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Inventory of Physico-Chemical Soil Properties and Spatial Pattern Analyses in the Everglades Ecosystem
Collaborators
PI: S. Grunwald, Soil and Water Science Department, UH
Collaborator: W.F. DeBusk, Soil and Water Science Department, UF
Acknowledgement
Data were made available for this project by the Wetland Biogeochemistry Laboratory, University of Florida http://wetlands.ifas.ufl.edu/
(courtesy of K.R. Reddy)
Time
10/1/2001 to 7/1/2002
Funding Source
School of Natural Resources, University
of Florida.
Summary
This project contributes to the restoration efforts outlined in the Comprehensive Everglades Restoration Plan (CERP) by revisiting physical and chemical soil properties measured within a time frame of more than a decade by the scientist of the Soil and Water Science Department, IFAS, University of Florida, in the Everglades Ecosystem. These geo-referenced point measurements of dissolved reactive phosphorus (P), HCL extractable P, total P, pH, bulk density, dissolved O2, and others are a valuable resource to describe spatial patterns of physico-chemical soil properties in the Everglades ecosystem. Recently, novel geostatistical methods emerged, which were not available at the time the data were collected. Quantifying these spatial patterns of soil properties enable us to look from a different perspective at historic conditions of the Everglades ecosystem. Given such a valuable spatial dataset, we can reconstruct the history of degradation of the Everglades ecosystem putting us in a position to better understand the ongoing dynamics, assess the potential for its reversal, and evaluate resilience of such a complex and fragile ecosystem. Preserving such a comprehensive dataset of geo-data in a readily available state-of-the-art GIS format is beneficial to the restoration effort in south Florida.
Objectives
(1) Restore and catalogue systematically physico-chemical soil properties measured in the wetlands biogeochemistry laboratory, Soil and Water Science Department, from 1987 to present in a GIS database.
(2) Integration of other readily available natural resource GIS data layers to assist in interpretation of spatial patterns of physico-chemical soil properties (e.g. hydrologic patterns, vegetation patterns).
(3) Explicit spatial modeling utilizing different types of geo-analytical methods:
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Global estimation methods
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Local estimation methods
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Interpolation using geostatistics (kriging) – these methods attempt to optimize interpolation by dividing spatial variation into 3 components: (i) deterministic variation, (ii) spatially autocorrelated but physically difficult to explain variations, and (iii) uncorrelated noise.
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Neighborhood analysis (technique: convolution). Common measures for diversity are ‘entropy’ or ‘lacunarity’, which measure the heterogeneity of specific property values across a study area.
(4) Improved 2D and 3D geographic visualization of physico-chemical soil properties utilizing ArcGIS and spatial modeling software.
Everglades Ecosystem
Many of the problems with declining ecosystem health in southern Florida revolve around four interrelated factors:
(1) Water quantity
(2) Water quality
(3) Timing
(4) Distribution
The major goal of restoration is to deliver the right amount of water that is clean enough to the right places and at the right time.
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EAA: Everglades Agricultural Area
LNWR: Loxahatchee National Wildlife Refugee
WCA: Water Conservation Area
ENP: Everglades National Park
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Major stresses imposed on the Everglades ecosystem:
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Water management (water quantity)
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Urban development
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Agriculture (input of phosphorus)
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Recreation
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Exotic species
STATSGO soil map, Florida |
Parent material in south Florida |
Related links:
South Florida Information Access (SOFIA): http://sofia.usgs.gov/
South Florida Water Management District: http://www.sfwmd.gov/
Everglades Restoration Plan: http://www.evergladesplan.org/
Geo-Database
Archive of chemical and physical soil properties (current status: 1987 - 1995).
Currently, there are 1905 data records stored in the geo-database.
