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Cold Regions Hydrological Model
Platform, CRHM: The Course
John Pomeroy
Centre for Hydrology
University of Saskatchewan
Saskatoon
Objectives of Course
„
„
„
„
„
„
Familiarisation with basic hydrological principles
necessary for modelling
Introduction to CRHM platform
Review of components and features of CRHM
Familiarisation with examples of hydrological
models using CRHM
Ability to create a purpose built hydrological
model using CRHM
Everyone becomes a modeller
Structure of Course
„
„
„
„
Review philosophy of modelling, model
structure, processes, model components
Review of model modules and features
using Help File and model examples
Review of example projects
Lab – YOU make a hydrological model
from scratch.
Models and Reality in Hydrology
„
„
„
„
„
„
The Hydrological Cycle is manifested with strong
regional variations around the world.
Hydrologists have created a vast number of models
(assumptions) to describe some aspects of this cycle.
It is generally not necessary or likely that one
hydrological model approach is applicable to all
environments, scales or predictive interest
Physically-based models attempt to describe reality
faithfully (but do not completely succeed)
Hydrological models predict most successfully in
catchments near where they were derived
Logical selection and design of model strategy, structure
and their inherent assumptions are governed by local
problems and local hydrology – this is not just
parameter selection.
Hydrological Cycle – elsewhere….
Cold Regions Hydrological Cycle
Bergeron Process
Snowfall
Sublimation
Ice
Blowing Snow
Evaporation
Snowmelt
Infiltration Frozen Ground
EvapoRainfall transpiration
Runoff
Interflow
Saturated Porous Media Flow
Distinctive Aspects
„
„
„
„
„
„
„
„
Snow storage,
redistribution, melt
Infiltration to frozen ground
Thick organic soils,
sometimes frozen
Poorly defined drainage
areas
Cool surface for evaporation
Frozen rivers and lakes
Seasonality of energy inputs
Sparse(r) data network
Why Physically-based Hydrological
Modelling?
„
Robust - can be more confidently extrapolated
to different climates and environments and
performs better in extreme situations (floods,
droughts).
„
Scientifically Satisfying - represents a
compilation of what is understood about
hydrology.
„
Can interface with chemistry and ecology aquatic chemistry and hydroecological
modelling require a sound hydrophysical base.
„
Elevates hydrological practice to
hydrological science.
What field information can help us
design models?
„
„
„
Identification of the principles governing the
primary physical processes responsible for most
water movement in basin (structure and
processes).
Fundamental boundary and initial conditions that
affect these processes (parameters).
Length scales for self-similarity and variability
associated with the properties affecting these
processes (scale).
Process observation and modelling
„
„
„
„
Understanding of hydrological processes results
from observing at multiple scales.
Modelling in hand
with observations
provides a way to
test and anticipate
algorithms, gaps
& assumptions
Different processes
are important in
different environments
No universal algorithm scale, structure, approach
Necessary Elements of Cold Regions
Hydrological Modelling
consideration of the following:
1. Transport of water in liquid, vapour and frozen states
(runoff, percolation, evaporation, sublimation,
blowing snow);
2. Coupled mass and energy balances;
3. Phase change in snow & soils (snowmelt, infiltration
in frozen soils, soil freezing and thawing);
4. Snow and rain interception in forest canopies;
5. Episodic flow between soil moisture, groundwater
and base flow.
Hydrological Response Units
„
„
A HRU is a spatial unit in the basin that has 3 groups of
attributes
„
biophysical structure - soils, vegetation, slope, elevation,
area (determine from GIS, maps)
„
hydrological state – snow water equivalent, snow internal
energy, intercepted snow load, soil moisture, water table
(track using model)
„
hydrological flux - snow transport, sublimation,
evaporation, melt discharge, infiltration, drainage, runoff
(determine using fluxes from adjacent HRU)
HRU need not be spatially continuous but must
have some approximate geographical location or
location in a hydrological sequence
Hydrological Response Units
Sequential HRU –
landscape connectivity
HRU 1
HRU 2
HRU 3
outlfow
Grouped HRU – must drain to stream
Rationale for CRHM Platform
„
„
„
„
„
„
Frustration with adding process algorithms to
existing hydrological models
Frustration with trying to fit inappropriate structure of
existing models to basins
Frustration with inability to fit gridded or other
conceptual spatial representations to reality.
