An Embedded Sensor Network for Measuring Elevation Effects on Temperature
Fall AGU
and Evapotranspiration Within a Tropical Alpine Valley 2006
C33C-1280
Dr. Rob Hellström, Geography, Bridgewater State College and Dr. Bryan G. Mark, Geography, The Ohio State University
Study Area (Llanganuco Valley)
Abstract
Looking SW down valley:
Sensors deployed from base of lower
lake to viewpoint
N
Nevados
Huandoy
(6395 m a.s.l)
Dry
5400
Wet
Linear (Wet)
4400
4200
4000
0.2
0
• Steepest lapse rates occur below the lakes for both wet and
dry seasons
• Dry season nocturnal inversion below the lakes is not
evident during wet season (graphs not shown here)
• Most precipitation (and cloud cover) occurs between sunset
and sunrise during the wet season, hence insolation at the
ground is strong during both seasons
• Valley winds dominate during sunlight hours in both wet and
dry seasons, but abruptly shift to katabatic winds during the
dry season at sunset
• The combination of valley winds and steep lapse rate below
the lakes suggest local warm air advection contributes to
evapotranspiration and glacial melt.
• Furthermore, the up-slope winds may enhance convective
precipitation, particularly during the wet season after sunset
• The BROOK90 output suggests that transpiration is a
significant source of ET during the wet season
Precip.
Infiltration
0:
00
3
Water Content (m /m )
3
00
00
22
:
00
00
00
00
20
:
18
:
16
:
14
:
0
0
00
12
:
10
:
0:
0
8:
00
10
:0
0
12
:0
0
14
:0
0
16
:0
0
18
:0
0
20
:0
0
22
:0
0
0
-0.2
0
0
-0.2
6:
00
Infiltration
0.2
0
4:
00
Intercepted Evap.
0.4
8:
0
0.2
Transpiration
0.6
0
0.4
6:
0
0.6
Soil Evap.
4:
0
Liquid Water (mm)
1
2:
0
Intercepted Evap.
0.8
0:
00
Liquid Water (mm)
0
:0
22
0
0
0
:0
:0
:0
18
Time of Day (hours)
20
:0
0
0.00
0
0.02
0
0
4
16
Time of Day (hours)
0.04
:0
0.00
0.06
8
:0
0.02
0
0.08
12
12
4
16
14
0.04
0.10
00
0.06
8
0.12
20
10
0.08
12
0.14
24
00
16
28
00
0.10
Transpiration
Time of Day (hour)
Dry & Wet Periods
2
Incident Solar (W/m )
0
:0
22
0
0
0
:0
:0
:0
16
18
20
0
0
0
:0
:0
:0
10
14
12
00
00
00
4:
6:
8:
00
00
0:
2:
Wind Speed (m/s)
Wind Speed (m/s)
Precipitation, swe (mm)
2
Incident Solar (W/m )
0
:0
22
0
0
0
:0
:0
:0
16
18
20
0
0
0
:0
:0
12
14
0
8:
0.01
200
6:
7.36
400
0.2
4:
0.08
1.07
0.12
20
Temperature (°C)
Precip.
(mm/day)
0.4
00
1.08
600
00
12.8
5033
0.6
2:
13.8
5377
0.14
24
Soil Evap.
1
0.8
BROOK90 Estimated Moisture Fluxes
1000
800
0:
0.10 m Soil
Temp. (°C)
0°C
Elevation
(m)
28
3
0.04
0.90
3
0.119
5.9
360
0.8
0
Water Content (m /m )
0.005
5.3
Dry Period
Dry Period: Modeled Hydrological Components
Time of Day (hour)
Dry Period Diurnal Variation of Average Air and Soil
Temperatures and Soil Water Content
Soil Water
Soil Temp
Air Temp
0
0.10 m Soil
Moist. (m3/m3)
Lapse Rate
(°C/km)
315
Wet Period Diurnal Variation of Average Air and Soil
Temperatures and Soil Water Content
Soil Water
Soil Temp
Air Temp
:0
0.75
270
1.00
0
0.72
9.48
22
0.54
9.49
0
Wind
Constancy
3469
0
N/A
135
180
225
Wind Direction (°)
Time of Day (hours)
:0
233
90
Dry Period Diurnal Variation of Average Precipitation
and Insolation
Precipitation
Insolation
1
0
:0
39
45
Time of Day (hours)
:0
1.20
0
1.20
18
1.18
6.24
200
20
1.42
7.49
400
0.2
0
Wind (m/s)
Wind Direction
(°)
3862
0.4
0
1.27
1.15
600
:0
13.21
7.45
0
1000
0.6
:0
16.82
8.59
360
800
14
Insolation
(MJ/m2)
3871
315
0.8
16
5.38
1.48
00
0.08
5.48
00
0.43
8.12
:0
VP Deficit
(kPa)
3948
270
Wet Period Diurnal Variation of Average Precipitation
and Insolation
Precipitation
Insolation
1
0
0.69
1.18
135
180
225
Wind Direction (°)
12
0.90
5.66
6:
0.62
6.68
90
8:
Vap. Press.
(kPa)
4148
45
10
0.64
0
00
92.4
1
0
00
59.5
2
0
:0
RH (%)
1.49
Precip.
1.5
0.5
6:
1.59
3.56
Wet Period
Wet Period: Modeled Hydrological Components
2.5
0.5
8:
9.1
5.31
1
10
14.5
4344
Time of Day (hour)
Valley Orientation
55° => 235°
3
2
00
0.42
1.43
3.5
1.5
4:
3.6
3.06
1.60
00
1.5
Temp. Range.
(°C)
4.39
1.77
10.0
Dry Period Wind Speed versus Direction
Valley Orientation
55° => 235°
3
8.0
Dry Period
2.5
4:
Min. Temp.
