Introduction
Drylands play an important role in global carbon (C) cycle and future C
sequestration (Houghton et al., 1999; Asner et al., 2003), as they cover
30–45 % of the earth's land surface (Asner et al., 2003; Reynolds et
al., 2007), store about 15 % of the global soil organic carbon
(Schlesinger, 1991), and represent 30–35 % of terrestrial net
primary production (Field et al., 1998). Driven by sporadic
precipitation (PPT) and nonlinear biological responses, dryland C fluxes are
especially variable across time and space (Maestre et al., 2012; Collins
et al., 2014), making the prediction of dryland C budgets a challenging task
(Jenerette et al., 2012). Moreover, climate models predict that the
intra- and interannual PPT variability may be further intensified in
dryland regions with longer drought durations and more large-sized events
(Solomon et al., 2007; Diffenbaugh et al., 2008; Cook and Seager, 2013).
Further, sequences of wet years followed by sequences of dry years and vice versa are
also increasingly likely (Peters et al., 2012; Sala et al., 2012).
Understanding the response of dryland ecosystem C fluxes to PPT variation
is, therefore, important to characterizing the global C cycle and predicting
how future PPT regime changes will affect dryland C balance.
As a measure of ecosystem C balance, net ecosystem production (NEP) has a
value that is positive when an ecosystem accumulates C and negative when an
ecosystem loses C. Dryland NEP is closely tied to current-year PPT amount,
with wetter-than-average years being a C sink, drier-than-average years
being a C source, and years with average rainfall being C neutral
(Flanagan et al., 2002; Hastings et al., 2005). Additionally, at seasonal
scales, the distribution of PPT in addition to the total amount can have
large influences on ecosystem production (Porporato et al., 2004; Katul et
al., 2007). At interannual scales a PPT legacy effect, defined as the impact
of past PPT conditions on the current structure and functioning of
ecosystems (Lauenroth and Sala, 1992; Sala et al., 2012; Monger et al.,
2015), has also been found to play an important role in shaping the temporal
variability of dryland ecosystem C fluxes (Knapp et al., 2002; Huxman et
al., 2004a, b; Heisler and Weltzin, 2006; Sala et al., 2012; Ogle et al.,
2015). For example, Hasting et al. (2005) attributed the C sink status of
a desert shrub ecosystem in the early spring of 2002 to the above-average
rainfall in the late fall of 2001. Scott et al. (2009) and Hamerlynck et al. (2013) found that a cool-season (December–April) drought was followed by
an unusually large net C loss during the following warm monsoon season (July–September) in a semiarid savanna and a semi-desert grassland. Moreover, the
savanna ecosystem has recently been a net C source, and one hypothesized but
untested explanation is due to an increase in current respiration of organic
C that accumulated in the preceding wetter decade (Scott et al., 2009).
While these studies reveal the existence of PPT legacy effects on NEP at the
seasonal scale, only a few studies have quantitatively assessed the
contribution of PPT legacy to the temporal variability of dryland NEP at
interannual and interdecadal timescales (Williams and Albertson, 2006),
mainly because it is methodologically difficult to separate the past and
current PPT impacts on C fluxes with the limited observational data (Sala
et al., 2012), and there is a general lack of field manipulative experiments
to address the PPT legacies at these scales (Reichmann et al., 2013a).
Much of our current understanding of the PPT legacy effects on dryland C
fluxes is based on aboveground net primary production (ANPP). A number of
studies have documented that dryland ANPP is not only linearly related to
current-year PPT but also closely related to the PPT amount and seasonality
several months to years before (Lauenroth and Sala, 1992; Oesterheld et
al., 2001; Huxman et al., 2004c). For example, field studies have found a
positive legacy impact where ANPP is higher than expected if preceded by a
wetter year, or lower than expected if preceded by a drier year (Jobbagy
and Sala, 2000; Oesterheld et al., 2001; Wiegand et al., 2004; Sherry et
al., 2008; Sala et al., 2012). Proposed mechanisms explaining such observed
positive PPT legacy effects on ANPP mainly involve the structural carryovers
between years, which can be leaf and root biomass
(Oesterheld et al., 2001); the composition of species
differing in rooting depth and phenology (Paruelo et al., 1999; Jobbagy
and Sala, 2000); or the density of seeds, tillers and plant individuals
(Oesterheld et al., 2001; Yahdjian and Sala, 2006; Reichmann et al.,
2013a). Alternatively, a negative legacy effect occurs when production is
lower than expected if preceded by a wet period or higher than expected if
preceded by a dry period (Jenerette et al., 2010). A
negative PPT legacy effects may be influenced more by biogeochemical
carryovers that influence the resource availability to respond to current
PPT (Evans and Burke, 2013; Reichmann et al., 2013b), whereby increased
growth in response to a higher PPT can reduce the available nutrients (e.g.,
nitrogen, N) for the following period and vice versa. Although various mechanisms
have been proposed for the PPT legacy impacts on ANPP, few of them have been
rigorously tested, and the key underlying mechanisms still remain poorly
understood (Sherry et al., 2008; Williams et al., 2009; Sala et al.,
2012; Monger et al., 2015).
Soil respiration (Rs), as a major component of ecosystem C efflux, has
also been found to have lagged responses to PPT variations (Huxman et
al., 2004b; Sponseller, 2007; Ma et al., 2012; Cable et al., 2013). This is
particularly true at the event scale; after a period of drought, a rainfall
event can result in a pulse of CO2 efflux that may be orders of
magnitude larger than that before the event and then decline exponentially
for a few days to weeks (Xu et al., 2004; Jenerette et al., 2008; Borken
and Matzner, 2009; Cable et al., 2013; Oikawa et al., 2014). At a seasonal
scale, Vargas et al. (2010) found no lags between Rs and soil
moisture across 13 vegetation types, including four grasslands; however,
Hamerlynck et al. (2013) presented longer-term ecosystem flux data that
suggest seasonal drought legacies affect ecosystem respiration (Re) in
a semi-desert grassland in southeastern AZ, USA. They posited that the
increased C substrate availability resulting from the previous cool-season
drought-induced plant mortality was responsible for the higher Re in
the following monsoon season. However, very few studies have been devoted to
understanding the PPT legacy impacts on dryland respiration at greater than
seasonal timescales.
