Accelerated climate change and associated forest disturbances in the
southwestern USA are anticipated to have substantial impacts on regional
water resources. Few studies have quantified the impact of both climate
change and land cover disturbances on water balances on the basin scale, and
none on the regional scale. In this work, we evaluate the impacts of forest
disturbances and climate change on a headwater basin to the Colorado River,
the San Juan River watershed, using a robustly calibrated (Nash–Sutcliffe efficiency
0.76) hydrologic model run with updated formulations that improve estimates
of evapotranspiration for semi-arid regions. Our results show that future
disturbances will have a substantial impact on streamflow with implications
for water resource management. Our findings are in contradiction with
conventional thinking that forest disturbances reduce evapotranspiration and increase
streamflow. In this study, annual average regional streamflow under the
coupled climate–disturbance scenarios is at least 6–11 % lower than
those scenarios accounting for climate change alone; for forested zones
of the San Juan River basin, streamflow is 15–21 % lower. The monthly
signals of altered streamflow point to an emergent streamflow pattern related
to changes in forests of the disturbed systems. Exacerbated reductions of
mean and low flows under disturbance scenarios indicate a high risk of low
water availability for forested headwater systems of the Colorado River
basin. These findings also indicate that explicit representation of land
cover disturbances is required in modeling efforts that consider the impact
of climate change on water resources.
Introduction
Widespread forest disturbances are projected to increase with climate change
(McDowell et al., 2016; Allen et al., 2010; Van Mantgem et al., 2009) and
this will have major implications for ecosystem services
(Anderegg et al., 2013). These ecosystem services impact
the provision of food, water, and energy and therefore necessitate a more
robust understanding of (a) how the landscape will respond to the associated
shifts in timing and frequency of streamflow and (b) the dominant processes
driving these changes. However, the impacts of coupled disturbances (e.g.,
climate-induced pest outbreaks, fires, drought) in forest ecosystems on
water resources remain understudied, despite its importance for natural
resource management and energy production in affected basins around the
globe. For instance, climate-induced changes in forests will feedback to the
climate system by altering the albedo and reducing the carbon sink, which is
anticipated to further transform ecosystems in either positive or negative
ways (Dale et al., 2001). This is particularly
salient for regions of the USA such as the Colorado River basin
(CRB), where forest cover is anticipated to be significantly impacted by a
higher incidence of wildfire, drought, and pest infestations (Williams
et al., 2013).
To date, predictions of future streamflow in forested river basins under
future changes in climate and land cover have exhibited wide disagreement as
to the strength and even the direction of change. Water yields and peak
streamflows in North American river basins are anticipated to either
increase, decrease, or show no response to changing forest cover (see
Table 2 in Adams et al., 2012; Schnorbus et al., 2010; Guardiola-Claramonte
et al., 2011; Somor, 2010; McDowell et al., 2018). Causes of the reported
changes have been related to topography (Schnorbus et al., 2010)
and climate variability (Allen et al., 2015). Other reported causes are
secondary impacts occurring as a result of forest mortality that enhance
processes such as radiation (Royer et al., 2011; Varhola et al., 2010),
changes in albedo (Winkler et al., 2010), evapotranspiration (Zou
et al., 2010; Kang et al., 2006), groundwater availability (Bearup et
al., 2014, 2016), and soil moisture states
(Dale et al., 2001). Snow has been reported to
play an important role in changes in streamflow
(Solander et al., 2017; Bennett et al., 2015) through
increased snow accumulation and snowmelt (Bewley et al., 2010; Boon,
2007), snow cover duration (Boon, 2009), reduced interception, canopy sublimation,
and evapotranspiration in disturbed forests
(Livneh et al., 2015b). Temperature changes were noted to
play a role in decreasing streamflow in one of eight catchments examined in
Somor (2010). Some research points to the fact that we have a limited
understanding on why or how streamflow changes in the future under land
cover shifts (Bonan, 2008; Buma and Livneh, 2015, 2017).
Disturbances inducing forest loss are poorly represented or entirely absent
from earth system models (ESMs) and eco-hydrologic models (Brovkin et al.,
2013a; Scheller and Mladenoff, 2007; McDowell et al., 2011). In the Coupled
Model Intercomparison Project phase 5 (CMIP5), several ESMs contain
dynamic vegetation models (Collins et al., 2011; Watanabe et al.,
2011; Brovkin et al., 2013b). ESMs in CMIP5 model fractional plant
functional types (PFTs) and include feedbacks to the climate and land
surface driven by atmospheric simulations. However, in general, ESMs contain
simplifications of the explicit scenario of evolving landscape ecology and
do not include a full suite of disturbances (e.g., pests, drought, wildfire),
which are extremely difficult to simulate due to the computational expense
associated with the coupling between disturbances and the disparate timescales
involved. Therefore, current ESMs are limited in their assessment of
the feedbacks driven by the disturbances and the threat of complete system crash, e.g., the
loss of large tracts of forests and/or strongly declining water resources
(McDowell et al., 2018).
Climate impact studies from the southwestern USA highlight the strong
influence of changing temperature and precipitation on forest distributions
(Dale et al., 2001), forest mortality
(Allen et al., 2015), and streamflow (McCabe and Wolock, 2007; Nash
and Gleick, 1991). Specifically, the strong interaction between forests and
water (Anderson et al., 1976) means that forest disturbances will have
a large impact on water volume and the timing of streamflow. Streamflow
decreases were previously reported for the upper CRB at Lee's Ferry using
the Variable Infiltration Capacity (VIC) hydrologic model (Liang et al.,
1994, 1996) forced with future climate change scenarios alone
and reported decreased future projected streamflow (Christensen and
Lettenmaier, 2002). A separate study reported that the CRB streamflow is highly
sensitive to precipitation and temperature shifts, with large reductions in
streamflow estimated for small increases in precipitation and temperature
(Nash and Gleick, 1991) resulting from enhanced rates of
evapotranspiration.
