Spatial Extrapolation of Soil Characteristics Using Whole‐Soil Particle Size Distributions

Shirazi, Mostafa A.; Boersma, L.; Haggerty, Patricia K.; Johnson, Colleen Burch

doi:10.2134/jeq2001.301101x

Cited by 16 publications

(19 citation statements)

References 10 publications

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“…We call this suite of variables the SCs. Shirazi et al (2001b) aggregated the SCs of layers and components for each of the 10463 map units in the conterminous United States and Hawaii. Of these, 882 map units were in the EMAPSW study region.…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…Models developed from the data matrices assume that water properties (WPs) co‐vary with SCs, which in turn are related to the whole‐soil PSD statistics (dg, σg). Shirazi et al (2001b)(Eq. [3] and [4]) used the whole‐soil PSD statistics and the USDA5 texture classes to determine a conditional mean (M q ) and conditional variance (V q ) for each SC as a linear function of the remaining SCs.…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…Shirazi et al (2001b) defined similar soils on the basis of relationships between 27 SCs of soil map units and two whole‐soil particle size distribution (PSD) statistics. These statistics were calculated in Shirazi et al (2001a) and were named the geometric mean particle diameter (dg) and the geometric particle standard deviation (σg).…”

mentioning

confidence: 99%

“…The SCs for the map units were selected from STATSGO (Soil Survey Staff, Soil Conservation Service, 1991). Shirazi et al (2001b) created mathematical models using SCs from one‐half of the map units across the entire United States and found that grouping map units of similar dg and σg values successfully predicted (i.e., spatially extrapolated) the SCs of map unit groups (MUGs) for the other half. By this modeling approach, the authors addressed the need for the appropriate spatial scale defined by Karlen et al (1997) In summary, the fundamental concept of aggregating relatively homogenous spatial features in order to classify or predict attributes of a similar, but unstudied, area was applied to SCs by Shirazi et al (2001b) using the whole‐soil PSD statistics.…”

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confidence: 99%

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Predicting Physical and Chemical Water Properties from Relationships with Watershed Soil Characteristics

Shirazi

Boersma

Johnson³

et al. 2001

J of Env Quality

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The Surface Waters component of the Environmental Monitoring and Assessment Program (EMAPSW) was developed by the USEPA to evaluate the extent and condition of lakes and streams over national and regional scales. Realistically, chemical or physical water properties (WPs) such as acidity or turbidity can be field-sampled for only a small portion of all lakes and streams. However, soil characteristics (SCs) affect WPs and broad-scale soil survey data have become available in the State Soil Geographic Data Base (STATSGO). We developed models relating observed WPs to SCs to extrapolate the sampled WPs to a region, potentially reducing extensive monitoring needs. Our study region consisted of 13 northeastern and Mid-Atlantic states and contained 882 STATSGO soil map units. We used map units as the spatial component of WP analysis. The WPs were sampled in 721 randomly selected EMAPSW study sites. The watersheds of these sites represent 7.1% of the region's total area and spatially intersect 400 of its soil map units. Each intersected map unit was assigned the weighted average WPs from the corresponding watersheds. Conditional expectation models were used to extrapolate sampled WPs to 882 map units. The relative standard errors ranged from low for pH (0.8%), intermediate for total P (12.1%), and very high for chloride (54.8%). The high extrapolation errors indicate outlier conditions from natural, non-soil, or anthropogenic sources.

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Section: Methods and Proceduresmentioning

confidence: 99%

Section: Methods and Proceduresmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Physical and Chemical Water Properties from Relationships with Watershed Soil Characteristics

Shirazi

Boersma

Johnson³

et al. 2001

J of Env Quality

Self Cite

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“…Average values of several soil characteristics were developed for the reach catchments in this study by using data sets associated with the State Soils (STATSGO) Geographic Database (U.S. Department of Agriculture, 1994;Miller and White, 1998;Shirazi et al, 2001aShirazi et al, , 2001b. For this model an area-weighted average soil hydrologic group variable was calculated for each reach catchment; lower values refer to less well-drained soils.…”

Section: Data Sourcesmentioning

confidence: 99%

A Regression-Based Approach to Understand Baseline Total Nitrogen Loading for TMDL Planning

McMahon¹,

Roessler²

2002

proc water environ fed

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Requirements under Section 303(d) of the Clean Water Act have led North Carolina environmental officials to prepare a total nitrogen (TN) total maximum daily load (TMDL) plan for the entire Neuse River Basin. The SPARROW (SPAtially Referenced Regression On Watershed attributes) watershed model was used to develop baseline estimates of TN inputs and delivery and a TN budget for the Neuse River Basin and two adjoining basins, the Cape Fear and Tar-Pamlico. The model explained 94% of the variability in log-transformed stream TN flux. Estimates of stream yield typically were within 25% of the observed values at the 44 monitoring stations used to calibrate the model. The model indicates that landscape factors, such as soil drainage characteristics, and channel transport factors, such as aquatic processes in streams and reservoirs, both exert a large influence on the transport of TN at both the reach and whole-basin scale. TN losses associated with in-stream processes occurred at a rate of about 5% per kilometer in streams with a mean annual discharge less than 1.04 cubic meters per second; losses in larger streams occurred at a rate of 0.2% per kilometer.

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Extent and drivers of vegetation type conversion in Southern California chaparral

Syphard

Brennan²,

Keeley

2019

Ecosphere

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The native chaparral shrublands of Southern California support exceptional biodiversity and provide critical ecological services, but increased fire frequency threatens to extirpate much of the chaparral due to long regeneration times needed between fires for many species. When short fire intervals inhibit shrub recovery, this favors invasion of exotic herbaceous species, and vegetation type conversion from woody shrubs to grassland is therefore a serious ecological concern in this biodiversity hotspot. Despite a history of field studies documenting the detrimental effect of short‐interval fire, the extent of vegetation type conversion and the conditions under which it occurs have not been documented at a landscape scale. Our objective was thus to provide an unbiased assessment of how and how much vegetation type conversion is occurring in Southern California chaparral. We used a chronosequence of aerial photographs to quantify percentage woody and herbaceous cover change from 1953 to 2016 across randomly sampled plots in San Diego County, then related conversion and decline to a range of explanatory variables including fire, proximity to human disturbance, and biophysical landscape characteristics. Within the 63‐yr study period, there was substantial net woody cover loss, and in the plots that were initially more than 75% woody cover in 1953, 59% experienced a decline, with a mean woody cover loss of 22.5%. Of these, 28% heavily type‐converted to the point that herbaceous vegetation covered more than 50% of the plot. The top drivers for woody conversion and decline included a fire interval shorter than 15 yr and total number of fires, actual evapotranspiration, and elevation. Although human land use variables were not strong independent contributors to either chaparral conversion or decline, 26% of the initial vegetated plots were directly converted to development or other human disturbance types. The combination of direct habitat loss and unintentional vegetation type conversion represents a substantial ecological impact in Southern California that can have far‐reaching impacts via loss of ecological services and by increasing the flammability of the landscape in general. Efforts to reduce fire frequency will be key to preventing further losses.

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Spatial Extrapolation of Soil Characteristics Using Whole‐Soil Particle Size Distributions

Cited by 16 publications

References 10 publications

Predicting Physical and Chemical Water Properties from Relationships with Watershed Soil Characteristics

Predicting Physical and Chemical Water Properties from Relationships with Watershed Soil Characteristics

A Regression-Based Approach to Understand Baseline Total Nitrogen Loading for TMDL Planning

Extent and drivers of vegetation type conversion in Southern California chaparral

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