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Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (15 December 2020) | Viewed by 44195

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Special Issue Editor

Prof. Dr. Venkatesh Merwade

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Guest Editor

Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
Interests: surface water hydrology; flood modeling and mapping; cyberinfrastructure for hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing frequency of high magnitude floods around the globe, there is a greater need to provide accurate information on the potential impacts of such floods in the future. Flood modeling and mapping has evolved over the last few decades from simulating single river reaches, to millions of reaches at continental scales. Similarly, research on issues such as uncertainty quantification, data resolution, model structure, scale, and dimensionality, continues to advance the science of flood forecasting, modeling, and mapping. With advances in weather forecasting, the desire to have near-real-time flood maps at street level is also growing. Recently, the need for large-scale holistic flood risk management has driven the scientific community towards a systems-based approach to flood modeling, by incorporating feedbacks between the atmospheric, hydrologic, and societal processes. The primary goal of this Special Issue is to take stock of all these new developments for charting the next phase of flood modeling research, by using the newly available technology, data, and cyber–physical systems.

Topics that may be relevant to this Special Issue, but are not limited to, include:

Theories and strategies for hyper-resolution urban flood modeling and maping;
Issues related to the mapping of flood inundation from extreme events such as typhoons and hurricanes;
Application of artificial intelligence, big data, and cyberinfrastructure for flood modeling and research;
Data driven approaches for flood forecasting, modeling, and mapping;
Novel methods for incorporating crowdsourcing or citizen science for improving flood research;
Strategies for improving flood modeling and mapping for data sparse regions.

Prof. Venkatesh Merwade
Guest Editor

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Keywords

Urban flood modeling
Hyper resolution flood modeling
Big data for floods
Citizen science and crowd sourcing
Cyberinfrastructure and artificial intelligence
Flood inundation mapping

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Published Papers (9 papers)

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Research

21 pages, 10570 KiB

Open AccessArticle

Assessment of Flood Forecast Products for a Coupled Tributary-Coastal Model

by Robert Cifelli, Lynn E. Johnson, Jungho Kim, Tim Coleman, Greg Pratt, Liv Herdman, Rosanne Martyr-Koller, Juliette A. FinziHart, Li Erikson, Patrick Barnard and Michael Anderson

Water 2021, 13(3), 312; https://doi.org/10.3390/w13030312 - 27 Jan 2021

Cited by 2 | Viewed by 3021

Abstract

Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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20 pages, 3876 KiB

Open AccessArticle

Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River

by Giovanni Massazza, Vieri Tarchiani, Jafet C. M. Andersson, Abdou Ali, Mohamed Housseini Ibrahim, Alessandro Pezzoli, Tiziana De Filippis, Leandro Rocchi, Bernard Minoungou, David Gustafsson and Maurizio Rosso

Water 2020, 12(12), 3504; https://doi.org/10.3390/w12123504 - 13 Dec 2020

Cited by 11 | Viewed by 4033

Abstract

In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models—Niger HYPE (NH) and World-Wide HYPE (WWH)—in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash–Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to “very good” for NH (Heidke skill score HSS = 0.53) and “good” for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10–20% of forecasts were unfortunately not produced in 2019, impacting operational availability. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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15 pages, 6073 KiB

Open AccessArticle

Effect of Fluvial Discharges and Remote Non-Tidal Residuals on Compound Flood Forecasting in San Francisco Bay

by Babak Tehranirad, Liv Herdman, Kees Nederhoff, Li Erikson, Robert Cifelli, Greg Pratt, Michael Leon and Patrick Barnard

Water 2020, 12(9), 2481; https://doi.org/10.3390/w12092481 - 4 Sep 2020

Cited by 14 | Viewed by 3412

Abstract

Accurate and timely flood forecasts are critical for making emergency-response decisions regarding public safety, infrastructure operations, and resource allocation. One of the main challenges for coastal flood forecasting systems is a lack of reliable forecast data of large-scale oceanic and watershed processes and the combined effects of multiple hazards, such as compound flooding at river mouths. Offshore water level anomalies, known as remote Non-Tidal Residuals (NTRs), are caused by processes such as downwelling, offshore wind setup, and also driven by ocean-basin salinity and temperature changes, common along the west coast during El Niño events. Similarly, fluvial discharges can contribute to extreme water levels in the coastal area, while they are dominated by large-scale watershed hydraulics. However, with the recent emergence of reliable large-scale forecast systems, coastal models now import the essential input data to forecast extreme water levels in the nearshore. Accordingly, we have developed Hydro-CoSMoS, a new coastal forecast model based on the USGS Coastal Storm Modeling System (CoSMoS) powered by the Delft3D San Francisco Bay and Delta community model. In this work, we studied the role of fluvial discharges and remote NTRs on extreme water levels during a February 2019 storm by using Hydro-CoSMoS in hindcast mode. We simulated the storm with and without real-time fluvial discharge data to study their effect on coastal water levels and flooding extent, and highlight the importance of watershed forecast systems such as NOAA’s National Water Model (NWM). We also studied the effect of remote NTRs on coastal water levels in San Francisco Bay during the 2019 February storm by utilizing the data from a global ocean model (HYCOM). Our results showed that accurate forecasts of remote NTRs and fluvial discharges can play a significant role in predicting extreme water levels in San Francisco Bay. This pilot application in San Francisco Bay can serve as a basis for integrated coastal flood modeling systems in complex coastal settings worldwide. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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27 pages, 5480 KiB

