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Advances in Large Scale Flood Monitoring and Detection

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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 72691

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Special Issue Editors

Dr. Salvatore Manfreda

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Department of Civil, Architectural and Environmental Engineering, University of Naples "Federico II", Napoli, Italy
Interests: stochastic processes; hydrological modelling; model calibration; flood risk; geomorphology; ecohydrology; UAS monitoring
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Dr. Caterina Samela

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Department of European Culture and the Mediterranean (DICEM), University of Basilicata, Potenza, Italy
Interests: flood hazard and risk; geomorphology, surface hydrology; automated pattern recognition; scarce data environments; large scales; Geographic Information System
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Dr. Alberto Refice

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Istituto di Studi sui Sistemi Intelligenti per l’Automazione, Consiglio Nazionale delle Ricerche (CNR-ISSIA), Bari, Italy
Interests: processing of remotely sensed data, with emphasis on synthetic aperture radar (SAR) and SAR interferometry (InSAR); applications of remote sensing to the retrieval of information about the Earth surface and to the monitoring of environmental hazards

Prof. Dr. Valerio Tramutoli

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School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: satellite remote sensing; robust satellite techniques for natural; environmental and industrial risks forecast and monitoring: floods, forest fires, earthquakes, volcanic eruptions, sand storms, air and water pollution, oil spills and energetic pipelines accidents
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Prof. Dr. Fernando Nardi

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Water Resources Research and Documentation Centre (WARREDOC), University for Foreigners of Perugia, Perugia, Italy
Interests: regional planning and sustainability; hazard and risk mapping and management; citizen science and public engagement; open geo data and big data; hydrology; natural hazards; GIS; geospatial data
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Special Issue Information

Dear Colleagues,

The last decades have seen a massive increase in new technologies for Earth observation (EO) and environmental monitoring, which provided scientists and engineers with valuable spatial information for studying hydrologic processes. At the same time, the power of computers and newly-developed algorithms have grown sharply.

Such advances have extended the range of possibilities for hydrologists, which are trying to exploit at the most these potentials, updating and re-inventing the way hydrologic and hydraulic analyses are carried out. A variety of research fields have progressed significantly, ranging from the evaluation of water features, to the classification of land-cover, the identification of river morphology, and the monitoring of extreme flood events.

In particular, the description of flood processes may particularly benefit from the coupled use of recent algorithms or data. In fact, flood exposure and risk over large areas and in scarce data environments has always been a challenging topic due to the limited information available on river basin hydrology, basin morphology, land cover and model uncertainty. The ability of new tools to carry out intensive analysis over huge datasets allows to produce flood studies over larger and larger extent and with a growing level of detail.

The challenge of this Special Issue is to describe the state-of-the-art on flood studies using innovative methods and identify the frontier of this research branch. The Special Issue is dedicated to contributions focusing on the benefit of the use of new algorithms, new measurements systems and EO data for flood assessment, monitoring, and management. The research presented might focus on:

New methods and technologies for river morphology monitoring;
Innovative methods for flood mapping over large areas;
Use of open/big data in hydrologic modelling of floods;
Advanced applications of EO and UAS data for hazard, vulnerability, risk mapping, and post-disaster recovery phase;
Innovative applications in support to disaster risk reduction strategies;
Development of tools and platforms for assessment and validation of hazard/risk models.
The special issue is intended to explore the potential of different types of sensors (e.g., thermal, visual, radar, laser, and/or the fusion of these) and algorithms for the prediction of extreme events and emergency management.

