Utah Energy Balance Snowmelt
Model
David Tarboton
Utah State University
http://www.engineering.usu.edu/dtarb/
dtarb@usu.edu
Outline
• USU Team
– Madeline Merck
– Tseganeh Gichamo
•
•
•
•
•
Overview of UEB
UEB Input data requirements
MERRA Downscaling
Project work
Possible partnerships
Overview
• A physically based snowmelt model designed to
quantify the surface energy balance involved in
snowmelt
• Physical basis reduces need for calibration and is
more robust for prediction under conditions of
non stationarity (climate change)
• Will configure as component of CHPS to enable
side by side examination of UEB versus index
based Snow17 to quantify forecast skill for each
model
Need for physically based snowmelt modeling
• Climate Change
– Advance in snowmelt timing
– Smaller snow pack area
– Uncertainty in regional variability
• Land Use Change
– Transitions to Agriculture or Urban
– Conifer/Deciduous
– Bark Beetles (in western US)
• Both
– Diminished statistical validity of past observations for current
conditions
Design Premises
• Physically based calculation of snow energy balance.
– Predictive capability in changed settings
– Strives to get sensitivities to changes right
• Simplicity. Small number of state variables and adjustable
parameters.
– Avoid assumptions and parameterizations that make no difference
• Transportable. Applicable with little calibration at different
locations.
• Match diurnal cycle of melt outflow rates
• Match overall accumulation and ablation for water balance.
• Distributed by application over a spatial grid.
• Effects of vegetation on interception, radiation, wind fields
Utah Energy Balance Snowmelt Model
e.g. Mahat, V. and D. G. Tarboton, (2012), "Canopy radiation transmission for an energy balance snowmelt model," Water Resour.
Res., 48: W01534, http://dx.doi.org/10.1029/2011WR010438.
Implementation
• Three State Variables
– Surface snow water equivalent, Ws
– Internal energy of the surface snowpack, Us
– Canopy snow water equivalent, Wc
• Energy fluxes
– Solar and longwave radiation
– Sensible heat and latent heat turbulent fluxes
– Conduction into snow
• Force restore parameterization to model surface temperature without
requiring multiple layers
• Vegetation layer that accounts for leaf and canopy density effects on radiation
transmission, turbulent transfers and interception
• 3 hr or less time step to quantify diurnal cycle of melt outflow rates
• Distributed by application over a spatial grid
• Flexible ASCII and netCDF based input/output data model to facilitate file
based coupling with other models
UEB Model Structure
State Variables
• Surface snow water equivalent, Ws
• Internal energy of the surface snowpack, Us
• Canopy snow water equivalent, Wc
State Equations
Surface mass and energy balance (beneath the canopy)
dU s
 Qsns  Qsnl  Q ps  Qg  Qhs  Qes  Qms
dt
dWs
 pr  ps  i  Rm  M c  Es  M s
dt
Canopy mass and energy balance
dWc
 i  Rm  M c  Ec
dt
Qcns  Qcnl  Q pc  Qhc  Qec  Qmc  0
Numerical Approach
𝐹1 (𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐 , 𝑖𝑛𝑝𝑢𝑡𝑠)
𝑑 𝑊𝑠
𝑈𝑠 = 𝐹2 (𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐 , 𝑖𝑛𝑝𝑢𝑡𝑠)
𝑑𝑡 𝑊
𝐹3 (𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐 , 𝑖𝑛𝑝𝑢𝑡𝑠)
𝑐
W
Predictor
𝑊𝑠
𝑈𝑠
𝑊𝑐
𝑝𝑟𝑒𝑑
𝑊𝑠
= 𝑈𝑠
𝑊𝑐
True
𝐹1 (𝑡)
+ 𝐹2 (𝑡) ∆t
𝐹3 (𝑡)
𝑡
Corrector
Predictor
t
∆t
Corrector
𝑊𝑠
𝑈𝑠
𝑊𝑐
𝑡+1
𝑊𝑠
= 𝑈𝑠
𝑊𝑐
𝐹1 [𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐 𝑡 ) + 𝐹1 [𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐
+ 𝐹2 [𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐 𝑡 ) + 𝐹2 [𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐
𝐹3 [𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐 𝑡 ) + 𝐹3 [𝑊𝑠 , 𝑈𝑠 , 𝑊𝑐
𝑡
𝑝𝑟𝑒𝑑 )
𝑝𝑟𝑒𝑑 )
𝑝𝑟𝑒𝑑 )
∆𝑡
2
UEB Grid Implementation
Time
• Model run separately at each
active grid cell
• Melt outputs aggregated for
subwatersheds
• Structured file based input-output
for linking with EPA BASINS
• ASCII for non-spatial data
• NetCDF for geospatial data
time
y
x
UEB Data Requirements
• Parameters
–thermal conductivities, emissivity, scattering coefficients, etc.
