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Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood...
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Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood and Water Pollution Assessment and Monitoring

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: 25 April 2024 | Viewed by 9628

Special Issue Editors

Dr. Yaohuan Huang

E-Mail Website
Guest Editor
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing; GIS; water cycle; hydrological model; precipitation extremes; floods; droughts; spatial analysis; land use and land cover change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yesen Liu

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research, Beijing, China
Interests: remote sensing; GIS; machine learning; waterlogging; flash floods; hydrological model; precipitation extremes; digital twin watershed; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Dr. Runhe Shi

E-Mail Website
Guest Editor
School of Geographic Sciences, East China Normal University, Shanghai, China
Interests: remote sensing of environment; GIS; quantitative remote sensing; wetland vulnerability; coastal monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Hongyan Ren

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: hyperspectral remote sensing; urbanization and water environment; geospatial analysis; water pollution and public health; agriculture and water resources
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuanyuan Liu

E-Mail Website
Guest Editor
Key laboratory of digital twin watershed, Ministry of Water Resources, Beijing, China
Interests: precipitation extremes; digital twin watershed; machine learning; data mining; neural network; flood prevention

Special Issue Information

Dear Colleagues,

Driven by global change and population pressure, droughts, floods and water pollution have been the most serious hydrological hazards that can lead to crop losses and economic havoc in many areas, affecting more people globally than any other natural hazard. As droughts, floods and water pollution are complex hydrological systems, they deserve a multidisciplinary monitoring effort in order to carry out appropriate and timely hazard assessments. Recently, various spatiotemporal data (e.g., remote sensing, big data, in-situ monitoring, etc.) have been widely applied to obtain a synoptic and punctual view over basin-scale monitored areas. The application of art-to-state spatiotemporal data can potentially provide an extra contribution to hydrological hazards of drought, flood and water pollution assessment and monitoring, for instance, in terms of the accuracy of results, the amount of information obtained, temporal availability, etc.

We are seeking contributions that integrate the application of spatiotemporal data such as remote sensing, Big data, etc., with a particular focus on and reference to drought, flood or water pollution monitoring and hazard assessment. In particular, contributions on various droughts or flood monitoring indexes from different spatiotemporal data resources are also welcome and encouraged. The investigative approach characterized by the integration of disciplines at different scales of vision and precision represents a modern challenge to strive for a more complete understanding of drought, flood and water pollution processes and, therefore, a better hazard evaluation.

Dr. Yaohuan Huang
Prof. Dr. Yesen Liu
Dr. Runhe Shi
Dr. Hongyan Ren
Prof. Dr. Yuanyuan Liu
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. 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

  • droughts
  • floods
  • water pollution
  • waterlogging
  • remote sensing observation
  • big data
  • digital twin watershed
  • hazard assessment
  • machine learning
  • public health

Published Papers (4 papers)

