nlda and cosmos how do they compare? cosmos workshop 11 december 2012 todd caldwell michael young...

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”