• Initial Conditions
–SWE, Snow Age, energy content, atmospheric pressure, etc.
• Spatially Varying, Time Constant
–Slope, Aspect, LAI, CCAN, HCAN, Watershed(s)
• Spatially Varying, Time Varying
–Temperature, Precipitation, Wind Speed, Humidity, Radiation
File-Based Input-output System
Overall control file
Input Files
Watershed
file
Provides
watershed ID
for each grid
from the
NetCDF file
Parameter
file
Provides
parameter
Values
Output Files
Site Initial file
Input Control file
Output Control file
Provides site
and initial
condition
values, or
points to 2-D
NetCDF files
where these
are spatially
variable
Provides start, end and
time step, and info. on
time varying inputs as
either from text file for
domain, from 3-D
NetCDF file where
spatially variable, or
constant for all time
Indicates which variables are to
be output and the file names to
write outputs.
2-D NetCDF files
Time series text file
3-D NetCDF files
Provides spatially
variable site and
initial condition
values
provides input
variables for each
time step and
assumes a constant
value for all the grid
points
Provides input variables
for each time step and
each grid point
Point detail
text file
3-D NetCDF
files
Holds all
output
variables for
all time steps
for a single
grid point
Holds a single
output
variable for all
time steps
and for entire
grid.
Aggregated
Output Control
Holds list of
variables for
which
aggregated
output is
required
Aggregated Output text file
Holds aggregated output
UEB Outputs
• Precip in the form of rain
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Precip in the form of snow
SWE
Surface Sensible Heat Flux
Surface Latent Heat Flux
Surface Sublimation
Average Snow Temperature
Snow Surface Temperature
Energy Content
Total outflow (Rain and Snow)
Canopy interception capacity
Canopy SWE
Canopy Latent and Sensible heat fluxes
Melt from Canopy
etc.
Model Domain
• Subwatershed ID for each grid cell
• Aggregation occurs over subwatersheds
• NetCDF file
Langtang Khola Watershed in Nepal
HIMALA
Project goals
• Use NASA remote sensing and climate
products to model hydrology in data scarce
Himalayan region
• Understand the impact of climate
variability on these high-mountain
hydrological systems
• Glacier and snow melt capability
• Decision support capability that integrates
information about snow and glacier ice melt
water in an easy to use modeling system
HIMALA
UEB Snowmelt Model as part of
BASINS modeling system
NASA Remote Sensing and climate products
MERRA
SRTM
MODIS
BASINS
Input pre-processing
UEB Model
Output post-processing
Visualization
GeoSFM
HIMALA
MERRA Spatial Downscaling for Hydrology
(MSDH)
MERRA Climate Data
0.67˚ x 0.5˚
01/01/1979 - present
MERRA Radiation
1.25˚ x 1.0˚
01/01/1979 - present
RFE2 precipitation
0.1˚ x 0.1˚
01/05/2001 - present
SRTM Digital
Elevation Model
(DEM)
Glacier outlines and
albedo
MODIS Land cover
Approx. 500 m
3 Hour Grid Surfaces
• Change data format
and units
• Resample using
bilinear interpolation
• Physically-based
downscaling using
elevation
•
•
•
•
•
•
temperature,
precipitation,
relative humidity,
wind speed,
shortwave radiation
longwave radiation
Elevation for Langtang Khola watershed
From MERRA geopotential height
SRTM
(m)
(m)
Temperature Downscaling
MERRA coarse resolution temperature (0.5˚ × 0.67˚)
(˚C)
(˚C)
• Linear lapse rate determined from climate SRTM grid scale temperature
stations or regional information
(˚C)
• Bilinearly interpolate temperature to fine
scale
• Compute temperature at fine scale using
lapse rate
𝑇 = 𝑇0 − 𝛤(𝑧 − 𝑧0 )
(June 25, 2003 at 00:00 UTC)
Specific humidity
MERRA coarse resolution specific humidity (0.5˚ × 0.67˚)
𝑒=
𝑞∗𝑃
(0.622 + 𝑞)
𝑇𝑑𝑔𝑟𝑖𝑑 = 𝑇𝑑 + 𝛥𝑧. 𝜆.
