The document provides an overview of the HYDROSYS project and its applications. The project aims to enable on-site data collection and visualization to support new ways of observing environmental processes. It provides an infrastructure for teams to monitor events and analyze natural resources using handheld devices and potentially UAVs. This improves monitoring for environmental scientists through integrating handhelds, sensor networks, and modeling. The document also describes applying the GEOtop modeling system to simulate rainfall-runoff for the instrumented La Fouly watershed in Switzerland, using available meteorological, soil, and water level data.

1. HYDROSYS
PROJECT OVERVIEW & APPLICATIONS
EPFL‐EFLUM Dec. 10th 2009

2. Project Goals

3. Project Goals
 Enabling on‐site data collec0on, visualiza0on and transfer to support new
ways of observing environmental processes.
 Providing a system infrastructure to support teams of users in the on‐site
monitoring of events and analysis of natural resources. It introduces the
innova0ve concept of event‐driven campaigns using handheld devices,
poten0ally supported by an unmanned aerial vehicle (UAV).
 Improving monitoring and management for environmental scien0sts,
ins0tu0ons, service providers, engineering companies and municipali0es
through its strong integra0on of handhelds, sensor networks and modeling.
 www.hydrosysonline.eu

4. La Fouly: GEOtop application to an instrumented
watershed in the Valais
Silvia Simoni
Environmental Fluid Mechanics Department - EPFL

5. Outline
1. Rationale for using a 3D-physically based model
Data spatial variability
2. GEOtop overview
for details: www.geotop.org
3. Rainfall-run off modeling using La Fouly data
4. Snow melt: how to deal with it in absence of measured snow
depth
Lausanne Dec. 10th 2009 - Silvia Simoni

6. La Fouly deployment
Location: La Fouly - Val Ferret, Orsiere VS. water level measuring point
catchment area=20 km2
z min=1785 m z max=3238 m
main stream: Dranse de Ferret

7. Available data
water level @ the outlet − hourly mean
Meteorological data
water level [mm]
300
(precipitation, wind speed and velocity,
100
air temperature and humidity, radiation)
0
18/06 25/06 02/07 09/07 16/07 23/07 30/07 05/08 12/08 19/08 26/08 02/09 09/09 16/09 23/09 30/09 07/10
hourly cumulated rain @ ST1033
Soil data
8
precipitation [mm]
6
(soil moisture, suction and temperature)
4
2
0
18/06 25/06 02/07 09/07 16/07 23/07 30/07 05/08 12/08 19/08 26/08 02/09 09/09 16/09 23/09 30/09 07/10
hourly averaged air Temperature @ ST1033 Water level data
20
Air Temperature [C]
Discharge from rating curve
10
5
0
−5
18/06 25/06 02/07 09/07 16/07 23/07 30/07 05/08 12/08 19/08 26/08 02/09 09/09 16/09 23/09 30/09 07/10
Incoming short wave radiation @ ST1033
Period
1200
ISW radiation [W/m2]
June - October 2009
800
400
0
18/06 25/06 02/07 09/07 16/07 23/07 30/07 05/08 12/08 19/08 26/08 02/09 09/09 16/09 23/09 30/09 07/10
10 Sensorscope stations
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

8. Discharge rating curve
8000
Measuments
Discharge rating curve Q=1.594 (h+15.56)2.03
7000
6000
5000
Q [l/s]
4000
3000
2000
1000
0
0 10 20 30 40 50
h [cm]
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

9. Spatial variability of rainfall
Hourly Cumulated Rainfall measured @ SS1032 Hourly Cumulated Rainfall @ ST1042
5
8
cumulated rain [mm]
cumulated rain [mm]
4
6
3
4
2
2
1
0
0
18/06 21/06 25/06 28/06 01/07 04/07 08/07 11/07 14/07 17/07 21/07 24/07 27/07 30/07 03/08 06/08 09/08 18/06 21/06 25/06 28/06 01/07 04/07 08/07 11/07 14/07 17/07 21/07 24/07 27/07 30/07 03/08 06/08 09/08
time (day/month) time (day/month)
Italy
G. S. Bernard
N
SG1044
SG1038
Lacs de Fenetre Italy
GPS base station
ST1041 ST1042
ST1040
SS1033 water level @ La Fouly 2009 data − hourly mean
ST1037
100 200 300 400 500
ST1043
water level [mm]
SS1032 ST1035
ST1036
GPRS base station Swiss-South Africa Joint Scientific Conference, Nov 5th 2009
ST1034 20/05 26/05 01/06 07/06 12/06 18/06 24/06 30/06 06/07 11/07 17/07 23/07 29/07 04/08 09/08 15/08 21/08 27/08 8

10. Spatial variability effect on Precipitation
Station Scattergram for the stations on the orographic left− Rainfall data Station Scattergram for the stations across the valley− Rainfall data
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x=1689 m
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0 2 4 6 8 0 2 4 6 8 10 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7
ST1033 mm ST1040 mm ST1032 mm ST1032 mm
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ST1034 mm
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0
0
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0
0
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0 2 4 6 8 0 2 4 6 8 10
0 1 2 3 4 5 6 7 0 2 4 6 8
ST1033 mm ST1040 mm
ST1032 mm ST1033 mm
10
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12
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x=251 m x=376 m
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x=1243 m
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ST1043 mm
ST1043 mm
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ST1042 mm
ST1036 mm
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0
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0 2 4 6 8 0 2 4 6 8
0 1 2 3 4 5 6 7 0 2 4 6 8
ST1033 mm ST1042 mm
ST1032 mm ST1033 mm
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

