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# Ancient volcanism may have influenced patterns of hydrated regolith on Mars

Tyler G. Paladino, Shannon Kobs Nawotniak, Ehouarn Millour, Suniti Karunatillake, Don R. Hood, Augustus Bates

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Tyler G. Paladino, Shannon Kobs Nawotniak, Ehouarn Millour, Suniti Karunatillake, Don R. Hood, et al.. Ancient volcanism may have influenced patterns of hydrated regolith on Mars. Icarus, 2024, 409, 10.1016/j.icarus.2023.115858. insu-04729909

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# Ancient volcanism may have influenced patterns of hydrated regolith on Mars

Tyler G. Paladino<sup>a, *, 1</sup>, Shannon Kobs Nawotniak<sup>a</sup>, Ehouarn Millour<sup>b</sup>, Suniti Karunatillake<sup>c</sup>, Don R. Hood<sup>d</sup>, Augustus Bates<sup>c</sup>

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

### Keywords:

- Volcanoes, geological processes

- Regolith

- Atmospheric dynamics

- Hydrosphere

## ABSTRACT

The hydration of the martian regolith at decimeter depth scales varies regionally, suggestive of geologically sustained processes, independent of diurnal soil-atmosphere exchange. A combination of factors, including residual hydration from ancient processes in the hydrosphere/cryosphere, seasonal exchange between the crust and atmosphere in the martian critical zone, or structural water in minerals or salts sourced via hydrothermal processes can contribute to such lateral changes in bulk regolith hydration. Here, we consider the theoretical extent of regolith hydration from explosive volcanic eruptions and subsequent hydration. While magmatic degassing has previously been considered as a contributing factor to regolith hydration, the relationship between hydrated regolith and the depositional footprint of volcanic eruption columns and associated atmospheric dispersal remains poorly known. We run simulations of eruptions in an ancient atmosphere over a martian year to account for seasonality and produce theoretical ash deposition maps which are then input into a geographic weighted regression model to predict hydrated regolith. We find that ash deposited via explosive volcanism can help explain certain patterns of hydration enrichment in the martian regolith, including those near Apollinaris Patera Arabia Terra, and Sabea Terra. While the global patterns of regolith hydration cannot be explained by one process alone, we found that hydrated ash likely contributed to hydrated regolith at a commensurable level to other leading hydration mechanisms.

**Plain language summary:** The surface of Mars is blanketed by a pervasive layer of regolith, a mixture of unconsolidated sediment of multiple grainsizes that range from very fine dust to larger pebbles. This regolith contains significant amounts of water that are stored in the chemical bonds that make up the individual regolith grains. The amount of water bound in the regolith is spatially inconsistent throughout the planet; some areas contain more water, while others contain less. Several different geologic processes have likely contributed to these inconsistencies, including volcanic processes. While gentle volcanic outgassing has already been considered, here we specifically looked at how explosive volcanism and the associated spreading of volcanic ash in the martian atmosphere may have contributed to hydrated regolith patterns. By using multiple numerical simulations, we found that explosive volcanism likely contributed to certain patterns of hydration in the regolith along with other geologic processes.

## 1. Introduction

The martian bulk regolith holds significant volumes (up to 7 wt%) of chemically-bound water at decimeter depth scales as shown by Gamma

Ray Spectrometer (GRS) data (Fig. 1; Hood et al., 2019; Karunatillake et al., 2014, 2016), other satellite observations (CRISM/OMEGA; Carter et al., 2013), and numerical models constrained by rover samples and meteorites (Boynton et al., 2007; Scheller et al., 2021). Crustal

<footnote><sup>a</sup> Department of Geosciences, Idaho State University, Pocatello, ID, USA</footnote>

<footnote><sup>b</sup> Laboratoire de Météorologie Dynamique/Institut Pierre Simon Laplace (LMD/IPSL), Sorbonne Université, ENS, Université PSL, École Polytechnique, Institut Polytechnique de Paris, CNRS, Paris, France</footnote>

<footnote><sup>c</sup> Department of Geology and Geophysics, Louisiana State University, Baton Rouge, LA, USA</footnote>

<footnote><sup>d</sup> Department of Geosciences, Baylor University, Waco, TX, USA</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: tylerpaladino@isu.edu (T.G. Paladino).</footnote>

<footnote n="1">¹ Now at USGS Cascades Volcano Observatory (tpaladino@usgs.gov).</footnote>
sequestration of an ancient surface cryosphere/hydrosphere has been hypothesized as a major contributor to the modern arid environment (Scheller et al., 2021). Beyond crustal sequestration, however, the relative role of water and the geologic processes hydrating the martian regolith remain poorly understood. Previous work has shown that hydrous Fe-sulfates from acid fog and leaching processes (Karunatillake et al., 2014; Lane et al., 2015), hydrous alteration (Hood et al., 2019), and dust cover variations (Jänchen et al., 2009; Milliken et al., 2007) may affect regional regolith hydration, but they cannot fully explain the spatial variability (Audouard et al., 2014). Furthermore, while magmatic degassing has been considered as a possible contributor to regolith hydration (Black et al., 2022), the consequent spatial footprints of explosive volcanism and associated ash dispersal in volcanic landscapes have not been considered in detail. Here, we consider the spatial extent of hydration that would be associated with ancient explosive volcanism.