The strong link between forests and water, coupled with the emerging threat
to future forest viability and integrity, provides a major impetus to study
the combined effects of climate and land cover change on streamflow in the
CRB. To this end, we apply a hydrologic model that incorporates projected
climate changes as well as forest and land cover changes based on recent
research. Unlike many other studies, we have included the impact of regrowth of
shrubs into our modeling approach since this is the most likely outcome in
the CRB (Rother et al., 2015). We determine the direction and shift in
streamflow under different scenarios of climate and land cover changes,
and we also identify the physical alterations occurring within watersheds across
scales and forest compositions to reveal the driving mechanisms behind the
streamflow changes. Knowledge of the physical mechanisms and dominant
triggers for streamflow alterations is critical because it will allow
decision makers to make more-informed assessments of near- and long-term
water operations in a water-constrained world with a changing climate
(Zou et al., 2010).
This paper is arranged as follows: Sect. 2 details information on
forest disturbances and the impact of climate change on streamflow, Sect. 3
outlines our study site and methodology, and Sects. 4, 5, and 6 provide
results, discussion, and conclusions, respectively.
Climate-driven forest disturbances and climate change impacts on
streamflow
As fire, pests, and drought modify forests in response to climate change, a
number of important energy and water fluxes become altered
(Adams et al., 2012). Land covers with a low canopy profile
and a small crown cover, e.g., shrubs or bare ground, partition water and
energy in different manners compared to forests that have a large crown cover. For
example, shrubs retain snow pack later into the melt season thus snow is
able to reach the ground and accumulate (Boon, 2007, 2009). This is
largely due to reduced canopy cover which results in reduced interception
and sublimation or transpiration (Pomeroy et al., 1998; Bearup et al., 2014)
from this above-ground storage reservoir, as well as higher solar radiation
and wind speeds through the open areas (Harpold et al., 2014).
Snow also melts quicker in response to increased shortwave
radiation and turbulent heat transfers linked to negative longwave radiative
fluxes (Burles and Boon, 2011). The responses have also been
linked to decreased albedo due to higher litter and/or darker soils
associated with dying trees (Bewley et al., 2010; Winkler et al.,
2010). Other effects of tree mortality on the water balance include changes
to soil moisture states, changes in groundwater recharge
(Bearup et al., 2016), and potential feedbacks to the
atmosphere (Bonan, 2008).
Variations in the type and magnitude of forest disturbances can also
strengthen or dampen some of these effects (Buma and Livneh, 2015). For
example, forest wildfires change soil properties and litter depths, which in
turn alter infiltration and runoff processes. In addition, mountain-pine-beetle infestations
change canopy conditions but do not change soil
properties or moisture states. Drought may affect the forest canopy and
the ability of soils to infiltrate water as trees die and soils desiccate
(Adams et al., 2012). Effects may also be impacted by the
type of post-disturbance regrowth and extent of disturbance that occurs,
such as increases or a difference in shrubs, grasses, forbs coverage, or
differences in the composition of tree species that replace the forests
(Buma and Livneh, 2017). For instance, water-yield declines have been
associated with changes in species compositions of forests in the southern
Appalachian Mountains (Caldwell et al., 2016). A recent study indicates
that soil water residence time is a key factor in water availability
post-disturbance owing to limited (or enhanced) evapotranspiration processes
(Buma and Livneh, 2017).
Selection of the appropriate timescale is an important aspect to consider in
vegetation disturbance studies. For example, research focused on forest
disturbance over a single summer season, such as post-fire hydrology in New
Mexico shows that surface runoff and recharge both rise following fire
(Atchley et al., 2018). However, these studies do not
measure the year-long change in water balance, which is critical for
snow-driven systems. Examination of water partitioning in disturbed forests
within time frames of less than 5 years may not adequately resolve
effects such as forest succession on the hydrologic regime of study basins
(Brown et al., 2005), which can result in a significant overestimation
of the impacts of changing forests on water yields over the long term
(Pugh and Gordon, 2013). Indeed, most studies that encompass forest
disturbance monitoring of greater than 5 years point to increased
evapotranspiration from understory regrowth and an associated decline or
mitigating effect of the forest cover removal (Brown et al., 2014;
Biederman et al., 2014; Guardiola-Claramonte et al., 2011). Other research
has shown that vegetation management, such as cutting or thinning of forests
in the face of climate change, could be used to ameliorate the impacts of
reduced streamflow in the CRB, noting that these effects would only last for
a period of approximately 10 years (Zou et al., 2010).
Finally, spatial heterogeneity also plays an important role in terms of
changing hydro-ecology in the wake of disturbances. Studies that focus on
plot-scale results, where the disturbed forest is the primary cover type,
illustrate different responses compared to locations where impacted trees are only
a component of the overall land cover, including grasses, shrubs, and
non-impacted tree species (Biederman et al., 2014; Pugh and Gordon,
2013). Recent studies examining climate change and extreme wildfire on
runoff erosion found that peak streamflow sediment yield will increase with
climate change and fire severity due to the lack of spatial heterogeneity in
land cover types (Gould et al., 2016). A study by Penn et al. (2016) compared hillslope to watershed-scale responses and found
a muted effect on the watershed scale in a headwater basin of the Colorado
Rocky Mountains.
