Water Resources Research, volume 61, issue 1

Real‐Time Flood Inundation Modeling With Flow Resistance Parameter Learning

Publication typeJournal Article
Publication date2025-01-03
scimago Q1
wos Q1
SJR1.574
CiteScore8.8
Impact factor4.6
ISSN00431397, 19447973
Abstract

Emergency response to flood plain inundations requires real‐time forecasts of flow depth, velocity, and arrival time. Detailed and rapid flood inundation forecasts can be obtained from numerical solution of 2D unsteady flow equations based on high‐resolution topographic data and geomorphologically informed unstructured meshes. However, flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data remain uncertain. In the present study, flow resistance parameters representing the effects of roughness, vegetation, and buildings are determined hydraulically in real‐time using flow depth observations. A detailed numerical reproduction of a real flood has been largely corroborated by observations and subsequently used as a surrogate of the ground truth target. In synthetic numerical experiments, flow depth observations are obtained from a network of in‐situ flow depth sensors assigned to hydraulically relevant locations in the flood plain. Starting from a generic resistance parameter set, the capability of a tandem 2D surface flow model and Bayesian optimization technique to achieve convergence to the target resistance parameter set is tested. Convergence to the target resistance parameter set was obtained with 50 or fewer tandem flow + optimization iterations for each forecasting cycle in which the difference between simulated and observed flow depths is minimized. The flood arrival time errors across a 52  flood plain inundation area were reduced by 3.13 hr with respect to results obtained without optimization from a fixed range of flow resistance parameters. Performance metrics like critical success index and probability of detection reach values above 90% across the flood plain.

Pizzileo S., Moretti G., Orlandini S.
Advances in Water Resources scimago Q1 wos Q1
2024-06-01 citations by CoLab: 2 Abstract  
Although high-resolution digital surface model (DSM) data derived from lidar surveys can describe land surface macrostructures like trees and buildings, digital terrain model (DTM) data obtained by filtering out these macrostructures are commonly used in flood inundation models. In the present study, it is shown for the first time that DSM data can be used directly in flood inundation models by employing automatically-extracted ridges as breaklines for the generation of geomorphologically-informed meshes (GIMs). Even under the simplifying assumption of impermeable macrostructures, especially when GIM refinement is applied, the use of DSM data in preference to DTM data leads to significant improvement in flood predictions. By comparing simulations and observations for a real flood inundation, it is found that the direct use of 1-m DSM data in place of the related DTM data leads to a 42% improvement in predicted flood area, a 36% improvement in predicted flood areal position, and a 25% improvement in predicted times of travel.
Pensoneault A., Krajewski W.F., Velásquez N., Zhu X., Mantilla R.
Advances in Water Resources scimago Q1 wos Q1
2023-11-01 citations by CoLab: 2 Abstract  
Data assimilation (DA) techniques such as the Ensemble Kalman filter (EnKF) and its extensions allow for real-time corrections of state-space models and model parameters based on an assumption of Gaussian error. The hydrological DA literature primarily documents applications of the EnKF to solve sequential state estimation problems. Recent advances in the DA literature demonstrate the potential of applying EnKF-based methods as efficient, derivative-free algorithms to solve various general Bayesian inverse problems, such as parameter estimation, while simultaneously providing Uncertainty Quantification (UQ). In this paper, the authors employ the Ensemble Kalman Inversion (EKI) algorithm to infer the distribution of a set of routing parameters. Through this correction, we improve streamflow at locations upstream of the gauged site in a virtual catchment setting. The algorithm enables learning spatially distributed routing parameters with observations available only at the outlet. The study reveals that this method sufficiently improves model performance throughout the basin. The performance of this method is demonstrated in a virtual catchment for three different model/data configurations. Favorable results, even with model misspecification, indicate that this method holds promise for operational application and more general hydrologic parameter estimation problems.
Cooper C.M., Sharma S., Nicholas R.E., Keller K.