Frustration with models that only focus on
streamflow response to precipitation
Frustration with attempts to teach modelling to
hydrologists using older computer languages, no
user interface, limited documentation of models
Frustration with the lack of a graphical system to
evaluate model inputs and outputs
CRHM Objectives
To develop a hydrological cycle simulation system that:
„
is spatially distributed such that the water balance for selected
surface areas can be computed;
„
uses natural landscape units that have hydrological importance;
„
is physically based so that the results contribute to a better
understanding of basin hydrology and are robust and so that
process parameters can be transferred regionally;
„
is sensitive to the impacts of land use and climate change;
„
Reflects landscape sequencing (e.g. catena) in natural drainage
basins;
„
does not require the presence of a stream in each land unit;
„
is flexible: can be compiled in various forms for specific needs;
„
is suitable for testing individual process algorithms.
„
is easy to use for all hydrologists and useful for teaching
„
IS NOT DEPENDENT UPON CALIBRATION!
Cold Regions Hydrological Model
Platform (CRHM)
„
„
„
„
Started in late 1990s as
NWRI land use
hydrology model.
BEERS
Attempted to write
Canadian modules for
USGS MMS
1999 Tom Brown
developed CRHM
platform in windows
environment
Development of modules
from MAGS, PAMF,
NERC, Quinton-CFCAS,
IP3 and other research
Multiple developers:
Brown, Gray, Granger,
Hedstrom, Pomeroy
The Cold Regions Hydrological Model Platf orm 2003
C:\Program Files\CRHM\Examples\badlakef low 7475.prj
g
b
c
d
e
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b
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e
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g
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220
210
200
190
180
170
160
150
140
130
(m3/s)
„
120
110
100
90
80
70
60
50
40
30
20
10
0
28/10/1974
27/11/1974
27/12/1974
26/01/1975
25/02/1975
27/03/1975
26/04/1975
26/05/1975
basinf low (1)
outf low (1)
outf low (2)
outf low (3)
SWE(1)
SWE(2)
SWE(3)
Building Physically-based,
Distributed Hydrological Models
with CRHM
„
„
„
„
„
uses a library of physically-based hydrological and
energy balance process modules;
handles the following aspects of the modelling
process:
„ data pre-processing,
„ module and model building,
„ results analysis
is easy to use: employs a Windows environment
with pull-down menus.
has an extensive HELP file and open module code
DLL version permits creating your own module and
linking it into CRHM – community model
development
Cold Regions Hydrological Model
DATA COMPONENT
Preparation of spatial and meteorological data.
„
„
„
„
„
„
Spatial data (e.g. basin area, elevation, cover type) is analyzed
using a Geographic Information System (GIS) that assists the
user in basin delineation, characterization and parameterization
of Hydrological Response Units (HRU). CRHM takes in ArcGIS
files with this information. The user can also simply enter this
information in a menu for less complex basins
Time-series meteorological data include air temperature,
humidity, wind speed, precipitation and radiation.
Adjustments for elevation (lapse rate), snowfall versus rainfall,
interpolation between input observations (stations)
Filters permit adjustment to data, changing time interval, creating
synthetic data using mathematical functions, interpolating data
Unit conversions to consistent SI units
Visualization of input data allows checking for quality
Observation Files
„
„
„
„
„
„
Created using weather station or other outputs
Sets model time step interval
Possible to have observations with varying time
steps
Interpolation, synthesis tools to fill in missing
data or create synthetic data
Observations are interpolated onto HRU
Visualization tools useful in assessing reliability
of observations
Parameters
„
„
„
„
„
Parameters describe basin, define HRU and set
operation of process modules (examples: tree height,
soil type, fresh snow albedo)
Parameters set based upon understanding of basin and
HRU (limits imposed in model)
Assumption that substantial transferability of process
parameters exists for similar HRU
If not known from basin then can be looked up or
guessed at
No facility to calibrate parameters exists in CRHM (but
some have figured it out anyway)
CRHM GIS Interface
„
„
„
„
The interface automates the
parameterization of CRHM.
