NLDA and COSMOSHow do they compare?

COSMOS Workshop11 December 2012

Todd CaldwellMichael Young

Bridget ScanlonDi Long

Soil Moisture Storage

WY05 +76.7 km3 +6.2x107 ac-ft

CY11 Drought -84.6 km3

-6.8x107 ac-ft ±11 cm of water

over TX

TEXAS

Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Sto

rag

e [km3

]-150

-100

-50

0

50

100

150

Soil Moisture (Noah)Total (GRACE)

Surface Water

Soil moisture is a large component of the water balance in Texas (676,000 km2)

Soil Moisture Modeling

Hard to quantify at basin+ scale We need a means to estimate and

predict

Soil moisture is enigmatic at large scales

Loukili et at., doi:10.2136/vzj2007.00810

The simplification and numerical representation of our world in 1-D columns

North American Land Data Assimilation System (NLDAS) by NASA A quality-controlled, and spatially and temporally consistent, land-surface multi-model (LSM) output from 1979 to present

Soil Representation in NLDASCONUS-SOIL STATSGO (1:250,000)

• 1 km grid• Dominant soil series

16 textural classes• 12 are actually soil

11 layers to 2m depth

NLDAS ⅛° grid (~14 km) %Class over each grid Noah, Mosaic, VIC

• Uniform soil texture from top 5cm layer

Miller and White, 1998, Earth Interactions, Paper 2-002.

Mitchel et al., 2004, JGR, D07S90, doi:10.1029 /2003JD003823.

Mosaic Noah SAC VIC

Soil Layers 3 4 2 buckets 3

Depth (cm)10, 40 200 10, 40, 100,

200 - 10 + 2 variable

Output θ (z) θ (z) SWS SWS

Soil Parameterization in NLDAS

Soil hydraulic properties for 12 soil classes• Mosaic PTF (Rawls et al., 1982)• Noah PTF (Crosby et al., 1984)

Flux between layers quasi-Richards’ equation Uniform soil with depth

Mosaic and Noah

Textural class at 5cm extracted for whole soil column

NLDAS-2 Data and Output Primary Forcing Data at Hourly Time Steps

Precipitation (PRISM) Solar Rad (NARR)

Convective Available PE PET

Air T and RH (2m) Wind Speed (10m)

GRIB outputs at hourly and monthly values http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings

52 Fields of parameters Soil Moisture Storage (4):

0-0.1m, 0.1-0.4m, 0.4-1.0m and 1.0-2.0m

Noah Output

Operational Scale of NLDAS-2

4169 nodes in TX 627 nodes in Colorado River Basin

NLDAS-2: ⅛° grid (~14 km), 224x464=104k nodesSTATE WATERSHED

Operational Scale of NLDAS-218 nodes in Travis CountyCOUNTY

SSURGOSSURGO SHC

SSURGO SWC

aws0100wta

0-2

2-4

4-6

6-8

8-10

10-12

12-14

14-16

16-18

18-20+

AWC(in)Austin

Current of Soil Moisture and Climate Observatories in the State of Texas

USDA SCAN Sites • 140 nationally• 5 (4%) in Texas, ~9 planned

NOAA USCRN Sites• 144 nationally, 538 planned• 7 (5%) in Texas

NSF COSMOS Sites• 50 nationally, 450 planned• 2 (4%) in Texas

AmeriFlux Sites • 212 nationally• 3 (1%) in Texas, ? Planned

NEON?

Freeman Ranch, TX

SCAN Data and NLDAS in Texas

VWC at 0-10 cm

Missing data?

Missing storm?

??

SCAN Data and NLDAS in Texas

VWC at 0-10 cm

A snapshot of COSMOS stations NSF COSMOS Sites

• Picked 6 of the oldest, more diverse station• Plus 2 in Texas• Not very scientific at this point

Extracted the daily mean of the Level 3, boxcar filtered hourly data (SM12H)

NLDAS-2 Model Data• Extracted nearest-node• Daily mean 0-10cm

Freeman Ranch, TX

COSMOS Data and NLDAS

COSMOS Data and NLDAS

So, how do they compare? Modeler’s viewpoint:

• Captures the soil moisture dynamics robustly, good correlation! • There’s a scale issue with the observational data• We need to refine our models and collect more data

Field hydrologist viewpoint:• Absolute values are way off, terrible correlation!• Non-synchronous and erroneous precipitation events• Oversimplified the soil system • We need to collect more data and refine our models

Personal viewpoint:• The models provide more spatiotemporal data then we can monitor

We can use the data to site future key monitoring locations (mean relative differences)

• The monitored data shows inadequacies in model structure We can update and refine the antiquated PTF through parameter optimization We can develop downscaling algorithms to better assess model performance

• We need to collect more data and refine our models Soil moisture is the “first-in-time, first-in-right”