I. Soil water storage is a huge and dynamic part of the water cycle in Texas. II. Soil moisture and evapotranspiration are difficult to measure and require sensors in the ground and satellites in space as well as good models. III. With accurate soil moisture data we can improve hydrologic forecasts, determine the start and end of droughts, forecast weather more accurately, and predict agriculture and water usage.

1. Texas Groundwater Summit, 26 August 2015
TxSON: the Texas Soil Observation Network
Todd G. Caldwell, Research Associate, todd.caldwell@beg.utexas.edu

2. Soil Moisture and the drought in Texas
I. How is drought linked to water resources?
II. Where does soil moisture fit into the picture?
III. At what scale is soil moisture operational?
IV. How are can we validate soil moisture at this scale?
V. How can stakeholders use soil moisture?
We cannot have drought without socio-economic
impact. Otherwise, it’s just desert
2011: ~$8 billion in losses from the agricultural sector

3. Let’s start with the RESULTS
• Water is the limiting resource in Texas
• Evapotranspiration (ET) is the largest consumer of water,
either directly from rain or indirectly through irrigation
• The start of a drought is easy; the end… not so much
• Soil water storage is huge in Texas and more dynamic than
any other piece of the water cycle
• Soil moisture is difficult to measure and highly variable, we
need sensors in the ground and satellites in space
• With good soil moisture data, we can do a lot

4. When will this drought end? How
much precipitation do we need to
end the drought?
Despite average rain, why are
reservoir levels NOT filling?
Why is my boat on dirt?
PROBLEM: The perplexity of drought
Photo by TPWD
Photo by TWDB
“Soil moisture is of modest value to
everyone but critical value to none”
- State (withheld) Climatologist
- How much water do we have?
Soil moisture partitions:
1. Rainfall into runoff or infiltration
2. The sun’s energy into heat or
evaporation

5. GRACE estimates of total water storage
Source: Long et al., 2013
∆TWS = ∆𝑹 + ∆SMS + ∆GW
Majority of depletion appears to be
in soil moisture storage
>60 maf = 6 maf + 70-80 % TWS + 4-8 maf

6. Texas Drought: Soil moisture deficit in Texas
Soil moisture from multiple LSM indicate that depletion in 2011
could range from 20% to 100% of TWS from GRACE
 The soil reservoir is BIG
 The model uncertainty is high
Long et al., 2013, GRL
-16 to -60
maf

7. Changes in Water Storage Compartments: Statewide
Residual = 𝑑𝑇𝑊𝑆 − 𝑑𝑅𝑒𝑠 − 𝑑𝑆𝑊𝑆

8. Statewide reservoir forecast from soil moisture
Monthly updated
MLR TWS, SWS, ERR,
and PPT
Lags at 30, 60, 90
days
 Deterministic
approach
 Need PC or step-
wise MLR to
determine
critical variables
24 month initialization

9. Changes in Total Water Storage: GRACE 1o Grid

10. Remote Sensing/LSM limitations: time and space
RIVER
BASIN
GRACE SMOS NLDAS
-- number of cells --
TEXAS 62 1102 4171
Neches 3 38 156
Trinity 5 74 286
Brazos 12 175 677
Colorado 9 167 627
Nueces 3 67 256
Rio Grande 12 210 773

11. Basin reservoir forecast from soil moisture

12.  Manually: shovel, oven, or portable probe
 In-situ sensors (TxSON, SCAN, USCRN)
Expensive, maintenance, small support volume
 Land surface models (NLDAS, GLDAS)
All the water must balance, but it is hard to
parameterize the globe, and harder to validate
 Satellites (SMAP, SMOS)
Easy to cover the globe, but resolution in time
and space is low and depth is shallow
How can we measure soil moisture?
TDR moisture sensors
AK Tundra
SMAP Satellite
- All models and satellites require
calibration and validation

