Research at Virginia Tech

ByDr. John M. Galbraith

May 2006

Jim Baker

• Updated the VALUES database in Virginia– Dataset of all soil series with soil properties and yields

of crops and forages– Data is used for land value assessment and for

nutrient management planning

• Research is conducted by VA Dept. Health employees supervised by Jim on septic system efficiency and suitability of C horizons for percolation

Dr. Matt Eick and Lucien Zelazny

• P-index for Virginia (progress)

Carl Zipper

• Powell River Research and Education Center Director– (With Galbraith and Prisley and Wynne) – Carbon sequestration potential for marginal

farmland in VA under various future land use scenarios

• Based on STATSGO and HEL definition• Identifies marginal cropland and current land use• Identifies buffers around streams in cropland that

may be reforested

W. Lee Daniels• Mineral sand mining reclamation

– Sand mining removes entire regolith, separates heavy mineral sands, replaces dispersed sediment back into pits.

– Problems• sands and clays (slimes) separated vertically and horizontally

in the deposit• Topsoil replaced when slimes are still wet or not replaced at

all– Solutions

• Work the soil when dry, rip 2 directions, apply phosphorus and lime and seed to a mixture of legumes and nurse crops

• Second or third year, return to normal cropping and fertilization

W. Lee Daniels

• Mountaintop Mining– (With Haering and Galbraith and Thomas)

Four articles published on mapping and reclamation of mine soils

– Recognition of densic horizons and restricted depth classes

– 2 new series proposed on mined areas– Recognition of impeded drainage classes– Subsidence interpretations– Land use interpretations

W. Lee Daniels

• N-index – With Pat Donovan, developing a N-index for

soil management in VA

W. Lee Daniels

• Wetlands– Working with VDOT to develop an

assessment of their wetland mitigation success – 10 pairs of mitigated wetlands-reference wetlands, study near completion

– Recommendations for reclamation, creation, and mitigation of wetlands for state agencies and consultants

Differential Soil Properties at Fort Lee (Cummings, 1999)

0-15 cm pH % C % N

Reference 4.76 2.89 0.18

Mitigation 5.31 0.82 0.07

Differential Soil Properties at Fort Lee (Cummings, 1999)

Bulk DensityMg/m3

Surface(0-15 cm)

Subsurface(70 cm)

Reference 0.71 1.42

Mitigation 1.75 1.71

W. Lee Daniels

• Acid-Sulfate soils– Problem

• Very low pH• Poor water quality• Corrosion steel and concrete• Toxic to fish and microbes• Subsidence

– Solution• Identify location of deposits• Classify the potential hazards of each deposit

Inside the culvert at Clifton Forge

o PPA < 10 Mg CaCO3/1000 Mg material (total-S < 0.2%): can readily be managed.

o PPA 10 - 60 Mg CaCO3/1000 Mg material (total-S = 0.2 - 2%): can be remediated with intense management.

o PPA > 60 Mg CaCO3/1000 Mg material (total-S > 2%): extremely difficult to remediate.

If sulfidic materials can have high pH values, then how do we know how bad they are?

Potential peroxide acidity (PPA) and total-S

Compiling a state-wide sulfide hazard map for Virginia: the final

map.

Sulfides undocumented1

2

3

4

Sulfides documentedN

Sulfides undocumented

Compiling a state-wide sulfide hazard map for Virginia: the final map.

All formations, based on draft version of digital

geologic map of Virginia.

CharacterizedNot characterized

Sulfides documented. Acid potential unknown.

Sulfides not documented.

1: PPA < 10; S < 0.5%.

2: PPA < 10; S > 0.5%.

3: PPA 10 - 60.

4: PPA - more than 10% of samples > 60.