11. Spatial variability effect on Precipitation
Influence of spatial distance on the spatial correlation between stations (based on rainfall data)
1.0
●
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correlation coefficient
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0.8
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0.6
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0.4
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0.2
0.0
0 500 1000 1500 2000
real distance between stations [m]
Influence of Aspect on the spatial correlation between stations (based on rainfall data)
1.0
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correlation coefficient
● ●
0.8
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0.6
●
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0.4
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0.2
0.0
0 30 60 90 120 150 180
aspect difference between stations [deg]
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

12. Spatial variability effect on cumulated Precipitation
Spatial variability impact on totally cumulated rainfall
400
total cum rainfall [mm]
● 1040
300
● 1043
● 1033
● 1042
● 1036
1032
200
●
● 1034
● 1037
100
1700 1800 1900 2000 2100 2200 2300 2400 2500
elevation [m a.s.l.]
ST1040 ST1041
ST1037 ST1042
SS1033
1. Data 2. Spatial variability 3. GEOtop SS1032 Rainfall-run off
4. ST1035 ST1043 5.Snow melt 6.Soil moisture
ST1036
Lausanne Dec. 10th 2009 - Silvia Simoni ST1034

13. Spatial variability effect on wind speed and directions
Meteorological station: 1033 Meteorological station: 1040
Wind velocity [m/s] Wind velocity [m/s]
N v >= 5.0 N Meteorological station: 1042
v >= 5.0 Wind velocity [m/s]
3.0 <= v < 5.0 3.0 <= v < 5.0
2.0 <= v < 3.0 N v >= 5.0
2.0 <= v < 3.0 3.0 <= v < 5.0
1.0 <= v < 2.0 1.0 <= v < 2.0 2.0 <= v < 3.0
NW NE 0.5 <= v < 1.0 0.5 <= v < 1.0 1.0 <= v < 2.0
NW
calm (v < 0.5) NE calm (v <NW
0.5) 0.5 <= v < 1.0
NE
calm (v < 0.5)
W E W E W E
0.0% 6.5% 13.0% 19.5% 26.0% 0.0% 5.0% 10.0% 15.0% 20.0% 0.0% 6.5% 13.0% 19.5% 26.0%
SW SE
SW SE SW SE
S
S S
Meteorological station: 1034 Meteorological station: 1035 Meteorological station: 1037
Wind velocity [m/s] Wind velocity [m/s]
N v >= 5.0 N v >= 5.0 N Wind veloci
3.0 <= v < 5.0 3.0 <= v < 5.0 v >=
2.0 <= v < 3.0 2.0 <= v < 3.0 3.0 <= v <
1.0 <= v < 2.0 1.0 <= v < 2.0 2.0 <= v <
0.5 <= v < 1.0 0.5 <= v < 1.0 1.0 <= v <
NW NE NW
calm (v < 0.5) NE NW NE 0.5 <= v <
calm (v < 0.5) calm (v < 0
W E W E W E
0.0% 7.5% 15.0% 22.5% 30.0% 0.0% 5.0% 10.0% 15.0% 20.0% 0.0% 6.0% 12.0% 18.0% 24.0%
SW SE SW SE
SW SE
S S
S
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

14. Spatial interpolation of Precipitation
Hourly Cumulated Rainfall @ ST1043
July 9th 2009
cumulated rain [mm]
8 10
6
N
4
2
0
18/06 25/06 02/07 09/07 15/07 22/07 29/07 05/08 12/08 19/08 25/08 01/09 08/09 15/09 22/09 29/09 05/10 12/10
time (day/month)
July 2th 2009
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

15. GEOtop model
snow, ice,
water cycle permafrost
Endrizzi 2007
Rigon et al., 2006
evapo- natural
transpiration, hazards
energy fluxes Simoni 2007
Bertoldi et al., 2006
14
Open source LGPL3

16. The GEOtop model
1. 3D topography
- distributed model
- DEM preprosessing: drainage directions => channel network
slopes and aspect => radiation
curvatures => shadowing
2. Water balance sky view factor => radiation
- effective rainfall
- surface flow
- subsurface flow
- snow/ice melt
3. Energy balance
- radiation
- boundary-layer interaction
- snow and glacier melt
- freezing soil
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

17. Rainfall-run off - infiltration mechanism
Effective rainfall: vegetation accounted for
Infiltration: Richards 3D or Richards 1D + lateral darcian flow
∂ψ dθ ∂ ∂ψ ∂ ∂ψ ∂ ∂ψ
= KL (ψ) − sin α + KL (ψ) + Kz (ψ) − cos α
∂t dψ ∂x ∂x ∂y ∂y ∂z ∂x
Drainage along slopes: kinematic scheme
c(x, t) = Cm h(x, t) [ z(x)] γ 0.5
W (x, t) = Ωc(x, t)
Channel routing: Saint-Venant equation
t L
sW (τ, s) (s − uc (t − τ ))2
Q(x, t) = AT exp − dsdτ
0 0 4πD(t − τ )3 4D(t − τ )
W water inflow from hillslopes into the channel network at a distance s from the outlet at time τ
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

