remote sensing
ArticleAn Assessment of the Filling Process of the Grand Ethiopian
Renaissance Dam and Its Impact on the Downstream Countries
Prakrut Kansara 1 , Wenzhao Li 2 , Hesham El-Askary 2,3,4, * , Venkataraman Lakshmi 1 , Thomas Piechota 2 ,Daniele Struppa 2 and Mohamed Abdelaty Sayed 5
1

2

3

4

5

*

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Citation: Kansara, P.; Li, W.;
El-Askary, H.; Lakshmi, V.; Piechota,
T.; Struppa, D.; Abdelaty Sayed, M.
An Assessment of the Filling Processof the Grand Ethiopian Renaissance
Dam and Its Impact on the
Downstream Countries. Remote Sens.
2021, 13, 711. https://doi.org/
10.3390/rs13040711
Academic Editors: Fumio Yamazaki,
Kenji Nakamura and Giovanni
Battista Chirico
Received: 28 December 2020
Accepted: 10 February 2021
Published: 15 February 2021

Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in

Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA;phk5e@virginia.edu (P.K.); vlakshmi@virginia.edu (V.L.)
Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA;
li276@mail.chapman.edu (W.L.); piechota@chapman.edu (T.P.); struppa@chapman.edu (D.S.)
Center of Excellence in Earth Systems Modeling and Observations, Chapman University,
Orange, CA 92866, USA
Department of Environmental Sciences, Faculty of Science, Alexandria University, Moharem Bek,
Alexandria 21522, Egypt
Egyptian Ministry of Water Resources & Irrigation, Cairo 12685, Egypt; abdelaty@mwri.gov.eg
Correspondence: elaskary@chapman.edu

Abstract: The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam,has been filling at a fast rate. This project has created issues for the Nile Basin countries of Egypt,
Sudan, and Ethiopia. The filling of GERD has an impact on the Nile Basin hydrology and specificallythe water storages (lakes/reservoirs) and flow downstream. In this study, through the analysis ofmulti-source satellite imagery, we study the filling of the GERD reservoir. The time-series generatedusing Sentinel-1 SAR imagery displays the number of classified water pixels in the dam from early
June 2017 to September 2020, indicating a contrasting trend in August and September 2020 for the
upstream/downstream water bodies: upstream of the dam rises steeply, while downstream decreases.
Our time-series analysis also shows the average monthly precipitation (derived using IMERG) in
the Blue Nile Basin in Ethiopia has received an abnormally high amount of rainfall as well as a highamount of runoff (analyzed using GLDAS output). Simultaneously, the study also demonstrates thedrying trend downstream at Lake Nasser in Southern Egypt before December 2020. From our results,
we estimate that the volume of water at GERD has already increased by 3.584 billion cubic meters,
which accounts for about 5.3% of its planned capacity (67.37 billion cubic meters) from 9 July–30
November 2020. Finally, we observed an increasing trend in GRACE anomalies for GERD, whereas,
for the Lake Nasser, we observed a decreasing trend. In addition, our study discusses potential
interactions between GERD and the rainfall and resulting flood in Sudan. Our study suggests that
attention should be drawn to the connection between the GERD filling and potential drought in
the downstream countries during the upcoming dry spells in the Blue Nile River Basin. This study
provides an open-source technique using Google Earth Engine (GEE) to monitor the changes in waterlevel during the filling of the GERD reservoir. GEE proves to be a powerful as well as an efficient
way of analyzing computationally intensive SAR images.

published maps and institutional affiliations.

Keywords: hydrology; Nile watershed; soil moisture; Sentinel-1; precipitation; Grand Ethiopian
Renaissance Dam

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.

1. Introduction

This article is an open access article

The Grand Ethiopian Renaissance Dam (GERD) is located on the Blue Nile River in
Ethiopia, close to the border with Sudan. It was a $4 billion initiative, and with a projected
capacity of 6000 megawatts, it is expected to become Africa’s biggest power exporter. The
Blue Nile impoundment behind GERD will encompass a spatial extent of about 1763.3 km2
and hold around 67.37 billion cubic meters of water with maximum seasonal load changes

distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Remote Sens. 2021, 13, 711. https://doi.org/10.3390/rs13040711

https://www.mdpi.com/journal/remotesensing

Remote Sens. 2021, 13, 711

2 of 15of around 27.93 (41% of total) to around 36.46 billion cubic meters (54% of total) during
projected operational scenarios [1]. A study by Elsayed et al. 2020 [2] suggests that the
GERD filling and operation would affect the WFE (water, food, and energy) nexus in Egypt,with the impact likely to be significant if the filling process occurred during a dry period.
A project of such a magnitude would definitely impact Egypt, whose population is around
100 million and depends on the Nile for about 90% of its fresh water. There is a concern
about the risk of drought occurrence that would arise from this project, such as those that
affected the Nile Basin in the late 1970s and early 1980s.
A report recently stated that that the GERD reservoir has been expanding—as seen
through a series of satellite images [3]. The streamflow in the Blue Nile river has significantly declined since Ethiopia started filling the reservoir [4,5]. This raised some serious
concerns in Egypt and Sudan for mainly two reasons—the filling of the GERD reservoir
despite no deal being reached during the multi-nation talks and the drought conditions
impacting the downstream water bodies [6–8].
There are numerous studies, which suggest that there is a lack of planning for thefilling process of the GERD reservoir, especially the consideration of the negative future
implications to Egypt and Sudan [9–11]. However, currently, there have been no studies
that involve the monitoring of the filling process using satellite remote sensing datasets.
With the increase in technical capabilities and advancements in data processing ofsatellites, the scientific community has been exploiting the remote sensing capabilities inthe estimation of reservoir extent and volume. For instance, a study by Amitrano et al. [12]in 2014 explores the usage of Sentinel imageries in DEM generation, volume estimationand land cover mapping. The study concludes that Sentinel imageries are suitable forvolume estimation as well as DEM generation. In addition, Pipitone et al. [13] monitor thewater surface level using different remote sensing approaches, including optical imagery(LANDSAT) and SAR imagery. The results from this study show that both optical and SAR
imagery techniques can be used to quantify surface water levels.
The motivation for this study is to provide up-to-date and accurate monitoring of
GERD filling process using satellite imagery using data from Sentinel-1 and Sentinel-2, andwater storage using the Gravity Recovery and Climate Experiment (GRACE), precipitation
using the Global Precipitation Measurement Mission (GPM) and topography from the
Shuttle Topography Measurement Mission (SRTM). This study will provide a basis for the
future management decisions for the operation of GERD that affects the three countries—
Egypt, Sudan and Ethiopia. We further investigate the flooding in Sudan on the Blue Nile
River Basin and present the impact of GERD on Lake Nasser. We estimate the increasein water storage in the GERD reservoir using SRTM DEM and the Sentinel imagery. To
promote the public usability of the research, this study uses an open-source code editor(GEE) to analyze the SAR images as well as uses Python language for analyzing thehydrological datasets. GEE is a cloud-based platform, which is easy to use and can provide
quick results without the need to download datasets to a local storage system. With theincreasing use of GEE and public availability of datasets and codes, our study will bevery useful for future researchers to examine the changes in the volume of the reservoirof GERD.
2. Data and Methods
2.1. Study Area
The GERD is located 15 km East of the Ethiopia–Sudan border inside Ethiopia, on theBlue Nile River (Figure 1). This dam is planned to provide 6.45 Gigawatts of hydro-electric
power to Ethiopia at its full capacity and store 67.37 billion cubic meters of water in theMillennium Reservoir formed by the dam. The construction of the dam started in 2011
and was completed in July 2020. The proposed filling of the reservoir has a timeline of
5–15 years depending on the not yet agreed upon trilateral policy agreement between
Egypt, Sudan and Ethiopia. The region around the reservoir is a mountainous area withshrubland and forests as the major land cover type. The dam is built on the Blue Nile