Explosive volcanism was mainly active early in martian history up until the Amazonian (~3.0 Ga to present) period as evidenced by physical features, such as paterae (i.e. calderas in a planetary context) and friable layered deposits, as well as from numerical models (Fig. 1; Brož et al., 2021; Robbins et al., 2011). Explosive eruptions on Earth can produce convective plumes of ash, gas, and rock that rise 10s of kilometers into the atmosphere where they are physically modified by winds and can spread over vast distances. In a modern martian atmosphere, numerical plume models break down (Glaze and Baloga, 2002) as atmospheric pressure is too low for the conservation equations plume models rely on to be satisfied. An ancient martian atmosphere, instead, likely had surface pressures on the order of 1 bar (Forget et al., 2013; Wordsworth et al., 2013, 2015) which is high enough for conservation equations to be satisfied (Glaze and Baloga, 2002).

Most volcanic features on Mars have been found to have erupted during the Hesperian (~3.5 Ga to ~3.0 Ga) and Noachian (~4.1 Ga to ~3.5 Ga) eons (Robbins et al., 2011; Tanaka et al., 2014). Some volcanoes, such as Tharsis or Elysium, though found to have more recent eruptions, were likely active for billions of years before the Amazonian (Robbins et al., 2011). These surfaces are dated via crater counting which mainly reflects when the last bout of volcanism occurred, rather than the age of the entire edifice, as older deposits have been resurfaced by younger deposits. Furthermore, a shift in volcanism on Mars from explosive to effusive at the Hesperian-Amazonian boundary has been suggested (Robbins et al., 2011). To reflect these considerations, we model the explosive volcanism of Mars in a dense (~ 1 bar), ancient (Noachian to Hesperian) atmosphere, in which explosive volcanism was more common and where convective volcanic plumes could exist (Glaze and Baloga, 2002).

The process of hydrating regolith via explosive volcanism depends on two main aspects of explosive volcanism: the ability for ash to absorb water, and the ability of ash to be transported over great distances. Ash of variable composition is capable of being hydrated (Seligman et al., 2016) both *syn*- and post-eruptively (Aomine and Wada, 1962; Hodder et al., 1990; Hudak et al., 2021; Mitchell et al., 2018). In a *syn*-eruptive timeframe, if the vent environment is particularly wet (i.e. a phreatic or phreatomagmatic eruption), ash grains can become hydrated up to 6% during the eruption (Hudak et al., 2021; Mitchell et al., 2018, 2022). In a post-eruptive timeframe, once ash beds have been deposited, they can adsorb-absorb external water over geologic timescales as they decay to phyllosilicates (Aomine and Wada, 1962; Hodder et al., 1990), gaining water as fast as 1% over 960 years in natural systems (Giachetti et al., 2020). Rates of water uptake of ash glasses depend on a variety of factors, including composition and external environment (Hudak and

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Overview map of all eruptive vents (red triangles) studied in this paper as well as the friable layered deposits (Black Lines; Tanaka, 2000). Background is the GRS H₂O data overlain on a Mars Orbital Laser Altimeter (MOLA) hillshade. On this map and all others, 1 degree of longitude is equal to 59.2 km at the equator. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
Bindeman, 2020; Seligman et al., 2016). Hydration can also be spatially inconsistent even on small spatial scales in the case of ignimbrites (Self et al., 2022).

It is difficult to posit which mechanism, syn- or post-eruptive hy-
dration, would have been dominate on Mars, but given the likelihood of
large portions of Mars being covered in water (Carr and Head III, 2003;
Carr and Head, 2010; Scheller et al., 2021) or in the presence of ice
(Boynton et al., 2002; Squyres and Carr, 1986) for extended periods of
time, the potential exists for both to have been active in tandem. GRS-
derived regional geochemical analyses have shown that much of the
southern highlands has experienced widespread aqueous interaction
that has produced stable hydrous minerals over geologic time (Hood
et al., 2019), meaning ash beds present in the southern hemisphere
could be even more readily susceptible to post-eruptive alteration.
Regardless of the ash hydration mechanism, explosive volcanism can
transport ash hundreds of kilometers on Mars in an ancient atmosphere
(Kerber et al., 2012), leading to areas that are susceptible to hydration
throughout the planet that are distal to their source. With the distribu-
tion of potentially explosive volcanic edifices throughout the surface of
Mars (Fig. 1), there is a valid pathway to hydrated regolith patterns by
invoking explosive volcanism.

If ash accumulation significantly affects variations in local regolith
hydration, it would suggest that some hydrated areas may contain H₂O
reservoirs that extend deeper into the subsurface that what can be
observed by GRS data and could be valuable for In Situ Resource Utili-
zation (ISRU) in the future. Also, understanding how water is trans-
ported via volcanism can help refine current models on the geologic
evolution of martian crustal hydration (i.e., what the magnitude of
water that can be readily sequestered into volcanic ash is; Scheller et al.,
2021).

## 2. Materials and methods

To characterize what effect explosive volcanism may have had on
hydrating regolith, we compared GRS maps of stoichiometric H₂O mass
fractions (wt%), i.e., water equivalent hydrogen, with modeled ash
deposition maps. We simulated realistic explosive plumes in a martian
atmosphere using the Active Tracer High-resolution Atmospheric Model
(ATHAM) (Herzog et al., 1998; Oberhuber et al., 1998). ATHAM pro-
vided ash mass loading into a realistic atmosphere at a horizontal scale
of 100 km that was then input into the Generic Planetary Climate Model
(Generic PCM) (Forget et al., 1999, 2013) used in an early Mars
configuration, which then tracked the dispersal and deposition of ash on
a regional to global scale. This work builds off of the work of Kerber et al.
(2012) in that we augmented their methodology with a 4D Navier-
Stokes based large eddy simulation plume model to better simulate
the mass of ash being loaded into the atmosphere versus becoming py-
roclastic density currents that deposit ash locally. This method also has
the added benefit of quantifying the spatial distribution of the atmo-
spheric ash in the convective plume.

