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Remote Sensing
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Remote Sensing of Geo-Hydrological Process in an Arid Region
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Remote Sensing of Geo-Hydrological Process in an Arid Region

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 5087

Special Issue Editors

Dr. Samy Elmahdy

E-Mail Website
Guest Editor
Department of Civil Engineering (Geospatial Analysis Center), American University of Sharjah, Sharjah, UAE
Interests: remote Sensing and geology Teaching; project management; image processing; geophysical
Prof. Dr. Alina Barbulescu

E-Mail Website
Guest Editor
Department of Civil Engineering, Transilvania University of Brașov, 500036 Brașov, Romania
Interests: hydrology; applied statistics; mathematical modelling; time series analysis; water quality assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

Water covers about 72% of the Earth’s surface, mostly with oceans and sea, and minor portions of water exist on Earth as groundwater, water vapour, and clouds. Geohydrological process can be defined hydrological characteristics of aquifers or rocks and their impacts on groundwater flow, groundwater quantity and groundwater quality. In an arid region, average annual precipitation was estimated to be less than 500 mm and average annual evapotranspiration greater than 800 mm as well as spatiotemporal variability in groundwater quantity and quality. This region covers more than a quarter of Earth's land surface.    

Recently, geospatial technology, hydrological models,  and machine learning models permitted better understanding and modelling geohydrological process. They have proven to be excellent tools for investigating the impacts of land use/ cover changes on climate change and the consequent impacts of these changes on groundwater quality and quality over a regional scale with low cost and time-consuming.

The main objective of this special issue is to gather the recent works that employed geohydrological process in an arid region. The potential research topics include the following: 

  • Water resources management
  • Geohydrology and remote sensing
  • Soil water science
  • Groundwater hydrology and information science
  • Hydrochemistry and groundwater pollution
  • Policy analysis and managements
  • Socio-hydrology
  • Aquifer characteristics
  • Groundwater risk assessment
  • Regional groundwater flow
  • Impact of LULC on groundwater quality and quantity

Dr. Samy Elmahdy
Dr. Alina Barbulescu
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. 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 2700 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

20 pages, 5332 KiB  
Article
Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms
by Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis and Simon Michael Papalexiou
Remote Sens. 2021, 13(3), 333; https://doi.org/10.3390/rs13030333 - 20 Jan 2021
Cited by 11 | Viewed by 4005
Abstract
Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions [...] Read more.
Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile levels 2.5% and 97.5% using linear models as base learners exhibit better performance compared to more flexible boosting models that use both linear models and stumps (i.e., one-level decision trees). On the contrary, boosting models that use both linear models and stumps perform better than boosting with linear models when used for point predictions. Moreover, it is shown that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures. Full article
(This article belongs to the Special Issue Remote Sensing of Geo-Hydrological Process in an Arid Region)
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