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River, which
is a major
tributary
theRiver.
Nile River.
Although
the White
Nile Riveris longer
which
is a major
tributaryof the of
Nile
Although
the White
Nile Riveris longer
than
than
the
Blue
Nile
River,
thestreamflow
in
the
Blue
Nile
is
significantly
higherthan
in thethethe Blue Nile River, the streamflow in the Blue Nile is significantly higher than in
White
Nile.
This
has
createdconcernforthecountries
of
Sudan
and
Egypt
since
they
are
White Nile. This has created concern for the countries of Sudan and Egypt since they are
dependent
on
the
water
from
the
Nile
River.
Further,thewatershed
upstreamofthedam
dependent on the water from the Nile River. Further, the watershed upstream of the dam
is referredreferred toto asas thethe Upper
Upper BlueisBlue Nile
Nile River
River Basin.
Basin.

Figure 1.
1. Subfigure
Subfigure (a)(a) shows
shows thethe Nile
Nile River
River Basin
Basin andsubbasins—White Nile
Figureand its
its subbasins—White
Nile River
River Basin
Basin andand BlueBlue Nile
Nile River
River Basin.
Basin.
The
White
Nile
River
and
the
Blue
Nile
River
merge
at
Khartoum,
Sudan,
which
is
shown
as
the
junctionpoint
in theThe White Nile River and the Blue Nile River merge at Khartoum, Sudan, which is shown as the junction point in the figure.
figure. Inset Figure (b) shows the Blue Nile River Basin along with the location of the Grand Ethiopian Renaissance Dam
Inset Figure (b) shows the Blue Nile River Basin along with the location of the Grand Ethiopian Renaissance Dam (GERD)(GERD) near the Ethiopian–Sudanese border.
near the Ethiopian–Sudanese border.

2.2.
2.2. Data
Data
2.2.1. Satellite
Satellite Synthetic
Synthetic Aperture
Aperture Radar and Optical Imagery
In thisthisstudy,
study,a acombination
combination
synthetic
apertureradar(Sentinel-1)
and opticalInof ofsynthetic
apertureradar(Sentinel-1)
and optical(Sen(Sentinel-2)
imagery[12] used,
was used,potentiallyovercome
the limitationoftinel-2)
imagery[12] waswhichwhich
couldcould
potentiallyovercome
the limitationof excluexclusive
one typeof imagery
landcoverchangedetection[13].
TheSentinel-1
Sentinelsive
usingusing
one typeof imagery
for forthe thelandcoverchangedetection[13].
The
1 imageryis isobtained
obtainedfrom
fromtwo
twoEuropean
European SpaceSpace Agency
Agency (ESA)(ESA) polar-orbiting satellites
imagery(Sentinel-1Aand
Sentinel-1B),
operating
day
and
night
a
C-band (central frequency of(Sentinel-1A and Sentinel-1B), operating day and5.404 GHz) synthetic aperture radar (SAR) imaging, with a global revisit time of 6–12 days
Sentinel-2 isis an
an ESA
ESA mission
mission asas aa constellation
constellation with
with two
two twin
twin satellites,
satellites,
based on the area. Sentinel-2
Sentinel-2A andandSentinel-2B,
Sentinel-2B,withwithaa5 5daydayrevisit
revisittime.
time.
Both
satellites
carry
a multispecSentinel-2ABothsatellites
carry
a multispectral
tral imaging
instrument(MSI),
which
acquireshigh-resolution
opticalimagesa setofimaging
instrument(MSI),
which
acquireshigh-resolution
opticalimagesin ainset
of 13
13
spectralbandsof
4visible,6near-infraredand3
infrared
shortwave
with
10
m,
20
m,
spectral bands of 4 visible, 6 near-infrared and 3 infrared shortwave with 10 m, 20 m, 60
60 spatial
m spatial
resolutions,
respectively.
m
resolutions,
respectively.