Calibration, validation, and entire period of record monthly statistics
for simulated to naturalized streamflow for the San Juan at Archuleta, NM, and San
Juan at Bluff, UT.
Objective functionSan Juan at Archuleta, NMSan Juan at Bluff, UTCalibration (2006–2010)volume bias0.910.90Nash–Sutcliffe efficiency0.780.76Nash–Sutcliffe efficiency log0.770.75Validation (2001–2005)volume bias0.930.93Nash–Sutcliffe efficiency0.830.60Nash–Sutcliffe efficiency log0.770.43Entire period (2001–2010)volume bias0.900.91Nash–Sutcliffe efficiency0.810.67Nash–Sutcliffe efficiency log0.780.58MethodsStudy site
To understand the impact of forest disturbances on streamflow under climate
changes, on different temporal scales and spatial settings, we implemented
the VIC model for the San Juan River basin, a sub-basin of the CRB, to
simulate streamflow responses to future changes in temperature,
precipitation, and land cover. The San Juan is a major headwater basin of the
CRB, accounting for 15 % of streamflow and 22 % of the area of the upper
CRB. Spanning four states – the Four Corners – the San Juan watershed is
also critical for thermoelectric and hydropower generation, substantial oil
and gas development, and extensive irrigated agriculture. Temperature ranges
from -2 to 23 ∘C from January to July, respectively, while
average annual precipitation is ∼666mm. The San Juan basin
captures the diversity present across the CRB. For instance, high-elevation
(>4000m) Colorado mountain ranges and large snowmelt-driven
rivers comprise the upper San Juan basin. The lower San Juan basin, located
in New Mexico and Arizona, is flat, semi-arid, and representative of the
lower CRB, with intermittent streams that drain into the main tributary of
the San Juan during the summer when they are charged by summer monsoonal
rains. The San Juan River eventually drains into the CRB, just below the
town of Bluff, Utah (Fig. 1).
San Juan River basin (61 560 km2) in the Colorado River
watershed (634 150 km2, inset), one of the most important water
resources in the western USA. The San Juan River basin (outline of HUC8
watersheds shown in red) spans the Four Corners region (CO, UT, AZ and NM)
of the USA, and supports multiple energy and water projects. Naturalized flow
gauge sites (USBR) at San Juan at Bluff, UT and San Juan at Archuleta, NM are
shown with closed blue triangles.
Hydrologic model
For this work, we used the Variable Infiltration Capacity (VIC) model
version 4.2 (Bohn and Vivoni, 2016; Liang et al., 1994) at a 1/16∘ (6 km) spatial resolution. In each grid cell, VIC simulates vertical
energy and water dynamics at a 1 h time step for a mosaic of land cover
tiles underlain by a 3-layer soil column. Sub-grid heterogeneity in
infiltration is represented by a statistical distribution (the variable
infiltration capacity curve). Surface runoff is generated via saturation
excess, while sub-surface runoff is characterized by the non-linear baseflow
curve of Franchini and Pacciani (1991). VIC version 4.2 includes fractional
canopy coverage derived from the normalized difference vegetation index (NDVI)
and a spatially varying monthly climatology of leaf area index (LAI),
albedo, and canopy fraction (Bohn and Vivoni, 2016). Historical
climate data used to run VIC (daily precipitation, minimum and maximum
temperature, and wind speed) were obtained from existing gridded data sets
for the USA (Livneh et al., 2015a). These daily
fields were disaggregated to hourly intervals within the VIC model via
algorithms as described in Bohn et al. (2013), which also estimated
hourly short- and long-wave radiation and humidity. Land cover fractional
areas were taken from the average of the years 2001–2012 of the Moderate-Resolution
Imaging Spectroradiometer (MODIS) MCD12Q1 Collection 5 Plant
Functional Type (PFT) product of Friedl et al. (2010), using the
International Geosphere-Biosphere Program (IGBP) classification. Repeating
climatological seasonal cycles of vegetation parameters (LAI, canopy
fraction, and albedo) were derived from the MODIS Collection 5 MOD15A2,
MCD43A3, and MOD13A1 products (Myneni et al., 2002; Schaaf et al., 2002;
Huete et al., 2002) over the period 2000–2012, and aggregated spatially
over the MCD12Q1 land cover classes within each 1/16∘ grid
cell.
Soil physical properties (e.g., bulk density, saturated hydraulic
conductivity, quartz content) were derived from global data sets such as the
United Nations Food and Agriculture Organization (FAO) Digital Soil Map of
the World (FAO, 1998). Vegetation structural parameters, such as
stomatal and canopy resistances, were taken from Ducoudré et al. (1993). Several other parameters were calibrated empirically: D2 and D3 (the
thicknesses of the 2nd and 3rd soil layers); binfilt (the
infiltration capacity curve shape parameter); Ds, Ws, and Dsmax (non-linear baseflow
parameters); and αsnow (the albedo of newly fallen snow). The
model was calibrated using an automatic calibration tool (Yapo et
al., 1998) to correct streamflow biases against the USA Bureau of Reclamation (USBR) naturalized gauged monthly streamflow (2006–2010) for the San
Juan River basin at Bluff, UT (Fig. 1; Table 1). Our calibration achieved a
Nash–Sutcliffe efficiency of 0.76 over the calibration period and 0.60
over the validation period for the San Juan at Bluff, UT, USBR monthly
naturalized flow data (Table 1).