𝑒𝑠 = 𝑎 exp
1
𝑐
𝑏
𝑏𝑇
𝑐+𝑇
SRTM grid scale relative humidity
𝑒
𝑐 𝑙𝑛 𝑎
𝑇𝑑 =
𝑒
𝑏 − 𝑙𝑛 𝑎
3
5
June 25, 2003 at 6:00 am
2
𝑏𝑇
𝑒 = 𝑎 exp
𝑐+𝑇
RH = 100
𝑒
𝑒𝑠
4
6
Incoming Shortwave Radiation Downscaling
Incoming shortwave
radiation
𝑃 = 𝑃𝑜
𝑇𝑜 + λ𝑧
𝑇𝑜
𝑔
−
𝑅λ
𝑆𝑊(𝑃) = SWtop 𝑒 −𝑘 𝑃
Attenuation due to the
thickness (mass) of
atmosphere traversed
MERRA Elevation (m)
Mountain
Assumption: Shortwave radiation attenuates based on
the thickness (mass) of atmosphere above.
Incoming shortwave radiation
MERRA coarse resolution solar radiation (1.25˚ × 1˚)
• Bilinear interpolation to fine scale
• Calculate atmospheric transmission factor
𝑇𝑓 =
𝑆𝑊𝑀𝐸𝑅𝑅𝐴
SWtop
• Calculate atmospheric transmissivity
𝑘=
− log 𝑇𝑓
𝑃𝑀𝐸𝑅𝑅𝐴
• Calculate pressure using elevation
𝑔
−
𝑇𝑜 + λ𝑧 𝑅λ
𝑃𝐷𝐸𝑀 = 𝑃𝑜
𝑇𝑜
• High resolution shortwave
𝑆𝑊𝐷𝐸𝑀 = SWtop 𝑒
−𝑘 𝑃𝐷𝐸𝑀
Langtang Khola watershed
Elevation (m)
Substrate Type
Albedo
watershed
Minimum
Maximum
Mean
Minimum
Maximum
Mean
area %
Bare ground
3700
6800
4882
0.09
0.74
0.26
57
Debris-cover glacier
3997
5554
4823
0.15
0.71
0.25
8
Clean glacier
4390
7204
5670
0.17
0.87
0.55
35
Total surface water used to drive hydrology model
(GeoSFM)
GeoSFM Calibration
GeoSFM Validation
Parallel Implementation
–
–
–
–
–
–
C++ with MPI implemenation
Little Bear River watershed
10/1/1999 – 6/1/2000
Hourly time steps
Grid size of 120 m (367 x 327 cells—rectangular region)
Total active grids = 49019
Task
Reading control file,
parameters, site variables
Reading weather forcing
Computation
Total time
Weather
Forcing as Spaceforcing as Single Time NetCDF
Time series
0.00008
0.00008
0.00078
41
16
16
13.7
55
96,000 – 330,000 cell time steps per minute
Work plan
• Configuration as Community Hydrologic Prediction
System (CHPS) component.
