Scaling Soil Survey
Background and Justification
The National Cooperative Soil Survey (NCSS) produces one of the most comprehensive, high resolution environmental datasets in the U.S., with generalized soil resource information for the entire nation and detailed data for nearly 80% of the country (Soil Survey Staff, 2006a). Recent developments in computing power coupled with the increasing availability of geographic information systems (GIS) on the desktop has made possible new and exciting applications of digital soil survey products. In many instances, however, the scale at which soil survey databases are produced and the use of "representative" values does not meet stakeholder needs (National Research Council, 1993; Brasher and Benham, 1996). Specifically, users have noted that there is a need for basic statistical measures (mean, variance, confidence intervals, etc.) on reported soil properties (Brown, 1988; Brown and Huddleston, 1991), along with some measure of spatial variability (Brubaker and Hallmark, 2001; Burrough, 2001). Thus digital soil survey data are often used incorrectly, miss-interpreted, or not used at all. Clearly, new products are needed which can meet the modern demands placed on soil survey. We intend to develop a set of methods for creating these products, by scaling (up and down) existing soil survey information, with an assessment of uncertainty. Evaluation of digital proxies for soil forming factors, historic pedon data (3000+ points), statistical models, and landscape-segmentation approaches will be used.
The NCSS has developed two digital soil survey products, representing two levels of generalization. The most detailed soil survey information, Soil Survey Geographic Database (SSURGO), is compiled at a scale of 1:12,000 to 1:24,000 from point observation and mental models of soil formation (Soil Survey Staff, 2006a). Generalized soils information is provided in the U.S. General Soil Map (STATSGO); a 1:250,000 scale interpretation of detailed soil survey data, with inferences about natural conditions where soils information is absent (Soil Survey Staff, 2006b). Both products make use of the "map unit" concept, where repeating combinations of soil type (usually expressed as Soil Series name or phase) are identified and delineated as polygons on a map (Soil Survey Staff, 2005).
Despite the obvious utility of digital soil survey databases, there are inherent drawbacks to using these products. The high degree of variability associated with soil bodies prohibits the mapping of soils at a 1:1 scale, hence map units are used to depict patterns of soil distribution. Map unit composition differs depending on the scale of the soil survey and when it was produced. On average, SSURGO map units in California consist of 4 soil types (components), whereas STATSGO map units average 14 components. Map unit composition adds considerable complication along with subjectivity to any analysis because the physical location of components within a map unit delineation is not explicitly documented. Furthermore, STATSGO is often unreliable because it is based on assemblages of dominant soil series from SSURGO. The concepts of SSURGO legends have changed through time and it often encompasses a wide range of geographic settings, particularly in mountainous terrain. SSURGO data are incomplete in several mountainous regions of California, and are missing for many public lands (BLM and National Forests). When SSURGO data are available in these regions, they are often mapped at coarser resolutions compared to agricultural lands; too coarse for many site specific operations such as restoration projects, urban uses, and some modeling efforts. Several workers have emphasized the importance of recognizing both scale-dependant and scale-invariant processes in natural systems when developing environmental models (Beven, 1995; Bloschl, 2001; Schoorl and Veldkamp, 2006), a difficult task with existing fixed-scale soil survey products. Moreover, no clear linkage between the development of SSURGO and STATSGO exists, thus the ability to work across multiple scales is impossible to evaluate.
Aggregating Soil Survey Information: Available Water Holding Capacity
4km Grid of AWC: generated using PostGIS/GRASS, based on USDA-NCSS SSURGO data.
Horizon thickness-weighted mean AWC (available water holding capacity), aggregated to a 4km grid, based on the detailed (SSURGO) soil survey database. Each grid cell is the component percentage / area fraction weighted mean of profile AWC. The variation in AWC tracks several important parent material induced patterns: with lower AWC in residual soils formed on steep granitic terrain (south flank of Sierra Nevada), and higher AWC in residual soils formed on the gentler slopes of meta-volcanic and meta-sedimentary terrain (central and northern flanks of Sierra Nevada). The higher AWC values one the east side of the San Joaquin Valley correspond with the characteristically finer soils formed from coast range alluvium. High AWC values of the Sacramento Valley correspond with the fine textured soils derived from a mixture of coast range alluvium, and meta-volcanic/sedimentary alluvium from the Sierra Nevada.
Maps of soil properties aggregated to this scale can serve an important role in near-surface, climate, or global circulation models. Since these data are ultimately derived from more detailed information, aggregated values should be more representative than a smaller (geographic) scale generalization such as STATSGO. These type of maps can be generated with a single query within a spatially-enabled database (PostGIS), loaded with multiple surveys from the SSURGO dataset. More examples here, and some sample SQL code below.
I wonder how this map would compare to a similar 0.5 degree res. map?
Example Code
SELECT id, sum(ST_Area(wkb_geometry) * mu_wt_mean_awc) / sum(ST_Area(wkb_geometry)) AS wt_mean_awc, sum(ST_Area(wkb_geometry) * mu_wt_mean_soil_depth) / sum(ST_Area(wkb_geometry)) AS wt_mean_soil_depth
FROM
grid_mapunit
JOIN
(
-- component pct weighted mean of AWC and soil depth
SELECT mukey, sum(comppct_r * hz_wt_mean_awc) / sum(comppct_r) AS mu_wt_mean_awc, sum(comppct_r * soil_depth) / sum(comppct_r) AS mu_wt_mean_soil_depth
FROM
component
JOIN
(
-- hz-thickness weighted AWC fraction and total soil depth
SELECT cokey, sum((hzdepb_r - hzdept_r) * awc_r) / sum(hzdepb_r - hzdept_r) AS hz_wt_mean_awc, sum(hzdepb_r - hzdept_r) AS soil_depth
FROM chorizon
WHERE awc_r IS NOT NULL AND awc_r != 0
GROUP BY cokey
) AS hz_data
ON component.cokey = hz_data.cokey
GROUP BY mukey
) AS mu_data
ON grid_mapunit.mukey = mu_data.mukey
GROUP BY id
ORDER BY id ASC;