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