Texas Groundwater Summit, 26 August 2015

TxSON: the Texas Soil Observation Network

Todd G. Caldwell, Research Associate, todd.caldwell@beg.utexas.edu

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

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

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

GRACE estimates of total water storage

Source: Long et al., 2013

TWS = SMS + GWMajority of depletion appears to

be in soil moisture storage

>60 maf = 6 maf + 70-80 % TWS + 4-8 maf

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

Changes in Water Storage Compartments: Statewide

R

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

Changes in Total Water Storage: GRACE 1o Grid

Remote Sensing/LSM limitations: time and space

RIVER BASIN GRACE SMOS NLDAS

-- number of cells --

TEXAS 62 1102 4171Neches 3 38 156Trinity 5 74 286Brazos 12 175 677Colorado 9 167 627Nueces 3 67 256Rio Grande 12 210 773

Basin reservoir forecast from soil moisture

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

How can we measure soil moisture?

SMAP 1000 km swath widthVereecken et al., 2008, WRR

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

Passive Radiometer (36km)

SMAP Level 2 data over Texas: 30 April 2015

Combined Active/Passive (9km)

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

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

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 = 3Soil moisture at 5, 10, 20, and 50 cmhttp://www.beg.utexas.edu/txson/

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?

Real-time web interface

http://www.beg.utexas.edu/txson

TxSON in action: micro-station Insert CS-655 sensors at - 5, 10, 20, and 50 cm

Add precip gage, cell modem Fence quickly!

TxSON Scaling: Inverse distance weighing

TxSON Scaling: PALScan Airborne Microwave Surveys

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

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.

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

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

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