Atmospheric Models 1993

1
Atmospheric Models (1993)

• Subgrid-scale spatial variations
• Surface water energy fluxes
• Soil moisture and runoff production
• Climate and weather prediction models
• Uniform soil characteristics within grid cell
• Ignore spatial variability of precipitation
• Simple treatment of land surface fluxes
• Important land-atmosphere interactions may be
  poorly represented (simulated)

2
A Key Challenge in Land-Atmosphere Modeling
Scale Issues Problems associated with the
transfer of information across scales.
(adopted from GEWEX, 1993)
3
Macroscale Hydrologic Models

• Hydrologic models designed to be coupled with
  atmospheric models (or run offline)
• Grid-based elements
• Improved representation of subgrid-scale
  processes
• Related to soil-vegetation-atmosphere transfer
  schemes (SVATS) (but macroscale models consider
  runoff processes)

4
Macroscale Model (VIC-3L)

• VIC Model Features
• Multiple vegetation classes in each cell
• Sub-grid elevation band definition (for snow)
• 3 soil layers used
• Energy and water budget closure at each time step
• Subgrid infiltration/runoff variability
• Non-linear baseflow generation

5
Model Development

• Model developed for 15 sub-regions, with
  consistent sources.
• Surface forcing data
• Daily precipitation maximum and minimum
  temperatures (from gauge measurements)

• Radiation, humidity parameterized from Tmax and
  Tmin
• Wind (from NCEP/NCAR reanalysis)
• Soil parameters derived from Penn State State
  STATSGO in the U.S., FAO global soil map
  elsewhere.
• Vegetation coverage from the University of
  Maryland 1-km Global Land Cover product (derived
  from AVHRR)

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LAI Calculation
UMD 1km Land Classification provides basis for
fractional land cover description
1/4o Monthly LAI for each cell with dominant
(gt80) one type of land cover
2o moving window for 1/8ocell
1/4o monthly LAI database from Myneni, et al.
Provides monthly LAIs
Each land cover in each 1/8o cell based on
average of dominant class LAI by month
7
Temperature and Precipitation
Precipitation and Temperature from gauge
observations gridded to 1/8o
Avg. Station density

• Within the U.S.
• Precipitation adjusted for time-of-observation
• Precipitation re-scaled to match PRISM mean for
  1961-90

8
River Routing
Runoff from VIC-3L is routed using a grid-based
topology to produce flow hydrographs for river
basins.
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Sample Hydrographs

• Good agreement of
• Seasonal cycle
• Low Flows
• Peak Flows

10
Validation with Soil Moisture
19 observing stations are compared to the 17 1/8º
modeled grid cells that contain the observation
points.
Moisture Level
Moisture Flux
Variability
Illinois Stations
Persistence
11
Validation of Energy Parameterization
Comparison with 4 SURFRAD Sites

• 3-minute observations aggregated to 3-hour
• Average Diurnal Cycle is for June, July, August
  1996-99
• Peak underestimated 3-15 at each site (avg. 10
  for all sites)
• Daily average within 10, (avg. 2)

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National LDAS
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Land Data Assimilation Selected Future Challenges
Data Assimilation Algorithm Development Link
calibration and assimilation in a logical and
mutually beneficial way and move towards
multivariate assimilation of data with
complementary information Land Observation
Systems Regular provision of snow, soil
moisture, and surface temperature with knowledge
of observation errors Land Modeling Better
correlation of land model states with
observations, and knowledge of prediction errors
and Advanced processes River runoff/routing,
vegetation and carbon dynamics, groundwater
interaction Assimilate new types of data
Streamflow, vegetation dynamics,
groundwater/total water storage (Gravity),
evapotranspiration Coupled feedbacks Understand
the impact of land assimilation feedbacks on
coupled system predictions.
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LDAS Soil Wetness Comparison
LDAS retrospective output example
(similar spread as in PILPS-2c)
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LDAS Soil Wetness Comparison
LDAS realtime output example (similar
spread as in PILPS-2c)
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Soil Moisture - Active Range
50-Year Soil Moisture Range Scaled by Annual
Precipitation

Scale indicates level of hydrologic interaction
of soil column
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Two main issues of scale in coupled modeling
(1) The effects of subgrid-scale variability in
rainfall (which is known to exist from
observations) on the land-surface are
neglected. (2) Parameterizations used within the
model are applied at the grid-scale, regardless
of the scale for which they have been optimized.