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2.2.2. SRTM DEM
The SRTM was an international collaboration by NASA in 2000 to map a complete nearglobal high-resolution data of Earth’s elevation [14]. This one-time mission mapped the
topography of the Earth’s surface using radar interferometry through a two-antenna setup
onboard satellite. The elevation difference at nadir location obtained through interferometryis transformed into the digital elevation model (DEM). SRTM void filled 1/3 arc-second
data were used for our study.
2.2.3. GPM Precipitation
GPM provides global observations of rainfall and snow using a network of satellites
launched by NASA, JAXA, CNES, ISRO, NOAA, and EUMETSAT [15]. This mission was
launched in February 2014 with the aim of providing precipitation amount as well as thethree-dimensional structure of the storm to better understand the atmospheric structure.
The observed data from the constellation of GPM satellites are calibrated with microwave
and infrared estimates from other satellite sources to provide high-quality precipitation.
This satellite data have improved the forecasting capabilities of disasters like floods anddroughts [16,17]. In this study, the Integrated Multi-satellitE Retrievals for GPM (IMERG)
Final run version 6 product was used at a spatial resolution of 0.1◦ for analysis for theperiod to June 2020 and IMERG Early Run product for analysis from July 2020 to present.
2.2.4. GRACE Total Water Storage
GRACE is a twin satellite setup that measures changes in water anomalies on a
global scale [18]. These anomalies depict the changes in the total water column, which
includes surface, sub-surface, and groundwater components. This dataset is corrected
for atmospheric pressure/mass changes along with the associated glacial isostatic adjustments [19,20]. This dataset has regional errors even after Gaussian filtering [21]. Further
details about the data processing and uncertainties pertaining to GRACE data can be foundon—https://grace.jpl.nasa.gov/data/get-data/monthlymass-grids-land/. In our study,
GRACE RL06 product and GRACE-FO RL06 mascon product from JPL for terrestrial water
storage anomalies (TWSA) [22] was used. The data are available at a spatial grid of 0.5◦
and monthly temporal resolution (https://grace.jpl.nasa.gov/data/grace_months/). In
terms of monthly terrestrial water anomalies.
2.2.5. GLDAS Runoff
The global land data assimilation system (GLDAS) is an assimilation model, which
combines satellite and ground-based observational data products through different land
surface models to generate land surface states and fluxes at a global scale [23]. Five different
land surface models are used for the assimilation of parameters and forcing into the models
to simulate hydrological variables at the surface and sub-surface level. Full descriptions ofeach parameter, forcing, and land surface models can be found at https://ldas.gsfc.nasa.
gov/gldas/model-output. In this study, total runoff (surface runoff + baseflow) output
from the GLDAS V2.1 assimilated data simulated from NOAH LSM model L4 with aspatial resolution of 0.25◦ at a monthly time step was used. We used the Early Run Productfrom GLDAS V2.1 outputs for data from July 2020 onwards.
2.3. Methods
2.3.1. SAR Image Processing
In this study, the orthorectified ground range detected (GRD) imagery was collectedfrom both Sentinel-1A/B in IW mode with 10 m spatial resolution. Each image waspreprocessed using the following steps: (1) Applying orbit file, (2) GRD border noise
removal, (3) Thermal noise removal, (4) Radiometric calibration, and (5) Terrain correction.
The band values were converted to the backscattering coefficient (σ◦ , dB). The pixels
with VV decibel smaller than -18 dB were identified as water pixels. The filling process
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a combination of VV and VH bands. In this study, Sentinel-2 Level-1C data (top of
atmosphere (TOA) reflectance) was collected and preprocessed to mask clouds and cirrus
areas. The modified normalized difference water index (mDNWI) [24] calculated from the−SWIRgreen and shortwave-infrared (SWIR) bands (mNDWI = GREEN
GREEN+SWIR ) with 20 m spatial
resolution to effectively differentiate the water and land surface. In addition, the true-color
images from China–Brazil Earth Resources Satellite 4 (CBERS) were also used to show thedam filling process.
2.3.2. DEM-Based Estimation of Reservoir Extent
The water volume was calculated for GERD upstream within an area of (35.06◦ E,
11.23◦ N, 35.35◦ E, 10.51◦ N). The calculation is implemented using Google Earth Engine
code editor [25] (see code in supplemental materials). We hypothesized a simple DEMbased estimation of the increase in volume (Equation (1)). Then, per-pixel change inelevation due to an increase in water level (∆h) was calculated. Per-pixel increase involume is calculated by multiplying the pixel size times the increase in water level (∆h).
∆h is determined using the elevation contours from DEM corresponding to the water levelas shown in the inset figure (Figure 2). Further, this per pixel calculation was averaged
over the entire area where water pixels were detected using the Sentinel images. The
mathematical formulation for change in volume is shown below:
A1 + A2∆V = ∆h(1)2
where ∆V is the increase in the volume of the reservoir storage and A1 , A2 are the initial
and the final surface areas of the reservoir, ∆h is the change in the elevation given by thedifference in the final and initial height as determined by the DEM. We obtain A1 and A2
from the spatial extent of water pixels estimated from SAR imagery. Moreover, the change
in height is obtained from the contour levels obtained from DEM corresponding to the SAR
imagery water pixels extent.
2.3.3. Precipitation, Runoff and LWE (GRACE) Analysis
We used IMERG final run data from January 2015 to June 2020, and IMERG Early
Run data from July 2020 to December 2020 for precipitation analysis. From the IMERG
dataset, the multi-satellite precipitation estimate with gauge calibration was used, which
was provided under the “precipitationCal” variable after removing the random errors
associated with this precipitation estimate. IMERG provides these errors as a separate
variable under “randomError”. Further information about the calculation of these errors
can be found inside the technical documentation of the dataset (https://gpm.nasa.gov/
resources/documents/IMERG-V06-Technical-Documentation). The monthly accumulated
values averaged over the watershed area were used to analyze the temporal patterns. The
total runoff (surface runoff + baseflow) was calculated using the “Qs_acc” and “Qsb_acc”
variables from the model outputs of GLDAS V2.1 with early run products being utilized fordata from July 2020 onwards. Since these values were provided as a 3-hour accumulation
for a particular month, they were converted to monthly accumulated values. Finally,
GRACE liquid water equivalent (LWE) was used for the total water anomaly to observe
the changes in the pixel corresponding to the GERD and Lake Nasser locations from April
2002 to November 2020. GRACE and GRACE-FO have missing data from October 2017
to May 2018 due to decommissioning of the GRACE mission. Lastly, the additive modelof time-series decomposition was used to remove seasonality from LWE estimates andanalyze the trend in seasonality removed observed data.