Prof. Dr. Salvatore Manfreda
Dr. Caterina Samela
Dr. Alberto Refice
Prof. Dr. Valerio Tramutoli
Prof. Dr. Fernando Nardi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Hydroinformatics
Flood Mapping
Flood Monitoring
Floodplains
Rivers Dynamics
DEM-based methods
Geomorphology
Data scarce environment

Published Papers (10 papers)

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Editorial

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4 pages, 196 KiB

Open AccessEditorial

Advances in Large-Scale Flood Monitoring and Detection

by Salvatore Manfreda, Caterina Samela, Alberto Refice, Valerio Tramutoli and Fernando Nardi

Hydrology 2018, 5(3), 49; https://doi.org/10.3390/hydrology5030049 - 03 Sep 2018

Cited by 3 | Viewed by 4238

Abstract

The last decades have seen a massive advance in technologies for Earth observation (EO) and environmental monitoring, which provided scientists and engineers with valuable spatial information for studying hydrologic processes. At the same time, the power of computers and newly developed algorithms have grown sharply. Such advances have extended the range of possibilities for hydrologists, who are trying to exploit these potentials the most, updating and re-inventing the way hydrologic and hydraulic analyses are carried out. A variety of research fields have progressed significantly, ranging from the evaluation of water features, to the classification of land-cover, the identification of river morphology, and the monitoring of extreme flood events. The description of flood processes may particularly benefit from the integrated use of recent algorithms and monitoring techniques. In fact, flood exposure and risk over large areas and in scarce data environments have always been challenging topics due to the limited information available on river basin hydrology, basin morphology, land cover, and the resulting model uncertainty. The ability of new tools to carry out intensive analyses over huge datasets allows us to produce flood studies over large extents and with a growing level of detail. The present Special Issue aims to describe the state-of-the-art on flood assessment, monitoring, and management using new algorithms, new measurement systems and EO data. More specifically, we collected a number of contributions dealing with: (1) the impact of climate change on floods; (2) real time flood forecasting systems; (3) applications of EO data for hazard, vulnerability, risk mapping, and post-disaster recovery phase; and (4) development of tools and platforms for assessment and validation of hazard/risk models. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

Research

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16 pages, 6116 KiB

Open AccessFeature PaperArticle

Floodplain Terrain Analysis for Coarse Resolution 2D Flood Modeling

by Francisco Peña and Fernando Nardi

Hydrology 2018, 5(4), 52; https://doi.org/10.3390/hydrology5040052 - 21 Sep 2018

Cited by 31 | Viewed by 7857

Abstract

Hydraulic modeling is a fundamental tool for managing and mitigating flood risk. Developing low resolution hydraulic models, providing consistent inundation simulations with shorter running time, as compared to high-resolution modeling, has a variety of potential applications. Rapid coarse resolution flood models can support emergency management operations as well as the coupling of hydrodynamic modeling with climate, landscape and environmental models running at the continental scale. This work sought to investigate the uncertainties of input parameters and bidimensional (2D) flood wave routing simulation results when simplifying the terrain mesh size. A procedure for fluvial channel bathymetry interpolation and floodplain terrain data resampling was investigated for developing upscaled 2D inundation models. The proposed terrain processing methodology was tested on the Tiber River basin evaluating coarse (150 m) to very coarse (up to 700 m) flood hazard modeling results. The use of synthetic rectangular cross sections, replacing surveyed fluvial channel sections, was also tested with the goal of evaluating the potential use of geomorphic laws providing channel depth, top width and flow area when surveyed data are not available. Findings from this research demonstrate that fluvial bathymetry simplification and DTM resampling is feasible when the terrain data resampling and fluvial cross section interpolation are constrained to provide consistent representation of floodplain morphology, river thalweg profile and channel flow area. Results show the performances of low-resolution inundation simulations running in seconds while maintaining a consistent representation of inundation extents and depths. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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15 pages, 9410 KiB

Open AccessArticle

Analyzing the December 2013 Metaponto Plain (Southern Italy) Flood Event by Integrating Optical Sensors Satellite Data

by Teodosio Lacava, Emanuele Ciancia, Mariapia Faruolo, Nicola Pergola, Valeria Satriano and Valerio Tramutoli

