HBVmodel09 - IARC Research - University of Alaska Fairbanks

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Introduction to runoff modeling
on the North Slope of Alaska
using the Swedish HBV
Model
Emily Youcha, Douglas Kane
University of Alaska Fairbanks
Water & Environmental Research Center
PO Box 755860
Fairbanks, AK 99775
Objective
Use existing
meteorological
datasets and
develop HBV
model parameters
to simulate runoff
in both small and
large North Slope
Basins
Kavik
Kuparuk
Sag
Upper
Kuparuk
Approach
 Begin
runoff simulations on North
Slope streams with abundance of
data
Imnaviat Cr, 2.2 km2 (1985-present)
Upper Kuparuk River, 146 km2 (1993present)
– Putuligayuk River, 417 km2 (1970-1979,
1982-1986, 1999-2007)
– Kuparuk River, 8140 km2 (1971present)
 Develop
parameter sets and apply to
other rivers (ungauged?)
HBV Model



Rainfall-runoff model, commonly used for forecasting in
Sweden
Developed by Swedish Meteorological and Hydrological
Institute
Semi-distributed conceptual model
– Divide into sub-basins
– Precipitation and temperature may be spatially distributed by
applying areal-based weights to station data
– Use of elevation and vegetation zones

Required data inputs (hourly or daily) include:
– Precipitation (maximum end-of-winter SWE and summer
precipitation)
– Air temperature
– Evapotranspiration (pan evaporation or estimated) daily or
monthly

Routines include:
–
–
–
–
Snow
Soil Moisture Accounting
Response
Transformation
HBV Routines and Input Data
Snow Routine
Inputs: Precipitation, Temperature
Outputs: Snowpack, Snowmelt
Soil Moisture Routine
Inputs: Potential Evapotranspiration,
Precipitation, Snowmelt
Outputs: Actual Evapotranspiration, Soil
Moisture,
Ground-Water Recharge
Response Routine
Input: Ground-Water Recharge/Excess soil
moisture
Output: Runoff, Ground-Water Levels
Transformation Routine
Input: Runoff
Simulated Runoff
SMHI
Soil Moisture Routine
Routines and Parameters:
Snow: 4 + parameters
Degree-day method: Snowmelt = CFMAX * (T –TT)
CFMAX=melting factor (mm/C-day)
TT=threshold temperature (C) (snow vs. rain)
CFR=refreezing factor to refreeze melt water
WHC=water-holding capacity of snow (meltwater is retained in
snowpack until it exceeds the WHC)
recharge
Soil Moisture Accounting : 3+ parameters
Modified bucket approach
Shape coefficient (BETA) controls the contribution to the response
function (runoff ratio)
Q0=K0 * (SZ- UZL0)
UZL0
Limit of potential evapotranspiration (LP), the soil moisture value UZL1
Response
Routine
SZ
Q1=K1 * (SZ- UZL1)
above which ET reaches Potential ET
Maximum soil moisture (FC)
Response 4+ parameters
Transforms excess water from soil moisture zone to runoff. Includes
both linear and non-linear functions. Upper reservoirs represent
quickflow, lower reservoir represent slow runoff (baseflow). Lakes
are considered as part of the lower reservoir. Lower reservoir may
not be used (PERC parameter is set to zero due to presence of
continuous permafrost).
Transformation/Routing
To obtain the proper shape of the hydrograph, parameter=
MAXBAS (/d)
Q2=K3 * SZ
Recharge: input from soil routine (mm/day)
Runoff
SZ: Storage in zone (mm)
UZL:Threshold parameter
Ki: Recession coefficient (/day)
Qi: Runoff component (mm/day)
Modified from Siebert, 2005
SMHI Manual, 2005
Transformation Routine
HBV Calibration





