Special Issue "Remote Sensing of Hydrometeorological Extremes"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 January 2020).

Special Issue Editors

Dr. Christian Massari
E-Mail Website
Guest Editor
National Research Council, Via Madonna Alta 126, Perugia, Italy
Interests: floods; drought; landslides; hydrological modelling; data assimilation; satellite soil moisture; satellite precipitation; water resource engineering
Special Issues and Collections in MDPI journals
Dr. Viviana Maggioni
E-Mail Website
Guest Editor
George Mason University, 4400 University Drive, MS-6C1; Fairfax, VA 22030, USA
Interests: Remote sensing applications in atmospheric and hydrologic science; Uncertainty analysis; Land data assimilation systems; Land surface modeling; Water resource engineering
Dr. Luca Ciabatta
E-Mail Website
Guest Editor
National Research Council, Via Madonna Alta 126, Perugia, Italy
Interests: Landslides; Remote sensing of precipitation; Floods
Dr. Manuela Girotto
E-Mail Website
Guest Editor
GESTAR, University Space and Research Association, Columbia, MD, USA; Global Modeling and Assimilation Office, NASA Goddard Space and Flight Center, Greenbelt, MD, USA
Interests: Hydrologic data assimilation; Snow hydrology modeling and remote sensing; Gravity Recovery And Climate Experiment (GRACE and GRACE-FO) missions; Groundwater
Dr. Yves Tramblay
E-Mail Website
Guest Editor
HSM (Univ. Montpellier, CNRS, IRD), 300, Avenue du Professeur Emile Jeanbrau, 34090, Montpellier, France
Interests: extreme hydrological events (floods, droughts, heavy rainfall, sediment); statistical hydrology (frequency analysis, regionalization methods, geostatistics); rainfall-runoff modeling (flood processes, water resources); climate change impacts on hydrology

Special Issue Information

Dear Colleagues,

The current intensification of hydroclimatic hazards due to climate change is posing several threats to human security causing humanitarian, environmental, and financial disasters. Climate change alters hydrometeorological variables, like temperature and precipitation, and produces changes in the partitioning of precipitation into evapotranspiration and runoff. This exacerbates hydroclimatic extremes like floods, droughts, heat waves and landslides, which have been revealing an unprecedented intensification during the last two decades.

In this scenario, monitoring hydroclimatic variables is of paramount importance for predicting and facing these extreme events, but it is also a difficult task due to the scarcity and sharp decline of in-situ measurement around the world. Remote sensing together with advanced modeling techniques offers an unprecedented opportunity to improve our understanding of underlying hydrological processes and allows assessing the likelihood, extent, and uncertainty of extreme events in order to mitigate their impacts.

This Special Issue aims at documenting not only the most recent progress in the methods used to monitor, model, and forecast hydroclimatic extremes, but also at understanding how changes in frequency and magnitude of hydroclimatic variables project into extreme events. The Special Issue also encourages studies that investigate the changes and trends of extreme events—e.g., river floods, flash floods, extreme temperatures, heat and cold waves, droughts and landslides—using remote sensing observations.

Dr. Christian Massari
Dr. Viviana Maggioni
Dr. Luca Ciabatta
Dr. Manuela Girotto
Dr. Yves Tramblay
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 2400 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
  • Landslides
  • Drought
  • Remote Sensing
  • Hydrological modeling

Published Papers (2 papers)

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Research

Open AccessArticle
A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture
Remote Sens. 2019, 11(19), 2221; https://doi.org/10.3390/rs11192221 - 24 Sep 2019
Cited by 7 | Viewed by 1809
Abstract
Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that [...] Read more.
Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coefficient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrometeorological Extremes)
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Open AccessArticle
Monitoring Soil Moisture Drought over Northern High Latitudes from Space
Remote Sens. 2019, 11(10), 1200; https://doi.org/10.3390/rs11101200 - 20 May 2019
Cited by 4 | Viewed by 1722
Abstract
Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with [...] Read more.
Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with a wider spatial and temporal coverage than that obtained from satellite SSM retrievals. We evaluate passive microwave brightness temperature observations from the Soil Moisture and Ocean Salinity (SMOS) satellite as a surrogate drought metric, and introduce a Standardized Brightness Temperature Index (STBI). We compute the STBI by fitting a Gaussian distribution using monthly brightness temperature data from SMOS; the normal assumption is tested using the Shapior-Wilk test. Our results indicate that the assumption of normally distributed brightness temperature data is valid at the 0.05 significance level. The STBI is validated against drought indices from a land surface data assimilation system (LDAS-Monde), two satellite derived SSM indices, one from SMOS and one from the ESA CCI soil moisture project and a standardized precipitation index based on in situ data from the European Climate Assessment & Dataset (ECA&D) project. When comparing the temporal dynamics of the STBI to the LDAS-Monde drought index we find that it has equal correlation skill to that of the ESA CCI soil moisture product ( 0.71 ). However, in addition the STBI provides improved spatial coverage because no masking has been applied over regions with dense boreal forest. Finally, we evaluate the STBI in a case study of the 2018 Nordic drought. The STBI is found to provide improved spatial and temporal coverage when compared to the drought index created from satellite derived SSM over the Nordic region. Our results indicate that when compared to drought indices from precipitation data and a land data assimilation system, the STBI is qualitatively able to capture the 2018 drought onset, severity and spatial extent. We did see that the STBI was unable to detect the 2018 drought recovery for some areas in the Nordic countries. This false drought detection is likely linked to the recovery of vegetation after the drought, which causes an increase in the passive microwave brightness temperature, hence the STBI shows a dry anomaly instead of normal conditions, as seen for the other drought indices. We argue that the STBI could provide additional information for drought monitoring in regions where the SSM retrieval problem is not well defined. However, it then needs to be accompanied by a vegetation index to account for the recovery of the vegetation which could cause false drought detection. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrometeorological Extremes)
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