MLRA: 15, 18, 27, 35
report version 2.0
2023-03-07

This report is designed to provide statistical summaries of the environmental properties for one or more MLRA Summaries are based on raster data extracted from fixed-density sampling of map unit polygons. Percentiles are used as robust metrics of distribution central tendency and spread.

Modified Box and Whisker Plots

Whiskers extend from the 5th to 95th percentiles, the body represents the 25th through 75th percentiles, and the dot is the 50th percentile.

Suggested usage:

• Gauge overlap between map units in terms of boxes (25th-75th percentiles) and whiskers (5th-95th percentiles).
• Non-overlapping boxes are a strong indication that the central tendencies (of select raster data) differ.
• Distribution shape is difficult to infer from box and whisker plots, remember to cross-reference with density plots below.

PRISM

ISSR-800

Aggregate soil properties developed from SSURGO/STATSGO at 800m resolution.

Gamma Spectroscopy

Population Density

Density Plots

These plots are a smooth alternative (denisty estimation) to the classic “binned” (histogram) approach to visualizing distributions. Peaks correspond to values that are most frequent within a data set. Each data set (ID / variable) are rescaled to {0,1} so that the y-axis can be interpreted as the “relative proportion of samples”. Note that density estimates are constrained to the range defined by the 1–99 percentiles.

Suggested usage:

• Density plots depict a more detailed summary of distribution shape.
• When making comparisons, be sure to look for:
  • multiple peaks
  • narrow peaks vs. wide “mounds”
  • short vs. long “tails”

PRISM

ISSR-800

Aggregate soil properties developed from SSURGO/STATSGO at 800m resolution.

Gamma Spectroscopy

Monthly Summaries

Median PPT vs. PET, bounded by 25th and 75th percentile.

Modified box-whisker comparisons by month.

Monthly inter-quartile range.

Tabular Summaries

Table of select percentiles, by variable. In these tables, headings like “Q5” can be interpreted as the the “5th percentile”; 5% of the data are less than this value. The 50th percentile (“Q50”) is the median.

Median Values

MLRA	Elevation (m)	Effective Precipitation (mm)	Frost-Free Days	Mean Annual Air Temperature (degrees C)	Mean Annual Precipitation (mm)	Growing Degree Days (degrees C)	Fraction of Annual PPT as Rain	Design Freeze Index (degrees C)
15	436	-245.28	276	15.18	529	2428	99	0
18	442	-229.67	272	16.07	597	2557	99	2
27	1356	-476.25	155	10.85	190	1858	93	242
35	1767	-426.54	170	11.64	257	2056	95	204

Elevation (m)

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	82	140	269	436	641	850	967.3
18	99	146	262	442	764	1229	1495.0
27	1174	1186	1222	1356	1562	1770	1906.8
35	1287	1404	1580	1767	1960	2148	2259.0

Effective Precipitation (mm)

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	-588.16	-525.98	-384.38	-245.28	-48.40	241.25	378.35
18	-549.78	-501.15	-397.52	-229.67	-41.76	143.77	279.67
27	-576.57	-569.79	-533.59	-476.25	-401.17	-330.62	-274.62
35	-654.56	-595.77	-523.71	-426.54	-331.65	-243.04	-190.22

Frost-Free Days

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	208	216	239	276	326	365	365
18	193	210	241	272	312	333	342
27	126	135	144	155	164	169	174
35	135	144	157	170	190	210	225

Mean Annual Air Temperature (degrees C)

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	13.75	14.08	14.63	15.18	15.79	16.45	16.78
18	12.64	13.56	15.16	16.07	16.66	17.17	17.55
27	8.94	9.31	10.17	10.85	11.41	11.81	12.10
35	8.65	9.29	10.39	11.64	12.73	14.05	15.13

Mean Annual Precipitation (mm)

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	238.7	287	386	529	721	1000	1165.30
18	295.0	326	424	597	791	958	1100.00
27	129.0	132	152	190	228	277	311.00
35	179.0	190	214	257	317	383	423.15

Growing Degree Days (degrees C)

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	1963.0	2058.4	2229.0	2428	2538	2647	2725
18	1937.5	2115.0	2404.5	2557	2660	2809	2898
27	1490.0	1594.0	1731.0	1858	1949	2020	2061
35	1534.0	1664.0	1851.0	2056	2245	2428	2613

