4-May-09 1-Jun-09 24/29-Jun-09 27-Jul-09 24-Aug-09 14/22-Sep-09 20-Oct-09 16-Nov-09 16-Dec-09 11-Jan-10. 8-Feb-10. 8-Mar-10. 5-Apr-10. 3-May-10.
A N���� A������� ��� C��������� M��������� �� D������ ��� S������� C������ �� N���-S������ E��������� R���������� Kaya Diker1, Remke L. van Dam1, Ajay K. Bhardwaj2, Stephen K. Hamilton2, and William Johnson1
D��������� �� G��������� S�������, M������� S���� U���������, E��� L������, MI 48824 - 2W. K. K������ B��������� S������, M������� S���� U���������, H������ C������, MI 49060
1
30 ~4.5cm depth
• The effect of different climatic conditions (solar radiation, air temperature, and precipitation) on resistivity measurements is illustrated for three separate periods.
Diurnal Temperature Effects 8-Feb-10
8-Mar-10
5-Apr-10
3-May-10
1-Jun-10
28-Jun-10
26-Jul-10
23-Aug-10 20-Sep-10 18-Oct-10
• The plot gives the temperature averaged between the 10 plots for each AGI data collection date.
Low tension suction lysimeter
400
Time domain reflectometry (TDR) Automated gas chamber 30
Stover non removal microplot
G8 Poplar
28m
G6 Miscanthus
G2 Corn-Soybean- Canola
G7 Native Grass mix
G3 Soybean -Canola- Corn
G8 Poplar
G4 Canola-Corn-Soybean
G9 Old field
G5 Switchgrass
G10 Native prairie
Objective:
To monitor root zone moisture dynamics of bio-energy crops with the Electical Resistivity Method.
6 5
200
4
1
0
60 40
-20
0
4-May-09
1-Jun-09 24/29-Jun-09 27-Jul-09 24-Aug-09 14/22-Sep-09 20-Oct-09 16-Nov-09
16-Dec-09 11-Jan-10
8-Feb-10
8-Mar-10
5-Apr-10
3-May-10
1-Jun-10
28-Jun-10
26-Jul-10
23-Aug-10 20-Sep-10 18-Oct-10
01 Oct 10
02 Oct 10
03 Oct 10
04 Oct 10
05 Oct 10
06 Oct 10
07 Oct 10
08 Oct 10
09 Oct 10
10 Oct 10
11 Oct 10
Precipitation Events
• Tick marks and vertical gridlines represent 2D (AGI) data collection dates.
10 0
Temperature Pole
4-Point Light HP Resistivity Instrument
Connection of an array to instrument
28m Temperature sensors & soil samples
ERI survey line
Underground wiring
2D Electrical Resistivity Instrument • Advanced Geosciences Inc (AGI) SuperSting R8/IP Multi-channel Resistivity Imaging System • Takeout boxes for underground cable connection in alleyways • Earthimager 2D (inversion software)
1D Electrical Resistivity Instrument • Lippmann Geophysikalische Messgeräte (LGM) 4-Point Light HP • 80 Programmable Active Electrodes 8 for each plot • Remotely controlled field computer • Geotest (data collection software)
electrodes
100 Ωm
400 Ωm
Red electrodes represent subset array for high temporal resolution soundings.
0.05
0.10
0.15
0.20
ERT derived Water Content
0.25
0.30
20 Oct 16 Nov 08 Feb 08 Mar 05 Apr 03 May 28 Jun
26 Jul
23 Aug 20 Sep
• Good correlation between ERT and TDR derived water content is observed for some plots.
18 Oct 2010
• In some plots, ERT underestimates water content values and its variability.
0.1
0.5
0.3
0.7 0.9 1.1
0.25
04 May 01 Jun 24 Jun 29 Jun 27 Jul 24 Aug 14 Sep 20 Oct 16 Nov 16 Dec 11 Jan 08 Feb 08 Mar 05 Apr 03 May 01 Jun 28 Jun 26 Jul 23 Aug 20 Sep 18 Oct 2009 2010
0.2
Plot 07 − TRT G7 − Native Grass Mix
Depth (m)
0.3
400
0.7 0.9
3
0.1
Buried Electrodes
16 Nov 10
17 Nov 10
18 Nov 10
19 Nov 10
20 Nov 10
21 Nov 10
22 Nov 10
23 Nov 10
24 Nov 10
25 Nov 10
CORN
26 Nov 10
Plot 08 − TRT G4
SOYBEAN
Volumetric Water Content (cm³/cm³)
0.1
a=0.3m
• Apparent resistivity drop is observed following two significant rain events.