John Galbraith

Focus Areas– Wetlands: Inventory, Soils, Hydrology, and

Mitigation– Mine Soil: Reforestation potential, Mapping,

Classification, Interpretation, and Hydrology– Urban Soil: Mapping, Classification,

Interpretation, and Hydrology– GIS Resource Inventory and Analysis (SOC)– Decision Support Systems and User Guides

DSS Projects

http://clic.cses.vt.edu/SoilsInfo/– Va_Septic – based on Soil Data Viewer– Va_On-site – based on VHD regulations– SSURGO 2.x User’s Guide– Pasture Land Management System (PLMS)– Charts for Ver. 5.0 Field Indicators of Hydric

Soils (soon to be available at Mid-Atlantic Hydric Soils Committee web site)

John Galbraith

• Andy Jones, Galbraith, Burger, Fox, Zipper– Problem – Abandoned mine soils are

intentionally compacted, smoothed, seeded to fescue and Sericea lespedeza, and grazed. Native seedlings cannot reestablish.

– Solution - Identify soils where establishment of trees is economically and environmentally feasible and cost-effective.

Forest Capability Class

• We developed a suitability classification based on B.D., % rocks, pH, E.C., slope, aspect, and rooting depth (water holding capacity)

• Soils can be rated to see if it is economical to plant either hybrid poplar, white pine, native oaks, or if it should be left alone and grazed

LandLand--use Effects on Growing Season use Effects on Growing Season Length Indicators in Southeast Virginia Length Indicators in Southeast Virginia

Wet FlatsWet Flats (Great Dismal Swamp) Amanda C. Burdt and John M. Galbraith

– Problem: Growing season misses the seasonal high water table, and the growing season used for regulations is incorrect

– Solution: Measure hydric soils indicators, soil temperatures, growing seasons, and hydrology under three land uses

(Published 2005)

Treatments

3

21

Forest

Early-successionalfield

Tilled bare ground

Hydroperiod for Hall Property in 2001

-50

-40

-30

-20

-10

0

10

20

12/30/00 2/18/01 4/9/01 5/29/01 7/18/01 9/6/01 10/26/01 12/15/01 2/3/02 3/25/02 5/14/02

Wat

er ta

ble

dept

h (c

m)

Forest (Well 10)Field (Well 5)Bare ground (Well 3)

0

1

2

3

4

5

6

7

8

9

12/30/00 2/18/01 4/9/01 5/29/01 7/18/01 9/6/01 10/26/01 12/15/01 2/3/02 3/25/02 5/14/02

Date

Prec

ipita

tion

(cm

)

frost-free days frost-free

30 cm depth

rainfall

Soil Temperature at 50 cm at Hall

Crossover of forest treatment occurs in March and SeptemberCrossover of forest treatment occurs in March and September

~ 3 to 5 °C

0

5

10

15

20

25

30

12/30/00 2/18/01 4/9/01 5/29/01 7/18/01 9/6/01 10/26/01 12/15/01 2/3/02 3/25/02 5/14/02 7/3/02Date

Soil

tem

pera

ture

(oC

)

ForestFieldBare

Crossover of forest treatment occurs in March and SeptemberCrossover of forest treatment occurs in March and September

~ 3 to 5 °C

Circles represent times where soil temp. was below 5C. Blue bars are “growing season”

Results and Conclusions1. For the Great Dismal Swamp Ecosystem in Southeast Virginia,

the published frost-free days “growing season” is March 14 to Nov. 29 (259 days) on the east side and March 29 to Nov. 7 (222 days) on the west side.

2. We found the growing season measured at 50cm to be year round under forest, and all but one week under early successional(field) and under bare cropland.

3. The coldest days were in the first two weeks of January.

4. The forest soil was warmer and drier than the field which was warmer and drier than the cropland.

5. A year round growing season should be declared for all thermicwetlands, maybe all mesic wetlands as well. Frigid?

Measuring CO2 efflux from wetland soils under three land uses

• Problem: Hard to measure soil temperature at 50 cm

• Solution: Measure CO2 efflux at surface as a measure of biological activity

• Results and Conclusions: CO2 efflux in Forest > Field > Cropland.

• Could use 5C to estimate base rate of efflux, use that to see when activity was at a minimum.

(published 2006)

CO2 and Soil Temperature Relations –Treatment Differences

Note: The relationships between the natural log of the soil CO2 efflux rates and measured soil temperature and moisture at 15 cm were analyzed using multiple regression analysis (Neter et al., 1996) using SAS™ software (Statistical Analysis Systems, Cary, NC).

