So, the goal of this project is to see if the temporal gradient due to basin incision is reflected in map-view, and to see if there is any pattern of soil colors that may indicate cyclicity in climate.
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(sp)
library(ggplot2)
# Load .csv
paleosolsdf <- read.csv("GonaPaleosols.csv") # this is a .csv containing the column/soil ID, avg value, approx age, if the soil formed in a glacial period or not, and lat/long from GPS. I pulled these from various field notebooks
paleosolsdf
## ï..SITE_ID MUSELL_VALUE AGE Glacial POINT_X POINT_Y
## 1 Kilatioli 19 2.0 11 0 11.07767 40.33602
## 2 Garsale Dora 2.0 11 0 11.07762 40.33208
## 3 Ma Our Dora 5.0 25 1 11.07462 40.34095
## 4 Burtele 4.0 45 1 11.10753 40.39680
## 5 Ounda Yaalu South 3.0 78 0 11.04632 40.42309
## 6 Bolo Dora 5.0 175 1 11.11941 40.39862
## 7 Yaalu North 5.5 220 1 11.06061 40.42621
## 8 Gawis 4.5 220 1 11.06705 40.42064
## 9 Erole 2.5 10 0 11.00579 40.34070
## 10 Odele 18 2.5 11 0 11.03337 40.36313
## 11 Kilatitoli 19 2 2.5 11 0 11.07962 40.33835
## 12 ASASH 11 3.0 150 0 11.06472 40.40662
## 13 ASB 18 2T 3.0 75 0 11.04276 40.40662
## 14 ASB 18 2B 4.5 120 1 11.04276 40.42127
## 15 YAL 18 1T 3.0 60 0 11.04771 40.42265
## 16 YAS 18 1B 3.0 75 0 11.04717 40.42265
## 17 Odele 18-72 4.0 30 1 11.03806 40.35193
## 18 Erole S 5.0 20 1 11.00484 40.34251
## 19 Busidima NB 4.0 NA NA 11.11830 40.39779
## 20 Busidima NT 5.0 130 1 11.11927 40.39779
## 21 Gawis N 4.0 30 1 11.06752 40.44396
## 22 Gawis M 2.0 30 1 11.06195 40.44308
## 23 Yaalu 3.0 75 0 11.05410 40.40919
## 24 Yaalu T 2.0 75 0 11.05531 40.40430
## 25 Burtele 18 3.5 145 0 11.12382 40.38645
## 26 ODE 17 4 3.0 12 0 11.03700 40.37449
## 27 Odele 3.0 12 0 11.04178 40.37172
## 28 ODE 17 6 4.0 NA NA 11.03094 40.37207
## 29 GFL 17 T 2.0 10 0 11.05842 40.39080
## 30 ASB 2.5 75 0 11.04372 40.42509
# Check Projection
st_crs(paleosolsdf)# ok, need to define
## Coordinate Reference System: NA
# Create geometry
paleosols_sf <- st_as_sf(paleosolsdf, coords = c("POINT_X", "POINT_Y"))
# Make sure it's working...
plot(paleosols_sf)
# Define projection
st_crs(paleosols_sf)
## Coordinate Reference System: NA
st_crs(paleosols_sf) <- "ESRI:37001" # I had a hard time finding systems that covered Ethiopia so I used this worldwide projection
# Check projection...
st_crs(paleosols_sf)
## Coordinate Reference System:
## User input: ESRI:37001
## wkt:
## GEOGCRS["GCS_WGS_1966",
## DATUM["D_WGS_1966",
## ELLIPSOID["WGS_1966",6378145,298.25,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["unknown"],
## AREA["World"],
## BBOX[-90,-180,90,180]],
## ID["ESRI",37001]]
# Define variables for plotting
age <- paleosols_sf$AGE # pulls out ages
value <- paleosols_sf$MUSELL_VALUE # pulls out values
glacial <- paleosols_sf$Glacial # pulls out glacial vs interglacial
# Plot those values
hist(age) #it's obvious we focused on young stuff...
hist(value) # seems to be bimodal, which is cool - light vs dark soils for glacial vs interglacials?
hist(glacial) # distribution of glacial (1) vs interglacials (0)
# Maps
plot(age, value) # This is interesting - this roughly corresponds with plots regarding East African climate over the past ~400 ka. Notably low values at 10 ka and 75 ka correspond with the African Humid Period and the end of the last interglacial. Light colors before this correspond with the last glacial period. There isn't enough data 100 ka and older to really determine any pattern.
plot(age, glacial) # Using the glacial index, it is more obvious. When compared to the figure I used in my presentation, these match up with glacial/interglacial cycles form the marine isotope record.
ggplot(paleosols_sf) +
geom_sf(aes(size=age)) +
ggtitle("Paleosol Age ka") +
scale_size_continuous(range = c(2, 7)) # Age plot - having size represent age. Larger circles = older paleosols. I had them range from size 2-7. I had some trouble with this accurately representing the data since most of the data is 10-75 ka, so the size won't vary too much, but these reflect two interglacials and one glacial period. But, you can see the linear trend of old deposits along the Busidima and Gawis Rivers, which run on either side of the cluster of old points. The younger points above this are from stratigraphically higher positions, thus the younger age.
## Warning: Removed 2 rows containing missing values (geom_sf).
ggplot(paleosols_sf) +
geom_sf(aes(color=value), size = 3) +
ggtitle("Paleosol Munsell Value") +
scale_color_gradient(low="#372A24", high="#C9AD97") # Color plot - having color represent paleosol value. I added a color gradient with hex codes I pulled directly from a Munsell Color page. I used the 10YR page, which contains most of the colors used for describing these soils. There are some clusters of similar colors, but nothing too exciting.
ggplot(paleosols_sf) +
geom_sf(aes(color=glacial), size = 3) +
ggtitle("Glacial vs Interglacial Cycles at Gona") # Glacial vs interglacial plot - blue = glacial period, black = interglacial.
ggplot(paleosols_sf) +
geom_sf(aes(color = value, size=age)) +
ggtitle("Paleosol Age ka and Value") +
scale_color_gradient(low="#372A24", high="#C9AD97") +
scale_size_continuous(range = c(2, 7)) # Combined the two - color = value and size = age.
## Warning: Removed 2 rows containing missing values (geom_sf).
ggplot(paleosols_sf) +
geom_sf(aes(color = value, size=glacial)) +
ggtitle("Relationship of Paleosol Color and Glaical Periods") +
scale_color_gradient(low="#372A24", high="#C9AD97") +
scale_size(range = c(2, 5)) # Combined color and glacial period info - large circles represent glacial periods. This is pretty evenly split up into dark interglacial cycles and light glacial period soils.
## Warning: Removed 2 rows containing missing values (geom_sf).