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a 27th June 2020b 7th September 2020

FigureFigure2.
2. (a)(a)Sentinel-1
Sentinel-1forfor27
27JuneJune2020
2020(prior(priorto
tothe
thefilling
fillingprocess)process)andand(b)(b)77September
September2020
2020(after(afterthe
thestart
startofofthe
thefilling
filling
process). The increased spatial extent of the reservoir can be observed in the second image on 7 September 2020. The inset
process). The increased spatial extent of the reservoir can be observed in the second image on 7 September 2020. The inset
figure shows the zoomed view of the region inside the red box to study the elevation contours inside the region. The
figure shows the zoomed view of the region inside the red box to study the elevation contours inside the region. The contour
contour values shown in the inset figure are in meters.
values shown in the inset figure are in meters.

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3. Results
3.1. The Monitoring of the Dam Filling
We analyzed the Sentinel-1 SAR images to estimate the filling process of the GERD
reservoir. We plotted a series of SAR images to observe the spatial changes in the area
covered by water before and after the filling process for the dam (Figure 2). From the SAR
image corresponding to 27 June 2020, normal water flow was observed through the river
channel for the Blue Nile River. On the other hand, in the 7 September 2020 SAR image
significant increase was found in the spatial extent of water-covered areas upstream of
GERD. The inset figures in Figure 2 show the elevation contours for the region shown in the
red box for each plot to give a sense of elevation around the region. The elevation contours
near the river on the 27 June range from 515–545 m, whereas for 7 September, the elevation
contour range increased to 560–580 m (an increase of 35–45 m). An increase in the elevation
obtained from the DEM corresponds to the rising water level in GERD. To quantify the areachanges, the SRTM DEM was used to estimate the increase in the reservoir area during thefilling process (Figure 3). We used two SAR images corresponding to 25 September 2019
and 7 September 2020, which represent the reservoir area occupied by water due to thefilling process. Further, SRTM DEM was used to construct a spatial map of the increased
reservoir area, as shown in Figure 3c. It was found that the reservoir area has increased byapproximately 237 km2 , which is 14% of the total surface area of the reservoir (1680 km2 as
estimated at full capacity). In order to track the water level changes during the ongoingfilling process, we calculated the estimated from the SAR images (Table 1) based on theobservations from the satellite images (see Supplemental Materials). The reservoir filling
process started around 8 July 2020 with an estimated water level at 545 m followed by asteady increase in the water levels as indicated in Table 1. From our estimation, the waterlevel at 580 m for the final SAR image on 7 September as analyzed in our study. Overall,
the water level increased by 45 m from 27 June 2020 to 30 November 2020. Finally, thecalculated estimated increase in the volume of the water stored in the reservoir due toan increase in water level and the areal extent of the reservoir (Table 1). A total increaseof 3.584 billion cubic meters from 27 June 2020 to 30 November was found. It was also
observed that the increase in volume is significantly higher for late-July and the full monthof August as an increase of 1.135, and 1.39 billion cubic meters of water volume was foundbetween 20 July and 14 August 2020, and 14 August and 7 September 2020, respectively.
Table 1. Water level estimated from the digital elevation model (DEM) and corresponding volume of
the reservoir.
Date(dd/mm/yy)

Estimated Level (m)Total Increased Storage(×109 m3 )27/6/20
8/7/209/7/2011/7/20
14/7/20
20/7/20
2/8/2014/8/20
7/9/2020
25/10/2020
30/11/2020

520
545
550
555
560
565
570
575
570
567
565

0.904
1.345
1.922
2.664
3.584
4.719
6.109
4.719
4.009
3.584

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Remote Sens. 2019, 11, x FOR PEER REVIEW

9 of 16abc

Figure3.3.(a–c)(a–c)Composite
CompositeSentinel-1
Sentinel-1images
imagesthat
thathighlighthighlightthethespatial
spatialextent
extentof
ofthe
theMillennium
MillenniumReservoir
Reservoirbefore
beforeandandafterafter
Figurethefilling
fillingprocessprocessstarted.
started.Figure
Figure(c)(c)depicts
depictsthetotalextentincreased
reservoirarea
as obtained
from
digitalelevathethetotalextentof ofincreased
reservoirarea
as obtainedfrom
the thedigitalelevation
tion model(DEM).
model(DEM).
Table 1. Water level estimated from the DEM and corresponding volume of the reservoir.

3.2. Downstream Impact of Dam Filling
Datethe impact of the filling process of the GERD Total
Increased
Storage
To observe
reservoir
on downstream
Estimated Level (m)9 m3)(dd/mm/yy)
locations,
we calculated the number of inundated pixels and plotted (×the10correspondingtime-series 27/6/20(June 2017 to December 2020) to520
compare the increase/decrease-of the amountof water on both
sides
(upstream
anddownstream)of GERD (Figure 4). Similarly,
8/7/20545
0.904 the waterarea in one of
the
areas
of
Lake
Nasser,
which
is
the
reservoir
of
the
Aswan
Dam in Egypt,
9/7/20550
1.345
was also presented
towards one of
11/7/20 (Figure 5). Additionally,
555a small region (Figure 5a–c)1.922
the areas of Lake Nasser in Egypt was selected for visual–spatial analysis. To analyze
14/7/20560
2.664
the water inundation near GERD, two regions were selected for the analysis. The up20/7/20
565
3.584
stream region corresponds to a polygon with boundaries at (35.064◦ E, 11.165◦ N), (35.087◦ E,
2/8/20570
4.719
11.154◦ N), (35.101◦ E, 11.220◦ N), and (35.093◦ E, 11.221◦ N) whereas the downstream region
14/8/20575
6.109◦
corresponds to a rectangular boundary with latitudinal extent of (11.210 N, 11.226◦ N) and7/9/2020
4.719
longitudinal
extent of (35.054◦ E, 35.087◦ E).570
In Figure 4, it was observed thatthe upstream
25/10/2020
567
4.009
of GERD in 2020 had four times the water inundated pixels as compared to the previous
30/11/2020
565 (each pixel area is 900 m23.584
three years,
with the number of inundated pixels) increased fromapproximately four times (2017 to 2020). In addition to this, there was no observed increase/decrease in the inundated pixels at the downstream end of GERD. For Lake Nasser,
it was observed that the water inundated pixels showed a stable increase with no notable

