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Applications of Remote Sensing and Modeling in Hydrological Systems
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Applications of Remote Sensing and Modeling in Hydrological Systems

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 (25 April 2025) | Viewed by 4059

Special Issue Editor

Prof. Dr. Xiaoping Rui

E-Mail Website
Guest Editor
Department of Surveying, School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Interests: remote sensing; hydrological modeling; water resource management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the impacts of global climate change and human activities intensify, the study and management of water resources and hydrological systems have become increasingly critical. Rapid advancements in remote sensing technology, combined with the growing sophistication of hydrological models, offer powerful tools for scientists and engineers to gain deeper insights into water systems and manage water resources effectively. This Special Issue, with a focus on “Applications of Remote Sensing and Modeling in Hydrological Systems”, will explore cutting-edge applications for these technologies and their potential in hydrological research.

Remote sensing provides large-scale, high-resolution data on various hydrological components, such as precipitation, soil moisture, surface water bodies, and land cover changes. These data are invaluable in real-time monitoring, especially in remote or inaccessible regions. When integrated with hydrological models, remote sensing data enable the simulation of water cycle dynamics and the prediction of hydrological events such as floods and droughts and support the sustainable management of water resources.

The Special Issue will gather the latest research findings and case studies, showcasing how remote sensing and hydrological modeling can be used to address contemporary hydrological challenges. In this way, this Special Issue will promote applications of these technologies in global water resource management, providing scientific evidence and technical support to address the hydrological challenges posed by climate change.

This Special Issue will serve as a platform for academic exchange among researchers and practitioners in the field, fostering innovation and collaboration. We invite scholars to submit high-quality papers and contribute to advancing this crucial area of research.

Prof. Dr. Xiaoping Rui
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 modeling
  • water resource management
  • watershed analysis
  • flood forecasting
  • surface water area identification and change detection

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

Published Papers (3 papers)

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Research

17 pages, 440 KiB  
Article
Urban Rivers and Corporate Environmental Performance: Evidence from Listed Companies in China
by Ju-Ping Wu, Fan-Yao Meng and Huan Wang
Water 2025, 17(7), 1052; https://doi.org/10.3390/w17071052 - 2 Apr 2025
Viewed by 295
Abstract
Civilization begins with rivers, and so does pollution. Examining and deciphering the possible ecological curse effect of abundant river resources has a profound impact on sustainable economic development. This paper empirically examines the impact of urban rivers on the environmental performance of listed [...] Read more.
Civilization begins with rivers, and so does pollution. Examining and deciphering the possible ecological curse effect of abundant river resources has a profound impact on sustainable economic development. This paper empirically examines the impact of urban rivers on the environmental performance of listed companies by constructing indicators of urban river length and density and measuring river resources in prefecture-level cities with a sample of listed companies in China from 2011 to 2022. It is found that the richer the urban rivers are, the lower the environmental performance of listed companies in the region, and the conclusion still holds after the robustness and endogeneity tests, proving that rivers as important natural resources also have the ecological curse effect. Further mechanism analysis reveals that urban river resources will reduce the production cost of enterprises and generate path dependence, which will have a crowding-out effect on the green innovation of enterprises; at the same time, abundant river resources will induce market failure, and institutional weakness accelerates the polluting behavior of enterprises. The research in this paper enriches the micro impacts and mechanisms of the ecological curse and provides useful references for river pollution management. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Modeling in Hydrological Systems)
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18 pages, 10356 KiB  
Article
Automatic Flood Monitoring Method with SAR and Optical Data Using Google Earth Engine
by Xiaoran Peng, Shengbo Chen, Zhengwei Miao, Yucheng Xu, Mengying Ye and Peng Lu
Water 2025, 17(2), 177; https://doi.org/10.3390/w17020177 - 10 Jan 2025
Cited by 4 | Viewed by 1570
Abstract
Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring [...] Read more.
Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Modeling in Hydrological Systems)
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19 pages, 9251 KiB  
Article
Water Balance Analysis in the Majalaya Watershed: Two-Step Calibration and Application of the SWAT+ Model for Low-Flow Conditions
by Hadi Kardhana, Abdul Wahab Insan Lihawa, Faizal Immaddudin Wira Rohmat, Siska Wulandari, Wendi Harjupa, Widyawardana Adiprawita, Edwan Kardena and Muhammad Syahril Badri Kusuma
Water 2024, 16(23), 3498; https://doi.org/10.3390/w16233498 - 5 Dec 2024
Viewed by 1613
Abstract
Understanding hydrological processes is crucial for effective watershed management, with SWAT+ being one of the widely adopted models for analyzing water balance at watershed scales. While hydrological components are often assessed through sensitivity analysis, calibration, and validation, parameter sensitivity during dry periods (low-flow [...] Read more.
Understanding hydrological processes is crucial for effective watershed management, with SWAT+ being one of the widely adopted models for analyzing water balance at watershed scales. While hydrological components are often assessed through sensitivity analysis, calibration, and validation, parameter sensitivity during dry periods (low-flow conditions) when baseflow is predominant remains a relevant focus, especially for watersheds like Majalaya, Indonesia, which experience distinct low-flow periods. This study analyzes water balance components in the Majalaya watershed, Indonesia, using SWAT+ across the 2014–2022 period, focusing on low-flow conditions. This study employs a two-step calibration approach using various datasets, including ground rainfall (2014–2022), NASA POWER meteorological data, MODIS land cover, DEMNAS terrain, and DSMW soil types, and the streamflow data for model calibration. The first calibration step optimized the overall performance (R2 = 0.41, NSE = 0.41, and PBIAS = −7.33), and the second step improved the baseflow simulation (R2 = 0.40, NSE = 0.35, and PBIAS = 12.45). A Sobol sensitivity analysis identified six primary parameters, i.e., CN3_SWF, CN2, LATQ_CO, PERCO, SURLAG, and CANMX, as the most influential for streamflow calibration, with CN3_SWF and CN2 being the most critical. This study demonstrates SWAT+’s effectiveness in watershed management and water resource optimization, particularly during low-flow conditions. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Modeling in Hydrological Systems)
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