Temperature (∘C, a), precipitation (%, b), and land
cover (c, d) changes to the 2080s (2070–2099) averaged over the ESMs and
spatially over the San Juan River basin (61 560 km2 or 1570 grid
cells). CMIP5 dynamic forest disturbances for the four ESMs as a difference from
the historical climatology (1970–1999) illustrate a more moderate change with
HadGEM2-ES projecting the largest decline and MIROC-ESM projecting an
increase in forest cover (c). The disturbed vegetation scenario based on McDowell et
al. (2016) (d) illustrates strongly declining forest covers (>50% forest loss) and increasing shrub covers.
IGBP classification names and codes from MODIS, our new
classification, and remapped values (RM). Values for each ESM follows
MIROC-ESM, MPI-ESM-LR, HadGEM2-ES, and IPSL-CM5A-LR. For each ESM, the
original code and the remapped (RM) values are given.
Our study focused on three vegetation projections and four climate
projections for a total of 12 different scenarios (Fig. 2). The four climate
projections employed to drive VIC were based on ESM simulations from CMIP5
(Taylor et al., 2012) climate data including
daily temperature, precipitation, and wind speed, downscaled using the
multivariate adaptive constructed analogue (MACA) approach (Fig. 2a and
b; Abatzoglou and Brown, 2012) and again disaggregated to hourly intervals
via the algorithms described in Bohn et al. (2013). We selected the four
ESMs from CMIP5 because they implemented dynamic vegetation processes:
HadGEM2-ES (Collins et al., 2011; Cox, 2001), MIROC-ESM (SIEB-DGVM;
Watanabe et al., 2011; Sato et al., 2007), MPI-ESM-LR (JSBACH; Giorgetta
et al., 2013; Reick et al., 2013), and IPSL-CM5B-LR (ORCHIDEE; Krinner
et al., 2005). We used the representative concentration pathway (RCP) 8.5
emissions scenario, which stipulates strongly increasing emissions by 2100
(Van Vuuren et al., 2011) and corroborates current emissions on par with
RCP 8.5 (Le Quéré et al., 2015).
The three vegetation projections used in this study are: (1) climate-only, which assumes static land cover (i.e.,
vegetation types do not change); (2) dynamic, which uses
the dynamic vegetation changes present within the four CMIP5 ESMs (Fig. 2c);
and (3) disturbed, which uses disturbance projections
based on empirical statistical estimates of forest mortality in the southwest USA (Fig. 2d; McDowell et
al., 2016). Vegetation classes were aggregated to six dominant cover types
from 16 classes in the IGBP vegetation classification and from 9–13 classes
in the ESMs (Table 2). Vegetation changes observed in the
dynamic and disturbed scenarios were
applied to the historical MODIS vegetation to alter forest coverage for
future runs using a simple delta-change approach. For both the
dynamic and disturbed scenarios,
historical forest cover fractions were reduced and concordantly replaced by
shrubs in increments of ∼10 years (2006–2010, 2010–2020,
and so forth); we then ran the projections in 10 year segments, with each
segment having a new (constant) forest fraction and starting with the state
from the previous time period. Forest cover was reduced by approximately
90 % by 2100 for the disturbed scenario (McDowell and
Allen, 2015; McDowell et al., 2016; Fig. 2c, d). Table 3 contains forest
and shrub vegetation fractions for each scenario, and LAI, canopy fraction,
and albedo for forest and shrubs used in all scenarios from the average of
grid cells with greater than 50 % forest cover in the San Juan River
basin.
Average values for cells with forest cover greater than 50 %.
Climate-only forest and shrub fractional values (top left), dynamic forest percentages for each
climate model and all decades, and disturbed forests and shrubs for each decade.
Below, leaf area index (LAI, unitless), albedo (unitless), and canopy spacing
(fraction) for all months for forest, with shrub values in brackets.
We ran the 12 different scenarios for 1950–2099. We analyzed daily,
seasonal, and annual streamflow as well as monthly statistics of
temperature, precipitation, evapotranspiration, and snow water equivalent
(SWE) to understand changes in the water balance in the San Juan River
basin. In addition, we investigated the aridity effect upon water
availability under forest disturbances using a one-cell analysis. Some
studies have suggested an aridity effect, whereby basins with less than 500 mm
annual precipitation will see streamflow decrease and vice-versa
(Guardiola-Claramonte et al., 2011; Adams et al., 2012),
although this finding is not supported in all work (Brown et al., 2010;
Caldwell et al., 2016). For the single cell, we considered climate change by
adding +3 ∘C (warm) and +6 ∘C (hot) to the
temperature time series and changing precipitation by 20 or -20 %. We
then changed vegetation characteristics in the single cell to simulate
changes in LAI or fractional vegetation spacing (canopy spacing).
Monthly average future streamflows (m3s-1, 2070–2099) compared
to historical (1970–1999) for the San Juan River basin at Bluff, UT. The
range of responses for each ESM is represented by the semi-transparent
envelope around the lines. The simulated historical streamflow is shown in
black (solid line, no envelope), the climate-only scenarios are shown in grey
(dashed dark grey line, grey envelope), and the disturbed scenarios are shown in green
(dashed dark green line, green envelope).