Remote Sens. 2021, 13, 711

9 of 15changes in the downstream end of GERD. This observation is again corroborated by the
Sentinel-2 imagery in Figure 5a–c, where an increase in the water inundation was found at
the Southern end of Lake Nasser. The Sentinel-2 imagery corresponds to 18 June 2020, 18
July 2020, and 27 October 2020. This is an interesting observation that the expected increasein water inundation at the downstream end of GERD was not observed. This suggeststhat there is a need for further investigation of the regional hydrology to understand theanomalous increase in water extent at Lake Nasser. After fitting a second-order polynomial
harmonic curve, Lake Nasser showed low water inundation pixels during July 2020 as the
GERD reservoir filling process started (Figure 5d). A first-order time-series model was used
to fit the trend and seasonality here. The model fitted to the time-series is given below:
y = βo + β1 × t + β2 × cos (2 × π × β4 × t) + β3 × sin (2 × π × β4 × t) + e(2)

where βi are the coefficients estimated by the model, and e is the error term associated withthe above estimation model.

Figure 4. (a,b) Number of inundated pixels underwater for selected upstream and downstream locations near GERD
as observed from the Sentinel-1 images. The threshold for identifying water/non-water pixels is −18 dB. The upstream
region corresponds to a polygon with boundaries at (35.064◦ E, 11.165◦ N), (35.087◦ E, 11.154◦ N), (35.101◦ E, 11.220◦ N), and(35.093◦ E, 11.221◦ N) whereas the downstream region corresponds to a rectangular boundary with latitudinal extent of(11.210◦ N, 11.226◦ N) and longitudinal extent of (35.054◦ E, 35.087◦ E).

Remote Sens. 2021, 13, 711

a

10 of 15bcdFigure 5. (a–d) Selected regions of Lake Nasser. Image retrieved from Sentinel-1 and processed to classify water and land
pixels using mNDWI. Figure (d) shows the water areas represented by pixel counts for the region. Please note that theorange and green circles denote anomalous events. In 2019, Sudan suffered from floods, which impacted the inundated
pixels at Lake Nasser. On the other hand, in July 2020, the impact of GERD filling can be seen by the low values of water
inundated pixels. The water area in “d” is for a rectangular region with a latitudinal extent of (22.655◦ N, 22.790◦ N) andlongitudinal extent of (33.169◦ E, 33.265◦ E).

In contrast to the fitted curve, which shows a stable increase of water inundation, theobserved values show low water inundation pixels during July 2020. Although low water
inundation was observed for July 2020, the values for August and September have a lesser
deviation from the fitted curve.
3.3. Analysis of Hydrological Variability
To understand the regional hydrology of the Nile river, we analyzed the two mostimportant hydrological variables- precipitation and runoff for the last five years for the
Nile River Basin and Blue Nile River Basin upstream of GERD (termed as Upper Blue Nile
River Basin) (Figure 6). It was observed that precipitation (370 mm) in the Upper Blue
Nile River Basin for the month of August 2020 exceeded the mean precipitation (260 mm)
in August for the previous five years (2015–2019) by more than 100 mm. Similarly, therunoff in the Upper Blue Nile River Basin for August 2020 (250 mm) exceeded the mean
runoff (110 mm) in the previous five years (2015–2019) by 140 mm. On the other hand, for
the Nile River Basin, there was no significant increase/decrease in the precipitation andrunoff for 2020 as compared to the previous five years. Furthermore, from the GRACE

Remote Sens. 2021, 13, 711

11 of 15

total water anomalies for the pixels corresponding to the GERD reservoir and Lake Nasser(Figure 7). Since the signal from desert pixels do not vary over time, the changes capturedby the GRACE total water anomaly can be attributed to the changes in water storage for theregion. Hence, we use the GRACE data to analyze the trend of overall increase/decrease ofwater in the region. It was also observed that the deseasonalized data shows an increasing
trend for liquid water equivalent (LWE) from 2002–2020. The most recent data available
Remote Sens. 2019, 11, x FOR PEER REVIEW
12 of 16from the GRACE satellite corresponds to November 2020, which is shown as the last value
in the figure. For GERD (Figure 7a), it was found that the August anomaly has exceeded
the anomalies corresponding to August for the previous five years. This increased anomthe anomalies corresponding to August for the previous five years. This increased anomaly
aly is partly due to the filling up of the reservoir. On the other hand, there was a decreasis partly due to the filling up of the reservoir. On the other hand, there was a decreasing
ing trend of GRACE anomaly at Lake Nasser. The absolute magnitude of these anomalies
trend of GRACE anomaly at Lake Nasser. The absolute magnitude of these anomalies isis significantly different, with the magnitude at GERD being higher than Lake Nasser bysignificantly different, with the magnitude at GERD being higher than Lake Nasser byorder of 10.
order of 10.

FigureFigure 6.
6. (a,b)(a,b) Time-series
Time-series ofof precipitation
precipitation fromfrom Global
Global Precipitation
PrecipitationMeasurement
MeasurementMission
Mission(GPM)(GPM)data.
data. Figure
Figure (b)(b) showsshows
thetime-seriesofrunofffromthegloballand
data
assimilation
system(GLDAS).
Both
figures
represent
spatially
the time-series of runoff from the global land data assimilation system (GLDAS). Both figures represent spatially average
averagemonthlymonthly accumulated
accumulated values
values forfor thethecorresponding
correspondingwatersheds.
watersheds.

Remote Sens. 2021, 13, 711

12 of 15

Remote Sens. 2019, 11, x FOR PEER REVIEW

13 of 16

Figure7. (a,b)
Total
water
anomaly
and Storage
ClimateExperiment
Experiment(GRACE)liquidwater
Figure7. (a,b)
Total
water
anomalyderived
derivedfrom
fromthetheGravity
Gravity Recovery
Recovery and(GRACE)liquidwater
equivalent
(LWE)
data
forfor
GERD
correspondtotothe
thesingle
single
pixel
where
GERD
Lake
equivalent
(LWE)
data
GERDandandLake
LakeNasser.
Nasser.These
These time-series
time-series correspond
pixel
where
GERD
andandLake
Nasserlocated.
The
GRACEanomalies
anomaliesnormalized
normalized forfor the baseline
Nasserarearelocated.
The
GRACEbaseline ofof2002–2020.
2002–2020.

