Hydrological and hydraulic modelling applied to the mapping of flood-prone areas

Monte, Benício Emanoel Omena; Costa, Denis Duda; Chaves, Mahelvson Bazilio; Magalhães, Louis de Oliveira; Uvo, Cíntia Bertacchi

doi:10.21168/rbrh.v21n1.p152-167

Cited by 26 publications

(22 citation statements)

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“…Lack of adequate topographic data can lead to problems for the description of flooded areas provided by the hydraulic model [8,34,35]. Riverbanks and floodplains are commonly identified by digital elevation models (DEM), cross section, or geometrical description of hydraulic structures [19,36].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of a Two-Dimensional Hydraulic Model

Oubennaceur

Chokmani

Nastev

et al. 2018

Water

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Abstract:A reliability approach referred to as the point estimate method (PEM) is presented to assess the uncertainty of a two-dimensional hydraulic model. PEM is a special case of numerical quadrature based on orthogonal polynomials, which evaluates the statistical moments of a performance function involving random variables. When applied to hydraulic problems, the variables are the inputs to the hydraulic model, and the first and second statistical moments refer to the mean and standard deviation of the model's output. In providing approximate estimates of the uncertainty, PEM appears considerably simpler and requires less information and fewer runs than standard Monte Carlo methods. An example of uncertainty assessment is shown for simulated water depths in a 46 km reach of the Richelieu River, Canada. The 2D hydraulic model, H2D2, was used to solve the shallow water equations. Standard deviations around the mean water depths were estimated by considering the uncertainties of three main input variables: flow rate, Manning's coefficient and topography. Results indicate that the mean standard deviation is <27 cm for the considered flow rates of 759, 824, 936, 1113 m 3 /s. Higher standard deviations were obtained upstream of the topographic shoal at the municipality of Saint-Jean-sur-Richelieu. The PEM method adds further value to the H2D2 model predictions as it indicates the magnitude and the spatial variation in uncertainties. The effort required to complete an uncertainty analysis using the PEM method is minimal and the resulting insight is meaningful. This knowledge should be incorporated into decision-making in the context of flood risk assessment.

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Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of a Two-Dimensional Hydraulic Model

Oubennaceur

Chokmani

Nastev

et al. 2018

Water

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“…The following parameters were used for calibration of the MGB-IPH model: soil moisture capacity (Wm); a parameter defining the shape of the storage-saturation relation (b); a parameter controlling flow during dry periods (Kbas); a parameter controlling quantity of soil water emerging as surface flow (Kint); a parameter controlling the shape of the reduction in intermediate or sub-surface drainage (XL); a parameter controlling flow from subterranian reservoir to the soil surface layer during a time-interval (CAP); residual storage (Wc); the coefficient for surface propagation in cells (CS); the coefficient for sub-surface propagation in cells (CI); and the delay for the subterranean reservoir (CB). A detailed description of the MGB-IPH model is presented by Collischonn et al (2007) and Monte et al (2016).…”

Section: Acta Amazonicamentioning

confidence: 99%

Hydrological calibration and validation of the MGB-IPH model for water resource management in the upper Teles Pires River basin in the Amazon-Cerrado ecotone in Brazil

et al. 2019

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The upper Teles Pires River basin is located in the Brazilian agriculture frontier in the north of Mato Grosso state and has experienced significant changes in land use and cover, which can cause major changes in its hydrological dynamics. Climatic and hydrologic data are scarce in the region, which poses uncertainties in the decision-making process aiming at the sustainable management of water resources in this strategic area. The aim of this study was to evaluate the performance of the Large-Scale Distributed Hydrological Model (MGB-IPH) to assess water availability of the upper Teles Pires basin and support water resource management in the Amazon-Cerrado ecotone. The MGB-IPH model was calibrated and validated using data from three streamflow stations available in the basin. In order to verify the model performance, the Nash-Sutcliffe (NS) and the PBIAS statistical parameters were applied. Our results show that, by using the MGB-IPH model with generally available data, the maximum and minimum flow regimes can be successfully assessed in the upper Teles Pires basin. The continuity curves of daily flow simulated by the model showed a good fit with the observed flow. Overall, the results demonstrated the applicability of the MGB-IPH model for water resource assessment and management in the basin.

