Large-scale hydrometeorology:
Integrating land-surface models with
observations
By: Ben Livneh
Overview
• Background: significance of large-scale land
surface modeling
• Past and ongoing projects
• Importance of linking models together to
capture key land surface processes.
• The emerging utility of large-scale remote
sensing data and the need for improving
geophysical information transfer
• Future directions
Big picture
Role of large-scale (several km2 to >105km2) land surface models (LSMs):
• Represent the exchanges of energy, water, and carbon at the land surface (affects
climate, water availability, ecology, etc…). These can be coupled with atmospheric
models, or run offline
• Major types of processes/sub-components for the land surface:

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


Hydrology specific models: surface water budget, soil moisture, stream flow.
Biogeophysical models: vegetation dynamics, crop yield, etc..
Water quality and sediment transport models
Reservoir model: dams, hydraulic controls
Snow model – including snow, ice, and glaciers
• Land surface models (LSMs) may include some, or all of these; represent an
integration of many complex processes that are often inter-related.
• Linking models is beneficial when one single sub-model /process is not detailed
enough. Entails interdisciplinary synergy and promotes learning
http://manmadeclimatechange.co.uk/dir./?p=13
Big picture
• Models provide a means to estimate conditions over large areas, where detailed
observations are not available. Sophisticated methods for transferring geophysical
information in and out of the modeling system are vital to maximize physical
realism.
 Geophysical inputs are needed to drive the models; assumptions about spatial
representativeness, interpolation, averaging;
 Transferring validated simulations to make predictions over data poor regions
(ungauged basins). Even for a “gauged” location, not continuous in space –
spatial a transfer scheme is needed
Satellite/remote sensing data are an emerging tool for understanding land surface
processes. However, as a stand-alone, they are presently inadequate (closure of water
and energy budgets); require careful interpretation
http://manmadeclimatechange.co.uk/dir./?p=13
Example: linking a LSM with a hydrology model
• The Noah LSM is used in most of the National Oceanic and
Atmospheric Administration (NOAA) coupled weather and climate
models. Its principal role in this setting is to partition net radiation
into turbulent surface and ground heat fluxes, which are required
to characterize the atmospheric model’s lower boundary.
• Historically, the quality and complexity of soil
RMSE of modeled
streamflow
moisture and runoff parameterizations have
received less attention than the processes
above1,2;
• Further, the Noah LSM was run offline to
simulate streamflow at the National Centers for
Environmental Prediction (NCEP) and National
Weather Service (NWS) and was shown to be
less skillful in streamflow prediction compared
Noah
VIC
with more hydrologically based models3.
Sac
1. Koster et al., 2000; 2. Bastidas et al., 2006; 3. Bohn et al., 2010
Obs
5
Hydrologic considerations
• Hydrologic factors such as soil moisture play an
important role in modulating climate4,5, cloud
formation6, and surface latent heat fluxes through
interaction with evapotranspiration (ET)7
• At very large scales, runoff from an LSM ultimately
becomes an input to the oceans, important for
modeling of oceanic circulation and climate through
salinity levels8,9.
• The risk is that poor hydrologic characterization from
an LSM could carry-through and produce unrealistic
estimates of other important quantities
6
4. Wang and Kumar, 1998; 5. Mahmood and Hubbard, 2003; 6. Wetzel et al., 1996; 7. Xiu, 2001 ; 8. Verseghy, 1996; 9. Arora, 2001
Hydrologic prediction
• Hydrologic-based models (e.g. the Sacramento Soil Moisture
Accounting model -Sac10) focus on accurately simulating
components of the surface water budget, especially
streamflow.
• Sac is used by the NWS River Forecast Centers (RFCs) has been
shown to simulate streamflow skillfully compared with other
models11.
NWS River Forecast
• Long history of Sac
usage for flood
forecasting/streamflow
prediction goes back to
1970s.
Centers
7
10. Burnash et al., 1973; 11 Reed et al., 2004
http://water.weather.gov/ahps/rfc/rfc.php
Sac – limitations
Despite extensive focus on streamflow prediction, 2 notable
obstacles prevent Sac from being coupled with atmospheric
models or estimate other states and fluxes
1. Lack of a surface energy budget
 radiative partitioning (downward
solar, longwave)
 surface heat fluxes (sensible, latent)
2. Absence of an explicit vegetation scheme.
 Affects surface exchanges of heat, moisture and momentum12,13,14
Controls the rate of moisture movement
to and from the soil, via canopy interception,
root-water uptake
T
T rates shown to vary by vegetation type15
P
↑Ecanopy
On global average, roughly 2/3 of precip.
interception
reaching the land surface is estimated returned
through↑Esoil
fall
to the atmosphere via ET!16
↑T
infiltration
12. Bonan et al., 1992; 13. Pan and Mahrt, 1987; 14; Pielke et al., 1998; 15. Zhang et al., 2001; 16. Tateishi and Ahn
8
Unified Land Model (ULM) Development
• Objective: combine aspects of the soil moisture accounting
scheme of the Sacramento model with the versatility of the
Noah LSM to create a unified land model (ULM17) capable of
representing land surface processes more realistically than
either of the existing models.
• Assess model performance in estimating important
components of the terrestrial water budget
17. Livneh et al., 2011
ULM
Noah LSM
SAC model
9
Results: evolution of soil moisture and surface
heat fluxes through warm season
(CA)
• Non-linearity of SM decline through dry
summer captured only by ULM:
dominated first by direct soil evaporation,
then transpiration, whereas Noah
combines ET sources in Richard’s
equation.
