HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-5517-2017Future shift of the relative roles of precipitation and temperature in
controlling annual runoff in the conterminous United StatesDuanKaikduan@ncsu.eduhttps://orcid.org/0000-0002-0808-7580SunGegesun@fs.fed.usMcNultySteven G.CaldwellPeter V.CohenErika C.SunShanleihttps://orcid.org/0000-0002-7237-2722AldridgeHeather D.ZhouDechengZhangLiangxiaZhangYangDepartment of Marine, Earth, and Atmospheric Sciences, North Carolina
State University, Raleigh, NC, USAEastern Forest Environmental Threat Assessment Center, USDA Forest
Service, Raleigh, NC, USACoweeta Hydrologic Laboratory, USDA Forest Service, Otto, NC, USAKey Laboratory of Meteorological Disaster of Ministry of Education,
Nanjing University of Information Science & Technology, Nanjing, Jiangsu,
ChinaState Climate Office of North Carolina, North Carolina State
University, Raleigh, NC, USAKai Duan (kduan@ncsu.edu) and Ge Sun (gesun@fs.fed.us)13November20172111551755293July201714July201710September20173October2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://hess.copernicus.org/articles/21/5517/2017/hess-21-5517-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/5517/2017/hess-21-5517-2017.pdf
This study examines the relative roles of climatic variables in altering
annual runoff in the conterminous United States (CONUS) in the 21st century,
using a monthly ecohydrological model (the Water Supply Stress Index model,
WaSSI) driven with historical records and future scenarios constructed from
20 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. The
results suggest that precipitation has been the primary control of runoff
variation during the latest decades, but the role of temperature will
outweigh that of precipitation in most regions if future climate change
follows the projections of climate models instead of the historical
tendencies. Besides these two key factors, increasing air humidity is
projected to partially offset the additional evaporative demand caused by
warming and consequently enhance runoff. Overall, the projections from 20
climate models suggest a high degree of consistency on the increasing trends
in temperature, precipitation, and humidity, which will be the major climatic
driving factors accounting for 43–50, 20–24, and
16–23 % of the runoff change, respectively. Spatially, while temperature
rise is recognized as the largest contributor that suppresses runoff in most
areas, precipitation is expected to be the dominant factor driving runoff to
increase across the Pacific coast and the southwest. The combined effects of
increasing humidity and precipitation may also surpass the detrimental
effects of warming and result in a hydrologically wetter future in the east.
However, severe runoff depletion is more likely to occur in the central CONUS
as temperature effect prevails.
Introduction
Precipitation and temperature are the two key climatic
variables that control land water balances and thus control water
availability for both ecosystem and humans (Lutz et al., 2014; Milly et
al., 2005; Seager et al., 2013; Piao et al., 2010). Changes in temperature
interact with changes in precipitation and cause profound shifts in water
balance, such as snowpack melting and accumulation (Barnett et
al., 2005; Zhang et al., 2015), intensification of hydrologic cycle (Creed et
al., 2015; Davis et al., 2015), precipitation partitioning (Duan et
al., 2016b; Zhou et al., 2015), extreme floods and droughts (Duan et
al., 2016a; Trenberth et al., 2014; Duan and Mei, 2014b), and can lead to
hydrological “nonstationarity” (Milly et al., 2008).
Surface and subsurface (shallow aquifers) runoff is a critical source of
fresh water for humans (Vörösmarty et al., 2000). The impacts of
temperature and precipitation changes on the magnitude and variability of
runoff (Ficklin et al., 2009; Arnell and Gosling, 2013; Nash and Gleick,
1991; Vano et al., 2012) have drawn particular attention due to its importance
for water supplies. Future changes in precipitation, evaporation, and plant
water use are direct driving forces of runoff generation. Climate change
alters both precipitation and the partitioning of precipitation into
evapotranspiration (ET) and runoff since a warmer climate generally
provides more energy for water fluxes between the land and the atmosphere.