UseTOPAZ and ARC/INFO AML
coding to divide the watershed into
sub-basins.
Each sub-basin is defined as a
polygon with drainage information,
ID, and can be assigned other
parameters.
The next step is to link the sub-basin
to other spatial information (land
cover, fetch, etc.) in order to derive
relevant HRU’s.
Cold Regions Hydrological Model
MODEL COMPONENT
„
Utilizes Windows-based series of pull-down menus
linked to the system features.
„
Modules, (process algorithms) are selected from the
library and grouped together by the CRHM processor.
„
Modules have a set order of execution with a common
set of variables and parameters.
„
Modules are created in C++ programming language.
„
Macro modules can be created from within the model
using a simple macro language.
CRHM Routing
„
„
„
„
„
HRU routing conceptualizes more complex
reality in characteristic sequences
HRU to HRU
Can include lake, wetland
Groundwater routed separately from near
surface and surface water
Flexible – HRU can route sequentially or
accumulate in an outflow HRU
HRU2
HRU5
HRU3
HRU4
HRU1
HRU0
Groups and Structures to Adapt to Real
Basin Hydrology
„
Group. A collection of modules executed in
sequence for all HRUs.
„
„
„
„
collection of modules which can be used in place of specifying
the individual modules.
if groups are defined with same modules, then can execute
modules in parallel using different parameters or driving
observations.
If groups are defined as different ‘models’, it is possible to
execute the models in parallel using identical parameters and
driving observations to check different responses.
Structure. A parallel collection of modules or Group
assigned to an HRU and run in sequence.
„
„
„
Comparison of algorithms
Customization of model to HRU characteristics - diverse sets
modules to be representative of the HRU and basin.
Dynamic structural change due to excess water or lack of it.
Groups
„
A collection of modules executed in sequence for all HRUs.
Define group:
Model
incorporation:
Groups
Group application:
1. If groups are defined with the same modules, it
is possible to execute the models in parallel using
different parameters or driving observations.
2. If groups are defined as different modules, it is
possible to execute the models in parallel using
identical parameters and driving observations to
check different responses.
Groups
„
E.g. estimate sub‐canopy SWE for forests of differing leaf‐area‐index
Structures
„
Running similar modules in parallel
e.g. snowmelt
Structures
Structure application:
1. Algorithm comparison. Intercomparison of algorithms
with similar driving data and parameters
2. Mixed Land Use in Basin. Permits differing model
structure for differing HRU (e.g. forest versus farmland
3. Dynamical Structural Change. Permits change in model
structure in response to changing hydrological state (e.g.
change grassland to a slough when leaving a
drought). The decision about which module to use would
be made by a preceding module based upon the availability
of moisture.
New Capabilities: Structures
e.g. comparison of SWE estimation by EBSM and SNOBAL:
Able to give
structures
meaningful
names
Representative Basins (RB)
RB1
RB1
RB2
RB3
RB2
RB4
RB1
„
„
„
„
„
„
Permits upscaling of CRHM to large, complex basins
using groupings of sub-basins
RB sub-basins determined in detail as assemblies of
HRU
RB types repeated with identical module structure,
similar parameters but differing geometry
Many RB types allowed in the larger basin
Muskingum routing module routes RBs through streams,
lakes, wetlands
Basin model is a network of RBs linked by a routing
module.
Cold Regions Hydrological Model
ANALYSIS COMPONENT
„
Used to display, analyze and export results (Excel,
ASCII, Obs).
„
Statistical and graphical tools are used to analyze model
performance, allowing for decisions to be made on the
best modelling approach.
