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NLDA and COSMOS How do they compare? . Todd Caldwell Michael Young Bridget Scanlon Di Long . Soil Moisture Storage. Soil moisture is a large component of the water balance in Texas (676,000 km 2 ). WY05 +76.7 km 3 +6.2x10 7 ac- ft CY11 Drought -84.6 km 3 -6.8x10 7 ac- ft - PowerPoint PPT Presentation
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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-ftCY11 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 Storage [km
3]-150
-100
-50
0
50
100
150Soil 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 VICSoil Layers 3 4 2 buckets 3Depth (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 SHCSSURGO SWCaws0100wta
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”