Functions with Questionable Future

Some functions represent temporary solutions to data delivery problems that made sense in the past. fetchKSSL() relies on an older snapshot of the NCSS LDM (2020), fetchRaCA() relies on a pre-release of the current RaCA database, and SoilWeb_spatial_query() has been superseded by soilDB functions which interface with Soil Data Access (see SDA_query() and SDA_spatialQuery).

• fetchKSSL(): get soil characterization and (limited) morphology data (soil color, structure, redoximorphic features)
• fetchRaCA(): get records associated with the RaCA collection
• SoilWeb_spatial_query(): simple interface to spatial intersection between SSURGO polygons and user supplied bounding-box

There are currently no replacements for full functionality provided by fetchKSSL() and fetchRaCA(). See fetchLDM() for a modern approach to downloading NCSS LDM snapshot data.

MLRA

Derived from the spatial intersection between MLRA and SSURGO polygons, with area computed on the ellipsoid from geographic coordinates. Membership values are area proportions by soil series.

MLRA “membership” for the LUCY soil series.

.mlra <- x$mlra[x$mlra$series == 'LUCY', ]
.mlra[order(.mlra$membership, decreasing = TRUE), ]

Soil series can be found in multiple MLRA, therefore MLRA membership can be modeled using directed graphs.

Geomorphic Summaries

Geomorphic summaries are computed from SSURGO component “geomorphology” tables. These represent a cross-tabulation of soil series name x geomorphic position in several landform and surface shape description systems. These systems are defined in the Field Book for Describing and Sampling Soils.

There are several associated functions in the sharpshootR package for visualizing these summaries (e.g. vizHillslopePosition()).

• hillslope position (2D)
• geomorphic component: hills (3D)
• geomorphic component: mountains (3D)
• geomorphic component: terrace (3D)
• geomorphic component: flats (3D)
• surface curvature across-slope
• surface curvature down-slope

Note that the n column in each table is the number of component geomorphic data records associated with each soil series. It is possible for a single component to have multiple geomorphic positions defined.

# hillslope position
head(x$hillpos)
# geomorphic component: hills
head(x$geomcomp)
# geomorphic component: mountains
head(x$mtnpos)
# geomorphic component: terraces
head(x$terrace)
# geomorphic component: flats
head(x$flats)
# surface curvature across slope
head(x$shape_across)
# surface curvature down slope
head(x$shape_down)

Siblings

SoilWeb defines the term “siblings” as those components or soil series that co-occur within map units. The siblings() function returns siblings for a single soil series, with a tabulation of how many times each sibling shares a common map unit.

Siblings of the PIERRE soil series, limited to just major components. The n column describes how many map units are shared between a sibling and the PIERRE series.

sib <- siblings('PIERRE', only.major = TRUE)
head(sib$sib)

Other

Percentiles of the National Commodity Crop Productivity Index are computed from SSURGO component records, by soil series name. These include both irrigated and non-irrigated versions of the NCCPI.

head(x$NCCPI)

Ecological classification membership are computed from map unit polygon area and component percentages.

head(x$ecoclassid)

Parent Material Summaries

Parent material kind and origin, tabulated by soil series name. The n column is the number of component parent material records associated with a specific parent material kind or origin. The total column is the total number of component parent material records by soil series. The final column, P, is the associated proportion.

head(x$pmkind)
head(x$pmorigin)

Frost-Free Period

Number of days in the 50%, 80%, and 90% probability frost-free period, derived from daily minimum temperatures greater than 0 degrees C.

These maps are based on 50/80/90 percent probability estimates for the last spring frost and first fall frost (day of year). See the related algorithm documentation for details.

Values have been cross-checked with 300+ weather stations in CA.

 ols(formula = ffd.50 ~ prism_ffd, data = z)
 

      Min       1Q   Median       3Q      Max 
 -278.344  -16.875    2.436   14.323  274.604 
 

Frost-Free Days

Percentiles of frost-free days (FFD) at the 50% probability threshold.