2D Electrical Resistivity
Snowmelt Event
• In 4-week intervals since May 2009, we have collected ERI data using a dipole-dipole configuration. • This leads to 89 readings of apparent resistivity per vegetation plot. • In order to convert 206 ERI datasets into water content we have applied several processing steps. » Modeling meshes were constructed with exact X and Z positions of buried electrodes. » Batch inversion process was done in Earthimager 2D. → 3 Mesh division, modified underwater algorithm 1000 » Spatial average was calculated for a 2-m wide zone in the center of the inverted section. » Statistical analysis was applied to 100 eliminate bad data points. » The data were corrected for temperature ρ using ρref = m (t - tref ) + 1 (Sen & Goode, 1992). Power (0-20) Power (20-40) Power (40-60) 0 - 20cm 20 - 40cm 40 - 60cm t Power (60-80) Power (80-100) Power (100-120) 60 - 80cm 80 - 100cm 100 - 120cm » Lab-derived petrophysical functions Power (120-140) 120 - 140cm 10 between resistivity and water content 0.03 0.30 Volumetric Water Content (cm 3 /cm 3 ) were applied. Different relationships are due to textural differences; fine grained from 0-40cm and sandy from 60-140cm.
• Bi-hourly sounding data have been collected since July 2010, using several array configurations. • Each dataset consists of 18 apparent resistivity readings for 10 different plots. • The daily data set thus consits of 18 x 10 x 12 = 2160 data points. • Custom Matlab scripts have been written to store data in MySql database for sorting and plotting.
0.3
Depth (m)
Surface Electrodes
15 Nov 10
0.05
0
300
1.2 0.6 0 20 10 0
0.5 0.7 0.9 1.1 1.3
04 May 01 Jun 24 Jun 29 Jun 27 Jul 24 Aug 14 Sep 22 Sep 20 Oct 16 Nov 16 Dec 11 Jan 08 Feb 08 Mar 05 Apr 03 May 28 Jun 26 Jul 23 Aug 20 Sep 18 Oct 2009 2010
-10
0.1
5
200
4 3 2 1 0 09 Feb 11
10 Feb 11
11 Feb 11
12 Feb 11
13 Feb 11
14 Feb 11
15 Feb 11
16 Feb 11
17 Feb 11
18 Feb 11
19 Feb 11
0.5 0.7 0.9 1.1 1.3
04 May 01 Jun 24 Jun 27 Jul 24 Aug 14 Sep 22 Sep 20 Oct 16 Nov 16 Dec 11 Jan 08 Feb 08 Mar 05 Apr 03 May 28 Jun 26 Jul 23 Aug 20 Sep 18 Oct 2009 2010
20 Feb 11
• Inversion of buried electrodes using underwater algorithm causes artifacts ~50cm depth in some plots.
• Distinctive reduction of apparent resistivity is observed in response of snowmelt event.
• Absolute water content values may not all be accurate but seasonal trends can be interpreted.
Air Temperature Solar Radiation
Plot8 (canola) Plot2 (soybean)
We would like to acknowledge Poonam Jasrotia and Kevin Kahmark (W.K. Kellogg Biological Station) and Bobby Chrisman, Ben Johnson, Samer Hariri, Brian Eustice, Mine Dogan, Anthony Kendall, and David Hyndman (MSU Dept. of Geological Sciences) for their assistance during various phases of this project. Support for this work was provided by grants from NSF and DOE. Support for this research was also provided by the NSF Long-Term Ecological Research Program at the Kellogg Biological Station and by Michigan State University AgBioResearch.
Jayawickreme, D. H., Van Dam, R. L., & Hyndman, D. W. (2010). Hydrological consequences of land-cover change: Quantifying the influence of plants on soil moisture with time-lapse electrical resistivity. Geophysics, 75(4), WA43-WA50.
• Diurnal fluctuations of apparent resistivity are not seen due to insulation of snow cover.
Precipitation
A���������������
R���������
0.3
6
• Further research is necessary to improve inversion of resistivity data from buried electrode arrays.
Plot 09 − TRT G6 − Miscanthus
-20
300
• Results of seasonal resistivity variations illustrate growing season drying that correlates with TDR results for some plots.
04 May 01 Jun 24 Jun 29 Jun 27 Jul 24 Aug 14 Sep 22 Sep 20 Oct 16 Nov 16 Dec 11 Jan 08 Feb 08 Mar 05 Apr 03 May 28 Jun 26 Jul 23 Aug 20 Sep 18 Oct 2009 2010
2 1
• Measured resistivity values are highly sensitive to climate dynamics, including diurnal temperature fluctuations, precipitation, and snowmelt.
1.3
4
400 Ωm
0.15
0.5
1.1
6
S������ • A permanent setup for monitoring diurnal and seasonal resitivity variation was installed at the Great Lakes Bioenergy Research Center.
1.3
500
5
1D Electrical Resistivity Electrodes were buried to avoid damage by farm equipment.
