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Remote Sensing in Water Resources Management Models, 2nd Volume
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College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
1. School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019-3072, USA
2. National Weather Center, ARRC Suite 4610, University of Oklahoma, 120 David L. Boren Blvd, Norman, OK 73072, USA
Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt
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Remote Sensing in Water Resources Management Models, 2nd Volume

Abstract submission deadline
closed (31 October 2024)
Manuscript submission deadline
31 December 2024
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3198

Topic Information

Dear Colleagues,

Almost 74% of the Earth's surface is covered with water. However, only 0.02% of all the water on Earth is in streams, lakes, rivers, and reservoirs as freshwater available for direct human consumption. The remaining freshwater is found underground (0.6%), in the atmosphere (0.001%), and in icecaps (2.2%). Freshwater is a scarce resource worldwide due to land use and climate changes. Hence, the need for spatiotemporal data on freshwater, for water resource management is increasing. However, the acquisition of spatial and temporal data on freshwater resources has been a major challenge facing ecological and hydrological researchers and policymakers. With the advent of remote sensing technology in the near past, data collection has fundamentally improved with the introduction of satellite sensors with higher spatial and temporal resolution on space-borne platforms. Most of these datasets are freely available on the Internet. This has been further advanced by the development of open-source remote sensing services, spatially distributed hydrological models, and software for data processing, analysis, and visualization. However, the performance of remote sensing data and hydrological models to capture the effect of ongoing development and management decisions has to be evaluated. Spatiotemporal analysis of freshwater dynamics under land use and climate changes using spatially explicit hydrological models and remote sensing data can provide information for water resource management, the effect of ongoing developments, management decisions, and policy implications. Therefore, water resource modeling is essential for sustainable water resource management. The Topic “Remote Sensing in Water Resources Management Models” invites high-quality papers focused on the design and development of methods, strategies, and new technologies for water resource management and development impact assessment using hydrological models and remote sensing technologies under land use and climate changes. Potential topics include, but are not limited to the following:

  • Land use change and water resource management;
  • Climate change and water scarcity;
  • Remote sensing and water resource management;
  • Hydrological modeling and remote sensing;
  • Water resource management and sustainable development;
  • Population growth and water resource scarcity;
  • Spatiotemporal dynamics of water resource management;
  • New technologies for water resource management;
  • Methods for water resource modeling.

Dr. Jinsong Deng
Prof. Dr. Yang Hong
Prof. Dr. Salah Elsayed
Topic Editors

Keywords

  • land use change
  • climate change
  • remote sensing
  • water resource management
  • hydrology and water security
  • water ecology and degradation
  • water economics
  • sustainable development
  • ecosystem service

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Environments
environments 		3.5 5.7 2014 25.7 Days CHF 1800 Submit
Forests
forests 		2.4 4.4 2010 16.9 Days CHF 2600 Submit
Land
land 		3.2 4.9 2012 17.8 Days CHF 2600 Submit
Remote Sensing
remotesensing 		4.2 8.3 2009 24.7 Days CHF 2700 Submit
Water
water 		3.0 5.8 2009 16.5 Days CHF 2600 Submit

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Published Papers (2 papers)

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20 pages, 18176 KiB  
Article
Estimation of Water Demand for Riparian Forest Vegetation Based on Sentinel-2 Data: A Case Study of the Kokyar River Basin
by Xianhe Liu, Yilinuer Alifujiang, Abdugheni Abliz, Halidan Asaiduli, Panqing Ye and Buasi Nurahmat
Forests 2024, 15(10), 1749; https://doi.org/10.3390/f15101749 - 4 Oct 2024
Viewed by 654
Abstract
In recent years, due to the shortage of water resources and the fragile ecological environment in arid areas, the relationship between vegetation and water resources has been relatively close. The unreasonable allocation of water resources and the excessive demand for ecological water use [...] Read more.
In recent years, due to the shortage of water resources and the fragile ecological environment in arid areas, the relationship between vegetation and water resources has been relatively close. The unreasonable allocation of water resources and the excessive demand for ecological water use have led to ecological and environmental problems such as river interruption, land desertification, and the extensive withering of vegetation in arid areas; therefore, rapid, accurate estimation of the vegetation ecological water demand has become a hot research topic in related fields. In this study, we classified the land use types in the lower reaches of the Kokyar River Basin based on Sentinel-2A data and calculated the water requirements of each type of vegetation using a combination of the area quota method and improved Penman–Monteith (PM) based on different vegetation coverage levels. The results revealed that in 2020, the water demand of planted woodlands within 0–2 km of the watershed will be the highest, and the water demand of naturally growing arboreal woodlands will be the lowest, and the water demand of the surrounding desert riparian vegetation forests will be very small in relation to the ecological base flow and will not affect the downstream water use for agriculture, industry, and domestic use for the time being. The ecological water demand of the vegetation in the study area can be accurately estimated using Sentinel-2A data, and the research results provide technical support and a theoretical basis for rapid estimation of the ecological water demand of vegetation in typical riparian forests in arid areas and for the allocation of water resources. Full article
(This article belongs to the Topic Remote Sensing in Water Resources Management Models, 2nd Volume)
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16 pages, 4510 KiB  
Article
A Comparison of Multiple DEMs and Satellite Altimetric Data in Lake Volume Monitoring
by Cui Yuan, Fangpei Zhang and Caixia Liu
Remote Sens. 2024, 16(6), 974; https://doi.org/10.3390/rs16060974 - 10 Mar 2024
Viewed by 1295
Abstract
Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric [...] Read more.
Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric data combined with the lake area, rarely has a comparison been made between the two methods. Therefore, we conducted a comparison between the two methods in Texas for 14 lakes with abundant validation data. First, we constructed the lake hypsometric curve by five commonly applied DEMs (SRTM, ASTER, ALOS, GMTED2010, and NED) or satellite altimetric products combined with the gauge lake area. Second, the lake volume was estimated by combining the hypsometric curve with the gauge lake area time series. Finally, the estimation error has been quantitatively calculated. The results show that the relative lake volume estimation error (rVSD) of the altimetric data (4%) is only 10–18% of that of the DEMs (22–41%), and the DEM with the highest resolution (NED) has the least rVSD with an average of 22%. Therefore, for large-scale lake monitoring, we suggest the application of satellite altimetric data with the lake area to estimate the lake volume of large lakes, and the application of high-resolution DEM with the lake area to calculate the lake volume of small lakes that are gapped by satellite altimetric data. Full article
(This article belongs to the Topic Remote Sensing in Water Resources Management Models, 2nd Volume)
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