Special Issue "Remote Sensing of Hydrological Extremes"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editors

Dr. Frédéric Frappart
Website
Guest Editor
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, UMR 5566, CNES/CNRS/IRD/UPS, Observatoire Midi-Pyrénées, 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: hydrology; land surface processes; remote sensing
Special Issues and Collections in MDPI journals
Dr. José Darrozes
Website1 Website2 SciProfiles
Guest Editor
Géosciences Environnement Toulouse (GET), UMR CNRS5563, CNRS/IRD/UPS, Observatoire Midi-Pyrénées (OMP), 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: earth observation; river morphology; near surface geophysics; soil moisture; GNSS-R; water cycle; soil contamination; remote sensing
Special Issues and Collections in MDPI journals
Dr. Fabrice Papa
Website
Guest Editor
Institut de Recherche pour le Développement (IRD), LEGOS (Laboratoire d’Etudes en Géophysique et Océanographie Spatiales), Observatoire Midi-Pyrénées (OMP), 14, Avenue Edouard Belin, 31400, Toulouse, France
Interests: remote sensing; hydrology; water cycle; tropical climate variability
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the technological advances and the development of new types of sensors/satellites (like Soil Moisture Active Passive (SMAP), Gravity Recovery And Climate Experiment (GRACE) Follow On, Cyclone Global Navigation Satellite System (CYGNSS), SENTINEL 1, 2 and 3 and many others), remotely-sensed observations offer new opportunities to accurately monitor hydrological extremes such as floods, tidal bores and droughts, as well as their causes (exceptional rainfall/snowfall or deficit, long term changes due to climate change or human activities, impact on ecosystems and riparian forest). The great diversity of satellite observations from atmospheric soundings to gravimetry from space measurements provide now and in the future a wide range of information on both the storage in hydrological reservoirs and the fluxes between water cycle compartments. This Special Issue aims to present reviews and recent advances of general interest in the use of remotely sensed observations for the monitoring of hydrological extremes and these consequences. Manuscripts can be related to any aspect of remote sensing technique or hydrological applications. We also encourage manuscripts resulting from applications of new technology and improvements expected from missions to be launched in the near future.

Dr. Frédéric Frappart
Dr. José Darrozes
Dr. Fabrice Papa
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 papers will be 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2200 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

  • Floods
  • Droughts
  • Extreme rainfall
  • Extreme snowfall
  • Tidal bores

Published Papers (6 papers)