4.4.Discussion
Discussion
Thefilling ofof GERDGERDhavehavecaused
causeddisagreements
disagreements
among
Ethiopia
Theconstruction
construction and filling
among
Ethiopiaandof Egypt
EgyptandandSudan.
Sudan.These
These
countries
situatedin theandthethedownstream
downstream countries
countries ofcountries
arearesituatedin thedrylands
to waterwatershortage.
shortage.Our
Ourstudy
studyconsists
consistsa simpleestimation
drylandsthat
thatareare vulnerable
vulnerable toof ofa simpleestimation
ofofthetheincrease
increaseininreservoir
reservoirvolume
volumeof
ofwaterwater(Figure(Figure22andand Table
Table1)
1)during
during thethe filling
filling process.
process. Figure
3 demonstrates
the impactof thedamon
filling
on theland morphology
andFigure3 demonstrates
the impactof the dam
filling
the landmorphology
and potentiallypotentially hydrologicalprocesses. we
Meanwhile,
we usedthe most
up-to-datepubliclyhydrologicalprocesses. Meanwhile,used the most
up-to-datepublicly
available
remoteavailable
remotesensingdata during
time ofIn
ouranalysis. In
particular,
the usage
synergistic
sensingdataduringthe timeof our the
analysis.
particular,thesynergisticof both
usage
of
both
Sentinel-1
andSentinel-2
imagery
has
provedtoimprovetheeffectiveness
Sentinel-1 and Sentinel-2 imagery has proved to improve the effectiveness of detectingof detectingininundation
this study(Figures
4 and5). This combinationof SAR
andimages
opinundation
this studyin(Figures
4 and5). Thiscombinationof SAR and
opticalticalcompensate
images can forcompensate
forfrom
limitations
from eachsuch
as SAR sometimes
canlimitations
each source,
suchsource,
as SAR
sometimes
misclassifies
misclassifies
areaswith low backscattering(e.g.,sand)while
as water,while
opticaltheareas withthelowbackscattering(e.g., sand) as
water,
opticalimagesareimages
strongly
are strongly
affected by atmosphericconditions(e.g.,
clouds). However,since theSRTM
affectedby atmosphericconditions (e.g.,clouds).
However,since the SRTM
DEM
has a
DEM hasa spatial
resolutionof 30 m, anyelevationthese
30 m will
× 30notm bespatialresolutionof 30
m, any elevation
changes
insidechanges
these 30inside
m × 30m pixels
pixelswill
notbeaccounted
for,
and
thus,
the
volumeestimationiscorrespondingly
unaccounted for, and thus, the volume estimation is correspondingly under/overestimated.
der/overestimated.
Further,
we usedsatellite
datasets,
whichSentinel-1
include data
SentinelFurther,
we used satellite
datasets,
which
includedata from
andfrom
Sentinel-2,
GPM,
1
and
Sentinel-2,
GPM,
SRTM,
and
GRACE,
as
well
as
model
outputfrom
GLDAS.
We to
SRTM, and GRACE, as well as model output from GLDAS. We suggest further studies
suggest further
to consider
addinguncertainties
to the analysis
to understandconsideraddingstudies
uncertainties
to theanalysisto understandthat may
impact the that
results
may impact the results derived in this study. Further, the runoff data from GLDAS is a
derived in this study. Further, the runoff data from GLDAS is a model output, which iscalibrated globally but lacks calibration at a regional scale. This situation is proved by theRemote Sens. 2021, 13, 711

13 of 15

fact that the number of functional hydrometeorological gauging stations declined by 88%
between the early 1960s and 2014 [26]. Furthermore, the unusually low number of water
inundation pixels (Figure 5d) at Lake Nasser cannot be explicitly explained by GERD filling
process since the low values correspond to July 2020 values. Since the dam filling started
in early July, it is unlikely that the low inundation is caused by the dam filling process
because Lake Nasser is located approximately 1300 km downstream of GERD. Any flowchanges in GERD can be observed at Lake Nasser only after a lag of a certain time (in theorder of months—flow time between GERD and Lake Nasser), but this study does notcontain any such analysis, and we suggest further investigation. In addition, the Early Run
IMERG dataset (Figure 6) was used, which is not calibrated with the global rain gauge
datasets. Runoff in 2015 and 2019 for the Upper Blue Nile River Basin is lower in 2015
and 2019 as compared to other years despite no decrease in precipitation. This deviationin runoff is caused by the high soil water deficit in Upper Blue Nile and the Nile River
Basin. Since there is no runoff due to snow in both of these river basins, soil water deficit
becomes a key parameter for estimating runoff [27]. The most recent data for GRACE
and GLDAS obtained was for November and December 2020, respectively, at the time ofanalysis. In Figure 7, the increased GRACE water storage anomaly in August is due tothe increased volume of the water in the GERD reservoir. Therefore, we hypothesize that
the GERD August anomalies will be sustained for the following months since September
precipitation was almost as high as August precipitation, and GRACE anomalies usually
lag the precipitation and runoff by around 1–2 months. Studies like the present one where
alteration in the natural hydrological processes (such as storage of water in the reservoir)
are required to be considered when evaluating water balance and extreme events in large
watersheds using satellite observations and model outputs [28].
Recently, Sudan experienced heavy flooding due to a significantly high amount of
rainfall during August 2020. This phenomenon was an extreme event, and it coincided withthe filling process of the GERD reservoir. Due to the occurrence of such flooding, the actual
implications of the shortage of water supply downstream of the GERD reservoir were notobserved in our analysis. Due to this excessive flooding, the downstream flow of water
into Northern Sudan and Egypt was observed to be significantly higher, which caused theincreased water levels in Lake Nasser. Since these extreme events are unlikely to occur
every year, consideration of such events should be taken into account using physical-basedhydrological models to simulate events where the Blue Nile River receives climatologicallyaverage rainfall. The analysis from such a model will be more accurate to provide thebasis for policy formulation for the governments of Egypt, Sudan and Ethiopia. Finally,
the ground observation networks should be established to support the scientific studies indecision-making and collaborations among the countries in the transboundary basin.
5. Conclusions
This study presents comprehensive monitoring of the filling process of GERD and its
implications on the downstream river hydrology. We monitored the spatial extent of thereservoir using Sentinel-1 SAR and Sentinel-2 optical imagery. In addition, SRTM DEM
was used to calculate the changes in water depth and increase in the volume of water
being stored at the reservoir of GERD. Further, Sentinel-1 imagery was used to calculate
the inundated water pixels at the upstream and downstream side of GERD as well as at
the southern tail of Lake Nasser. Finally, the precipitation, runoff and GRACE total water
anomaly were analyzed for the Nile River Basin and the Upper Nile River Basin. From
our analysis, we found that the estimated storage of water in the reservoir has increasedby 3.584 billion cubic meters to 30 November 2020, which is approximately 5.3% of the
final capacity. In addition to that, we found that the increased reservoir area is alreadyat 14% of the estimated surface area of the reservoir as of 7 September 2020. Further, it
was observed that although the number of inundated water pixels did not increase at thedownstream end of GERD, there is a significant increase in the water inundation at Lake
Nasser. To further investigate the reason behind this observation, precipitation and runoff