(°C)
4559
2.84
4.0
6.0
Air Temperature (°C)
Wet Period Wind Speed versus Direction
3.5
Precipitation, swe (mm)
1.26
4742
2.0
Wet Period
Dry/
Wet
00
12.7
1.15
Wet
(°C)
00
16.0
6.6
Dry
(°C)
0:
Max. Temp.
(°C)
7.6
Wet
iButton
Elevation
(m)
2:
Air Temp.
(°C)
Dry
Dry/
Wet
0.0
6:
00
-0.4
4:
00
Conclusions
3600
2:
00
3800
-0.2
iButton Air
Temperature
Temperature (°C)
HOBO
Weather
Station
WetETRef
0.4
3400
HOBO Weather Station
DryETRef
0.6
2:
00
Elevation (m)
4600
WetBRK90
0.8
Twet = -0.0059(Z) + 29.7
2
R = 0.97
4800
DryBRK90
1
Liquid Water (mm)
5000
Sources of ground-based data within valley
– HOBO Automatic Weather Station (AWS)
– Specially designed iButton temperature stakes at
different elevations
• ET-REF and BROOK90 models
– Estimating potential and actual ET within valley
• Compare diurnal variations in meteorological forcing for
dry and wet seasons (25-day periods)
– Dry: 17 June => 12 July 2005
– Wet: 7 December 2005 => 1 Jan 2006
– Precipitation, Insolation, Air Temperature and Wind
Vector
– Hourly data were synthesized and analyzed
Estimated ET: Dry and Wet Period (+ loss from surf.)
Linear (Dry)
Tdry = -0.0053(Z) + 28.5
2
R = 0.95
5200
•
Model Output
Average Temperature Profiles: Dry and Wet Periods
00
•
Snow and ice mass is connected to climate change
– Meltwater is a source of water for agriculture, human
consumption and hydroelectric
Moisture in the atmosphere—a key component of the
mass balance in alpine valleys
Alpine regions modulate atmospheric moisture depending
largely on wind direction and speed—can we measure this
effect within an alpine valley?
The models parameterize the subgrid-scale energetics of
alpine valleys—do models adequately represent alpine
contributions?
The magnitudes of regional latent heat flux and
evapotranspiration are unverified in tropical alpine regions
The Cordillera Blanca is seasonally isothermal
The seasonal migration of the ITCZ creates distinct dry
(May-Sept.) and wet (Oct.-April) seasons
• How do local atmospheric conditions (wind vectors and
lapse rate) within Tropical alpine valleys alter seasonal
surface moisture fluxes
(evapotranspiration/sublimation)?
• Driving questions
– Does microscale variability of temperature and
topography significantly modulate catchment
hydrology?
– Can a sustainable, low-cost embedded sensor network
verify remote sensing and climate model results?
Data & Methods
Lower lake
3862 m
Measurements
00
•
1.7 cm
11 km
2:
•
HOBO AWS
3850 m
iButton logger
0:
•
Casa de control
3469 m
Nevado
Huascarán
(6768 m a.s.l)
Hydroclimatic Significance of Tropical
Alpine Regions
•
•
•
Portachuelo
4742 m
8:
00
10
:0
0
12
:0
0
14
:0
0
16
:0
0
18
:0
0
20
:0
0
22
:0
0
Conditions of glacier recession in the seasonally dry tropical Peruvian
Andes motivate research to better constrain the hydrological balance in
alpine valleys. Studies suggest that glaciers in the tropical Andes are
particularly sensitive to seasonal humidity flux due to the migration of the
Intertropical Convergence Zone (ITCZ). However, there is an outstanding
need to better measure and model the spatiotemporal variability of energy
and water budgets within pro-glacial valleys. In this context, we introduce a
novel embedded network of low-cost, discrete temperature microloggers
and an automatic weather station installed in the Llanganuco valley of the
Cordillera Blanca. Here we present data recorded over a full annual cycle
(2004-2005) and report on network design and results during the dry and
wet seasons. The transect of sensors ranging from about 3500 to 4700 m
reveals seasonally characteristic diurnal fluctuations in up-valley lapse rate.
We use a process-based water balance model (Brook90) to examine the
influence of meteorological forcing on evapotranspiration (ET) rates in the
valley. The model results suggest that cloud-free daylight conditions
enhances ET during the wet season. ET was insignificant throughout the
dry season. In addition, we report on the effects of elevation on ET.
Background & Questions
Embedded Temperature Stakes
Period
Dry
% of
Precip.
Wet
% of
Precip.
Wet/Dry
Pre*
(mm)
0.09
Inf
(mm)
0.09
SEv
(mm)
0.02
Trs
(mm)
0.01
IEv
(mm)
0.00
ET
(mm)
0.03
100
7.06
99
6.82
22
0.35
11
2.06
0
0.22
33
2.63
100
78
97
76
5
18
29
206
3
n/a
37
88
*Pre = precipitation; Inf = infiltration; SEv = surface
evaporation; Trs = transpiration; IEv = intercepted
evaporation; ET = evapotranspiration
Acknowledgements
Funding for this project was provided by The Ohio State University,
Department of Geography and Office of International Affairs. We are
grateful for the collaboration with Peruvian Institute of Natural
Resources (INRENA), especially the timely collection and
transmission of data by Jesús Gómez, INRENA-Huaraz, Perú.
Model References
Federer, C.A. 1995. BROOK90. A Simulation Model for Evaporation,
Soil Water, and Streamflow. Version 3.1 Computer Freeware and
Documentation. USDA Forest Service, P.O. Box 640, Durham, N.H.
03824.
Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. 1998. Crop
evapotranspiration-Guidelines for computing crop water requirements,
FAO irrigation and drainage paper 56, FAO. ISBN 92-5-104219-5.