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\begin{document}

\title{The Importance of Continental Evaporation for Precipitation in Colombia:
A Baseline Combining Observations from Stable Isotopes and Modeling
Moisture Trajectories}



\author[1]{Maritza Escobar}%
\author[2]{Isabel Hoyos}%
\author[1]{Isabel Hoyos}%
\author[1]{Juan Camilo Villegas}%
\affil[1]{Universidad de Antioquia}%
\affil[2]{Universidad del Quindío}%


\vspace{-1em}



  
  \date{March 13, 2025}


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\selectlanguage{english}
\begin{abstract}
The hydroclimatology of Northern South America responds to
strongly-coupled dynamics of oceanic and terrestrial surface-atmosphere
exchange, as moisture evaporated from these sources interact to produce
continental rainfall. However, the relative contributions of these two
source types through the annual cycle have been described only in
modeling studies, with no observational tools used to corroborate these
predictions. The use of isotopic techniques to study moisture sources
has been common in assessing changes in the water cycle and in climate
dynamics, as isotopes allow tracking the connection between evaporation,
transpiration, and precipitation, as well as the influence of large
scale hydroclimatic phenomena, such as the seasonal Inter Tropical
Convergence Zone migration. We characterize the isotopic composition of
moisture sources becoming precipitation in the Andes and Caribbean
regions of Colombia, using stable isotopes data (\selectlanguage{greek}δ\selectlanguage{english}18O, \selectlanguage{greek}δ\selectlanguage{english}2H) from the
Global Network of Isotopes in Precipitation (1971-2016) and contrasting
it with moisture trajectory tracking from the FLEXPART model, using
input from ERA-Interim reanalysis to compute the relative contribution
of oceanic and terrestrial sources through the annual cycle. Our results
indicate that most precipitation in the region comes from terrestrial
sources including recycling (\textgreater{}30 \% for all months),
Orinoco (up to 28 \% monthly for April), and the northern Amazon (up to
17 \% monthly for June, July, and August); followed by oceanic sources
including the Tropical South Pacific (up to 30 \% monthly in October,
November, December) and Tropical North Atlantic (up to 30 \% monthly for
January). These outcomes highlight the utility of combining stable
isotopes in precipitation and modeling techniques to discriminate
terrestrial and oceanic sources of precipitation. Further, our results
highlight the need to assess the hydrological consequences of land cover
change in South America, particularly in a country like Colombia where
water, food and energy security all depend directly on precipitation. .%
\end{abstract}%



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\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

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\textbf{1. INTRODUCTION}

The hydrological cycle explains water distribution and its availability
across the globe. Processes such as evaporation, transpiration, and
precipitation connect the terrestrial and atmospheric components of the
hydrological cycle through water and energy exchange. Atmospheric
circulation allows regional-to-global water redistribution, establishing
teleconnections between remote areas. These teleconnections are vital
for the sustainability of ecosystems and biodiversity as well as for
water security, and socioeconomic development (Wagener et al., 2010;
Martinez \& Dominguez, 2014; Swann, Longo, Knox, Lee, \& Moorcroft,
2015; Molina, Salazar, Mart\selectlanguage{ngerman}ínez, Villegas, \& Arias, 2019).~
Understanding moisture origin and the mechanisms driving atmospheric
transport is key for defining the interdependence of a territory with
its surroundings, and also for the definition of proper spatio-temporal
dynamics that determine regional atmospheric processes.

The conventional methods to identify moisture source regions, and to
estimate the proportion of incoming atmospheric moisture associated with
each source include i) analytical or box models, ii) numerical water
vapor tracers, and iii) physical water vapor tracers (Durán-Quesada,
Gimeno, Amador, \& Nieto, 2010). The first two are
theoretical-computational models based on the Eulerian and Lagrangian
notion of trajectory, respectively. These models often use input
information from gauge stations and reanalysis data (Wang et al., 2004;
Fuka et al., 2014; Liu et al., 2020). The third method is based on
physical water vapor tracers in the isotopic composition of
precipitation. The interpretation of moisture tracers is useful to infer
the sources and the processes inducing fractionation in water isotopic
composition throughout the movement of air masses (Simpson \& Herczeg,
1991; Martinelli, Victoria, Sternberg, Ribeiro, \& Moreira, 1996; Clark
\& Fritz, 1999). More specifically, the isotopic composition of
rainwater allows the interpretation of prevailing meteorological
conditions in the formation of air masses (temperature, humidity, wind
speed) and the origin of moisture (evaporation and/or transpiration)
(Gat \& Carmi, 1970; Clark \& Fritz, 1999; Duran-Quesada et al., 2010;
Gimeno, Drumond, Nieto,~Trigo, \& Stohl, 2010; Van der Ent, Savenije,
Schaefli, \& Steele-Dunne, 2010; Gimeno et al., 2012). The study of
moisture sources through stable isotopes has been widely used in
understanding long-term changes in the water cycle and the dynamics of
climate (Gat \& Carmi, 1970; Salati, 1979; Gat \& Gonfiantini, 1981;
Clark \& Fritz, 1999; Aggarwal, Froehlich, \& Gat, 2005; Van der Ent et
al., 2010; Gimeno et al., 2012; Négrel, Petelet-Giraud, \& Millot, 2016;
Sánchez-Murillo et al., 2016; Alexandre, 2020). However, the lack of
isotopic data in many locations around the world is still a disadvantage
for proper long-term analyses (Benjamin et al., 2005).

