Elmer, Nicholas

Hain, Christopher

McCreight, James

English

NASA Technical Reports Server

Clapp-Hornberger B 
exponent (soil moisture)
Soil moisture maximum Saturated soil 
conductivity
Soil infiltration Soil 
drainage
Retention depth Saturated soil lateral
conductivity
Groundwater 
maximum depth
Groundwater exponent 
(bucket model)
Canopy 
wind (ET)
Maximum caboxylation
at 25°C
Ball-Berry conductance 
relationship slope
Snowmelt
parameter
Improving Soil Moisture States in WRF-Hydro Using SWOT Observations
Nicholas Elmer (nicholas.j.elmer@nasa.gov)1,2, Christopher Hain2,3, James McCreight4
H43I-2126
4. Methodology
• SWOT orbit was simulated using expected orbit parameters
• At each SWOT overpass, WSE was calculated for every channel
reach within SWOT swath extent and with stream order ≥4
(estimate of rivers with widths > 50 m)
• Randomly generated noise (σ=0.25 m for WSE) was added to
mimic SWOT instrument and algorithm errors
• Result is proxy SWOT observation at each channel grid point
within the simulated SWOT orbit (Figure 4)
• Proxy SWOT WSE assimilated into WRF-Hydro using
HydroDART to update Noah-MP soil moisture states.
Figure 4. WRF-Hydro-derived synthetic SWOT elevations (colorbar) for the
Chena River watershed (outlined in blue) for a simulated SWOT overpass
(swath shaded in blue; nadir track shown by the thick black line). Randomly
sampled virtual gauges (colored circles outlined in black), current USGS stream
gauge sites (blue triangles), and terrain (grayscale; NED DEM) are also shown.
1. Background
• Surface Water Ocean Topography (SWOT) mission (Rodriguez
2016; Biancamaria et al. 2016)
• 2021 launch
• Wide-swath (120 km), bistatic, Ka-band (36 GHz) radar
interferometer
• Global measurements of channel water surface elevation
(WSE) and estimated discharge for rivers with widths greater
than 50-100 meters
• Weather Research and Forecasting hydrological extension
package (WRF-Hydro) (Gochis et al. 2018)
• High-resolution hydrologic routing and streamflow modeling
framework
• Couples column land surface, terrain routing, and channel
routing modules (Figure 1)
• Basis of the NOAA National Water Model (NWM; OWP 2019)
• HydroDART (McCreight et al. 2018)
• Data assimilation system built on National Center for
Atmospheric Research (NCAR) Data Assimilation Research
Testbed (DART; Anderson et al. 2009)
• For offline implementation of WRF-Hydro
Figure 1. WRF-Hydro modules and output variables (NCAR 2019)
Figure 2. Experiment workflow. Blue boxes indicate models or processing
tools, white boxes indicate datasets and model inputs/outputs, and yellow
boxes provide brief descriptions.
2. Motivation
• In situ stream and soil moisture gauges are spatially limited
• SWOT mission will provide global observations at high spatial
resolution
• Can SWOT measurements of streamflow improve soil moisture
estimates of the land surface?
Acknowledgements
This work is supported by NASA Headquarters under the NASA
Postdoctoral Program at NASA Marshall Space Flight Center
administered by Universities Space Research Association.
Table 1. Parameters used during calibration and ensemble generation.
References
Biancamaria, S., D. P. Lettenmaier, and T. M. Pavelsky (2016), 
The SWOT mission and its capabilities for land hydrology, 
Surv. Geophys., 37(2), 307-337, doi:10.1007/s10712-015-
9346-y.
Gochis, D.J., M. Barlage, A. Dugger, K. FitzGerald, L. Karsten, M. 
McAllister, J. McCreight, J. Mills, A. RafieeiNasab, L. Read, 
K. Sampson, D. Yates, W. Yu, (2018), The WRF-Hydro 
modeling system technical description, (Version 5.0). NCAR 
Technical Note. 107 pages. Available online at 
https://ral.ucar.edu/sites/default/files/public/WRF-
HydroV5TechnicalDescription.pdf. Source Code 
DOI:10.5065/D6J38RBJ.
McCreight, J. L., T. Hoar, D. J. Gochis, W. Yu, and A. Dugger
(2018), HydroDART (v0.1): Ensemble Data Assimilation 
with (offline) WRF Hydro and the Data Assimilation 
Research Testbed (DART). Geoscientific Model 
Development, in preparation.
Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, and Coauthors 
(2006), North American Regional Reanalysis, Bull. Amer. 
Meteorol. Soc., 87, 343–360, doi:10.1175/BAMS-87-3-343.
NCAR (2019), WRF-Hydro modeling system, National Center for 
Atmospheric Research Research Applications Laboratory, 