„
Sensitivity-analysis tools are provided to optimize
selected model parameters and evaluate the effects of
model parameters on simulation results.
„
Mapping tools use ArcGIS files to map ouputs for
geographical visualization.
CRHM Modules
PROCESSES
DATA
ASSIMILATION
„
„
Data from multiple sites
Interpolation to the HRUs
„
„
„
„
„
„
SPATIAL
PARAMETERS
„
Basin and HRU parameters
are set. (area, latitude,
elevation, ground slope,
aspect)
„
„
„
„
„
„
„
Infiltration into soils (frozen and
unfrozen)
Snowmelt (open & forest)
Radiation – level, slopes
Wind speed variation – complex topo
Evapotranspiration
Blowing snow transport
Interception (snow & rain)
Sublimation (dynamic & static)
Soil moisture balance
Pond/depression storage
Surface runoff
Sub-surface runoff
Routing (hillslope & channel)
Radiation
„
„
„
„
„
„
α, Ts
Diffuse and Direct
Slopes
Longwave
Forest canopy
Albedo estimation
Limited data requirements
References: Brunt, Brutsaert,
Garnier & Ohmura,
Granger & Gray, Gray and Landine,
Pomeroy et al., Satterlund,
Sicart et al.
Shortwave Radiation
„
„
„
„
„
Direct and diffuse
Uses lat/long, sunshine hours or
measured incoming shortwave to estimate
direct and diffuse radiation to a level plane
Correction for slope, aspect, self-shading
using Garnier and Ohmura
A variety of albedo routines
Forest canopy transmissivity model
Longwave Radiation
„
„
„
„
Can be estimated as part of net radiation from
shortwave using Granger and Gray algorithm
Incoming longwave can be estimated from
Brutsaert relationship modified by Sicart et al. –
requires incoming shortwave, air temperature,
humidity
Outgoing longwave from surface temperature of
vegetation or snow
Forest canopy and surrounding topography
effects on longwave
Blowing Snow – ‘water’ transport
Blowing snow modelling
„
„
„
„
„
„
Saltation and suspension transport
Sublimation loss
Threshold condition of snowpack
Vegetation, horizontal fetch effects
Links to windflow module for complex
terrain
Full PBSM or simplified version available
Blowing Snow
⎡
E B ( x) dx ⎤
dS
∫
⎥−E−M ,
( x) = P − p ⎢∇F ( x) +
dt
x
⎢
⎥
⎣
⎦
P
wind direction
zb
EB
Fsusp
Fsusp
suspension layer
h*
dS/dt
Fsalt
Fsalt
saltation layer
snowpack
M
Fetch = x
References: Pomeroy & Gray, Pomeroy & Male, Li & Pomeroy, Pomeroy & Li
Distribution of Blowing Snow over
Landscapes
Blowing snow transport, and
sublimation relocate
snow across the
landscape from sources
to sinks depending on
fetch, orientation and
area.
Source
Sink
Fallow
Field
Stubble
Field
Grassland
Brush
Trees
Interception: snow and rain
Sublimation
Evaporation
Rainfall
Snow
Snowfall
interception
Rainfall
Interception
Storage
Throughfall
Throughfall
Unloading
Drip
References: Rutter et al., Granger & Pomeroy,
Hedstrom & Pomeroy, Parviainen & Pomeroy, Pomeroy et al 1998
Interception Efficiency I/P
Controlled by
„
„
„
Leaf + stem area index
(surface to collect snow)
Air temperature (elasticity
of branch, adhesion and
cohesion of snow)
Wind speed (particle
trajectory, impact rate,
branch bending,
scouring)
Weekly Snow Interception
Weekly Interception
(mm SWE)
14
12
Snow Interception by Pine
10
8
6
4
2
Measured
Modelled
0
0
5
10
15
20
Weekly Snowfall (mm SWE)
25
Interception Efficiency - model
1
1
T= -1.0 °C
T= -5.0 °C
T= -30.0 °C
0.8
I/P
0.6
0.4
0.4
0.2
0.2
0
0
2
4
6
1
LAI
0
0
5
P=2.0 m m SWE
P=10.0 m m SWE
P=20.0 m m SWE
0.8
0.6
I/P
I/P
0.6
Lo = 1.0 m m SWE
Lo = 3.0 m m SWE
Lo = 5.0 m m SWE
0.8
10
15
20
P (mm SWE)
0.4
0.2
0
0
2
4
6
L 0 (mm SWE)
8
10
25
30
Annual Sublimation Losses
Sublimation (mm)
60
50
Sublimation loss as
a percent of
annual snowfall.
