Flood Frequency Estimating Techniques for Small Watersheds

Fleming, George; Franz, Delbert D.

doi:10.1061/jyceaj.0003072

Cited by 11 publications

(4 citation statements)

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“…It was often the case that longer rainfall time series were available, so that fitting a stochastic model of rainfalls would allow the generation of long rainfall sequences as an input to a rainfall‐runoff model calibrated to whatever discharge data were available. This approach had already used based directly on observed rainfall series, for example, using the Hydrocomp version of the Stanford Watershed Model in Fleming and Franz (1971).…”

Section: Generating Stochastic Inputs To Hydrological Modelsmentioning

confidence: 99%

“…Such an approach has less often been used to generate the statistics of droughts given the limitations of many rainfall‐runoff models in predicting low flows as connectivities in flow pathways start to break down. One of the first studies to use continuous simulation to estimate flood frequency made use of historical rainfall records and evapotranspiration estimates (Fleming & Franz, 1971). This limits the length of simulations can be made, whereas an approach based on distribution functions can generate records of any required length.…”

Section: Generating Stochastic Inputs To Hydrological Modelsmentioning

confidence: 99%

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Issues in generating stochastic observables for hydrological models

Beven

2021

Hydrological Processes

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This paper provides a historical review and critique of stochastic generating models for hydrological observables, from early generation of monthly discharge series, through flood frequency estimation by continuous simulation, to current weather generators. There are a number of issues that arise in such models, from uncertainties in the observational data on which such models must be based, to the potential persistence effects in hydroclimatic systems, the proper representation of tail behaviour in the underlying distributions, and the interpretation of future scenarios.

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Section: Generating Stochastic Inputs To Hydrological Modelsmentioning

confidence: 99%

Section: Generating Stochastic Inputs To Hydrological Modelsmentioning

confidence: 99%

Issues in generating stochastic observables for hydrological models

Beven

2021

Hydrological Processes

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“…Even in a small basin, the error in the peak flow based on the RF was found very big (Hiemstra and Reich 1967;Fleming and Franz 1971;Linsley 1986). More recent studies in natural basins also showed that the RF overestimates the peak flow.…”

Section: Introductionmentioning

confidence: 99%

Analysis of error in the peak flow of rational formula due to basin storage effect

Lee,

Yoo

2023

Preprint

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This study analyzes the error in the peak flow of the Rational Formula (RF) or the Modified Rational Formula (MRF). For this objective, this study derives the RF and the MRF based on a simple rainfall-runoff analysis in a rectangular basin. Additionally, the storage effect in a basin is considered by introducing a linear reservoir at the exit of the basin. The derived runoff hydrograph is found to be identical to the Clark unit hydrograph (Clark UH). That is, the RF, MRF and Clark UH are found to be related to each other in a consistent procedure of rainfall–runoff analysis. Analysis of error in the peak flow, as well as that in the peak time, of RF or MRF is done by deriving and analyzing its ratio to that of the derived Clark UH. The result shows that the error (or ratio) can be explained effectively by the Russel coefficient (α), that is, the relative role of storage effect in a basin. Obviously, the RF or MRF is acceptable only when the Russel coefficient is very small (i.e., when the storage effect is very small). For example, when \({\alpha }=0.5\), the error in peak flow of RF or MRF is 30%. The error becomes higher to be 50% when \({\alpha }=1.0\). Additionally, it is found that the sum of the two ratios, one for the peak flow and the other for the peak time, is always 2. That is, the increase rate of peak time and the decrease rate of peak flow, introduced by considering the storage effect, are always the same.

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“…In most cases, regionalization methods have been developed for the estimation of the characteristics of flood frequency distributions (Fleming and Franz, 1971;Lamb, 1999;Blazkova and Beven, 2002), flow duration curves (Holmes et al, 2002;Castellarin et al, 2007;Li et al, 2010), and the parameters of hydrological models (Nash, 1960;Abdulla and Lettenmaier, 1997;Fernandez et al, 2000;Heuvelmans et al, 2006;Boughton and Chiew, 2007;Bastola et al, 2008;Hundecha et al, 2008;Wallner et al, 2008) at ungauged sites. Especially, Oudin et al (2008) compared three regionalization scheme, namely, spatial proximity, physical similarity, and regression scheme in France and showed that spatial proximity showed best results while the regression approach showed the least compared to other schemes, but all regionalization results were far behind the results with full calibration.…”

Section: Introductionmentioning

confidence: 99%

Prediction of direct runoff hydrographs utilizing stochastic network models: a case study in South Korea

Seo

Park

2014

Preprint

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Abstract. In this study, we combine stochastic network models that reproduce the actual width function and the width function based instantaneous unit hydrograph (WFIUH) that directly makes use of a width function and converts it into runoff hydrographs. We evaluated the stochastic network models in terms of reproducing the actual width function and also the robustness of the semi-distributed model (WFIUH) in application to a test watershed in South Korea. The stochastic network model has an advantage that it replicates width functions of actual river networks, whereas the WFIUH has an advantage that the parameter values are physically determined, which can be potentially advantageous in prediction of ungauged basins. This study demonstrates that the combination of the Gibbsian model and the WFIUH is able to reproduce runoff hydrographs not just for the case of uniform rainfall over the test catchment but also for moving storms. Therefore, results of this study indicate that the impact of spatial and temporal rainfall variation on runoff hydrographs can be evaluated by the suggested approach in ungauged basins even without detailed knowledge of river networks. Once the regional similarity in river network configuration is identified, the proposed approach can be potentially utilized to estimate the runoff hydrographs for ungauged basins.

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Flood Frequency Estimating Techniques for Small Watersheds

Cited by 11 publications

References 0 publications

Issues in generating stochastic observables for hydrological models

Issues in generating stochastic observables for hydrological models

Analysis of error in the peak flow of rational formula due to basin storage effect

Prediction of direct runoff hydrographs utilizing stochastic network models: a case study in South Korea

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