A current challenge for land and water resource managers is the
definition and understanding of the potential implications of
environmental change on the availability of water resources (Newman et
al., 2006; Gain et al., 2020; Rivadeneira et al., 2020). This challenge
has been generally addressed with local-scale management plans and
strategies. However, in a region like Northern South America
(particularly in Colombia), water, food and energy security (which
largely supports the country's economy) depend, almost exclusively, on
surface water, which in turn is associated with short-term rainfall
generation processes (Álvarez-Villa, Vélez, \& Poveda, 2011; Díaz,
Saurral, \& Vera, 2020; Mesa, Urrea, \& Ochoa, 2021). In addition,
Colombia is one of the most biodiverse regions in the world (Myers,
Mittermeier, Mittermeier, Da Fonseca, \& Kent, 2000; Churchill, 2009;
Bruijnzeel, Scatena, \& Hamilton, 2011; Herzog \& Kattan, 2011;
Ehrendorfer, 2013; Hutter, Lambert, \& Wiens, 2017; Hoorn, Perrigo, \&
Antonelli, 2018; Bax \& Francesconi, 2019). The connection with the
Pacific and Atlantic oceans, the Amazon-Andes interactions, and the
orographic barrier of the regional Andes are the major drivers of
atmospheric circulation and ecological diversity in the country
(Sakamoto, 2011; Hoyos, 2017; Poveda, Jaramillo, \& Vallejo, 2014;
Espinoza et al., 2020). For biodiversity conservation, ecosystem health,
and socioeconomic development, the availability of water is an important
basis that depends on hydrologic functioning (Pringle, 2001). Therefore,
understanding the origin and dynamics of moisture that becomes rainfall
in the two most populated regions of the country is fundamental to
maintaining water security. Yet, these analyses have only been performed
with models that, to date, have not been contrasted with actual field
measurements such as those from environmental tracers.

In this study, we explore the hydroclimatic features underlying the
composition of Colombian atmospheric moisture by establishing the
isotopic baseline for the regional precipitation on a seasonal time
scale. We include data from 33 stations distributed along the
inter-Andean mountain region and the Caribbean region in Colombia,
available in the Global Network of Isotopes in Precipitation (GNIP)
project. We analyze the monthly variation of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H values, and the spatio-temporal reconstruction of
D-excess during 1971-2016. Comparing the Local Meteoric Water Line
(LMWL) with the Global Meteoric Water Line (GMWL) provides criteria of
depletion or enrichment of hydrogen and oxygen isotopic composition of
precipitation that, in turn, allow the identification of the oceanic or
terrestrial origin of air-water masses that effectively precipitates
over the target area. We use the results from the Lagrangian FLEXPART
model to contrast the information inferred from isotopic composition
with the regional moisture contributions structure based on the air
masses trajectories, providing a more comprehensive understanding of
moisture sources to the country and the potential implications of
alterations in these dynamics associated with land use and vegetation
cover change.

\textbf{2. DATA AND METHODS}

\textbf{2.1} \textbf{Study area}

This research focuses on atmospheric moisture transport over central and
northern Colombia. The target region (Fig. 1) corresponds to the largest
hydrological system in the country, draining the Andes Mountain chain
towards the Caribbean Sea and encompasses two regions with marked
orographic differences: the Magdalena-Cauca River basin (Andean), and
the Caribbean regions. Due to their location, the oceanic influences in
the regional climate are characterized by the transport of moisture from
the Tropical and Subtropical Atlantic and Pacific Oceans, and
terrestrial sources from the Orinoco and Amazon basins, as well as local
recycling (Hoyos, 2017; Hoyos and Rodriguez, 2020).

{[}Insert Figure 1{]}

The Magdalena-Cauca River basin system is the major Andean catchment in
the country. It is characterized by a complex orography defined by the
division of the Andes into three branches, i) the western branch crosses
Colombia from south to north with approximately 1200 km in length,
parallel to the pacific coast, ii) the central branch between the Cauca
and Magdalena River valleys and iii) the eastern branch with an
extension of approximately 1500 km and heights up to 4500 meters above
sea level, towards the Amazon and Orinoco basins. The Magdalena River
flows in a Valley between the Eastern and central branches, and the
Cauca flows between the Central and Western branches. Their confluence
occurs at the lowlands that mark the transition to the Caribbean region.
The interaction between the large-scale circulation systems, the trade
winds, and the orographic systems results in differentiated climatic
regions over the territory.

The rainiest areas generally occur on the eastern slopes of the eastern
cordillera and the western slopes of the western cordillera, while
rainfall is lower (and more variable) in the inter-Andean valleys (Snow,
1975; Hoyos, Baquero-Bernal, \& Hagemann, 2013). Likewise, the Amazon
region exports water vapor to the Andean and inter-Andean zones
generating high rainfall intensity, due to the ascent of trade winds and
the orographic effect of the Andes (Espinoza et al., 2020). The
distribution of precipitation in this region has significant spatial and
temporal variability (Poveda, Waylen, \& Pulwarty, 2006; Hoyos et al.,
2013;~Poveda et al., 2014; Espinoza et al., 2020). The most influential
seasonal event in the region is the migration of ITCZ that determines
the annual distribution of rainfall, characterized by a bimodal regime
with two wet seasons between March to May and September to November
(Giannini, Kushnir, \& Cane, 2000; Alvarez-Villa et al., 2011; Espinoza
et al., 2020) and its interaction with the Atlantic and the Pacific
Ocean, the moisture contribution from the Caribbean Sea, and the Amazon
and Orinoco basins (Hoyos, 2017).