Earth's Future scimago Q1 wos Q1 Open Access
2022-11-04 citations by CoLab: 4 PDF Abstract  
The increasingly urgent need to develop knowledge and practices to manage flood risks drives innovative information design. However, experts often disagree about design practices. As a result, flood-risk estimates can diverge, leading to different conclusions for decision-making. Using examples of household-scale fluvial (riverine) flood-risk information in the United States, we assess design features and risk communication approaches that may lead to more actionable information for decision-making. We argue that increased attention to uncertainty characterization and model diagnostics is a critical intermediate step for developing simpler approaches for designing flood-risk information. Simpler frameworks are desirable because flood risks change over time, and simpler frameworks are cheaper and less costly to reproduce. Developing frameworks for large spatial domains require collaboration grounded in principles of open science. Finally, systematically evaluating how decision-makers access and use information can provide new insights to guide risk communication and information design.
Kahl D.T., Schubert J.E., Jong-Levinger A., Sanders B.F.
Advances in Water Resources scimago Q1 wos Q1
2022-10-01 citations by CoLab: 15 Abstract  
Dual-grid models address the computational bottlenecks of large-scale ( > 1 0 3 km 2 ) urban flood modeling with solution updates on a coarse grid that are informed by topographic data on a fine grid. However, dual-grid models may poorly resolve levees, leading to loss of accuracy. Here we present a grid edge classification method whereby specific edges of the coarse grid are flagged to gather nearby topographic data from the fine grid and create a contiguous physical barrier. The method relies on levee location data in a polyline format, and does not require levee height data since that information is stored on the fine grid. Using a 6804 km 2 model of the Los Angeles Metropolitan Region with 3 m topographic data and 987 km of levees, the proposed method is implemented and evaluated. Simulations using coarse grids of 15, 30 and 60 m capture flood extent consistent with fine-grid models based on a critical success index ( CSI ) of 90, 87 and 82%, respectively. Edge classification improves CSI up to 7 percentage points over a model with unclassified coarse grid edges, and reduces the false alarm ratio up to 10 percentage points. Differences in model performance across the study area are noted, including lower accuracy on urbanized alluvial fans. With compute costs that scale with the coarse grid, dual-grid models can efficiently realize more accurate large-scale models of urban flood hazards. • Spatial accuracy of large-scale flood model improved by 10% using levee polyline data. • Dual-grid model formulation supports high spatial accuracy with low compute costs. • Grid edge classification guides fine-grid data sampling to improve spatial accuracy.
Schubert J.E., Luke A., AghaKouchak A., Sanders B.F.
Water Resources Research scimago Q1 wos Q1
2022-09-19 citations by CoLab: 36 Abstract  
Urban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic framework to simulate flood inundation over metropolitan-wide areas at fine resolution (3 m). A dual-grid shallow-water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi-dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5-day period with a median MAE and Nash-Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs.
Pujol L., Garambois P., Monnier J.
Geoscientific Model Development scimago Q1 wos Q1 Open Access
2022-08-03 citations by CoLab: 10 Abstract  
Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic–hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydrometeorology and satellite data. The multi-D hydraulic model relies on the 2D shallow-water equations solved with a 1D–2D adapted single finite-volume solver. One-dimensional-like reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1D-like–2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on virtual and real cases of increasing complexity, including using the second-order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry friction, upstream and lateral hydrographs and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is, backward hydrology. A 1D-like model of part of the Garonne River is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, with inflow from the semi-distributed hydrological model, is built. High-resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1D-like reaches. The current work constitutes an upgrade of the DassFlow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.
Silverman A.I., Brain T., Branco B., Challagonda P.S., Choi P., Fischman R., Graziano K., Hénaff E., Mydlarz C., Rothman P., Toledo-Crow R.
Water Research scimago Q1 wos Q1
2022-07-01 citations by CoLab: 18 Abstract  
Flooding is expected to increase due to intensification of extreme precipitation events, sea-level rise, and urbanization. Low-cost water level sensors have the ability to fill a critical data gap on the presence, depth, and duration of street-level floods by measuring flood profiles (i.e., flood stage hydrographs) in real-time with a time interval on the order of minutes. Hyperlocal flood data collected by low-cost sensors have many use cases for a variety of stakeholders including municipal agencies, community members, and researchers. Here we outline examples of potential uses of flood sensor data before, during, and after flood events, based on dialog with stakeholders in New York City. These uses include inputs to predictive flood models, generation of real-time flood alerts for community members and emergency response teams, storm recovery assistance and cataloging of storm impacts, and informing infrastructure design and investment for long-term flood resilience project planning.