In this study, we conducted simulation experiments with a widely used
dryland ecosystem model, Patch Arid Land Simulator (PALS; Kemp et al., 1997,
2003; Reynolds et al., 2004; Shen et al., 2009), to analyze the PPT legacy
effects on ecosystem-level C fluxes including NEP, gross ecosystem
production (GEP), and Re. The PALS model was built on the pulse-reserve
concept (Noy-Meir, 1973) and had been used to analyze the
impacts of antecedent moisture conditions and the lagged responses of
different plant functional types (FTs) in three North American deserts at the
rainfall event scale (Reynolds et al., 2004). We parameterized,
calibrated, and evaluated the model based on the long-term
eddy-covariance-measured fluxes in a semi-desert savanna ecosystem in the southwestern USA
(Scott et al., 2009) to analyze the PPT legacy effects at interannual and
interdecadal scales. Specifically, we addressed the following two questions.
First, what are the direction and magnitude of ecosystem C flux responses to
dry and wet legacies? We expected that the PPT legacy impacts would occur
over annual and decadal scales in correspondence to PPT fluctuations at
these scales, and the dry- and wet-legacy impacts would differ in direction
and magnitude. Second, how are the direction and magnitude of PPT legacy
effects related to the PPT characteristics of both the previous and the
current year/period? We expected that greater variability in PPT would
lead to corresponding increases in legacy effect. For PPT characteristics,
we were interested not only in the annual and seasonal PPT amount but also in
between-event interval and event size distribution, since these variables
are widely recognized key PPT features to dryland ecosystems (Porporato et
al., 2004; Katul et al., 2007; Shen et al., 2008a).
Methods
Model description
PALS is a process-based ecosystem model that consists of four modules:
atmospheric forcing, a water cycling and energy budget, plant production and
respiration, and soil organic matter (SOM) decomposition and heterotrophic
respiration (Rh). The four modules are interactively linked by the
cycling of C, N, and H2O through the atmosphere–plant–soil continuum.
The PALS model explicitly considers seven plant FTs
commonly found in the North American warm deserts: evergreen shrub,
deciduous shrub, perennial forb, perennial C3 and C4 grasses, and
native and exotic C3 annual grasses (Reynolds et al., 1997; Shen et
al., 2009). Since the detailed model structure and mechanistic relationships
have been presented in several publications (Kemp et al., 1997, 2003;
Reynolds et al., 1997, 2000, 2004; Gao and Reynolds, 2003; Shen et al.,
2005, 2008a, b, 2009), here we briefly describe the four modules and
refer to the specific literature for detailed description.
The atmospheric driving force module reads in data for atmospheric driving
variables (e.g., atmospheric [CO2], N deposition rate, daily maximum and
minimum air temperatures, PPT, relative humidity, and solar radiation) and,
based on these driving variables, calculates other important variables such
as vapor pressure deficit (VPD), which directly influences stomatal
conductance and indirectly influences soil temperature, SOM decomposition and
soil respiration. Calculations of VPD and soil temperature can be found in Eqs. (2)–(7)
in Shen et al. (2005).
The water cycling and energy budget module mainly calculates soil water
contents at six layers, the rates of water infiltration into and percolation
out of a layer, and water losses via evaporation and transpiration from
different layers. Water infiltration and percolation rates of a layer are
determined by the effective PPT reaching the soil surface, previous water
content, and the water holding capacity as a function of soil texture
(Shen et al., 2005). Soil evaporation is determined by soil water
availability and energy available in the two top soil layers (10 cm in
depth). Water uptake by plants is partitioned among the soil layers
according to the proportion of roots in each layer for all plant FTs
(Kemp et al., 1997; Shen et al., 2008b). Canopy transpiration is
calculated by using the energy budget and the canopy stomatal resistance
(Reynolds et al., 2000; Gao and Reynolds, 2003).
The plant production and respiration module mainly simulates phenology,
primary production, growth and maintenance respiration, photosynthate
allocation, and litterfall of each plant FT. Three major phenophases (i.e.,
dates of germination, leafing, and dormancy) are determined in PALS based on
the observed dates, air temperature, and PPT (Shen et al., 2009). Primary
production for each FT is calculated based on the leaf area, potential net
photosynthetic rate, stomatal conductance, leaf N content modifier, and the
difference between intercellular and atmospheric [CO2]. The plant
photosynthesis rate is estimated as a product of stomatal conductance and
the partial pressure gradient between atmospheric and intercellular
[CO2]. The stomatal conductance is calculated as an exponential
function of leaf water potential that decreases linearly with atmospheric
vapor deficit (see Eqs. (10)–(14) in Shen et al., 2005).
Photosynthate is allocated to different plant organs (leaf, stem, and root)
using fixed allocation ratios after subtracting the maintenance respiration,
which is estimated as a function of live biomass, basal respiration rate,
and modifiers of temperature and plant water potential (Shen et al.,
2008a). Growth respiration is calculated based on the growth yield
coefficient and the net photosynthate used for growth (Shen et al.,
2008a). Litterfall amount is mainly determined as a function of observed
dormancy dates, maximum air temperature and drought conditions (Shen et
al., 2008a, 2009).
The SOM decomposition and heterotrophic respiration module simulates the
decomposition of metabolic and structural litter material; SOM in active,
slow and passive pools; and CO2 emissions associated with these
decomposition processes (Kemp et al., 2003; Shen et al., 2009). The SOM
decomposition rate or heterotrophic rate is calculated as a first-order
kinetic rate with a decomposition coefficient multiplied by the pool size
and the temperature and moisture scalars (see Eqs. (A4)–(A11) in Shen
et al., 2009). In addition, this module also simulates the dynamics of soil
mineral N pool by using N mineralization and atmospheric deposition as the
major inputs, and plant N uptake and leaching loss as the major outputs.