2.1. ATHAM

ATHAM is a time varying plume simulation that solves the full set of
Navier-Stokes equations for momentum, mass, and energy using a large
eddy simulation closure for turbulence (Herzog et al., 1998; Oberhuber
et al., 1998). ATHAM acts in a Eulerian reference frame with a variable
grid space and uses an implicit time stepping scheme. Particles, such as
ash, water, or other various chemical species are treated as active
tracers, meaning that the particles can influence the overall dynamics of
the plume as they move through it (e.g., water moving through changes
of state release and absorb latent heat). To reduce computational
expense, all particles are assumed to be in dynamic and thermal equi-
librium with the surrounding fluid, so that they exchange momentum
and heat instantaneously. For this assumption to hold true, all tracer
particles are idealized as ≤1 mm in diameter, consistent with Plinian,

subplinian, and vulcanian eruptions on Earth (Herzog et al., 1998;
Oberhuber et al., 1998). Particles of ash and water can coexist in
ATHAM, but they do not combine together to form new, different sized
ash-ice aggregates. To use ATHAM on Mars, we made several changes to
the code that include lower gravity, specific heat capacity that reflects a
majority CO₂ atmosphere (Wordsworth et al., 2015), smaller planetary
radius, and a slower planetary angular velocity. Surface pressure
remained at 1 bar to reflect a thicker ancient martian atmosphere
(Wordsworth et al., 2015).

2.1.1. Eruption characteristics

ATHAM allows parameters to be set for grain-size distribution, post-
fragmentation volatile percentage, tracer density, tracer specific heat,
vent velocity, and eruptive mixture temperature. For a grain-size dis-
tribution, we chose to use Unit 3 of the 25.4 ka Oruanui phreato-
magmatic eruption (Self, 1983; Van Eaton and Wilson, 2013) as it
preserves a large degree of fine grains as well as coarse ones, is well
studied, and provides an excellent phreatomagmatic grain-size distri-
bution to draw from (Fig. 2). A phreatomagmatic grain-size distribution
was chosen to acknowledge that phreatic or phreatomagmatic eruptions
likely have occurred on Mars during the Noachian/Hesperian periods as
rising magma bodies interacted with saturated sediments, icy sediments,
or standing bodies of water (Broż et al., 2021; Broż and Hauber, 2013;
Sears et al., 2022; Wilson and Head III, 2004; Wilson and Mouginis-
Mark, 2003). We also aimed to be consistent with past eruption
modeling on Mars in which modified phreatomagmatic grain-size dis-
tributions were used (Kerber et al., 2012; Wilson and Head, 2009).
Furthermore, phreatomagmatic eruption models effectively set an upper
bound on the extent of regolith hydration by water effected explosive
volcanism dispersion, broadening the insight from prior works (e.g.
Black et al., 2022) that focused exclusively on magmatic degassing.

It should be noted that there is little evidence for primary high-silica
volcanic deposits on Mars, with the tridymite found at Gale Crater being
the only in-situ chemical observation (Morris et al., 2016). Despite this,
our choice of the Oruanui eruption as a grain-size distribution source is
justified for several reasons. The Oruanui deposit covers a wide range of
grainsizes, fully capturing a phreatomagmatic event, which is rare to
find and is the type of eruption we wanted to model. Previous workers
modeling explosive eruptions on Mars used a grainsize distribution from
a rhyolitic Plinian eruption phase (Sparks et al., 1981) that was then
manually modified to have a larger degree of fine grains to better
simulate increased fragmentation and a greater proportion of finer grain
sizes (Kerber et al., 2012; Wilson and Head, 2009). While a basaltic
Plinian eruption, such as the 1886 Tarawera eruption would fit the bulk
chemistry of Mars better, the grain-size distributions for this eruption
does not cover the degree of fine grains we were interested in modeling
(Carey et al., 2007). Regardless of the chemistry of input volcanic pa-
rameters, we do not explicitly model the process of ash becoming hy-
drated in this work, meaning the chemistry of the grainsize distribution
is not critical.

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Total grain size distribution (TGSD) from unit 3 of the 25.4 ka Oruanui eruption on the north island of New Zealand. Open circles represent the grainsize diameters input into the models.</figcaption></figure>
To reduce computational expense, we chose 7, 6, 3, and 0 φ grain sizes from this distribution to be input as tracers into each model (Fig. 2). These specific values were chosen to maintain the general proportions of the grain-size distribution while still allowing for computational efficiency. A value of 4% H₂O of the post eruptive mixture at the vent was chosen to be consistent with past literature sources (Glaze and Baloga, 2002; Hort and Weitz, 2001). This value of 4% H₂O only effects the dynamics of the plume in the ATHAM simulation and does not have any bearing on the final regolith hydration maps produced later. The mass fraction percentages (wt%) of the ash tracers chosen from the grain-size distribution (7,6,3, and 0 φ) were then renormalized to 96% to account for 4% H₂O in the post eruptive mixture. Ash tracer density for all four ash tracers was set to 700 kg/m³ (Wilson and Head, 2009) as a generic pyroclast value in the range of terrestrial literature values (Shipley and Sarna-Wojcicki, 1983; Walker et al., 1971; Wilson, 1976). A pyroclast temperature of 1450 K for each tracer was used, again to be consistent with literature values of martian pyroclasts (Glaze and Baloga, 2002; Hort and Weitz, 2001). A mass eruption rate of 6.65 × 10⁶ kg/s was chosen as a moderately powerful Plinian eruption.