Fraction of Annual PPT as Rain

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	96	97	98	99	99	100	100
18	90	93	98	99	99	99	99
27	86	89	92	93	95	96	96
35	90	92	94	95	96	97	98

Design Freeze Index (degrees C)

MLRA	Q5	Q10	Q25	Q50	Q75	Q90	Q95
15	0	0	0	0	3	5	8.0
18	0	0	0	2	9	33	56.5
27	171	180	198	242	298	328	348.0
35	66	103	152	204	264	315	350.0

Geomorphon Landform Classification

Proportion of samples within each map unit that correspond to 1 of 10 possible landform positions, as generated via geomorphon algorithm. Landform classification by this method is scale-invariant and is therefore not affected by computational window size selection.

Suggested usage:

• Use the graphical summary to identify patterns, then consult the tabular representation for specifics.
• “Flat” is based on a 3% slope threshold.
• Map units are organized (in the figure) according to the similarity, computed from proportions of each landform position.
• The dendrogram on the right side of the figure describes relative similarity. “Lower branch height” (e.g. closer to the right-hand side of the figure) denotes more similar landform positions.
• Landform class labels and colors are aligned with an idealized shedding → accumulating hydrologic gradient.

Geomorphon Proportions

mlra	flat	summit	ridge	shoulder	spur	slope	hollow	footslope	valley	depression
15	0.047	0.047	0.158	0.004	0.153	0.191	0.127	0.017	0.207	0.048
18	0.041	0.035	0.150	0.011	0.157	0.198	0.149	0.026	0.198	0.035
27	0.409	0.014	0.053	0.007	0.076	0.214	0.094	0.046	0.081	0.005
35	0.265	0.020	0.119	0.056	0.097	0.167	0.074	0.063	0.117	0.021

Landcover Summary

These values are from the 2011 NLCD (30m) database.

Landcover Proportions

mlra	Open Water	Developed, Open Space	Developed, Low Intensity	Developed, Medium Intensity	Developed, High Intensity	Deciduous Forest	Evergreen Forest	Mixed Forest	Shrub/Scrub	Cultivated Crops	Woody Wetlands
15	0.02	0.07	0.02	0.01	0	0.01	0.12	0.16	0.54	0.04	0
18	0.04	0.05	0.01	0.00	0	0.12	0.33	0.00	0.43	0.01	0
27	0.02	0.00	0.00	0.00	0	0.00	0.04	0.00	0.92	0.01	0
35	0.00	0.01	0.00	0.00	0	0.00	0.17	0.00	0.81	0.00	0

Multivariate Summary

The following “ordination” summarizes environmental variables by MLRA. The flattening of multivariate data (16 dimensions) onto an optimal 2D projection is performed using principal coordinates. Ellipses represent 50% probability contours via multivariate homogeneity of group dispersions. MLRA delineations with more than 1,000 samples are (sub-sampled via cLHS). MLRA with very low variation in environmental variables can result in tightly clustered points in the ordination. See this chapter, from the new Statistics for Soil Scientists NEDS course, for an soils-specific introduction to these concepts.

Suggested usage:

• Colors match those used in the density plots above. Be sure to cross-reference this figure with density plots.
• The relative position of points and ellipses are meaningful; absolute position will vary each time the figure is generated.
• Look for “diffuse” vs. “concentrated” clusters: these suggest relatively broadly vs. narrowly defined map unit concepts.
• Nesting of clusters (e.g. smaller cluster contained by larger cluster) suggests superset/subset relationships.
• Overlap is proportional to similarity.

Pair-wise comparisons at the 90% level of confidence.

Raster Data Correlation

The following figure highlights shared information among raster data sources based on Spearman’s Ranked Correlation coefficient. Branch height is associated with the degree of shared information between raster data.

Suggested usage:

• Look for clustered sets of raster data: typically PRISM-derived and elevation data are closely correlated.
• Highly correlated raster data sources reduce the reliability of the “raster data importance” figure.

Raster Data Importance

The following figure ranks raster data sources in terms of how accurately each can be used to discriminate between map unit concepts.

Suggested usage:

• Map unit concepts are more consistently predicted (by supervised classification) using those raster data sources with relatively larger “Mean Decrease in Accuracy” values.
• Highly correlated raster data sources will “compete” for positions in this figure. For example, if elevation and mean annual air temperature are highly correlated, then their respective “importance” values are interchangeable.

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Report configuration and source code are hosted on GitHub.