Open AccessArticle

Risk-Based and Hydrodynamic Pluvial Flood Forecasts in Real Time

by Julian Hofmann and Holger Schüttrumpf

Water 2020, 12(7), 1895; https://doi.org/10.3390/w12071895 - 2 Jul 2020

Cited by 21 | Viewed by 5108

Abstract

The effective forecast and warning of pluvial flooding in real time is one of the key elements and remaining challenges of an integrated urban flood risk management. This paper presents a new methodology for integrating risk-based solutions and 2D hydrodynamic models into the early warning process. Whereas existing hydrodynamic forecasting methods are based on rigid systems with extremely high computational demands, the proposed framework builds on a multi-model concept allowing the use of standard computer systems. As a key component, a pluvial flood alarm operator (PFA-Operator) is developed for selecting and controlling affected urban subcatchment models. By distributed computing of hydrologic independent models, the framework overcomes the issue of high computational times of hydrodynamic simulations. The PFA-Operator issues warnings and flood forecasts based on a two-step process: (1) impact-based rainfall thresholds for flood hotspots and (2) hydrodynamic real-time simulations of affected urban subcatchments models. Based on the open-source development software Qt, the system can be equipped with interchangeable modules and hydrodynamic software while building on the preliminary results of flood risk analysis. The framework was tested using a historic pluvial flood event in the city of Aachen, Germany. Results indicate the high efficiency and adaptability of the proposed system for operational warning systems in terms of both accuracy and computation time. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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26 pages, 5171 KiB

Open AccessEditor’s ChoiceArticle

Spatial Dependence Modeling of Flood Risk Using Max-Stable Processes: The Example of Austria

by Hansjörg Albrecher, Dominik Kortschak and Franz Prettenthaler

Water 2020, 12(6), 1805; https://doi.org/10.3390/w12061805 - 24 Jun 2020

Cited by 6 | Viewed by 3031

Abstract

We propose a new approach to model the dependence structure for aggregating the risk of flood damages from a local level to larger areas, which is based on the structure of the river network of a country and can be calibrated with publicly available data of river discharges. Building upon a suitable adaptation of max-stable processes for a flood-relevant geometry as recently introduced in the literature, this enables the assessment of flood risk without the need for a hydrological model, and can easily be adapted for different countries. We illustrate its use for the particular case of Austria. We first develop marginal flood models for individual municipalities by intertwining available HORA risk maps with the actual location of buildings. As a second alternative for the marginal modeling, we advocate an approach based on suitably normalized historical damage data of municipalities together with techniques from extreme value statistics. We implement and compare the two alternatives and apply the calibrated dependence structure to each of them, leading to estimates for average flood damage as well as its extreme quantiles on the municipality, state, and country level. This also allows us to quantify the diversification potential for flood risk on each of these levels, a topic of considerable importance in view of the natural and strong spatial dependence of this particular natural peril. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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21 pages, 3320 KiB

Open AccessArticle

Comparison of Methods for Imputing Non-Wetting Storm Surge to Improve Hazard Characterization

by Matthew P. Shisler and David R. Johnson

Water 2020, 12(5), 1420; https://doi.org/10.3390/w12051420 - 16 May 2020

Cited by 7 | Viewed by 3118

Abstract

Joint probability methods for characterizing storm surge hazards involve the use of a collection of hydrodynamic storm simulations to fit a response surface function describing the relationship between storm surge and storm parameters. However, in areas with a sufficiently low probability of flooding, few storms in the simulated storm suite may produce surge, resulting in a paucity of information for training the response surface fit. Previous approaches have replaced surge elevations for non-wetting storms with a constant value or truncated them from the response surface fitting procedure altogether. The former induces bias in predicted estimates of surge from wetting storms, and the latter can cause the model to be non-identifiable. This study compares these approaches and improves upon current methodology by introducing the concept of “pseudo-surge,” with the intent to describe how close a storm comes to producing surge at a given location. Optimal pseudo-surge values are those which produce the greatest improvement to storm surge predictions when they are used to train a response surface. We identify these values for a storm suite used to characterize surge hazard in coastal Louisiana and compare their performance to the two other methods for adjusting training data. Pseudo-surge shows potential for improving hazard characterization, particularly at locations where less than half of training storms produce surge. We also find that the three methods show only small differences in locations where more than half of training storms wet. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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16 pages, 7505 KiB

