Reducing uncertainties in flood inundation outputs of a two-dimensional hydrodynamic model by constraining roughness

Bhola, Punit Kumar; Leandro, Jorge; Disse, Markus

doi:10.5194/nhess-19-1445-2019

Cited by 19 publications

(10 citation statements)

References 31 publications

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“…Hence, another key issue is that collaborative flood modeling can achieve what is critical for making any type of science useful: iterative interaction between researchers (modelers) and end‐users (DeLorme et al, ; Dilling & Lemos, ). Finally, we note that fine‐resolution urban flood modeling is rapidly advancing and now supports realistic, street‐level flood visualizations (Bhola et al, ; Fewtrell et al, ; Kobayashi et al, ; Sanders, ; Sanders & Schubert, ; Xia et al, ).…”

Section: Introductionmentioning

confidence: 65%

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

et al. 2020

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Existing needs to manage flood risk in the United States are underserved by available flood hazard information. This contributes to an alarming escalation of flood impacts amounting to hundreds of billions of dollars per year and countless disrupted lives and affected communities. Making information about flood hazards useful for the range of decisions that dictate the consequences of flooding poses many challenges. Here, we describe collaborative flood modeling, whereby researchers and end‐users at two coastal sites co‐develop fine‐resolution flood hazard models and maps responsive to decision‐making needs. We find, first of all, that resident perception and awareness of flooding are enhanced more by fine‐resolution depth contour maps than Federal Emergency Management Agency (FEMA) flood hazard classification maps and that viewing fine‐resolution depth contour maps helps to minimize differences in flood perception across subgroups within the community, generating a shared understanding. We also find that collaborative flood modeling supports the engagement of a wide range of end‐users in contemplating the risks of flooding and provides strong evidence that the co‐produced knowledge can be readily adopted and applied for Flood Risk Management (FRM). Overall, collaborative flood modeling advances FRM by providing multiple points of entry for diverse groups of end‐users to contemplate the spatial extent, intensity, timing, chance, and consequences of flooding, thus enabling the web of decision‐making related to flooding to be better informed with the best available science. This transdisciplinary approach emphasizes vulnerability reduction and is complementary to FEMA Flood Insurance Rate Maps used for flood insurance administration.

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Section: Introductionmentioning

confidence: 65%

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

et al. 2020

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“…Relative Uncertainty (%) = δX/ δX O (11) where δX is the absolute uncertainty of X and X O is the central value of the variable. The uncertainty of indirect measurements (W in Equation ( 12)), which was estimated based on direct measurements of X, Y, and Z, is given by Equation ( 12) [22].…”

Section: Uncertainty Measurement Analysis Prpmentioning

confidence: 99%

“…The variation in the weighting coefficient did not alter the output of the model but influenced the number of valid runs. Bhola [11] performed a study in a 2-D unsteady HEC-RAS model with water height as calibration data. The reach was divided into five land uses, each with a certain range of roughness values.…”

Section: Introductionmentioning

confidence: 99%

Patterns of Difference between Physical and 1-D Calibrated Effective Roughness Parameters in Mountain Rivers

Cedillo

Sánchez-Cordero

Timbe

et al. 2021

Water

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Due to the presence of boulders and different morphologies, mountain rivers contain various resistance sources. To correctly simulate river flow using 1-D hydrodynamic models, an accurate estimation of the flow resistance is required. In this article, a comparison between the physical roughness parameter (PRP) and effective roughness coefficient (ERC) is presented for three of the most typical morphological configurations in mountain rivers: cascade, step-pool, and plane-bed. The PRP and its variation were obtained through multiple measurements of field variables and an uncertainty analysis, while the ERC range was derived with a GLUE procedure implemented in HEC-RAS, a 1-D hydrodynamic model. In the GLUE experiments, two modes of the Representative Friction Slope Method (RFSM) between two cross-sections were tested, including the variation in the roughness parameter. The results revealed that the RFSM effect was limited to low flows in cascade and step-pool. Moreover, when HEC-RAS selected the RSFM, only acceptable results were presented for plane-bed. The difference between ERC and PRP depended on the flow magnitude and the morphology, and as shown in this study, when the flow increased, the ERC and PRP ranges approached each other and even overlapped in cascade and step-pool. This research aimed to improve the roughness value selection process in a 1-D model given the importance of this parameter in the predictability of the results. In addition, a comparison was presented between the results obtained with the numerical model and the values calculated with the field measurements

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“…Además, el factor de rugosidad implica diferentes elementos según la estructura del modelo: 1D, 2D o 3D. En los modelos 1D, el parámetro contiene una representación incorrecta de turbulencia (Bhola, Leandro y Disse, 2019), mientras que en algunos modelos 2D la representación de rugosidad no incluye turbulencia (Morvan y col., 2008). En esta investigación se analiza el desempeño de tres ecuaciones empíricas con datos recolectados en un río de montaña.…”

Section: Introductionunclassified

Efecto del refinamiento de la descripción de la rugosidad en una aproximación 2D para un río de montaña: un caso de estudio

Cedillo

Castro

Samaniego

et al. 2021

lgr

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La predicción de niveles de agua en ríos es importante para prevenir pérdidas económicas así como de vidas humanas causadas por inundaciones. Los modelos hidráulicos son comúnmente usados para predecir estos niveles de agua y tomar acciones para mitigar el daño debido a inundaciones. En la presente investigación, se analizó una aproximación 2D para resolver las ecuaciones promediadas en profundidad de Reynolds Average Navier Stokes (RANS), llamado Conveyance Estimation System (CES), para explorar sus capacidades predictivas. Este artículo presenta una ampliación del estudio realizado por Knight et al. (2009). De igual forma, en esta investigación se explora una caracterización más detallada del parámetro de rugosidad y del número de zonas de rugosidad produciendo diversos escenarios. Se evaluó el desempeño de cada escenario mediante diferentes funciones de ajuste usando curvas de descarga para comparación. La investigación muestra que el uso de una adecuada descripción de la rugosidad, como un factor de rugosidad calibrado para toda la sección transversal o un modelo de rugosidad para cantos rodados calibrado para el lecho junto con valores de rugosidad obtenidos en valores sugeridos por el CES para los bancos, produce resultados del modelo óptimos en un río de montaña.

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Reducing uncertainties in flood inundation outputs of a two-dimensional hydrodynamic model by constraining roughness

Cited by 19 publications

References 31 publications

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

Patterns of Difference between Physical and 1-D Calibrated Effective Roughness Parameters in Mountain Rivers

Efecto del refinamiento de la descripción de la rugosidad en una aproximación 2D para un río de montaña: un caso de estudio

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