Hydrology 2018, 5(3), 43; https://doi.org/10.3390/hydrology5030043 - 07 Aug 2018

Cited by 4 | Viewed by 3705

Abstract

Timely and continuous information about flood dynamics are fundamental to ensure an effective implementation of the relief and rescue operations. Satellite data provided by optical sensors onboard meteorological satellites could have great potential in this framework, offering an adequate trade-off between spatial and temporal resolution. The latest would benefit from the integration of observations coming from different satellite systems, also helping to increase the probability of finding cloud free images over the investigated region. The Robust Satellite Techniques for detecting flooded areas (RST-FLOOD) is a sensor-independent multi-temporal approach aimed at detecting flooded areas which has already been applied with good results on different polar orbiting optical sensors. In this work, it has been implemented on both the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375 m Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The flooding event affecting the Basilicata and Puglia regions (southern Italy) in December 2013 has been selected as a test case. The achieved results confirm the RST-FLOOD potential in reliably detecting, in case of small basins, flooded areas regardless of the sensor used. Flooded areas have indeed been detected with similar performance by the two sensors, allowing for their continuous and near-real time monitoring. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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23 pages, 12560 KiB

Open AccessArticle

GEV Parameter Estimation and Stationary vs. Non-Stationary Analysis of Extreme Rainfall in African Test Cities

by Francesco De Paola, Maurizio Giugni, Francesco Pugliese, Antonio Annis and Fernando Nardi

Hydrology 2018, 5(2), 28; https://doi.org/10.3390/hydrology5020028 - 18 May 2018

Cited by 48 | Viewed by 9086

Abstract

Nowadays, increased flood risk is recognized as one of the most significant threats in most parts of the world, with recurring severe flooding events causing significant property and human life losses. This has entailed public debates on both the apparent increased frequency of extreme events and the perceived increases in rainfall intensities within climate changing scenarios. In this work, a stationary vs. Non-Stationary Analysis of annual extreme rainfall was performed with reference to the case studies of the African cities of Dar Es Salaam (TZ) and Addis Ababa (ET). For Dar Es Salaam (TZ) a dataset of 53 years (1958–2010) of maximum daily rainfall records (24 h) was analysed, whereas a 47-year time series (1964–2010) was taken into account for Addis Ababa (ET). Both gauge stations rainfall data were suitably fitted by Extreme Value Distribution (EVD) models. Inference models using the Maximum Likelihood Estimation (MLE) and the Bayesian approach were applied on EVD considering their impact on the shape parameter and the confidence interval width. A comparison between a Non-Stationary regression and a Stationary model was also performed. On this matter, the two time series did not show any Non-Stationary effect. The results achieved under the CLUVA (Climatic Change and Urban Vulnerability in Africa) EU project by the Euro-Mediterranean Centre for Climate Change (CMCC) (with 1 km downscaling) for the IPCC RCP8.5 climatological scenario were also applied to forecast the analysis until 2050 (93 years for Dar Es Salaam TZ and 86 years for Addis Ababa ET). Over the long term, the process seemed to be Non-Stationary for both series. Moreover, with reference to a 100-year return period, the IDF (Intensity-Duration-Frequency) curves of the two case-studies were estimated by applying the Maximum Likelihood Estimation (MLE) approach, as a function of confidence intervals of 2.5% and 97.5% quantiles. The results showed the dependence of Non-Stationary effects of climate change to be conveniently accounted for engineering design and management. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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23 pages, 5326 KiB

Open AccessFeature PaperArticle

An Operational Method for Flood Directive Implementation in Ungauged Urban Areas

by George Papaioannou, Andreas Efstratiadis, Lampros Vasiliades, Athanasios Loukas, Simon Michael Papalexiou, Antonios Koukouvinos, Ioannis Tsoukalas and Panayiotis Kossieris