24 Aug 14 Sep
0.1
Interface
Interface
27 Jul
Plot 05 − TRT G5 − Switchgrass
Precipitation (mm)
Apparent Resistivity - ohm m
100 Ωm
29 Jun
-20
Precipitation (mm)
15m
electrodes
24 Jun
-10
600
Apparent Resistivity - ohm m
11.7m
0.9
1.3
0.6
Resistance (ohms)
6m
40m
Electrode array prior to installation
1.3m Canola 0.00
04 May 01 Jun 2009
1.2
• Forward modeling dipole-dipole configuration was conducted with Res2DMod sofware. • This result illustrates the effect of buried electrodes on apparent resistivity
Takeout Box
0.9m Canola
0.3
• Solar radiation, air temperature, and precipitation data was obtained from a Michigan Automated Weather Network (MAWN - Enviro-Weather) weather station 1.35 km from the field site.
Effect of Buried Electrodes
Preparation for underground cabling
0.9m Corn
1.1
• Diurnal fluctuations of apparent resistivity correlated with air temperature and solar radiation. This shows the importance of the temperature correction to translate electrical resistivity to soil moisture.
P���������
Total • 10 Plots • 2000m of Cables Per Plot • 40 Electrodes • Temperature Sensors (3 depths) Electrodes • Material : Graphite • Length : 10 cm • Spacing : 30 cm • Depth : 25-30 cm below surface
0.5m Canola
0.00
12 Oct 10
20
F���� S���� I��������������
0.10
1.3m Corn
0.7
0
80
Approach? • Monitor seasonal dynamics and interpret in terms of moisture variation.
0.5m Corn
0.05
0.1
0.5
0
• Setup a system for continuous monitoring of diurnal resistivity variation.
0.15
0.3
10
20
How do candidate cellulosic biofuel crops differ in their ability to exract soil moisture, and how do they compare with conventional grain crops?
-20
CANOLA
0.20
2
-10
Research Question:
Plot 04 − TRT G2
SOYBEAN
Depth (m)
G6 Miscanthus
G1 Continuous corn
04 May 01 Jun 24 Jun 29 Jun 27 Jul 24 Aug 14 Sep 22 Sep 20 Oct 16 Nov 16 Dec 11 Jan 08 Feb 08 Mar 05 Apr 03 May 01 Jun 28 Jun 26 Jul 23 Aug 20 Sep 18 Oct 2009 2010
-10
300
3
Precipitation (mm)
Air Temperature (°C)
Treatment Legend
0.9
1.3
Depth (m)
Unfertilized microplot (G10-fertilized)
0
20
15m 49ft 40m 131ft
Trime TDR
G4 Canola
G7 Grass mix
0
Temperature (oC) Solar Rad. (kJ/m²)
G10 Native Prairie
200
Apparent Resistivity - ohm m
Trace gas flux chamber
10
Lippmann resistivity instrumentation shed
G5 Switchgrass
20
0.7
1.1
Depth (m)
16-Dec-09 11-Jan-10
Precipitation (mm)
1-Jun-09 24/29-Jun-09 27-Jul-09 24-Aug-09 14/22-Sep-09 20-Oct-09 16-Nov-09
• Soil temperature has been measured bi-hourly using sensors at three depths in each vegetation plot.
Plot Legend
G2 Corn
0.5
0.6 0
0.25
0.3
1.2 0
CORN
0.1
Temperature (oC) Solar Rad. (kJ/m²)
10
Solar Rad. (kJ/m²)
G9 Oldfield
Plot 02 − TRT G3
TDR derived Water Content
• Apparent resistivity values are shown for dipole-dipole measurement with a=n=0.3m, for two vegetation types.
Plot 02 − TRT G3
CANOLA
Depth (m)
20
4-May-09
G3 Soybean
~100cm depth
0.30
In this study we installed permanent multi-electrode arrays beneath various vegetation types at the Great Lakes Bioenergy Research Center in southwest Michigan. We implemented a novel ER measurement system to provide data at high temporal resolution for a subset of the electrodes, and collected multi-electrode surveys at 4 week intervals. G1 Corn
~45cm depth
D���������
S������� S��� M������� V��������
Temperature (oC) Solar Rad. (kJ/m²)
It is important to obtain information about soil moisture dynamics for understanding agricultural, hydrological, ecological, and climatic processes. Traditional procedures for in-situ measurement of soil moisture are invasive and become increasingly challenging at greater depths. Methods for indirect measurement of soil moisture, such as time-domain reflectometry (TDR) and neutron probes, can overcome some of these issues, but typically offer spatially limited coverage. Geophysical methods, on the other hand, can often achieve better spatial coverage and are minimally invasive. In recent years, electrical resistivity (ER) imaging methods have become increasingly popular for soil moisture research (e.g., Jayawickreme et al., 2010). A disadvantage of the ER imaging method, however, is the relatively high cost of field deployment.
D������ R���������� V��������
S��� ��� C������ D��� Soil Temperature (°C)
I�����������
• In most plots near surface water content is higher than below due to textural differences. • During growing seasons in both 2009 and 2010 soils experience drying.
Sen, P., & Goode, P. (1992). Influence of temperature on electrical conductivity on shaly sands. Geophysics, 57(1), 89-96.