The Caribbean region, located in the northernmost portion of Colombia
(and South America), is low and flat in the north (except for the
occurrence of the Sierra Nevada de Santa Marta complex, a coastline
isolated mountain with a height of 5.7 km that influences local
atmospheric circulation in the North), in contrast with the southern
part of this zone, which is framed by the foothills of the Andes
Mountain range. The annual cycle of rainfall in this zone has a
different pattern from the Andes: a dry period from November to April
and a rainy period between May to October, explained by the ITCZ
migration, the occurrence of synoptic disturbances associated with the
Tropical Easterly Waves (TEWs), and the Caribbean Low-Level Jet (Poveda
et al., 2006; Arias, Mart\selectlanguage{ngerman}ínez, \& Vieira, 2015; Cárdenas, Arias, \&
Vieira, 2017).

\textbf{2.2. Regional moisture contributions from FLEXPART}

We set the regional sources of atmospheric moisture (Fig. 2), based on
Hoyos et al. (2018), as follows: Tropical North Pacific (TNP), Tropical
South Pacific (TSP), Subtropical North Atlantic (STNA), Tropical North
Atlantic (TNA), Tropical Atlantic (TA), Tropical South Atlantic (TSA),
Caribbean Sea (CARS), Northern South America (NOSA), Orinoco Basin
(ORIC), Northern Amazon Basin (NAMZ), and Southern Amazon Basin (SAMZ).
In Hoyos et al. (2018), these zones were determined by the best
agreement among the Dynamical Recycling Model (DRM), the
Quasi-Isentropic Model (QIBT), and the Flexible Particle Dispersion
Model (FLEXPART).

{[}Insert Figure 2{]}

The FLEXPART is a 3D Lagrangian dispersion model that accounts for the
net loss (or gain) of specific humidity, \emph{q,} along with a large
number of backward/forward trajectories between source and target areas
(Stohl \& James, 2005, 2004; Gimeno et al., 2012; Sodeman et al., 2015).
The moisture contribution from a source area is estimated by accounting
for the vertically-integrated long-term balance of precipitation P and
evaporation E along the trajectories for a large number of computational
particles (2 million). Net E \selectlanguage{english}- P \textless{} 0 represents a loss of
moisture (precipitation exceeds evaporation), and E \selectlanguage{english}- P \textgreater{} 0
represents a gain of moisture (evaporation exceeds precipitation) (Stohl
\& James, 2005, 2004). The diagnostic precipitation (P) corresponds to
the climatological sum of \textbar{}E - P \textless{} 0\textbar{} over
the entire period.

Here, we estimate the composition of moisture contributions using the
experiment developed by Hoyos et al., (2018). However, we calculate the
seasonal values considering the optimal transport day (that is, the day
when the moisture transference is maximum) instead of the 10-day global
mean lifetime of water vapor in the atmosphere (Numaguti, 1999; Gimeno,
Nieto,~Drumond,~Castillo, \& Trigo, 2013; Van der Ent \& Tuinenburg,
2017; Gimeno et al., 2021). This experimental design accounts for the
fact that the time scale at which the regional atmospheric moisture is
exchanged depends on the dynamic relationship between source and target
regions (including not only distance but also the intensity of the
advective processes and mechanisms that cause precipitation). We expect
that, as the integration time of the moisture trajectories increases,
the contribution of each source region correspondingly increases until
reaching the maximum contribution value, after which moisture
contribution shows a decreasing or asymptotic behavior (Hoyos et al.,
2017; Nieto \& Gimeno, 2019). Setting integration times at the optimum
allows us to avoid over (or under) estimations in the relative
contributions of moisture sources. The period simulated 1980-2012,
includes the availability period of rainwater isotope records.

\textbf{2.3. Physical Water Vapor Tracers}

We use a combination of isotopic indices derived from local and global
meteoric water lines to confirm moisture sources to the study region.

2.3.1 Local Meteoric Water Line

The Global Meteoric Water Line (GMWL) {[}Craig (1961){]} provides the
relationship between the global isotopic contents of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H, referred to the Vienna
Standard Mean Ocean Water (VSMOV), as:

\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H= 8\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O+10%0 VSMOV. (1)

At the local scale, this relationship is represented by the Local
Meteoric Water Line (LMWL) which can be used to determine how (i) the
spatial variability of the linear statistical relationship between
isotopic ratios, and (ii) the variation in slope that provides
information about the seasonal climatology of a particular site
(Rozanski, Aragu\selectlanguage{ngerman}ás-Araguás, \& Gonfiantini, 1993; Putman, Fiorella,
Bowen, \& Cai, 2019). Therefore, the extent (and direction) of the
deviation of the LMWL from the GMWL is an indicator of the thermodynamic
state of vapor formation processes, as well as the
strengthening/weakening of processes or mechanisms involved in the
atmospheric vapor transport. We constructed the LMWL for the study area,
based on the linear regression of the monthly average of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H isotope composition for
the period between 1971 and 2016. Data of isotope composition of local
precipitation were obtained from the Global Network of Isotopes in
Precipitation (GNIP) project (IAEA, 2020).~ We included up to as many as
33 sampling points (shown in Fig 1.)

