![Overview of the target area. (a): CTX-image-based mosaic of the Jezero delta, in which the crater interior can be seen in the right half of the image. The study area with the delta feature is marked with a white rectangle, which can be seen in inset. (b): Geological map of the target area by [63] (inset c). Note that the blue-toned color codes refer to the fluvial features of the delta (after Williams et al. 2020. 51st LPSC #2254).](https://www.researchgate.net/profile/Steinmann-Vilmos/publication/384450533/figure/fig1/AS:11431281283917669@1729033197510/Overview-of-the-target-area-a-CTX-image-based-mosaic-of-the-Jezero-delta-in-which_Q320.jpg)
Access to this full-text is provided by MDPI.
Content available from Remote Sensing
This content is subject to copyright.
Citation: Steinmann, V.; Bahia, R.S.;
Kereszturi, Á. Selecting Erosion- and
Deposition-Dominated Zones in the
Jezero Delta Using a Water Flow
Model for Targeting Future In Situ
Mars Surface Missions. Remote Sens.
2024,16, 3649. https://doi.org/
10.3390/rs16193649
Academic Editor: Christian Wöhler
Received: 13 August 2024
Revised: 31 August 2024
Accepted: 12 September 2024
Published: 29 September 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Article
Selecting Erosion- and Deposition-Dominated Zones in the
Jezero Delta Using a Water Flow Model for Targeting Future
In Situ Mars Surface Missions
Vilmos Steinmann 1,2,3 , Rickbir Singh Bahia 4and Ákos Kereszturi 1,2,3,5,*
1
Research Centre for Astronomy and Earth Science HUN-REN, Konkoly Thege Miklós Astronomical Institute,
1121 Budapest, Hungary; steinmann.vilmos@csfk.org
2Department of Physical Geography, Faculty of Science, Doctoral School of Earth Sciences,
Eötvös Loránd University, 1053 Budapest, Hungary
3Research Centre for Astronomy and Earth Sciences, CSFK, MTA Centre of Excellence,
1121 Budapest, Hungary
4European Space Research and Technology Centre, European Space Agency, 2201 Leiden, The Netherlands;
riccibahia@hotmail.com
5European Astrobiology Institute, 67000 Strasbourg, France
*Correspondence: kereszturi.akos@csfk.org
Abstract: Identifying surface sites with significant astrobiological potential on Mars requires a
comprehensive understanding of past geological processes and conditions there, including the
shallow subsurface region. Numerical modelling could distinguish between regions dominated by
erosion and those characterized by sediment accumulation in ancient wet environments. The target
area of Jezero Crater is relatively well explored and thus is an ideal site to evaluate model calculations;
however, important works are still missing on expectations related to its shallow subsurface . In this
work, the best available approaches were followed, and only surface morphology was considered
(supposedly formed by the last fluvial episode). The shallow subsurface became an important target
recently, and this model could provide new inputs in this area. Erosion–accumulation models are
suitable for terrestrial surface features, but few have been applied to Mars. This work addresses
this challenge using the SIMWE (SIMulated Water Erosion) model on the Jezero Crater delta, the
landing site of the Perseverance rover. For calculations, the average grain size according to the
THEMIS TI data was applied to the target area. The flow depth varied between 1.89 and 34.74 m
(average of 12.66 m). The water-filled channel width ranged from 35.3 to 341.42 m. A flow velocity
of 0.008–11.6 m/s, a maximum erosion rate of 5.98 g/m
2
/h, and a deposition 4.07 g/m
2
/h were
estimated. These calculated values are close to the range of estimations from other authors assuming
precipitation of 1–20 mm/h and discharges of 60–400 m
3
/s. The model was able to distinguish
between erosion- and accumulation-dominated areas about 1 m above Jezero Crater’s delta that are
not visible from above. This model helps to identify the accumulation-dominated areas with the
finest grain size with good preservation capability for the shallow but invisible subsurface.
Keywords: Mars; hydrology; geography; landscape; THEMIS; HiRISE
1. Introduction
Ongoing and upcoming in situ Mars missions, e.g., NASA’s Perseverance Rover and
ESA’s Rosalind Franklin Rover (former ExoMars Rover), are poised to investigate Mars’s
surface and shallow subsurface regions. These missions play a pivotal role in selecting
scientifically significant sample return targets [
1
] and locations that were important for past
habitability and are also suitable for potential future human habitation [
2
]. Mars astrobi-
ology research is primarily centered around locations where liquid water endured over
substantial durations [
3
–
5
], boasting strong potential for weathering and organic material
preservation, notably in phyllosilicate-rich areas [
6
]. Although phyllosilicate-rich areas
Remote Sens. 2024,16, 3649. https://doi.org/10.3390/rs16193649 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2024,16, 3649 2 of 23
are observable from orbit, the challenge arises from the fact that astrobiological indicators
are likely to be preserved not on the surface but only within the Martian subsurface [
7
].
Additionally, phyllosilicate detection of bedrock from orbit can be obscured by surficial
dust deposits [
8
]. Hence, while both the Perseverance Rover and Rosalind Franklin Rover
possess subsurface sampling capabilities, the precise identification of subsurface drilling
targets before the acquired sample can be analyzed there or returned to the Earth for
more detailed investigation remains poorly constrained. The modeling-based approach
presented in this work might help, but its verification requires on-site shallow subsurface
confirmation in the future, which could be conducted by future missions. Regarding the
verification of the used and calculated numerical values, beside expected numerical un-
certainties (see the last part of the Section 4), as other published models mainly calculate
discharge but rarely consider sediment transport, comparison to other models cannot be
performed in detail.
This study explores the model-based identification and evaluation of surface or shal-
low subsurface sites with potential astrobiological significance, which are characterized by
extended periods of past wet conditions and the presence of clay-like sediment accumu-
lation in low-energy ancient fluvial settings. The model allows the identification of areas
where fine sediment has likely accumulated and subsequently undergone lithification but
where surface layers have not been covered or resurfaced much by later processes (e.g.,
aeolian ripples). The Jezero delta is such a target, where the undulating surface suggests the
possibility that substantial differences in flow conditions and directions could emerge, thus
revealing accumulation- and sedimentation-dominated areas close to each other to allow
the testing of this model. The future sample return of cores collected by Perseverance could
undergo detailed Earth-based laboratory analysis of fluvial sediments there (including the
smallest grain size), which would support the development of this and similar models.
This work goes beyond the morphological analysis of surface features with numerical
calculations linked to the observed morphology.
The aim of this research is to provide examples and experience with an Earth-tested
specific surface erosion accumulation model for flowing liquid water called SINWE as
applied to Mars. Although it is an Earth-type planet, there are several poorly known
aspects of how the fluvial process and related erosion known on Earth happens on Mars,
including specific numerical parameters (see the Section 2), as differences are expected
due to the different gravity acceleration of solid grain fallout related to Stoke’s law and
different behavior of vortex formation in the flowing medium. This paper aims to make
one step forward in applying Earth-based modelling to Mars. While images provide
information only on the surface, the model-based approach provides information on the
shallow subsurface, which is not visible from above but might be an important target
in the future. The adaptation of any specific model to another planet is difficult and a
long-lasting procedure; thus, here, only an early test was conducted in order to see how
effectively such a model could be applied to Mars and reveal the possibilities and benefits
of its usage. Beside various physical parameters, including the reduced surface gravity of
Mars compared to those of the Earth, the surface topography on Mars influences the results
substantially—thus, such terrains are useful for the tests, which have not been modified
much since the last fluvial activity there. The surface of the Jezero delta seems to be such
an area.
Due to the unavailability of subsurface stratigraphic information extending beyond the
visible surface (radar data do not show the shallow subsurface and neutron spectrometry
indicates only the hydrogen content), our aim is to reconstruct only the topmost layers
accessible to in situ missions (both considering planetary geology [
9
] and astrobiology, plus
mission relevance). In this shallow subsurface region, the stratigraphy is expected to be
variable with different sedimentary features that exist within about 1 m of depth [
10
,
11
],
which will be reachable by shallow drilling in the near future to sample specific fluvial
sedimentary units. Although the topographic data and infrared-data-based grain size
values exhibit lower resolutions than optical images, they can still offer valuable insights to
Remote Sens. 2024,16, 3649 3 of 23
identify the potential locations of shallow buried sediments and support methodological
development, especially being a global coverage dataset. The importance of this work is
that such locations may not be directly observable in HiRISE images but can be inferred
through erosion and accumulation modelling.
In this work, the SIMWE method is applied to a specific example area: the delta in
Jezero Crater, the landing site of the Mars 2020 Perseverance rover. This location was
examined in order to demonstrate the rationality of such analysis and show the type of
results and uncertainties that emerge. The work aims to enhance a new type of model
focused on reconstructing late fluvial events at the upper part of the delta, using observable
topographical data, excluding the comprehensive delta-building process of deeper units.
This SIMWE model can estimate runoff and related precipitation from the topography and
the eroded/deposited volumes without the direct measurement of precipitation, a value
that is not currently constrained for ancient Mars. The rationale for employing the current
topography to reconstruct ancient processes by concentrating on the shallow subsurface,
rather than deeper regions (largest volume unit of the delta), is partly that the observable
topography gives the only possibility for this type of reconstruction. Also, upcoming rover
missions in the next few years lack deep but have shallow drilling capabilities. Although
the forthcoming Rosalind Franklin Rover can drill down to 2 m, drilling deeper would
likely necessitate a stable platform, which is currently unable to achieve sampling from old
fluvial sedimentary locations. This advanced drilling capability is anticipated in the more
distant future, making the shallow subsurface the primary focus for the coming decade(s).
Similar types of modelling-based analysis have been performed for Mars surface
missions that primarily rely on the remote or in situ direct identification of key surface
features, such as sediments [
12
] and phyllosilicates [
13
], to draw conclusions regarding
ancient Martian surface conditions. However, the full potential of process-based modelling
is still awaiting exploitation. Existing models generally aim to reconstruct large scale
processes at low resolutions, such as global precipitation patterns, with few performing
detailed analyses of specific locations [
14
–
16
] at high spatial resolution. Examples of models
simulating ancient environmental conditions at specific locations include models of climate-
related river transport [
17
], glaciation at Erebus Montes [
18
], Hesperian aged aeolian
bedforms in Gale crater [
19
], paleohydraulic conditions in Ebro Basin [
20
], widespread
formation of an ice layer in the shallow mid-latitude region [
21
], silica precipitation from
ancient water [
22
], pedogenic weathering [
23
], and rainfall estimation [
24
]. Although
complex landscape evolution for Mars has rarely been modelled, a few published examples
include models exist of Endeavour crater [
25
], Gale crater [
26
], aeolian ripple formation [
27
],
and general paleoclimatic evolution [
28
]. Few numerical, calculation-based models have
been presented for a fluvial erosion/deposition.
Limited efforts have been undertaken to employ numerical methods in estimating
erosion and accumulation processes, particularly to discern the areas conducive for the
accumulation of specific materials, such as fine-grained clays, and where liquid water
persisted for extended periods. These models have been limited in capability, though
some of them could be adapted to environments other than the Earth by modifying the
physical parameters of the target area, e.g., Martian surface gravity, hypothetical flow
depth (water thickness on each pixel), grain size, erodibility, and other parameters of soil or
regolith. The main advantage of this SIMWE model is that it estimates erosion/deposition
without data on the precipitation and provides a step toward estimating the occurrence of
sedimentary features (as different ones are expected to occur in different location types).
Thus, the authors of this work were motivated to apply modelling approaches for rover-
acquired sampling at localization on Mars to reveal the spatial arrangement of sediment
accumulation in Jezero Crater. In particular, this open-source model uses the global THEMIS
dataset, which can work without precipitation data and which no one has applied to Mars
before. The target area of this work is the sedimentary structure at the termination of the
main inlet at Neretva Vallis in Jezero Crater. At present, many of these depositional areas
are covered with aeolian ripples, resulting in partly obscured deposits. However, at the
Remote Sens. 2024,16, 3649 4 of 23
analyzed target, only a few, scattered sand ripples are present, thus providing a useful target
structure for evaluating fluvial features related to erosional and depositional processes.