Hydrology 2018, 5(2), 24; https://doi.org/10.3390/hydrology5020024 - 20 Apr 2018

Cited by 71 | Viewed by 7471

Abstract

An operational framework for flood risk assessment in ungauged urban areas is developed within the implementation of the EU Floods Directive in Greece, and demonstrated for Volos metropolitan area, central Greece, which is frequently affected by intense storms causing fluvial flash floods. A scenario-based approach is applied, accounting for uncertainties of key modeling aspects. This comprises extreme rainfall analysis, resulting in spatially-distributed Intensity-Duration-Frequency (IDF) relationships and their confidence intervals, and flood simulations, through the SCS-CN method and the unit hydrograph theory, producing design hydrographs at the sub-watershed scale, for several soil moisture conditions. The propagation of flood hydrographs and the mapping of inundated areas are employed by the HEC-RAS 2D model, with flexible mesh size, by representing the resistance caused by buildings through the local elevation rise method. For all hydrographs, upper and lower estimates on water depths, flow velocities and inundation areas are estimated, for varying roughness coefficient values. The methodology is validated against the flood event of the 9th October 2006, using observed flood inundation data. Our analyses indicate that although typical engineering practices for ungauged basins are subject to major uncertainties, the hydrological experience may counterbalance the missing information, thus ensuring quite realistic outcomes. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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17 pages, 59264 KiB

Open AccessFeature PaperArticle

Socioeconomic Impact Evaluation for Near Real-Time Flood Detection in the Lower Mekong River Basin

by Perry C. Oddo, Aakash Ahamed and John D. Bolten

Hydrology 2018, 5(2), 23; https://doi.org/10.3390/hydrology5020023 - 10 Apr 2018

Cited by 16 | Viewed by 7493

Abstract

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these events. Flood damage assessments, critical for understanding the effects of flooding on the local population and informing decision-makers about future risks, are frequently used to quantify the economic losses due to storms. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further highlighting the need for improved flood monitoring and impact analysis. In this study we integrate a socioeconomic damage assessment model with a near real-time flood remote sensing and decision support tool (NASA’s Project Mekong). Direct damages to populations, infrastructure, and land cover are assessed using the 2011 Southeast Asian flood as a case study. Improved land use/land cover and flood depth assessments result in rapid loss estimates throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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20 pages, 12615 KiB

Open AccessArticle

Floods and Countermeasures Impact Assessment for the Metro Colombo Canal System, Sri Lanka

by Mohamed Mashood Mohamed Moufar and Edangodage Duminda Pradeep Perera

Hydrology 2018, 5(1), 11; https://doi.org/10.3390/hydrology5010011 - 26 Jan 2018

Cited by 11 | Viewed by 9928

Abstract

A 15th-century canal system in the Metro Colombo area of Sri Lanka was studied to identify its capacity in controlling floods. The canal system was modelled by MIKE FLOOD for 10, 25 and 50-year return periods of rainfalls to achieve respective floods. The impacts of the considered rainfalls were analyzed considering the flood levels, inundation distributions and affected people. Two simulation scenarios which were based on the river boundary conditions were carried out in the study and they were categorized as favourable and least favorable. It was identified that under the existing conditions, the canal system could handle only a 10-year rainfall flood event under the favourable condition. Therefore, the canal system's sustainability for future anticipated extreme events is suspicious. To mitigate such floods, four countermeasures were introduced and their impacts were analyzed. When the countermeasures were introduced one at a time, the flood water levels were lowered locally and they were not up to the flood safety levels of the surrounding area. When all four countermeasures were introduced together, the flood water levels were significantly lowered below the flood safety levels for a 50-year design rainfall under the favourable condition. The reduction of the inundated area was significant in the case of applying all four countermeasures together. In that case, a 46% inundation area reduction and a 49% reduction in the number of affected people were achieved. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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4277 KiB

Open AccessArticle

RCP8.5-Based Future Flood Hazard Analysis for the Lower Mekong River Basin

by Edangodage Duminda Pradeep Perera, Takahiro Sayama, Jun Magome, Akira Hasegawa and Yoichi Iwami