The deviation of LMWL from GMWL explains the depletion/enrichment of
isotopic composition of precipitation concerning the GMWL and how it can
be interpreted in terms of processes and water vapor formation
conditions that air masses suffer in their evolution before becoming
precipitation. This information is useful to infer the terrestrial or
oceanic origin of water vapor. Pairs of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H in the LMWL result from the interaction of
fractionation generated in the advance into the continent of moisture
flow and meteorological conditions at the site (Dansgaard,1964). Since
the GMWL is used as the `expected' equilibrium relationship (Putman et
al., 2019), the LWML is often evaluated in the context of its deviation
from the GMWL, in terms of the variability of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H pairs. Pairs of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H in the LMWL bring information of the history of
fractionation of water in the air mass due to phase changes such as
evaporation, condensation, and transpiration, as well as variations due
to meridional and altitudinal changes that are arising from the
progressive rainout of heavy isotopes during the evolution of a
precipitating air mass (Rayleigh distillation, Gat, 1996). Depletion or
enrichment of isotopes in precipitation is expressed by the deviation
from the VSMOW. In general, expected values from terrestrial regions
exhibit depletion in isotopic composition due to Rayleigh distillation,
and expected oceanic values from cold sources (for instance the Pacific
Ocean or the South Atlantic Ocean for the study area) are more depleted
than the isotopic composition originated from warmer sources (for
example the Tropical Atlantic).

We compiled the main findings from (Putman et al., 2019) and (Clark \&
Fritz, 2013) in order to produce a conceptual scheme for the
interpretation of moisture origin based on the comparison between the
relative position of isotopic observations in precipitation and the GMLW
(Fig. 3). When \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H pairs
are located in the upper-right (lower-left) portion of the line (Fig.
3), indicates that moisture was originated in warmer (colder) regions,
low (high) altitude, low (high) latitude, and coastal (terrestrial)
zones (Clark \& Fritz, 2013). Similarly, terrestrial (and water) sources
in this scheme are located above (below) the GMWL (Putman et al., 2019).

{[}Insert Figure 3{]}

2.3.2 D-excess

Deuterium excess (hereafter D-excess, \emph{d} ) is understood as a
measure of the deviation of the LMWL from the GMWL. D-excess is also a
key parameter to infer the origin of atmospheric vapor regarding the
evaporation conditions in the region source of precipitation (Petit,
White, Young, Jouzel, \& Korotkevich, 1991; Froehlich, Gibson, \&
Aggarwal, 2002; Froehlich et al., 2008). D-excess follows the
relationship:

\emph{d} = \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H - 8\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O. (2)

Higher values of \emph{d} refer to moisture originated under conditions
of lower relative humidity, where the kinetic fractionation is more
pronounced. Lower values of \emph{d} correspond to~air masses where the
fractionation was close to equilibrium (Clark \& Fritz, 1999). However,
some secondary processes can change the initial value. For
instance,\emph{d} is an indicator of moisture recycling as evaporation
from the land surface to the atmosphere increases its value (Froehlich
et al., 2002; Froehlich et al., 2008). Spatial reconstruction of
\emph{d} values was developed through coKriging interpolation, using
precipitation and temperature as auxiliary variables for improving the
estimations over areas where there is no sampling data. The spatial
resolution of D-excess value in the study zone is 1\selectlanguage{ngerman}°x1° degree.

\textbf{3. RESULTS}

\textbf{3.1. Oceanic and continental contributions to precipitation}

Our results show that the spatial and temporal scales at which each
source region influences precipitation in the study area vary and,
importantly, vary from the mean 10-day residence time commonly used as
integration time (Table 1). For instance, while the oceanic contribution
from the Pacific reaches the Andean region in 2 to 7 days (TSP and TNP),
moisture originated in the Atlantic Ocean takes longer than 10 days to
become precipitation (TA, TSA, STNA). Atlantic sources have a time scale
for the optimal transport day in the 10-day global mean lifetime of
water vapor, except for CARS (that is between 4 and 6 days for the
Andean region and between 3 and 4 for the Caribbean region). Transport
time scales for Pacific sources have differentiated features. TNP spends
2 days reaching the optimum transport time while TSP varies from 2 to 7
days for the Andean region, and from 8 to 9 for the Caribbean region.
ORIC is the second largest terrestrial source with a time scale of
moisture transport of around 4 days. The time transport from NAMZ to the
Andean region is between 6 to 7 days and to the Caribbean region is 8
days. As expected, the largest spatial extents correspond to more
distant source regions while the closest source regions have the lowest
spatial extents and integration times. (Appendix A includes detailed
information about climatological monthly diagnostic precipitation for
different integration times).

{[}Insert Table 1{]}

We estimated the effective amount of precipitation from each source
based on the percentage of diagnostic precipitation accounted for
FLEXPART. Observational data from 1506 gauge stations along the entire
region provided by the IDEAM (Colombia's National hydroclimatic agency)
were included in order to regionally validate the model results (Fig.
4). In general, the annual mean moisture contribution from the Atlantic
(Pacific) ocean is larger in the Caribbean (Andean) region than for the
Andean (Caribbean) region (Figure 5 and 6). The main atmospheric
Atlantic sources for both target regions are TNA, TA, and CARS (Fig 5a,
5d). In the Andean region, TSP contributions are larger than TNP (Fig.
5b), while both Pacific sources have similar contributions for the
Caribbean region (Fig. 5e). Terrestrial recycling accounts for more than
50\% of the total (59.40\% to the Andean and 56.74\% to the Caribbean;
Fig. 5c, 5f respectively and Fig. 6). The local atmospheric moisture
from NOSA reaches the maximum contribution in 1 day. Overall, NOSA is
the largest contributor among the terrestrial and oceanic sources, with
an annual contribution of 37.98\% (32.10\%) for the Caribbean (the
Andean) region. For the Caribbean (Andean) region, the atmospheric
moisture from ORIC is comparable to the total contribution from the
Pacific (Atlantic) Ocean. For both target areas, the contribution from
the TNA (TSP), is the largest Atlantic (Pacific) source. As expected,
moisture coming from the Amazon basin is higher for northern Amazon
(NAMZ), and the contribution is greater in the Andean compared to the
Caribbean region (Fig. 5c, 5f), but its relative importance is, on
average, smaller than the other two terrestrial sources.

{[}Insert Figure 4{]}

{[}Insert Figure 5{]}

Moisture contributions to both regions have a marked seasonal behavior,
with the relative contribution of oceanic and terrestrial sources
varying through the year. For all months, terrestrial sources dominate
moisture contributions in both regions (Fig. 6). The predominant oceanic
moisture source for the first season of the year (JFM) is the Atlantic
Ocean, with total monthly contributions greater than 26\% for the Andean
region, and greater than 41\% for the Caribbean region (Fig. 6). More
specifically,~the TNA region has the largest contributions, with more
than 17\% for the Andean region (Fig. 5a) and more than 27\% per month
for the Caribbean region (Fig. 5d). In the Andean region, the Pacific
Ocean is the most predominant oceanic moisture source From May to
December, with the contributions are over 36\% for the months SOND (Fig.
6a). Particularly the TSP is the major moisture contributor to the
Andean region exceeding 33\% per month in November (Fig. 5b). Between
May and October, the Caribbean region receives moisture predominantly
from the Pacific Ocean (Fig. 6b), intensifying these contributions in
October -- November as the ITCZ stays in the northern hemisphere over
the Atlantic and eastern Pacific.

{[}Insert Figure 6{]}

\textbf{3.2. Local Meteoric Water Line and hydroclimatic mechanisms~}

The resulting LMWLs (Fig. 7) follow the expected trends based on
depletion or enrichment of isotopic composition of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H from theoretical
considerations such as i) the orographic distillation that generates
depletion of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H values due
to fractionation generated in the advance of moisture flow into the
continent~ (Dansgaard, 1964; Rozanski et al., 1993; Clark \& Fritz,
1999;~ Mook, 2002; Aggarwal et al., 2005) ii) the proximity to
atmospheric moisture sources (Guan, Zhang, Skrzypek, Sun, \& Xu, 2013);
iii) the thermodynamic conditions of sources; iv) the mixing ratio of
sources (Rindsberger, Magaritz, Carmi, \& Gilad, 1983; Rindsberger,
Jaffe, Rahamim, \& Gat, 1990), and v) the amount effect that generates
depletion in hydrogen and oxygen isotopes with the increase in monthly
and annual precipitation of different places and with the intensity of
the storms (Dansgaard, 1964).

{[}Insert Figure 7{]}

As in our Flexpart experiment, isotopic signals from the LMWLs also
highlight the seasonal behavior of moisture sources for the two regions.
More specifically, from December through February (Fig. 7a, 7b, 7c), the
Andean region exhibits a variation range of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H
(\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O) that oscillates between -20 and -60 ( -5 and
-9) %0 VSMOW, and the Caribbean also varies with the highest values of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H oscillating between 0 and -20 %0 VSMOW, and for
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O between 0 and -5 %0 VSMOW. In the Andean region
these values represent the depletion of the heavy isotopes from coastal
regions towards the continental interior (orographic distillation),
while in the Caribbean, more depleted values represent the first
condensate from marine moisture, indicative of meteorological conditions
(such as relative humidity (72\%-79\%) and sea surface temperature
(25-28\selectlanguage{ngerman}°\selectlanguage{russian}С)) from warm sources (Yurtsevert,1981; Rozanski et al. 1993),
indicating the prevalence of oceanic sources (from the Atlantic ocean as
indicated in our modeling results in Fig. 5). More specifically, the
values of Oxygen and Hydrogen isotopes for both regions are similar to
those proposed by Bowen (2003) for the TNA zone, and the FLEXPART
results indicate that TNA was the most active source in this month for
the study area (\textgreater{}27\%, Fig. 5).

For the MAM season (Fig. 7d, 7e, 7f) in the Andean region, March
represents the highest enrichment for this season, and April- May also
exhibit an oscillation range for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H between 0 and
-120 %0 VSMOW, and for~\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O between 0 and -20 %0 VSMOW.
In the Caribbean region, March exhibits similar variations to DJF, while
April-May observations vary along the LMWL with oscillations for
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H between 0 and -120 %0 VSMOW, and for
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O between 0 and -20 %0 VSMOW. The main regional
terrestrial contribution for MAM corresponds to ORIC and NOSA sources
(Fig. 5). For both regions, the footprint of these terrestrial
contributions can be seen in the local LMWL located above the GMWL
(Interpretation of Fig. 7, following guidelines on Fig. 3). Moisture
from ORIC is located in the lower part of the figure, indicating its
warm origin, and moisture from NOSA along the LMWL indicating sources
with different temperatures. The large variation for both regions in
April-May (Fig. 7d, Fig. 7e) responds to the mixing ratio effect. This
spread in the isotopic composition of rainwater is produced by the
mixing of different air masses that precipitate over Colombia. During
this season, the study area is characterized by a complex combination of
terrestrial and oceanic sources of moisture that contribute in different
relative amounts to regional precipitation, associated with the dynamics
of the ITCZ (Tables 2 and 3).