ResultsChanging climate and land cover
Temperature and precipitation changes are consistent with previous modeling
efforts for the region (Gangopadhyay et al., 2011). The four ESMs
projected consistent increases in the annual average temperature
(4.3–7.1 ∘C, mean change of 5.7 ∘C) but
variable changes in the annual average precipitation (both increases and
decreases, -6.6–8.2 %, mean change of 1.7 %) for the San Juan River
basin by comparing the last 30 years of this century to the last 30 years
of the previous century (not shown). For both temperature and precipitation, changes are
most dramatic and variable after the 2050s, concurrent with increasing
greenhouse gas emissions. These four ESMs represent the range of warm/hot
and wet/dry changes for the San Juan River basin by the 2080s in CMIP5
(Taylor et al., 2012; Brekke et al., 2013). Seasonal variations
in temperature and precipitation change indicate important regional process
shifts. For instance, summer and winter differences show that the summer is
warming slightly more than the winter (5.8 ∘C compared to
5.6 ∘C). Annual average fall and winter precipitation is
projected to increase, while spring and summer precipitation is projected to
decrease slightly (-1 %) with a large range in variability across the
basin. We note that the signals of change for both temperature and
precipitation differ from the results offered by the U.S. Department of the Interior Bureau of Reclamation
data sets at 1/8th of a degree and downscaled using a slightly
different technique (Bureau of Reclamation, 2011).
Our study identifies a key challenge in representing land cover: land cover
change is represented differently by each of the four ESMs due to the
variable representations of land use and the application of different
dynamic models within each ESM (Arora, 2002). The
dynamic trajectory of change is variable depending on the
ESM considered (Fig. 2c). The MIROC-ESM model projects increasing forest
cover, while the HadGEM2-ES model projects the most amount of change in
terms of forest loss. Both IPSL-CM5B-LR and MPI-ESM-LR show only a small
amount of change in terms of land cover shifts by the 2080s. The
disturbed scenario reflects regional changes expected in
the southwestern USA (McDowell et al., 2016) and therefore projects a more
severe, and likely realistic, scenario in terms of projected forest cover
change in the San Juan River basin (Fig. 2d).
Changing streamflow and water balances
Annually, simulated streamflow in the San Juan River basin under the
climate-only scenario exhibits differences of -15 to
45 %, while the disturbed scenario indicates a shift of
-21 to 34 % for the 2080s as compared with historical streamflow,
dependent on the ESM (Fig. 3). The dynamic scenario changes
the streamflow by -16 to 50 %, but is generally very similar to the
climate-only scenario. An exception to this is the
MIROC-ESM dynamic scenario, which projects an increase in
streamflow during winter and a decrease in summer peak flow in response to
increasing forest cover and thus decreasing shrubs represented in this model
(Fig. 2c). Due to the lack of a large distinction in the vegetation changes
under the dynamic scenarios and resultant similarities
between the climate-only and dynamic
scenarios, we focus on the differences between the
climate-only and the disturbed scenarios
for the remainder of this paper.
Seasonal streamflow in the climate-only scenario versus
historical simulated streamflow illustrates a shift in the timing of peak
flow and increased winter streamflow in the San Juan River basin (Fig. 3).
Peak streamflow occurs approximately 1 month earlier, owing to earlier
snowmelt due to a temperature increase. This shift means that winter flows
are higher and also indicates more mid-winter warming events. Under
disturbed forest cover conditions, seasonal streamflow
shows a different hydrograph that represents a shift in timing of winter
streamflow and a change in the magnitude of both low (December–January) and high
(April–May) streamflow compared to the climate-only
scenario (green envelope and line, Fig. 3). The VIC simulations driven by
disturbed scenarios project a lower late-fall and winter
streamflow, with a delay in spring ice melt and subsequent increase in the pulse
of peak streamflow during April–May. Recessional streamflow (May–July) is
also slightly higher in the disturbed
scenario than the climate-only scenario,
resulting in greater water availability in summer (Fig. 3).
Monthly water balance panels for forested (greater than
50 %) regions in the San Juan River basin. Panels include precipitation
(mm) and temperature (∘C) for historical (1970–1999, black) and
future (2070–2099, grey, a), snow water equivalent (SWE, m3s-1, b),
snowmelt (m3s-1, c), sublimation from snow (m3s-1, d), sublimation from
canopy (m3s-1, e), soil evaporation (m3s-1, f), transpiration
(m3s-1, g), canopy evaporation (m3s-1, h), soil moisture from second
soil later (L2, m3s-1, i), soil moisture from the third soil layer (L3,
m3s-1, j), leaf area index (LAI, unitless, k), and runoff (m3s-1,
l) for simulated historical (black line), climate-only (grey line), and disturbed scenarios
(green line). The months of April and June are indicated with vertical grey
lines.
The mechanisms causing these differences in streamflow response to climate
change and forest disturbance are illustrated in Fig. 4 for forest-dominant
(more than 50 %) regions. In the climate-only
scenario, the streamflow response is dominated by the impact of temperature
on snow pack. Warmer winter temperatures and reduced March snowfall (Fig. 4a)
lead to a reduction in SWE, snowmelt, and sublimation
from both the pack and the canopy (Fig. 4b–e). Warmer temperatures, earlier
snowmelt, and greater April rainfall subsequently lead to increases in soil
evaporation (Fig. 4f) and transpiration (Fig. 4g) in the spring. Warmer
temperatures also lead to increased canopy evaporation in late summer (Fig. 4h).