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Research

Open AccessArticle
Evaluation of Six Satellite-Based Precipitation Products and Their Ability for Capturing Characteristics of Extreme Precipitation Events over a Climate Transition Area in China
Remote Sens. 2019, 11(12), 1477; https://doi.org/10.3390/rs11121477 - 21 Jun 2019
Cited by 5
Abstract
Extreme precipitation has received much attention because of its implications for hazard assessment and risk management. However, accurate precipitation information for extreme precipitation research from dense rain gauges is still difficult to obtain in developing countries or mountainous regions. Satellite-based precipitation products (SPPs) [...] Read more.
Extreme precipitation has received much attention because of its implications for hazard assessment and risk management. However, accurate precipitation information for extreme precipitation research from dense rain gauges is still difficult to obtain in developing countries or mountainous regions. Satellite-based precipitation products (SPPs) with high spatial and temporal resolution offer a new way of supplementing data from gauge-based observations. This study aims to evaluate the precision of six SPPs in detail at multiple temporal and spatial scales and explore their ability to capture the characteristics of extreme precipitation from 2009 to 2014 over a semi-arid to semi-humid climate transition area (Wei River basin) in China. The six products are TRMM 3B42RT, TRMM 3B42V7, PERSIANN, PERSIANN CDR, CMORPH RAW, and CRORPH CRT. China gauge-based daily precipitation analysis (CGDPA) provided by the China Meteorological Administration is used as the benchmark reference data. Various statistical evaluation techniques and extreme precipitation indices are used to evaluate and compare the performance of the selected products. The results show that the post real-time products (TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT) agreed better with the reference data than PERSIANN and CMORPH RAW. On a daily scale, TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT displayed similarly good performance. However, at the monthly or annual scale, TRMM 3B42V7 was superior to the other products. With regard to the spatial distribution of precipitation, the datasets performed better over plains and were disappointing over mountainous areas. Additionally, TRMM 3B42V7 provided higher precision and less spatial uncertainty when monitoring extreme precipitation. This study provides a basis for selecting alternative precipitation data for climate transition basins. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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Open AccessArticle
Identifying 2010 Xynthia Storm Signature in GNSS-R-Based Tide Records
Remote Sens. 2019, 11(7), 782; https://doi.org/10.3390/rs11070782 - 01 Apr 2019
Cited by 6
Abstract
In this study, three months of records (January–March 2010) that were acquired by a geodetic Global Navigation Satellite Systems (GNSS) station from the permanent network of RGP (Réseau GNSS Permanent), which was deployed by the French Geographic Institute (IGNF), located in Socoa, in [...] Read more.
In this study, three months of records (January–March 2010) that were acquired by a geodetic Global Navigation Satellite Systems (GNSS) station from the permanent network of RGP (Réseau GNSS Permanent), which was deployed by the French Geographic Institute (IGNF), located in Socoa, in the south of the Bay of Biscay, were used to determine the tide components and identify the signature of storms on the signal to noise ratio (SNR) during winter 2010. The Xynthia storm hit the French Atlantic coast on the 28th of February 2010, causing large floods and damages from the Gironde to the Loire estuaries. Blind separation of the tide components and of the storm signature was achieved while using both a singular spectrum analysis (SSA) and a continuous wavelet transform (CWT). A correlation of 0.98/0.97 and root mean square error (RMSE) of 0.21/0.28 m between the tide gauge records of Socoa and our estimates of the sea surface height (SSH) using the SSA and the CWT, respectively, were found. Correlations of 0.76 and 0.7 were also obtained between one of the modes from the SSA and atmospheric pressure from a meteorological station and a mode of the SSA. Particularly, a correlation reaches to 0.76 when using both the tide residual that is associated to surges and atmospheric pressure variation. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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Open AccessArticle
Remote Sensing of Water Use Efficiency and Terrestrial Drought Recovery across the Contiguous United States
Remote Sens. 2019, 11(6), 731; https://doi.org/10.3390/rs11060731 - 26 Mar 2019
Cited by 12
Abstract
Ecosystem water-use efficiency (WUE) is defined as the ratio of carbon gain (i.e., gross primary productivity; GPP) to water consumption (i.e., evapotranspiration; ET). WUE is markedly influential on carbon and water cycles, both of which are fundamental for ecosystem state, climate and the [...] Read more.
Ecosystem water-use efficiency (WUE) is defined as the ratio of carbon gain (i.e., gross primary productivity; GPP) to water consumption (i.e., evapotranspiration; ET). WUE is markedly influential on carbon and water cycles, both of which are fundamental for ecosystem state, climate and the environment. Drought can affect WUE, subsequently disturbing the composition and functionality of terrestrial ecosystems. In this study, the impacts of drought on WUE and its components (i.e., GPP and ET) are assessed across the Contiguous US (CONUS) at fine spatial and temporal resolutions. Soil moisture simulations from land surface modeling are utilized to detect and characterize agricultural drought episodes and remotely sensed GPP and ET are retrieved from the moderate resolution imaging spectroradiometer (MODIS). GPP, as the biome vitality indicator against drought stress, is employed to investigate drought recovery and the ecosystems’ required time to revert to pre-drought condition. Results show that drought recovery duration indicates a positive correlation with drought severity and duration, meaning that a protracted drought recovery is more likely to happen following severe droughts with prolonged duration. WUE is found to almost always increase in response to severe (or worse) drought episodes. Additionally, ET anomalies are negatively correlated with drought severity and ET is expected to decrease during severe (or worse) drought episodes. Lastly, the changes of WUE are decomposed in relation to its components and the cross-relation among the variables is revealed and a consistent changing pattern is detected. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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Open AccessArticle