Among these the N mineralization and plant uptake processes are modeled in
more detail while the rates of the other processes are basically assigned
with empirical constant values. The N mineralization processes are directly
coupled to litter and SOM decomposition processes and are calculated as a
product of the C flow rates and the C / N ratio of the corresponding litter or
SOM pools (Parton et al., 1993; Kemp et al., 2003). The plant N uptake is
a product of water transpiration and N concentration in soil solution (see
Eq. 8 in Shen et al., 2008b).
Model parameterization
For this study, we modified and parameterized PALS to represent an upland
mesquite savanna ecosystem in the Santa Rita Experimental Range (SRER;
31.8214∘ N, 110.8661∘ W, elevation 1116 m), about 45 km south of
Tucson, AZ, USA. Soils at this site are a deep sandy loam (Scott et al.,
2009), and the mean groundwater depth likely exceeds 100 m
(Barron-Gafford et al., 2013). PPT was therefore considered as the only
source of water input into the system. Based on the vegetation composition
(Scott et al., 2009), there were five major plant FTs included in PALS:
shrub (e.g., Prosopis velutina), subshrub (e.g., Isocoma tenuisecta), C4 perennial grass (e.g., Digitaria californica), perennial
forb (e.g., Ambrosia psilostachya), and C3 annual grass, among which the velvet mesquite
shrub with average height of ca. 2.5 m accounted for ∼ 35 %
of the total canopy cover and other FTs (mainly perennial grasses) accounted
for ∼ 22 % (Scott et al., 2009). Therefore, we derived
the site-characteristic parameters for the two major FTs (shrub and
perennial grass) from previous studies carried out in SRER, with those for
the other FTs being adopted from a generic parameter data set for the PALS
model to be used in the North American warm deserts (Reynolds et al.,
2004; Shen et al., 2005). These site-specific parameters mainly included
plant-related parameters (e.g., canopy cover, C allocation ratio, rooting
distribution ratio, and the initial values of living and dead plant biomass
pools) and soil-related parameters (e.g., soil chemical and physical
properties, C / N ratios, decomposition rates, and initial values of the
litter and SOM pools). The values of these parameters are provided in
Table S1 in the Supplement, with the cited literature also being listed below the
table.
For the climatic variables used to drive the PALS model, we compiled a
30-year meteorological data set that included daily PPT, maximum and minimum
air temperatures (Tmax and Tmin), relative humidity (RH), and
total solar radiation (Srad) from 1981 to 2010. The Tmax,
Tmin, RH, and Srad data from 1981 to 1990 were observations from the
Tucson weather station (about 50 km north of the mesquite savanna site and
lower elevation) and obtained by accessing Arizona Meteorological Network
online data (AZMET: http://ag.arizona.edu/azmet). The
remaining 20 years (1991–2010) of Tmax, Tmin, RH and Srad
data were observations from the Kendall Grassland meteorological site (about
85 km east of the mesquite savanna site and slightly higher elevation) and
obtained by accessing Southwest Watershed Research Center (SWRC) online data
(http://www.tucson.ars.ag.gov/dap/). The 30-year PPT
data were observations from the Santa Rita watershed rain gage no. 5 (1.5 km
from the site) and obtained also from the SWRC online data access. These
different sources of meteorological data were adjusted based on the 7 years
(2004–2010) of meteorological data obtained from the AmeriFlux
eddy-covariance flux tower at the mesquite savanna site (US-SRM; see
Fig. S1 in the Supplement). Lastly, we used the AZMET and SWRC data from 1981
to 2003 plus the flux tower data from 2004 to 2010 to drive the model.
Precipitation characteristics in the 30 years (1981–2010) at the
Santa Rita mesquite savanna site. (a) Annual and seasonal precipitation
amount; (b) frequency distribution of daily rainfall; (c) mean and maximum
between-event interval (BEI). Horizontal lines within (a) indicate mean
annual and seasonal precipitation. The warm growing season (warm-GS) is from
July through September, the cool dry season (cool-DS) from October to November, the cool
growing season (cool-GS) from December through March, and the warm dry season
(warm-DS) from April through June. Error bars in panel (c) represent standard
deviations, and n is the number of rain event pairs used to calculate the
between-event interval in the 30 years.
Since our simulation experiment was based on the manipulations of the
30-year (1981–2010) PPT data, we report the PPT characteristics here in more
detail. In the past 30 years, the mean annual PPT (MAP) amount was 401 mm at
the site, slightly greater than the long-term (1937–2007) mean of 377 mm
(Scott et al., 2009). These 30 years were divided into two periods: a wet
period from 1981 to 1994 with a MAP of 449 mm and a dry period from 1995 to
2010 with a MAP of 347 mm (Fig. 1a). For the analysis of PPT legacy effects
at interdecadal scale, the wet period was treated as the previous period and
the dry period as the current period. For the analysis of PPT legacy effects
at interannual scale, the annual scale was defined as being from July through June
of the next year. To analyze the relationship between PPT legacy effects and
seasonal rainfall characteristics, each year was further divided into four
seasons (with their mean rainfall in parentheses): the main warm growing
season from July to September (warm-GS, 224 mm), the cool dry season from
October to November (cool-DS, 48 mm), the minor cool growing season from
December to March (cool-GS, 104 mm), and the warm dry season from April to
June (warm-DS, 26 mm). At the site, as in many other dryland regions
(Sala et al., 1992; Heisler-White et al., 2008), most rainy days have
only light-rainfall events. About 80 % of daily rainfall was < 10 mm, with medium- to large-sized events (10–50 mm) accounting for about
20 % and only 10 events larger than 50 mm in the 30 years (Fig. 1b). The
no-rain-day duration between events (hereafter between-event interval or
BEI) was ∼ 5 days on average in the warm-GS and
∼ 10 days in the cool-GS (Fig. 1c). The average BEI was
∼ 17 days in the cool-DS and 24 days in the warm-DS, but there
could be no rain for 3 months in these dry seasons (Fig. 1c).