Although an increase in precipitation may cause increase in both ET and
runoff, the enhanced evaporative demand can result in decreases in runoff
efficiency (ratio of runoff to precipitation) (McCabe and Wolock, 2016). Both
observation and simulation studies in the US suggest that higher ET
induced by rising temperature is unlikely to be counterbalanced by the
increase in precipitation, and will lead to less runoff at large scales (Duan et
al., 2016b; Jackson et al., 2005; Duan et al., 2017). Further, warming may
also cause precipitation decrease in some regions and exacerbate the effects
of temperature on runoff change.
Several studies have examined the relative contributions of historical
changes in precipitation and temperature to runoff variation at watershed
(Karl and Riebsame, 1989), regional (Ryberg et al., 2014; Gupta et
al., 2015), and continental (McCabe and Wolock, 2011) levels across the
conterminous US (CONUS). These studies all agree that precipitation, instead
of temperature, explains most of the long-term change and variability in
runoff during the past century. McCabe and Wolock (2011) suggested that the
effects of temperature on runoff may become more substantial under a warming
climate. However, no study in the literature has rigorously investigated the
potential changes in the roles of precipitation and temperature under future
climate scenarios. According to the Parameter-elevation Relationships on
Independent Slopes Model (PRISM) dataset
(http://prism.oregonstate.edu/) (Daly et al., 2008), the rate of
decadal change in temperature over the CONUS fluctuated between -0.03 and
+0.28 ∘C from the 1960s to the 2000s. The rate of warming is
likely to accelerate under intermediate or high-emission scenarios and
increase the pressure of water scarcity in many regions in this century
(IPCC, 2014; Schewe et al., 2014). In addition, future change in climate is
projected to vary spatiotemporally in both direction and magnitude in the
CONUS (Mearns et al., 2012). Thus, sensitivity of water budget to climate
change may be discrepant across time and space. Although the possible
underestimation of the influence of temperature in altering regional water
resources has been discussed recently (Sospedra-Alfonso et al., 2015;
Woodhouse et al., 2016), a comprehensive evaluation under different climate
backgrounds and land-cover compositions is still lacking.
We aim to address two questions in this study: (1) to what extent, if any,
will the relative roles of precipitation and temperature in controlling
runoff shift, if future climate changes follow the projections of climate
models instead of the tendencies documented in the recent decades, and
(2) how will runoff change in the future and what are the potential roles of
other climatic driving forces besides precipitation and temperature? In the
remainder of the paper, we first describe the methodology of runoff
simulation and sensitivity assessment, and the hydro-climatic datasets used,
followed by the results. Then, the advantages, limitations, and implications
of this study are discussed and the conclusions are drawn.
Location of the 18 water resource regions (WRRs) in the conterminous
United States (CONUS).
MethodsStudy area
The CONUS covers the 48 adjoining states and the District of Columbia. In the
hydrologic unit system developed by the US Geological Survey (USGS)
(http://water.usgs.gov/GIS/huc.html), the nation is divided into six
levels of hydrologic units and each unit is identified by a unique hydrologic
unit code (HUC) consisting of 2–12 digits. The first level of classification
divides the CONUS into 18 two-digit HUC areas that are also commonly referred
to as water resource regions (WRRs) (Fig. 1). These regions can be further
divided into 2099 eight-digit HUC areas, or HUC-8 watersheds. This study
investigates climate and runoff variations at the resolution of HUC-8
watershed, as well as the aggregations in each WRR and the entire CONUS. The
full lists and boundaries of hydrologic units at different levels can be
found in the Watershed Boundary Dataset
(https://datagateway.nrcs.usda.gov/).