Metadata
| Variable |
Description |
Units |
| SOILS_ID |
Index |
- |
| DATE |
Date of collection of sample |
- |
| PROJECT_ID |
Project identifier |
- |
| LAT |
Latitude |
degrees |
| LONG |
Longitude |
degress |
| BEGIN |
Beginning depth of sample |
cm |
| END |
Ending depth of sample |
cm |
|
Soil physical parameters
|
| pH |
pH of wet-sample |
- |
| ASH |
Ash content of dried sub-sample |
% |
| WATER |
Water content of fresh sub-sample |
% |
| TC |
Total carbon content * |
mg kg-1 |
|
Parameters related to phosphorus
|
| CA |
1 M hydrochloric acid extractable calcium content * |
mg kg-1 |
| MG |
1 M hydrochloric acid extractable magnesium content * |
mg kg-1 |
| FE |
1 M hydrochloric acid extractable iron content * |
mg kg-1 |
| AL |
1 M hydrochloric acid extractable aluminum content * |
mg kg-1 |
|
Phosphorus (P)
|
| TP |
Total phosphorus content * |
mg kg-1 |
| TPI |
Summation of all inorganic phosphorus forms |
mg kg-1 |
| TPO |
Summation of all organic phosphorus forms |
mg kg-1 |
| NAHCO3 |
Sodium bicarbonate extractable phosphorus (represents the amount of bioavailable P) ** |
mg kg-1 |
| KCLP1M |
Potassium chloride extractable phosphorus content (P that can be easily desorbed from soil and sediment, and thus is also considered bioavailable) ** |
mg kg-1 |
| NAOHPI |
Sodium hydroxide extractable inorganic phosphorus content (the sodium hydroxide extraction is thought to liberate P associated with iron and aluminum compounds) ** |
mg kg-1 |
| NAOHPO |
Sodium hydroxide extractable organic phosphorus content (This form of P is considered moderately labile and its mobility is related to the iron and aluminum content of the soil and redox conditions) ** |
mg kg-1 |
| HCLPI |
1 N hydrochloric acid extractable inorganic phosphorus content ** |
mg kg-1 |
| HCLP1M |
1 M hydrochloric acid extractable phosphorus * |
mg kg-1 |
| SOL_P |
Porewater dissolved reactive phosphorus concentration |
mg L-1 |
| FLUX_P |
Field-measured flux of phosphorus |
mg m-2 day-1 |
|
Nitrogen (N)
|
| TN |
Total nitrogen content * |
mg kg-1 |
| KCLN2M |
2M potassium chloride extractable ammonium-N * |
mg kg-1 |
| SOL_N |
Porewater ammonium-N concentration |
mg L-1 |
| FLUX_N |
Field-measured flux of N |
mg m-2 day-1 |
* of dried sample
** of wet sub-sample
Interactive Analysis of Geo-Data
You can access, analyze, select, and filter all soil physical and chemical data collected in Water Conservation Area 2 interactively utilizing the Treemap tool.
Treemaps are a space-filling visualization for hierarchical structures that are extremely effective in showing attributes by size and color coding. Treemaps enable users to compare sizes of nodes and of sub-trees, and are especially strong in spotting unusual patterns. They were developed by Ben Schneiderman at the Human-Computer Interaction Laboratory (HCIL) of the University of Maryland.
Treemaps functions:
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Access raw data |
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Select variables and display them by size and color. Identify pattern in datasets. For example, show differences in total phosphorus between different soil depths and sampling locations. |
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Use the filter routine to highlight values of an attribute exceeding a user-defined threshold value. For example, highlight all values of total phosphorus exceeding 500 mg/kg. |
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Phosphorus Mechanisms
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Phosphorus (P) pools and mechanisms |
Orthophosphate (H2PO4- and HPO42-) that is released by mineralization is rapidly adsorbed by the soil particles where its availability steadily declines with time (phosphate fixation). Soil pH, clay, sesquioxide content, and redox potential influence the phosphorus availability. Phosphorus pools range from labile phosphorus that can be readily desorbed plus phosphorus in solution, to inorganic phosphorus associated with calcium, magnesium, iron, and aluminum, to non-labile (stable) phosphorus held in insoluble metallo-organic complexes. The reduction of redox potential following flooding can cause transformations of crystalline aluminum and iron minerals to the amorphous forms. Amorphous aluminum and iron hydrous oxides have higher phosphorus sorption capacity than crystalline oxides due to their larger number of singly coordinated surface hydroxyl ions.