Potočki K., Hartmann T., Slavíková L., Collentine D., Veidemane K., Raška P., Barstad J., Evans R.
Earth's Future scimago Q1 wos Q1 Open Access
2022-03-07 citations by CoLab: 17 PDF Abstract  
Flood risk management (FRM) aims to integrate necessary technical measures with environmental and societal approaches. Focusing on the process and governance of how to plan, implement, and maintain solutions therefore becomes essential. Among the different stakeholders, landowners are a key group to be considered. This contribution elaborates on the interconnections between land policy, FRM and private land ownership. It is based on the European COST Action network LAND4FLOOD, which brings together academics and stakeholders from various disciplines and more than 35 countries. We argue for a less project oriented and more process oriented approach, a focus on land management and more emphasis on small-scale measures. This represents a break with some of the recent working paradigms of FRM.
Annis A., Nardi F., Castelli F.
2022-02-22 citations by CoLab: 15 Abstract  
Abstract. Hydro-meteo hazard early warning systems (EWSs) are operating in many regions of the world to mitigate nuisance effects of floods. EWS performances are majorly impacted by the computational burden and complexity affecting flood prediction tools, especially for ungauged catchments that lack adequate river flow gauging stations. Earth observation (EO) systems may integrate the lack of fluvial monitoring systems supporting the setting up of affordable EWSs. But, EO data, constrained by spatial and temporal resolution limitations, are not sufficient alone, especially at medium–small scales. Multiple sources of distributed flood observations need to be used for managing uncertainties of flood models, but this is not a trivial task for EWSs. In this work, a near-real-time flood modelling approach is developed and tested for the simultaneous assimilation of both water level observations and EO-derived flood extents. An integrated physically based flood wave generation and propagation modelling approach, that implements an ensemble Kalman filter, a parsimonious geomorphic rainfall–runoff algorithm (width function instantaneous unit hydrograph, WFIUH) and a quasi-2D hydraulic algorithm, is proposed. An approach for assimilating multiple stage gauge observations is proposed to overcome stability issues related to the updating of the quasi-2D hydraulic model states. Furthermore, a methodology to retrieve distributed observed water depths from satellite images to update 2D hydraulic modelling state variables is implemented. Performances of the proposed approach are tested on a flood event for the Tiber River basin in central Italy. The selected case study shows varying performances depending on whether local and distributed observations are separately or simultaneously assimilated. Results suggest that the injection of multiple data sources into a flexible data assimilation framework constitutes an effective and viable advancement for flood mitigation to tackle EWS uncertainty and numerical stability issues. Specifically, our findings reveal that the simultaneous assimilation of observations from static sensors and satellite images led to an overall improvement of the Nash–Sutcliffe efficiency (NSE) between 5 % and 40 %, the Pearson correlation up to 12 % and bias reduction up to 80 % with respect to the open-loop simulation. Moreover, this combined assimilation allows us to reduce the flood extent uncertainty with respect to the disjoint assimilation simulations for several hours after the satellite image acquisition.
Nguyen T.H., Ricci S., Fatras C., Piacentini A., Delmotte A., Lavergne E., Kettig P.