CO2 and the Baseline in a Forest Plot

0

5

10

15

20

25

30

35

40

2/11

2/16 3/7 3/20 4/7 5/7 6/6 7/3 8/5 9/2 10/7

11/3

11/14 12

/112

/14 1/1 1/20 2/2 2/17 3/4 3/16 4/6 5/5 6/13

0

1

2

3

4

5

6

7

8

9

2001 and 2002 Sampling Dates

Soil

CO

2Ef

flux

Rat

es (µ

mol

m-2

s-1)

Soil

Tem

pera

ture

(°C

) and

Soi

l Moi

stur

e (%

)

Soil Temperature at 15 cm Soil CO2 Efflux Soil Moisture to 15 cm

(0.3 CO2 baseline)

CO2 efflux is dependent on soil temperature and moisture. Red – temperature dip

HGM, Hydrology and Hydric Soil Indicators in Piedmont Slope Wetlands and Wet

Floodplains

• Problem: Soils at inflection point of hill and in floodplain are wet, don’t always meet an indicator

• Solution: Measure the hydrology and hydric soil technical standard

• Results and Conclusions: Soils are hydric. In backswamps, water may be oxygenated. At the inflection point, water flows through, may take Fe with it. High OC at inflection point masks indicators.

(in publication draft stage)

Discharge in Humid Riverine Systems With Seeps

GroundwaterDischarges in rock fissures

River

Regional Water Table

Impermeable layer, possibly bedrock

Gravelly layer

Flooding Recharge

Runoff recharge

YazoostreamBackswamp

Study Site:5 Landscape Positions

Terraces and Uplands

InflectionMidpointMidpoint

LeveeLevee

River

Monitoring Well

Rt.6

29

Levee

Midpoint

InflectionInflection

Upland

HighlandTerrace

Monitoring Well

River

Non-hydric

Non-hydric

Used to develop New Indicator F19

Using public domain data to predict the occurrence of hydric soil

Published 2004

Integrating National Wetland Integrating National Wetland Inventory maps using Remote Inventory maps using Remote

Sensing and GIS toolsSensing and GIS tools

Eva Pantaleoni and John Galbraith, Virginia Tech

2006(in progress)

Using Remote Sensing to Improve NWI

• Purpose: To explore the ability of raw ASTER bands and GIS data layers in predicting wetland location using a logistic regression model

• ASTER bands are able to detect wetland with an accuracy of 28% when open water features are not included in the model.

• The model improves significantly when soil, hydrographyand elevation data are introduced in the model (accuracy=52% at 0.95 probability level).

• Most of misclassified wetlands were forested wetlands• Other errors were due to the dry year and

georectification problems (pixels did not match)

Compare this areato ASTER wetlands

Compare this areato NWI wetlands

Assessment of National Wetlands Inventory (NWI) Landscape Position

Classification SystemAlexis E. Sandy and John M. Galbraith

• The National Wetland Inventory (NWI) is a project of the FWS that has produced wetland maps for approximately 90 percent of the coterminous United States (US Fish and Wildlife Service, 2002).

• They are adding hydrogeomorphic and other characteristic descriptors that are needed for assessment of wetland functions

• Updating process is still conducted manually

Functions Implied by Landscape Position Descriptors

• Functions Implied– Surface water detention– Stream flow maintenance– Shoreline stabilization– Nutrient transformation– Sediment retention

• Procedure: 180 wetlands representing 6 landscape positions were visited and soils, vegetation, and hydrology indicators recorded.

• The data were input to see if they clustered and differentiated enough to be diagnostic for making an automated method to identify the 6 positions.