Runoff modeling
The runoff responses to climate change and variability were modeled with the
Water Supply Stress Index model (WaSSI). WaSSI is a monthly ecohydrological
model that was developed to capture land-cover specific large-scale water
balance in the CONUS based on empirical and physically based
parameters (Caldwell et al., 2012; Sun et al., 2011b). It was integrated from
a snow model, an ET model, and a soil moisture accounting model. A
conceptual snow sub-model (McCabe and Markstrom, 2007) is used to partition
the total precipitation into rainfall and snowfall, and to estimate snowpack
melt/accumulation and snow water equivalent with concern of the mean
elevation, latitude, and air temperature in the watershed. ET is
calculated with an ecosystem ET model developed from the empirical
relationships between ET and precipitation, potential evapotranspiration
(PET), and leaf area index (LAI) (Sun et al., 2011a, b). These
ET functions were established for 10 different land-cover classes
independently to account for the different water demand within different
vegetation, ranging from cropland, deciduous forest, evergreen forest, mixed
forest, grassland, shrubland, wetland, open water, urban area, to barren
land. Then, this ET estimation is further constrained by soil water
availability, which is simulated using the algorithms of Sacramento Soil
Moisture Accounting model (SAC-SMA) (Burnash, 1995), as well as the processes
of infiltration and runoff generation at monthly basis. SAC-SMA is a classic
rainfall–runoff conceptual model that has been successfully used by the US
National Weather Service (NWS) to issue river forecasts across the country
for decades. Necessary inputs for WaSSI include monthly precipitation, air
temperature, PET, LAI, and land-cover composition. In this study, the spatial
distribution of LAI and the 10 land-cover classes were assumed to be static
over time. Monthly climate data were first scaled to watersheds by the
area-weighted averages. All the water balance components were calculated
independently for each land-cover class within each watershed, and then were
aggregated into monthly means. The model parameters were acquired from several
previous studies, including (1) the parameters of snow sub-model (four
parameters for each WRR) were estimated for each WRR by comparing regional
monthly mean snow water equivalent to remotely sensed values from the Snow
Data Assimilation System (McCabe and Markstrom, 2007; Caldwell et al., 2012);
(2) the parameters of ET sub-model (three empirical parameters for
each land-cover type) were estimated by empirical relationships derived from
eddy covariance or sapflow measurements at multiple sites (Sun et
al., 2011a, b); and (3) SAC-SMA parameters (11 parameters for each
watershed) used to drive the soil water balance sub-model were developed from
soil physical characteristics documented by the State Soil Geographic
Database (http://soildatamart.nrcs.usda.gov) (Anderson et al., 2006;
Koren et al., 2003).
The WaSSI model has been validated against observations at USGS gauged sites
at the levels of both 8-digit (Caldwell et al., 2012) and 12-digit HUC
watersheds (S. Sun et al., 2015). We here verify the model performance at
CONUS and WRR scales to complement to previous validations. The simulated
annual runoff, driven by monthly precipitation and temperature from the PRISM
dataset, was compared against the USGS measurements over the entire CONUS
(Fig. 2a and c) and in the 18 WRRs (Fig. 2b and d) for the time period of
1961–2010. Despite a slight overestimation of the minima, WaSSI shows
reliable accuracy in capturing annual runoff at both CONUS and WRR scales,
with R-square statistic reaching 0.91 and 0.95, and root mean squared error
(RMSE) limited to 29 and 55 mm yr-1, respectively.
Validations of the WaSSI model at the conterminous United States
(CONUS) and water resource region (WRR) levels. (a, b) Comparisons
of simulated annual runoff (R) (mm yr-1) against USGS observed data
in 1961–2010 over the entire CONUS (a) and in 18 WRRs (b).
(c, d) Comparisons of simulated runoff coefficient
(runoff / precipitation, R/P) against that derived from USGS observed
data in the CONUS (c) and WRRs (d).
Quantifying the independent effects of climatic variables
Large-scale water balance can be described as runoff (R) equals precipitation
(P) minus ET and changes in soil moisture (SM) and the hydrologically
connected snowpack (SP):
R=P-ET+dSM/dt+dSP/dt.
