1. Introduction
Climate
change is accompanied by increasing variability in seasonal rainfall
(Feng et al., 2013) and alters rainfall regimes, where water
availability and timing are key factors controlling primary productivity
(Briggs & Knapp, 1995; Weltzin et al., 2003; Huxman et al., 2004) and
the phenology of growth and reproduction (Dirzo et al., 2011; Walther et
al., 2002). Evapotranspiration (ET ) links regional climate and
forest function and plays an important role in the hydrological cycle.
It is also an internal connection with CO2 flux during
the transpiration process and an indicator for ecosystem function. It is
broadly known that the Amazon basin transfers large amounts of water
from the land surface to the atmosphere by ET every day, which
has a huge impact on the global energy budget (Christoffersen et al.,
2014; Hasler & Avissar, 2006; Restrepo-Coupe et al., 2016). Therefore,
any impact on the ET over the Amazon tropical forest may affect
the global carbon cycle and provide further feedback to climate change.
Nonetheless, the spatio-temporal variation of ET across the
Amazon basin, as well as the relative contributions of multiple drivers
to this process, are still uncertain. Thus, assessing the factors
controlling ET in the Amazon basin is fundamentally important and
largely depends on how tropical vegetation processes available energy
and water (Nepstad et al., 1994; Saleska et al., 2003).
Controls of ET across the Amazon basin vary. Based on
observations and model syntheses, the response of forest cover to
seasonal disparities in both water availability and solar radiation can
reduce drought susceptibility by temporally adjusting net leaf flush
(Jones et al., 2014). Wagner et al. (2016) found that the seasonal
variation of canopy photosynthetic capacity is positively correlated
with precipitation (P ) (water-limited forests) when rainfall is
less than ~2000 mm yr-1, and
negatively correlated with radiation (light-limited forests). Costa et
al. (2010) concluded that ET in the dry season is larger than
that in the wet season and that surface net radiation is the main
controller of ET in wet equatorial sites. Some models (Baker et
al., 2008; da Rocha et al., 2009; Kleidon & Heimann, 2000; Werth &
Avissar, 2004) predict water-limited ET seasonality that
resembles P variations. Considering the large scale of previous
studies, the evaluation of ET drivers has not been conclusive in
some cases, or only analyzed for the whole Amazon basin. Malhi et al.
(2002) were the first to measure a full annual trend of latent heat flux
for the Cuieiras forest close to Manaus. Their regression results
indicate that water limitation and stomatal control were the main
factors driving seasonal ET . Nemani et al. (2003) concluded that
light is more limiting than water for tropical forest productivity,
consistent with greening of Amazon forests during the dry season from
satellite data (Brando et al., 2010; Doughty & Goulden, 2008; Huete et
al., 2006; Myneni et al., 2007; Samanta et al., 2012). By evaluating
potential mechanisms for the seasonal green-up phenomenon, Morton et al.
(2014) found that the light-limited net primary production in Amazon
forest and enhanced forest growth during drought conditions are
influenced by the stability of Amazon forest structure and reflectance
over seasonal timescales. Recent studies based on eddy flux measurements
indicate seasonal ET is driven by radiation, rather than water
availability, in the Amazon (da Rocha et al., 2009; Juárez et al., 2007)
and across the tropics (Fisher et al., 2009). Maeda et al. (2017)
suggested that both annual mean and seasonality of ET are driven
by a combination of energy and water availability, as rainfall or
radiation alone could not explain ET patterns. This led to
continued controversy regarding the seasonality of ET and its
control. Therefore, more detailed studies are needed to explore the
factor driving ET (i.e., radiation or water availability).
The modeling results from Verbeeck et al. (2011) show that forests in
some regions of the Amazon maintain high transpiration during the dry
season. Therefore, the flux of ET will also be changed by various
external conditions, resulting in great uncertainty for ETmeasurements that are sparse and often indirect due to the limited
spatial coverage and the complex plant components (Culf et al., 2008).
Moreover, there are systematic biases of hydrologic and carbon fluxes
and responses in Earth system models. For example, Tang et al. (2015)
found that ET predicted using CLM4.5 at the Tapajos forest site
in the Amazon basin compares poorly and is out of phase with MODIS data
(MOD16A2). The latest MOD16 global ET product agrees well with
measurements from Amazon tropics eddy flux towers (Mu et al., 2011), and
shows higher ET in the dry season and lower ET in the wet
season. On the one hand, ET includes contributions from the
evaporation of the ground or other wet surfaces, as well as
transpiration flux from plants. Thus, ET reflects aspects of the
aspiration functioning of plants (Swann et al., 2017) and is largely
affected by precipitation, as the vegetation canopy of the Amazon
rainforest is highly sensitive to changes in precipitation patterns.
Reduction in rainfall has diminished vegetation greenness, which
coincides with the decline in terrestrial water storage (Hilker et al.,
2014). This pattern is supported by severe drought suppressed
photosynthesis (Doughty et al., 2015).
On the other hand, groundwater has a
strong influence on hydrologic responses in the Amazon (Tomasella et
al., 2010). Previous empirical
studies indicate that errors result if groundwater is not included in
hydrologic balances (Leopoldo et al., 1995; Lesack, 1993). Several
modeling studies have also concluded that surface runoff is rare and
groundwater plays a key role in Amazon hydrology (Hodnett et al., 1997b,
1997a; Leopoldo et al., 1995; Miguez-Macho & Fan, 2012a), and
groundwater has significant influence on soil moisture and ET(Miguez-Macho & Fan, 2012b). Our recent analysis with a
three-dimensional hydrologic model applied to Amazon watersheds (Niu et
al., 2017) demonstrated that lateral fluxes, especially groundwater
flows, have a large impact on hydrologic responses.
To detect and analyze oscillations of
different hydrologic components on a given scale, wavelet coherence
analysis qualitatively estimates the temporal evolution of the degree of
linearity of the relationship between two signals (Labat, 2005). Wavelet
spectral and correlation analyses have been applied widely in previous
studies to identify the annual periodicity of the hydrologic and climate
fluxes and detect their long-term trends (Andreo et al., 2006); to
detect potential flood triggering conditions (Schaefli et al., 2007);
and to extract significant information and the characteristic time scale
of the dominant hydrologic processes (Molénat et al., 1999; Zhang et
al., 2016). The method has also been applied to Amazon River monthly
discharges to suggest physical explanations for time-scale dependent
relationships (Labat et al., 2005). To the best of our knowledge, due to
the scarcity of observations, especially of water table depth, no
previous studies using wavelet spectral has analyzed the relationships
between P and ET , or groundwater and ET, in the
Amazon. The phase difference of wavelet analysis can obtain the dynamic
correlation among hydrological components, and its application is
relatively rare. In addition, the Budyko curve framework is a classic
empirical approach to analyze annual hydrological budgets and their
inter-annual variability (Bukydo, 1974). The Budyko framework has been
used to evaluate the inter-annual variability of annual water balances
(Yang et al., 2007; Wang, 2012). A recent study found that the errors
between observations and the traditional Budyko curve could be reduced
if the equation was corrected using information extracted from the
Gravity Recovery and Climate Experiment (GRACE) terrestrial water
storage anomalies (TWSA ) (Fang et al., 2016).
In this paper, we explore the relationships and seasonal-to-annual
variations among P , ET , and TWSA across the Amazon
basin using wavelet analysis. By analyzing the phase differences amongP (TWSA ) and ET , we can explore the interaction
between ET fluxes and rainfall (groundwater) at different scales
(for sub-basins, the individual grid cells, different climatic zones,
and averaged for the whole Amazon basin), including the effects of
drought. The purpose is to dynamically analyze the main factors
controlling ET across the Amazon basin. We also apply the Budyko
framework to evaluate the annual hydrological budgets in sub-basins of
the Amazon and analyze the degree of radiation and rainfall limitation,
as well as their interactive effects on ET . With these tools we
address the following hypotheses: (1) in years with sufficient P ,ET is energy limited, while during drought years, plants have
sufficient water supply from groundwater to maintain photosynthesis andET compared to normal years; (2) after a drought year, the
groundwater system recovers, and the phases between ET andP , and between ET and TWSA, reflect this recovery;
and (3) the above two hypotheses vary spatially and the spatial
heterogeneity of the water-energy balance account for their spatial
variation. Hypotheses (1) and (2) were addressed using wavelet coherence
phase analysis for the whole Amazon (Section 3.1), sub-basins (Section
3.2), and sub-regions (Section 3.5). The analyses at different scales
(from Section 3.2 to Section 3.5) help address hypothesis (3),
specifically, applying the wavelet coherence phase for analyzing the
individual grid cells and the Bydyko framework for analyzing each
sub-basin.