### 2.1.2. Modeled volcanoes

We modeled all major volcanoes detailed and summarized by (Kerber et al., 2012) as these volcanoes have long been hypothesized to be capable of producing explosive eruptions (Kerber et al., 2012 and references therein). We augmented these volcanoes with three proposed volcanoes in the Arabia Terra region, as there is a growing body of geophysical and geochemical evidence that these volcanoes existed and were explosive in origin (Bates et al., 2023; Michalski and Bleacher, 2013; Whelley et al., 2021). In total, 20 volcanoes were modeled (Fig. 1 and Table 1).

## 2.2. The generic PCM

The Generic Planetary Climate Model (Generic PCM), formally known as the LMD GCM is a global circulation model (GCM) that can

<caption>**Table 1**</caption>

All volcanoes used in our modeling with their approximate location coordinates and sources for being deemed an explosive volcano. Longitude convention is -180 to 180.

<table><thead><tr><th>Volcano Name</th><th>Longitude (°)</th><th>Latitude (°)</th><th>Literature Reference</th></tr></thead><tbody><tr><td>Apollinaris Patera</td><td>174.4</td><td>-9.3</td><td>Robinson et al. (1993)</td></tr><tr><td>Elysium Mons</td><td>147</td><td>24.8</td><td>Horvath et al. (2021); Mouginis-Mark (1985)</td></tr><tr><td>Cerberus Fossae</td><td>176.6</td><td>9</td><td>Keszthelyi et al. (2000)</td></tr><tr><td>Olympus Mons</td><td>-133.9</td><td>18.7</td><td>Head and Wilson (1998); Hynek (2003)</td></tr><tr><td>Arsia Mons</td><td>-120.46</td><td>-9.14</td><td></td></tr><tr><td>Pavonis Mons</td><td>-112.85</td><td>0.662</td><td></td></tr><tr><td>Ascraeus Mons</td><td>-104.37</td><td>11.1</td><td></td></tr><tr><td>Syrtis Major</td><td>66.4</td><td>9.85</td><td>Fassett and Head (2007)</td></tr><tr><td>Tyrrhena Patera</td><td>106.55</td><td>-21.32</td><td>Greeley and Crown (1990)</td></tr><tr><td>Hadriaca Patera</td><td>92.18</td><td>-30.44</td><td>Crown and Greeley (1993)</td></tr><tr><td>Peneus Patera</td><td>60.76</td><td>-58.05</td><td>Williams et al. (2010)</td></tr><tr><td>Amphritrites Patera</td><td>52.66</td><td>-58</td><td></td></tr><tr><td>Pityusa Patera</td><td>36.87</td><td>-66.77</td><td></td></tr><tr><td>Malea Patera</td><td>50.96</td><td>-63.09</td><td></td></tr><tr><td>Hecates Tholus</td><td>150.08</td><td>31.68</td><td>Mouginis-Mark et al. (1982)</td></tr><tr><td>Alba Patera</td><td>-111.11</td><td>39.19</td><td>Mouginis-Mark et al. (1988) and Wilson and Mouginis-Mark (1987)</td></tr><tr><td>Electris</td><td>-173.21</td><td>-37.35</td><td>Scott and Tanaka (1986); Wilson and Head III (2002)</td></tr><tr><td>Eden Patera</td><td>-11.1</td><td>33.6</td><td>Bates et al. (2023); Michalski and Bleacher (2013); Whelley et al. (2021)</td></tr><tr><td>Siloe Patera</td><td>6.6</td><td>35.2</td><td></td></tr><tr><td>Ismenia Oxus</td><td>0</td><td>38.5</td><td></td></tr></tbody></table>

simulate a generic atmosphere of most planetary bodies (Forget et al., 1999, 2013). The Generic PCM contains two packages, one that solves for dynamics, including general equations for atmospheric motion and circulation, and another that solves for atmospheric physical processes such as radiative transfer, cloud effects, or water cycles. There are various physics packages that can be used with the PCM, for instance some specific to modern day planetary atmospheres, such as Venus or Mars. Here, we use the generic physics package, which was designed to be general and applicable to any atmosphere, as in the present case, to represent ancient martian atmospheres (Forget et al., 2013).

The generic atmosphere was initialized with input parameters consistent with Wordsworth et al. (2015): a surface pressure of 1 bar, a CO₂ atmosphere including radiative transfer schemes specific to a dominantly CO₂ atmosphere, gravitational acceleration of 3.72 m/s², planetary angular velocity of 7.08 × 10⁻⁵ rad/s, and obliquity set to a modern-day value of 25°. While obliquity has likely changed significantly from the present to the Noachian/Hesperian periods when explosive volcanism was most active (Ćuk et al., 2020; Laskar et al., 2004), ash dispersal on Mars is not significantly altered at more extreme obliquities than present (Kerber et al., 2012). Solar flux was set to a faint young sun flux at Mars of 441.1 W/m².

The model was run on a 128 × 96 × 23 grid (longitude × latitude × altitude) which corresponds to a horizontal grid spacing of 2.8125° in longitude and 1.875° in latitude. The vertical spacing is variable and is done on a hybrid sigma coordinate system that extends ~87 km above ground level. Modern topography was used, except for removing the Tharsis rise (Fig. 3) to better reflect ancient terrain conditions consistent with our modeling being done in Hesperian or Noachian times when the majority of Tharsis had not yet been fully constructed (Anderson et al., 2001; Bouley et al., 2018; Carr and Head, 2010; Johnson and Phillips, 2005). A sensitivity test with modern Tharsis topography is discussed in Section 2.5. The grid size was chosen to be significantly finer than the GRS grid of 72 × 36 (longitude × latitude) so that interpolation and comparison of the finer PCM grid to the coarser GRS grid was trivial. The GRS grid corresponded to a horizontal spacing of 5° in longitude and 5° in latitude. The GCM was run separately for each volcano for an entire martian year to remove seasonal bias. We separated each volcano into separate model runs to be able to isolate the effects of one volcano versus another. Ash particles are sedimented onto the planetary surface according to a version of Stokes' law (Rossow, 1978). Once ash has been sedimented, it cannot be remobilized.

