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IMPROVING THE SNOWMELT MODELLING IN MESOSCALE HYDROLOGIC MODEL (mHM) with SATELLITE BASED DYNAMICALLY CALCULATED
DEGREE-DAY FACTOR
Birkan ERGUC Zuhal AKYUREK
Birkan ERGUC
November 2019
Reading, UK
AGENDA
• Motivation
• Objective
• Study Areas
• Methodology
• Results
• Discussion and Future Works
2METU - GGIT
MOTIVATION
• Fresh water sources are under stress
• Better management for mountain watersheds
• More realistic modelling
• Snow-related dynamics dominant in mountains
• Need for more realistic snow modelling
3METU - GGIT
Taurus Mountains, TurkeyEncyclopædia Britannica
OBJECTIVE
• Achieve More Realistic Snow Modelling:
• Degree-day factor
• Based on satellite imagery
• Spatio-temporally dynamic
• Test results
• mHM hydrologic model
• Karanlıkdere basin
• Bahcelik basin
4METU - GGIT
Word Art https://wordart.com/create
1 / 1 23.11.2019, 16:04
STUDY AREA: Karanlikdere• Karanlıkdere Basin
• Mountain basin
• 1295 - 2332m elevation
• ~30 km2 area
5METU - GGIT
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Monthly Average Flow (m3/s)
STUDY AREA: Bahcelik• Bahcelik Basin
• Mountain basin
• 1392 - 2705m elevation
• ~2500 km2 area
6METU - GGIT
0
2
4
6
8
10
12
Monthly Average Flow (m3/s)
METHODOLOGY
• Degree-day Factor Conventionally:
• Simple computation
• Reduced data need
• Relatively good performance
• Generally accepted constant in basin
• Spatially and/or Temporally
• Followed Methodology1 (He et al., 2014):
• Daily average temperature
• Daily precipitation
• MODIS Snow cover data
7METU - GGIT
1 He, Z. H., et al. "Estimating degree-day factors from MODIS for snowmelt runoff modeling." Hydrology and Earth System Sciences18.12 (2014): 4773-4789.
METHODOLOGY (Cont.)
8METU - GGIT
𝑉𝑠 = 𝑆𝐷 ∙ 𝑆𝐶𝐴 (1)
VS : Volume per area of snow (mm3 / mm2)
SD : Snow depth (mm)
SCA: Snow-covered area (%)
𝑝𝑠 .𝑑𝑉𝑠
𝑑𝑡= ቐ
𝑃, 𝑇 < 𝑇𝑆 𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑃𝑠 −𝑀, 𝑇𝑆 ≤ 𝑇 ≤ 𝑇𝑅 𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 + 𝐴𝑏𝑙𝑎𝑡𝑖𝑜𝑛
−𝐷𝐷𝐹 𝑇 − 𝑇𝑚 , 𝑇 > 𝑇𝑅 𝐴𝑏𝑙𝑎𝑡𝑖𝑜𝑛(2)
𝑝𝑠 : Snow density (%)
VS : Volume per area of snow (mm3 / mm2)
P : Daily precipitation (mm)
Ps : Daily snowfall(mm)
M : Daily snowmelt depth (mm)
Ts : Temperature threshold for snowfall (oC)
TR : Temperature threshold for rain (liquid) (oC)
Tm : Temperature threshold for melting (oC)
METHODOLOGY (Cont.)
9METU - GGIT
• Preparing MODIS Data
• Pixel based approach
METHODOLOGY (Cont.)
10METU - GGIT
• MODIS data process: Karanlikdere Basin
METHODOLOGY (Cont.)
11METU - GGIT
• MODIS data process: Bahcelik Basin
METHODOLOGY (Cont.)
12METU - GGIT
• Daily Degree-day Factor Calculation:
• At days of ablation
• TR = 2,5 oC
• TS = TM = 0 oC
• SCA = From MODIS data (daily)
• 𝑝𝑠 = Month Snow Density (%)
December 0,25
January 0,25
February 0,27
March 0,30
𝑝𝑠 .𝑑𝑉𝑠𝑑𝑡
= −𝐷𝐷𝐹 𝑇 − 𝑇𝑚 , 𝑇 > 𝑇𝑅
−𝐷𝐷𝐹 𝑇(𝑡) − 0𝑝𝑠 . 𝑉𝑠 𝑡 − 𝑉𝑠(t − 1) =
𝐷𝐷𝐹(𝑡) = −𝑝𝑠 . 𝑉𝑠 𝑡 − 𝑉𝑠(t − 1)
𝑇(𝑡)Daily
METHODOLOGY (Cont.)
• Mesoscale Hydrologic Model (mHM)
• 102 – 104 km2 basins
• Distributed model
• Tested on 400+ basin
• Utilize sub-grid variability
• Multi-parameter Regionalization
• Use data at finest scale available
• Link to global parameters
• Max. 53 calibration parameters
• Open source
13METU - GGIT
METHODOLOGY (Cont.)