Remote Sens. 2021, 13, 711

14 of 15in the Blue Nile River Basin were analyzed. We found that the precipitation and runoff forAugust 2020 were significantly higher for the Upper Blue Nile River Basin as compared tothe previous five years. In addition to this, we find an increasing trend in deseasonalized
GRACE anomaly for the GERD reservoir, whereas Lake Nasser shows a decreasing trendof anomaly. Finally, the GRACE observations for the month of August and September 2020
show very high anomalies consistent with the very high precipitation and runoff duringthat month. We conclude that the filling of GERD is not due to natural pooling, and theanomalously high precipitation in the Upper Blue Nile river basin, as well as parts of the
Nile River Basin, has attributed to an increase in water inundation at Lake Nasser. Our
study shows that the Upper Blue Nile River Basin is experiencing one of the wettest spells,
which is overshadowing the negative implications due to dry spells at the downstream
parts of the Nile River in Sudan and Egypt. From our observations, we conclude that thecurrent filling process may have drastic downstream implications during dry spells in theUpper Blue Nile River Basin. The dry/wet spells in the Nile River basin have shown direct
linkage with the El Niño–Southern Oscillation (ENSO) cycle depicting strong correlations
with decadal scale precipitation variations inside the Nile River basin [29]. In addition, thisstudy provides a cloud-based analysis technique to monitor the increase in the volume
of water in the reservoir. This method provides easy, computationally light, and quick
method for analyzing these datasets. The codes used in this study can be found in the
supplementary materials. Finally, we suggest tri-lateral policies for regulating the flow in
the Blue Nile River for fair water distribution to the countries of Egypt and Sudan.
Supplementary Materials: The following are available online, (1) The water volume variations
during the dam filling are calculated using Google Earth Engine JavaScript code: https://code.
earthengine.google.com/a794cd8260c2fe1e6f8ce9ff780616c7, (2) Extra plots corresponding to Figure 3
showing the dam filling process is shared in the link: https://drive.google.com/drive/folders/1Yzr2
E09nCb_-D1O3nZnsnpBt2ISOmF4y?usp=sharing.
Author Contributions: Conceptualization, P.K., H.E.-A., V.L., M.A.S. and W.L.; data analysis, P.K.
and W.L.; funding acquisition, H.E.-A. and V.L.; methodology, P.K., W.L., H.E.-A. and V.L.; validation,
P.K. and W.L.; writing—original draft, P.K. and W.L.; writing—review and editing, H.E.-A., W.L., P.K.,
T.P., M.A.S. and D.S. All authors have read and agreed to the published version of the manuscript.
Funding: We would like to acknowledge the support from the Earth Systems Science and Data
Solutions (EssDs) Lab, Schmid College of Science and Technology, Chapman University. PK and VL
acknowledge the support from NASA Earth Sciences Program—Terrestrial Hydrology (Program
Manager: Jared Entin) and Applied Sciences—Water Resources (Program Manager: Bradley Doorn).
Data Availability Statement: Publicly available datasets were analyzed in this study. SRTM data
was downloaded from USGS Earth Explorer (https://earthexplorer.usgs.gov/ accessed on 17 August2020). GPM and GLDAS data was downloaded from NASA Earthdata inventory (https://search.
earthdata.nasa.gov/search accessed on 26 January 2021). GRACE data was downloaded from NASA
JPL PODAAC drive (https://podaac.jpl.nasa.gov/ accessed on 26 January 2021). The processed
SAR imagery analyzed in this study are available to download at: https://drive.google.com/drive/
folders/1Yzr2E09nCb_-D1O3nZnsnpBt2ISOmF4y?usp=sharing (accessed on 26 January 2021).
Acknowledgments: The first author acknowledge the support from the Department of Engineering
Systems and Environment, and from School of Engineering and Applied Sciences, University ofVirginia and the support from Earth Systems Science and Data Solutions (EssDs) Lab, Schmid Collegeof Science and Technology, Chapman University.
Conflicts of Interest: The authors declare no conflict of interest.

References
1.
2.

Madson, A.; Sheng, Y. Reservoir Induced Deformation Analysis for Several Filling and Operational Scenarios at the Grand
Ethiopian Renaissance Dam Impoundment. Remote Sens. 2020, 12, 1886. [CrossRef]
Elsayed, H.; Djordjević, S.; Savić, D.A.; Tsoukalas, I.; Makropoulos, C. The Nile Water-Food-Energy Nexus under Uncertainty:
Impacts of the Grand Ethiopian Renaissance Dam. J. Water Resour. Plan. Manag. 2020, 146, 04020085. [CrossRef]

Remote Sens. 2021, 13, 711

3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.

14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.