While P is the primary water input, changing temperature (T) and other
climatic factors interact with each other and affect R by altering the
melt/accumulation of snowpack and controlling ET with the constraints of
vegetation and soil moisture.
Here we develop a simple approach of sensitivity test to examine the
relative roles of climatic variables in R variation, as
ΔR=∑i=1NECi+EInt,
where ΔR denotes the change in R, which equals the combined effect
of variations in all the climatic variables (Ci,i=1,2,…,N). ΔR can be decomposed into the independent effects of each
variable (ECi) and the effect of interactions between them
(EInt). From a pre-change period (t1) to a post-change period
(t2), ΔR is quantified by R change (%) driven by changes in
all the variables, as the difference between R(C1t2,…,Cit2,…,CNt2) and R(C1t1,…,Cit1,…,CNt1), while ECi is
estimated by R change driven by changes in the variable Ci only, as the
difference between R(C1t1,…,Cit2,…,CNt1) and R(C1t1,…,Cit1,…,CNt1). EInt is calculated as the ΔR minus
∑i=1NECi, representing the changes in R that
cannot be accounted for by the independents effects. Given that the changing
climatic variables may cause either positive or negative effects on R,
their contributions (%) are quantified by the relative weights, as
C(Ci)=100⋅|ECi|/∑i=1NECi+EInt.
Temporal variations of annual mean precipitation (a),
surface air temperature (b), solar radiation at
surface (c), wind speed near surface (d), and specific
humidity near surface (e) over the CONUS. Thick lines and the
shading denote the multi-model ensemble means and uncertainty ranges of the
20 GCMs, respectively.
Modeling experimentsClimate projection
Climate data downscaled from the raw outputs of 20 global climate models
(GCMs) (Table 1) of the fifth phase of the Coupled Model Inter-comparison
Project (CMIP5) (the MACAv2-LIVNEH dataset, Livneh et al., 2013, available at
http://maca.northwestknowledge.net/) were used to test the potential
future changes in R. This dataset includes the CMIP5 experiments of
“historical”, Representative Concentration Pathways (RCP) 4.5, and RCP8.5,
which correspond to the climate forcings (i.e., greenhouse gases emissions,
aerosols, land use feedbacks, etc.) observed in the history and projected in
a future with the radiative forcing reaching 4.5 and 8.5 W m-2 in 2100
(equivalent to 650 ppm and 1370 ppm CO2), respectively (Moss et
al., 2010; IPCC, 2014). The used climatic variables include monthly P,
maximum and minimum T, solar radiation (Rs), wind speed
(Ws), and specific humidity (Sh) spanning from 1950 to 2099
(Fig. 3).
List of the 20 climate models and the changes in mean annual
precipitation and temperature over the conterminous United States (CONUS)
from the baseline scenario (B) to future scenarios S1 (RCP4.5/2030s), S2
(RCP4.5/2080s), S3 (RCP8.5/2030s), and S4 (RCP8.5/2080s).
To evaluate the R responses to various changes in future climates, we
conducted four 30-year simulation experiments: (i) RCP4.5/2030s (S1 scenario),
near-future 2020–2049 under RCP4.5; (ii) RCP4.5/2080s (S2), far-future
2070–2099 under RCP4.5; (iii) RCP8.5/2030s (S3), near-future 2020–2049
under RCP8.5; (iv) RCP8.5/2080s (S4), far-future 2070–2099 under RCP8.5.
These four future scenarios cover two post-change time periods (2030s and
2080s) and are compared to the historical condition in 1970–1999 (1980s)
that represents the baseline level. Traditional sensitivity test methods
usually assume a fixed amount of change (Karl and Riebsame, 1989) or allow
one (or more) of the variables to remain constant over time (McCabe and
Wolock, 2011). In this study, the 30-year-long continuous climate series were
used to examine the long-term patterns while implicitly incorporating the
inter- and intra-annual variations. This large set of climate projections was
collected to enable a robust quantification of the major uncertainties from
GCM structure and emission scenario.