14METU - GGIT
Data Type Data Source
Meteorological Data State Meteorological Service (MGM) Joint Research Center (JRC)
Hydrometric Data State Hydraulic Works (DSI)
Elevation Data General Directorate of Mapping (HGM)
Land Use Data Ministry of Agriculture and Forestry
Geological Data DG Mineral Research and Exploration (MTA)
Leaf-Area_Index Data MODIS Land Cover Type Product
Soil Data Harmonized World Soil Database
Satellite Data MODIS 500m resolution Snow Products
• Data Sources:
METHODOLOGY (Cont.)
• mHM Calibration
• Modelling grid: 1 x 1 km
• Sub-grid: 100 x 100 m
• Meteo-grid: 25 x 25 km
• Dynamically Dimensioned Search (DDS) algorithm
• 7500 iteration
• Objective Function:
• Simulation period:
• Karanlikdere Basin: 1.12.2014 - 30.09.2017 – Warm up: 180 days
• Bahcelik Basin: 1.10.2003 – 30.04.2017 – Warm-up : 365 days
15METU - GGIT
min𝑍 = 1 −1
2𝑁𝑆𝐸 − ln 𝑁𝑆𝐸
Z : Objective Function
NSE : Nash-Sutcliffe efficiency
RESULTS
• Daily Degree-day factors for Karanlikdere Basin
16METU - GGIT
-15
-10
-5
0
5
10
15
20
25
30
10/1
/13
11/1
/13
12/1
/13
1/1/
142/
1/14
3/1/
144
/1/1
45/
1/14
6/1
/14
7/1/
148
/1/1
49
/1/1
410
/1/1
411
/1/1
412
/1/1
41/
1/15
2/1/
153/
1/15
4/1
/15
5/1/
156
/1/1
57/
1/15
8/1
/15
9/1
/15
10/1
/15
11/1
/15
12/1
/15
1/1/
162/
1/16
3/1/
164
/1/1
65/
1/16
6/1
/16
7/1/
168
/1/1
69
/1/1
610
/1/1
611
/1/1
612
/1/1
61/
1/17
2/1/
173/
1/17
4/1
/17
5/1/
176
/1/1
77/
1/17
8/1
/17
9/1
/17
10/1
/17
11/1
/17
12/1
/17
1/1/
182/
1/18
3/1/
184
/1/1
85/
1/18
6/1
/18
7/1/
188
/1/1
89
/1/1
8
0
1
2
3
4
5
6
7
8
9
10
Sn
ow
dep
th (
dm
)T
emp
erat
ure
(C
)
Date (days)
DD
F (
mm
/day
/C)
DDF
snowdepth (dm) DDF Avg. DDF tavg
Avg. calculated DDF = 2.431 | mHM calibrated DDF (forest): 2.395
RESULTS (Cont.)
• Daily Degree-day factors for Bahcelik Basin
17METU - GGIT
-30
-10
10
30
50
700
0.5
1
1.5
2
10/1
/02
5/1/
03
12/1
/03
7/1/
04
2/1/
05
9/1
/05
4/1
/06
11/1
/06
6/1
/07
1/1/
08
8/1
/08
3/1/
09
10/1
/09
5/1/
10
12/1
/10
7/1/
11
2/1/
12
9/1
/12
4/1
/13
11/1
/13
6/1
/14
1/1/
15
8/1
/15
3/1/
16
10/1
/16
5/1/
17
12/1
/17
7/1/
18
Sn
ow
dep
th (
cm)
Avg
.Tem
p(C
)
Deg
ree-
Day
Fac
tor
(mm
/day
/C
DDF at Grid 64200
DDF Snowdepth(cm) Avg.DDF Avg.Temp
0
1
2
3
4
5
6
7
01-
01
01-
11
01-
21
01-
31
02-
10
02-
20
03-
01
03-
11
03-
21
03-
31
04
-10
04
-20
04
-30
05-
10
05-
20
05-
30
06
-09
06
-19
06
-29
07-
09
07-
19
07-
29
08
-08
08
-18
08
-28
09
-07
09
-17
09
-27
10-0
7
10-1
7
10-2
7
11-0
6
11-1
6
11-2
6
12-0
6
12-1
6
12-2
6
Avg
. D
egre
e-D
ay F
acto
r
Average DDF for 2002 – 2018 (at Grid 64200)
Avg. calculated DDF = 0.2826 | mHM calibrated DDF (pervious): 0.1237
RESULTS (Cont.)
• Flow simulations for Karanlikdere Basin
18METU - GGIT
DDF Calibrated mHM NSE: 0,45785 KGE: 0.292mHM NSE: 0.45078 KGE: 0.281
-16
-6
4
14
24
34
44
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
12
/1/1
4
1/1
/15
2/1
/15
3/1
/15
4/1
/15
5/1
/15
6/1
/15
7/1
/15
8/1
/15
9/1
/15
10
/1/1
5
11
/1/1
5
12
/1/1
5
1/1
/16
2/1
/16
3/1
/16
4/1
/16
5/1
/16
6/1
/16
7/1
/16
8/1
/16
9/1
/16
10
/1/1
6
11
/1/1
6
12
/1/1
6
1/1
/17
2/1
/17
3/1
/17
4/1
/17
5/1
/17
6/1
/17
7/1
/17
8/1
/17
9/1
/17
Pre
cip
itat
ion
(m
m),
Avg
.Tem
p.