## 2.3. Modeling workflow

To compare modeled ash deposits with GRS data, we developed the following workflow (summarized in Fig. 4): 1) The Generic PCM is initialized in a cold and icy scenario using inputs detailed in Section 2.2 and run for one martian year to generate appropriate inputs for ATHAM (this only needs to be done once). 2) Vertical atmospheric profiles containing humidity, temperature and windspeed/direction for multiple volcano locations and for each season are extracted from the GCM output and input into ATHAM. 3) ATHAM is run at each volcano location and for each season for one hour of simulation time to generate a representative steady-state eruption column. 4) The ash in the steady-state column produced in ATHAM is summed into a vertical ash concentration profile, with one profile calculated for each season. The concentration profile is used as a year-long source in the GCM, 5) The GCM is run for a martian year with the ash profiles from ATHAM inserted at every timestep (switching ash sources depending on the season) as a passive tracer and allowed to advect, diffuse, and sediment. Each volcano location is run in a separate GCM simulation. 6) The deposited ash output is downloaded and analyzed. The result of this methodology is ash deposition maps that generally characterize the distribution of ash produced by individual volcanoes over many eruptions. For steps where output must move from one model to the other (steps 2 and 4), we use python scripts that are described in the following
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Surface atmospheric pressure plots showing the effects of removing the Tharsis Rise. The top panel shows modern topography derived from MOLA data while the bottom panel shows the same MOLA modern topography with the Tharsis Rise removed.</figcaption></figure>

subsections.

### 2.3.1. Generic PCM to ATHAM

To transfer atmospheric profiles from the Generic PCM to ATHAM, we first read in a .csv of latitude/longitude coordinates of proposed explosive volcanoes (Table 1) and the output netCDF file of the PCM. For each volcano location in the .csv, we find the index of the PCM grid point that is closest to that volcano's specific latitude/longitude coordinate. Next, using this index, we extract vertical profiles of temperature, humidity, and wind from the 4D timeseries of the PCM at $L_s$ values that correspond to each season: 0°/360° for spring, 90° for summer, 180° for fall, and 270° for winter. These profiles are then formatted into a tab delimited text file that can be read in by ATHAM. Four ATHAM compatible profiles per volcano are produced in this code.

### 2.3.2. ATHAM to generic PCM

Once ATHAM has output a plume simulation for each season, the ash produced must be summed and reshaped into a format to be read by the GCM. ATHAM tracks ash dispersion and deposition in a 100 × 100 km grid. Locally deposited ash is ignored, while ash lofted into the air is tracked. Any ash that leaves the domain of the model is summed into planes that correspond to each open domain of the model (the sides and the top). Each side domain plane is summed row-wise into a vertical profile while the top domain plane is summed into a point. At the last timestep, any ash that is still present within the domain of the model is summed into a vertical profile and added to the side and top domain profiles. This process is repeated for every ash tracer present. The result is a delimited text file that contains ash concentration values for each tracer at every altitude level within ATHAM. We only track the 7 phi

grainsize tracer for this work. This process is shown graphically in Fig. 5.

### 2.3.3. Modifications to the generic PCM

The Generic PCM was modified to provide a means of inputting a vertical one-dimensional profile of ash from a specific volcano location in the model as a passive tracer; the ash does not provide any feedbacks or affect any of the physical or dynamic parameterization present during the simulation. The Generic PCM takes an input of the volcano name, which the volcano module uses to find the latitude/longitude location in a lookup table. The code then finds which PCM grid point is closest to the volcano coordinates in question and which processor that grid point is on. Once the grid point is chosen, the delimited ash concentration file created earlier is read in for each season. As time progresses through the model, each season-specific concentration array is fed into the PCM at every timestep at the appropriate season (at $L_s$ = 0–90 for spring, $L_s$ = 90–180 for summer, $L_s$ = 180–270 for Fall, and $L_s$ = 270–360 for Winter).

The height values associated with the delimited ash concentration files from ATHAM are different than those used in the PCM. To remedy this, the code loops through each ash concentration line and finds the closest PCM height level. If one or more ATHAM concentrations are closest to a single PCM height level, the concentrations are summed. Ash is added in this manner at every timestep for an entire martian year. This is done to minimize any bias towards one specific season (Kerber et al., 2013).

## 2.4. Regression analysis

At the end of the modeling workflow (Fig. 4), ash deposition maps for
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Flow chart showing how ATHAM and the Generic PCM interact. Rounded rectangles represent starting or stopping points, parallelograms represent inputs or outputs and rectangles represent processes. Processes either have an F denoting a Fortran model or a python symbol, denoting a python script.</figcaption></figure>

each volcano were generated. These maps (Fig. 6) show ash deposition in areal density (kg/m²) and can be compared qualitatively to observe which volcanoes may individually influence the GRS hydration data.

To gain a quantitative sense of the relationship between modeled ash deposition and GRS hydration, we employed a multivariate Geographically Weighted Regression (GWR; Brunsdon et al., 1996) model, where the modeled ash deposition for each volcano are the predictor variables and the GRS hydration data is the response variable. The output of the GWR model is in the units of the response variable: in this case H₂O wt%. There are no conversions being made of the degree of hydration occurring from the areal density to the output of the GWR model. This methodology is simply finding the set of coefficients for the predictor variables that best fit the response variable; it is purely a statistical relationship rather than a modeled physical process.