15 of 15

Siddig, K.; Basheer, M.; Luckmann, J.; Grethe, H. Long-Term Economy-Wide Impacts of the Grand Ethiopian Renaissance Damon Sudan. In Proceedings of the 22nd Annual Conference on Global Economic Analysis, Warsaw, Poland, 19–21 June 2019.
Salman, S.M.A. The Grand Ethiopian Renaissance Dam: The road to the declaration of principles and the Khartoum document.
Water Int. 2016, 41, 512–527. [CrossRef]
Kahsay, T.N.; Kuik, O.; Brouwer, R.; Van Der Zaag, P. Estimation of the transboundary economic impacts of the Grand Ethiopia
Renaissance Dam: A computable general equilibrium analysis. Water Resour. Econ. 2015, 10, 14–30. [CrossRef]
Zhang, Y.; Erkyihum, S.T.; Block, P. Filling the GERD: Evaluating hydroclimatic variability and impoundment strategies for Blue
Nile riparian countries. Water Int. 2016, 41, 593–610. [CrossRef]
Zhang, Y.; Block, P.; Hammond, M.; King, A. Ethiopia’s Grand Renaissance Dam: Implications for Downstream RiparianCountries. J. Water Resour. Plan. Manag. 2015, 141, 05015002. [CrossRef]
Gebreluel, G. Ethiopia’s Grand Renaissance Dam: Ending Africa’s Oldest Geopolitical Rivalry? Wash. Q. 2014, 37, 25–37.
[CrossRef]
Basheer, M.; Wheeler, K.G.; Elagib, N.A.; Etichia, M.; Zagona, E.A.; Abdo, G.M.; Harou, J.J. Filling Africa’s Largest Hydropower
Dam Should Consider Engineering Realities. One Earth 2020, 3, 277–281. [CrossRef]
Dandrawy, M.E.; Omran, E.-S.E. Environmental Remote Sensing in Egypt; Springer: Cham, Switzerland, 2020; pp. 533–574.
[CrossRef]
Wheeler, K.G.; Jeuland, M.; Hall, J.W.; Zagona, E.; Whittington, D. Understanding and managing new risks on the Nile with theGrand Ethiopian Renaissance Dam. Nat. Commun. 2020, 11, 1–9. [CrossRef] [PubMed]
Amitrano, D.; Di Martino, G.; Iodice, A.; Mitidieri, F.; Papa, M.N.; Riccio, D.; Ruello, G. Sentinel-1 for Monitoring Reservoirs: A
Performance Analysis. Remote Sens. 2014, 6, 10676–10693. [CrossRef]
Pipitone, C.; Maltese, A.; Dardanelli, G.; Brutto, M.L.; La Loggia, G. Monitoring Water Surface and Level of a Reservoir Using
Different Remote Sensing Approaches and Comparison with Dam Displacements Evaluated via GNSS. Remote Sens. 2018, 10, 71.
[CrossRef]
Li, W.; El-Askary, H.; Lakshmi, V.; Piechota, T.; Struppa, D. Earth Observation and Cloud Computing in Support of Two
Sustainable Development Goals for the River Nile Watershed Countries. Remote Sens. 2020, 12, 1391. [CrossRef]
Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The
Shuttle Radar Topography Mission. Rev. Geophys. 2007, 45. [CrossRef]
Huffman, G.J.; Bolvin, D.T.; Nelkin, E.J. IMERG Technical Documentation. Available online: https://gpm.nasa.gov/sites/
default/files/document_files/IMERG_doc_190909.pdf (accessed on 26 January 2020).
Tang, G.; Ma, Y.; Long, D.; Zhong, L.; Hong, Y. Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products overMainland China at multiple spatiotemporal scales. J. Hydrol. 2016, 533, 152–167. [CrossRef]
Tapley, B.D.; Bettadpur, S.; Ries, J.C.; Thompson, P.F.; Watkins, M.M. GRACE Measurements of Mass Variability in the Earth
System. Science 2004, 305, 503–505. [CrossRef] [PubMed]
Cheng, M. Correction to “Variations in the Earth’s oblateness during the past 28 years”. J. Geophys. Res. Space Phys. 2005, 110.
[CrossRef]
Swenson, S.; Chambers, D.P.; Wahr, J. Estimating geocenter variations from a combination of GRACE and ocean model output. J.
Geophys. Res. Solid Earth 2008, 113, 08410. [CrossRef]
Landerer, F.W.; Swenson, S.C. Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res. 2012, 48, 48.
[CrossRef]
Syed, T.H.; Famiglietti, J.S.; Rodell, M.; Chen, J.; Wilson, C.R. Analysis of terrestrial water storage changes from GRACE andGLDAS. Water Resour. Res. 2008, 44, 44. [CrossRef]
Rodell, M.; Houser, P.R.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.-J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.;
Bosilovich, M.; et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [CrossRef]
Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery.
Int. J. Remote Sens. 2006, 27, 3025–3033. [CrossRef]
Li, W. Code for SAR Imagery Processing. Available online: https://code.earthengine.google.com/a794cd8260c2fe1e6f8ce9ff78061
6c7 (accessed on 25 November 2020).
Lv, M.; Lu, H.; Yang, K.; Xu, Z.; Lv, M.; Huang, X. Assessment of Runoff Components Simulated by GLDAS against UNH–GRDC
Dataset at Global and Hemispheric Scales. Water 2018, 10, 969. [CrossRef]
Zheng, D.; Van Der Velde, R.; Su, Z.; Wen, J.; Wang, X. Assessment of Noah land surface model with various runoff parameterizations over a Tibetan river. J. Geophys. Res. Atmos. 2017, 122, 1488–1504. [CrossRef]
Lakshmi, V.; Fayne, J.; Bolten, J. A comparative study of available water in the major river basins of the world. J. Hydrol. 2018,
567, 510–532. [CrossRef] [PubMed]
Le, J.A.; El-Askary, H.M.; Allali, M.; Sayed, E.; Sweliem, H.; Piechota, T.C.; Struppa, D.C. Characterizing El Niño-Southern
Oscillation Effects on the Blue Nile Yield and the Nile River Basin Precipitation using Empirical Mode Decomposition. Earth Syst.
Environ. 2020, 4, 699–711. [CrossRef]