(C)
Q (
m3
/s)
Flow Results for DDF-fed mHM Model - Bahcelik Basin
Precipitation Qobs mHM DGF Fed DGF calibrated Avg.Temp.
RESULTS (Cont.)
• Flow simulations for Bahcelik Basin
19METU - GGIT
-20
-10
0
10
20
30
40
50
600
10
20
30
40
50
60
200
310
01
200
3122
920
04
032
720
04
06
2420
04
09
2120
04
1219
200
5031
820
050
615
200
509
1220
051
210
200
60
309
200
60
60
620
06
09
03
200
612
01
200
7022
820
070
528
200
708
2520
071
122
200
80
219
200
80
518
200
80
815
200
811
1220
09
020
920
09
050
920
09
08
06
200
911
03
2010
013
120
100
430
2010
072
820
1010
2520
110
122
2011
04
2120
110
719
2011
1016
2012
011
320
120
411
2012
070
920
1210
06
2013
010
320
130
40
220
130
630
2013
09
2720
1312
2520
140
324
2014
06
2120
140
918
2014
1216
2015
031
520
150
612
2015
09
09
2015
120
720
160
305
2016
06
02
2016
08
3020
1611
2720
170
224
Avg
. P
reci
pit
atio
n (
mm
), A
vg.T
emp
erat
ure
(C)
Q (
m3/
s)
Flow Results for DDF-fed mHM Model - Bahcelik Basin
Avg. Precipitation Qobs mHM DDF Calibrated Avg. Temp
DDF Calibrated mHM NSE: 0.07003 KGE: 0.21543mHM NSE: 0.11149 KGE: 0.25381
RESULTS (Cont.)
• Snowpack Simulations: Karanlikdere Basin
20METU - GGIT
-16
-6
4
14
24
34
44
0
50
100
150
200
250
300
350
400
450
500
12/1
/14
1/1/
15
2/1/
15
3/1/
15
4/1
/15
5/1/
15
6/1
/15
7/1/
15
8/1
/15
9/1
/15
10/1
/15
11/1
/15
12/1
/15
1/1/
16
2/1/
16
3/1/
16
4/1
/16
5/1/
16
6/1
/16
7/1/
16
8/1
/16
9/1
/16
10/1
/16
11/1
/16
12/1
/16
1/1/
17
2/1/
17
3/1/
17
4/1
/17
5/1/
17
6/1
/17
7/1/
17
8/1
/17
9/1
/17
Pre
cip
itat
ion
(m
m),
Avg
. T
emp
. (C
)
Sn
ow
pac
k (
mm
)
Observed vs Simulated SWE (mm)
Precipitation Obs. SWE (mm) mHM DDF Fed Avg. Temp.
RESULTS (Cont.)
• Snowpack Simulations: Bahcelik Basin
21METU - GGIT
-20
-10
0
10
20
30
40
500
20
40
60
80
100
120
140
160
180
200
10/2
/03
2/2/
04
6/2
/04
10/2
/04
2/2/
05
6/2
/05
10/2
/05
2/2/
06
6/2
/06
10/2
/06
2/2/
07
6/2
/07
10/2
/07
2/2/
08
6/2
/08
10/2
/08
2/2/
09
6/2
/09
10/2
/09
2/2/
10
6/2
/10
10/2
/10
2/2/
11
6/2
/11
10/2
/11
2/2/
12
6/2
/12
10/2
/12
2/2/
13
6/2
/13
10/2
/13
2/2/
14
6/2
/14
10/2
/14
2/2/
15
6/2
/15
10/2
/15
2/2/
16
6/2
/16
10/2
/16
2/2/
17
Avg
.Tem
p.(
C),
Pre
cip
itat
ion
(m
m)
Sn
ow
pac
k (
mm
)
Observed vs Simulated SWE
Precipitation AgroGrid SWE (mm) DDF Calibrated mHM Calibrated Avg.Temp.
RESULTS (Cont.)
22METU - GGIT
• Bahcelik Basin Simulated SWE vs MODIS Imagery
DISCUSSION and FUTURE WORKS
• Results
• Slight differences in terms of flow simulation
• Improvement on SWE simulations
• Consistency between average calculated DDF and mHM
calibrated DDF
• Error Sources
• Data quality (meteorological and flow data)
• Relatively short period of availability
• Size of basin for Karanlikdere
• Future Works
• Add spatial snow distribution as calibration parameter to mHM
• Improve meteorological data with ground-based observations
• Investigate other remote sensing snow data
23METU - GGIT
24
Thank You.
Kackar Mountains, Turkey