However, the higher rates of soil evaporation and transpiration in the
spring deplete the middle layer soil moisture (Fig. 4i), which diminishes
their rates in the summer (Fig. 4f, g). In the fall, higher rainfall and
warmer temperatures lead to greater soil evaporation but only minimal
replenishment of soil moisture. Both bottom layer soil moisture (Fig. 4j)
and total (surface and subsurface) runoff (Fig. 4l) exhibit earlier and
smaller peaks in the spring, reflecting the earlier melting of the reduced
snow pack and lower levels in summer due to greater evapotranspiration in the spring.
Two factors in the disturbed scenario further impact the
snow pack, partially compensating for the effects of climate change. First,
replacement of forest with shrubs leads to an increase (relative to the
climate-only scenario) in the on-the-ground snow pack
accumulation (Fig. 4b), a prominent feature observed in disturbed forests
across North America (Boon, 2007; Zou et al., 2010; Biederman et al.,
2015; Brown et al., 2014; Harpold et al., 2014). Shrubs in our VIC
simulations have no canopy thus they have no mechanism to intercept snow.
This disturbance-driven increase in on-the-ground snow pack partially
compensates for the climate-induced streamflow increase during this time of
the year. In addition, the larger on-the-ground snow pack and smaller canopy
snow pack are accompanied by proportionally higher and lower rates of
sublimation from the ground and canopy snow packs, respectively, relative to
climate-only (Fig. 4d, e). Second, the increase in snow on
the ground caused by the higher shrub coverage leads to higher rates of snowmelt
(approximately 30 %, Fig. 4c), releasing water during the late spring
and early summer (April–June; Fig. 4l). The larger snowmelt flux leads to
substantial increases in transpiration in spring and early summer, not only
relative to the climate-only scenario but also relative
to historical conditions (Fig. 4g). A similar increase in soil evaporation
(Fig. 4f) does not occur due to the upper and middle soil layers already
being saturated in the spring in the climate-only case
(Fig. 4i). The high shrub transpiration rate in the
disturbed scenario delays the larger snowmelt flux in
reaching the bottom soil layer (Fig. 4j), leading to a delayed peak in
runoff (Fig. 4j, l). Meanwhile, LAI values are similar between shrubs and
forests (Fig. 4k), indicating that the water and energy balance differences
are mainly due to snow process shifts between those two land cover types.
Runoff differences for the climate-only scenario (x axis) plotted against
disturbed scenario (y axis). Increasing sizes indicate results from the entire San
Juan River basin (smallest), to all forests (next largest size), and then
forests with greater than 10, 50, 70, and 90 % coverage
(largest). All changes are shown as projected differences from the
historical (in % change).
Differences in streamflow among disturbed and
climate-only scenario results are most notable at fine
scales (100 to 3000 km2, 72 grid cells in the San Juan) where
the forest cover dominates (more than 50 %) the land cover (Fig. 5). As
basin size increases (smaller circles in Fig. 5), and forest cover becomes
sparser with respect to other types of land cover (e.g., mixed forest,
deciduous, shrubs and grass covers), the differences between the
climate-only scenario and the disturbed
scenario begin to decrease, which is corroborated by several other studies
(Zhang et al., 2014; Anderegg et al., 2015). The difference between the
two scenarios for the densest forest and smallest portion of the basin is
approximately 20 %, as observed for the larger circles in Fig. 5. This
dynamic occurs even under changing precipitation and temperature projections
that would otherwise cause increasing or decreasing streamflow (observed in
Fig. 5 color ramps that change from red to blue for precipitation and
different symbols for different temperature ranges). Even as the ESMs
project increasing precipitation and temperature, we see the variability in
responses through the cascade of scale and land cover variability. This
finding is consistent with other studies that observed that variability
(forest cover composition and topography) in the area and size of
forest–shrub conversion can buffer responses of streamflow or evapotranspiration shifts from
climate change (Winkler et al., 2014; Harpold et al., 2014; Caldwell et
al., 2016).
Cumulative density distributions are used here to illustrate the
variations in (a) climate and (b) vegetation parameterizations for a single
cell in the San Juan River basin. Climate alterations are shown for a
hot/wet, hot/dry, warm/wet and warm/dry scenarios for both forest and
shrubs. For the vegetation parameterization, forests, forests with 2 times
leaf area index (LAI), shrubs, and shrubs with
fractions of 0.5 and 1.0 are shown. A shrub fraction of 1 indicates that there are no
spaces between the plants. The third quantile in the average of the
distributions is indicated in each plot with intersecting lines.
The dynamic between temperature, precipitation, and vegetation changes is
examined in more detail by using a simple single-cell analysis where climate
and vegetation is altered in a sensitivity framework. Figure 6 illustrates the
single-cell changes in terms of runoff from the grid cell to investigate the
aridity effect. We can see that forests and shrubs act similarly in hot/warm
and dry environments, while the differences are more pronounced between
forests and shrubs in wetter environments (Fig. 6a). For the vegetation
changes, we see that changing forest structure (e.g., fraction of the canopy
that may occur as a result of pest outbreaks) results in small shifts for
forests but very large shifts for shrubs (Fig. 6b). For example, shrubs with
50 % canopy spacing produce more water than the forests for the majority
of runoff conditions (below the 3rd quantile, Fig. 6b). On the other
hand, under high runoff conditions (above the 3rd quantile, Fig. 6b)
shrubs act similarly to regularly spaced shrubs. These changes are, generally speaking, on the
same order as changes within the climate. Therefore, we
hypothesize that larger changes in hydrology under disturbances are more
likely to occur not from the forest disturbance itself but the secondary
effects such as regrowth of shrubs, the type of regrowth, and the pattern of
that regrowth on the landscape. This is why timescale is such an important
consideration in studies of this nature, as regrowth patterns are key to
understanding how water is partitioned across a disturbed landscape.