Design Freeze Index

• number of degree days below 0 deg C, 90th percentile

From NSSH Part 618.33 Frost Action, Potential:

Part 618, Subpart B, Exhibits, Section 618.85 is a map that shows the design freezing index values in the continental United States. The values are the number of degree days below 0 deg C for the coldest year in a period of 10 years . The values indicate duration and intensity of freezing temperatures. The 250 isoline is the approximate boundary below which frost action ceases to be a problem.

Methods:

• using units of degrees Celsius, and daily average air temperature (\(Tavg\))
• freezing degree days for a single year: \(FI = sum( abs( min(0, Tavg) ) )\)
• design freezing index, over 30 year record: \(DFI = Q90( FI )\) where FI is the stack of annual FI

Notes:

• There is a fairly large difference in where the 250 DFI isoline falls, depending on the temperature units.
• The 90th percentile of FI seems to track the notion of “coldest year in 10 years”.
• The “average of 3 coldest years in 30” method gives different results, but spatial patterns are the same.
• Related conversation on the calculation and interpretation.

Growing Degree Days (C)

Mean (Celsius) growing degree days, derived from the 800m PRISM daily minimum/maximum temperature data over the interval of 1981–2010.

Calculation reference: http://agron-www.agron.iastate.edu/courses/Agron541/classes/541/lesson02b/2b.1.1.html

\[GDD_i = [ min(T_{max}, upper_{threshold}) + max(Tmin, lower_{threshold}) / 2 ] - T_{base}\]

\[GDD_i = max(GDD_i, 0)\]

Effective Precipitation

Annual sum of monthly (total) precipitation - monthly (estimated) evapotranspiration, averaged over the interval of 1981–2010. Potential evapotranspiration (PET) estimated via Thornthwaite’s method of 1948. Input sources included:

• 800m resolution, monthly, total precipitation (PRISM group)
• 800m resolution, monthly, mean air temperature (PRISM group)

Processing in GRASS GIS.

Fraction of Precipitation as Rain

This map contains estimates of the fraction of total (annual) precipitation as rain, derived from 800m daily PRISM Tmax and PPT grids (1981–2010). Calculations were performed using GRASS GIS, with methods and estimated parameters of the conditional snow probability function from Rajagopal and Harpold (2016).

Partition PPT into snow/rain:

\[rain = PPT - snow\]

\[snow = PPT * Pr(snow)\]

compute \(Pr(snow)\) as a function of \(Tmax\) using exponential identity for hyperbolic tangent function:

Evaluate conditional probability (fraction) of snow on a daily basis:

\[Pr(snow) = a * ( tanh(b * (Tmax - c) ) - d )\]

a:-0.5, b:0.21, c:0.5, d:1

\[tanh(x) = (1 - exp(-2*x)) / (1 + exp(-2*x))\]

\[Pr(snow) = -0.5 * ( (1 - exp(-2 * (0.21 * (Tmax - 0.5) ))) / (1 + exp(-2 * (0.21 * (Tmax - 0.5) ))) - 1 )\]

\[rain = PPT - (PPT * Pr(snow))\]

For each year(\(i\)):

\[rain fraction_i = sum(rain_i) / sum(PPT_i)\]

Percentages have been converted to integers ranging from 0 to 100.

Rajagopal, S. and A.A. Harpold. 2016. Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States. Journal of the American Water Resources Association, 52: 1142-1154.

Geomorphons

A cross-tabulation of geomorphon by soil series name was computed from the current gSSURGO (30m) grid and a 30m grid of geomorphons. Currently, these data are only available within CONUS.

These maps were generated using the r.geomorphon GRASS GIS module, with the following parameters:

r.geomorphon --o dem=elev30_int forms=forms30 search=75 skip=5 flat=1.718

The source DEM was a 10m / 30m resolution compilation of USGS NED data, rounded to integers. The “flat” threshold (1.718 deg) is based on a 3% slope break.

Jasiewicz, J., Stepinski, T., 2013, Geomorphons - a pattern recognition approach to classification and mapping of landforms, Geomorphology, vol. 182, 147-156. (https://doi.org/10.1016/j.geomorph.2012.11.005)

Proportions are weighted by total soil series area (within CONUS) as informed by the component name and associated component percentage.

head(x$geomorphons)