40
30
20
„
10
„
0
„
Mixedwood
93-94
Pine
94-95
Spruce
95-96
Spruce 38% to 45%
Pine 30% to 32%
Mixed-wood 10% to
15%
Snowmelt
„
„
Degree Day Method
has problems in open
environments, slopes & forests.
Energy Balance CAN be estimated using
reliable methods
M=[QN+Qh+Qe+Qg+Qp-du/dt]/(ρ Lf B)
Snowmelt
dU
Qm + Qn + Q H + Q E + QG + Q D =
,
dt
α
Qm
,
M=
ρw B hf
-Daily EB
-Hourly EB
-Advection
-SCA Depletion
-Meltwater Flow
-Degree Day
-Radiation Index
References: Gray & Landine, Kustas et al., Essery, Shook, Marks et al. 1999
Snow
cover
depletion
is not
even!
South Face
North Face
Melt Energy Variability on
Slopes
120
Melt + Internal
Net Radiation
Ground Heat
Sensible Heat
Latent Heat
100
80
Mean Energy 60
2
(W/m )
40
20
0
-20
Valley
Bottom
South Face
North Face
Forest Snowmelt (Pomeroy & Dion, ’96)
„
Sensible and latent heat fluxes very small
„
Snowmelt rate
under mature
forests 3 times
less than in
open sites.
Cumulative Melt Energy (kJ)
Snow Melt Rate in Forests
20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Pine
Mixed-wood
Clear-cut
Regenerating
94
96
98
100
102
104
Julian Day - April 1996
106
108
110
Forest Cover
Modules
Forest Canopy Effects on Radiation
Shortwave irradiance: The transmissivity (τ) of the canopy layer to abovecanopy shortwave irradiance (K↓) is estimated as a function of the
effective-leaf-area index (LAI`) and solar elevation angle (θ) by:
τ= exp[-1.081 (θ) cos(θ) LAI`/sin(θ)]
Sub-canopy longwave irradiance (Lsc↓) is determined as the sum of abovecanopy longwave irradiance (L↓) and forest emissions weighted by the
relative proportions of canopy-cover (1- ν) and open sky (ν) of the overhead
forest scene:
Lsc↓= L↓ (v) + (1-v)εσT4
where ε is the emissivity of the forest (~0.98) , σ is the Stephan-Boltzmann
constant (W m-2 K-4) and T is the physical temperature of the forest (K).
Snow Interception and Sublimation
Interception : Intercepted snow and rain by the canopy is subject to
sublimation and evaporation back to the atmosphere, respectively. The
amount of snowfall, P (kg m-2) that may be intercepted by the canopy prior
to unloading is related to the (i) antecedent intercepted load, Lo (ii) the
maximum intercepted load, I* (which is related to LAI` and the density of
falling snow) and the ‘canopy-leaf’ contact area, Cp via (Hedstrom and
Pomeroy, 1998):
I= (I*-Lo)(1-exp[-Cp P/I*])`
Sublimation: estimated by a multi-scale
model approach: ice-sphere to branch to
canopy
Duration of Melt, T
„
„
Controls runoff, infiltration, snow-covered
period, contributing area
Can be simply described as a function of
SWE, S and melt rate, M.