Discussion
Climate change in the CRB is anticipated to cause large impacts to water
resource sustainability (Christensen and Lettenmaier, 2007; Rasmussen et
al., 2014; Dawadi and Ahmad, 2012). However, to our knowledge, few modeling
studies have considered the impacts of climate change coupled with changes
in vegetation (Buma and Livneh, 2015; Carroll et al., 2017; Pribulick et
al., 2016). In this study, we incorporated changes from the CMIP5 dynamic
vegetation models and from an estimate of forest mortality (McDowell et al.,
2016) to consider the impacts of both climate and vegetation changes on the
water balance in a headwater system of the CRB, the San Juan River basin. We
found that failing to consider climate change coupled with vegetation
disturbances could result in a ∼10% over-estimation of the
annual water availability for this basin. For a river system such as the CRB
that is already gravely stressed, 10 % less water in the system may lead
to significant water management challenges. Furthermore, considering seasonality in
flows, the changes we illustrated in our monthly and seasonal scenarios
indicate that less water will runoff during spring with more water arriving
at peak melt. This could lead to water shortages and flooding, and lead to
planning issues for short-term water delivery.
In this work, we considered not only the impacts of changes to streamflow
but also the reasons why streamflow is changing. As in other studies, we
found that at the size of the basin and the land cover variability can
obscure the signal of change (Biederman et al., 2015; Penn et al., 2016).
When we consider forested regions only, we are able to understand how and
why streamflow is projected to change under the disturbed conditions. The
main mechanism that is shifting streamflow is the manner in which shrubs
impact the water balance during the cold and warm seasons. Snow pack is
retained further into the melt season and when snow starts to melt, it melts
more quickly and results in higher peak flows. These peak flows, however,
occur during a time that the shrubs are using the water, resulting in large
transpiration losses. We found differences in canopy evaporation, soil
evaporation, and sublimation, but these differences are quite small overall
when comparing the overall volume of water held back in the snowpack (SWE)
and leaving via shrub transpiration. Overall, the differences in the
mechanisms and delivery of the water changed, and this results in a 20 %
reduction in the amount of water that is available for runoff through the
year compared to the historical streamflow conditions for the forested
regions of the San Juan River basin.
We also found that climate and disturbance have opposing influences on snow
pack. In the early season, the forest disturbance partially compensates for
streamflow impacts caused by warmer temperatures (Fig. 3). We see that the
shift towards an earlier snowmelt is compensated in part by the fact that
shrubs held on to snow for longer into the snowmelt season, releasing snow
at a later date. Overall, this effect results in less water in the system and
peak flows that occur at the same point in the year, but are higher in
magnitude which results in lower soil moisture, such that the effect of
having a late melting pack may not be beneficial for water resources.
However, the snow on the ground could have important consequences for early
season energy balances.
The findings presented herein represent a deviation from the more broadly
accepted viewpoint that forest disturbances will lead to reduced
evapotranspiration and increased streamflow. Previous findings tend to be
based on studies carried out over short time periods (first 1–2 year
responses, <5 year studies), paired-basin analyses in watersheds
disturbed by clear cuts, and where climate variability may have obscured
results (Brown et al., 2005). Studies pointing towards increased
streamflow also broadly found evapotranspiration from the canopy decreased,
leading to an increase in runoff. However, work based on observations across
scales and encompassing multiple disturbances indicates that the regrowth
potential for understory, such as shrubs used in this study, is high and
that the regrowth is a major controlling factor for water availability and direction of
change for evapotranspiration and runoff (Caldwell et al., 2016;
Biederman et al., 2014, 2015; Brown et al., 2014;
Pribulick et al., 2016). Moreover, ecologists project that global forest
covers are expected to decline and be replaced with species and understory
compositions that are more water intensive. It is therefore paramount to
treat regrowth correctly within models to align these two currently
disparate principles and account accurately for changing streamflow. This is
a fundamental issue because ESMs rely upon the research principles developed
on plot-scale and watershed-scale observational studies and modeling work.
We also investigated the aridity effect upon water availability under forest
disturbances. By comparing vegetation changes to climate shifts in a single
cell, we highlighted the impacts of changing temperature and precipitation
versus the impacts of changing forest cover properties. We found that the
greatest differences in our results for forests and shrubs under climate
change occurred under wet conditions, and vice versa for dry conditions.
This may be due to the large component of water that is leaving via
evaporation in arid environments. Additionally, we found that an important
controlling factor for water availability in shrub environments was the canopy spacing between
the shrubs that lead to changes in water balances and water partitioning
within the environment. Thus, we believe that not only climate (i.e.,
aridity) but also vegetation characteristics (i.e., canopy spacing) may play
a fundamental role in the impact of disturbances on water availability.
Our study did not incorporate fine-scale processes such as lateral flow, as
these processes are not possible to implement using the VIC modeling approach.
Suggestions from studies examining vegetation structure and patchiness
illustrate that interconnected hillslope and riparian vegetation, such as
shrublands, can receive water from wet upland catchment areas
(Thompson et al., 2011). A recent study using the ParFlow–CLM
model, which incorporates lateral flow processes, found results that are
very similar to our findings (Pribulick et al., 2016). For
example, Pribulick et al. (2016) show non-linear declines in streamflow in
response to vegetation and temperature changes, with enhanced responses in
snowmelt-driven transects and where percent vegetation change was largest.