13. How can we measure soil moisture?
SMAP 1000 km swath width
Vereecken et al., 2008, WRR

14.  NASA Soil Moisture Active/Passive (SMAP) Mission
 First dedicated -satellite
 Global coverage at 3, 9, 36 km resolution
 January 31, 2015 launch date
 1000 km swath provides data ~50 hours globally to 5 cm depth
 Microwaves are emitted as radiation from the land surface
proportional to surface moisture and temperature (Tb)
Soil moisture from satellite measurements
SMAP Radar/radiometer SMAP 1000 km swath width

15. Passive Radiometer (36km)
SMAP Level 2 data over Texas: 30 April 2015
Combined Active/Passive (9km)

16. HOW VALID IS MODEL OR SATELLITE ‘DATA’?
HOW CAN WE UTILIZE LARGE-SCALE DATA LOCALLY?
WHAT CAN YOU DO 0-5CM SOIL MOISTURE?
The perplexity of validating soil moisture data
 Buried sensors are point measurements, bias
depending on the soil type, and difficult to maintain
 Soil moisture variability depends on climate,
topography, vegetation, landuse and soil type
— All can change a lot of 3, 9, or 36km

17. Soil moisture networks in Texas (i.e. data)
Fredericksburg
Network Total Texas
USCRN 151 8
SCAN 220 14
WTX 75 70
OK Meso 120 0
TxSON 41 41

18. SMAP CORE Cal/Val site – Fredericksburg, TX
TxSON:
 41 soil moisture stations (expanding
throughout Texas)
 6 meteorological stations
 7 Participating LCRA stations
- 36 km footprint, n = 1
- 9 km footprint, n = 2
- 3 km footprint, n = 3
Soil moisture at 5, 10, 20, and 50 cm
http://www.beg.utexas.edu/txson/

19. 36 km Footprint
• LSM to determine
inherent variability(?)
• Land Accessibility
– Too limited to spatially
distribute stations
– Variability in landuse and
climate
• SSURGO
– Shallow soils in to north
– Deep soils along
Pedernales (south)
Where to put our
stations?

20. Real-time web interface
http://www.beg.utexas.edu/txson

21. TxSON in action: micro-station
 Insert CS-655 sensors at
- 5, 10, 20, and 50 cm
 Add precip gage, cell modem
 Fence quickly!

22. TxSON Scaling: Inverse distance weighing

23. TxSON Scaling: PALScan Airborne Microwave Surveys

24. SMAP Soil Moisture (5cm) over TxSON, 36 km

25. Soil moisture and rain coupling in the afternoon
Here’s what we are learning:
a) Temporally (+), rain is
more likely when soil is
wet
b) Spatially (-), rain is more
likely over drier patches
c) Rain is most likely in wet
conditions, but more
probable over the dry
areas
Source: Guillod et al., 2015, Nature Comm.

26. I. The start of a drought is easy; it’s end… not so much
II. Soil water storage is huge and more dynamic than any other piece
of the water cycle
III. Soil moisture and ET are difficult to measure, we need sensors in
the ground and in space; and good models.
Here’s what you can do with good soil moisture data:
 Improve hydrologic forecasts for water resources
 Determine the 3 W’s of droughts and floods
 Forecast weather with improved accuracy
 Predict agriculture and rangeland production
 Predict irrigation demand and water usage
• Water (and drought) in Texas

27. Texas Soil Observation Network (TxSON)
 Fully operational December
2014
 41 stations, 20 land owners
 Focal point of hydrologic
research in Texas and the US
 Scaling up and down
 NASA Airborne campaigns:
PALScan (4 flights) and
SLAPEX15 (this fall)
 Expansion throughout TX

28. http://www.beg.utexas.edu/soilmoisture/
Dave Murdoch and Quinten Zoeller
(LCRA) Mike Cosh (USDA)
Richard Casteel (UT)
Paul Tybor (HCUWCD)
SMAP (NASA):
Tom Jackson , Seung-bum Kim,
Andreas Colliander , Simon Yueh