Investigating the Distribution of Soil Inorganic Carbon, and the Role of Dust
Deposition in Dryland Soils
Authors: Ghahremani Zahra 
Published: January 2025

The importance of dust deposition in the biogeochemical cycling of nutrients,
sediment redistribution, and soil formation is well-known. Anthropogenic
activities such as changing land use and agricultural practices can influence
dust flux deposition rates, dust organic matter content, and the chemical
composition of dust. This study examines the influence of agricultural
activities on aeolian deposition in a the xeric intermontane westerns USA. We
investigate seasonal and spatial variations in dust deposition rates, dust
organic matter, dust inorganic matter, and dust geochemistry from passive dust
traps. The Reynolds Creek Experimental Watershed (RCEW; Owyhee Mountains, Idaho)
and the Northwest Irrigation and Soils Research Lab (Snake River Plain, Idaho)
are considered non-agricultural (native) and agricultural (heavily managed)
sites respectively. We use aeolian deposition flux, dust organic and inorganic
content, and geochemical analysis to assess the impact of human activities on
aeolian processes in these semi-arid study areas. Our results show high
variability in monthly and annual aeolian mass flux. Notably, the annual dust
flux at the agricultural site (50.2 g/m2/year) is 7.6 times higher
than the annual dust flux rate at the non-agricultural site (6.6
g/m2/year). We compare measured loess accumulation rates with loess
accumulation on millennial timescales inferred from loess thickness and loess
ages from Optically Stimulated Luminescence (OSL). Based on the annual dust flux
measured in this study, the dust accumulation rates for soils in the Snake River
Plain 4 cm ka-1. OSL ages between 50.14 ± 9.19 ka to 8.32 ± 1.88 ka
from the Snake River Plain site indicate average accumulation rates from ~ 50 ka
to 34 ka and ~ 34 ka to 18 ka are 6 ± 4 cm ka-1 and 6 ± 3 cm
ka-1 respectively. However, dust accumulation rates increase to
17 ± 4 cm ka-1 in the interval between 18 ka to 8 ka. We infer
increased rates of deposition may reflect increased sediment availability
following the ~18 ka Bonneville Flood on the ancestral Snake River. Over the
last 8,000 years, the rate decreased to 7 ± 2 cm ka-1.

Dust organic matter analysis indicates a relatively high percentage of organic
matter in dust. While aeolian deposits in both agricultural and non-agricultural
areas show organic matter content of around 18 g/m2/year (~26%) and 4
g/m2/year (~38%), respectively, the soil in both locations
exhibits a low organic matter percentage of 1.3% and 1.8%, respectively.
Geochemical analysis shows the concentration of most elements is generally lower
than the average upper continental crust concentration for both study areas;
however, dust deposition is enriched in some trace elements such as Co, Cd, and
Zn at both the agricultural and non-agricultural sites.

Dust is a well-known source of calcium, which can lead to the formation of soil
inorganic carbon (SIC) in arid and semi-arid areas. SIC plays a vital role in
shaping global carbon cycles, influencing hydrological processes, and affecting
climate models. However, it is somewhat surprising that there has been a notable
dearth of modeling studies dedicated to comprehensively understanding the
formation, dynamics, and distribution of SIC in North America. Numerous surveys
have characterized the distribution of SIC in dryland soils, showing
accumulation of SIC usually occurs below 50 cm in the Bk horizon, with the depth
or presence of SIC appears to be influenced by various factors, including
precipitation, slope, and parent material. Nonetheless, high variation in the
distribution of SIC is a significant challenge to mapping and spatio-temporal
modeling of SIC as uncertainty increases with distance from the sampled area. In
this study, we look for alternative land surface parameters, paired with
satellite imagery and machine learning methods, to lower the uncertainty in
unsampled regions compared to traditional spatial interpolation methods. We
utilize the Random Forest Regressor (RFR) and Random Forest Classifier (RFC)
models, identified as the top-performing models, to predict SIC across the
continental United States (CONUS). To manage the high number of samples with
zero SIC values the models are divided into two pieces by training a classifier
to distinguish zero from non-zero SIC, and then training a regressor/classifier
(RFC and RFR) on the non-zero target dataset.

The results indicate the moderate effectiveness of the RFR model with a
coefficient of determination (R2) of 0.37, Root Mean Square error
(RMSE) of 3.97, and Mean Absolute Error (MAE) of 3.1 in predicting SIC. The RFC
model with an overall accuracy of 0.61 classified the SIC into three classes. We
fed several soil properties, climate variables, land surface parameters, and
geographical information (latitude and longitude) as SIC controlling factors to
the ML models. Then, we used the developed model in the form of Google Earth
Engine to produce distribution maps of SIC.

Soil pH influences SIC accumulation and crystallization, with higher soil pH
resulting in precipitation of SIC. Both RFR and RFC models highlighted soil pH
as the primary controlling factor in SIC accumulation. It is important to note
that the relationship between pH and SIC is indeed complex; pH can both
influence and be influenced by SIC levels, creating a dynamic relationship where
each factor can affect the other. In addition to pH, geographical data (latitude
and longitude) are primary factors which control SIC in both models, suggesting
these variables might encapsulate essential information related to regional
climate variations, terrain characteristics, or soil properties strongly
associated with SIC content. Prior studies identify precipitation and soil
parent materials as important variables in SIC formation. We identified a
precipitation threshold of 1700 mm for SIC accumulation across the CONUS,
differing from the threshold of approximately 500 mm annual precipitation
identified in other arid and semi-arid regions. Despite utilizing lithology as a
substitute for parent material, the model did not recognize it as a significant
factor in SIC formation, likely due to the limited resolution of the lithology
data.

This thesis provides two comprehensive chapters related to dust deposition and
the role of dust in the development of calcic soils. Chapter 1 examines rates
and seasonality of dust accumulation in managed and native sites in southwestern
Idaho, and inferred changes in dust accumulation over modern to millennial
timescales. Chapter 2 provides a new analysis and map of calcic soil
distribution in the continental United States and predicts controls on SIC
distribution using a novel combination of models.