Model calibration and evaluation
After model parameterization, we calibrated the model based on 4 years
(2004–2007) of CO2 and H2O flux data monitored using the
eddy-covariance technique at the savanna site. Detailed descriptions of
instrumentation, sensor heights and orientations, and data-processing
procedures for the eddy-covariance data can be found in Scott et al. (2009).
During model calibration, we mainly adjusted the parameter values of
photosynthate allocation ratios, live biomass death rates, and SOM
decomposition rates to achieve a best fit between modeled and observed GEP
and Re, since these parameters have been identified as the most
sensitive and uncertain ones (e.g., photosynthate allocation ratios) in
influencing the modeled ecosystem carbon fluxes (Shen et al., 2005). The
model performed well in capturing the seasonal variation patterns of actual
evapotranspiration (AET), GEP, Re, and NEP in the 4 calibration
years (Fig. S2), with larger C fluxes during the warm-GS than
in the other seasons. At the annual scale, simulated AET, GEP, and Re
explained over 60 % of the variations in the observations (Fig. 2, left
panels), but the correlation between the simulated and observed NEP was very
weak (Fig. 2d). This was mainly because the model substantially
overestimated GEP (120 g C m-2 simulated vs. 52 g C m-2 observed) in
the cool-GS of 2006 (Fig. S3b). Further explanations on the
possible causes of the GEP overestimation in 2006 shall be provided later
in the Discussion section. If the data of this year were excluded, the explanatory power
for annual NEP could reach 74 %. Since our goal was to use an empirically
plausible model to understand the long-term temporal variations in ecosystem
fluxes, we consider the calibration results acceptable.
Comparison of the model-simulated water and carbon fluxes with the
eddy-covariance observations at the mesquite savanna site. Left panels show
the comparison between the modeled and observed fluxes in 4 calibration
(2007–2007; solid dots) and 3 validation years (2008–2010; open dots).
Right panels show the relationships of the simulated (solid dots) and
observed (open dots) fluxes with precipitation in the 7 years
(2004–2010). R2 is the coefficient of determination describing the
proportion of the variance in measured fluxes explained by the model for the
left panels or that explained by precipitation for the right panels. AET
represents actual evapotranspiration, GEP gross ecosystem production,
Re total ecosystem respiration, and NEP net ecosystem production.
The model performance was further evaluated by assessing the degree of
correlation between the PALS-simulated and flux-tower-measured C and
H2O fluxes from 2008 through 2010, which were not used for model
calibration. The coefficients of determination (R2), which describe the
proportion of the variance in measured data explained by the model, were all
larger than 0.9 in the 3 validation years (2008–2010; Fig. 2, left
panels). These evaluation results indicate that the model was capable of
capturing the temporal variability of observed fluxes at the annual scale.
Furthermore, we also analyzed the relationships between the observed and
simulated fluxes with the corresponding current-year PPT to see how the flux
variations were explained by current-year PPT under baseline conditions
(i.e., the PPT variations shown in Fig. 1). The explanatory power (R2)
for both the observed and simulated fluxes were mostly over 70 % (Fig. 2,
right panels), which further indicates that the model is capable of
capturing the impacts of PPT variability on ecosystem fluxes. The following
simulation experiments were therefore designed to discriminate the
contributions by previous- and current-year PPT impacts.
Simulation experiments
We designed two sets of simulation experiments to examine the interdecadal
and interannual PPT legacy effects. To analyze the interdecadal legacy
effects, we first changed the PPT of the 14-year previous period (1981–1994)
by 0, ±10, ±30, ±50 and ±80 %
(multipliers of existing daily PPT amounts in the record) while keeping the
16-year current-period (1995–2010) PPT unchanged. After these manipulations,
the average PPT of the previous period ranged from 93 mm, corresponding to
the 80 % decrease, to 837 mm, corresponding to the 80 % increase.
This design detects how changes in previous-period PPT influence the
current-period C fluxes and the associated C pool dynamics. On top of each
previous period PPT manipulation level, we further changed the
current-period PPT by 0, ±10, ±30, ±50,
and ±80 %, which resulted in the average current-period PPT varying
from 69 to 621 mm. This design detects how changes in the current-period
PPT influence the legacies resulting from changes in the previous-period
PPT. As a result, we conducted 73 simulation runs, corresponding to the 73
combinations of the above previous- and current-period PPT manipulations (9
previous PPT levels times 8 current PPT levels plus 1 baseline run).
To analyze the interannual legacy, we changed the PPT of each individual
year by ±30 % while keeping the PPT of the subsequent years
unchanged. This design resulted in 54 simulation runs (27 years from
1981 to 2007 times 2 PPT manipulation levels) and illustrates the effects of
changes in the PPT of the previous 1 year on the C fluxes and resource
pools of the current year(s). After a 30 % PPT change, annual PPT
ranged from 162 to 925 mm in the 27 years, which was large enough to
cover the PPT interannual variation at the study site. Another consideration
of using 30 % as the PPT manipulation level was that future projected
annual PPT variation in dryland regions will be -30 to +25 %
(Bates et al., 2008; Maestre et al., 2012).
Data analysis
The legacy effect was quantified as the C flux (or resource pool size) of the
current period/year after PPT changes in the previous period/year minus that
without PPT changes in the previous period/year. As an example, the
following equation calculates the legacy effect of increasing the
previous-period PPT by 30 % on the current-period NEP:
LegacyNEP=ΔNEP=NEPPPT+30%CP-NEPPPT+0%CP,
where NEPPPT+30%CP is the cumulative NEP throughout the current
period (1995–2010) under a 30 % previous-period (1981–1994) PPT
increase; NEPPPT+0%CP is the cumulative NEP throughout the
current period with no previous-period PPT change (i.e., the baseline PPT
conditions shown in Fig. 1). This method directly quantifies whether changes
in PPT of the previous period will impose a positive, a negative, or no legacy
effect on the C fluxes (or resource pools) of the current period. For
simplicity, hereafter we refer to the legacy effect resulting from the
decreased previous-period/year PPT as the dry legacy and that resulting from
the increased previous-period/year PPT as the wet legacy. Spearman
correlation analysis was used to detect the relationships between legacy
effects and PPT characteristics, including PPT amount, BEI, and the number
of large (≥ 10 mm) vs. small (< 10 mm) events at yearly and
seasonal scales. The correlation analysis was performed in SPSS 16.0
(Chicago, IL, USA).