• Identify test/case study watersheds.
• Evaluation of lumped (SNOW-17) and spatially
distributed snow model configurations.
• Evaluation of alternative downscaling methods to scale
inputs to the model grid
• Evaluation of methods for assimilating observations
into model states and quantifying uncertainty in
forecasts using ensembles to quantify variability in
parameters, forcing inputs and initial conditions, and
then select ensemble members whose outputs most
closely match observations.
Items for Discussion
• CHPS/FEWS. Replicate CHPS environment in
our development system
• Test basins
• Evaluating skill
• Inputs (Albedo, Forcing Variables)
• Assimilation and Ensembles (the nuts and
bolts)
Possible Partnerships
• Martyn Clark and Andy Wood
– SUMMA – Clark NASA project to increase
technology readiness level at CONUS scale
– Wood real time streamflow prediction (consider
same sites)
A unified approach for process‐based hydrologic modeling: 1. Modeling
concept
Water Resources Research
Volume 51, Issue 4, pages 2498-2514, 18 APR 2015 DOI: 10.1002/2015WR017198
http://onlinelibrary.wiley.com/doi/10.1002/2015WR017198/full#wrcr21399-fig-0002
A unified approach for process‐based hydrologic modeling: 1. Modeling concept
Water Resources Research
Volume 51, Issue 4, pages 2498-2514, 18 APR 2015 DOI: 10.1002/2015WR017198
http://onlinelibrary.wiley.com/doi/10.1002/2015WR017198/full#wrcr21399-fig-0003
Web sites
• http://www.neng.usu.edu/cee/faculty/dtarb/s
now/snow.html
Repositories
• http://bitbucket.org/dtarb/ueb (Fortran)
• https://github.com/dtarb/UEB (C++)
Other points (on the fly)
• GRACE
• SMAP
• Metrics on impacts (verification measures)
• ADHydro (Ogden from CI-WATER)
Papers
•
•
•
•
•
•
Yuan, X., and E. F.Wood (2012), Downscaling precipitation or bias-correcting streamflow?
Some implications for coupled general circulation model (CGCM)-based ensemble seasonal
hydrologic forecast, Water Resour. Res., 48, W12519, doi:10.1029/2012WR012256.
Welles, E., S. Sorooshian, G. Carter and B. Olsen, (2007), "Hydrologic Verification: A Call for
Action and Collaboration," Bulletin of the American Meteorological Society, 88(4): 503-511,
DOI:10.1175/BAMS-88-4-503.
Werner, M., J. Schellekens, P. Gijsbers, M. van Dijk, O. van den Akker and K. Heynert, (2013),
"The Delft-FEWS flow forecasting system," Environmental Modelling & Software, 40(0): 6577, http://dx.doi.org/10.1016/j.envsoft.2012.07.010.
Clark, M. P., et al. (2015), A unified approach for process-based hydrologic modeling: 1.
Modeling concept, Water Resour. Res., 51, 2498–2514, doi:10.1002/2015WR017198.
Newman, A. J., M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L.
Brekke, J. R. Arnold, T. Hopson and Q. Duan, (2015), "Development of a large-sample
watershed-scale hydrometeorological data set for the contiguous USA: data set
characteristics and assessment of regional variability in hydrologic model performance,"
Hydrol. Earth Syst. Sci., 19(1): 209-223, http://dx.doi.org/10.5194/hess-19-209-2015.
Sen Gupta, A., (2014), "Improving the Physical Processes and Model Integration Functionality
of an Energy Balance Model for Snow and Glacier Melt," Ph.D. Thesis, Civil and Environmental
Engineering, Utah State University, http://digitalcommons.usu.edu/etd/3875, 213 pp.
Show and Tell
Images from http://www.anri.barc.usda.gov/emusnow/default.htm