1. NLDA and COSMOS How do they compare? COSMOS Workshop 11 December 2012 Todd Caldwell Michael Young Bridget Scanlon Di Long

2. Soil Moisture Storage WY05  +76.7 km 3  +6.2x10 7 ac-ft CY11 Drought  -84.6 km 3  -6.8x10 7 ac-ft  ±11 cm of water over TX Soil moisture is a large component of the water balance in Texas (676,000 km 2 )

3. 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

4. Soil Representation in NLDAS CONUS-SOIL STATSGO (1:250,000) STATSGO (1:250,000) 1 km grid1 km grid Dominant soil seriesDominant soil series 16 textural classes 16 textural classes 12 are actually soil12 are actually soil 11 layers to 2m depth 11 layers to 2m depth NLDAS ⅛° grid (~14 km) ⅛° grid (~14 km) %Class over each grid %Class over each grid Noah, Mosaic, VIC 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. MosaicNoahSACVIC Soil Layers 342 buckets3 Depth (cm) 10, 40 200 10, 40, 100, 200 - 10 + 2 variable Output θ (z) SWS

5. Soil Parameterization in NLDAS Soil hydraulic properties for 12 soil classes Soil hydraulic properties for 12 soil classes Mosaic PTF (Rawls et al., 1982)Mosaic PTF (Rawls et al., 1982) Noah PTF (Crosby et al., 1984)Noah PTF (Crosby et al., 1984) Flux between layers quasi-Richards’ equation Flux between layers quasi-Richards’ equation Uniform soil with depth Uniform soil with depth Mosaic and Noah Textural class at 5cm extracted for whole soil column Textural class at 5cm extracted for whole soil column

6. NLDAS-2 Data and Output Primary Forcing Data at Hourly Time Steps Precipitation (PRISM)Solar Rad (NARR) Convective Available PEPET Air T and RH (2m)Wind Speed (10m) GRIB outputs at hourly and monthly values GRIB outputs at hourly and monthly valueshttp://disc.sci.gsfc.nasa.gov/hydrology/data-holdings 52 Fields of parameters 52 Fields of parameters Soil Moisture Storage (4): Soil Moisture Storage (4): 0-0.1m, 0.1-0.4m, 0.4-1.0m and 1.0-2.0m Noah Output

7. Operational Scale of NLDAS-2 4169 nodes in TX 627 nodes in Colorado River Basin NLDAS-2: ⅛° grid (~14 km), 224x464=104k nodes STATE WATERSHED

8. Operational Scale of NLDAS-2 18 nodes in Travis County COUNTY SSURGO AWC(in) Austin

9. Current of Soil Moisture and Climate Observatories in the State of Texas USDA SCAN Sites USDA SCAN Sites 140 nationally140 nationally 5 (4%) in Texas, ~9 planned5 (4%) in Texas, ~9 planned NOAA USCRN Sites NOAA USCRN Sites 144 nationally, 538 planned144 nationally, 538 planned 7 (5%) in Texas7 (5%) in Texas NSF COSMOS Sites NSF COSMOS Sites 50 nationally, 450 planned50 nationally, 450 planned 2 (4%) in Texas2 (4%) in Texas AmeriFlux Sites AmeriFlux Sites 212 nationally212 nationally 3 (1%) in Texas, ? Planned3 (1%) in Texas, ? Planned NEON? NEON? Freeman Ranch, TX

10. SCAN Data and NLDAS in Texas VWC at 0-10 cm Missing data? Missing storm? ??

11. SCAN Data and NLDAS in Texas VWC at 0-10 cm

12. A snapshot of COSMOS stations NSF COSMOS Sites NSF COSMOS Sites Picked 6 of the oldest, more diverse stationPicked 6 of the oldest, more diverse station Plus 2 in TexasPlus 2 in Texas Not very scientific at this pointNot very scientific at this point Extracted the daily mean of the Level 3, boxcar filtered hourly data (SM12H) Extracted the daily mean of the Level 3, boxcar filtered hourly data (SM12H) NLDAS-2 Model Data NLDAS-2 Model Data Extracted nearest-nodeExtracted nearest-node Daily mean 0-10cmDaily mean 0-10cm Freeman Ranch, TX

13. COSMOS Data and NLDAS

15. So, how do they compare? So, how do they compare? Modeler’s viewpoint: Modeler’s viewpoint: Captures the soil moisture dynamics robustly, good correlation!Captures the soil moisture dynamics robustly, good correlation! There’s a scale issue with the observational dataThere’s a scale issue with the observational data We need to refine our models and collect more dataWe need to refine our models and collect more data Field hydrologist viewpoint: Field hydrologist viewpoint: Absolute values are way off, terrible correlation!Absolute values are way off, terrible correlation! Non-synchronous and erroneous precipitation eventsNon-synchronous and erroneous precipitation events Oversimplified the soil systemOversimplified the soil system We need to collect more data and refine our modelsWe need to collect more data and refine our models Personal viewpoint: Personal viewpoint: The models provide more spatiotemporal data then we can monitorThe models provide more spatiotemporal data then we can monitor We can use the data to site future key monitoring locations (mean relative differences) We can use the data to site future key monitoring locations (mean relative differences) The monitored data shows inadequacies in model structureThe monitored data shows inadequacies in model structure We can update and refine the antiquated PTF through parameter optimization We can update and refine the antiquated PTF through parameter optimization We can develop downscaling algorithms to better assess model performance We can develop downscaling algorithms to better assess model performance We need to collect more data and refine our modelsWe need to collect more data and refine our models Soil moisture is the “first-in-time, first-in-right” Soil moisture is the “first-in-time, first-in-right”