Estimation of potential evapotranspiration
Hamon's PET equation has been used for PET estimation in previous WaSSI
simulations because it only requires mean temperature as input and has shown
reliable correlation with actual ET in historical periods (Lu et
al., 2005; Vörösmarty et al., 1998). Essentially, temperature-based
methods perform well because T is correlated with radiation and humidity at
monthly timescale (Sheffield et al., 2012). Such correlations are the
physical bases of the empirical ET functions, through which variability
in P, T, and LAI was able to explain the main controls of evaporation and
transpiration fluxes without including the radiative and aerodynamic
variables. However, recent studies revealed that the bias in
temperature-based methods could be amplified in future scenarios of global
warming, leading to overestimation of PET and ultimately ET and the
severity of land surface drying (Milly and Dunne, 2011; Sheffield et
al., 2012). Penman–Monteith (PM) reference ET (Allen et al., 1998), as a
commonly used alternative PET model, incorporates the effects of surface
temperature, humidity, wind, and radiation, and is considered the most
reliable PET approach where sufficient meteorological data exist (Kingston et
al., 2009; Feng and Fu, 2013).
Temporal variations of changes in annual potential
evapotranspiration (PET) over the CONUS against the baseline level
(1970–1999). Thick lines and the shading denote the ensemble means and
uncertainty ranges of the 20 GCMs, respectively.
In this case, using the Hamon equation would lead to 130 mm yr-1 larger
PET increase from the baseline to RCP8.5/2080s than that using PM equation
(Fig. 4). We assume that the PM PET projections are more reasonable because
the effects of future changes in Rs, Ws, and Sh are
included as well as T. In the remainder of this paper, we focus on
analyzing the R changes and the independent effects of five climatic
variables based on PM PET, i.e., P, T (including changes in maximum T,
minimum T, and mean T that was estimated as the average of maximum and
minimum), Rs, Ws, and Sh. Effects of P and T
evaluated from simulations of Hamon PET will also be investigated to address
the consistency and discrepancy caused by using different PET methods.
Projected changes in multi-year mean annual runoff (%) at HUC-8
watershed scale. (a–d) Changes from the baseline to S1
(RCP4.5/2030s) (a), S2 (RCP4.5/2080s) (b), S3
(RCP8.5/2030s) (c), and S4 (RCP8.5/2080s) (d) scenarios.
The maps display the multi-model mean changes from the 20 GCMs.
ResultsProjected changes in runoff
Changes in mean annual R under future climate change scenarios vary among
HUC-8 watersheds (Fig. 5) and WRRs (Fig. 6) across the CONUS. Runoff
depletion is projected to cover most part of the central CONUS across
WRR7–WRR12, with largest decreases over 50 % found in the
south of WRR10 (Missouri) under RCP8.5. Increases are mainly projected in the
southwest, the north of Missouri, and regions along the Atlantic coast and
Pacific coast. Extreme increases over 100 % are projected in several arid
watersheds in WRR15 (Lower Colorado) and WRR16 (Great Basin). However, this
may be caused by the inability of GCMs in reproducing the low P values in
these extremely dry areas. Although the general spatial patterns appear to be
similar in the four scenarios, there is an evident expansion of the areas
showing either extreme increasing or decreasing trend from the 2030s to the 2080s
under both RCP4.5 (Fig. 5a, b) and RCP8.5 (Fig. 5c, d) scenarios.