https://www.ral.ucar.edu/projects/wrf_hydro.
Niu, G.-Y., et al. (2011), The community Noah land surface 
model with multiparameterization options (Noah-MP): 1. 
Model description and evaluation with local-scale 
measurements, J. Geophys. Res., 116, D12109, 
doi:10.1029/2010JD015139.
OWP (cited 2019), The National Water Model, 
http://water.noaa.gov/about/nwm.
Pavelsky, T. M., M. T. Durand, K. M. Andreadis, R. E. Beighley, R. 
C. D. Paiva, G. H. Allen, and Z. F. Miller (2014), Assessing 
the potential global extent of SWOT river discharge 
observations, J. Hydrol., 519, 1516-1525, 
doi:10.1016/j.jhydrol.2014.08.044.
Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, 
C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. 
Bosilovich, J. K. Entin, J. P. Walker, D. Lohmann, and D. Toll 
(2004), The Global Land Data Assimilation System, Bull. 
Amer. Meteor. Soc., 85(3), 381-394.
Rodriguez, E. (2016), Surface Water and Ocean Topography 
Mission (SWOT) Project, Science Requirements Document, 
Rev A, 28 pp., https://swot.jpl.nasa.gov/files/swot/D-
61923_SRD_Rev%20A_20160318%20with%20signatures1.
pdf.
http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.
USGS (cited 2017), National Elevation Dataset (NED), 
https://nationalmap.gov/elevation.html.
5. Preliminary Results (Chena River)
• Open loop WRF-Hydro 0-10 cm volumetric soil moisture is of 
similar magnitude as SNOTEL observations
• Point-by-point comparison is poor, likely a result of no 
calibration and no data assimilation being performed
6. Next Steps
• Expand analysis to full domain to 
include upper Tanana, Susitna, and 
Copper River basins
• Complete simulations with 
assimilated proxy SWOT WSE
• Fine-tune radius of influence for 
updating soil moisture states
• Integrate Soil Moisture Ocean
Salinity (SMOS) and Soil Moisture
Active Passive (SMAP) soil moisture
observations for improved spatial 
validation.
Figure 3. Alaska domain used in this study depicting major basins (thick
black lines), minor basins (thin black lines), in situ USGS gauges (red and
white dots; white gauge points delineate minor basins), SWOT observable
rivers (blue lines), and topography (background).
3. Model Configuration
• Alaska domain containing Susitna River, Copper River, and upper
Tanana River basins (Figure 3)
• WRF-Hydro v5.0 coupled with 1-km resolution Noah Land
Surface Model with Multi-Parameterization options (Noah-
MP)(Niu et al. 2011)
• Regridded, 2 arc-second National Elevation Dataset (NED)(USGS
2017) for WRF-Hydro subsurface flow, overland flow, and
diffusive wave channel routing (Figure 2)
• Global Land Data Assimilation System Version 2 (GLDAS-2; Rodell
et al. 2004) meteorological forcing (0.25° spatial res.) (Fig. 2)
• Control run (100-m WRF-Hydro simulation)
• 2-year model spin-up (Mar 2009 – Mar 2011) followed by
three-year calibration (Mar 2011 – Mar 2014) performed
using USGS stream gauges for parameters shown in Table 1
• Assumed to be free of errors and representative of truth
• Used to derive proxy SWOT observations (see Section 4 and
Figure 4)
• Corrupted runs (uncalibrated 250-m WRF-Hydro simulations)
• 8-year model spin-up (Mar 2009 – Mar 2017)
• 80 ensemble members created by randomly varying
calibration parameters (Table 1) within valid parameter ranges
1NASA Postdoctoral Program, NASA Marshall Space Flight Center, Huntsville, AL, USA
2NASA Short-term Prediction Research and Transition (SPoRT) Center, Huntsville, AL, USA
3Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL, USA
4National Center for Atmospheric Research, Boulder, CO, USA
Figure 5. 0000 UTC 14 May 2017 Chena River watershed 0-10 cm volumetric
soil moisture simulated by open-loop (no data assimilation) WRF-Hydro and
USDA Natural Resources Conservation Service (NRCS) Snow Telemetry
(SNOTEL) 5 cm soil moisture measurements (filled circles).
Fairbanks
10 km
7. Future Work
• Integrate Centre National D'Etudes 
Spatiales (CNES) SWOT Hydrology 
Simulator to create proxy SWOT 
WSE to account for dark water and 
geolocation errors
• Assimilate both SWOT and SMAP 
observations
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https://ntrs.nasa.gov/search.jsp?R=20190033996 2020-03-11T16:17:51+00:00Z


Improving Soil Moisture States in WRF-Hydro Using SWOT Observations

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