Other research suggests that when water availability declines, plants adapt
and become more efficient at using water (Troch et al.,
2009), which is not incorporated in the approaches undertaken in this study
or Pribulick et al. (2016). Additionally, VIC model parameters that impact
changes in water balances have inherent uncertainty under climate change
(Bennett et al., 2017, 2012), and some of these parameters, such
as canopy overstory, are represented in a binary fashion, which is not
necessarily indicative of the forest and shrubland mixtures observed during
forest recovery. This is a clear example of improvements that could be made
to the modeling approach presented herein that should be investigated in
future work.
Our study also points to the challenges associated with incorporating
spatially variable estimates of changing vegetation patterns. Although CMIP5
contains information from dynamic vegetation models, we find a large
disparity between the results from CMIP5 (dynamic) and the
current estimates from cutting-edge research on forest cover mortality
(disturbed); these methods are a scenario-style approach
that do not necessary reflect future changing land cover conditions. The
importance of accurately representing the effects that are observed on the
catchment scale and in offline models, such as VIC, cannot be understated.
ESMs require the capability to incorporate these changes in a meaningful way
that can be validated using our current understanding of changes to forests
in the southwestern USA and globally. This will enable the science
community to accurately capture the range of responses and the impacts of
changing climate and changing land cover on water resources. ESMs such as
the U.S. Department of Energy's E3SM model have started down this path with the
incorporation and testing of the FATES dynamic vegetation model
(Fisher et al., 2015).
The differences in future streamflow projections in comparison to historical
conditions we observed in this study are notable, and suggest that
streamflow will decline by a minimum of ∼10% for headwater
systems of the CRB. However, in the San Juan River basin forest cover
accounts for only 17 % of the area, and only 4 % of these cells have
greater than 80 % forest cover. Impacts may be much larger for adjacent
systems, such as the Gunnison, CO. Future work will investigate the impacts of
changing forest cover using sensitivity analysis to understand the
cumulative effect of changing climate and vegetation cover across the entire
CRB.
Conclusion
The implications of declining streamflow due to forest disturbances in the
San Juan River basin are significant for the CRB. Given that the San Juan
basin is one of the least forested portions of the CRB and assuming similar
rates of forest decline will occur nearby (Van Mantgem et al., 2009),
other regions of the CRB are likely to experience streamflow declines of
much higher severity due to changes in forest cover. Failure to adequately
understand the direction and magnitude of changes could have
catastrophic consequences for those who rely on this resource. The CRB – roughly 11 % area of the continental USA – directly
supports water supply for more than 30 million people, accounts for
approximately 15 % of the USA's crops and livestock (Cohen et al.,
2013), and 53 GW of power generation capacity (Buono and Eckstein,
2014; Cohen et al., 2013). Pressing work includes improvements in the spatial
representation of changing forest covers across the CRB, further
investigations into drivers of change including an understanding of the role
of aridity and variability, a stronger link between ecological dynamics and
hydrological knowledge that is translated into models, and upward
propagation of these findings into global climate models and ESMs.
Data availability
Downscaled CMIP5 climate model projections may be
downloaded via the MACA web portal:
https://climate.northwestknowledge.net/MACA/ (Abatzoglou, 2018). VIC model may be downloaded via
GitHub: https://github.com/UW-Hydro/VIC (Computational Hydrology Group, 2018). CMIP5 dynamic vegetation changes may
be downloaded via the ESGF portal:
https://esgf-node.llnl.gov/projects/esgf-llnl/. Historical VIC forcing data
may be obtained from
ftp://gdo-dcp.ucllnl.org/pub/dcp/archive/OBS/livneh2014.1_16deg/ (Livneh, 2018). Naturalized
streamflow data for the Colorado River basin may be obtained from USBR:
https://www.usbr.gov/lc/region/g4000/NaturalFlow/current.html (U.S. Bureau of Reclamation, 2018). Other model
parameter files and McDowell et al. (2016) vegetation disturbances may be
obtained by contacting the authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Observations and
modeling of land surface water and energy exchanges across scales: special
issue in Honor of Eric F. Wood”. It is a result of the Symposium in Honor of
Eric F. Wood: Observations and Modeling across Scales, Princeton, New Jersey,
USA, 2–3 June 2016.
Acknowledgements
We acknowledge the support of John Abatzaglou and Katherine Hegewisch for
providing us with additional models not in the original MACA data set. We
acknowledge the World Climate Research Programme's Working Group on Coupled
Modeling, which is responsible for CMIP, and we thank the climate modeling
groups for producing and making available their model output. For CMIP, the
U.S. Department of Energy's Program for Climate Model Diagnosis and
Intercomparison provides coordinating support and led development of
software infrastructure in partnership with the Global Organization for
Earth System Science Portals. Katrina E. Bennett, Kurt Solander, Richard S. Middleton,
Nathan G. McDowell, and Chonggang Xu acknowledge the Los Alamos National Lab's LDRD program for
supporting this work. Nathan G. McDowell further acknowledges support of Pacific
Northwest National Laboratories LDRD program. Theodore J. Bohn was supported by grant 1216037
from the U.S. National Science Foundation (NSF) Science, Engineering
and Education for Sustainability (SEES) Post-Doctoral Fellowship program and
NSF grant 1462086, DMUU: Decision Center for a Desert City III:
Transformational Solutions for Urban Water Sustainability Transitions in the
Colorado River Basin.
Edited by: Reed Maxwell
Reviewed by: two anonymous referees
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