18. Rainfall-run off contribute
Discharge @ La Fouly 2009 data from ST1043
4000
GEOtop no snow
run off [l/s]
Q measured
2000
0
27/06 03/07 10/07 16/07 22/07 29/07 04/08 10/08 17/08 23/08 29/08 05/09 11/09 17/09 24/09 30/09 06/10 13/10
time (day/month)
Determining a stage-discharge relationship
Modeled discharge at La Fouly: sensitivity analysis on D and C_m
15
D=100 m2/s C_m=0.5 m^1/3/s, n_Dt_w=10
Discharge [m3/s]
D=50 m2/s C_m=0.25 m^1/3/s, n_Dt_w=2
10
D=50 m2/s, C_m=0.25 m^1/3/s, n_Dt_w=10
5
0
18/06 25/06 01/07 08/07 14/07 21/07 27/07 03/08 09/08 16/08 22/08 29/08 04/09 11/09 17/09 24/09 30/09 07/10
time
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

19. Soil moisture
Measured soil moisture
Soil Moisture − 20cm @ SS1043
50
Hourly cumulated rain [mm]
Soil Moist. (V)
45
8 10
Soil moisture [−]
Precip
40
6
35
4
30
2
25
0
18/06 25/06 02/07 09/07 15/07 22/07 29/07 05/08 12/08 19/08 25/08 01/09 08/09 15/09 22/09 29/09 05/10 12/10
time (day/month)
1043
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

20. Soil moisture
Measured soil moisture
Soil Moisture − 20cm @ SS1043
50
Hourly cumulated rain [mm]
Soil Moist. (V)
45
8 10
Soil moisture [−]
Precip
40
6
35
4
30
2
25
0
18/06 25/06 02/07 09/07 15/07 22/07 29/07 05/08 12/08 19/08 25/08 01/09 08/09 15/09 22/09 29/09 05/10 12/10
time (day/month)
1043
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

21. Soil moisture
Measured soil moisture
Soil Moisture − 20cm @ SS1043
50
Hourly cumulated rain [mm]
Soil Moist. (V)
45
8 10
Soil moisture [−]
Precip
40
6
35
4
30
2
25
0
18/06 25/06 02/07 09/07 15/07 22/07 29/07 05/08 12/08 19/08 25/08 01/09 08/09 15/09 22/09 29/09 05/10 12/10
time (day/month)
1043
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

22. Soil moisture
Measured soil moisture
Soil Moisture − 20cm @ SS1043
50
Hourly cumulated rain [mm]
Soil Moist. (V)
45
8 10
Soil moisture [−]
Precip
40
6
35
4
30
2
25
0
18/06 25/06 02/07 09/07 15/07 22/07 29/07 05/08 12/08 19/08 25/08 01/09 08/09 15/09 22/09 29/09 05/10 12/10
time (day/month)
Modeled soil moisture
1043
Soil Moisture @ ST1043
0.50
0.40
soil moisture [−]
0.3 m measured
0.30
0.3 m
0.125 m
0.20
24/06 24/06 25/06 26/06 27/06 28/06 29/06 30/06 01/07 02/07 03/07 04/07 05/07 05/07 06/07 07/07 08/07
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

23. Adding snow melt
Discharge @ La Fouly 2009 data from ST1043
4000
GEOtop no snow
run off [l/s]
Q measured
2000
0
27/06 03/07 10/07 16/07 22/07 29/07 04/08 10/08 17/08 23/08 29/08 05/09 11/09 17/09 24/09 30/09 06/10 13/10
time (day/month)
Challange:
setting initial condition on snow depth
how to???
MODIS data June 27th 2009
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

24. Adding snow melt
First attempts Discharge @ La Fouly 2009 data from all stations
4000
GEOtop no snow
run off [l/s]
Q measured
2000
0
27/06 03/07 10/07 16/07 22/07 29/07 04/08 10/08 17/08 23/08 29/08 05/09 11/09 17/09 24/09 30/09 06/10 13/10
time (day/month)
Snow depth distribution according to topography,
slope and aspect
1. Data 2. Spatial variability 3. GEOtop 4. Rainfall-run off 5.Snow melt 6.Soil moisture
Lausanne Dec. 10th 2009 - Silvia Simoni

25. Further work
1. Introducing snow melt
2. Characterizing the soil more in detail to improve soil moisture modeling
3. Comparing fluxes computed from flux tower with the ones computed by the
model.
4. Investigating subsurface flows and their link with potential hazards such as debris-
flows and shallow landslide.
.....
Lausanne Dec. 10th 2009 - Silvia Simoni

26. Enjoy GEOtop!
snow, ice,
water cycle permafrost
evapo- natural
transpiration, hazards
energy fluxes