• ULM surface heat fluxes (not shown)
compare favorably with Noah and
observations. Important benchmark for
model focused on land-atmosphere
interaction.
Noah
ULM
Observations
Study domain for further testing and development with
remote sensing data, precipitation and streamflow
CB
MO
UM
GL
GB
CO
CA
OH
AR
LM
EA
RG & GU
Precipitation stations 13 major hydrologic regions
Shaded
areas defined
USGS
gaugeprecipitation
(*suitable scale
for density18
250 MOPEX
basinsby
with
adequate
gauge
comparison with large-scale remote sensing products)
11
18. Schaake et al., 2006
Multi-criteria calibration data
Study domain
Estimate soil water and
snow (TWSC) on the land
surface via changes in the
Earth’s gravitational field.
Gravity Recovery and
Climate Experiment
(GRACE)
Q
Streamflow
gages
TWSC
http://www.cosmosmagazine.com/features/online/1678/gravity-ball
Estimate ET from the land surface by closing an
atmospheric water balance(AWB). Uses gauge
precipitation data and North American Regional
reanalysis (NARR).
Estimate ET from
satellite data –
differences in
vegetation diversity
and surface skin
temperature
ETSAT
ETAWB
Single and multi-criteria results19
Arkansas
Red
California
Colorado
Columbia
Missouri
Ohio
Upper Miss.
• At large-scales (>105 km2), highquality single criterion calibrations
(TWSC, ET, Q) were tenable.
• Multi-criteria calibrations
considering Q performed best,
while ET and/or TWSC alone did not
contain enough information to
significantly improve Q simulation.
MULTI-CRITERIA CALIBRATIONS
(102
NASH-SUTCLIFFE EFFICIENCY (NSE)
ideal
TWSC
At catchment scales
–
104 km2), the best Q
predictions resulted from
simultaneous calibration
towards Q and ETsatellite for
1/3 of cases (80 basins),
demonstrating the potential
for remote sensing in LSM
development
NSE (-∞,1)
NSE = 1
Perfect model;
NSE = 0
Equivalent to
climatology
19. Livneh et al., 2012a
Parameter transfer/regionalization
• Motivated towards extending rigorous calibration effort to
new domains.
• In general, domain-specific model calibration either
impossible (due to lack of data), or infeasible (e.g. University
of Washington global drought monitor), thus a broadly
applicable method was employed.
• Procedure – derive predictive relationships between
catchment attribute data (meteorological, geomorphic, landcover, remote-sensing, other databases) and calibrated model
parameters.
• A large number of candidate catchment attributes available,
therefore, a step-wise principal components analysis (PCA)
procedure20 was selected to maximize their explanatory skill
and minimize potential redundancy.
20. Garen, 1990
Regionalization experiment example
• Relate calibrated model parameter to spatially varying
catchment attributes.
• Could be applied to other physical features, model
parameters, or processes.
“free”
“tension”
http://www.terragis.bees.unsw.edu.au/terraGIS_soil/sp_water-soil_moisture_classification.html
1 of 388 attributes considered
Tension water model parameter
% clay (average)
Parameter transfer results
NSE
Local basin
LOCAL-ZONAL
• Used a jack-knifing approach – i.e. only information from
other basins used to predict parameters for target basin
• An additional (global) experiment reduced the catchment
attributes to only those that are globally available.
220 Basins
ULM calibrated
ULM regionalized
ULM regionalized global
Mean NSE
0.54
0.44
0.41
Rank
• Modest loss of skill for the regionalized model
• The approach worked comparatively well for the global
case [show mean NSE scores, sequence the plot above]
Opportunities for linking models
• Improving vegetation representation/dynamics to better
understand implications of global change on the biosphere.
• A comparison of several state-of-the-art LSMs revealed
considerable disagreement in their ET partitioning (between
transpiration, canopy, and soil evaporation)
• Alternatively, models can be run as a multi-model ensemble. This
is a good way to ‘bracket’ a solution; collaborate; learn about
different ways to parameterize a processes.
1. Multi-model streamflow forecasting project 21 demonstrated the
ability of LSMs to make skillful streamflow forecasts at seasonal
leads, with only knowledge of soil moisture and snow states
2. Model intercomparison projects: snow model intercomparison in
the Colorado headwaters (snowpack a vital water resource for
montane regions)
21. Koster et al., 2000
Opportunities for transferring/inferring
geophysical features
• Current strategy of transferring model parameters through land
surface attributes, statistics could be extended globally and to other
physical phenomena.
• Geophysical features can be inferred/derived through physical
relationships
1. Ongoing global project22 testing algorithms that estimate downwelling radiation and humidity (infrequently observed) via inputs of
only daily temprature and precipitation (more frequently observed)
2. Related work23 (nearly complete) to make publicly available a 96
year dataset (1915-2010) of sub-daily hydrometeorological fluxes
over the U.S. at approximately 6km resolution.
• Can incorporate a combination of inference and transfer.
22. Bohn et al., 2012; 23. Livneh et al., 2012b
Future directions
• Continue developing/linking models to address
scientific problems; global change; flood
forecasting, infrastructure loadings.
• Driver for collaboration, interdisciplinary synergy
and building a diverse research environment
• Further explore ways at incorporating emerging
observational data into land-surface modeling
• Bristol is an ideal place for this, given the diversity
of its faculty, high quality facilities, and
encouragement towards inter-departmental and
international collaboration.
Thank you!