Interdecadal legacy effects of changing the previous-period
(1981–1994) precipitation on the cumulative carbon fluxes of the current
period (1995–2010). Interdecadal legacy effects on carbon fluxes (e.g.,
ΔNEP) are calculated as the difference between the current-period
flux with previous-period PPT changes and that without previous-period PPT
changes. Dashed lines with open symbols represent different levels of
decreasing the current-period precipitation (left panels). Solid lines with
filled symbols represent increasing the current-period precipitation (right
panels).
Results
Interdecadal legacy
Changes in PPT of the previous period (1981–1994) imposed obvious legacy
impacts on the C fluxes of the current period (1995–2010). The direction of
the simulated interdecadal dry and wet legacies on GEP and Re was
dependent upon the direction of both the previous- and current-period PPT
changes. When the current-period PPT was reduced (Fig. 3, left panels), the
simulated dry legacies mostly increased the current-period GEP (Fig. 3a) but
decreased Re (Fig. 3c); whereas wet legacies imposed little impacts on
the current-period GEP (Fig. 3a) but mostly increased Re (Fig. 3c).
When the current-period PPT was enhanced (Fig. 3, right panels), both the
dry and wet legacies mostly increased GEP and Re (Fig. 3b, d).
Regardless of current-period PPT changes, NEP always increased with dry
legacies and decreased with wet legacies (Fig. 3e, f), indicating a
consistent negative NEP response to PPT legacies.
The simulated absolute magnitude of the PPT legacy influence on ecosystem C
fluxes (i.e., GEP, Re, and NEP) generally increased with the absolute
magnitude of changes in the previous-period PPT (Figs. 3, 4). Increasing
the current-period PPT generally amplified the legacy effects compared to
decreasing the current-period PPT (comparing the left to the right panels of
Fig. 3). The magnitude of the PPT legacies was also significantly correlated
with the PPT difference between the current and previous period (ΔPPT, equals to the current-period PPT minus the previous-period PPT; Fig. 4). If the previous period was wetter than the current period (i.e., ΔPPT < 0 or a wet-to-dry period transition), the legacy effect on
Re was negatively correlated with ΔPPT (Fig. 4c) but that on NEP
was positively correlated with ΔPPT (Fig. 4e), indicating more
current-period C release after a wetter previous period. In contrast, if the
previous period was drier than the current period (i.e., ΔPPT
>0 or a dry-to-wet period transition), the correlations were all
positive for GEP, Re and NEP (Fig. 4, right panels), indicating more
current-period C sequestration after a drier previous period.
Spearman correlations of interdecadal precipitation legacy effects
with the precipitation difference between periods (ΔPPT).
Interdecadal ΔPPT is calculated as the mean PPT of the current
period (1995–2010) minus that of the previous period (1981–1994).
Interdecadal legacy effects on carbon fluxes (e.g., ΔNEP) are
calculated as the difference between the current-period flux with
previous-period PPT changes and that without previous-period PPT changes.
Sample size is 41 for the wet-to-dry period transition (left panels) and 23
for the dry-to-wet period transition (right panels). GEP represents gross
ecosystem production, Re ecosystem respiration, and NEP net ecosystem
production. R2 is the coefficient of determination, and P is
probability.
The resource pool dynamics were also shaped by the alterations in the
previous- and current-period PPTs. We only showed the 30 % decrease and
increase in the previous- and current-period PPT (i.e., 4 out of 72 pairs of
PPT change combinations) as representative examples in Fig. 5, because the
major response patterns for the other paired combinations were similar.
The PPT legacy impacts generally lasted for about 6–8 years for
plant biomass, litter mass and soil water content (SWC), and much longer for
soil organic matter (SOM) and soil mineral N (Nsoil; Fig. 5). Based on
the resource pool responses in the early 1–2 years (i.e., 1995 and 1996) of
the current period, the dry legacies decreased biomass, litter and SOM (Fig. 5a–f), but positively impacted Nsoil (Fig. 5g–h). Contrastingly, the
wet legacies increased biomass, litter and SOM (Fig. 5a–f) but negatively
impacted Nsoil (Fig. 5g–h). Similar to the influences on C fluxes,
increasing the current-period PPT (Fig. 5, right panels) amplified the PPT
legacy impacts on biomass and litter (Fig. 5a–d), and hastened the recovery
rates of SOM and Nsoil to their baseline levels (Fig. 5e–h).
Interannual legacy
At the interannual scale, a 30 % decrease or increase in PPT could have
legacy impacts on ecosystem C cycling lasting for 2–12 years (Fig. 6a–b).
Notably, the direction of GEP and Re responses to decreasing or
increasing previous-year PPT could be positive or negative (Fig. 6c–f). The
dry- or wet-legacy effects on these two fluxes were variable; idiosyncratic;
and, in some cases, large at this timescale. However, the simulated dry
legacies mostly increased NEP (Fig. 6g), whereas the simulated wet legacies
mostly decreased NEP (Fig. 6h), which was similar to legacy responses at the
interdecadal scale (Fig. 3e–f).
Interdecadal precipitation legacy effects on the resource pool
dynamics. Left panels show the resource pool responses under a 30 %
decrease, while right panels show those under a 30 % increase in the
precipitation (PPT) of the current period from 1995 to 2010. Legacy effects on
pool size (e.g., ΔBiomass) are quantified as the difference between
the current-period pool size with previous-period PPT change and that
without previous-period PPT change. Dashed lines represent a 30 %
decrease, while solid lines represent a 30 % increase in the PPT of the
previous period from 1981 to 1994. SOM represents soil organic matter,
Nsoil soil mineral nitrogen, and SWC soil water content.