2022-01-27 citations by CoLab: 20 Abstract  
Flood simulation and forecast capability have been greatly improved, thanks to the advances in data assimilation (DA). Such an approach combines in situ gauge measurements with numerical hydrodynamic models to correct the hydraulic states and reduce the uncertainties in model parameters. However, these methods depend strongly on the availability and quality of observations, thus necessitating other data sources to improve the flood simulation and forecast performances. Using Sentinel-1 images, a flood extent mapping method was carried out by applying a Random Forest algorithm trained on past flood events using manually delineated flood maps. The study area concerns a 50-km reach of the Garonne Marmandaise catchment. Two recent flood events are simulated in analysis and forecast modes, with a +24-h lead time. This study demonstrates the merits of using synthetic aperture radar (SAR)-derived flood extent maps to validate and improve the forecast results based on hydrodynamic numerical models with Telemac2D-ensemble Kalman filter (EnKF). Quantitative 1-D and 2-D metrics were computed to assess water-level time-series and flood extents between the simulations and observations. It was shown that the free run experiment without DA underestimates flooding. On the other hand, the validation of DA results with respect to independent SAR-derived flood extent allows to diagnose a model–observation bias that leads to over-flooding. Once this bias is taken into account, DA provides a sequential correction of area-based friction coefficients and inflow discharge, yielding a better flood extent representation. This study paves the way toward a reliable solution for flood forecasting over poorly gauged catchments, thanks to the available remote sensing datasets.
Bates P.D.
2022-01-05 citations by CoLab: 82 Abstract  
Every year flood events lead to thousands of casualties and significant economic damage. Mapping the areas at risk of flooding is critical to reducing these losses, yet until the last few years such information was available for only a handful of well-studied locations. This review surveys recent progress to address this fundamental issue through a novel combination of appropriate physics, efficient numerical algorithms, high-performance computing, new sources of big data, and model automation frameworks. The review describes the fluid mechanics of inundation and the models used to predict it, before going on to consider the developments that have led in the last five years to the creation of the first true fluid mechanics models of flooding over the entire terrestrial land surface.
Ma J., Zhang J., Li R., Zheng H., Li W.
2022-01-01 citations by CoLab: 21 Abstract  
A framework that integrates Bayesian optimization (BO) and high-performance computing was developed, to automate calibration of complex hydrological models. It adopts a loosely coupled web architecture, integrating Tornado and SpringBoot, to facilitate bidirectional transfer of variables between BO and model evaluation. Extensive model evaluations were implemented on a Hadoop cluster, to wrap the model into the calculation flexibly and separate the calculation process from the algorithm execution effectively. A case study, calibrating a SWAT model in the Meichuan Basin (Jiangxi Province, China), indicated that the framework provides an ideal environment for assessment of the capability of BO to quantify the efficient estimation of SWAT parameters. Compared with that of the built-in SWAT-CUP tool, the number of executions was reduced from 1500 to 150, while maintaining similar accuracy. The framework also allows evaluation of the performance of different surrogate models and acquisition functions and provides instant visualization for searching for optimal parameters. • Automatic model calibration framework for Bayesian optimization (BO) was developed. • Tornado and SpringBoot were integrated to enable transfer of variables between them. • Hadoop cluster wrapped complex model internally and performed model evaluations simultaneously. • Capability of BO to quantify efficient estimation of SWAT parameters was assessed. • Performance of surrogate models and acquisition functions of BO was evaluated.
Balistrocchi M., Moretti G., Ranzi R., Orlandini S.
Water Resources Research scimago Q1 wos Q1
2021-12-02 citations by CoLab: 7 Abstract  
Mammal bioerosion is an emergent threat to the functionality of levees. In the present paper, the problem of assessing the failure probability of levees affected by mammal bioerosion is addressed. A fully bivariate description of peak flow discharge and flood duration is combined with a deterministic unsteady seepage flow model to obtain a suitable model of variably disturbed levee response to the observed natural variability of floods. Monte Carlo analysis is also implemented to evaluate the epistemic uncertainty connected to the description of the river system. The obtained model is tested with respect to a real-world levee located along the Secchia River in northern Italy, which underwent a disastrous failure caused by mammal bioerosion in 2014. The convex linear combination of two Archimedean copulas is found to fit the empirical dependence structure between peak flow discharge and flood duration. The reliability of the unsteady seepage flow model is tested against detailed numerical simulations of the seepage occurring through the levee body. A limit state function is obtained by comparing the maximum extent of the seepage front to the distance between the den end and the riverside levee slope, and the corresponding levee safety and failure regions are delimited. Results obtained from the developed model reveal a significant impact of mammal dens located near the levee crest in terms of failure probability and related return period. This impact is consistent with failures observed in the study area.

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