Flow regionalization in the upper Paraguay basin, Brazil

Tucci, Carlos Eduardo Morelli; Silveira, ANDRé; Sanchez, J. C. Robredo; Albuquerque, Fernando

doi:10.1080/02626669509491434

Cited by 22 publications

(14 citation statements)

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“…Since there are no discharge stations at the interface of the Jacuí and Guaíba rivers, continuous discharge values for the Jacuí River were estimated based on a regionalization method (Tucci et al, 1995;Samuel et al, 2011). The regionalization method involved transferring discharge data from subsidiaries of the Jacuí River to its outlet by using interpolation techniques that depend on geographic location.…”

Section: Input Datamentioning

confidence: 99%

Assessment of numerical schemes for solving the advection–diffusion equation on unstructured grids: case study of the Guaíba River, Brazil

Pereira

Fragoso

Uvo

et al. 2013

Nonlin. Processes Geophys.

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Abstract. In this work, a first-order upwind and a high-order flux-limiter schemes for solving the advection-diffusion equation on unstructured grids were evaluated. The numerical schemes were implemented as a module of an unstructured two-dimensional depth-averaged circulation model for shallow lakes (IPH-UnTRIM2D), and they were applied to the Guaíba River in Brazil. Their performances were evaluated by comparing mass conservation balance errors for two scenarios of a passive tracer released into the Guaíba River. The circulation model showed good agreement with observed data collected at four water level stations along the Guaíba River, where correlation coefficients achieved values up to 0.93. In addition, volume conservation errors were lower than 1 % of the total volume of the Guaíba River. For all scenarios, the higher order flux-limiter scheme has been shown to be less diffusive than a first-order upwind scheme. Accumulated conservation mass balance errors calculated for the flux limiter reached 8 %, whereas for a first-order upwind scheme, they were close to 18 % over a 15-day period. Although both schemes have presented mass conservation errors, these errors are assumed negligible compared with kinetic processes such as erosion, sedimentation or decay rates.

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Section: Input Datamentioning

confidence: 99%

Assessment of numerical schemes for solving the advection–diffusion equation on unstructured grids: case study of the Guaíba River, Brazil

Pereira

Fragoso

Uvo

et al. 2013

Nonlin. Processes Geophys.

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show abstract

“…[1] fitting a probability distribution to a flow series, or parameters to a flow duration curve, and then relating the model parameters to physical catchment characteristics (e.g., Smakhtin et al, 1997;Tucci et al, 1995;van der Wateren-de Hoog, 1995); [2] relating index flows with specific return periods (e.g., the mean or median annual flood) to physical catchment characteristics (e.g., NERC, 1975;Schreiber and Demuth, 1997;Vogel and Kroll, 1992);or [3] deriving the parameters of an intermediate conceptual rainfall-runoff model from physical catchment characteristics and then simulating the required discharge sequences (e.g., Ibrahim and Cordery, 1995;Pirt, 1983;Post and Jakeman, 1996;Sefton and Howarth, 1998).…”

Section: Introductionmentioning

confidence: 99%

Flood estimation at ungauged sites using artificial neural networks

Dawson

Abrahart

Shamseldin

et al. 2006

Journal of Hydrology

224

111

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Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists.Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance. KeywordsArtificial neural networks, flood estimation, ungauged catchments Page 2 / 39 IntroductionThe UK Flood Estimation Handbook (FEH) notes that "many flood estimation problems arise at ungauged sites for which there are no flood peak data" (Reed and Robson, 1999:12 Pirt, 1983;Post and Jakeman, 1996;Sefton and Howarth, 1998). Page 3 / 39The FEH involves the use of an index flood procedure to derive the flood frequency curve at ungauged sites. The index flood is a middle-sized flood for which the mean or median of the flood data series is typically used (Grover et al., 2002) Hall et al. (2000) used between four and twelve input catchment characteristics to predict the same two EV1 parameter outputs using data from sites in Sumatra and Java; whereas Dastorani and Wright (2001) found that seven catchment inputs were sufficient to predict the index flood for selected catchments in the UK. This paper discusses the application of ANNs to predict the index flood for a much larger sample of selected catchments in the UK. It also considers the estimation of 10-, 20-and 30-year flood event magnitudes at such sites. Given the range of record lengths available, the 20-year flood event was chosen for further discussion as it is a convenient metric that is often used for the purposes of comparison in other studies (for example, see Reynard et al., 2004).

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“…Das 180.000 km 2 große Gebiet wird von Savannen, Waldinseln, galeriewaldgesäumten Flüssen und Seen geprägt (Tucci et al, 1995).Während der jährlichen Regenzeit werden ausgedehnte Flä-chen überschwemmt. Ein Großteil des Wassers verdunstet bereits vor dem Rücklauf in den Rio Paraguay (Ponce, 1995).…”

Section: Kasten D 44-2unclassified

Wege zu einem nachhaltigen Umgang mit Süßwasser

1998

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Flow regionalization in the upper Paraguay basin, Brazil

Cited by 22 publications

References 1 publication

Assessment of numerical schemes for solving the advection–diffusion equation on unstructured grids: case study of the Guaíba River, Brazil

Assessment of numerical schemes for solving the advection–diffusion equation on unstructured grids: case study of the Guaíba River, Brazil

Flood estimation at ungauged sites using artificial neural networks

Wege zu einem nachhaltigen Umgang mit Süßwasser

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