Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water and Watersheds

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 3702

Share This Special Issue

Special Issue Editors

Dr. Adnan Rajib

E-Mail Website
Guest Editor

Department of Environmental Engineering, Texas A&M University, Kingsville, TX, USA
Interests: ecosystem modeling; climate risks; earth observations; environmental informatics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Venkatesh Merwade

E-Mail Website
Guest Editor

Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
Interests: surface water hydrology; flood modeling and mapping; cyberinfrastructure for hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Zhu Liu

E-Mail Website
Guest Editor

College of Hydrology and Water Resource, Hohai University, Nanjing, China
Interests: climate change; flood modeling; watershed hydrology; uncertainty quantification; bayesian analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing availability of open access data, models, remote sensing, and machine learning techniques is taking the science of water and watersheds to a new direction. The goal of this Special Issue is to aggregate research contributions along this new direction. We welcome high-quality articles (original research, technical notes, and reviews) on the use of open access data, modeling, remote sensing, and machine learning techniques to assess floods, droughts, and water quality as well as their interactions with climatic, anthropogenic, and ecological drivers. Depending on the topic and authors’ interests, the authors may submit their articles either to the Remote Sensing or the Water journal.

Potential topics include but are not limited to the following:

Flood and drought hazard forecasting, mapping, and management.
Water quality predictions in lakes, rivers, and estuaries.
Water use and irrigation efficiency in agricultural landscapes.
Improved land use/land cover mapping and change detection.
Improved process representation in hydrologic models via assimilation of remotely sensed Earth observations.
Climate change impacts on water availability and water extremes.
Next-generation remote sensing techniques (e.g., unmanned aerial vehicles) for improved representation of landscape features.
Deeping learning techniques in surface and subsurface hydrology.
Tools, workflows, and web-based decision-support frameworks for watershed management.

You may choose our Joint Special Issue in Water

Dr. Adnan Rajib
Prof. Dr. Venkatesh Merwade
Dr. Zhu 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. 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.

Keywords

hydrology
water quality
floods
droughts
earth observations
data assimilation
GIS
climate change
land use change

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

16 pages, 7221 KiB

Open AccessArticle

Projecting the Impact of Climate Change on Runoff in the Tarim River Simulated by the Soil and Water Assessment Tool Glacier Model

by Gonghuan Fang, Zhi Li, Yaning Chen, Wenting Liang, Xueqi Zhang and Qifei Zhang

Remote Sens. 2023, 15(16), 3922; https://doi.org/10.3390/rs15163922 - 08 Aug 2023

Cited by 3 | Viewed by 1251

Abstract

Analyzing the future changes in runoff is crucial for efficient water resources management and planning in arid regions with large river systems. This paper investigates the future runoffs of the headwaters of the Tarim River Basin under different emission scenarios by forcing the hydrological model SWAT-Glacier using six regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project. Results indicate that compared to the period of 1976~2005, temperatures are projected to increase by 1.22 ± 0.72 °C during 2036~2065 under RCP8.5 scenarios, with a larger increment in the south Tianshan mountains and a lower increment in the north Kunlun Mountains. Precipitation is expected to increase by 3.81 ± 14.72 mm and 20.53 ± 27.65 mm during 2036–2065 and 2066–2095, respectively, under the RCP8.5 scenario. The mountainous runoffs of the four headwaters that directly recharge the mainstream of the Tarim River demonstrate an overall increasing trend in the 21st century. Under the RCP4.5 and RCP8.5 scenarios, the runoff is projected to increase by 3.2% and 3.9% (amounting to 7.84 × 10⁸ m³ and 9.56 × 10⁸ m³) in 2006–2035. Among them, the runoff of the Kaidu River, which is dominated by rainfall and snowmelt, is projected to present slightly decreasing trends of 3~8% under RCP4.5 and RCP8.5 scenarios. For catchments located in the north Kunlun Mountains (e.g., the Yarkant and Hotan Rivers which are mix-recharged by glacier melt, snowmelt, and rainfall), the runoff will increase significantly, especially in summer due to increased glacier melt and precipitation. Seasonally, the Kaidu River shows a forward shift in peak flow. The summer streamflow in the Yarkant and Hotan rivers is expected to increase significantly, which poses challenges in flood risk management. Full article

(This article belongs to the Special Issue Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water and Watersheds)

► Show Figures

Figure 1

18 pages, 5301 KiB

Open AccessArticle

Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models

by Wenting Zhang, Bin Hu, Yongzhi Liu, Xingnan Zhang and Zhixuan Li

Remote Sens. 2023, 15(14), 3678; https://doi.org/10.3390/rs15143678 - 23 Jul 2023

Cited by 1 | Viewed by 1857

Abstract

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk. Full article

(This article belongs to the Special Issue Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water and Watersheds)

► Show Figures

Journal Menu

Journal Browser

Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water and Watersheds

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI