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Seasonal isotope hydrology of a coffee agroforestry watershed in Costa Rica
J. Boll1, K. Welsh*1,2, O. Roupsard2,3 1University of Idaho, Moscow, ID, USA; 2Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), Turrialba, Costa Rica; 3UMR Eco&Sols, CIRAD, Montpellier, France
H21F-0794
Introduction
In the tropics, the “amount effect” has been
recognized as one of the primary influences on
stable isotope values in tropical precipitation for
monthly samples (Figs 1 and 2). However, when
examining event-based precipitation events at a
regional scale, lifting condensation levels and
surface relative humidity have a greater influence
(Sánchez-Murillo et al. 2014). The main goals of
this research were to examine what microclimate
factors influence local stable isotope (δ18O and δD)
values and whether stable isotopes can be used to
trace seasonality in this region at different temporal
scales. Methods
• Sampled precipitation (event-based samples),
groundwater (weekly samples), and stream water
(weekly samples at 4 locations) for 2+ years.
• Micrometeorological data were collected at an
eddy-flux tower located on site.
• Study Site: The Mejías watershed (Cartago,
Costa Rica) is an agroforestry microwatershed
(0.9 km2) located on the Aquiares Coffee Farm.
Results
Acknowledgments This work was funded by an NSF IGERT (Integrative
Graduate Education and Research Traineeship) and a U.S.
Borlaug Fellowship in Global Food Security. The
CoffeeFlux project provided site infrastructure and the
Aquiares Farm provided site access. The authors would
like to thank Alvaro Barquero, Alejandro Barquero, Alexis
Pérez, and Amilkar Moncada for field assistance.
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8O
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Monthly δ18O values: Study data
MSD MSD
Fig. 5. Monthly averaged values for δ18O in precipitation at the study site. Precipitation isotope values were correlated with air temperature, wind speed, and wind direction.
Fig. 6. The Local Meteoric Water Line (LMWL) for precipitation samples (collected between Aug 2011 and Dec 2013) for the watershed.
y = 8.4987x + 18.02
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δD
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δ18O (‰)
LMWL Precipitation
Groundwater
Streamwater
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Jan-02 Apr-02 Jul-02 Oct-02 Feb-03 May-03 Aug-03 Dec-03 δ
18O
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Monthly δ18O values: Historic GNIP data
MSD MSD
Dry Dry
Rainy Rainy
Dry Dry
Rainy Rainy
Fig. 4. Monthly averaged values of δ18O in precipitation from historic GNIP database for Turrialba. MSD = Mid-Summer Drought.
F. Gomez-Delgado et al.: Modelling hydrological behaviour of coffee agroforestry basin in Costa Rica 371Figure 1
Fig. 1. (a) Location of Reventazon river basin in Costa Rica, Central America. (b) Position of experimental basin inside of Reventazon
basin. (c) The “Coffee-Flux” experimental basin in Aquiares farm and its experimental setup to measure the water balance components.
In the present study we attempted to couple two lumped
models into a new integrative one, chosen to be scalable
and parsimonious: a basin reservoir model similar to the
CREC model (Cormary and Guilbot, 1969) and employing
the Diskin and Nazimov (1995) production function as pro-
posed by Moussa et al. (2007a,b), and the SVAT model pro-
posed by Granier et al. (1999). While the basin model was
considered appropriate for its simplicity and capacity to sup-
port new routines, the SVAT model was chosen for its par-
simony (three parameters in its basic formulation), its ro-
bustness (uses simple soil and stand data in order to pro-
duce model runs for many years, avoiding hydraulic param-
eters that are difficult to measure and scale up), its ability
to quantify drought intensity and duration in forest stands,
and for its successful past validation in various forest stands
and climatic conditions, including tropical basins (Ruiz et al.,
2010).
This paper aims to explain and model the hydrological
behaviour of a coffee AF micro-basin in Costa Rica, as-
sessing its infiltration capacity on andisols. The method-
ology consists of experimentation to assess the main water
fluxes and modelling to reproduce the behaviour of the basin.
First, we present the study site and the experimental design.
