Special Issue "Remote Sensing Applications for Water Scarcity Assessment"

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: 31 January 2022.

Special Issue Editors

Dr. Aondover Tarhule
E-Mail Website
Guest Editor
State University of New York (SUNY) at Binghamton, Couper Administration Building (AD), Room 134, The Graduate School, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Interests: hydrology; water resources,;stochastic hydrology; physical geography; water scarcity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yang Hong
E-Mail Website
Guest Editor
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
Interests: remote sensing of global water cycle (precipitation, evapotranspiration, soil moisture, waterbody, streamflow); hydrology and water resources; natural hazards; hydrometeorology; hydroclimatology
Special Issues, Collections and Topics in MDPI journals
Dr. Emad Hasan
E-Mail Website
Guest Editor
Department of Geological Sciences and Environmental Studies, State University of New York (SUNY) at Binghamton OJ124, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Interests: remote sensing hydrology; GIS applications in water resources
Special Issues, Collections and Topics in MDPI journals
Dr. Guoqiang Tang
E-Mail Website
Guest Editor
1. Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada
2. University of Saskatchewan Coldwater Lab, Canmore, AB, Canada

Special Issue Information

Dear Colleagues,

The availability of fresh water resources in sufficient quantity is critical for human well-being, agro-ecological and socio-economic systems. Yet, determining the status of available water i.e. whether adequate, vulnerable, stressed or inadequate/scarce, using ground based hydroclimatic networks, is fraught with considerable challenges. Notably, the data tend to be unavailable, inaccessible, or discontinuous over space and time. Recently, a number of studies have demonstrated considerable promise in using remote sensing data and methodologies to overcome these limitations. As example, satellite missions, such as GRACE (Gravity Recovery and Climate Experiment), and its successor program GRACE-FO (GRACE Follow-On) have provided unprecedented data on complex water cycle information significantly improving our understanding of water resources occurrence, storage, fluxes and variability over time and space. Several other platforms also provide similar or complementary advantages.

This special issue calls for original contributions that utilize remote sensing technologies in innovative ways and methodologies for observing, monitoring and assessing water scarcity regionally and globally. We encourage especially contributions that utilize frameworks that merge and integrate different remote sensing observations, numerical models and algorithms to address global water scarcity assessment and prediction. Examples include, but are not limited to:

  1. Methods and theories that utilize remote sensing platforms and data to locate, observe and predict “available water resources”.
  2. Contributions that refine and improve assessment of per capita water resources availability, withdrawal and use.
  3. Changes in available water resources over time and space in response to complex interactions between climatic variability and anthropogenic processes.
  4. New and emerging remote sensing applications for water scarcity monitoring that support decision making to mitigate possible conflicts over shared water resources.  
Dr. Aondover Tarhule
Dr. Yang Hong
Dr. Emad Hasan
Dr. Guoqiang Tang
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 papers will be 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 2400 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

  • Climate Change and Climate variability
  • GRACE
  • Hydrology and Water Resources
  • Remote Sensing
  • Water Scarcity

Published Papers (5 papers)