The erosion accumulation model presented in this study examines the changes that occur
during a specified rainfall event. The intensity and duration of the rainfall event exert an
influence on the flow depth and flow discharge values that are measured in the study area.
The model is capable of discerning the erosion and accumulation processes occurring in the
area under study during a rainfall event based on the derived data (e.g., flow velocity, shear
stress) and the grain size data of the area, with the latter determined from the THEMIS
TI data. In accordance with precedent, cross-sectional sampling is employed, whereby
the hydrological and subsequent erosion/accumulation values for a specified valley are
estimated from the data derived from the cross-sections.
The southern highlands of Mars are incised by vast arrays of fluvial valleys that
have been explored by numerous studies [
29
–
33
]; however, paleo-discharge values are
poorly constrained but important for climatic reconstruction. Surface material transport
models for Mars have also been applied to understand precipitation, infiltration, and
runoff [
34
]; various aspects of fluvial systems [
35
–
37
]; sediment deformation [
38
]; and
sediment deposition [
39
]. The specific target area for the model tested here is the top
surface of the Jezero delta, where a range of fluvial features are available, and with this
being a landing site, high-resolution topographic data are also available.
The delta in Jezero Crater was deposited during the early wet period on Mars before
the Noachian/Hesperian transition, when fluvial activity was present across the planet [
31
].
Data retrieved by the Perseverance rover indicates that the western margin of the deposit
shows delta/alluvial fan stratigraphy [
40
], which likely formed in the Late Noachian–Early
Hesperian (~3.6–3.8 Ga) [
41
,
42
], on the top of a volcanic basement [
43
]. Dip and strike
measurements of sedimentary beds identified in Mastcam-Z stereo-images indicate an
increasing, subaqueous fan by deposition of meter-scale, gravity driven, sediment-rich
flows [
44
]. Deposition of variable grain size fractions in the bottom sets was observed (from
Devils Tanyard to Knob Mountain members), with a change to siltstones at Hogwallow
Flats. Coarser-grained sandstones (Rocky Top) might have been deposited as turbidity
currents. Folding deformation is visible mainly in the lower members, with tight folding
sandwiched between the sub-horizontal layers, while many folds present in soft sediment
deformation features were formed by vertical loading [
45
]. The Shenandoah Formation
spans more than 50 m of vertical stratigraphy at the delta front, where filled fractures imply
late-phase fluid flow with cementation and lithification [
46
]. Some evidence indicates that
hydrodynamic sorting has had an effect on mineralogy related to temporal changes in the
watershed [
47
], and spectral data of the delta front records variable aqueous conditions at a
range of redox parameters, both during deposition and post-deposition due to alteration by
fluids [
48
], with aqueous alteration minerals like Fe/Mg smectites, alumino-phyllosilicates,
serpentines, and Fe/Mg carbonates [
49
]. The original size of the delta was likely larger
than that is currently visible, which is partly indicated by the sedimentary geometry feature
with Gilbert-like succession [
50
], especially at Kodiak Butte, located 1 km away from the
front of the continuous deposit structure of the delta [
51
]. Similarly, Hawksbill Gap also
presents stratigraphic patterns similar to those at Kodiak [48].
There is a proposed localized variation in meanders and bars formed in the deposi-
tional regime during the later stages of this delta’s history [
52
]. For example, there is a
wide range of various fluvial features on the top of the delta, including point bar strata in
meandering channels, inverted channel-filling deposits by alluvial distributary channels,
and incised valley deposits with very few post-fluvial dune stripes. Although in theory
it is possible that the very topmost part of the original delta has been eroded away, there
are no signatures supporting this possibility; thus, we consider that the currently observ-
able upper surface of the delta is representative of the last fluvial episode. The deposit
is mainly composed of finer-than-conglomerate (sand and cobble grain size) lithologies,
resembling pebbly sandstones, and locally holds boulders within the topset, indicating
episodic high-discharge floods. It shows the presence of phosphate, carbonate, olivine, and
Remote Sens. 2024,16, 3649 5 of 23
sulphates, with certain variations between bedforms [
53
]. Horvath and Andrews-Hanna
(2022) [
54
] ran hydrological models for the formation of the deposit and found that under
semiarid conditions, a lake might have been stable there. Analyses of the modern and
paleo-topography of Jezero Crater reveals no strong evidence for lake terraces or shorelines.
However, analyses of the deposit at the end of Neretva Vallis, such as the stratigraphic
sequence, the thicknesses, and the dip angles measured, all indicate an increasing delta
deposit, suggesting a lake was present in Jezero at one time.
At the top of the delta, there are several inverted channel forms that are not covered
by sand or other, later aeolian sediment, and only a few sands of aeolian origin are found
on the walls of the delta front [
55
] in general. The original rock material of the delta is
prominently exposed in various locations, with multiple outcrops of the same sediment.
Although it is possible that the top surface was modified by post-fluvial erosion, like
wind scour or sand deposition and cementation, such morphological features could not
be identified at the top of the delta, while several fluvial-related features are still present.
However, these ripples are really small and cover only a small fraction of the area, below
0.1%. Beside this aspect, there is no other possibility than using the currently available
surface topography for modelling. In this work we target such shallow subsurface locations
where the top layer, influenced by flowing water, could be visited by mobile rovers—and
the last flowing-water-influenced top layer could be analyzed, regarding the influence
of the direction, speed, etc., of the last liquid flow. The reconstruction of such a terrain
evolution that produced the large delta-like structure of a dozen meters’ thickness is beyond
the scope of this work; here, only a simpler, related starting step was undertaken. The main
benefit of the approach and methodology presented in this work is not the identification
of deltas on Mars but the numerical evaluation of erosion and sedimentation from fluvial
activity, which helps in the reconstruction of ancient processes.
The method presented in this work could serve as an additional line of evidence
in determining the dimensions and locations of accumulation- and erosion-dominated
zones, partly as a complementary approach of higher resolution geomorphological data.
These predictions come from the present-day topography, which is the best-established
available data, although in the future, subsurface fluvial features might be observed in
theory by shallow radar or at frequent and nearby deep outcrops, which would expose
subsurface settings. But till such data become available, we need to rely on the present-day,
observable topography. This is a reasonable approach as there are no signatures indicating
that substantial thickness of further, even-younger layers had been deposited there but
were eroded away from the current top surface.
2. Materials and Methods
This section provides a concise summary of prior models for context, an overview of
available datasets for calculations, and an introduction to the applied specific modelling
system, including the rationality of the parameters used.
Surface evolution models emphasizing the balance of erosion and deposition have
been applied and validated in terrestrial contexts [
56
–
58
], though further improvements
are needed to apply them to another planet. Studies using the Universal Soil Loss Equation
(USLE) are inherently complex and rely on numerous experimentally derived values
and constants from terrestrial studies [
59
]. In contrast, Mars lacks the range of specific
parameters necessary for implementing the USLE model for the Red Planet, demanding a
more generalized approach. The parameters highly specific to Earth include the rainfall
erosivity factor (R) and the cropping management factors (C—cover management factor,
P—conservation practice factor). Given the unmeasurable nature of factors P and C, their
application to the Martian environment is not viable. Alternatively, the SIMWE (SIMulated
Water Erosion) model, relying on more calculable, simpler physical parameters and fewer
empirical ones, can be employed. In this study, we adapted this numerical erosion and
sedimentation model for Mars, with its primary parameters influenced by shear stress,
detachment, and transport coefficients. However, most of these parameters have not been
Remote Sens. 2024,16, 3649 6 of 23
adjusted for Mars as no Earth-based laboratory tests are available, excluding the data on the
local gravity acceleration on Mars and the remote-sensing-acquired grain size distribution.
These parameters can be estimated based on theoretical principles using accessible Martian
datasets, such as the THEMIS (Thermal Emission Imaging System) for dominant grain size;
HiRISE (High Resolution Imaging Experiment), CTX (Context Camera), and HRSC (High
Resolution Stereo Camera) data for surface morphological evaluation; and topography
from stereo images.
In this work, the SIMWE model [
60
] was applied and adapted to Mars using erosion
and accumulation estimations based on surface grain size and topography. This model
relies only on calculated physical parameters, making it particularly suitable for Mars,
where empirical data are scarce. The core of this model comprises two scripts integrated
into GRASS GIS (an open-source GIS) for numerical calculations. The r.sim.sediment script
focuses on erosion and accumulation processes and requires the following parameters:
grain size, sediment detachment coefficient (D
c
), transport coefficient (T
c
), topographic
elevation, x and y derivatives of the slope (where x is the first order partial derivative of the
slope in the E-W direction, and y is the first order partial derivative in the N-S direction),
critical shear stress (
τ
cr), Manning’s value, and the infiltration rate. For further technical
explanation, see the abbreviations part. It is essential to specify a time interval to match
the value set in the r.sim.water script (a landscape simulation method with overland flow
dominated hydrological simulation using the path sampling method, with inputs: elevation,
flow gradient vector, and a surface roughness coefficient given by Manning’s n), which
can be divided into 60 min units what is easily convertible to other time units. 60 min was
selected to make a simple approach that is relevant for moderately short precipitation event
characteristic in desert-like terrains, possibly connected with chaotic terrain formation or
snow melting by hot volcanic dust fall [
61
], which are relevant for Mars—however, the
durations of such events are very poorly constrained for Mars.
The following datasets were used in this study: The DTM and the THEMIS TI datasets
were initially digitized to two different coordinate systems and have different spatial
resolutions. For consistency between datasets, we reprojected the HRSC DTMs from the
initial MARS_Sinusoidal coordinate system to the Simple_Cylindrical_Mars coordinate
system [
62
], the coordinate system of the THEMIS TI dataset. The values of the DTM were
then re-interpolated at the resolution of the THEMIS TI dataset. These conversions were
made in ESRI ArcMap using the Project Raster tool. Such steps were necessary to result in
a homogeneous topographic dataset of the whole target area.
The optical CTX images (D15_033216_1989_XN_18N282W and D20_035141_1987_XN_
18N282W), HRSC DTM-h5270_0000_dt4, and the THEMIS infrared image (a clip from the
original dataset) were analyzed for surface morphology (Figure 1) were used. On the right
side of the geological map from [
63
], the units colored with different shades of blue mark
the fluvial features, including those that are situated at the top of the delta structure and
considered to represent the remnants of the last observable fluvial episode. For further
details on these topographic fluvial morphological features, please see the last part of the
Section 1. HRSC [
64
] Digital Terrain Models (DTM—50 m/pixel) were used for analysis.
THEMIS TI [
65
] (TI for Thermal Inertia—Mars Odyssey Mission), which has a 100 m/pixel
spatial resolution, shows the thermal behavior of the surface material and was utilized to
indicate the dominant grain size.
This method offers the ability to estimate an average grain size for extensive terrains
due to THEMIS’s resolution limitations. In the future, an improved grain size estimation
method will be needed, but here, we had to apply the available global dataset first to test
the methodology in general. The grain size data from THEMIS are based on the thermal
inertia measurement, and larger grains provide slower daytime warming and slower
nighttime cooling, while smaller grains are composed of material that undergoes faster
temperature change. It stands as the sole remote method available for obtaining meaningful
grain size estimations across almost all Martian terrains by assessing the target’s thermal
characteristics. The distinctive advantage of global coverage of thermal inertia is that it
Remote Sens. 2024,16, 3649 7 of 23
facilitates erosion and deposition calculations across all Martian fluvial surface structures,
not only the few sites where some ground truth is available.