Qsim  Qobs

1
 Qobs Qobs
2
2
Each model routine has parameters requiring model
calibration
– over 20 parameters, and may be varied throughout the
simulated period (i.e. spring vs. summer)
Explained variance (observed vs. simulated) is the NashSutcliffe (1970) model efficiency criterion good model fit is Refficiency=1. Also looked at accumulative volume difference
and visually inspect the hydrograph.
Used the commercially available HBV software to manually
calibrate the model by trial and error
We tried HBV automated calibration to estimate parameters
(Monte Carlo procedure using “HBV-light” by Siebert, 1997).
Produced many different parameter sets that would solve the
problem. Many parameters were not well defined
Most of the time, model validation results not very good.
GUI – easy to
use and view
results quickly
(sort of)
Observed Hydrographs for Imnaviat
and Upper Kuparuk:
1996, 1999, 2002, 2005
130
4.0
120
Imnaviat Creek - Observed Flow
3.5
3.0
110
Q1996
Q1999
Q2002
Q2005
100
90
80
3
Flow (m /s)
3
Flow (m /s)
2.5
2.0
1.5
Upper Kuparuk - Observed Flow
Q1999
Q1996
Q2002
Q2005
Snowmelt
Summer
70
60
50
40
1.0
Summer
Snowmelt
30
20
0.5
10
0.0
120
140
May
160
June
180
200
July
220
August
Day of Year
240
260
September
280
0
120
140
May
160
180
June
200
July
Day of Year
220
August
240
260
September
280
HBV Parameter
Example:
Upper
Kuparuk,
21
parameters
to calibrate!
1996
1999
2002
2005
Snow Routine
TT (C)
1
1
1
1
SFCF
1
1
1
1
WHC
0.1
0.1
0.1
0.1
4
4
4
4
0.05
0.05
0.05
0.05
PCORR
1
1
1
1
RFCF
1
1
1
1
PCALT
0.1
0.1
0.1
0.1
TCALT
0.6
0.6
0.6
0.6
CFMAX (mm/C-d)
CFR
Soil Moisture Routine
BETA spring
0.2
0.2
0.2
0.2
BETA summer
0.2
0.2
0.2
0.2
FC (mm)spring
10
10
10
10
FC (mm)summer
50
50
50
50
LP
0.9
0.9
0.9
0.9
ECALT
0.1
0.1
0.1
0.1
CFLUX
1
1
1
1
Response Routine
k0 (/d) spring
0.4
0.4
0.4
0.4
k0 (/d) summer
0.9
0.9
0.9
0.9
k1 (/d) spring
0.1
0.1
0.1
0.1
k1 (/d) summer
0.5
0.5
0.5
0.5
k3 (/d) spring
0.06
0.06
0.06
0.06
k3 (/d) summer
0.09
0.09
0.09
0.09
UZL0 (mm) spring
40
40
40
40
UZL0 (mm) summer
30
30
30
30
UZL1 (mm) spring
10
10
10
10
0
0
0
0
UZL1 (mm) summer
PERC
Transformation Routine
Preliminary Automated Calibration
Results (Monte Carlo Procedure)
 Dotty
plots – look for parameters
that are well defined
Automated Calibration Results
(Monte Carlo Procedure)
Snowmelt 2002
Snowmelt Parameters
1.00
0.95
0.90
0.85
0.80
0.75
Upper Kuparuk Spring Snowmelt 2002
Automated Calibration
Runoff (mm/hr)
0.4
0.3
0.2
1.00
0.95
0.90
0.85
0.80
0.75
2
0.5
Qobserved
P37
P65
P99
P112
P116
P129
P132
P144
P155
P212
-1
1.00
0.95
0.90
0.85
0.80
0.75
1
5/16
5/26
2002
6/5
6/15
1
2
2
3
4
5
Snowfall Correction Factor
SFCF:0.5-0.75
0.75
1.00
1.25
1.50
Refreezing Factor
1.00
0.95
