Special Issue "Remote Sensing in Hydrological Modelling"

A special issue of Hydrology (ISSN 2306-5338).

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Dr. Catherine Ottlé

Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Orme des Merisiers, 91191 Gif-sur-Yvette, France
Website | E-Mail
Interests: Earth Observation; Land Surface Modeling (carbon-water-energy exchanges); Data Assimilation
Guest Editor
Dr. Aaron Boone

Centre National de Recherches Météorologiques (CNRM) Météo-France/CNRS, Toulouse, France
Website | E-Mail
Interests: land-atmosphere feedbacks; the modeling of continental surface processes; hydrometeorology

Special Issue Information

Dear Colleagues,

Satellite remote sensing can now provide very useful observations for water resource monitoring. Land use, land cover, river networks, lakes, and snowpack, as well as certain atmospheric variables that are determinant of surface–atmosphere exchanges, such as those related to rainfall or evapotranspiration, are variables that can be assessed from space with various types of instruments. Moreover, they can be measured at unprecedented temporal and spatial scales, thanks to recent on-board instruments. Hydrological models are often largely parameterized and require observations for their calibration in order to model water resources accurately. For that purpose, EO data provide valuable information that can be used to constrain hydrological models. However, it is also important to assess their limitations and to quantify their uncertainties in order to exploit these data optimally.The objective of this Special Issue is to describe state-of-the-art applications of satellite remote sensing in hydrological modelling. Contributions presenting the use of new algorithms and/or new EO data to assess land surface variables impacting the energy and water cycles at regional or global scales are expected. The research presented might focus on:

  • Innovative methods and observations to map land use and catchment characteristics and to characterize atmospheric forcing, especially rainfall and snowfall and their spatial and temporal variability.
  • Innovative methods and observations to retrieve or monitor hydrological variables or parameters such as soil moisture, snowpack, evapotranspiration, interception, etc.
  • New approaches to assess observations and model uncertainties.
  • New approaches and metrics to evaluate hydrological models.
  • Advanced methods to upscale/downscale hydrological variables.
  • Innovative techniques to assimilate EO products in hydrological models.
  • Advanced applications in irrigation hydrology and water management, including hydrological monitoring and forecasting.
  • Review papers on potential and limitations of various EO products.

Dr. Catherine Ottlé
Dr. Aaron Boone
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. Hydrology is an international peer-reviewed open access quarterly 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 350 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

  • Surface water hydrology
  • Irrigation hydrology
  • Global water and energy cycles
  • Streamflow simulation
  • Data assimilation
  • Water reservoir monitoring

Published Papers (3 papers)

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Research

Open AccessArticle Mitigating Spatial Discontinuity of Multi-Radar QPE Based on GPM/KuPR
Received: 30 June 2018 / Revised: 23 August 2018 / Accepted: 30 August 2018 / Published: 1 September 2018
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Abstract
Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly
[...] Read more.
Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrological Modelling)
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Open AccessArticle Indications of Surface and Sub-Surface Hydrologic Properties from SMAP Soil Moisture Retrievals
Received: 30 June 2018 / Revised: 22 July 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
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Abstract
Variability and covariability of land properties (soil, vegetation and subsurface geology) and remotely sensed soil moisture over the southeast and south-central U.S. are assessed. The goal is to determine whether satellite soil moisture memory contains information regarding land properties, especially the distribution karst
[...] Read more.
Variability and covariability of land properties (soil, vegetation and subsurface geology) and remotely sensed soil moisture over the southeast and south-central U.S. are assessed. The goal is to determine whether satellite soil moisture memory contains information regarding land properties, especially the distribution karst formations below the active soil column that have a bearing on land-atmosphere feedbacks. Local (within a few tens of km) statistics of land states and soil moisture are considered to minimize the impact of climatic variations, and the local statistics are then correlated across the domain to illuminate significant relationships. There is a clear correspondence between soil moisture memory and many land properties including karst distribution. This has implications for distributed land surface modeling, which has not considered preferential water flows through geologic formations. All correspondences are found to be strongest during spring and fall, and weak during summer, when atmospheric moisture demand appears to dominate soil moisture variability. While there are significant relationships between remotely-sensed soil moisture variability and land properties, it will be a challenge to use satellite data for terrestrial parameter estimation as there is often a great deal of correlation among soil, vegetation and karst property distributions. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrological Modelling)
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Open AccessArticle Analysing the Potential of OpenStreetMap Data to Improve the Accuracy of SRTM 30 DEM on Derived Basin Delineation, Slope, and Drainage Networks
Received: 1 June 2018 / Revised: 2 July 2018 / Accepted: 17 July 2018 / Published: 18 July 2018
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Abstract
Terrain slope and drainage networks are useful components to the basins morphometric characterization as well as to hydrologic modelling. One way to obtain the slope, drainage networks, and basins delineation is by their extraction from Digital Elevation Models (DEMs) and, therefore, their accuracy
[...] Read more.
Terrain slope and drainage networks are useful components to the basins morphometric characterization as well as to hydrologic modelling. One way to obtain the slope, drainage networks, and basins delineation is by their extraction from Digital Elevation Models (DEMs) and, therefore, their accuracy depends on the accuracy of the used DEM. Regional DEMs with high detail and accuracy are produced in many countries by National Mapping Agencies (NMA). However, the use of these products usually has associated costs. An alternative to those DEMs are the Global Digital Elevation Models (GDEMs) that can be accessed freely and cover almost the entire surface of the world. However, they are not as accurate as the regional DEMs obtained with other techniques. This study intends to assess if generating new, modified DEMs using altimetric data from the original GDEMs and the watercourses available for download in the collaborative project OpenStreetMap (OSM) improves the accuracy of the rebuilt DEMs, the slope derived from them, as well as the delineation of basins and the horizontal and vertical accuracy of the extracted drainage networks. The methodology is presented and applied to a study area located in the United Kingdom. The GDEMs used are of 30 m spatial resolution from the Shuttle Radar Topography Mission (SRTM 30). The accuracy of the original data and the data obtained with the proposed methodology is compared with a reference DEM, with a spatial resolution of 50 m, and the rivers network available at the Ordnance Survey website. The results mainly show an improvement of the horizontal accuracy of the drainage networks, but also a decrease of the systematic errors of the new DEMs, the derived slope, and the vertical position of the drainage networks, as well as the basin’s identification for a set of pour points. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrological Modelling)
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