Spearman correlation coefficients between interannual legacy
effects and precipitation characteristics. Significant correlations are
indicated with * for 0.01 < P≤ 0.05 and ** for P≤ 0.01
(2-tailed; n= 27).
Precipitation
Dry legacy (previous-year PPT -30 %)
Wet legacy (previous-year PPT +30 %)
characteristics
ΔGEP
ΔRe
ΔNEP
ΔGEP
ΔRe
ΔNEP
Previous-year PPT characteristics
Yearly rainfall
0.134
0.033
0.0.270
-0.324
-0.180
-0.374
Warm-GS rainfall
0.303
0.072
0.519**
-0.430*
-0.065
-0.579**
Warm-GS BEI
-0.069
0.137
-0.399*
-0.075
0.053
-0.262
Warm-GS NE > 10 mm
0.329
0.067
0.636**
-0.535**
-0.227
-0.619**
Current-year PPT characteristics
Yearly rainfall
0.278
0.162
0.484*
-0.466*
-0.600**
-0.224
Cool-GS rainfall
0.528**
0.338
0.495*
-0.277
-0.331
-0.218
Yearly BEI
-0.512**
-0.285
-0.686**
0.359
0.352
0.255
Cool-GS BEI
-0.519**
-0.286
-0.510**
0.151
0.088
0.214
Yearly NE > 10 mm
0.331
0.178
0.512**
-0.567**
-0.583*
-0.398*
Cool-GS NE < 10 mm
0.614**
0.577**
0.398*
-0.105
-0.075
-0.128
PPT difference (ΔPPT) between current and previous year
Yearly rainfall
0.088
-0.135
0.466*
0.078
-0.088
0.252
Warm-GS rainfall
-0.059
-0.042
0.074
0.206
-0.096
0.326
Cool-GS rainfall
0.326
0.048
0.374*
0.248
0.160
0.209
Abbreviations: PPT: precipitation; GEP: gross primary production; Re:
ecosystem respiration; NEP: net ecosystem production; GS: growing season;
BEI: between-event interval; NE: number of rainfall events.
The correlation analysis showed that not only rainfall amount but also BEI
and event size distribution could influence the magnitude of the simulated
dry and wet legacies (Table 1). The warm-GS PPT of a previous year was
significantly correlated with the dry legacies for NEP and the wet legacies
for GEP and NEP (Table 1). On the other hand, the cool-GS PPT of a
current year influenced the dry and wet legacies for C fluxes, but not all
of them were statistically significant (Table 1). These results indicate
that the legacies were mainly generated in the warm-GS of a previous year,
but the current-year cool-GS PPT conditions could influence the C flux
responses to the previous-year legacies. Unlike at the interdecadal scale
(Fig. 4), our correlation analysis showed that only the dry legacies for NEP
had significant correlations with the PPT difference (ΔPPT) between
2 consecutive years or cool-GSs (Table 1), indicating that the larger the
PPT difference between a previous dry year and a current wet year, the
greater the legacy impacts on NEP imposed by the previous dry year.
Interannual precipitation legacy effects on the ecosystem carbon
fluxes. (a) and (b) show the lasting duration of dry (left panels) and wet
(right panels) legacies, respectively. The legacy lasting duration is
quantified as the number of years during which the legacy impacts on
ecosystem fluxes exist after a previous-year PPT change. (c) through
(h) show the responses of gross ecosystem production (GEP), ecosystem
respiration (Re) and net ecosystem production (NEP) to dry (left
panels) and wet (right panels) legacies. Bars in the background of (a) and
(b) represent the previous-year PPT amount after a 30 % decrease and
increase, respectively.
To analyze the interannual PPT legacy impacts on the dynamics of resource
pools (i.e., biomass, litter, SOM, Nsoil, and SWC), 2 wet years (1983
and 1994) and 2 dry years (1986 and 1995) were chosen as examples (see
Fig. 1a). The simulated dry legacies reduced biomass, litter and SOM but
increased Nsoil and SWC in the first current year (Fig. 7). In
contrast, the simulated wet legacies imposed the opposite direction of
impacts on the five resource pools (Fig. 7). The simulated PPT legacy
impacts on the resource pools could also last for several years, and the
direction and magnitude of the legacy impacts in the following years could
differ from those in the first year as described above. For example,
increasing the PPT of 1995 by 30 % caused a positive legacy impact on the
biomass of the first following year (i.e., 1996; Fig. 7b), but it became
negative in the later following years (e.g., in 1998; Fig. 7b), further
indicating that current-year PPT conditions can influence the direction and
magnitude of previous-year PPT legacies.
Discussion
Direction and magnitude of the simulated PPT legacies
Through this simulation analysis we demonstrated that previous PPT could
impose substantial legacy impacts on current ecosystem C fluxes at
interannual and interdecadal timescales. Notably, our simulation results
support the hypothesis proposed for our study site (Scott et al., 2009) that
the accumulated SOM during the previous wet period contributed to the net C
release from the ecosystem during the current dry period. This specific test
illustrates a major finding from our simulation study of a negative PPT
legacy effect on NEP; i.e., decreasing previous PPT increased current NEP,
whereas increasing previous PPT decreased current NEP (Figs. 3, 6).
Increasing prior PPT (wet legacy) led to limited changes in GEP but
consistently increased Re. Decreasing prior PPT (dry legacy) led to
more variable effects for both GEP and Re that were strongly
conditioned on current-period PPT such that increasing current PPT was
associated with increases in the dry-legacy effect. Overall, the effects on
GEP were larger than Re for reduced prior PPT and smaller for increased
prior PPT, which resulted in a consistent negative PPT legacy on NEP
regardless of current PPT. The complexity in the legacy effects on ecosystem
C cycling we show here are in part influenced by the contrasting PPT legacy
responses of C uptake and emission and their distinct interactions with
current PPT distributions.