0.90
0.85
0.80
0.75
0.0
0
Snowmelt Factor (mm/C-day)
CFMAX: 1-2
1.00
0.95
0.90
0.85
0.80
0.75
0.1
5/6
Threshold Temperature (C)
R
0.6
Upper Kuparuk 2002 Spring Period
0.04
0.06
0.08
Water Holding Capacity of Snow
0.0
0.2
0.4
0.6
20
0
3
10
5/16
5/31
6/15
6/30
7/15
Summer
R-efficiency=0.42
Accum Diff = -18 mm
Rel. Accum Diff =-19%
7/30
8/14
8/29
9/13
20
10
0
0
-10
90
80 R-efficiency=0.55
70 Accum Diff = 39 mm
Rel. Accum Diff = 20%
60
50
40
Snowmelt
30
R-efficiency=-1.17
Accum Diff = 15 mm
20
Rel. Accum Diff = 49%
10
0
5/1 5/16 5/31 6/15
Qsimulated
Qobserved
Summer
R-efficiency=0.56
Accum Diff = 23 mm
Rel. Accum Diff = 15%
6/30
0
-20
-40
-60
-80
7/15
7/30
8/14
8/29
9/13
9/28
1999
20
Accum. Diff.
Simulated Snow
Observed Snow UKmet
40
20
9/28
1996
180
160
140
120
100
80
60
40
20
0
50
30
Snow Water Equivalent (mm)
Snowmelt
R-efficiency=0.85
Accum Diff = -4 mm
Rel. Accum Diff =-3%
20
160
140
120
100
80
60
40
20
0
50
Accum. Diff.
Simulated Snow
Observed Snow UKmet
40
30
20
10
0
-10
15
100
Qsimulated
Qobserved
Accum Diff = -60 mm
Rel. Accum Diff = -22%
80
60
40
20
0
5/1
Summer
R-efficiency=0.25
Accum Diff = -83 mm
Rel. Accum Diff = -37%
Snowmelt
R-efficiency=0.51
Accum Diff = 20 mm
Rel. Accum Diff = -41 %
5/16
5/31
6/15
6/30
7/15
2002
7/30
8/14
8/29
9/13
9/28
10
R-efficiency=0.64
Accum Diff = 16 mm
Rel. Accum Diff = 16%
Qsimulated
Qobserved
3
120 R-efficiency=0.29
3
Runoff (m /s)
30
0
5/1
Snow Water Equivalent (mm)
Qsimulated
Qobserved
40
Runoff (m /s)
3
Runoff (m /s)
40
R-efficiency=0.85
Accum Diff = -23 mm
Rel. Accum Diff =-9%
60
Accum. Diff.
Simulated Snow
Observed Snow UKmet
Accum. Diff. (mm)
40
60
Snowmelt
R-efficiency=0.40
Accum Diff = 28 mm
Rel. Accum Diff = 49%
5
0
5/1
5/16
5/31
6/15
6/30
Summer
R-efficiency=0.82
Accum Diff = -13 mm
Rel. Accum Diff = -28%
7/15
2005
7/30
8/14
8/29
9/13
9/28
Accum. Diff. (mm)
60
Snow Water Equivalent (mm)
80
Accum. Diff. (mm)
100
50
Runoff (m /s)
20
10
0
-10
-20
-30
-40
-50
-60
Simulated Snow
Accum. Diff.
Accum. Diff. (mm)
Snow Water Equivalent (mm)
120
Summary






Need an automated calibration procedure to
develop unique parameter sets
For Upper Kuparuk, model generally
predicted timing of events (onset of
snowmelt and timing of peak events). When
it did not predict the proper timing, the
model efficiency was poor.
For Upper Kuparuk, model overpredicted
snowmelt flow volume and underpredicted
extreme peak runoff events during summer
For both Upper Kuparuk and Imnavait, model
did not predict the magnitude of peak flow
Problems may be attributed to not using a
long enough simulation period
Many improvements are needed to increase
the Nash-Sutcliffe model efficiency
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