Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Water
Special Issues
Remote Sensing in Water Cycle Management
share announcement

Remote Sensing in Water Cycle Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 10959

Special Issue Editor

Dr. Evangelos Rozos

E-Mail Website
Guest Editor
National Observatory of Athens, Institute for Environmental Research & Sustainable Development, Athens, Greece
Interests: urban water; hydrology; water resources management; integrated modelling; numerical methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ever-increasing economic activity combined with the prevalent climatic trends is mounting the pressure on water resources. This places utmost importance on water cycle management, and efficient management of the same requires that one gain comprehensive knowledge of hydrological processes, which is not an easy task. Even for a local project such as the water supply of a town, processes including precipitation, evaporation, surface runoff, infiltration, and so on need to be measured over spatial regions much larger than the area of interest over considerably long temporal periods. For instance, precipitation requires a carefully designed network of rain gauges in order to obtain a reliable estimation of the precipitation over the area of interest. Likewise, doing so is necessary for the estimation of evapotranspiration. On the other hand, surface runoff does not require an extensive network since measurements obtained using a stream gauge include the information regarding runoff from upstream areas. However, these measurements require the construction of the rating curve, which necessitates multiple visits to the stream gauge location to perform discharge measurements. For the aforementioned reasons, remote sensing techniques have long been used to assist with the measurements of hydrological processes, and consequently, the water cycle management. For instance, over four decades have passed since radars are being used to measure rainfall in the USA. Similarly, techniques to estimate evapotranspiration from satellite images have been applied by scientists from a long time. The scope of this Special Issue involves the study of remote sensing techniques that facilitate the management of the water cycle, with an emphasis on modern low-cost solutions.

Dr. Evangelos Rozos
Guest Editor

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. Water 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 2600 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

  • remote sensing
  • hydrological processes
  • hydrological modelling
  • water management
  • UAS
  • satellite data
  • image processing
  • photogrammetry
  • machine learning.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 4122 KiB  
Article
Probabilistic Evaluation and Filtering of Image Velocimetry Measurements
by Evangelos Rozos, Katerina Mazi and Antonis D. Koussis
Water 2021, 13(16), 2206; https://0-doi-org.brum.beds.ac.uk/10.3390/w13162206 - 13 Aug 2021
Cited by 4 | Viewed by 1475
Abstract
The recent technological advances in remote sensing (e.g., unmanned aerial vehicles, digital image acquisition, etc.) have vastly improved the applicability of image velocimetry in hydrological studies. Thus, image velocimetry has become an established technique with an acceptable error for practical applications (the error [...] Read more.
The recent technological advances in remote sensing (e.g., unmanned aerial vehicles, digital image acquisition, etc.) have vastly improved the applicability of image velocimetry in hydrological studies. Thus, image velocimetry has become an established technique with an acceptable error for practical applications (the error can be lower than 10%). The main source of errors has been attributed to incomplete intrinsic and extrinsic camera calibration, to non-constant frame rate and to spurious low velocities due to moving objects that are irrelevant to the streamflow. Some researchers have even employed probabilistic approaches (Monte Carlo simulations) to analyze the uncertainty introduced during the camera calibration procedure. On the other hand, the endogenous uncertainty of the image velocimetry algorithms per se has received little attention. In this study, a probabilistic approach is employed to systematically analyze this uncertainty. It is argued that this analysis may not only improve the performance of the image velocimetry methods but it can also provide information regarding the impact of the video recording conditions (e.g., low density of features, oblique camera angle, low resolution, etc.) on the accuracy of the estimated values. The suggested method has been tested in six case studies of which the data have been previously made publicly available by independent researchers. Full article
(This article belongs to the Special Issue Remote Sensing in Water Cycle Management)
►▼ Show Figures

Figure 1

20 pages, 6906 KiB  
Article
Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil
by Altemar L. Pedreira Junior, Marcelo S. Biudes, Nadja G. Machado, George L. Vourlitis, Hatim M. E. Geli, Luiz Octávio F. dos Santos, Carlos A. S. Querino, Israel O. Ivo and Névio Lotufo Neto
Water 2021, 13(3), 333; https://0-doi-org.brum.beds.ac.uk/10.3390/w13030333 - 29 Jan 2021
Cited by 9 | Viewed by 5146
Abstract
The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable [...] Read more.
The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management. Full article
(This article belongs to the Special Issue Remote Sensing in Water Cycle Management)
►▼ Show Figures

Figure 1

22 pages, 14105 KiB  
Article
Evaluation of Multi-Satellite Precipitation Products and Their Ability in Capturing the Characteristics of Extreme Climate Events over the Yangtze River Basin, China
by Shuai Xiao, Jun Xia and Lei Zou
Water 2020, 12(4), 1179; https://0-doi-org.brum.beds.ac.uk/10.3390/w12041179 - 20 Apr 2020
Cited by 34 | Viewed by 3694
Abstract
Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research [...] Read more.
Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003–2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in the mountainous areas of the upper reach. Only CMORPH and GPM IMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis. Full article
(This article belongs to the Special Issue Remote Sensing in Water Cycle Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop