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Remote Sensing Applications in Hydrology and Human-Natural Systems Management

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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 July 2021) | Viewed by 11793

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Special Issue Editors

Prof. Dr. Mekonnen Gebremichael

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Guest Editor

Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
Interests: hydrologic remote sensing; food-energy-water nexus; hydrologic forecasting; reservoir operation
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Dr. Noel Aloysius

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Guest Editor

Department of Chemical and Biomedical Engineering, School of Natural Resources University of Missouri, Columbia, MO 65211, USA
Interests: terrestrial hydrology; remote sensing; GIS
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Special Issue Information

Dear Colleagues,

The use of remotely sensed data in land-surface hydrology and watershed and ecosystem management is rapidly increasing. Multispectral and hyperspectral imageries at high spatial and temporal resolution are increasingly becoming available and cover global land areas. Advances in geospatial data processing, computational power, and cloud services enable rapid assessment of the terrestrial water cycle and the fate and transport of chemical constituents. The field is in an exciting period of expansion, as new satellite sensors and unmanned aerial vehicles now permit us to revolutionize the measurement of atmosphere, land, and water variables by adding new capabilities and greater detail.

For this Special Issue, we invite contributions on the innovative use of remote sensing data in hydrology and human–natural systems management applications. Topics of interest include, but are not limited to, the use of new and upcoming remote sensing datasets in flood forecasting, reservoir operation, water quality management, agricultural watershed management, the food–energy–water nexus, and the modeling of human–natural systems.

Prof. Dr. Mekonnen Gebremichael
Prof. Dr. Noel Aloysius
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

remote sensing
hydrology
watershed management
agricultural water management
modeling of human–natural systems
water quality
flood forecasting
reservoir operation
energy–water nexus in urban areas

Published Papers (4 papers)

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Research

15 pages, 3133 KiB

Open AccessArticle

The Accuracy of Precipitation Forecasts at Timescales of 1–15 Days in the Volta River Basin

by Mekonnen Gebremichael, Haowen Yue and Vahid Nourani

Remote Sens. 2022, 14(4), 937; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040937 - 15 Feb 2022

Cited by 3 | Viewed by 1694

Abstract

Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two satellite-gauge merged products: NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations, and the University of California Santa Barbara’s Climate Hazard’s group Infrared Precipitation with Stations (CHIRPS). The performance of GFS depends on the climate zone, with underestimation bias in the dry Sahel climate, overestimation bias in the wet Guinea Coastal climate, and relatively no bias in the moderately wet Savannah climate. Averaging rainfall over the watershed of the Akosombo dam (i.e., averaging across all three climate zones), the GFS forecast indicates low skill (Kling-Gupta Efficiency KGE = 0.42 to 0.48) for the daily, 1-day, lead GFS forecast, which deteriorates further as the lead time increases. A sharp decrease in KGE occurred between 6 to 10 days. Aggregating the forecasts over long timescales improves the accuracy of the GFS forecasts. On a 15-day accumulation timescale, GFS shows higher skills (KGE = 0.74 to 0.88). Full article

(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Human-Natural Systems Management)

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17 pages, 6454 KiB

Open AccessArticle

Impacts of Human Activities on the Variations in Terrestrial Water Storage of the Aral Sea Basin

by Xuewen Yang, Ninglian Wang, Qian Liang, An’an Chen and Yuwei Wu

Remote Sens. 2021, 13(15), 2923; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152923 - 25 Jul 2021

Cited by 11 | Viewed by 2499

Abstract

Assessing the impacts of human activities on the variations in terrestrial water storage (TWS) is essential for water resource management, particularly in regions like the Aral Sea Basin which suffers from severe water scarcity. In this study, the variations in TWS anomalies (TWSA) of the Aral Sea Basin during the period of April 2002 to June 2017 were analyzed using Gravity Recovery and Climate Experiment (GRACE) data and the Global Land Data Assimilation System (GLDAS) Noah model outputs. The impacts of human activities on TWS variations were further quantified through the variations in TWS components and the comparison of TWS obtained from GRACE and GLDAS. The results indicate that TWSA of the entire Aral Sea Basin derived from GRACE experienced a significant decreasing trend of 4.12 ± 1.79 mm/year (7.07 ± 3.07 km³/year) from 2002 to 2017. Trends in individual TWS components indicate that the reduction in TWS of the Aral Sea Basin was primarily attributed to surface water loss, followed by groundwater depletion, which account for ~53.16% and 11.65 ± 45.39 to 42.48 ± 54.61% of the total loss of TWS, respectively. Precipitation (P) and evapotranspiration (ET) both exhibited increasing trends, indicating that ET played a dominant role in TWS depletion from the perspective of water balance. The variations in ET and TWS induced by human activities contributed ~45.54% and ~75.24% to those in total ET and TWS of the Aral Sea Basin, respectively. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Human-Natural Systems Management)

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18 pages, 7376 KiB

Open AccessArticle

Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

by Joan Grau, Kang Liang, Jae Ogilvie, Paul Arp, Sheng Li, Bonnie Robertson and Fan-Rui Meng

Remote Sens. 2021, 13(10), 1997; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101997 - 20 May 2021

Cited by 8 | Viewed by 2471

Abstract

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Human-Natural Systems Management)

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19 pages, 5954 KiB

Open AccessArticle

What Drives Crop Land Use Change during Multi-Year Droughts in California’s Central Valley? Prices or Concern for Water?

by Mekonnen Gebremichael, P. Krishna Krishnamurthy, Lula T. Ghebremichael and Sarfaraz Alam

Remote Sens. 2021, 13(4), 650; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040650 - 11 Feb 2021

Cited by 14 | Viewed by 4324

Abstract

The recent multi-year droughts in California have highlighted the heightened risk of longer and more intense droughts, thus increasing the interest in understanding potential impacts for major economic activities, such as agriculture. This study examines changes in cropping pattern in California’s Central Valley between 2007 and 2016 in response to two consecutive droughts (2007–2009 and 2012–2016), factors driving these changes, and the impact of these changes on groundwater level. Results indicate that Central Valley experienced a shift in cropping pattern from alfalfa, cereals (rice, winter wheat, corn, and oats), and cotton, to nut (almonds, walnuts, and pistachios) and fruit (grapes, oranges, and tomatoes) tree crops. This shift in cropping pattern was likely driven by high crop prices, increasing trend in crop price, and increasing water pumping cost, particularly in the relatively water-stressed southern parts of Central Valley. While the total cropland water use for Central Valley remained the same during 2007–2016 (during both wet and dry years), they vary from county to county. Some counties experienced large reductions in cropland water use, while other counties experienced large increases in cropland water use, indicating the need for county-specific water resource management. The results also indicate that both land management (determining size of fallow land), as well as crop management (choice of crop types), are key factors in water resource management. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Human-Natural Systems Management)

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