Remote Sens. 2024, 16, x FOR PEER REVIEW 7 of 24
Figure 1. Overview of the target area. (a): CTX-image-based mosaic of the Jezero delta, in which the
crater interior can be seen in the right half of the image. The study area with the delta feature is
marked with a white rectangle, which can be seen in inset. (b): Geological map of the target area by
[63] (inset c). Note that the blue-toned color codes refer to the fluvial features of the delta (after
Williams et al. 2020. 51st LPSC #2254).
This method offers the ability to estimate an average grain size for extensive terrains
due to THEMIS’s resolution limitations. In the future, an improved grain size estimation
method will be needed, but here, we had to apply the available global dataset first to test
the methodology in general. The grain size data from THEMIS are based on the thermal
inertia measurement, and larger grains provide slower daytime warming and slower
nighime cooling, while smaller grains are composed of material that undergoes faster
temperature change. It stands as the sole remote method available for obtaining meaning-
ful grain size estimations across almost all Martian terrains by assessing the target’s ther-
mal characteristics. The distinctive advantage of global coverage of thermal inertia is that
it facilitates erosion and deposition calculations across all Martian fluvial surface struc-
tures, not only the few sites where some ground truth is available.
One limitation of employing THEMIS for grain size estimation is the potential sub-
pixel variations in grain size, introducing uncertainties to the method (Table A1). The po-
tential drawbacks stemming from THEMIS’s restricted resolution and its provision of av-
erage grain size data are offset by the model’s capacity for analyzing erosion–deposition
processes along the extensive, meandering former fluvial channel, spanning a significant
number of THEMIS pixels; thus, it still provides a starting point for model development.
Consequently, the spatial scale for assessing variations in surface alterations due to fluvial
activity exceeds THEMIS’s threshold, as indicated by the grain size range presented in
Table 1 later. Thus, the grain size estimation needs to be further improved, but the THE-
MIS data still provide a starting point for model development.
The THEMIS TI values can be matched to the different sediment size classes, and
these sediment sizes also can be roughly matched to the corresponding shear stress and
critical shear stress values [66]. Hence, several raster operations were applied to this da-
taset: (1) to classify the THEMIS TI (Thermal Inertia) images to average grain size per pixel
(in mm); (2) to determine the Shields parameter (Y) to characterize the initiation of motion
Figure 1. Overview of the target area. (a): CTX-image-based mosaic of the Jezero delta, in which
the crater interior can be seen in the right half of the image. The study area with the delta feature
is marked with a white rectangle, which can be seen in inset. (b): Geological map of the target area
by [
63
] (inset c). Note that the blue-toned color codes refer to the fluvial features of the delta (after
Williams et al. 2020. 51st LPSC #2254).
One limitation of employing THEMIS for grain size estimation is the potential subpixel
variations in grain size, introducing uncertainties to the method (Table A1). The potential
drawbacks stemming from THEMIS’s restricted resolution and its provision of average
grain size data are offset by the model’s capacity for analyzing erosion–deposition processes
along the extensive, meandering former fluvial channel, spanning a significant number of
THEMIS pixels; thus, it still provides a starting point for model development. Consequently,
the spatial scale for assessing variations in surface alterations due to fluvial activity exceeds
THEMIS’s threshold, as indicated by the grain size range presented in Table 1later. Thus,
the grain size estimation needs to be further improved, but the THEMIS data still provide a
starting point for model development.
The THEMIS TI values can be matched to the different sediment size classes, and these
sediment sizes also can be roughly matched to the corresponding shear stress and critical
shear stress values [
66
]. Hence, several raster operations were applied to this dataset: (1) to
classify the THEMIS TI (Thermal Inertia) images to average grain size per pixel (in mm);
(2) to determine the Shields parameter (Y) to characterize the initiation of motion in a fluid
flow related to shear stress for the average grain size; and (3) to determine the critical shear
stress (
τ
cr) [
66
]. To convert the THEMIS TI values to sediment size classes and also to shear
stress values, the following formula was used in the QGIS raster calculator tool:
((THEMIS TI image ≥x AND THEMIS TI image < y)z) + [. . .]
In this formula, the x and y represent the minimum and the maximum value of the
THEMIS TI for the considered target area to approach a realistic range, while z is the
Remote Sens. 2024,16, 3649 8 of 23
sediment grain size class value. Although the calculated values obtained in this way
might differ from the real average grain size due to THEMIS subpixel mixing and possible
adhesion-driven flocculation during the fluvial sediment transport (which could enlarge
the grain size during the wet phase transport compared to the dry sediment relevant grain
size), the approach is reasonable for testing the method using only remote observations,
which is currently applicable for any Martian surface currently.
Prior to executing the erosion–accumulation model, the HRSC DTM underwent modi-
fications using the GRASS GIS r.hydrodem tool. This tool serves to fill enclosed topographic
depressions, such as small impact craters, and rectify errors and noise within the DTM. This
depression-filling process is essential for preventing anomalies in the simulation arising
from the DTM and from small crater depressions. These depressions, likely formed after
the period of fluvial activity (as observable impact craters formed later, overprinting fluvial
features, while former craters are buried by the fluvial sedimentation), would otherwise
serve as flow terminators, leading to pooling rather than continued flow. This uncertainty
is unavoidable but, as will be demonstrated later, does not change the general outcome of
the modelling.
The erodibility of the surface reflects the resistance of the rocks there against the force
of the erosive mechanism, in this case the fluvial incision. In this paper, the Dg model [
67
]
was applied for the estimation of the USLE-K factor, where Kis the erodibility and Dg is
the geometric mean diameter of the surface particle (mm). In this model, the Kformula
was chosen because all of the other related equations use different particle sizes like clay,
which can also be used to calculate the Kfactor. The weight percentage of the particle size
fraction (%) is the arithmetic mean of the particle size limits, and n is the number of the
particles in the given size fraction.
Dgcan be estimated with the following equation:
Dg=exp(0.01
n
∑
i=1
filnmi)
The SIMWE model was run in the GRASS GIS 7.8 software environment. The model
contains two different scripts, called r.sim.water and r.sim.sediment, which estimate the
flow depth (m) and flow discharge (m
3
/s) from given precipitation values (r.sim.water)
and the erosion–accumulation balance of the analyzed area. The T
c
and D
c
were used as
the main parameters for the r.sim.sediment tool. In this work, only the r.sim.sediment tool
was applied.
This Kvalue is part of the main detachment coefficient (D
c
), where D
c
[
68
] is calcu-
lated as:
Dc=K(τ−τc)
where the shear stress (
τ
) and the critical shear stress (
τc
) in Pa are considered. Although
there are many empirically acquired parameters for the Earth in discharge estimations,
these two stress parameters could be calculated for Mars too, as they are based on physical
characteristics of the given granular material and can be calculated from the Shields
parameter (Y) [69] both as normal and critical values, as follows:
Y=τ
Ds(ρs−ρ)gM
where D
s
is the diameter of the sediment (in meters);
ρs
is the density of the sediment,
which was estimated using onsite data-based analogue rock types from the Earth;
ρ
is
the density of water; and gis the gravity of the given planet, in this case Mars. The
threshold shield parameter on Earth is around 0.03–0.07 m
2
/s, though overall the Shields
parameter has a certain error that should have been accounted for in sediment hiding and
erosion/accumulation, but without a better approach, it is worth applying here.
To determine the transport coefficient (T
c
), the specific volumetric transport rate
(m
2
/s) [
70
] was used. For the volumetric sediment transport, the shear stress was calculated
Remote Sens. 2024,16, 3649 9 of 23
for the bedload (particles in a flowing fluid that are transported along the bottom of the
stream bed). The transport coefficient was calculated with the following formula:
Tc=λ
1−λ∗β∗(R∗gM)0.5 ∗Dg(3
2)
where
β
is the nondimensional transport parameter, and Ris the relative submerged density.
βcan calculated with the following formula:
β=0.1
f∗Y2.5
where fis the friction factor. The flow depth was calculated using the following equation:
h=w∗s0.2
7(1)
where wis the flow width and sis the slope of the upstream channel [
71
], which was later
calculated in GrassGis with the r.stream.direction tool. The estimation of the flow width
was made in SAGA GIS with the multiple flow direction (MFD) method [
72
]. This is a useful
means to describe the downslope movement. The logic behind the MFD algorithms is
similar to the single direction method, but the MFD diverts the flow to multiply downslope
cells in proportion to the slope between them. The upstream slope values were calculated
based on the filled DTM’s flow accumulation and flow direction parameters. The flow
depth (h) is equal to the estimated water depth in the channel or valley, not to be confused
with the valley depth, which is the estimated total depth of the valley or channel according
to the topography. The valley depth was estimated with the valley depth tool in SAGA GIS.
The flow width was considered using the same tool in SAGA GIS. The Manning roughness
was set as Mars relevant at 0.0545, and the roughness value obtained by [73] was used.
Considering the presented images, which visualize the targets and the results of this
work, Figure 1shows the target area, Figure 2shows the flow route calculation-related
results, and Figure 3 shows the calculated accumulation and erosion values in the target
areas; Figure 4 also shows erosion- and accumulation-related specific parameters (sediment
size, flow width, flow velocity, and flow depth map); Figure 5 shows the rover-recorded
image of the delta front, from which the profiles are marked, presented, and graphed as
values along them in Figure 6.
Table 1. Summary of the parameters used for the model.
Parameter Name Background Physics Values on the Earth
Mars Relevant Aspects
Future Directions for
Improvement
DTM-based surface
topography
Slope-related processes
and gravity action
Resolutions have a
wide range from
km/px to cm/px
Available HRSC
(50 m/px) or HiRISE
(~1 m/px) images
based DTM
Improved automatized
stereo images based on
high-resolution DTM
generation
Grain diameter of
surface-covering
material (D50)
Influence the adhesion
and erodibility of target
material; however, the
role of cementation is
as yet poorly
constrained
0.95 nm–256 mm
THEMIS TI
dataset-based
calculations, some
surface in situ
measurements
Improved grain size
estimation fusing
ground truth at all of
the planet surface and
remote data
Shear stress between
transportable grains (
τ
)
Force of the friction
from a flowing fluid
acting on a body/grain
Depends on the local
sediment’s
hydrological properties
Estimation from
classified diameter;
however, little
information on
cementation is
available
Improved evaluation of
future landing sites,
implementation of
laboratory tests to
support model
calculations
Remote Sens. 2024,16, 3649 10 of 23
Table 1. Cont.
Parameter Name Background Physics Values on the Earth
Mars Relevant Aspects
Future Directions for
Improvement
Shields number (Y)
Dimensionless number
related to the fluid
force and the particle
weight used to
calculate the initiation
of the motion of a grain
by the transport media
Depends on the shear
stress From equation
Improved Earth-based
laboratory analysis and
theoretical modelling
might provide a better
understanding of the
Shields parameter
under Martian
conditions
Transport coefficient
(Tc)
Maximum potential
soil transport by
overland flow
Equal to the volumetric
transport rate
Equal to the volumetric
transport rate
Inferred data from the
calculation, future
theoretical evaluation
Detachment coefficient
(Dc)
Maximum potential
soil detachment by
overland flow
From different
equations
From different
equations
Inferred data from the
calculation, future
theoretical evaluation
3. Results
To see the effectivity of this modelling method, in this section, the general parameters
used are outlined first, and the calculated variables second. Our calculations reveal that the
water level of the final flow event that formed the top part of the Jezero western delta ranged
between 1.89 and 34.74 m, and the mean water depth was 12.66 m, while the depth of the
deep, engraved channels ranged up to 385.54 m. The flow width, which is the width of the
water (not of the whole valley), ranged between 35.3 and 341.42 m, while the valley width
was up to 1200 m based on a CTX-image-based measurement. The channel parameters
used here may have been a slightly different at the time of their formation due to long-term
aeolian erosion and sedimentation—however, not much difference is expected (see the
related part in the Section 4). Flow velocity rates varied between 0.008 and 11.6 m
2
/s for the
whole period (Figure 3), considering no substantial erosion happened after the formation of
the delta top surface. This range is large, but further development of this model and further
background parameter acquisition for the equations will decrease it. Better parameter
acquisition might also improve it. For example, improved grain parameters or better
resolution data will provide a basis for more-detailed calculations but also more spatially
heterogeneous results in the future. These values (and the sediment diameter plus the
formerly calculated coefficients, which are summarized in Table 1) influence the results of
the erosion–accumulation simulation. The script is able to estimate the erosion/deposition
values for each pixel in a given timestep; in this article we used a single, 60 min-long
simulation. The estimation of the detachment coefficient (D
c
)is based on a calculation
from the sediment diameter dataset (in mm), which is divided by 1000 to give the numerical
value of the diameter in meters.