Open AccessArticle

A 2D Real-Time Flood Forecast Framework Based on a Hybrid Historical and Synthetic Runoff Database

by Giampaolo Crotti, Jorge Leandro and Punit Kumar Bhola

Water 2020, 12(1), 114; https://doi.org/10.3390/w12010114 - 30 Dec 2019

Cited by 8 | Viewed by 3831

Abstract

Operational real-time flood forecast is often done on the prediction of discharges at specific gauges using hydrological models. Hydrodynamic models, which can produce inundation maps, are computationally demanding and often cannot be used directly for that purpose. The FloodEvac framework has been developed in order to enable 2D flood inundations map to be forecasted at real-time. The framework is based on a database of pre-recorded synthetic events. In this paper, the framework is improved by generating a database based on rescaled historical river discharge events. This historical database includes a wider variety of runoff curves, including non-Gaussian and multi-peak shapes that better reflect the characteristics and the behavior of the natural streams. Hence, a hybrid approach is proposed by joining the historical and the existing synthetic database. The increased number of scenarios in the hybrid database allows reliable predictions, thus improving the robustness and applicability of real-time flood forecasts. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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14 pages, 6212 KiB

Open AccessArticle

Case Study of HEC-RAS 1D–2D Coupling Simulation: 2002 Baeksan Flood Event in Korea

by Lea Dasallas, Yeonsu Kim and Hyunuk An

Water 2019, 11(10), 2048; https://doi.org/10.3390/w11102048 - 30 Sep 2019

Cited by 89 | Viewed by 12545

Abstract

Recent studies strongly suggest the possibility of more frequent extreme events as a result of the changing climate. These weather extremes, such as excessive rainfall, result in debris flow, river overflow and urban flooding, which can pose a substantial threat to the community. An effective flood model is therefore a crucial tool in flood disaster control and mitigation. A number of flood models have been established in recent years. However, the major challenge in developing effective and accurate flood models is the disadvantage of running multiple models for separate, individual conditions. Among the solutions in recent research is the development of combined 1D–2D flood modeling. Coupled 1D–2D flood modeling allows the channel flows to be represented in 1D and the overbank flow to be modeled in 2D. In order to test the efficiency of the approach, this research aims to assess the capability of the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System (HEC-RAS) model’s implementation of the combined 1D–2D hydraulic computation in simulating river overflow inundation. For verification, the simulation is applied to the Baeksan river levee breach event in South Korea in 2011. The simulation results show similarities of the observed data and the outputs from widely used flood models. This proves the applicability of the HEC-RAS 1D–2D coupling method as a powerful tool in simulating accurate inundations for flood events. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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19 pages, 11757 KiB

Open AccessArticle

A Study on the Improvement of Flood Forecasting Techniques in Urban Areas by Considering Rainfall Intensity and Duration

by Yeon Moon Choo, Deok Jun Jo, Gwan Seon Yun and Eui Hoon Lee

Water 2019, 11(9), 1883; https://doi.org/10.3390/w11091883 - 10 Sep 2019

Cited by 13 | Viewed by 4446

Abstract

Frequent localized torrential rains, excessive population density in urban areas, and increased impervious areas have led to massive flood damage that has been causing overloading of drainage systems (watersheds, reservoirs, drainage pump sites, etc.). Flood concerns are raised around the world in the events of rain. Flood forecasting, a typical nonstructural measure, was developed to help prevent repetitive flood damage. However, it is difficult to apply flood prediction techniques using training processes because training needs to be applied at every usage. Other techniques that use predicted rainfall data are also not appropriate for small watershed, such as single drainage area. Thus, in this paper, a flood prediction method is proposed by improving four criteria (50% water level, 70% water level, 100% water level, and first flooding of water pipes) in an attempt to reduce flooding in urban areas. The four criteria nodes are generated using a rainfall runoff simulation with synthetic rainfall at various durations. When applying real-time rainfall data, these nodes have the advantage of simple application. The improved flood nomograph made in this way is expected to help predict and prepare for rainstorms that can potentially cause flood damage. Full article

(This article belongs to the Special Issue Recent Advances and New Directions in Flood Forecasting, Modeling, and Mapping)

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