Interannual precipitation legacy effects on resource pool
dynamics. Left panels show the legacy effects on pool dynamics in 2
representative wet years, while right panels for 2 representative dry
years. Legacy effects on pool size (e.g., ΔBiomass) are quantified as
the difference between the current-year pool size with previous-year PPT
change and that without previous-year PPT change. Solid lines represent a
30 % decrease, while dashed lines represent a 30 % increase in the
previous-year precipitation (PPT). SOM represents soil organic matter,
Nsoil soil mineral nitrogen, and SWC soil water content.
In projecting future dryland C dynamics, the effects of PPT legacies
increase the complexity of ecosystem responses to PPT variability. One
consistent interaction between legacy and current PPT effects was that
larger between-period PPT differences could result in larger legacy effects
(Fig. 4), which is in agreement with what has been found in some field
studies. For example, the magnitude of drought legacy on ANPP is
proportional to the severity of the drought (Yahdjian and Sala, 2006;
Swemmer et al., 2007), and dry- or wet-year legacies on ANPP are linearly
related to the PPT difference between years (Sala et al., 2012; Reichmann
et al., 2013a). Our simulation analysis detected that not only annual PPT
amount but also finer-scale PPT characteristics such as GS rainfall, BEI,
and event size could be important in determining the interannual-scale PPT
legacy effects (Table 1). These simulation results suggest that PPT legacy
effects may play a more important role in shaping the temporal variability
of dryland ecosystem C fluxes under the projected increase in future PPT
variability (Solomon et al., 2007; Cook and Seager, 2013) but that their
characterization remains a challenge.
The influence of PPT legacies on dryland ecosystem C balance may strongly
interact with other sources of variability in dryland C balance, including
current-year PPT (Flanagan et al., 2002; Hastings et al., 2005),
growing-season length (Xu and Baldocchi, 2004; Ma et al., 2007), seasonal drought
(Scott et al., 2009, 2010; Hamerlynck et al., 2013), and
other factors such as temperature and vegetation composition (Hui et al.,
2003; Hamerlynck et al., 2010; Barron-Gafford et al., 2012; Scott et al.,
2014). These interactions are shown by several examples from our
simulations. While PPT was wetter than normal in 1987 (537 mm), the NEP was
-85 g C m-2 yr-1 (a C source), due to the negative wet-legacy
impacts on NEP from several previous wet years before (1982–1985; see Fig. 6h).
PPT was nearly normal in 2008 (402 mm), but the simulated NEP was 80 g C m-2 yr-1 (a C sink), due to the positive dry-legacy impacts on
NEP from several previous dry years (2002–2007; see Fig. 6g). Our findings
of substantial PPT legacy effects are consistent with a recent analysis of
14 years (1997–2011) of eddy-covariance measurements, where Zielis et al. (2014) reported that inclusion of previous year's weather (PPT and
temperature) into the linear predicting models for NEP increased the
explained variance to 53 % compared to 20 % without accounting for
previous year's weather, indicating that previous year's weather also played
an important role in determining the C balance of the subalpine
spruce forest in Switzerland. Although response patterns generated from this simulation
study compared well with previous field observations, there exists no field
study that, to our knowledge, provides a similarly comprehensive analysis of
PPT legacies. The simulation experimental design of this study provides
helpful insights into designing field manipulative experiments to further
test the modeled patterns by focusing on contrasting wet and dry legacies,
separating ecosystem production and decomposition, and exploring the
difference in prior and current PPT on the magnitude of the PPT legacy
effect.
Potential mechanisms of the modeled PPT legacies
There are three basic mechanisms explaining why PPT legacy impacts can occur
in a model system like PALS. First, the rate of C fluxes is a function of
not only various environmental factors (e.g., PPT and temperature) but also
the pool size itself. For example, soil heterotrophic CO2 efflux
(Rh) rate is a product of the decomposition coefficient, two scalar
functions accounting for temperature and moisture influences, and also the
size of the SOM pool (Kemp et al., 2003; Shen et al., 2009). Change in
the SOM pool size from previous PPT thereby affects current Rh. Second,
different C pools have different turnover rates that determine whether
biogeochemical materials (e.g., biomass or SOM) can be carried over. If the
material produced in a previous year has a turnover rate of less than 1 year,
it will not be carried over to the next year to form a legacy impact as
explained in the first mechanism. In addition, the turnover rates of
different C pools also determine legacy duration. For example, SOM pools in
the model have relatively slower turnover rates than biomass pools (Shen
et al., 2005, 2008b), thus resulting in the longer-lasting
legacy impacts on SOM than on biomass or litter pools (Figs. 5 and 7).
Third, the interactions between C fluxes and resource (e.g., N or water)
availability also determine the direction and magnitude of legacy effects.
For example, N carryover as a legacy of a prior dry period (Fig. 5g, h) can
impose impacts on the current-period GEP only when the current-period PPT is
not so limiting (Fig. 3b). These are the general mechanisms explaining the
occurrence of the modeled PPT legacies from a systems perspective. Below we
discuss more specifically the major patterns and the responsible
biogeochemical carryovers found in this study.
An intuitive first explanation of the simulated wet legacies would be the
carryover of water. However, in most cases soil water carryover did not
occur because the wet legacies on SWC were mostly negative or close to zero
at the beginning of the current period/year (Figs. 5i–j; 7i–j). Soil
water carryover was therefore not the major contributor to the modeled PPT
legacy effects at interdecadal and interannual scales. This simulation
result corroborates field studies that have shown that carryover of
water across long temporal scales is rare in dryland ecosystems, because the
rainy growing seasons or wet years are often separated by dry dormant
seasons or dry years resulting in short residence times (Oesterheld et
al., 2001; Reichmann et al., 2013a; Scott et al., 2014).