A simulation with a duration of 60 min resulted in a maximum erosion of 5.986 g/m
2
and maximum deposition of 4.072 g/m
2
(Figure 2), which should be considered as a rough
approach only, but useful for testing the model, which should be further improved. The
rationality of selecting only 60 min for the tested duration of precipitation is twofold: As
a first attempt to apply this model to Mars, it is easier to start interpreting the results
from shorter duration events and under simpler conditions to see the realization of model
runs, and also to make interpretations easier. However, longer tests in the future could be
also realized if the model gets further validation. The second reason is that this current
paleoclimate model favors short episodic precipitation events on Mars [
74
,
75
] instead of a
long-term, stable, humid climate [76,77].
Remote Sens. 2024,16, 3649 11 of 23
Remote Sens. 2024, 16, x FOR PEER REVIEW 11 of 24
Figure 2. Results of the SIMWE erosion–accumulation model of the western part of Jezero Crater
(18.48°N; 77.37°E), where the rim can be followed by the most obvious thick, red-and-blue arc-
shaped curve at the middle part of the image, curving from top right to lower left. The blue color
represents the net erosion (negative values in the model), and the red color shows the net accumu-
lation (positive values) that characterize the area. The black lines in the center left represent the
proposed possible traverse plan made by NASA for 2023 and later periods. Note that the visualized
area is almost the same as in Figure 1, but here, the erosion–accumulation rates are indicated.
Figure 3. Flow depth (blue) and river routes (red) based on model calculations in the target area.
Note that the edge of the Jezero Crater runs as an arc-shaped darker blue (steep and thus small flow
depth, dominated by fast runoff) area, from the upper right toward lower left. The indicated terrain
is the same as in Figure 4 at 18.48°N; 77.37°E.
Figure 2. Results of the SIMWE erosion–accumulation model of the western part of Jezero Crater
(18.48
◦
N; 77.37
◦
E), where the rim can be followed by the most obvious thick, red-and-blue arc-shaped
curve at the middle part of the image, curving from top right to lower left. The blue color represents
the net erosion (negative values in the model), and the red color shows the net accumulation (positive
values) that characterize the area. The black lines in the center left represent the proposed possible
traverse plan made by NASA for 2023 and later periods. Note that the visualized area is almost the
same as in Figure 1, but here, the erosion–accumulation rates are indicated.
Remote Sens. 2024, 16, x FOR PEER REVIEW 11 of 24
Figure 2. Results of the SIMWE erosion–accumulation model of the western part of Jezero Crater
(18.48°N; 77.37°E), where the rim can be followed by the most obvious thick, red-and-blue arc-
shaped curve at the middle part of the image, curving from top right to lower left. The blue color
represents the net erosion (negative values in the model), and the red color shows the net accumu-
lation (positive values) that characterize the area. The black lines in the center left represent the
proposed possible traverse plan made by NASA for 2023 and later periods. Note that the visualized
area is almost the same as in Figure 1, but here, the erosion–accumulation rates are indicated.
Figure 3. Flow depth (blue) and river routes (red) based on model calculations in the target area.
Note that the edge of the Jezero Crater runs as an arc-shaped darker blue (steep and thus small flow
depth, dominated by fast runoff) area, from the upper right toward lower left. The indicated terrain
is the same as in Figure 4 at 18.48°N; 77.37°E.
Figure 3. Flow depth (blue) and river routes (red) based on model calculations in the target area.
Note that the edge of the Jezero Crater runs as an arc-shaped darker blue (steep and thus small flow
depth, dominated by fast runoff) area, from the upper right toward lower left. The indicated terrain
is the same as in Figure 4at 18.48◦N; 77.37◦E.
Remote Sens. 2024,16, 3649 12 of 23
This 60 min simulation time is a test time and can be freely changed in the r.sim.sediment
script. The diameter of the sediment used in the simulation is between 0.0031 and 1.6 m
based on the THEMIS data pixels from the target area (Figure 4a inset). This range is
quite wide but helps to see how the simulations run under the observed and available
THEMIS values. These values of maximum erosion and deposition are unrepresentative
of the entire system, as they are only present in one pixel of the modelled domain, but
they provide an objective calculations-based approach that is available today. The erosion–
accumulation model results show that at the front, the delta is dominated by accumulation.
The sedimentation-dominated areas are continuous and large, covering approximately
60–70% of the whole analyzed area, while the erosion-dominated areas are scattered plus
isolated and mainly connected to the steep slope angle areas, with a size between roughly
500 and 1000 m. It is a fallacy to assume that accumulation areas are necessarily linked
to the erosion areas that are in closest proximity to them. Due to the characteristics of the
surface in question, material from accumulation areas may in fact originate from areas that
are more erosion dominated, yet a larger proportion of the accumulated material is more
likely to originate from the nearest erosion-dominated area.
Remote Sens. 2024, 16, x FOR PEER REVIEW 13 of 24
Mars using the currently available topography. The updated model is capable of roughly
identifying the flow paths, which are observable as remnant surface morphology, and de-
lineating erosion–accumulation zones for the top of the Jezero Crater delta system.
Figure 4. Overview of the generated maps used for the erosion–accumulation simulation. The visu-
alized area is the same as in Figures 2 and 3 (18.48N; 77.37E). Note that the crater rim runs from top
right toward lower left as a curved feature, while the delta structure is located at the middle of the
four images. Inset a: sediment size map (m); inset b: flow width map (m); inset c: flow velocity map
(m/sec); inset d: calculated flow depth map (m). Please note that the calculated depth is only a
model-based approach that should be further improved in the future. The coarse sand fraction
grains are primarily located in the riverbed that flows into the Jezero Crater.
Erosion dominates certain areas, such as the steep walls of the main valley, larger
crater interiors, peaks, and the edges of the delta. Deposition or accumulation zones are
located near erosion sources and spread out from those areas along low-slope-angle ter-
rains. The amount of accumulated material decreases as the distance from the erosion
source increases (Figure 3).
The coarse sand fraction grains are primarily located in the riverbed that flows into
the Jezero Crater (Figure 5, inset a, red rectangle), as well as in craters and other
Figure 4. Overview of the generated maps used for the erosion–accumulation simulation. The
visualized area is the same as in Figures 2and 3(18.48◦N; 77.37◦E). Note that the crater rim runs
Remote Sens. 2024,16, 3649 13 of 23
from top right toward lower left as a curved feature, while the delta structure is located at the middle
of the four images. Inset (a): sediment size map (m); inset (b): flow width map (m); inset (c): flow
velocity map (m/s); inset (d): calculated flow depth map (m). Please note that the calculated depth is
only a model-based approach that should be further improved in the future. The coarse sand fraction
grains are primarily located in the riverbed that flows into the Jezero Crater.
Altogether, seven cross-sectional-profiles were made crossing the frontal outcrop of the
Jezero Crater’s delta. The result of the model shows that the area in the field of view (FOV)
in front of the rover is dominated by erosion, in agreement with the expectations. The area
in which erosion and accumulation are balanced (i.e., the area not dominated by erosion
or accumulation) is approximately 1.02 km
2
(~33% of the whole area of the FOV). The
erosion–accumulation survey was made along the seven cross-sectional profiles (Figure 5),
which are located about ~460 m from each other along the delta front. The average length
of the profiles is ~3000 m. On average, along the profiles, the erosion-dominated area is
~200 m shorter than the accumulation-dominated area.
The slopes at the accumulation sites are gentle at the analyzed profiles. In most cases,
the slope values range from around 3 to 2 degrees (Figure 5). The steepest accumulation area
can be found at profile seven, where the slope values change between 9.1 and 1.7 degrees in
a short, 500 m distance. The lowest slope angle change can be found at profile three, where
the change is only 0.6 degrees along 750 m (Figure 5).
Remote Sens. 2024, 16, x FOR PEER REVIEW 14 of 24
depressions within the delta. Sediments with similar grain sizes are also present in the
foreground of the delta. The simulation suggests that sediment in the accumulation areas
has a finer grain size, possibly due to the reduced flow speed of the fluvial process during
the last wet period. Gravel and other larger blocks are rare or sparsely distributed
throughout the valley. The flow velocity (as shown in Figure 4, inset c) is closely related
to the slope of the terrain. Therefore, steeper slopes exhibit higher values than the observ-
able confined large channels.
Cross-sectional profiles show that the dominant accumulation areas are in a transi-
tion zone between the end of the steep slopes and the surrounding flat surface of the crater
boom (Figure 4). Here, the simulated water velocity is low enough to deposit fine-
grained sediment and prevent further sediment movement. This agrees with the fact that
the fluvial sediment possibly accumulated close to the wall of the delta and appears as a
gentle slope near the delta’s wall (Figure 5). This fluvial sediment might be partly buried
by aeolian material, which is visible on the last image of the Perseverance rover (Figure 6,
inset b) taken from the edge of Jezero’s delta, but the aeolian coverage rate is small in
general.
Figure 5. Location of the cross-sectional profiles (from Figure 6) in the FOV of the rover’s image with
the following insets (the area is part of the target region indicated in Figure 1): In inset (a), the loca-
tions with numbers represent the cross-profiles from Figure 6. The names scarp A, B etc., were given
by the authors to specifically mark certain locations along the frontal edge of the delta that the rover
recorded by images. Inset (b) is an example image, from the area of the interest. Inset (c) shows a
magnified version of the boxed area in inset (b) with two examples of large boulders below fine
layering. The image was taken by Perseverance rover’s Mastcam-Z in 2021. Several images were
stacked to take the final mosaic. Inset (d) shows a Mastcam Z image taken by Perseverance on sol
402 and shows an example of the layered sediment on the Jezero delta’s wall. This outcrop is located
at cross-section profile scarp 3. (NASA/JPL-Caltech/LANL/CNES/CNRS/ASU/MSSS).
The inset c shows scarp A, where larger boulders transported by a former high dis-
charge are located below layers composed of smaller grain sizes, indicating a temporal
decrease in the discharge and transition from more to less erosive periods. Such locations
could be estimated using this model purely from the remotely accessible data, which are
Figure 5. Location of the cross-sectional profiles (from Figure 6) in the FOV of the rover’s image
with the following insets (the area is part of the target region indicated in Figure 1): In inset (a), the
locations with numbers represent the cross-profiles from Figure 6. The names scarp A, B etc., were
given by the authors to specifically mark certain locations along the frontal edge of the delta that the
rover recorded by images. Inset (b) is an example image, from the area of the interest. Inset (c) shows
a magnified version of the boxed area in inset (b) with two examples of large boulders below fine
layering. The image was taken by Perseverance rover’s Mastcam-Z in 2021. Several images were
stacked to take the final mosaic. Inset (d) shows a Mastcam Z image taken by Perseverance on sol 402
and shows an example of the layered sediment on the Jezero delta’s wall. This outcrop is located at
cross-section profile scarp 3. (NASA/JPL-Caltech/LANL/CNES/CNRS/ASU/MSSS).