SWE
T=
M
Infiltration into Frozen Soils
Snowmelt Water
Restricted Infiltration
Limited Infiltration
INF = (1 − θ p ) SWE 0.584 or Parametric Equation
Unlimited Infiltration
Soils
References: Granger et al., Gray et al., Zhao & Gray
Runoff
Infiltration to
Frozen Soils
„
„
„
Frozen soils can be
permeable, but show
reduced infiltration
compared to unfrozen conditions
‘Frozen’ means a frost depth of at least
0.5 m
Simple grouping of soil types and
physically-based equations
Empirical Model of Infiltration into Frozen
Soils - Prairie Environment
120
Unlimited
Restricted
100
1:1
80
Saturation
0.3
0.4
0.5
0.6
0.7
Infiltration
60
(mm)
40
20
0.9
0
0
30
60
90
120
150
Snow Water Equivalent (mm)
180
Infiltration to Frozen Soils
Heat Flux
0
Saturation
.
Q (kW/hm2)
50
100
150
Ice Content
θi
S
200
0.2
0.4
0.6
0
0.8
0
0.1
0.1
0.1
0.2
0.2
0.2
Z (m)
0.3
0.4
t =2h
t =8h
0.5
t = 12 h
t =2h
0.4
t =8h
0.5
t = 24 h
0.6
0.3
0.3
0.4
t = 12 h
t = 2h
t = 8h
0.5
t = 12 h
t = 24 h
t = 24 h
0.6
0.1
Z (m)
0
Z (m)
0
0.05
0.6
0.15
Infiltration Rate and Ground Heat
Flux during Snowmelt Infiltration
0.4
140
Transient
0.3
Quasi-steady state
100
80
0.2
60
40
0.1
dINF/dt
20
dQ/dt
0
0
5
10
0
15
Time (h)
20
25
dQ/dt (kJ/hm2)
dINF/dt (cm/h)
120
Parametric Equation for Infiltration
to Frozen Soils
INF = C ⋅ S
2.92
0
⋅ (1 − S I )
1.64
⎛ 273.15 − TI ⎞
⋅⎜
⎟
⎝ 273.15 ⎠
C is a coefficient
So is surface saturation
SI is saturation in the top 40 cm
TI is initial soil temperature
to is infiltration opportunity time
−0.45
⋅ t0
0.44
Infiltration into Unfrozen Soils
Green Ampt Infiltration
Depends on Ponding Time
Iterative Solution
References: Green & Ampt, Ogden and Saghafian, Pietroniro in Pomeroy et al
Evaporation
„
„
„
Empirical models
fail in spring (cold
soils), over
permafrost and
with changing land use
Penman-Monteith energy balance hard to
implement & parameterise
Granger extension of Penman offers a
physically based, practical solution (Granger
‘90)
Evaporation Modelling – Land
cover effects Granger & Pomeroy ‘97
Evapotranspiration (mm)
400
350
300
250
200
Pine
150
Mixed-wood
100
Regenerating
Cleared
50
0
120
140
160
180
200
220
Julian Day 1996
240
260
280
Evaporation
Granger-Gray QN
or
Priestly-Taylor
or
Penman-Monteith
or
ShuttleworthWallace
QE
G [ s (Q * − Q G ) + C vdd
=
sG + γ
a
/ ra ]
Ta, ea, uz
QE
QH
QA=QN-QG
es = f (ea, Ta-Ts)
Ts, es, z0
Surface Vegetation & Soil
QG
Water drawn from
1) canopy
2) recharge zone
3) deep soil
4) groundwater
References: Granger & Gray, Granger & Pomeroy,
Priestly & Taylor
Soil
Moisture
Balance (SMBAL)
Interception
Snowmelt
Evapotranspiration
Snowmelt Infiltration
Mass balance and flow
from
2 soil layers &
groundwater
Green-Ampt Infiltration
Runoff
Recharge Zone
Soil Column
Sub Surface
Discharge
INF − GW − SSR − E SURFACE − Trans − Δθ = 0
Alternative is SOIL,
better for northern soils and
sub surface flow generation
References: Leavesley et al.
