This project uses data from an embedded sensor network in Peru's Cordillera Blanca mountains to analyze factors influencing evapotranspiration over nearly a decade. The network found that valley topography and land cover influence diurnal and seasonal variability of evapotranspiration. Analysis of temperature, moisture, and wind patterns suggests atmospheric boundary layer processes within the valley impact evapotranspiration and potentially glacial mass balance. The long-term data could improve understanding of multi-scale interannual variability, such as El Niño and La Niña influences, in pro-glacial valleys.

1. RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
El Norte del Perú se enfrentará a una crisis en sus recursos de agua en un futuro cercano
debido al retiro del hielo permanente en los Andes. Procesos hidrometeorológicos
parcialmente controlados por la variabilidad en la cobertura del suelo, las fuertes
pendientes y la orientación de los valles pro-glaciales a lo largo de la vertiente
occidental de los Andes, no son usualmente tomados en cuenta por la predicción de los
modelos climáticos actuales. Se carece de estudios que comparen factores de
calentamiento regionales y locales, particularmente en los Andes peruanos. En 2005,
nosotros instalamos una red integrada de sensores automáticos discretos y rentables a lo
largo del eje del valle, en conjunto con cuatro estaciones meteorológicas automáticas,
con el propósito de entender los factores que controlan el flujo de humedad en la capa
límite del valle Llanganuco, Cordillera Blanca, Perú (9 º Sur). Durante casi una década,
nuestra red de sensores integrados (embedded sensor network [ESN]), que se extiende a
través de la cresta de la Cordillera de los Andes peruanos, ha proporcionado datos para
evaluar las condiciones de clima y cambio climático, indicadores críticos a la gestión de
recursos hídricos. La permanencia de una alta densidad de mediciones autónomas en
extremas condiciones meteorológicas impone desafíos técnicos y logísticos. En este
trabajo, reportamos nuestros enfoques para superar estos desafíos y el valor científico de
casi una década de observaciones a resolución horaria.
Este proyecto incorpora imágenes de LandSat ETM +, ESN, y simulaciones de
evapotranspiración (ET) para demostrar la influencia de la topografía del valle y la
cobertura de suelo en la variabilidad estacional diurna de la ET. Comparamos ciclos
diurnos y estacionales de temperatura, humedad y viento obtenidos de la ESN, con datos
de reanálisis tomados cada seis horas. Los resultados sugieren que procesos atmosféricos
de capa límite en el valle de influencian la ET y probablemente el balance de masa
glacial. Teniendo en cuenta la longitud del registro, los datos ESN pueden adicionalmente
otorgar una mejor comprensión de la variabilidad interanual de procesos que ocurren
sobre múltiples escalas en valles pro-glaciales, como la influencia del Niño y La Niña.
Palabras clave: redes de sensores integrados, valle pro-glacial, evapotranspiración,
múltiples escalas, Andes peruanos
Resúmen
Evapotranspiration
Abstract (eng)
Northern Peru will face critical water resource issues in the near future as permanent ice in the Andes retreats.
Hydrometeorological processes partially controlled by land cover variability, steep terrain, and orientation of pro‐
glacial valleys along the western slope of the Andes are largely disregarded in current climate model predictions.
Studies that compare regional and local warming factors are lacking, particularly in the Peruvian Andes. In 2005, we
installed an embedded network of discrete, cost‐effective automatic sensors along the valley axis and four automatic
weather stations to better understand near‐surface boundary layer processes controlling moisture flux from the
glacierized Llanganuco Valley, Cordillera Blanca, Peru (9º south). Over nearly a decade, an “embedded” sensor
network (ESN) of ground‐based automated instruments extending across the crest of the Cordillera Blanca of the
Peruvian Andes has provided data for evaluating weather conditions and climate change indicators critical to water
resource management. Sustaining high density autonomous ESNs exposed to extreme meteorological conditions
poses technical and logistical challenges. We report on approaches to overcome these challenges and the scientific
value of nearly a decade of hourly observations.
This project incorporates LandSat ETM+ images, the ESN, and evapotranspiration (ET) modeling to demonstrate
that control of valley topography and land cover on diurnal to seasonal variability of ET. We compare diurnal and
seasonal cycles of temperature, moisture and wind patterns from the ESN with six‐hourly upper air reanalysis data.
Results suggest that atmospheric boundary layer processes within the valley influence ET and plausibly glacial mass
balance. Given the length of record, the ESN data can also inform a better understanding of the inter‐annual
variability of multi‐scale processes in pro‐glacial valleys, including the influence of El Niño and La Niña.
Keywords: embedded sensor networks, pro‐glacial valley, evapotranspiration, multi‐scale, Peruvian Andes
Valley Winds
References and Acknowledgements
• SST Anomaly Plot: http://www.ncdc.noaa.gov/sotc/enso/2007/7
• Evapotranspiration model: Allen RG, Pruitt WO, Wright JL, Howell TA, Ventura F, Snyder R, et al (2006) A
recommendation on standardized surface resistance for hourly calculation of reference ET0 by the FAO56
Penman–Monteith method. Agric Water Manage 81:1–22.