We chose to use GWR over other regression methods such as ordinary least squares (OLS) because the modeled ash output is spatially non-stationary, meaning there is spatial autocorrelation and statistical variations in space over some scales, which needs to be accounted for. Furthermore, a global OLS model, by definition, misses variation at smaller spatial scales. Because we are interested in regional patterns of regolith hydrations, GWR becomes the obvious choice as it calculates regression functions at any set of locations in the study area and at a user-set spatial scale, known as the bandwidth.

At its base, GWR is similar to OLS in that it uses linear equations to describe the response variable with the predictor variables. As such, linear relationships must exist between each predictor and the response; a Box-Cox transformation was used on all the predictor variables to achieve a more normal and linear relationship. GWR, like OLS, also requires there to be no multicollinearity among predictor variables, though this requirement has been found to not be as important as previously thought, except at very high degrees of multicollinearity (Fotheringham and Oshan, 2016). We still try to minimize multicollinearity by choosing two sets of predictor variables with minimal variance inflation factors (VIF). VIF describes the severity of multicollinearity of one predictor against all others. Here we chose two scenarios: the first with VIFs <35, the second with VIFs <60 (Table 2). These VIF thresholds were chosen to provide two scenarios, one conservative, the other liberal. Note that both are well below extreme levels of multicollinearity found by Fotheringham and Oshan (2016) to effect GWR.

To calculate GWR, we ran the GWR tool within the ArcGIS Pro spatial statistics toolbox. GWR requires an input for the kernel size, either via the number of neighbors around a point or the bandwidth distance around a point. Bandwidth distance refers to the distance around a point with which to include points in the GWR model. To find optimal value for this bandwidth, we ran the incremental spatial autocorrelation (ISA) tool in ArcGIS Pro on all of the predictor variables, which identified the distance at which the highest degree of spatial autocorrelation occurred throughout our model outputs (Fig. 7a). The average ISA peak occurred at 2735 km, so we chose to set the bandwidth distance far above this value to avoid autocorrelation issues. We tested several bandwidth distances above the average, but decided to use a value of 15,000 km to capture the regional scale of variability we were interested in. We also report R² values for a multitude of bandwidths in Fig. 7b.

To approximate the relative importance of each predictor volcano on the GRS H₂O response, we individually ran each predictor by itself through the GWR model at a bandwidth of 15,000 km. The R² values given for each volcano give a first order approximation of which volcanoes are contributing the most to the overall GWR model (Fig. 8).

## 2.5. Sensitivity tests

Sensitivity of the Generic PCM to volcanic ash deposition has been well characterized by Kerber et al. (2012, 2013) in terms of obliquity, eccentricity, atmospheric pressure, dust loading, pyroclast release height, pyroclast density, grain size, and seasonality. In summary, higher obliquities result in slightly greater dispersion of ash; eccentricity has little effect; increased atmospheric pressure results in smaller, more concentrated ash deposits; dust loading alters the shape of the deposited ash pattern, but not the dispersal range; higher pyroclast release height results in greater dispersion of ash; higher ash density results in a slightly less dispersion of ash; larger grain sizes result in smaller dispersion of ash; and seasonality affects the shape of the deposited ash pattern (Kerber et al., 2012, 2013). Our work here allows us to extend these tests to terms specific to ATHAM, such as mass eruption rate, pyroclast temperature, and volatile content. We also extend to terms shared between models, including pyroclast density. To test these parameters, we ran a case study of Apollinaris Patera. Ash deposition maps for all separate sensitivity tests for Apollinaris Patera are summarized in Fig. 9.

From our tests (Fig. 9), we found that ATHAM and the Generic PCM
<figure><img src="image_9.png" /><figcaption>(caption on next column)</figcaption></figure>

**Fig. 5.** Schematic detailing the process summing ash in ATHAM. A. Full 3D plume visualization. The red plane shows where ash is leaving the domain and also represents the extent of panel B. B. Plane referenced in panel A showing ash leaving the domain. This represents an instantaneous snapshot of ash leaving the domain. C. 1D profile of the ash pattern seen in panel B. The ash in panel B is summed row wise to create a 1D vertical profile. This step and panel B are occurring at every timestep as ash is leaving the model domain. D. The final ash domain profile. This profile is created only at the last timestep to capture any ash that is still present in the domain by summing all ash into a vertical profile. E. The total ash profile created by adding the instantaneous profiles at every timestep (C) to the final full domain ash profile (D). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

together are insensitive to volatile weight percentage and temperature and are somewhat sensitive to mass eruption rate and ash density. The presence of Tharsis changes the shape of the ash distribution slightly, but does not affect the range (note that if the sensitivity test had been done for a Tharsis volcano rather than Apollinaris Patera, a larger change would likely be present). Increasing mass eruption rate resulted in greater ash dispersion and concentration likely due to a higher maximum plume height releasing pyroclasts at a greater height, allowing for a higher chance to be dispersed over larger distances. A larger ash tracer density resulted in less dispersion and higher localized deposition, as the denser clasts will settle out of the atmosphere earlier than less dense clasts. Increasing the grainsize distribution to be coarser overall results in far less dispersion and more concentrated localized deposits.

## 3. Results

### 3.1. VIF < 35 case

Including all volcanoes with a VIF <35 (Table 2) and a GWR bandwidth of 15,000 km resulted in an $R^2$ of 0.342. Fig. 10a shows the predicted output of this model along with the volcanoes used in the model. Comparing this with the GRS hydration data (Fig. 10c) shows strong similarities near Apollinaris Patera and Cerberus Fossae. There is also good agreement in the eastern Arabia Terra region, though most of Arabia Terra tends to be underpredicted. This model also slightly over predicted hydration northwest of Olympus Mons.