Remote Sens. 2024,16, 3649 14 of 23
A further example of the results of the applied model can be seen in Figure 3, focusing
on local runoff rate and regional flow route estimations. Here, these two different aspects
are indicated: the local slope-related flow thickness (indicated by blue color), where the
shades indicate how fast the water runs off downward from every pixel, and the larger
flow depth (darker shade) marking lower slope angle pixels, where water flowed down
slower. The brighter shades of blue mark larger slope angles (steeper terrains in each pixel),
where the water has flowed away and run off faster, producing a smaller water thickness
at a hypothetical moment. The red lines mark the reconstructed flow routes, e.g., ancient
riverbeds with the main flow directions where the water convergence happened, producing
a hierarchical system (Figure 3). These later lines help to reconstruct former flow-produced
channel locations.
4. Discussion
In this section, first, the links between the model and the delta structure are summa-
rized; secondly, the strengths of the modelling approach are presented; thirdly, knowledge
gaps that would further advance the model are given; and fourthly, an overview of how
such an approach can specifically support future missions is discussed.
Our prior iteration of the Mars-specific SIMWE model employed a hypothetical pre-
cipitation value [
78
] that produced the ancient flow paths using the best available approach,
providing a realistic simulation of the locations of former water flow tracks. However,
we acknowledge that multiple processes could lead to the poor reconstruction of ancient
flow paths on Mars, such as later topographic modification [
29
], or the newly acknowl-
edged potential of Martian aeolian–fluvial interactions [
79
]. However, this work employs a
unique and moderately simple methodology that has not yet been applied to Mars using
the currently available topography. The updated model is capable of roughly identifying
the flow paths, which are observable as remnant surface morphology, and delineating
erosion–accumulation zones for the top of the Jezero Crater delta system.
Erosion dominates certain areas, such as the steep walls of the main valley, larger crater
interiors, peaks, and the edges of the delta. Deposition or accumulation zones are located
near erosion sources and spread out from those areas along low-slope-angle terrains. The
amount of accumulated material decreases as the distance from the erosion source increases
(Figure 3).
The coarse sand fraction grains are primarily located in the riverbed that flows into the
Jezero Crater (Figure 5, inset a, red rectangle), as well as in craters and other depressions
within the delta. Sediments with similar grain sizes are also present in the foreground
of the delta. The simulation suggests that sediment in the accumulation areas has a finer
grain size, possibly due to the reduced flow speed of the fluvial process during the last
wet period. Gravel and other larger blocks are rare or sparsely distributed throughout the
valley. The flow velocity (as shown in Figure 4, inset c) is closely related to the slope of
the terrain. Therefore, steeper slopes exhibit higher values than the observable confined
large channels.
Cross-sectional profiles show that the dominant accumulation areas are in a transition
zone between the end of the steep slopes and the surrounding flat surface of the crater
bottom (Figure 4). Here, the simulated water velocity is low enough to deposit fine-grained
sediment and prevent further sediment movement. This agrees with the fact that the fluvial
sediment possibly accumulated close to the wall of the delta and appears as a gentle slope
near the delta’s wall (Figure 5). This fluvial sediment might be partly buried by aeolian
material, which is visible on the last image of the Perseverance rover (Figure 6) taken from
the edge of Jezero’s delta, but the aeolian coverage rate is small in general.
Remote Sens. 2024,16, 3649 15 of 23
Remote Sens. 2024, 16, x FOR PEER REVIEW 16 of 24
gravity regimes on Mars compared to those on Earth, which limit our understanding of
vortex formation and their intensity inside Martian flowing rivers, which influence the
sedimentation as well. However, possibilities for improvement also exist, especially the
determination of other parameters used here, like the detachment coefficient, which re-
quires microscopic-scale physical tests.
Figure 6. Results of the erosion/accumulation calculations along the selected profiles (for profile
locations, please refer to the Figure 5). The blue lines show the HRSC (100 m/pixel) profile, and the
red lines show the results of the SIMWE model, which all were situated almost perpendicular to the
frontal edge of the currently visible delta. The number of the insets corresponds to the number of
the profiles in Figure 5. The profiles extend radially from the rover’s position on 17 April 2021 to the
present-day topographic front of the Jezero delta system. The numbers between the accumulation
marker arrows represent the slope in degrees. Although errors exist in the data used for the visual-
ization, it is useful to roughly estimate the related specific values (making error envelopes around
the curves) as there are too many parameters to firmly use such error values.
The model’s uncertainties stem from various sources, as outlined in Table 1 (param-
eter numbers correspond to table rows): Parameter 1—Uncertainties in the DTM intro-
duced variability in the calculated accumulation and erosion values, but do not signifi-
cantly impact the locations of these processes, as topographic undulations below the DTM
spatial resolution do not change the flow path direction. These errors are expected to be
distributed uniformly across the terrain, thus minimally affecting the overall erosion/ac-
cumulation paern. Parameter 2—The grain size determination is based on THEMIS TI
Figure 6. Results of the erosion/accumulation calculations along the selected profiles (for profile
locations, please refer to the Figure 5). The blue lines show the HRSC (100 m/pixel) profile, and
the red lines show the results of the SIMWE model, which all were situated almost perpendicular
to the frontal edge of the currently visible delta. The number of the insets corresponds to the
number of the profiles in Figure 5. The profiles extend radially from the rover’s position on 17 April
2021 to the present-day topographic front of the Jezero delta system. The numbers between the
accumulation marker arrows represent the slope in degrees. Although errors exist in the data used for
the visualization, it is useful to roughly estimate the related specific values (making error envelopes
around the curves) as there are too many parameters to firmly use such error values.
The inset c shows scarp A, where larger boulders transported by a former high dis-
charge are located below layers composed of smaller grain sizes, indicating a temporal
decrease in the discharge and transition from more to less erosive periods. Such locations
could be estimated using this model purely from the remotely accessible data, which are
much more available than in situ images from the surface. In the case of further-improved
DTM (both by remote sensing and surface roving missions) with higher spatial resolution,
the 1–10 m scale features identified by on-site images could be linked to model-based data,
validating the method to find favorable sites for fine-grained accumulation. In addition
to surface morphometry, grain size and estimated flow depth also strongly influence the
model adapted to Mars.
The presented method for determining grain size is important as it could be sampled
for all of the Martian surface using THEMIS data, and the resulting grain size distribution
Remote Sens. 2024,16, 3649 16 of 23
is the best available approach to test the methodology. However, the method clearly should
be improved in the future.
The original bedrock of the delta is visible in several Mastacam Z images (Figure 5,
inset d). These layered sediments are evidence of former fluvial activity. During the
lifetime of the last fluvial activity of the Jezero delta, these previously deposited sediments
were eroded and redeposited further from the currently visible wall of the delta. These
last accumulation fields could be covered with aeolian sand and dust. An example of a
layered sediment outcrop is located at cross-section profile three, shown in Figure 5. These
sedimentary layers are on the top and edge of the delta’s wall, where the last fluvial erosion
event occurred, and the eroded material could be accumulated, as shown by the result of
the simulation.
Although this model is not able to directly separate sites showing the accumulation of
boulders vs. clays, fine grains are expected to deposit at the locations of lowest flow speed.
To find these sites, further development of the model focusing on separating different
depositional regimes is needed and could probably be achieved, supported by the results
of this work as a first step. The strength of this model lies in its application to ancient
Martian fluvial features in the future, using only remote data without in situ information
and lacking exact knowledge of ancient precipitation. Although specific in situ data
would have made the calculations more accurate, such information from Mars is poorly
available and scattered, while remote-sensing-based data are abundant. These conditions
allow the comparison of differently aged fluvial networks, supporting the identification of
the temporal climatic changes that happened during the geological history of the planet.
However, such work requires the usage and testing of this model by the community on
other, already analyzed fluvial networks.
A possible traverse plan according to NASA communication (Press Release 13 Septem-
ber 2021) was overlain on the erosion/sedimentation color code map in Figure 2as black
lines. These proposed tracks mainly cross sedimentation-dominated areas (indicated by
yellow-red colors); however, there are several possibilities for passing by erosion-dominated
locations (smaller, isolated blue areas). The comparison of Mastcam images from the Perse-
verance rover of erosion- and sedimentation-dominated areas will provide opportunities
to further link this model to observations and identify areas in need of improvement. If
on high-resolution images sedimentary- versus erosion-dominated features were visible
(possible at steep outcrops), this model could be verified by such on-site images from Mars
in the future. This should be conducted after Perseverance has passed along a route of
several km and sampled enough sites to correlate several THEMIS pixel-based values to
grain size values measured on local, nearby recorded images. A further possibility in the
coming years is to evaluate the accumulation-/erosion-dominated locations by the analysis
of outcrops on other missions’ images, if a large enough number of outcrops can be found
and recorded—unfortunately, at the time of this writing (early 2024) few such matching
locations have been recorded, with most images at less than the ideal resolution and quality.
Besides the planned on-site activities, Earth-based laboratory analysis also helps the
advancement of this model. One important problem in such development is that the pH
and chemistry of possible flowing waters on Mars are poorly known (salt concentrations
with melting point decreases would present in the water), which influence flocculation,
grain aggregation, and thus deposition processes. Another critical point is the different
gravity regimes on Mars compared to those on Earth, which limit our understanding
of vortex formation and their intensity inside Martian flowing rivers, which influence
the sedimentation as well. However, possibilities for improvement also exist, especially
the determination of other parameters used here, like the detachment coefficient, which
requires microscopic-scale physical tests.
The model’s uncertainties stem from various sources, as outlined in Table 1(parameter
numbers correspond to table rows): Parameter 1—Uncertainties in the DTM introduced
variability in the calculated accumulation and erosion values, but do not significantly
impact the locations of these processes, as topographic undulations below the DTM spatial
Remote Sens. 2024,16, 3649 17 of 23
resolution do not change the flow path direction. These errors are expected to be distributed
uniformly across the terrain, thus minimally affecting the overall erosion/accumulation
pattern. Parameter 2—The grain size determination is based on THEMIS TI values, which
introduce subpixel-level variations and related errors into the model. Additionally, the
presence of non-fluvial sediments (e.g., aeolian deposits) can contribute to uncertainties,
especially with very small grain size, although this proportion is estimated to be less than 1%
based on examples from Goudge et al. (2018) [
55
]. Parameters 3 to 6 represent mechanical
properties for those values that could be determined through laboratory tests. However,
due to the limited number of such tests conducted under Mars-relevant conditions, the
potential errors associated with these parameters remains poorly understood.
When testing the sensitivity of the approach used here, by increasing the duration
or the intensity of the precipitation event, the final total erosion or accumulation values
increase linearly. The length of the rainfall event and its magnitude exert the greatest
influence on the final results, namely the erosion/accumulation rate, flow depth, the
derived flow velocity and flow discharge values. However, precipitation of high-intensity
but short-duration results in erosion and accumulation rates that are similar to or somewhat
more pronounced than those resulting from less intense but longer-duration precipitation.
The increase in shear stress as one basic parameter also modifies the final outcome: a
twofold increase (along with the increase in simulated flow speed and water thickness)
enhances the maximal erosion rate from 21.55 to 28.24 kg/ha/h, and the accumulation
increases by roughly the same scale also. Despite the various uncertainties in the parameters
used, the spatial pattern of the erosion-/accumulation-dominated locations do not change,
though the absolute values were modified. These indicate that the method used to find
the targets of accumulation sites with the longest duration and lowest flow speed related
looks to be useful already in its current state. Nevertheless, the equations that use both
transport and detachment coefficients and shear stress values should be further tested,
bearing in mind that when introducing a new modeling approach, the error level of the
method is always uncertain. However, through continuous improvement and comparison
to other erosion/deposition models, our understanding of these uncertainties will gradually
decrease and became better known in the future.