Second, we develop a new lumped eco-hydrological model
with balanced ecophysiological/hydrological modules (that
we called Hydro-SVAT model). This model was tailored to
the main hydrological processes that we recorded (stream-
flow, evapotranspiration, water content in the non-saturated
zone and water table level) and that are described in the sub-
sequent sections. Third, we propose a multi-variable calibra-
tion/validation strategy for the Hydro-SVAT model so we cal-
ibrate using the streamflow in 2009 and validate using the re-
maining three variables in 2009 and the streamflow in 2010.
Fourth, we make an uncertainty analysis to produce a confi-
dence interval around our modelled streamflow values, and
a sensitivity analysis to assess from which parameters this
uncertainty might come. Fifth, we attempt to simplify the
initially proposed model based on the results of the sensitiv-
ity analysis. Finally, we discuss the main findings concerning
the water balance in our experimental basin.
2 The study site
2.1 Location, soil and climate
The area of interest is located in Reventazon river basin, in
the Central-Caribbean region of Costa Rica (Fig. 1a and b).
It lies on the slope of the Turrialba volcano (central vol-
canic mountain range of the country) and drains to the
Caribbean Sea. The Aquiares coffee farm is one of the
largest in Costa Rica (6.6 km2), “Rainforest AllianceTM”
certified, 15 km from CATIE (Centro Agronomico Tropical
de Investigacion y Ensenanza). Within the Aquiares farm,
we selected the Mejıas creek micro-basin (Fig. 1c) for the
“Coffee-Flux” experiment. The basin is placed between the
www.hydrol-earth-syst-sci.net/15/369/2011/ Hydrol. Earth Syst. Sci., 15, 369–392, 2011
Fig. 1. Location of study site. Source: Gómez-Delgado et al. 2011
References Gómez-Delgado, F.., et al. 2011. Modelling the
hydrological behaviour of a coffee agroforestry basin in
Costa Rica. Hydrology and Earth System Sciences 15(1):
369-392.
Sánchez-Murillo, R., et al. 2014. Spatial and temporal
variation of stable isotopes in precipitation across Costa
Rica: An analysis of historic GNIP records. Open Journal of
Modern Hydrology 3: 226-240.
Fig. 9. Hourly values of δ18O (‰) measured in stream water (11/26/12 – 12/1/12), compared with stream flow and precipitation levels. Both δ18O and δD are positively correlated with precipitation amount (p=0.02 and 0.00005).
Discussion
• For monthly averaged samples (Figs 4 and 5),
dry season precipitation is enriched with respect
to the rainy season, but at an hourly or daily
basis other trends are evident, such as
correlation with meteorological measurements
and stream response to precipitation events (Fig
9).
• Air temperature, wind speed, and wind direction
are all significantly correlated with δ18O and δD
values in precipitation, which could be due to
seasonal differences in air mass sources.
• Examining stream water δ18O and δD values at a
finer scale (Fig. 9) shows how streams respond
to precipitation, as the stream becomes more
enriched with higher amounts of rain due to
source contribution of groundwater and overland
flow from spring and roads (Figs. 10 and 11).
• Stable isotopes are useful for examining
seasonality and watershed dynamics when
examined at different temporal scales. For
example, measuring isotopes in stream samples
during storm events assists in our understanding
of when storm flow reaches streams.
• Further work will include partitioning stream
water sources and quantifying spring contribution
to storm flow.
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δ18O in groundwater WTL-1 WTL-2 WTL-4 WTL-5
Fig. 7. δ18O values in groundwater (weekly samples) at four groundwater wells: WTL-1 and WTL-2 (low elevations), WTL-4 (mid-elevation), and WTL-5 (high elevation).
*Contact: Kristen Welsh [email protected]
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δ18O in stream water Flume
Lower
Middle
Upper
Fig. 8. δ18O values in stream water (weekly samples) at four locations in the watershed. Values generally become enriched in δ18O from upper elevations to lower elevations.
Fig 2. The land cover at the
site is dominated by Coffea
arabica coffee plants
interspersed with Erythrina
poeppigiana shade trees.
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δ18O, streamflow, and precipitation (hourly) O18
Fig 3. The study site is
part of the global
network of FLUXNET
micrometeorological
sites.
Storm flow Low flow
Fig. 11. Low flow conditions compared with storm flow conditions at the flume. Fig. 10. Intermittent springs (a) and road and path runoff (b) at the
site contribute significantly to storm flow.
a b