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Research

17 pages, 9529 KiB  
Article
An Analysis of Rainfall Characteristics and Rainfall Flood Relationships in Cities along the Yangtze River Based on Machine Learning: A Case Study of Luzhou
by Yuanyuan Liu, Yesen Liu, Jiazhuo Wang, Hancheng Ren, Shu Liu and Wencai Hu
Water 2023, 15(21), 3755; https://doi.org/10.3390/w15213755 - 27 Oct 2023
Viewed by 966
Abstract
Cities along rivers are threatened by floods and waterlogging, and the relationship between rainstorms and floods is complex. The temporal and spatial distributions of rainstorms directly affect flood characteristics. The location of the rainstorm center determines the flood peaks, volumes, and processes. In [...] Read more.
Cities along rivers are threatened by floods and waterlogging, and the relationship between rainstorms and floods is complex. The temporal and spatial distributions of rainstorms directly affect flood characteristics. The location of the rainstorm center determines the flood peaks, volumes, and processes. In this study, machine learning algorithms were introduced to analyze the rain–flood relationship in Luzhou City, Sichuan Province, China. The spatial and temporal patterns of rainstorms in the region were classified and extracted, and flood characteristics generated by various types of rainstorms were analyzed. In the first type, the center of the rainstorm was in the upper reaches of the Tuojiang River, and the resulting flood caused negligible damage to Luzhou. In the second type, the center of the rainstorm occurred in the Yangtze River Basin. Continuously high water levels in the Yangtze River, combined with local rainfall, supported urban drainage. In the third type, the rainstorm center occurred in the upper reaches of the Yangtze and Tuojiang rivers. During the flooding, rainfall from Yangtze River and Tuojiang River moved towards Luzhou together. The movement of the rainstorm center was consistent with the flood routing direction of the Yangtze and Tuojiang rivers, both of which continued to have high water levels. The flood risk is extremely high in this case, making it the riskiest rainfall process requiring prevention. Full article
(This article belongs to the Special Issue Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood and Water Pollution Assessment and Monitoring)
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19 pages, 8962 KiB  
Article
Comparing Six Vegetation Indexes between Aquatic Ecosystems Using a Multispectral Camera and a Parrot Disco-Pro Ag Drone, the ArcGIS, and the Family Error Rate: A Case Study of the Peruvian Jalca
by Jaris Veneros, Segundo Chavez, Manuel Oliva, Erick Arellanos, Jorge L. Maicelo and Ligia García
Water 2023, 15(17), 3103; https://doi.org/10.3390/w15173103 - 30 Aug 2023
Viewed by 1532
Abstract
A Parrot Sequoia four-band multispectral camera mounted on a Parrot Disco-Pro Ag drone allowed us to study six vegetation indexes in four lakes within the Tilacancha Private Conservation Area (PCA) in 2021. These lakes are a source of water for consumption for more [...] Read more.
A Parrot Sequoia four-band multispectral camera mounted on a Parrot Disco-Pro Ag drone allowed us to study six vegetation indexes in four lakes within the Tilacancha Private Conservation Area (PCA) in 2021. These lakes are a source of water for consumption for more than 32,000 people in the province of Chachapoyas in the Amazon region of Peru. To obtain the six vegetation indexes (Green Normalized Difference Vegetation Index—GNDVI; Leaf Chlorophyll Index—LCI; Modified Chlorophyll Absorption in Reflective Index—MCARI; Normalized Difference Red Edge—NDRE; Normalized Difference Vegetation Index—NDVI; and Structure Intensive Pigment Index 2S—SIPI2), Pix4DFields 1.8.1 software was used. The sensitivity and distribution of pixel values were compared in histograms and Q–Q plots for each index. Statistical differences were established for each index, and the SIPI2 obtained the highest level of sensitivity concerning the degree of pixel distribution in the ranges shown in the histogram according to the standard deviation; however, the values of all the indexes were not disregarded, because they showed statistical differences between lakes despite their closeness. The family error rate and Tukey-Kramer HSD statistics allowed for establishing statistical differences between pairs of lakes. The six vegetation indexes can be used to detect and analyze the dynamics of biological beings with photosynthetic activity in aquatic ecosystems of the Peruvian Jalca. Full article
(This article belongs to the Special Issue Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood and Water Pollution Assessment and Monitoring)
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21 pages, 9627 KiB  
Article
Numerical Simulation of Two-Dimensional Dam Failure and Free-Side Deformation Flow Studies
by Haoyu Jiang, Bowen Zhao, Zhang Dapeng and Keqiang Zhu
Water 2023, 15(8), 1515; https://doi.org/10.3390/w15081515 - 13 Apr 2023
Cited by 2 | Viewed by 1884
Abstract
A dam breaking is a major flood catastrophe. The shape, depth, and wave Doppler effect of initial water flow are all modified as a result of the interaction of the water body with downstream structures after a dam breach, forming a diffraction and [...] Read more.
A dam breaking is a major flood catastrophe. The shape, depth, and wave Doppler effect of initial water flow are all modified as a result of the interaction of the water body with downstream structures after a dam breach, forming a diffraction and reflection flow field. This study investigates the dam breaking problem of a single liquid, by creating a two-dimensional simplified numerical model using the VOF approach, analysing the interaction and effect between barriers of various forms and the dam failure flow, and explains the problem of a complex flow mechanism involving significant deformation of the free surface of a medium. According to the findings, obstacles of varying forms could obstruct the dam break’s water flow to various degrees, and the viscous dissipation characteristic of the water body at the edge of the obstacle is closely related to the slope of the site. The numerical simulation presented in this study is validated, demonstrating its accuracy for both the gate-pulling and downstream wet-bed scenarios. Full article
(This article belongs to the Special Issue Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood and Water Pollution Assessment and Monitoring)
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17 pages, 5160 KiB  
Article
Mapping of Groundwater, Flood, and Drought Potential Zones in Neom, Saudi Arabia, Using GIS and Remote Sensing Techniques
by Talal Alharbi
Water 2023, 15(5), 966; https://doi.org/10.3390/w15050966 - 02 Mar 2023
Cited by 4 | Viewed by 4737
Abstract
Neom is expected to face climate and environmental challenges, including the provision of water and the mitigation of flood and drought risks. The field data for identifying the potential risk zones are limited. I utilized remote sensing data and geographic information system (GIS) [...] Read more.
Neom is expected to face climate and environmental challenges, including the provision of water and the mitigation of flood and drought risks. The field data for identifying the potential risk zones are limited. I utilized remote sensing data and geographic information system (GIS) techniques to identify such zones. The datasets used here included drainage density, lineament density, precipitation, elevation, lithology, slope, soil, and land use/land cover. These data were analyzed using a weighted overlay analysis in a GIS environment. The analysis successfully mapped the potential groundwater, flood, and drought zones in Neom. The zone with a good potential for groundwater covered 515 km2 of Neom, whereas 11,562, 10,616, and 289 km2 of land had a moderate, poor, and very poor chance of having groundwater, respectively. The area with the lowest flood danger covered only 195 km2, whereas the areas with a low, moderate, and high flooding risk covered 4355, 13,542, and 4910 km2 of land, respectively. The results of the overlay analysis showed that low and very low drought risks were associated with 4322 and 44 km2 of land, respectively. In turn, 10,615 and 8266 km2 of the region were at a moderate and high drought risk, respectively. Full article
(This article belongs to the Special Issue Application of Spatiotemporal Data in Hydrological Hazards of Drought, Flood and Water Pollution Assessment and Monitoring)
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