The primary focus of this article was to develop a model for identifying erosion
and deposition at the analyzed site based on a hypothetical precipitation event. The
exact temporal duration could not be considered, but the total amount of rain (fallen
vertical water thickness) was used. This study applied the hypothetical duration of the
rainfall event to be 60 min, without specifying its intensity. However, a comprehensive
model encompassing the entire delta’s evolution is planned for future iterations. To
provide a context to better understand how the gained results fit with other researchers’
projects, we compare our numerical values to estimates from other studies. The mean
flow rate, as determined in the study for the entire area, is 35,524.24 m
3
/s, which falls
between previously cited values. Although comparison of different models is difficult, the
following values give insight into the correctness of this model and its rough relation to
other models. Grotzinger et al. [
80
] estimated a peak discharge of 1 to 5 million m
3
s
−1
.
Tate et al. [
81
] employed Darcy–Weisbach equations to estimate discharge rates for the
area, including Kodiak Butte, ranging from 1.63 to 8.64 ms
−1
for velocities and 76 to
3000 m
3
s
−1
for discharge. They calculated discharge rates for the western portion of
the delta at around 500 m
3
s
−1
. The mean flow rate, as determined in the study for the
entire study area, is 35,524.24 m
3
/s, which falls between previously cited values. It is
crucial to acknowledge that these discharge figures represent estimated flow discharge
data, reflecting the maximum possible value for the given methodology and morphology.
In this study, the flow discharge was calculated using the Darcy–Weisbach equation. For a
broader perspective, considering other fluvial systems on Mars, highland valley networks
typically form with discharges of 300 to 3000 m
3
/s [
82
], while outflow valleys feature the
largest discharges, reaching around one billion m
3
/s [
83
,
84
]. The model presented here
proves to be valuable not only for identifying accumulation-dominated sites, but especially
Remote Sens. 2024,16, 3649 18 of 23
for comparing them regarding the expected grain size of sediments there and the rate of the
finest grade grains. Such comparison could not be performed from the optical observations
from above, but on-site verification will require shallow drill-based sampling in the future.
The identified accumulation-dominated areas, characterized by the lowest slope angles and
slowest flow speeds, are most likely to host fine-grained deposits, particularly clay-sized
particles, which favor organic preservation [
41
,
80
–
84
]. It is also worth noting that the model
applied here is available for the community from now and could be further used and tested
with different input parameters for different Mars surface locations.
Possible Future Usage of the Modelling Approach
Recent Mars surface missions have exhibited increased sophistication, encompassing
both traverse or rover route planning and instrumental enhancements [
85
], while landing
accuracy has also been improved [
86
]; thus, sophisticated targeting is also needed. For the
planned international Mars Sample Return project, meticulous site selection and safe access
are critical prerequisites. The advanced modelling approach outlined in this work could
prove valuable in determining the potential sites of sample acquisition. While not directly
applicable to landing site selection, this model could be employed to evaluate and select
one or more surface targets among several potential rover destinations, particularly where
lithologies of interest have been obscured by aeolian deposits but reachable for shallow
drilling. This model allows the identification of such former accumulation-dominated sites,
even if their morphology is not readily evident from the results above. This enables a more
precise identification of accumulation-dominated fluvial sites even without conspicuous
indicative surface features.
The model’s output facilitates comparisons between accumulation and sedimentation
patterns, particularly for identifying locations with prolonged wet periods that favor the
deposition of fine grains, enhancing the preservation of ancient organics and biosignatures.
Improved future versions could also support and guide future research and potentially
even on-site analysis and target selection [85,86].
Although waterborne sediments could deposit in accumulation-favoring locations,
they are typically found within depressions that are challenging for rovers to traverse
and analyze. Yet, in the future, if a rover identifies multiple depositional locations, this
model could assist in selecting the most promising one among them by providing context
to compare these different locations based on the volume of water that flowed through
them or the expected rate of former material accumulation.
As a summary, this model aids in the identification of locations that might not be
discernible through orbital optical imagery, such as concealed sedimentary formations and
minor drainage pathways [78].
5. Conclusions
For proper targeting of Martian fluvial features for the purpose of astrobiology-related
in situ analysis, shallow subsurface targets are of import due to their preservation potential.
The identification of such locations is somewhat difficult from visual imagery alone, and
although locations with abundant phyllosilicates can be detected as evidence of prominent
former water abundance, these need to be exposed at the surface. The modelling approach
shown in this article could support the identification of sites where drilling is able to reach
former accumulation-dominated locations at reachable shallow depths.
The SIMWE erosion model was applied to Mars, in the Jezero Crater fluvial delta
deposit, with parameters adjusted for Martian conditions. For identifying the erosion- and
accumulation-dominated areas formed during the last flow episode, using the top part of
the delta surface in the FOV of the recent Perseverance images (Figure 5), we estimated
the area (in km
2
) and the size (in m) of the best possible areas for acquiring fine-grained
deposited sediment samples. Although the currently observable surface need not reflect the
real final flow episode, it still provides the best currently available approach for estimating
these last flow features and related accumulation locations. At a few hundred meters to a
Remote Sens. 2024,16, 3649 19 of 23
kilometer away from the current delta front, some isolated hills are present (with similar
appearances of height and a flat top, together with a spatial correlation with the edge of the
delta), which could be classified as outliers [
81
], representing the former, more extended
but eroded remnants of a larger original delta. However, this is only a possibility and does
not influence the currently observable topography of the remnant delta. At any site, there
are no such surface features visible that would indicate that thicker or thinner deposits have
accumulated there before. The finest grains accumulated during the last fluvial event, with
the probable occurrence of the smallest grain size when the vanishing discharge supported
low flow speed, thus providing the potential location for finding deposited weathering
products. The longest ponding sites favor the sedimentation of the smallest grains with
possible bound organics.
Using the model calculations with a hypothetical short 60 min precipitation event,
the average flow depth is 12.66 m, the maximum erosion is 5.98 g/m
2
, and the maximum
deposition is 4.07 g/m
2
. The average size of the accumulation-dominated areas by the
cross-section profiles (Figure 6) is ~1000 m. The erosion-dominated areas have an average
size of ~800 m, which are typically found along the steep walls, including the major craters
or various peaks. The rate of accumulation decreases as the erosion moves away from the
source, and accumulation areas are located immediately next to erosion-dominated areas at
a lower elevation.
The model can be further advanced as there are current knowledge gaps, such as
sediment size estimation and surface reconstruction from the last fluvial activity. The
correlation of the model-based erosion-/deposition-dominated area types is possible by
high-resolution on-site images, although analysis of more locations (at further landing sites)
could greatly improve such connections. Better sediment detachment coefficient modelling
is also needed, which future laboratory tests and theoretical calculations might also help to
obtain. However, it is apparent that the model, even in its current state, is already useful
for identifying potential locations of shallow subsurface deposits of interest, dominated
by small grain size accumulation during the latest phase of fluvial activity. This approach
is valuable for uncovering such areas, which are likely concealed beneath surficial covers
of alluvial material that increases shielding plus preservation, and it could be applied at
future landing sites, including those of the Rosalind Franklin Rover, which plans to drill to
a depth of 2 m.
Author Contributions: V.S. completed the model calculations and text writing, R.S.B. the validation,
and Á.K. the conceptualization and manuscript organization. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Data will be made accessible on request from the first author by email.
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
hflow depth (m)
sslope of the surface (m/m)
wflow width (m)
YShield stress (nondimensional)
τshear stress (Pa)
τcr critical shear stress (Pa)
Kerodibility coefficient
Dggeometric mean diameter of the soil particle (mm)
Dcdetachment coefficient
βnondimensional transport parameter
Rrelative submerged density
Remote Sens. 2024,16, 3649 20 of 23
Appendix A
Table A1. The relationship between THEMIS TI values and grain size [13].
Name Phi Diameter Thermal Inertia
J m−2s−0.5 K−1
Pebbles −4 to −2 4–16 mm 417–580
Granules −2 to −1 2–4 mm 353–417
Very coarse sand −1 to 0 1–2 mm 300–353
Coarse sand 0 to 1 0.5–1 mm 254–300
Medium sand 1 to 2 250–50 µm 215–254
Fine sand 2 to 3 125–250 µm 182–215
Very fine sand 3 to 4 63–125 µm 155–182
Coarse silt 4 to 5 31–63 µm 131–155
Medium silt 5 to 6 16–31 µm 112–131
Fine silt 6 to 7 8–16 µm 95–112
Very fine silt 7 to 8 4–8 µm 80–85
Clay 8+ <4 µm <80
References
1.
Farley, K.A.; Williford, K.H.; Stack, K.M.; Bhartia, R.; Chen, A.; de la Torre, M.; Hand, K.; Goreva, Y.; Herd, C.D.K.; Hueso, R.; et al.
Mars 2020 Mission Overview. Space Sci. Rev. 2020,216, 142. [CrossRef]
2.
Muirhead, B.K.; Nicholas, A.K.; Umland, J.; Sutherland, O.; Vijendran, S. Mars Sample Return Campaign Concept Status. Acta
Astronaut. 2020,176, 131–138. [CrossRef]
3.
Adeli, S.; Hauber, E.; Kleinhans, M.; Le Deit, L.; Platz, T.; Fawdon, P.; Jaumann, R. Amazonian-aged fluvial system in the southern
mid-latitude regions, Mars. In Proceedings of the 48th Lunar and Planetary Science Conference, The Woodlands, TX, USA, 20–27
March 2017.
4.
Hargitai, H.I.; Gulick, V.C.; Glines, N.H. Evolution of the Navua Valles region: Implications for Mars’ paleoclimatic history. Icarus
2019,330, 91–102. [CrossRef]
5.
Pajola, M.; Rossato, S.; Baratti, E.; Mangili, C.; Mancarella, F.; McBride, K.; Coradini, M. The Simud-Tiu Valles hydrologic system:
A multidisciplinary study of a possible site for future Mars on-site exploration. Icarus 2016,268, 355–381. [CrossRef]
6.
Abrevaya, X.C.; Anderson, R.; Arney, G.; Atri, D.; Azua-Bustos, A.; Bowman, J.; Brazelton, W.J.; Brennecka, G.A.; Carns, R.;
Chopra, A.; et al. The Astrobiology Primer v2.0. Astrobiology 2016,16, 561–653. [CrossRef] [PubMed]
7.
Westall, F.; Hickman-Lewis, K.; Cavalazzi, B.; Foucher, F.; Clodoré, L.; Vago, J.L. On biosignatures for Mars. Int. J. Astrobiol. 2021,
20, 377–393. [CrossRef]
8.
Riu, L.; Carter, J.; Poulet, F. The M3 project: 3-Global abundance distribution of hydrated silicates at Mars. Icarus 2022,374, 114809.
[CrossRef]
9.
Changela, H.G.; Chatzitheodoridis, E.; Antunes, A.; Beaty, D.; Bouw, K.; Bridges, J.C.; Capova, K.A.; Cockell, C.S.; Conley, C.A.;
Dadachova, E.; et al. Mars: New insights and unresolved questions. Int. J. Astrobiol. 2021,20, 394–426. [CrossRef]
10.
Dundas, C.; Byrne, S.; Chojnacki, M.; Diniega, S.; Daubar, I.; Hamilton, C.; Hansen, C.; McEwen, A.; Portyankina, G.; Sizemore, H.
Current Activity on the Martian Surface: A Key Subject from Future Exploration. Bull. Am. Astron. Soc. 2021,53, 2023–2032.
[CrossRef]
11. Serla, J.K.; Christensen, P.R. Thermophysical Modeling of Surface and Shallow-Subsurface of Mars Using THEMIS-IR Measure-
ments and KRC Numerical Thermal Model: Characterization of Small-Scale Surface Structure Using Thermal Infrared Data. In
Proceedings of the Tenth International Conference on Mars, Pasadena, CA, USA, 22–25 July 2024.
12.
Mangold, N.; McLennan, S.M.; Czaja, A.D.; Ori, G.G.; Tosca, N.J.; Altieri, F.; Amelin, Y.; Ammannito, E.; Anand, M.; Beaty, D.W.;
et al. Seeking Signs of Life on Mars: The Importance of Sedimentary Suites as Part of a Mars Sample Return Campaign. In
Proceedings of the Second International Mars Sample Return, Berlin, Germany, 25–27 April 2018; p. 6045. [CrossRef]
13.