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“…The Northeastern region of Brazil (NEB) is the most vulnerable socio-economic area of the country [6,7]. It exhibits a very large interannual variability of precipitation [8][9][10] during the rainy season with a high intensity of extreme events such as the drought from 2012 to 2016 [11] and the 2010 flood episodes [12]. These extreme events are normally associated with large-scale climate phenomena…”

Section: Introductionmentioning

confidence: 99%

Key Climate Oscillation Factors Controlling Precipitation Variability during the Dry Season in Eastern Northeast Brazil: Study Case of Mundaú and Paraíba Do Meio River Basins

Pereira

Costa

Fragoso

et al. 2018

Water

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In Brazil, the northeastern region (NEB) is considered one of the most vulnerable areas of the country in terms of precipitation variability due to frequent drought episodes during the rainy season. Differently from the Northern NEB (NNEB), where dry season is consistently dry, the Eastern NEB (ENEB) exhibits a high interannual variability of precipitation during the dry season, including years exceeding 400 mm. This work aims at understanding key large-scale climate factors that modulate the high pluviometric variability of ENEB during the dry season. Multivariate statistical techniques were applied to identify the time-frequency relationship between precipitation variability and global climate phenomena. The results suggested that hydrological extreme events during the dry season became more frequent after the 1990s. Moreover, our findings also indicated a relationship, at multiannual time scales, between the state of El Niño Southern Oscillation (ENSO) and Atlantic Meridional Mode (AMM) and precipitation variability during the dry season. This additional knowledge may contribute to the formation of new perspectives of drought management, leading support to the development of a long-term drought forecasting framework, as well as to the improvement of the water resources management of the region. such as ENSO [13][14][15] and/or anomalous position or intensity of the Intertropical Convergence Zone-ITCZ due to sea surface temperature (SST) variabilities in the tropical Atlantic Ocean [8,16,17]. The NEB is exposed to different spatial-temporal rainfall regimes throughout the year. Precipitation at the Northern NEB (NNEB) is characterized by a rainy season from January to May marked by expressive interannual variability [8,16,18,19], and a dry season from June to December characterized by very low amounts of precipitation (near zero). On the other hand, Eastern NEB (ENEB) has the highest annual average precipitation of the NEB, with a wet season from March to August, is also susceptible to annual variations [20,21], but in a different way than the NNEB, while the ENEB dry season might receive substantial amount of precipitation in some years.Many studies have been conducted in order to analyze and characterize such interannual variability of precipitation in NEB, which, however, were predominantly focused on NNEB rainy season [8,10,17,[22][23][24][25][26]. There is also a growing interest in understanding the dynamics of the precipitation variability over the ENEB [18,20,[27][28][29][30]. However, most of these are focused on the rainy season of the region, its relationship with Atlantic SST patterns and the occurrence of easterly wave disturbance over the tropical Atlantic [18,19,31]. Nevertheless, the dry season in the ENEB may also exhibit high variabilities in terms of volume precipitated, as seen recently (e.g., 96 mm was precipitated in 2016-2017, and more than 400 mm in 2017-2018), and low-rainfall indices during the dry season of this region may directly impact basic economic activities (i.e., mainly rainfe...

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Hydrological and hydraulic modelling applied to the mapping of flood-prone areas

Cited by 26 publications

References 0 publications

Uncertainty Analysis of a Two-Dimensional Hydraulic Model

Uncertainty Analysis of a Two-Dimensional Hydraulic Model

Hydrological calibration and validation of the MGB-IPH model for water resource management in the upper Teles Pires River basin in the Amazon-Cerrado ecotone in Brazil

Key Climate Oscillation Factors Controlling Precipitation Variability during the Dry Season in Eastern Northeast Brazil: Study Case of Mundaú and Paraíba Do Meio River Basins

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