Water Conservation Areas
The level of impairment in phosphorus differed in Water Conservation Areas 1, 2, and 3. Typically phosphorus content decreased with depth.
Total phosphorus (TP)
|
Porewater dissolved reactive phosphorus concentration (Sol_P)
|
1 M hydrochloric acid extractable phosphorus (HCLP1M)
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Water Conservation Area 2
Generally, correlations between total phosphorus (TP) and other physical and chemical properties were low for most projects in the geo-database.
Example: Significant correlations for Water Conservation Area 2 are listed in the table below.
Correlations
| |
TP |
| TN |
-0.363** |
| TC |
-0.229* |
| SOL_P |
0.663** |
| SOL_N |
0.396** |
| NAHCO3 |
0.589** |
| KCLN2M |
-0.343** |
| HCLP1M |
0.855** |
| CA |
0.375** |
| Mg |
0.461**
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** correlation is significant at the 0.01level (2-tailed)
* correlation is significant at the 0.05 level (2-tailed)
It is difficult to develop general empirical prediction models for phosphorus due to many interrelating factors and processes. To gain an understanding of underlying processes and factors across an area of interest it is necessary to analyze the spatial variability.
Geostatistics
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Variography: The process of estimating the theoretical semivariogram.
Steps: (1) exploratory data analysis, (2) check for global trend, (3) computation of the empirical semivariogram, (4) binning and fitting a semivariogram model, (5) computation of directional variograms to identify anisotropy. |
Geostatistics is based on the assumption that observations close to each other are more likely to be similar than observations at a larger distance from each other. The spatial correlation between observations is described by the semivariance, which is half the average squared difference between paired data values. The fitted semivariogram model is defined by nugget, sill, and range. The nugget represents the measurement error and fine-scale variability (variation at spatial scales too fine to detect). The distance at which the sill is reached is called the range. Beyond the range the observations are not spatially correlated. The identified spatial structure in the variogram is used to estimate values at previously unsampled locations to create a continuous map (kriging). Kriging, which is a weighted interpolation method, is similar to inverse distance in that it weights the surrounding measured values to derive a prediction for each location. However, the weights are based no only on the distance between the measured points and the prediction location but also on the overall spatial arrangement among the measured points. To use the spatial arrangement in the weights the spatial autocorrelation must be quantified.
Results
Spatial pattern analyses can reveal relationships between variables at geographic locations. Three-dimensional (3D) spatial models were created identifying cross-relationships between soil chemical and physical variables. Total phosphorus (TP)
was represented as height and physical and chemical properties by color
(e.g. bulk density, magnesium content). For example we found that bulk
density was large in the south-west of the study area while total
phosphorus was large. In contrast, small bulk densities were predicted in
the north-east of the study area associated with large total phosphorus.
Likewise other spatial cross-relationships between physical and chemical
variables were identified in the models below.
Multi-dimensional ordinary kriging was used to create a 3D model showing the spatial distribution of total phosphorus in 3D geographic space.
Download VRML plug-ins to access VRML models
(the plug-in will be installed in your web browser)
Blaxxun Contact: http://www.blaxxun.com/services/support/download/install.shtml
Cosmo Player (Windows 95/NT)
Cortona
Download standalone VRML Viewer:
GLView
Select publications:
Mathiyalagan V., S. Grunwald, K.R. Reddy and S.A. Bloom. 2005. A WebGIS
and geodatabase for Florida's wetlands. Computers & Electronics in
Agriculture, 47: 69-75.
Grunwald S., K.R. Reddy, S. Newman and W.B. DeBusk. 2004. Spatial
variability, distribution, and uncertainty assessment of soil phosphorus in
a south Florida wetland. J. of Environmetrics, 15: 811-825.
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Archive of chemical and physical soil properties (current status: 1987 - 1995).
Currently, there are 1905 data records stored in the geo-database.
Metadata
Total phosphorus (TP)
Porewater dissolved reactive phosphorus concentration (Sol_P)
1 M hydrochloric acid extractable phosphorus (HCLP1M)
