh
    Shamsi, Zahra
    IRRIGATION AND DRAINAGE, 2023, 72 (01) : 38 - 47
  • [29] Data-based bivariate uncertainty assessment of extreme rainfall-runoff using copulas: comparison between annual maximum series (AMS) and peaks over threshold (POT)
    Esmaeel Dodangeh
    Kaka Shahedi
    Karim Solaimani
    Jenq-Tzong Shiau
    John Abraham
    Environmental Monitoring and Assessment, 2019, 191
  • [30] Data-based bivariate uncertainty assessment of extreme rainfall-runoff using copulas: comparison between annual maximum series (AMS) and peaks over threshold (POT)
    Dodangeh, Esmaeel
    Shahedi, Kaka
    Solaimani, Karim
    Shiau, Jenq-Tzong
    Abraham, John
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2019, 191 (02)