{[}Insert Table 2{]}

{[}Insert Table 3{]}

In JJA (Fig. 7g, 7h, 7i) the range of variations for
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H in both regions oscillates between -20 and -80 %0
VSMOW, and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O between -3 and -12 %0 VSMOW. Especially
for the months of June-July (Fig. 7g, 7h), observations are located in
the middle of the variation range, coinciding with a dry period, and the
contributions are a combination of terrestrial and oceanic moisture
sources. For the Caribbean region, although not predominant, Atlantic
sources are important (Fig. 6b), as indicated by enrichment observations
of the LMWL. Similarly, terrestrial sources make their contributions
from ORIC and NOSA (Fig. 5f), explaining the combination of terrestrial
and oceanic sources. This is similar for the Andean region, which
receives more contributions from terrestrial sources, followed by
contributions from the Pacific Ocean (Fig. 6a). Particularly, for the
Andean region the major contributions of terrestrial sources from NAMZ
occurred during June (\textgreater{}17\%), and coincide with
observations localized above of the GMWL and the lower part of LMWL due
to the minor temperature of the NAMZ source.

In the SON season (Fig. 7j, 7k, 7l), the isotopic composition of
precipitation shows more depleted values compared to the rest of the
year, with a variation range for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H
(\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O) between 0 and -120 (-2%0 and -15) %0 VSMOW. In
particular, October and November exhibit more depleted values,
coinciding with an increment of the contribution from the cold Pacific
Ocean (Fig. 6a). Also, this season corresponds to one of the two rainy
seasons of the year, generated by the seasonal migration of the ITCZ,
producing an increase of monthly precipitation and intensity of the
storms, causing depletion in hydrogen and oxygen (amount effect).
Likewise, the Caribbean zone receives predominant moisture sources from
the Pacific Ocean, intensifying these contributions in October --
November due to the ITCZ staying in the northern hemisphere over the
Atlantic and eastern Pacific.

\textbf{3.3. D-excess analysis}

Spatial variations of reconstructed D-excess are explained by
theoretical considerations such as the effects of (i) distance or
proximity to main moisture sources to the study areas (Guan et al.,
2013); (ii) influence of oceanic or terrestrial sources (Aemisegger et
al., 2014); (iii) depletion of the heavy isotopes from coastal regions
toward the continental interiors (Gat \& Matsui,1991; Rozanski et al.,
1993). Particularly, for the Andean region, the effects of oceanic or
terrestrial sources and the depletion of heavy isotopes with distance to
coastal regions are most evident (Upper left inserts in Fig. 7). This
can be explained by the predominance of terrestrial sources depleted in
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H and the moisture
transport route (e.g. from the Atlantic Ocean is larger than the
Caribbean region causing major fractionations). For the Caribbean
region, the proximity effect of main moisture sources to the target
region is the most visible, given that, for this region, D-excess
reconstruction shows predominant values close to the Caribbean Sea and
the Atlantic Ocean, which agrees with previous reports from Terzer et
al., (2021).

The seasonal variation of D-excess ranged from 7 to 15 %0 VSMOW, reaching
its maximum in December/January (Fig. 7a, Fig 7b) and its minimum in
June/July (Fig. 7g, Fig 7h). These changes are explained by air-sea
conditions at distinct water vapor sources with specific D-excess values
(Gat \& Gonfiantini, 1981) and modified by the mixing of inland water
vapor from evapotranspiration with the air-mass (Gat, Joel, Bowser, \&
Kendall, 1994) and by secondary evaporation (Frits, Drimmie, Frape, \&
Oshea, 1987). In continental areas, the D-excess value is strongly
controlled by temperature effects (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O temperature
coefficient increases to about 0.5 %0/\selectlanguage{ngerman}°C) (Frits et al., 1987). This
effect is evident in our interpolation with lower D-excess in the
Caribbean region, and with higher temperatures and higher D-excess
compared to the Andean region. In addition to the temperature effect,
and considering the Rayleigh distillation phenomenon,
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O values become lower when water vapor rains out at
lower temperatures than oceanic sources (Dansgaard, 1964). There is an
inverse relationship between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and D-excess. As
expected, our data in the Andean region shows this pattern, as its
predominant moisture source is terrestrial, and is located away from the
ocean. In the Andean region, the months that showed the highest values
of D-excess correspond to the season with the low values of rainfall,
with December and January being the months with the highest values of
D-excess (Fig. 7a, Fig 7b), reaching up to 15%0 VSMOW. D-excess reduces
in June and July (Fig. 7g, Fig 7h), due to the effect of secondary
evaporation which can be especially large with small amounts of
precipitation during hot dry months. April and May (the rainy season)
report the lowest concentrations of deuterium excess, with a range
variation between 8%0 VSMOW and 10%0 VSMOW.

\textbf{4. DISCUSSION}

Our results illustrate how physical water vapor tracers, such as stable
isotopes in precipitation, allow tracking water sources in atmospheric
circulation as they connect evaporation and precipitation, and their
concentration indicates the meteorological conditions and the proximity
to the main atmospheric moisture sources that originate precipitation,
as expected. Although the use of stable isotopes in precipitation does
not provide quantitative information about the amount of atmospheric
moisture from the sources, it complements the information generated by
models, such as in our FLEXPART experiment.~ More generally, this
information has the potential to be integrated into atmospheric models
to diagnose the percentage of moisture originated from the different
sources (Wang et al., 2004; Fuka et al., 2014; Arias et al., 2015; Hoyos
et al., 2018; Molina et al., 2019; Liu et al., 2020).~ Further, the
combined use of physical tracers and models are particularly useful in
regions with complex topographical and meteorological setups where model
results can be highly uncertain.