Bishop, J.L.; Fairén, A.G.; Michalski, J.R.; Gago-Duport, L.; Baker, L.L.; Velbel, M.A.; Gross, C.; Rampe, E.B. Surface clay formation
during short-term warmer and wetter conditions on a largely cold ancient Mars. Nat. Astron. 2018,2, 206–213. [CrossRef]
14.
Roback, K.P.; Runyon, K.D.; Avouac, J.P. Craters as sand traps: Dynamics, history, and morphology of modern sand transport in
an active martian dune field. Icarus 2020,342, 113642. [CrossRef]
15.
Salese, F.; McMahon, W.J.; Balme, M.R.; Ansan, V.; Davis, J.M.; Kleinhans, M.G. Sustained fluvial deposition recorded in Mars’
Noachian stratigraphic record. Nat. Commun. 2020,11, 1–8. [CrossRef]
Remote Sens. 2024,16, 3649 21 of 23
16.
Chojnacki, M.; Fenton, L.K.; Weintraub, A.R.; Edgar, L.A.; Jodhpurkar, M.J.; Edwards, C.S. Ancient Martian Aeolian Sand Dune
Deposits Recorded in the Stratigraphy of Valles Marineris and Implications for Past Climates. J. Geophys. Res. Planets 2020,125,
e2020JE006510. [CrossRef]
17.
Kamada, A.; Kuroda, T.; Kasaba, Y.; Terada, N.; Nakagawa, H. Global climate and river transport simulations of early Mars
around the Noachian and Hesperian boundary. Icarus 2021,368, 114618. [CrossRef]
18.
Sinha, R.K.; Ray, D.; Sinha, R.K.; Ray, D. Extensive glaciation in the Erebus Montes region of Mars. Icarus 2021,367, 114557.
[CrossRef]
19.
Banham, S.G.; Gupta, S.; Rubin, D.M.; Edgett, K.S.; Barnes, R.; Van Beek, J.; Watkins, J.A.; Edgar, L.A.; Fedo, C.M.; Williams, R.M.;
et al. A Rock Record of Complex Aeolian Bedforms in a Hesperian Desert Landscape: The Stimson Formation as Exposed in the
Murray Buttes, Gale Crater, Mars. J. Geophys. Res. Planets 2021,126, e06554. [CrossRef]
20.
Heath Geil-Haggerty. Paleohydraulic Investigation of the Ebro Basin: Implications for Mars. In Proceedings of the 23rd EGU
General Assembly, Gather Online, 19–30 April 2021. [CrossRef]
21.
Dundas, C.M.; Mellon, M.T.; Conway, S.J.; Daubar, I.J.; Williams, K.E.; Ojha, L.; Wray, J.J.; Bramson, A.M.; Byrne, S.; McEwen,
A.S.; et al. Widespread Exposures of Extensive Clean Shallow Ice in the Midlatitudes of Mars. J. Geophys. Res. Planets 2021,126,
e06617. [CrossRef]
22.
Pan, L.; Carter, J.; Quantin-Nataf, C.; Pineau, M.; Chauviré, B.; Mangold, N.; Le Deit, L.; Rondeau, B.; Chevrier, V. Voluminous
Silica Precipitated from Martian Waters during Late-stage Aqueous Alteration. Planet. Sci. J. 2021,2, 65. [CrossRef]
23.
Ye, B.; Michalski, J.R.; Ye, B.; Michalski, J.R. Precipitation Driven Pedogenic Weathering of Volcaniclastics on Early Mars. Geophys.
Res. Lett. 2021,48, e91551. [CrossRef]
24.
Palumbo, A.M.; Head, J.W.; Wilson, L. Rainfall on Noachian Mars: Nature, timing, and influence on geologic processes and
climate history. Icarus 2020,347, 113782. [CrossRef]
25.
Hughes, M.N.; Arvidson, R.E.; Grant, J.A.; Wilson, S.A.; Howard, A.D.; Golombek, M.P. Degradation of Endeavour Crater Based
on Orbital and Rover-Based Observations in Combination With Landscape Evolution Modeling. J. Geophys. Res. Planets 2019,124,
1472–1494. [CrossRef]
26.
Day, M.D.; Kocurek, G.; Grotzinger, J.P.; Day, M.D.; Kocurek, G.; Grotzinger, J.P. Landscape Evolution on Mars—A model of
Aeolian Denudation in Gale Crater. In Proceedings of the AGU Fall Meeting, San Fransico, CA, USA, 14–18 December 2015.
27.
Matsubara, Y.; Howard, A.D.; Irwin, R.P. Reversing transverse dunes: Modelling of airflow switching using 3D computational
fluid dynamics. Earth Planet Sci. Lett. 2020,544, 116363. [CrossRef]
28.
Matsubara, Y.; Howard, A.D.; Irwin, R.P. Constraints on the noachian paleoclimate of the martian highlands from landscape
evolution modeling. J. Geophys. Res. Planets 2018,123, 2958–2979. [CrossRef]
29.
Bahia, R.S.; Covey-Crump, S.; Jones, M.A.; Mitchell, N. Discordance analysis on a high-resolution valley network map of Mars:
Assessing the effects of scale on the conformity of valley orientation and surface slope direction. Icarus 2022,383, 115041.
[CrossRef]
30.
Galofre, A.G.; Bahia, R.S.; Jellinek, A.M.; Whipple, K.X.; Gallo, R. Did Martian valley networks substantially modify the landscape?
Earth Planet Sci. Lett. 2020,547, 116482. [CrossRef]
31.
Carr, M.H. The Martian drainage system and the origin of valley networks and fretted channels. J. Geophys. Res. Planets 1995,100,
7479–7507. [CrossRef]
32.
Bahia, R.S.; Jones, M. The Subjectivity in Identification of Martian Channel Networks and Its Implication for Citizen Science
Projects. Earth Moon Planets 2020,123, 45–59. [CrossRef]
33.
Hynek, B.M.; Beach, M.; Hoke, M.R.T. Updated global map of Martian valley networks and implications for climate and
hydrologic processes. J. Geophys. Res. 2010,115, 9008. [CrossRef]
34.
Boatwright, B.D.; Head, J.W. Simulating early Mars hydrology with the MARSSIM landform evolution model: New insights from
an integrated system of precipitation, infiltration, and groundwater flow. Planet Space Sci. 2019,171, 17–33. [CrossRef]
35.
Lapôtre, M.G.A.; Ielpi, A.; Lamb, M.P.; Williams, R.M.E.; Knoll, A.H. Model for the Formation of Single-Thread Rivers in Barren
Landscapes and Implications for Pre-Silurian and Martian Fluvial Deposits. J. Geophys Res. Earth Surf. 2019,124, 2757–2777.
[CrossRef]
36.
Blois, G.; Christensen, K.T.; Gundersen, D.; Blois, G.; Christensen, K.T.; Gundersen, D. PIV measurements of intracrater flow
dynamics utilizing a model of Gale crater in a refractive index matched environment. In Proceedings of the AGU Fall Meeting,
San Francisco, CA, USA, 9–13 December 2019.
37.
Warren, A.O.; Holo, S.; Kite, E.S.; Wilson, S.A. Small crater outlet valleys on Mars: A breach erosion modelling perspective. In
AAS/Division for Planetary Sciences Meeting Abstracts; Division for Planetary Sciences: Washington, DC, USA, 2020; Volume 52.
38.
Pozzobon, R.; Pesce, D.; Massironi, M.; Pozzobon, R.; Pesce, D.; Massironi, M. 3D Geomodel of the Deformed Deposits in
Crommelin Crater (Mars). In Proceedings of the 14th Europlanet Science Congress 2020, Virtually, 1 September–9 October 2020.
[CrossRef]
39.
Limaye, A.B.; Adler, J.; Whipple, K.X.; Limaye, A.B.; Adler, J.; Whipple, K.X. Numerical modelling of fan-shaped fluvial deposits
in low-latitude regions of Mars and their relationship to basin boundary conditions. In AGU Fall Meeting Abstracts; American
Geophysical Union: Washington, DC, USA, 2019; Volume 2019, p. EP24A-02.
Remote Sens. 2024,16, 3649 22 of 23
40.
Mangold, N.; Gupta, S.; Gasnault, O.; Dromart, G.; Tarnas, J.D.; Sholes, S.F.; Horgan, B.; Quantin-Nataf, C.; Brown, A.J.; Le
Mouélic, S.; et al. Perseverance rover reveals an ancient delta-lake system and flood deposits at Jezero crater, Mars. Science (1979)
2021,374, 711–717. [CrossRef] [PubMed]
41. Fassett, C.I.; Head, J.W. Fluvial sedimentary deposits on Mars: Ancient deltas in a crater lake in the Nili Fossae region. Geophys.
Res. Lett. 2005,32, 1–5. [CrossRef]
42.
Goudge, T.A.; Milliken, R.E.; Head, J.W.; Mustard, J.F.; Fassett, C.I. Sedimentological evidence for a deltaic origin of the western
fan deposit in Jezero crater, Mars and implications for future exploration. Earth Planet Sci. Lett. 2017,458, 357–365. [CrossRef]
43.
Annex, A.M.; Ehlmann, B.L. Fill History of Jezero: Volcanic Floor Units and Their Relationships to the Many Lakes of Jezero Crater.
In Proceedings of the 54th Lunar and Planetary Science Conference, The Woodlands, TX, USA, 13–17 March 2023. [CrossRef]
44.
Ives, L.R.; Stack-Morgan, K.M.; Barnes, R.; Gupta, S.; Caravaca, G.; Russell, P.; Annex, A.M. Lithofacies, Flow Directions, and
Preliminary Depositional Interpretations of Ledge-Forming Sandstones at Alagnak, Jezero Crater, Mars. In Proceedings of the
54th Lunar and Planetary Science Conference, The Woodlands, TX, USA, 13–17 March 2023; Lunar and Planetary Institute: The
Woodlands, TX, USA, 2023. Available online: https://hal.science/hal-04051962 (accessed on 10 October 2023).
45.
Barnes, R.; Gupta, S.; Paar, G.; Stack-Morgan, K.M.; Horgan, B.; Crumpler, L.; Tebolt, M.; Caravaca, G.; Holm-Alwmark, S.; Ortner,
T.; et al. Constructing Geological Cross-Sections to Constrain the Three-Dimensional Stratigraphic Architecture of The Jezero
Delta Front. In Proceedings of the 54th Lunar and Planetary Science Conference, The Woodlands, TX, USA, 13–17 March 2023;
Lunar and Planetary Institute: The Woodlands, TX, USA, 2023.
46.
Broz, A.P.; Horgan, B.H.; Kalucha, H.; Garczynski, B.; Haber, J.; Dehouck, E.; Hurowitz, J.; Johnson, J.; Bell, J.F.; Mastcam-Z
Team. Diagenetic Alteration of Hogwallow Flats, Jezero Crater, Mars. In Proceedings of the 54th Lunar and Planetary Science
Conference, The Woodlands, TX, USA, 13–17 March 2023.
47.
Horgan, B.; Duflot, L.; Garczynski, B.; Gupta, S.; Johnson, J.R.; Kathir, B.; Million, C.C.; Nunez, J.; Rice, M.; Clair, M.S.; et al.
Provenance of Sediments in the Jezero Delta From Perseverance Rover and or-Bital Observations. In Proceedings of the 54th
Lunar and Planetary Science Conference, The Woodlands, TX, USA, 13–17 March 2023.
48.
Núñez, J.I.; Johnson, J.R.; Rice, M.S.; Horgan, B.N.; Vaughan, A.; Garczynski, B.J.; Duflot, L.E.; Million, C.C.; St. Clair, M.; Merusi,
M.; et al. Spectral Diversity along The Delta Front In Jezero Crater, Mars as Seen With Mastcam-Z on The Mars 2020 Perseverance.
