Spatial discretization influence on flood modeling using unit hydrograph theory

Steinmetz, Alice Alonzo; Beskow, Samuel; Terra, Fabrício da Silva; Nunes, Maria Cândida Moitinho; Vargas, Marcelle Martins; Horn, João Francisco Carlexo

doi:10.1590/2318-0331.241920180143

Cited by 6 publications

(5 citation statements)

References 42 publications

(37 reference statements)

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“…The application of the MUSLE model is reiterated and subordinated to the T parameter from the probabilistic model. The tools used include QGIS for mapping works [42], and HEC-HMS for hydrometeorological simulation [27,28,43]. The factors of the MUSLE model, integrated in HEC-HMS, are estimated in a semi-distributed way in the different delimited sub-basins (Figure 3d).…”

Section: Applied Methodologymentioning

confidence: 99%

Sensitivity Analysis in Mean Annual Sediment Yield Modeling with Respect to Rainfall Probability Distribution Functions

Antonio

Rodríguez-Pérez

Alonso

et al. 2022

Land

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An accurate estimation of the mean annual sediment yield from basins contributes to optimizing water resources planning and management. More specifically, both reservoir sedimentation and the damage caused to infrastructures fall within its field of application. Through a simple probabilistic combination function implemented in hydrometeorological models, this sediment yield can be estimated on a planning and management scale for ungauged basins. This probabilistic combination methodology requires the use of probability distribution functions to model design storms. Within these functions, SQRT-ET max and log-Pearson type III are currently highlighted in applied hydrology. Although the Gumbel distribution is also relevant, its use has progressively declined, as it has been considered to underestimate precipitation depth and flow discharge for high return periods, compared to the SQRT-ET max and log-Pearson III functions. The quantification of sediment yield through hydrometeorological models will ultimately be affected by the choice of the probability distribution function. The following four different functions were studied: Gumbel type I with a small sample size, Gumbel type I with a large sample size, log-Pearson type III and SQRT-ET max. To illustrate this, the model with these four functions has been applied in the Alto Palmones basin (South Iberian Peninsula). In this paper, it is shown that the application of Gumbel function type I with a small sample size, for the estimation of the mean annual sediment yield, provides values on the conservative side, with respect to the SQRT-ET max and log-Pearson type III functions.

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Section: Applied Methodologymentioning

confidence: 99%

Sensitivity Analysis in Mean Annual Sediment Yield Modeling with Respect to Rainfall Probability Distribution Functions

Antonio

Rodríguez-Pérez

Alonso

et al. 2022

Land

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“…According to the Köppen classification, the climate of the region is characterized as Cfa, with an average temperature of the warmest month exceeding 22ºC and well-distributed monthly rainfall (Alvares et al, 2014). The average annual rainfall depth in CRW is 1,367 mm (Steinmetz et al, 2019).…”

Section: Characterization Of the Study Areamentioning

confidence: 99%

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

et al. 2021

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The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter – referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.

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“…A bunch of research found different overestimation of the peak flow with a maximum reported up to 7 times (Brunner et al, 2018;Cunha et al, 2015;Hoffmeister & Weisman, 1977;Singh et al, 2014;Steinmetz et al 2019;Tucci, 1998;Thorvat & Patel, 2016;Zakizadeh & Malekinezhad, 2015;). We highlight the definition of bold and conservative combination of parameters which produced as output, respectively, the lower and higher peak flows.…”

Section: /12mentioning

confidence: 99%

“…Considering the uncertainty of semi-distributed and concentrated modeling, Steinmetz et al (2019) explored it with SCS and Clark SUH in a 120 km 2 area basin in Brazil. The errors in the concentrated model were up to 137% and 85%, respectively for SCS and Clark methods.…”

Section: Introductionmentioning

confidence: 99%

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The uncertainties of synthetic unit hydrographs applied for basins with different runoff generation processes

Pedroso

Männich

2021

RBRH

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Synthetic unit hydrographs (SUH) are useful tools for the estimation of maximum flows in basins lacking historical records of measurements. However, these methods have many uncertainties and do not always produce results consistent with reality. This study comparatively analyzed the uncertainty of the application of the Snyder, SCS, and Clark HUS methods, widely used, in relation to the observed hydrographs, in the Pequeno River and the Espingarda River basins, located in the State of Paraná, considered small from the point of view of the drainage area. The simulation was performed using the HEC-HMS 4.2.1 software considering a combination of parameters that produced the higher and lower peak flow, respectively named as conservative and bold approaches. It was verified that the SUH methods, in general, overestimated the peak flows for both basins under study. In addition, the results obtained showed that SUH are fundamentally conservative models so that a bold approach in estimating the parameters input leads to results with smaller errors in simulated peak flows. Even running the SUH with the real excess rainfall as input there is an overestimation of the peak flow. SCS SUH produced the highest peak flows and consequently the largest errors while Snyder’s SUH produced the smallest errors. The magnitude of the overestimation of the peak flow for the Pequeno River was up to 60 folds. Its geology features suggest a Dunnian runoff generation process, which explains the larger errors.

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Spatial discretization influence on flood modeling using unit hydrograph theory

Cited by 6 publications

References 42 publications

Sensitivity Analysis in Mean Annual Sediment Yield Modeling with Respect to Rainfall Probability Distribution Functions

Sensitivity Analysis in Mean Annual Sediment Yield Modeling with Respect to Rainfall Probability Distribution Functions

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

The uncertainties of synthetic unit hydrographs applied for basins with different runoff generation processes

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