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Research

Article
Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin
Remote Sens. 2021, 13(23), 4831; https://doi.org/10.3390/rs13234831 (registering DOI) - 28 Nov 2021
Viewed by 191
Abstract
Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical [...] Read more.
Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)
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Article
Monitoring Drought through the Lens of Landsat: Drying of Rivers during the California Droughts
Remote Sens. 2021, 13(17), 3423; https://doi.org/10.3390/rs13173423 - 28 Aug 2021
Cited by 1 | Viewed by 538
Abstract
Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative [...] Read more.
Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative to in-situ observations. This study investigates the seasonal drying dynamics of river extent in California where severe droughts have been occurring more frequently in recent decades. Our methods combine the use of Landsat-based Global Surface Water (GSW) and global river bankful width databases. As an indirect comparison, we examine the monthly fractional river extent (FrcSA) in 2071 river reaches and its correlation with streamflow at co-located USGS gauges. We place the extreme 2012–2015 drought into a broader context of multi-decadal river extent history and illustrate the extraordinary change between during- and post-drought periods. In addition to river extent dynamics, we perform statistical analyses to relate FrcSA with the hydroclimatic variables obtained from the National Land Data Assimilation System (NLDAS) model simulation. Results show that Landsat provides consistent observation over 90% of area in rivers from March to October and is suitable for monitoring seasonal river drying in California. FrcSA reaches fair (>0.5) correlation with streamflow except for dry and mountainous areas. During the 2012–2015 drought, 332 river reaches experienced their lowest annual mean FrcSA in the 34 years of Landsat history. At a monthly scale, FrcSA is better correlated with soil water in more humid areas. At a yearly scale, summer mean FrcSA is increasingly sensitive to winter precipitation in a drier climate; and the elasticity is also reduced with deeper ground water table. Overall, our study demonstrates the detectability of Landsat on the river surface extent in an arid region with complex terrain. River extent in catchments of deficient water storage is likely subject to higher percent drop in a future climate with longer, more frequent droughts. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)
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Article
Analysis on the Variation of Hydro-Meteorological Variables in the Yongding River Mountain Area Driven by Multiple Factors
Remote Sens. 2021, 13(16), 3199; https://doi.org/10.3390/rs13163199 - 12 Aug 2021
Viewed by 490
Abstract
In recent decades, strong human activities have not only brought about climate change including both global warming and shifts in the weather patterns but have also caused anomalous variations of hydrological elements in different basins all around the world. Studying the mechanisms and [...] Read more.
In recent decades, strong human activities have not only brought about climate change including both global warming and shifts in the weather patterns but have also caused anomalous variations of hydrological elements in different basins all around the world. Studying the mechanisms and causes of these hydrological variations scientifically is the basis for the management of water resources and the implementation of ecological protection. Therefore, taking the Yongding River mountain area as a representative watershed in China, the changes of different observed and simulated hydro-meteorological variables and their possible causes are analyzed on an inter-annual scale based on ground based observations and remotely sensed data of hydrology, meteorology and underlying surface characteristics from 1956 to 2016. The results show that the annual natural runoff of Guanting hydrological station in the main stream of the Yongding River, Cetian hydrological station and Xiangshuibao hydrological station in the tributary of the Yongding River all have a significant decreasing trend and abrupt changes, and all the abrupt change points of the annual natural runoff series of the three hydrological stations appear in the early 1980s. On the inter-annual scale, the water balance model with double parameters is unable to effectively simulate the natural surface runoff after the abrupt change points. The annual average precipitation after the abrupt change points decreases by no more than 10%, compared with that before the abrupt change points. However, the precipitation from July to August, which is the main runoff-production period, decreases by more than 25%, besides the intra-annual temporal distribution of precipitation becoming uniform and a significant decrease in effective rainfall, which is the source of the runoff. Meanwhile, the NDVI in the basin show an increasing trend, while the groundwater level and land water storage decrease significantly. These factors do not lead only to the continuous reduction of the annual natural runoff in the Yongding River mountain area from 1956 to 2016, but also result in significant changes of the hydro-meteorological relationship in the basin. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)
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Article
Twentieth and Twenty-First Century Water Storage Changes in the Nile River Basin from GRACE/GRACE-FO and Modeling
Remote Sens. 2021, 13(5), 953; https://doi.org/10.3390/rs13050953 - 04 Mar 2021
Cited by 4 | Viewed by 851
Abstract
This research assesses the changes in total water storage (TWS) during the twentieth century and future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and a land surface [...] Read more.
This research assesses the changes in total water storage (TWS) during the twentieth century and future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and a land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (generalized additive model for location, scale, and shape) model. The model performance was tested using standard indicators including coevolution plots, the Nash–Sutcliffe coefficient, cumulative density function, standardized residuals, and uncertainty bounds. All model evaluation results are satisfactory to excellent. The drought and flooding severity/magnitude, duration, and recurrence frequencies were assessed during the studied period. The results showed, (1) The NRB between 2002 to 2020 has witnessed a substantial transition to wetter conditions. Specifically, during the wet season, the NRB received between ~50 Gt./yr. to ~300 Gt./yr. compared to ~30 Gt./yr. to ~70 Gt./yr. of water loss during the dry season. (2) The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS of ~17% during the wet season. Moreover, the TWS storage had witnessed a recovery of ~28% during the dry season. (3) The projected TWSA between 2021 to 2050 unveiled a positive increase in the TWS during the rainy season. While during the dry season, the water storage showed insubstantial TWS changes. Despite these projections, the future storage suggested a reduction between 10 to 30% in TWS. The analysis of drought and flooding frequencies between 1901 to 2050 revealed that the NRB has ~64 dry-years compared to ~86 wet-years. The exceedance probabilities for the normal conditions are between 44 to 52%, relative to a 4% chance of extreme events. The recurrence interval of the normal to moderate wet or dry conditions is ~6 years. These TWSA trajectories call for further water resources planning in the region, especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)
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Article
Comprehensive Comparisons of State-of-the-Art Gridded Precipitation Estimates for Hydrological Applications over Southern China
Remote Sens. 2020, 12(23), 3997; https://doi.org/10.3390/rs12233997 - 06 Dec 2020
Cited by 4 | Viewed by 855
Abstract
Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze [...] Read more.
Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Water Scarcity Assessment)
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