Dornes et al
Groundwater
Groundwater
Discharge
New Modules
„
Soil- Depressional storage
- sub-HRU
- can form subsurface runoff or ground water recharge or fill and
spill.
- transfer of flows between HRUs
- Pond storage
all of HRU water covered.
- parameterization of maximum pond storage.
- possible to: (i) leak to subsurface flow or groundwater recharge
(ii) fill and spill.
Interflow between HRU
- subsurface flow can enter downhill HRU as surface or
subsurface flow.
-
-
Routing – Clark Lag and Route
Clark (1945) showed that routing a flood wave through a reach of a stream
channel could be done by shifting the wave a time equal to the travel time of the
reach, and then routing it through an amount of reservoir storage that gives the
equivalent "action" as the channel storage in the reach. The practice visualises a
reservoir that has storage characteristics, S = KO, at the outlet of a watershed.
Substituting this relation for storage into the continuity equation gives:
I1 + I 2 O1 − O2 k (O2 − O1 )
−
=
2
2
Δt
Where I1, I2, O1 and O2 are respectively inflow and outflow rates at the
beginning and end of routing interval, Δ t. k, the storage constant can be
obtained from the hydrograph or other analyses.
Baker Creek, NWT
•
•
Sub‐arctic shield lakes
Parameterized CRHM with 16 HRU’s ‐ 8 trunk lakes and their contributing areas
• The storage interaction between HRU’s was manipulated within the model to evaluate effect on water budget
CRHM Test - Yukon
Modular structure
HRU based
INF = 5 ⋅ (1 − θp ) ⋅ SWE 0.584
INF = C ⋅ S
2.92
0
⋅ (1 − S I )
1.64
⎛ 273.15 − TI ⎞
⋅⎜
⎟
⎝ 273.15 ⎠
−0.45
⋅ t0
0.44
Modelling Approach
Aggregated vs. Distributed
Basin Areal SWE
NF, SF, and VB
2002
2003
180
250
160
Obs SWE
Aggregated
Distributed
120
200
SWE [mm]
SWE [mm]
140
100
80
Obs SWE
Aggregated
Distributed
150
100
60
40
50
20
0
4/17/02 4/24/02
0
5/1/02
5/8/02
5/15/02 5/22/02 5/29/02
Time [days]
6/5/02
4/17/03
4/26/03
5/05/03
5/14/03
Time [days]
5/23/03
6/01/03
Basin discharge
2002
2003
1.0
Obs
Aggregated
Distributed
0.4
Q [m3/s]
Q [m3/s]
0.8
0.5
0.6
0.4
Obs
Aggregated
Distributed
0.3
0.2
0.2
0.1
0.0
5/01/02 5/09/02 5/17/02 5/25/02 6/02/02 6/10/02
0.0
4/17/03 4/26/03 5/05/03 5/14/03 5/23/03 6/01/03
Time [days]
Time [days]
CRHM Evaluation – open
environment snow dynamics and
spring runoff
„
„
„
Modules: radiation, blowing snow, energy
balance snowmelt, evaporation, infiltration
to frozen soils, soil moisture balance,
hillslope flow, routing
Parameters: from local scale observations
HRU structure: based on observed
landscape units for processes
Parameter Estimation
„
„
„
„
„
„
„
„
Blowing snow: fetch, vegetation height
Radiation: land surface albedo
Snowmelt: initial snow albedo
Infiltration: fall soil moisture content, cracking
Evaporation: vegetation type, height
Soil moisture balance: soil type, vegetation type,
groundwater connection
Hillslope flow: porosity, bulk density, thermal
conductivity, initial frost table depth
Routing: lag time, storage
Sub-arctic alpine tundra
Water Balance Wolf Creek-Alpine 1998/99
250
200
mm
150
CRHM Runo ff
CRHM Sno wfall
CRHM Infiltratio n
CRHM M elt
CRHM Gro und SWE
