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, Jin Gu

doi:10.1007/s12205-012-1646-x

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…Characterization of the events applied to the flood modeling in scenario 1, with emphasis to the total rainfall for each event, total duration of rainfall event, mean intensity, effective rainfall, duration of effective rainfall, initial abstractions obtained by cumulative rainfall until the start time of the DSR observed on the hydrograph, 5-day antecedent rainfall, estimated Curve Number from Clark's UH and tabulated Curve Number to determine the effective rainfall hyetographs according to CN-NRCS method (SCS, 1971 Direct surface runoff hydrographs estimated from the SCS UH and Clark's IUH models and the direct surface runoff hydrograph monitored at the Cadeia river watershed's outlet for each analyzed event according to scenario 1 (lumped approach) and scenario 2 (semi-distributed approach). Spatial discretization influence on flood modeling using unit hydrograph theory 8/12 notable variation (KUMAR et al, 2002;SAHOO et al, 2006;AHMAD, 2009;ADIB et al, 2010;YOO;YOON, 2012). Although equations often take fixed parameters into account to estimate t c , its value changes among events since it depends on characteristics that are not commonly included in these equations, such as magnitudes associated with rainfall and antecedent soil moisture.…”

Section: Scenario 1 (Lumped Approach)mentioning

confidence: 99%

Spatial discretization influence on flood modeling using unit hydrograph theory

Steinmetz

Beskow

Terra

et al. 2019

RBRH

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The Unit Hydrograph (UH) is the most popular method for flood related applications. There are several conceptual and geomorphological models based on UH and coupled with different spatial discretizations. However, there are few studies concerning the evaluation of UH models according to semi-distributed approaches. This study aimed to assess the influence of lumped and semi-distributed modeling on the applicability of Soil Conservation Service UH (SCS UH) and Clark’s Instantaneous UH (Clark’s IUH) for estimation of flood hydrographs. The methodological procedures were conducted in the Hydrological Modeling System (HEC-HMS) using rainfall-runoff events of a gauged watershed in Southern Brazil. The main conclusions were: a) CIUH under the semi-distributed approach provided slightly superior performance; b) CIUH was able to effectively estimate the direct surface runoff hydrographs even for long duration rainfall events; c) SCS UH presented more accurate hydrographs for lumped modeling; d) the Nelder and Mead algorithm may have limited application.

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Section: Scenario 1 (Lumped Approach)mentioning

confidence: 99%

Spatial discretization influence on flood modeling using unit hydrograph theory

Steinmetz

Beskow

Terra

et al. 2019

RBRH

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“…Yoo et al . () found that error in rainfall events could lead to uncertainty in the model parameters by about 30% in the case of Chugnju Dam Basin, Korea. The results of these studies indicate that error in the input rainfall data can lead to significant uncertainty in the outputs of a rainfall–runoff model.…”

Section: Introductionmentioning

confidence: 99%

Parameter uncertainty of the AWBM model when applied to an ungauged catchment

Haque

Rahman

Hagare

et al. 2014

Hydrological Processes

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Abstract:In this study, a quantitative assessment of uncertainty was made in connection with the calibration of Australian Water Balance Model (AWBM) for both gauged and ungauged catchment cases. For the gauged catchment, five different rainfall data sets, 23 different calibration data lengths and eight different optimization techniques were adopted. For the ungauged catchment case, the optimum parameter sets obtained from the nearest gauged catchment were transposed to the ungauged catchments, and two regional prediction equations were used to estimate runoff. Uncertainties were ascertained by comparing the observed and modelled runoffs by the AWBM on the basis of different combinations of methods, model parameters and input data. The main finding from this study was that the uncertainties in the AWBM modelling outputs could vary from À1.3% to 70% owing to different input rainfall data, À5.7% to 11% owing to different calibration data lengths and À6% to 0.2% owing to different optimization techniques adopted in the calibration of the AWBM. The performance of the AWBM model was found to be dominated mainly by the selection of appropriate rainfall data followed by the selection of an appropriate calibration data length and optimization algorithm. Use of relatively short data length (e.g. 3 to 6 years) in the calibration was found to generate relatively poor results. Effects of different optimization techniques on the calibration were found to be minimal. The uncertainties reported here in relation to the calibration and runoff estimation by the AWBM model are relevant to the selected study catchments, which are likely to differ for other catchments. The methodology presented in this paper can be applied to other catchments in Australia and other countries using AWBM and similar rainfall-runoff models.

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