The consideration of the optimal transport day (when the moisture
transference is maximum), instead of the canonical 10-day mean lifetime
of water vapor in the atmosphere, allowed us to improve the
quantification of contributions from atmospheric moisture sources,
highlighting the predominance of terrestrial sources all through the
year. The natural scales of distance and time are closely linked to the
regional structure of moisture source composition. Therefore,
assumptions about uniformity or similarity to a global mean need to be
revisited for each region, after consideration of the particular
climatic and meteorological features. Conversely, the proper transport
scales are footprints for each particular target area and include
regional features like the mechanisms involved in the atmospheric
moisture transport, orographic features, and distance between the target
and source regions. The lack of knowledge of these scales could lead to
over (or under) estimations of the degree of contribution from different
sources and reduce the forecasting power of teleconnections.

Physical water vapor tracers help identify signals of the origin of
incoming atmospheric moisture when the source is not influenced by
mixing effects associated with different oceanic and terrestrial sources
present in the majority of the seasonal analysis. Our results allowed us
to identify oceanic sources connected with the seasonality migration of
ITCZ, as well as terrestrial sources such as Amazon and Orinoco basins,
that agree with meteorological criteria. Both of these observations are
evident in the reconstruction of the D-excess, even when the spatial and
temporal coverage of isotopic data is very low for Colombia. Although we
have reconstructed a baseline for the isotopic structure of
precipitation, it is also evident that a better sampling network is
necessary to improve the monitoring network of stable isotopes to
produce a more detailed analysis of the moisture transport processes, as
they have done in more instrumented areas (Friedman, Smith, Gleason,
Warden, \& Harris, 1992; Kendall \& Coplen, 2001; Bowen, Ehleringer,
Chesson, Stange, \& Cerling, 2007;~Bowen, Kennedy, Liu, \& Stalker,
2011).

Our results showed a multiannual moisture convergence average from
terrestrial sources of 59\% for the Andean region and for the Caribbean
region of 56\% (Figure 6), which is greater than previous estimations by
our group (38\% in Hoyos et al., 2018). By using a combination of
physical tracers and modeling, we confirm previous modeling results
(Hoyos et al., 2018) that indicate how, in the study area, the
contribution of terrestrial moisture sources to local precipitation is
significant (always greater than 44\%), such that most ecosystems and
water security for society and the economy may depend on the stability
of major regional ecosystems such as the Orinoco plains (8\% - 28\% per
month) and the Northern Amazon (17\% contribution). More importantly,
our results highlight that most terrestrial moisture originates within
the same region (NOSA), with contributions larger than 23\% per month in
some seasons and up to 40\% per month in other seasons.

The fact that a significant proportion of rainfall comes from recycling
(Fig. 6), highlights how precipitation and, more generally, water
availability in the Andean and Caribbean regions of Colombia could
potentially be altered by changes in vegetation and land cover, directly
affecting transpiration and atmospheric circulation. This is a clear
indication that the region is particularly vulnerable to ongoing
widespread ecosystem transformation in the region and the surrounding
basins. According to Ruiz-V\selectlanguage{ngerman}ásquez et al. (2020), under scenarios that
consider deforested areas of approximately 28\% and 38\% of the Amazon
basin, terrestrial sources reduce their annual contributions to northern
South America by an incredible average of 40 and 43\%. Likewise, Badger
and Dirmeyer (2016), confirm that the rise of air masses over northern
South America is inhibited with Amazon deforestation, which could also
induce inhibition of precipitation over the region. Similarly, the
teleconnections with the Orinoco basin reveal that the regional regime
of precipitation is highly dependent on a zone in which raising cattle
is one of the main economic sources since this is the predominant
activity in the area, occupying more than 50\% of the productive
territory. The expansion of the areas dedicated to this activity is the
main source of deforestation (González-González, Villegas, Clerici,~\&
Salazar, 2021). Overall, our results indicate the importance of the
hydrological coupling of terrestrial ecosystems in Northern South
America. Particularly in Colombia, rainfed agriculture and hydropower
generation are an important proportion of the nation's economy.

Overall, our results highlight an advantage of considering stable
isotopes of precipitation over using only numerical modeling, given that
many models can either underestimate or overestimate the amount of
atmospheric moisture (Hoyos, 2017). Additionally, many atmospheric
models have difficulties to represent the topography of the area (Inse,
Poulsen, \& Ehlers, 2010). Here, this difficulty is faced by analyzing
moisture transport phenomena with stable isotopes in precipitation,
precisely due to the inverse correlation between the amount of isotopic
composition and altitude (Dansgaard, 1964; Rozanski et al., 1993; Mook,
2002) The simultaneous application of both techniques (physical tracer
and atmospheric modeling) results in a better interpretation of the
transport of atmospheric moisture. The integration of stable isotopes of
precipitation and the spatial-temporal modeling can be an accurate tool
that reduces the uncertainty associated with the understanding of the
climate system and provides information for foreseeing possible changes
in hydroclimatic patterns provenience from sources region (Risi, Bony,
Vimeux, \& Jouzel, 2010; Sánchez-Murillo et al., 2013; Hu \& Dominguez,
2015).