M easured M elt
M easured SWE
100
50
0
-50
-100
22-Sep
11-Nov
31-Dec
19-Feb
10-Apr
30-May
Boreal forest clearing
mm
Water Balance Bittern Creek-Clearcut 1996/97
300
250
200
150
100
50
0
-50
-100
-150
-200
-250
12-Oct
CRHM Runo ff
CRHM Sno wfall
CRHM Infiltratio n
CRHM M elt
CRHM Gro und SWE
M easured M elt
M easured SWE
1-Dec
20-Jan
11-Mar
30-Apr
Prairie wheat field
Water Balance Creighton-Stubble 1981/82
200
150
mm
100
50
CRHM Runo ff
CRHM Sno wfall
CRHM Infiltratio n
CRHM M elt
CRHM Gro und SWE
M easured M elt
M easured SWE
0
-50
-100
-150
10-Nov
20-Dec
29-Jan
10-Mar
19-Apr
Effect of forest cover on snow accumulation and
melt
SnowMIP2 runs: Alptal, Switzerland:
2002-03
open site
forest site
2003-04
SnowMIP2 runs: BERMS, Sask
Snow Accumulation, Melt & Runoff
Simulation in Prairies
180
Fallow Runoff
Stubble Runoff
Grass Runoff
Fallow SWE
Stubble SWE
Grass SWE
Snowfall
160
140
100
80
60
40
21-M ay-75
21-Apr-75
21-M ar-75
21-Feb-75
21-Jan-75
21-Dec -74
0
21-Nov -74
20
21-O ct-74
mm
120
Basin Hydrograph – no calibration
4
3.5
M o de lle d Flow
O bse rve d Flo w
3
2
1.5
1
26-May
19-May
12-M ay
05-May
28-Apr
21-Apr
14-Apr
07-Apr
31-M ar
24-Mar
0
17-Mar
0.5
10-Mar
3
m /s
2.5
0
2 6 -M ay
1 9 -M ay
1 2 -M ay
0 5 -M ay
28-A pr
21-A pr
14-A pr
250
07-A pr
300
31-M ar
24-M ar
17-M ar
10-M ar
3
m
Water Export Simulation – no
calibration used
350
Modelled Flow
Observed Flow
200
150
100
50
Evaporation – summer testing
550
500
Boreal fen site, BERMS, Saskatchewan
450
400
350
mm
300
250
200
150
Cum ulative Rainfall
100
Eddy Correlation Evaporation
50
CRHM Modelled Evaporation
0
-50
01/05/2004
Cum ulative Qn-Qh-Qg
05/06/2004
10/07/2004
14/08/2004
18/09/2004
23/10/2004
Prairie Basin Water Balance
With 30% Summer Fallow
Fallow
Stubble
Coulee
Basin
500
mm water equivalent
400
300
200
100
0
-100
-200
-300
-400
-500
Snowfall
Rainfall
Runoff
Sublimation
Drifting Snow
Evaporation
Stubble
Basin
500
400
mm water equivalent
Changed to
Continuous
Grain
Cropping
Coulee
300
Snowfall
200
Rainfall
100
Runoff
Sublimation
0
Drifting Snow
-100
Evaporation
-200
-300
-400
ru n o ff
in filt ra t io n
s n o w m e lt
s u b lim a t io n
d rift
e va p o ra t io n
s n o w fa ll
ra in fa ll
-4 0
-2 0
0
20
% Change
40
60
Conclusions
„
„
„
„
„
Process algorithms can form the basis for physicallybased hydrological model structure and parameter
selection
Flexible model structure and physically based
components can lead to appropriate and robust
hydrological simulation
Errors in simulation identify gaps in understanding of
processes, structure or parameters
A process based modular model is able to simulate key
components of the cold regions hydrological cycle from
an understanding of principles, and without calibration of
parameters except for routing
Modular models are relatively simple to update as our
science advances.
Problem Set
„
„
Using an observation file provided
Develop a process hydrology project that
„
„
„
Calculates snow accumulation, snowmelt,
infiltration, evaporation and runoff over at
least three HRU (land types)
Set parameters for this project
Show the sensitivity of the runoff to changes
in paramters