The carryover of soil N (Nsoil) is mainly responsible for the modeled
GEP responses. In the PALS model, the photosynthetic rate is linearly
related to N availability if plant N demand is not fulfilled (Reynolds et
al., 2004; Shen et al., 2005). Therefore, the enhanced Nsoil from dry
legacies (Figs. 5g, h and 7g, h) generated mostly positive responses of
GEP (Figs. 3a, b and 6c). The simulated dry legacies increased Nsoil
mainly through suppressed plant growth (e.g., the reduced biomass and litter
production shown in Figs. 5 and 7) that limited N uptake, which is
consistent with the results of many field measurements that Nsoil accumulates under
drought conditions (Reynolds et al., 1999; Yahdjian et al., 2006;
Yahdjian and Sala, 2010; de Vries et al., 2012; Evans and Burke, 2013;
Reichmann et al., 2013b). Although diverse mechanisms of inorganic N
accumulation during dry periods have been proposed in field studies – such as
the diffusion restriction of N ions in thin water films of dry soil, the
reduced N immobilization by microbial growth and plant uptake, and the
reduced N loss from the soil via leaching (Yahdjian et al., 2006) – our
simulation results suggest that reduced plant uptake may be the main
contributor to the Nsoil accumulation during dry periods. Given the
accumulated Nsoil as a dry legacy, how ecosystem C fluxes such as GEP
respond to this dry legacy may be influenced by current PPT conditions. When
current PPT conditions were favorable (e.g., the increasing current-period
PPT treatment shown in Fig. 3b and the relatively wet years shown in Fig. 6c), GEP mostly increased with a dry legacy (or the accumulated N) because
both N and H2O availabilities were favorable for plant growth (or GEP).
Contrastingly, when current PPT conditions were unfavorable (e.g., the
decreasing current-period PPT treatment shown in Fig. 3a and the relatively
dry years shown in Fig. 6c), the GEP responses could be reduced because of
the constrained plant growth and the reduced biomass in previous dry years
(see Figs. 5c and 7b).
Similarly, the mostly negative responses of GEP to wet legacies (see Figs. 3a, b and 6d) can be explained by the reduced Nsoil
(Figs. 5g, h and 7g, h). The decrease of Nsoil with increasing PPT in the PALS
model is mainly attributed to the increases in plant N uptake and the N
leaching loss that is calculated as a linear function of PPT amount (Shen et
al., 2005). Similar to our simulation results, several field studies found
that N uptake increases and Nsoil decreases under wet conditions in
dryland ecosystems (McCulley et al., 2009; McCalley and Sparks, 2009;
Yahdjian and Sala, 2010; Reichmann et al., 2013b). However, contrary to our
model assumption that N leaching loss is greater in wet than in dry years,
some recent field studies have reported that the N leaching loss actually is
higher in dry than in wet years or at wet sites (McCulley et al., 2009;
Evans et al., 2013; Reichmann et al., 2013b; Homyak et al., 2014), resulting
in a more “open” N cycle under drier conditions. If these recent field
study results are also true for our semi-desert savanna ecosystem, the model
assumption could potentially cause an overestimation of Nsoil carryover
effects as shown in Figs. 3 and 6. Further studies are needed to
discriminate the relative contributions of different N processes (e.g., plant
uptake, microbial immobilization and mineralization, denitrification,
ammonia volatilization, and leaching) to the dynamics of soil inorganic N
pools. Nevertheless, this simulation analysis highlights the importance of
interactions between N and H2O availabilities in creating the legacy
impacts of PPT and in shaping the temporal variability of dryland ecosystem
C fluxes.
The carryover of organic material (biomass, litter and SOM) is mainly
responsible for the modeled Re responses. In the PALS model, the
autotrophic (Ra) and heterotrophic (Rh) respiration rates are
linearly related to the size of biomass, litter and SOM pools (Kemp et
al., 2003; Shen et al., 2008a, 2009). The previous wet
condition benefited biomass, litter and SOM accumulation (Figs. 5 and 7),
which resulted in the mostly positive wet-legacy impacts on Re (Figs. 3c, d and 6f). Conversely, the dry legacy decreased these pools
(Figs. 5 and 7) and therefore resulted in the mostly negative dry-legacy impacts
on Re (Figs. 3c, d and 6e). Contrary to our simulation results that
dry legacies are mostly negative on SOM and Rh, some field studies
suggest that the labile C resulting from litter decomposition in a dry
season may stimulate Rh in the following wet season (Jenerette et
al., 2008; Scott et al., 2009; Ma et al., 2012). This is likely because the
labile soil C pool in the PALS model only accounts for ∼ 3 %
of the total SOM and has a very short residence time (1.7 year; see
Table S1); small amounts of seasonal labile C carryover
therefore may not exert obvious legacy impacts on the total SOM pool size
and Rh across interannual and interdecadal scales. These results imply
that the PPT legacy effects differ in direction and magnitude, depending on
the type of C fluxes under consideration, the type of legacies (i.e., dry vs.
wet), and the temporal scale of analysis.
Several lines of future research will likely be needed to continue improving
the understanding of ecosystem legacy dynamics. Structural shifts in
vegetation composition such as woody-plant encroachment (Potts et al.,
2008; Scott et al., 2014), exotic-species invasion (Hamerlynck et al.,
2010; Scott et al., 2010), and changes in microbial communities (de Vries
et al., 2012; Evans and Wallenstein, 2012; Collins et al., 2014) may also
interact with the biogeochemical processes to shape the PPT legacy effects
on the temporal variability of dryland C fluxes. Furthermore, we need to
better understand the legacy effects of extreme events such as the cool-GS
drought in 2006 (see Fig. 1a) so that these important events can be
adequately simulated. This cool-GS drought may have caused increased plant
mortality as reported for a semi-desert grassland near our study site
(Scott et al., 2010; Hamerlynck et al., 2013), but that is poorly
represented in the model and may have caused the overestimation of the
modeled GEP in comparison with the observation (see Fig. S3b).
Finally, our approach that uses a highly resolved process model provides
information complementary to contrasting analytical approaches that evaluate
ecosystem responses to statistical rainfall regimes (Rodrigo-Iturbe et al.,
2006; Katul et al., 2007; Porporato and Rodríguez-Iturbe, 2013). Improvement of these
alternative modeling approaches is needed to understand both general and
specific ecosystem responses to changing PPT regimes at temporal scales from
events to decades.