Hydrology 2017, 4(4), 55; https://doi.org/10.3390/hydrology4040055 - 23 Nov 2017

Cited by 20 | Viewed by 8497

Abstract

Climatic variations caused by the excessive emission of greenhouse gases are likely to change the patterns of precipitation, runoff processes, and water storage of river basins. Various studies have been conducted based on precipitation outputs of the global scale climatic models under different emission scenarios. However, there is a limitation in regional- and local-scale hydrological analysis on extreme floods with the combined application of high-resolution atmospheric general circulation models’ (AGCM) outputs and physically-based hydrological models (PBHM). This study has taken an effort to overcome that limitation in hydrological analysis. The present and future precipitation, river runoff, and inundation distributions for the Lower Mekong Basin (LMB) were analyzed to understand hydrological changes in the LMB under the RCP8.5 scenario. The downstream area beyond the Kratie gauging station, located in the Cambodia and Vietnam flood plains was considered as the LMB in this study. The bias-corrected precipitation outputs of the Japan Meteorological Research Institute atmospheric general circulation model (MRI-AGCM3.2S) with 20 km horizontal resolution were utilized as the precipitation inputs for basin-scale hydrological simulations. The present climate (1979–2003) was represented by the AMIP-type simulations while the future (2075–2099) climatic conditions were obtained based on the RCP8.5 greenhouse gas scenario. The entire hydrological system of the Mekong basin was modelled by the block-wise TOPMODEL (BTOP) hydrological model with 20 km resolution, while the LMB area was modelled by the rainfall-runoff-inundation (RRI) model with 2 km resolution, specifically to analyze floods under the aforementioned climatic conditions. The comparison of present and future river runoffs, inundation distributions and inundation volume changes were the outcomes of the study, which can be supportive information for the LMB flood management, water policy, and water resources development. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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Review

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36 pages, 4061 KiB

Open AccessReview

Applications of Open-Access Remotely Sensed Data for Flood Modelling and Mapping in Developing Regions

by Iguniwari Thomas Ekeu-wei and George Alan Blackburn

Hydrology 2018, 5(3), 39; https://doi.org/10.3390/hydrology5030039 - 31 Jul 2018

Cited by 25 | Viewed by 8294

Abstract

Flood modelling and mapping typically entail flood frequency estimation, hydrodynamic modelling and inundation mapping, which require specific datasets that are often unavailable in developing regions due to financial, logistical, technical and organizational challenges. This review discusses fluvial (river) flood modelling and mapping processes and outlines the data requirements of these techniques. This paper explores how open-access remotely sensed and other geospatial datasets can supplement ground-based data and high-resolution commercial satellite imagery in data sparse regions of developing countries. The merits, demerits and uncertainties associated with the application of these datasets, including radar altimetry, digital elevation models, optical and radar images, are discussed. Nigeria, located within the Niger river basin of West Africa is a typical data-sparse country, and it is used as a case study in this review to evaluate the significance of open-access datasets for local and transboundary flood analysis. Hence, this review highlights the vital contribution that open access remotely sensed data can make to flood modelling and mapping and to support flood management strategies in developing regions. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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Other

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10 pages, 2970 KiB

Open AccessTechnical Note

Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions

by Amir Javaheri, Mohammad Nabatian, Ehsan Omranian, Meghna Babbar-Sebens and Seong Jin Noh

Hydrology 2018, 5(1), 9; https://doi.org/10.3390/hydrology5010009 - 21 Jan 2018

Cited by 11 | Viewed by 4652

Abstract

This study proposes a framework that (i) uses data assimilation as a post processing technique to increase the accuracy of water depth prediction, (ii) updates streamflow generated by the National Water Model (NWM), and (iii) proposes a scope for updating the initial condition of continental-scale hydrologic models. Predicted flows by the NWM for each stream were converted to the water depth using the Height Above Nearest Drainage (HAND) method. The water level measurements from the Iowa Flood Inundation System (a test bed sensor network in this study) were converted to water depths and then assimilated into the HAND model using the ensemble Kalman filter (EnKF). The results showed that after assimilating the water depth using the EnKF, for a flood event during 2015, the normalized root mean square error was reduced by 0.50 m (51%) for training tributaries. Comparison of the updated modeled water stage values with observations at testing locations showed that the proposed methodology was also effective on the tributaries with no observations. The overall error reduced from 0.89 m to 0.44 m for testing tributaries. The updated depths were then converted to streamflow using rating curves generated by the HAND model. The error between updated flows and observations at United States Geological Survey (USGS) station at Squaw Creek decreased by 35%. For future work, updated streamflows could also be used to dynamically update initial conditions in the continental-scale National Water Model. Full article

(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)

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