PRELIMINARY RESULTS: Wetland Plant Status

ES1

ES2ES3ES4

ES5ES6

ES7ES8ES9ES10

ES11

ES12ES13ES14ES15

ES16

ES17

ES18ES19

ES20

ES21ES22

ES23

ES24

ES25ES26

ES27ES28ES29

ES30

LE1

LE2

LE3LE4

LE5

LE6

LE7

LE8

LE9

LE10

LE11

LE12LE13LE14

LE15LE16

LE17LE18LE19LE20

LE21

LE22

LE23

LE24

LE25LE26

LE27

LE28

LE29

LE30

LR1LR2

LR3

LR4LR5

LR6LR7

LR8LR9

LR10

LR11

LR12

LR13

LR14

LR15

LR16

LR17

LR18

LR19LR20

LR21

LR22LR23LR24LR25

LR26LR27

LR28LR29

LR30

LS1 LS2

LS3LS4

LS5

LS6

LS7

LS8LS9

LS10

LS11

LS12

LS13

LS14

LS15LS16

LS17

LS18

LS19LS20LS21

LS22

LS23

LS24

LS25

LS26LS27 LS28

LS29LS30

TE1

TE2

TE3

TE4

TE5

TE6

TE7TE8

TE10

TE11

TE12TE13TE14

TE15

TE16

TE19TE20

TE23

TE25

TE26

TE27TE28

TE29

TE30

TE31

TE32

TE33

TE34

TE35

TE36

PO1PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11PO12

PO13

PO14

PO15

PO16PO17

PO18 PO19

PO20

PO21

PO22

PO23

PO24

PO25

PO26

PO27

PO28

PO29

PO30

Axis 1

Axis 2

•Stress = 0.136

•Dimensions = 3

Legend:ES = EstuarineLE = LenticLS = Lotic StreamLR = Lotic RiverPO = PondTE = Terrene

PRELIMINARY RESULTS: Hydrology Primary & Secondary

Indicators

ES1ES2ES3ES4ES5ES6ES7ES8ES9ES10ES11ES12ES13ES14ES15ES16ES17ES18ES19ES20ES21ES22ES23ES24ES25ES26ES27ES28ES29ES30

LE1

LE2

LE3LE4LE5

LE6

LE7

LE8

LE9

LE10

LE11

LE12

LE13

LE14

LE15LE16

LE17 LE18

LE19

LE20

LE21

LE22

LE23 LE24

LE25

LE26

LE27

LE28

LE29

LE30

LR1LR2LR3LR4LR5LR6LR7LR8LR9LR10LR11LR12LR13LR14LR15LR16LR17LR18LR19LR20LR21LR22LR23LR24LR25LR26LR27LR28LR29LR30

LS1LS2

LS3

LS4

LS5

LS6

LS7

LS8LS9

LS10

LS11LS12

LS13

LS14

LS15

LS16

LS17

LS18

LS19

LS20

LS21 LS22

LS23

LS24

LS25 LS26LS27

LS28

LS29

LS30

TE1

TE2

TE3

TE4

TE5

TE6

TE7

TE8

TE10

TE11

TE12

TE13

TE14

TE15TE16

TE19

TE20

TE23

TE25

TE26

TE27TE28

TE29

TE30

TE31TE32

TE33

TE34

TE35

TE36

PO1

PO2

PO3

PO4PO5PO6PO7

PO8PO9

PO10

PO11PO12

PO13PO14

PO15PO16

PO17

PO18PO19

PO20

PO21

PO22

PO23PO24

PO25

PO26

PO27

PO28

PO29

PO30

Axis 1

Axis 2

•Stress = 0.097

•Dimensions = 3

Legend:ES = EstuarineLE = LenticLS = Lotic StreamLR = Lotic RiverPO = PondTE = Terrene

PRELIMINARY RESULTS: Field Hydric Soil Indicators

•Stress = 0.117

•Dimensions = 3

Legend:ES = EstuarineLE = LenticLS = Lotic StreamLR = Lotic RiverPO = PondTE = Terrene