In summary, most studies have focused on atmospheric moisture from
oceanic sources (Rueda \& Poveda, 2006; Sakamoto et al., 2011; Arias et
al., 2015; Yepes, Poveda, Mejía, Moreno, \& Rueda, 2019) and although a
few show the importance of terrestrial sources (Kumar et al., 2016;
Hoyos, 2017; Hoyos et al 2018; Molina et al., 2019; Ruiz-Vásquez, Arias,
Martínez, \& Espinoza, 2020). Our results illustrate the influence of
moisture recycling in two important areas of Colombia, with the largest
population in the country, and whose economy and ecosystems vitally
depend on water availability for ensuring moisture security. Regional
atmospheric moisture composition largely controls ecosystem structure,
increasing the vulnerability to climate and environmental change in the
study area. Further, these natural systems are directly threatened by
human activities such as deforestation and intensive agriculture,
altering the exchange of water in the land-atmospheric interactions, the
energy balance, evapotranspiration, and therefore, the atmospheric
moisture content and transport.

\textbf{5. CONCLUSIONS}

In this work, we developed a description of the atmospheric moisture
sources that define rain over the Caribbean and Andean subregions in
Colombia. This contribution integrates relevant information from the
isotopic composition of precipitation with results from the mathematical
modeling FLEXPART to better describe the structure of the regional
moisture sources and the relative contribution in terms of amount and
ecological relevance. This approach refines the strategy of moisture
accounting by considering the optimal transport day instead of the
canonical 10-day mean lifetime of water vapor in the atmosphere,
avoiding, as much as possible (under)overestimations in the moisture
contributions. In this sense, our calculations are more realistic and
robust.

The differentiation between regions in the country allowed us to
identify a variation in the amount and seasonality of moisture
contribution from different sources.~ Our results indicate a
predominance of terrestrial moisture sources (56\% and 59\% annual mean
contributions for the Caribbean and Andes regions, respectively),
followed by oceanic moisture sources, dominated by the contribution from
the Pacific in the Andean Region (26\% contribution) and the Atlantic in
the Caribbean region (28\%).

The quantification of moisture sources in each region reveals that
precipitation regimes depend on the seasonality of the regional
composition of moisture sources and the diverse underlying mechanisms,
and the physical variables related to each zone, including variables
such as topography, geographic location, and environmental features. The
isotopic analysis results reveal that the processes which give origin to
precipitation in the Andean region are linked to the advance of moisture
flow into the continent, and are related to orographic ascent, as can be
seen in the LMWLs. For the Caribbean region, the closeness factor to the
main sources such as the Caribbean Sea and the Atlantic Ocean has a high
influence on the precipitation regime. However, an important influence
from the Pacific Ocean is also evident for this region.

The teleconnections between the sources and targets clarify that water
resources management should consider not only surface hydrology, but
also the links that directly influence the precipitation regime. The
dynamics of the climate system at the local level is conditioned by
phenomena that depend synergistically on other places, involving the
transfer of vital ecosystem services. In this work, in particular, we
found that the precipitation regime of the Colombian Andes is highly
conditioned by meteorological and ecological processes within the
region, as well as in neighboring Amazon and the Orinoco, such that
ecological alterations associated with deforestation or land use could
have impacts on the amount of moisture that is transported from these
regions, threatening water, energy and economic security in the region.

\textbf{ACKNOWLEDGEMENTS}

Maritza Escobar was funded through the postgraduate scholarship program
at Universidad de Antioquia.~ We thank the EPhysLab (University of Vigo)
where the FLEXPART experiment was developed.

\textbf{DATA AVAILABILITY}

All data in this paper is available upon request to the corresponding
author

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\textbf{FIGURE LEGENDS}

FIGURE 1. Study area. Regional orography (m) from Global Land
One-kilometer Base Elevation (GLOBE, (Hastings and Dunbar 1998)). Andean
and Caribbean regions, in yellow and green respectively. GNIP Stations
highlighted in dark blue dots.

FIGURE 2. Regional source of atmospheric moisture considered in the
experimental setting for the FLEXPART model. We adopt source regions and
names as {[}Hoyos et al., 2018{]}. Tropical North Pacific (TNP),
Tropical South Pacific (TSP), Subtropical North Atlantic (STNA),
Tropical North Atlantic (TNA), Tropical Atlantic (TA), Tropical South
Atlantic (TSA), Caribbean Sea (CARS), Northern South America (NOSA,
target region), Orinoco Basin (ORIC), Northern Amazon Basin (NAMZ),
Southern Amazon Basin (SAMZ).

FIGURE 3: Qualitative summary of how hydrologic processes affect oxygen
and hydrogen isotopic composition of rainwater. Pairs of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H can be distributed
along with the entire possible variation range, according to the
depletion or enrichment of observations and the meteorological
conditions of the atmospheric moisture sources.

FIGURE 4. Annual cycle of precipitation from observational data
(black)~from 1506 sampling points distributed as shown in a.) and
estimations from the percentage of diagnostic precipitation accounted
for FLEXPART (gray). b.) Caribbean and c.) Andean regions.

FIGURE 5. Annual cycle of precipitation contribution from the main
Atlantic (a,d), Pacific (b,e) and terrestrial (c,f) sources. Top
(bottom) panels for the Andean (Caribbean) region. Precipitation amount
is estimated from percentage of contributions based on diagnostic
precipitation.

FIGURE 6. Seasonal summary of Oceanic (Atlantic and Pacific) and
terrestrial percentages of moisture contributions to the Andean (a) and
Caribbean (b) regions.

FIGURE 7\textbf{.} Seasonal reconstruction of LMWL and Deuterium excess.
Blue dashed lines correspond to GMWL. Green (orange) lines correspond to
the LMWL in the Colombian Andean (Caribbean) region. Interpolation of
seasonal D-excess is shown in the box located in the upper left.\selectlanguage{english}
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