In Proceedings of the 54th Lunar and Planetary Science Conference, The Woodlands, TX, USA, 13–17 March 2023.
49.
Royer, C.; Poulet, F.; Wiens, R.C.; Mandon, L.; Fouchet, T.; Clavé, E.; Montmessin, F.; Forni, O.; Johnson, J.R.; Gasnault, O.; et al.
Jezero Delta Mineralogical Diversity Revealed by SuperCam Infrared Spectral Modeling. In Proceedings of the 54th Lunar and
Planetary Science Conference 2023, The Woodlands, TX, USA, 13–17 March 2023; Lunar and Planetary Institute: The Woodlands,
TX, USA, 2023. Available online: https://hal.science/hal-04035423 (accessed on 10 October 2023).
50.
Caravaca, G.; Dromart, G.; Mangold, N.; Gupta, S.; Le Mouélic, S.; Gasnault, O.; Kah, L.C.; Maurice, S.; Wiens, R.C. Flow Direction
Assessed From 3d Geometry Reconstruction Of Kodiak Butte In Jezero Crater (Mars). In Lunar and Planetary Science Conference;
Lunar and Planetary Institute: The Woodlands, TX, USA, 2022.
51.
Gupta, S.; Yelle, R.V.; Schneider, N.M.; Jain, S.K.; González-Galindo, F.; Verdier, L.; Braude, A.S.; Montmessin, F.; Mayyasi, M.;
Deighan, J.; et al. Thermal Structure of the Martian Upper Mesosphere/Lower Thermosphere From MAVEN/IUVS Stellar
Occultations. J. Geophys. Res. Planets 2022,127, e2022JE007534. [CrossRef]
52.
Gupta, S.; Mangold, N.; Bell, J.F.; Gasnault, O.; Dromart, G.; Tarnas, J.; Sholes, S.F.; Horgan, B.; Quantin-Nataf, C.; Brown, A.J.;
et al. A DELTA-LAKE SYSTEM AT JEZERO CRATER (MARS) FROM LONG DISTANCE OBSERVATIONS. In Lunar and Planetary
Science Conference; Lunar and Planetary Institute: The Woodlands, TX, USA, 2022.
53.
Sun, V.Z.; Hand, K.P.; Stack, K.M.; Farley, K.A.; Simon, J.I.; Newman, C.; Sharma, S.; Liu, Y.; Wiens, R.C.; Williams, A.J.; et al.
Overview and Results From the Mars 2020 Perseverance Rover’s First Science Campaign on the Jezero Crater Floor. J. Geophys.
Res. Planets 2023,128, 10. [CrossRef]
54.
Horvath, D.G.; An-Drews-Hanna, J.C. THE CLIMATE AND HYDROLOGY OF THE JEZERO CRATER PALEOLAKE. In
Proceedings of the 53rd Lunar and Planetary Science Conference, The Woodlands, TX, USA, 7–11 March 2022.
55.
Goudge, T.A.; Mohrig, D.; Cardenas, B.T.; Hughes, C.M.; Fassett, C.I. Stratigraphy and paleohydrology of delta channel deposits,
Jezero crater, Mars. Icarus 2018,301, 58–75. [CrossRef]
56.
Takáts, T.; Mészáros, J.; Albert, G. Spatial Modelling of Vineyard Erosion in the Neszmély Wine Region, Hungary Using Proximal
Sensing. Remote Sens. 2022,14, 3463. [CrossRef]
57.
Mitasova, H.; Barton, M.; Ullah, I.; Hofierka, J.; Harmon, R.S. 3.9 GIS-Based Soil Erosion Modeling. Treatise Geomorphol. 2013,1–14,
228–258. [CrossRef]
58. Willgoose, G. Mathematical modeling of whole landscape evolution. Annu. Rev. Earth Planet Sci. 2005,33, 443–459. [CrossRef]
59.
Alewell, C.; Borrelli, P.; Meusburger, K.; Panagos, P. Using the USLE: Chances, challenges and limitations of soil erosion modelling.
Int. Soil Water Conserv. Res. 2019,7, 203–225. [CrossRef]
60.
Mitasova, H.; Thaxton, C.; Hofierka, J.; McLaughlin, R.; Moore, A.; Mitas, L. Path sampling method for modeling overland water
flow, sediment transport, and short term terrain evolution in Open Source GIS. Dev. Water Sci. 2004,55, 1479–1490. [CrossRef]
61.
Wdowiak, T.J. A NEW MECHANISM FOR H2O PRECIPITATION ON AN EARLIER MARS. In Proceedings of the 37th Lunar
and Planetary Science Conference, League City, TX, USA , 13–17 March 2006.
62.
Malin, M.C.; Edgett, K.S. Evidence for recent groundwater seepage and surface runoff on Mars. Science (1979) 2000,288, 2330–2335.
[CrossRef]
Remote Sens. 2024,16, 3649 23 of 23
63.
Williams, N.R.; Stack, K.M.; Calef, F.J., III; Sun, V.; Williford, K.; Farley, K. Photo-Geologic Mapping of the Mars 2020 Landing Site,
Jezero Crater, Mars. 2020. Available online: https://github.com/NASA (accessed on 10 March 2024).
64.
Neukum, G.; Jaumann, R. HRSC: The High Resolution Stereo Camera of Mars Express—NASA/ADS. In Mars Express: The
Scientific Payload; Wilson, A., Ed.; ESA Publications Division: Noordwijk, The Netherlands, 2004; pp. 17–35.
65.
Christensen, P.R.; Bandfield, J.L.; Iii, J.F.B.; Gorelick, N.; Hamilton, V.E.; Ivanov, A.; Jakosky, B.M.; Kieffer, H.H.; Lane, M.D.;
Malin, M.C.; et al. Morphology and composition of the surface of Mars: Mars Odyssey THEMIS results. Science (1979) 2003,300,
2056–2061. [CrossRef]
66.
Fenton, L.K. Aeolian Processes on Mars: Atmospheric Modeling and GIS Analysis. 2003. Available online: https://www.
researchgate.net/publication/41478778 (accessed on 10 October 2023).
67.
Wang, B.; Zheng, F.; Guan, Y. Improved USLE-K factor prediction: A case study on water erosion areas in China. Int. Soil Water
Conserv. Res. 2016,4, 168–176. [CrossRef]
68.
Kuznetsov, M.S.; Gendugov, V.M.; Khalilov, M.S.; Ivanuta, A.A. An equation of soil detachment by flow. In Soil and Tillage Research;
Elsevier B.V.: Amsterdam, The Netherlands, 1998; pp. 97–102. [CrossRef]
69.
Lamb, M.P.; Dietrich, W.E.; Venditti, J.G. Is the critical shields stress for incipient sediment motion dependent on channel-bed
slope? J. Geophys. Res. Earth Surf. 2008,113, F02008. [CrossRef]
70.
Kleinhans, M.G.; van de Kasteele, H.E.; Hauber, E. Palaeoflow reconstruction from fan delta morphology on Mars. Earth Planet
Sci. Lett. 2010,294, 378–392. [CrossRef]
71. Parker, G. Progress in the modeling of alluvial fans. J. Hydraul. Res. 1999,37, 805–825. [CrossRef]
72.
Quinn, P.; Beven, K.; Chevallier, P.; Planchon, O. The prediction of hillslope flow paths for distributed hydrological modelling
using digital terrain models. Hydrol. Process. 1991,5, 59–79. [CrossRef]
73.
Kleinhans, M.G. Flow discharge and sediment transport models for estimating a minimum timescale of hydrological activity and
channel and delta formation on Mars. J. Geophys. Res. Planets 2005,110, 1–23. [CrossRef]
74.
Seybold, H.J.; Kite, E.; Kirchner, J.W. Branching Geometry of Valley Networks on Mars and Earth and Its Implications for Early
Martian Climate. Available online: http://advances.sciencemag.org/ (accessed on 20 March 2024).
75.
Craddock, R.A.; Howard, A.D.; Irwin, R.P.; Tooth, S.; Williams, R.M.E.; Chu, P.S. Drainage network development in the Keanakkoi
tephra, Klauea Volcano, Hawaii: Implications for fluvial erosion and valley network formation on early Mars. J. Geophys. Res.
Planets 2012,117, 8009. [CrossRef]
76. Kite, E.S. Geologic Constraints on Early Mars Climate; Springer: Amsterdam, The Netherlands, 2019. [CrossRef]
77.
Fairén, A.G.; Haqq-Misra, J.D.; McKay, C.P. Reduced albedo on early Mars does not solve the climate paradox under a faint
young Sun. Astron. Astrophys. 2012,540, A13. [CrossRef]
78.
Steinmann, V.; Kereszturi, Á.; Mari, L. Geomorphological analysis of Tinto-B Vallis on mars. Hung. Geogr. Bull. 2020,69, 333–348.
[CrossRef]
79.
Bahia, R.S.; Bohacek, E.V.; Braat, L.; Boazman, S.; Sefton-Nash, E.; Orgel, C.; Wilson, C.; Riu, L. A Major Missing Component-
Aeolian-Fluvial Interaction on Ancient Mars. In Proceedings of the 54th Lunar and Planetary Science Conference, The Woodlands,
TX, USA, 13–17 March 2023.
80.
Grotzinger, J.P.; Sumner, D.Y.; Kah, L.C.; Stack, K.; Gupta, S.; Edgar, L.; Rubin, D.; Lewis, K.; Schieber, J.; Mangold, N.; et al. A
habitable fluvio-lacustrine environment at Yellowknife Bay, Gale crater, Mars. Science (1979) 2014,343. [CrossRef]
81.
Tate, C.D.; Hayes, A.H.; Kanine, O.A.; Gupta, S.; Caravaca, G.; Paar, G. Stratigraphic Reconstruction and Analysis of the Delta
Remnant Kodiak in Jezero Crater, Mars. In Proceedings of the 54th Lunar and Planetary Science Conference 2023, The Woodlands,
TX, USA, 13–17 March 2023.
82.
Irwin, R.P.; Craddock, R.A.; Howard, A.D. Interior channels in Martian valley networks: Discharge and runoff production.
Geology 2005,33, 489–492. [CrossRef]
83.
Rodriguez, J.A.P.; Bourke, M.; Tanaka, K.L.; Miyamoto, H.; Kargel, J.; Baker, V.; Fairén, A.G.; Davies, R.J.; Bridget, L.; Santiago,
R.L.; et al. Infiltration of Martian outflow channel floodwaters into lowland cavernous systems. Geophys. Res. Lett. 2012,39.
[CrossRef]
84.
Wilson, L.; Ghatan, G.J.; Head, J.W.; Mitchell, K.L. Mars outflow channels: A reappraisal of the estimation of water flow velocities
from water depths, regional slopes, and channel floor properties. J. Geophys. Res. Planets 2004,109. [CrossRef]
85.
Hayes, A.G.; Corlies, P.; Tate, C.; Barrington, M.; Bell, J.F.; Maki, J.N.; Caplinger, M.; Ravine, M.; Kinch, K.M.; Herkenhoff, K.; et al.
Pre-Flight Calibration of the Mars 2020 Rover Mastcam Zoom (Mastcam-Z) Multispectral, Stereoscopic Imager. Space Sci. Rev.
2021,217, 1–95. [CrossRef]
86.
Mastropietro, M.; Pajola, M.; Cremonese, G.; Munaretto, G.; Lucchetti, A. Boulder Analysis on the Oxia Planum ExoMars 2022
Rover Landing Site: Scientific and Engineering Perspectives. Sol. Syst. Res. 2020,54, 504–519. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Content uploaded by Steinmann Vilmos
Author content
All content in this area was uploaded by Steinmann Vilmos on Sep 30, 2024
Content may be subject to copyright.