A Remote Sensing Based Integrated Approach to Quantify the Impact of Fluvial and Pluvial Flooding in an Urban Catchment
Remote Sens. 2019, 11(5), 577; https://doi.org/10.3390/rs11050577 - 08 Mar 2019
Cited by 5
Abstract
Pluvial (surface water) flooding is often the cause of significant flood damage in urban areas. However, pluvial flooding is often overlooked in catchments which are historically known for fluvial floods. In this study, we present a conceptual remote sensing based integrated approach to [...] Read more.
Pluvial (surface water) flooding is often the cause of significant flood damage in urban areas. However, pluvial flooding is often overlooked in catchments which are historically known for fluvial floods. In this study, we present a conceptual remote sensing based integrated approach to enhance current practice in the estimation of flood extent and damage and characterise the spatial distribution of pluvial and fluvial flooding. Cockermouth, a town which is highly prone to flooding, was selected as a study site. The flood event caused by named storm Desmond in 2015 (5-6/12/2015) was selected for this study. A high resolution digital elevation model (DEM) was produced from a composite digital surface model (DSM) and a digital terrain model (DTM) obtained from the Environment Agency. Using this DEM, a 2D flood model was developed in HEC-RAS (v5) 2D for the study site. Simulations were carried out with and without pluvial flooding. Calibrated models were then used to compare the fluvial and combined (pluvial and fluvial) flood damage areas for different land use types. The number of residential properties affected by both fluvial and combined flooding was compared using a combination of modelled results and data collected from Unmanned Aircraft Systems (UAS). As far as the authors are aware, this is the first time that remote sensing data, hydrological modelling and flood damage data at a property level have been combined to differentiate between the extent of flooding and damage caused by fluvial and pluvial flooding in the same event. Results show that the contribution of pluvial flooding should not be ignored, even in a catchment where fluvial flooding is the major cause of the flood damages. Although the additional flood depths caused by the pluvial contribution were lower than the fluvial flood depths, the affected area is still significant. Pluvial flooding increased the overall number of affected properties by 25%. In addition, it increased the flood depths in a number of properties that were identified as being affected by fluvial flooding, in some cases by more than 50%. These findings show the importance of taking pluvial flooding into consideration in flood management practices. Further, most of the data used in this study was obtained via remote sensing methods, including UAS. This demonstrates the merit of developing a remote sensing based framework to enhance current practices in the estimation of both flood extent and damage. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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Open AccessArticle
The Use of Unmanned Aerial Vehicles to Estimate Direct Tangible Losses to Residential Properties from Flood Events: A Case Study of Cockermouth Following the Desmond Storm
Remote Sens. 2018, 10(10), 1548; https://doi.org/10.3390/rs10101548 - 26 Sep 2018
Cited by 4
Abstract
Damage caused by flood events is expected to increase in the coming decades driven by increased land use pressures and climate change impacts. The insurance sector needs accurate and efficient loss adjustment methodologies for flood events. These can include remote sensing approaches that [...] Read more.
Damage caused by flood events is expected to increase in the coming decades driven by increased land use pressures and climate change impacts. The insurance sector needs accurate and efficient loss adjustment methodologies for flood events. These can include remote sensing approaches that enable the rapid estimation of (i) damage caused to property as well as (ii) the number of affected properties. Approaches based on traditional remote sensing methods have limitations associated with low-cloud cover presence, oblique viewing angles, and the resolution of the geomatic products obtained. Unmanned aerial vehicles (UAVs) are emerging as a potential tool for post-event assessment and provide a means of overcoming the limitations listed above. This paper presents a UAV-based loss-adjustment framework for the estimation of direct tangible losses to residential properties affected by flooding. For that purpose, features indicating damage to property were mapped from UAV imagery collected after the Desmond storm (5 and 6 December 2015) over Cockermouth (Cumbria, UK). Results showed that the proposed framework provided an accuracy of 84% in the detection of direct tangible losses compared with on-the-ground household-by-household assessment approaches. Results also demonstrated the importance of pluvial and, from eye witness reports, lateral flow flooding, with a total of 168 properties identified as flooded falling outside the fluvial flood extent. The direct tangible losses associated with these additional properties amounted to as high as £3.6 million. The damage-reducing benefits of resistance measures were also calculated and amounted to around £4 million. Differences in direct tangible losses estimated using the proposed UAV approach and the more classic loss-adjustment methods relying on the fluvial flood extent was around £1 million—the UAV approach providing the higher estimate. Overall, the study showed that the proposed UAV approach could make a significant contribution to improving the estimation of the costs associated with urban flooding, and responses to flooding events, at national and international levels. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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Open AccessArticle
The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas
Remote Sens. 2018, 10(4), 583; https://doi.org/10.3390/rs10040583 - 09 Apr 2018
Cited by 24
Abstract
Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the [...] Read more.
Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable of significantly improving the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5% and the User’s Accuracy increased by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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