### 3.2. VIF < 60 case

Including volcanoes with a VIF <60 (Table 2) and a GWR bandwidth of 15,000 km resulted in an $R^2$ of 0.482. Fig. 10b shows the predicted output of this model along with volcanoes used in the model. The predicted hydration from this model is very similar to the VIF < 30 case, though there are some subtle differences. Like the VIF < 30 case, there is good agreement with the GRS H$_2$O data around Apollinaris/Cerberus. The VIF < 60 case tends to capture more of hydration around southern Arabia Terra and Terra Sabaea, though still overpredicts hydration northwest of Olympus Mons.

### 3.3. Other bandwidths

We also tested other bandwidths in addition to 15,000 km that are greater than the average ISA peak (Fig. 7b). In these tests, we found a general trend of decreasing $R^2$ with increasing distance band (Fig. 8). The VIF < 60 case maintains better $R^2$ values than the VIF < 35 case at all distance bands tested. It is worth noting that the largest distance band tested, 25,000 km, is larger than the circumference of Mars, meaning the GWR model is using all available points and is essentially calculating an OLS $R^2$ value.
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Plot showing the deposited ash of every volcano simulated. Each panel shows cumulative deposited ash from a single eruption over a year, where color corresponds to ash area density. Each panel is on the same log color scale. For this visualization, only the 7 phi ash tracer is shown.</figcaption></figure>

<caption>**Table 2**</caption>

Volcanoes with VIF values below either 35 or 60 for use in the GWR model.

<table><thead><tr><th>VIF &lt; 35</th><th>VIF &lt; 60</th></tr></thead><tbody><tr><td>Apollinaris Patera</td><td>Alba Patera</td></tr><tr><td>Cerberus Fossae</td><td>Apollinaris Patera</td></tr><tr><td>Olympus Mons</td><td>Arsia Mons</td></tr><tr><td>Syrtis Major</td><td>Ascraeus Mons</td></tr><tr><td>Tyrrhena Patera</td><td>Cerberus Fossae<br>Electris<br>Elysium Mons<br>Hadriaca Patera<br>Hecates Tholus<br>Olympus Mons<br>Syrtis Major<br>Tyrrhena Patera</td></tr></tbody></table>

# 4. Discussion

## 4.1. Volcanism as a hydrator of regolith

As mentioned earlier, explosive volcanism can be a pathway to regolith hydration both syn- and post-eruptively either through hydration of pyroclasts as the eruption is occurring or through adsorption of water into pyroclast deposits over geologic timescales.

From our analysis, we have found that modeled deposited ash from major volcanoes overlap spatially with regions of enriched H₂O in GRS-derived chemical maps. In particular, H₂O enrichment near Apollinaris Patera, including sections within the Medusae Fossae Formation, overlap well with modeled ash deposition (Fig. 10). We also see good agreement in parts of the Arabia Terra and Terra Sabaea regions, consistent with the geochemical interpretations by Bates et al. (2023). This may be due to wind patterns concentrating more ash in these areas, or the proximity of multiple nearby volcanoes may provide greater degrees of ash deposition in these areas. The presence of sustained bodies of water in these areas may also have been higher than others. The individual R² results in Fig. 8 corroborate these interpretations, showing Cerberus Fossae and Syrtis Major as having the highest correlation with the GRS H₂O data.

It is worth noting that because we are invoking an ancient Mars scenario, ash deposits, if present, had billions of years to be modified, reworked, and remobilized. However, cross referencing the extent of the proposed friable layered deposits (Tanaka, 2000) (FLDs, see Fig. 1) with global geologic mapping by Tanaka et al. (2014) shows that all the FLDs lie predominantly within Noachian or Hesperian aged units (Fig. 11), except for the polar layered deposits, which are Amazonian aged. Further work by Kerber et al. (2012) found many of the FLDs to be feasibly formed by volcanic airfall. Therefore, the precedent exists for very old ash deposits to be preserved over geologic time on Mars. Furthermore, old geologic units mapped as phyllosilicates may be ash
<figure><img src="image_11.png" /><figcaption>Fig. 7. a. Plot showing both volcano autocorrelation (ISA) and multicollinearity (VIF) metrics. Autocorrelation is represented by the blue line and axis, showing the distance in that particular volcano deposition model output at which the most autocorrelation is occurring. The dashed line shows the average ISA peak for all volcanoes. Multicollinearity is represented by the orange dashed line and axis. Notice this axis is on a log scale. b. Plot showing R² vs distance band in kilometers. The VIF < 35 case is shown in blue while the VIF < 60 case is shown in orange. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

beds that have been altered to clays (e.g. Ehlmann et al., 2013; Thollot et al., 2012; Weitz et al., 2015; Ye and Michalski, 2021), and therefore are not recognized as friable layered deposits in the geologic record.

## 4.2. Explosive volcanic derived hydration in the context of other hypotheses

We have shown that the eruption and transport of explosive volcanic ash and subsequent/concurrent hydration may have contributed to lateral changes in martian regolith hydration, especially proximal to Apollinaris Patera and Arabia Terra. Ash hydration, however, likely acted along with other processes to hydrate regolith over geologic

timescales. Fe-sulfate, dust cover related processes, and passive magmatic degassing are considered here. Fe-sulfates can hydrate the bulk regolith via leaching from hydrothermal vents or fumaroles (King and McLennan, 2010), and via acid fog redox reactions (Tosca et al., 2008). In the southern highlands in particular, GRS derived sulfur and H₂O maps have a strong correlation implying that sulfates may play a large role in hydrating the bulk martian regolith in the southern mid-latitudes of Mars (Hood et al., 2019; Karunatillake et al., 2014, 2016).