• Valley Wind Diagram: Whiteman CD (2000) Mountain Meteorology. Fundamentals and Applications, Oxford
University Press, NewYork: 355 pp.
• Special thanks to students Aimee Higgins and Derek Ferris for assitance with evapotranspiration modeling and
processing NCEP data. Thanks to Jesus Gomez for his support for the field portion and data download and
transmission.
1 Bridgewater State university, Department of Geography, Conant Science and Math Center, Bridgewater, MA 02325, rhellstrom@bridgew.edu
2The Ohio State University, Department of Geography, Byrd Polar Research Center, 1136 Derby Hall, 154 N Oval Mall, Columbus, OH 43210
3Parque Nacional de Huascaran, Huaraz, Perú
4Unidad de Glaciologia Y Recursos Hidricos, Autoridad Nacional de Agua, Huaraz, Perú
Robert Å. Hellström1, Bryan G. Mark2, Ricardo Jesus Gomez3, and Alejo Cochachin Rapre4
Applications and Limitations of Sustaining a Cost‐Effective Embedded
Sensor Network in Complex Terrain: Cordillera Blanca, Perú
The Study Area and Methods Lapse Rates
Southwest‐looking view of Llanganuco valley from
Portachuela site near the highest elevation iButton
logger (a) and instrumentation: HOBO AWS and
precipitation logger (Kaser, U. Innsbruck) (b), iButton
sensor and shield (c), Lascar temperature and
humidity logger in tree (d), porometer measuring
leaf conductance (e), and tensiometer measuring
soil water potential (f). Note the highland grasses,
steep rocky walls, terminus of glacier above the
north wall, and Santa River valley in the distance.
Dry and wet periods are compared for the 2005‐06
seasons. Note the significantly warmer. and higher
freezing line elevation of the ground‐based lapse rate at
1200hr noon. This suggests strong thermally drive
warming of the lower elevations in the valley not
detected by the NCEP reanalysis. In combination with
the valley wind shown above, it is likely that significant
local warming may affect glacial mass extending below
about 5700 m.
2005‐06 2006‐07
Period P ET0
PET
0
P/ET0
(mm) (mm) (mm)
Dry 0.09 3.51 -3.42 0.03
Wet 7.06 1.47 5.59 4.80
a)18Z Dry AWS. b) 18Z Dry NCEP. c) 18Z
Wet AWS. d) 18Z Wet NCEP: The results
demonstrate that there is a decoupling
between the regional (synoptic) and local
scale valley wind systems, particularly
during the wet season during periods of
strong solar afternoon surface heating.
0
5
10
15
20
0 2 4 6 8 10 12 14 16 18 20 22
AirTemperature(C)
Time of Day (hour)
Dry Wet(a)
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18 20 22
RelativeHumidity(%)
Time of Day (hours)
Dry Wet(b)
0
200
400
600
800
1000
1200
0 2 4 6 8 10 12 14 16 18 20 22
Insolation(Wm2)
Time of Day (hour)
Dry Wet(c)
0
0.5
1
1.5
2
2.5
3
0 2 4 6 8 10 12 14 16 18 20 22
Precipitation(mm)
Time of Day (hour)
Dry Wet(d)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 2 4 6 8 10 12 14 16 18 20 22
VapourPressure(kPa)
Time of Day (hour)
Dry Wet(e)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 2 4 6 8 10 12 14 16 18 20 22
VPDeficit(kPa)
Time of Day (hour)
Dry Wet(f)
0
5
10
15
20
25
30
0 2 4 6 8 10 12 14 16 18 20 22
SoilTemperature(C)
Time of Day (hour)
Dry Wet(g)
0
0.05
0.1
0.15
0.2
0 2 4 6 8 10 12 14 16 18 20 22
SoilMoisture(m3m3)
Time of Day (hour)
Dry Wet(h)
(2006‐07) Depicted are composite diurnal cycles as
recorded by the AWS at the base of the lower lake
compared with the upper station a Portachuela.
Comparisons for the dry and wet periods include: air
temperature (a), vapour pressure (b), lapse rate (c),
relative humidity (d), dry period precipitation (e), wet
period precipitation (f), solar radiation (g) and wind
speed (h). Time is UTC‐5hrs.
(2005‐06) Depicted are composite diurnal cycles as
recorded by the AWS at the base of the lower lake.
Comparisons for the dry and wet periods include: air
temperature (a), relative humidity (b), incoming global
solar radiation (c), hourly total precipitation—note dry is
negligible (d), vapour pressure (e), vapour pressure
deficit (f), and soil temperature (g) and volumetric water
content (h) at a depth of 10 cm. Day‐to‐day variation is
shown by the 1st and 3rd quartiles (25th and 75th
percentile) and is designated by small symbols above or
below the mean. Time is UTC‐5hrs
El Niño La Niña
Dry
Wet
Diurnal and Seasonal Variability