ES1ES2ES3ES4ES5ES6ES7ES8ES9 ES10ES11ES12ES13ES14ES15 ES16ES17ES18ES19 ES20ES21ES22ES23ES24ES25ES26 ES27ES28ES29 ES30

LE1

LE2

LE3

LE4

LE5

LE6LE7

LE8

LE9

LE10

LE11

LE12

LE13

LE14

LE15

LE16

LE17

LE18

LE19

LE20

LE21

LE22

LE23

LE24

LE25

LE26

LE27LE28

LE29

LE30

LR1LR2LR3LR4LR5LR6LR7LR8

LR9

LR10LR11

LR12LR13

LR14LR15

LR16

LR17LR18

LR19

LR20

LR21

LR22LR23LR24LR25LR26LR27

LR28LR29

LR30

LS1LS2LS3LS4

LS5

LS6LS7

LS8

LS9LS10LS11LS12LS13LS14LS15LS16LS17LS18LS19LS20LS21LS22LS23LS24LS25

LS26LS27

LS28

LS29

LS30

TE1

TE2

TE3

TE4

TE5

TE6

TE7

TE8TE10TE11

TE12

TE13TE14

TE15

TE16

TE19TE20

TE23

TE25

TE26TE27TE28

TE29TE30

TE31TE32

TE33TE34TE35TE36

PO1

PO2

PO3

PO4

PO5PO6

PO7PO8

PO9

PO10PO11

PO12

PO13

PO14

PO15

PO16

PO17

PO18

PO19

PO20

PO21

PO22 PO23

PO24

PO25

PO26PO27PO28

PO29

PO30

Axis 1

Axis 2

PRELIMINARY RESULTS

Distance Measure Axis Stress† Increment Cumulative Axis Pair r Orthogonality

-------%-------1 0.255 0.660 0.660 1 vs 2 -0.438 80.92 0.190 0.116 0.776 1 vs 3 0.288 91.73 0.136 0.118 0.893 2 vs 3 -0.205 95.8

1 0.344 0.441 0.441 1 vs 2 -0.167 97.2Jaccard 2 0.179 0.247 0.687 1 vs 3 0.095 99.1

3 0.097 0.272 0.960 2 vs 3 -0.130 98.3

1 0.368 0.237 0.237 1 vs 2 -0.058 99.7

Jaccard 2 0.185 0.334 0.571 1 vs 3 -0.164 97.3

3 0.117 0.318 0.889 2 vs 3 0.346 88.0† Kruskal's Stress Measure; stress of 0.20 = poor; 0.10 = fair; 0.050 = good; 0.025 = excellent; 0.000 = perfect.

Hydrology Primary and Secondary Indicators

Hydric Soil Characteristics

and Field Indicators of Hydric Soil

Bray-Curtis (Sorenson)

R2

Wetland Plant Indicator Status

Table I-1. Measures of Kruskal's stress, proportion of variance explained (R2), and orthogonality for dimensions retained for NWI landscape position classes as a result of a Non-metric Multidimensional Scaling (NMS) analysis. Analysis is based on field validated wetland plant occurrence, hydrology primary and secondary indicators, and hydric soil characteristics and field indicators of hydric soil.

Other Wetland-related Studies– Multi-state Problem Red Parent Materials Study

– Multi-state Piedmont Floodplains Indicator Study

– Proposed (Multi-state Young Sandy Dune Soils Indicator) may include use of Lidar and multispectral data to ID and locate sandy wetlands and use of photospectroscopy to ID SOC content in sandy soils

– Eh change/time in-situ study (leave for 4 hrs)

– Impact of roads on wetlands in rural and suburban Virginia Coastal Plain areas

– Using Wetness Index to enhance NHD stream network data in the Ridge and Valley

– Mine Soil Wetlands and Series Drainage Class Study at Powell River

Hydrology and drainage class study for coal mine soils

Neo-wetlands formed in 20 years on mine soil in southwest Virginia. Soils with dense, compacted subsoils perch water on or near the surface, forming wetlands. The soils range from somewhat excessively drained to very poorly drained, yet are mapped as a consociation of excessively drained soils with a few wet spot symbols.

Urban Soils Hydrology Study

• In Fairafx Co., VA we have selected two-three sites where we will install piezometernests to determine hydrologic drainage patterns in representative urban soils

• Study to begin in 2006

Research Programs• Wetlands and Stream Ecology http://clic.cses.vt.edu/soils/wetlands/Wetlands_VT_2006.pdf

• GIS, GPS, and Remote Sensing http://clic.cses.vt.edu/soils/OGIS.pdf