Increased fine grained dust cover can lead to hydration via adsorption of H₂O onto dust grains with specific surface areas and porosities (Jänchen et al., 2009; Milliken et al., 2007). However, Audouard et al. (2014) have suggested dust cover to have a negligible effect on
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Bar graph showing individual $R^2$ values in the GWR model for all volcanoes with a VIF < 60 at a 15,000 km bandwidth.</figcaption></figure>

hydration. Furthermore, work by Hood et al. (2019) found dust cover to have weak cumulative correlation with any of the GRS regolith chemistry maps, including water.

The moderate correlations found here in the GWR analysis of explosive volcanism are within the range for the Fe-sulfate hydration mechanism correlations found by Hood et al. (2019). The processes creating the hydrated Fe-sulfates are likely distinct from the hydration of explosively derived ash beds considered in this work. Although Fe-sulfates form via volcanically derived processes, they exist localized in hydrothermal or fumarolic systems. Explosive volcanic ash, conversely, can become hydrated several hundred kilometers away from its source as ash is transported through the atmosphere in a hydrated state or later hydrated over geologic timescales.

the martian regolith. To constrain every factor is not realistic, however, deposits derived from explosive volcanism are likely sinks for mineralogically bound H₂O that develop as ash breaks down and hydrates. Through this work, we modeled explosive volcanism depositional patterns and confirmed a moderate positive correlation with GRS-derived patterns of martian regolith hydration. Regions proximal to Apollinaris Patera such as the Medussae Fossae Formation are particularly well explained, while the elevated hydration region in Arabia Terra is moderately well explained. When comparing to other hydration mechanisms that have strong correlations with hydrated regolith, such as Fe-sulfate related processes, we find hydration of explosive volcanic deposits to be roughly commensurable. Future work can include the possible role of ashfall resurfacing of glaciers as a contributor to chemically weathered landscapes that preserve chemically bound H₂O at depth. Ash within glaciers subject to insolation can undergo chemical alteration at low pH due to scavenged S-phases. As the glaciers sublimate over geologic time scales, the residual weathered sediment may accumulate as indurated units, resembling isochemically weathered sedimentary strata. For example, Niles and Michalski (2009) and Baccolo et al. (2021) have hypothesized that hydrated sulfate lithostrata on Mars, such as at Meridiana Planum, arose in such glacial environments instead of sustained groundwater systems. Additional geochemical data can be incorporated into such analyses in hierarchical multivariate linear regression models, including major elements – iron, silicon – and volcanically derived elements – sulfur, chlorine – along with others.

Furthermore, we tested only one climatic scenario: that of a cold and dry atmosphere at 1 bar. Future studies could investigate a warm and wet atmosphere or different atmospheric pressures. Conducting similar statistical analysis as in this study with these new scenarios would provide a broader outlook on possible ash distribution and its influence on hydrated regolith.

## 5. Conclusions

There are likely myriad factors that influence the hydration state of

<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Sensitivity tests for various parameters of a hypothetical Apollinaris Patera eruption, including H₂O wt%, pyroclast density, pyroclast temperature, mass eruption rate, grainsize distribution, and Tharsis topography. The color map is on a log scale. Notice the differences from the base simulation, namely the high mass eruption rate (MER) simulation, the differing density simulations, and the coarser grainsize distribution simulation. A lower H₂O wt% simulation was run, but was not numerically stable.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 10.** GWR results plotted with GRS hydration data. Each panel shows a global view of mars. A. The VIF < 35 case where color corresponds to GWR-predicted H₂O wt%. B. The VIF < 60 case where color corresponds to GWR-predicted H₂O wt%. Red triangles in A and B are locations of volcanoes that are used in the corresponding GWR model C. GRS hydration data for reference. Panels A and B have a 2% histogram stretch applied. Notice too their data range is less than that of panel C. The hydration weight percents given in panels A and B are derived from the output of the GWR model, and not from any physical process modeled, (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Fig. 11.** Martian geologic units (Tanaka et al., 2014) constrained within friable layered deposit boundaries (Tanaka, 2000). Notice that most friable layered deposits contain Hesperian or Noachian aged units.</figcaption></figure>

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

GRS H₂O data along with other chemical datasets are available at the LSU Digital Commons (https://digitalcommons.lsu.edu/geo_psl/1/; Rani et al., 2022). MOLA data used in background maps is available from the USGS Astropedia database (https://astrogeology.usgs.gov/search). The GIS version of the Tanaka et al. (2014) map is available at the USGS publication page (https://pubs.usgs.gov/sim/3292/). All code as well as input/output files are available at the following GitHub repository (https://github.com/palatyle/LMD_MATHAM_Utils). Links to the model output data are also available at the LMD_MATHAM_Utils repository. The Generic PCM modified for this manuscript methodology is available at the following GitHub repository (https://github.com/palatyle/LMD_GCM_Paladino).

## Acknowledgments

This work was supported by NASA MDAP grant 80NSSC18K1375. The authors wish to thank Dr. Michael Herzog for the use of the ATHAM software and Dr. Laura Kerber for assistance in compiling and running the volcano submodule of the PCM. The authors also wish to thank Dr. Ken Aho and Dr. Sarah Godsey for helpful conversations regarding statistical methods. This research made use of the resources of the High Performance Computing Center at Idaho National Laboratory, which is supported by the Office of Nuclear Energy of the U.S. Department of Energy and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517.

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