The large variability of regional changes in R (Fig. 6) indicates
considerable uncertainties from GCM structure. In most cases, the uncertainty
range is limited to -30–+30 %, showing both
positive and negative changing signals. The distributions of the median lines
and interquartile ranges (IQRs) suggest a hydrologically drier future in
WRR7–12 and WRR14 (Upper Colorado), where consistent
decreasing signal is found in all the scenarios. Increasing trend can be
found in WRR1 (New England), WRR2 (Mid-Atlantic), WRR17 (Pacific Northwest),
and WRR18 (California). Generally, the uncertainty ranges tend to increase
from 2030s to 2080s under both RCPs, and reach a particularly high level
under RCP8.5/2080s. There is a noticeable consistency in the pattern that
the GCMs agree more on the simulations in 2030s while the uncertainty
aggregates over time toward the 2080s, which implies the limitation of the
state-of-the-art GCMs in predicting the farther future.
Independent effects of climate variables
The changes in R discussed above are under the combined impact of changing
P, T, Rs, Ws, and Sh. The independent effects of
these factors over the entire CONUS are illustrated in Fig. 7a, b. The P and
T are clearly the two most influential factors, which are projected to
cause divergent changes in R due to the increase in P
(+15–31 mm yr-1) and T
(+1.8–5.3∘). The median values show that annual R
under the independent P effect is expected to increase by 13 mm yr-1
(4 %) in 2030s and 24 mm yr-1 (8 %) in 2080s under RCP4.5, and
by 21 (7 %) and 30 (10 %) mm yr-1 at the same time under
RCP8.5. In contrast, the independent effects of T reach -32
(-11 %), -50 (-17 %), -34 (-12 %), and -80
(-28 %) mm yr-1 in the scenarios S1–S4. The negative
effect of rising T is expected to exceed the positive effect of increasing
P and lead to overall decrease in R. However, Sh, the third largest
contributor, will enhance R by 3–12 % and largely offset the
T effects. A significant increasing trend in Sh is projected under both
RCP4.5 and RCP8.5 (Fig. 3e), which will suppress vapor pressure deficit and
thus partially counterbalance the increasing evaporative demand caused by
warming. Meanwhile, the effects of Rs (slightly negative),
Ws (slightly positive), and interactions among the factors (Int)
are relatively minimal (< 3 %), suggesting that the variations in T,
P, and Sh can explain the major changes in R.
Area-averaged changes in runoff in the 18 water resource regions
(WRRs) in the future scenarios. The four future scenarios are denoted by S1
(RCP4.5/2030s), S2 (RCP4.5/2080s), S3 (RCP8.5/2030s), and S4 (RCP8.5/2080s)
on the x axis. The vertical spread of the box–whisker plots shows the
different results projected from the 20 GCMs, with the boxes covering the
ranges from the 25 % quartile to the 75 % quartile of the distributions and the red lines within each box marking the
median values. Points outside the whiskers are taken as extreme outliers and
marked by plus signs.
Independent effects of the climate variables over the conterminous
United States (CONUS) in the future scenarios S1 (RCP4.5/2030s), S2
(RCP4.5/2080s), S3 (RCP8.5/2030s), and S4 (RCP8.5/2080s).
(a, b) Effects of precipitation (P), temperature (T), solar
radiation (Rs), wind speed (Ws), specific humidity
(Sh), interactions among the variables (Int), and their sum (Total) on runoff
based on the projections of Penman–Monteith PET. (c) Effects of
precipitation (P), temperature (T), interaction between P and T
(Int), and their sum (Total) on runoff based on the projections of Hamon PET.
The format of the box–whisker plots is the same as that in Fig. 6.
It is worth noticing that much larger uncertainty ranges can be found in the
P effects. Compared to the highly consistent increases in T and Sh, the
20 GCMs constantly disagree on the changing direction of P. Under
RCP8.5/2080s, the multi-model result of P effect ranges from -11 to
24 %, and the IQR also reaches the highest level (13 %). This indicates
that uncertainty in P projection is still the largest contributor to the
uncertainty in R simulations, especially in the far future.
We also compared these results with those evaluated based on Hamon PET
(Fig. 7c), and found some similar features. The differences in independent
effects of P and T between the two sets of results are mostly smaller
than 5 %, and both results show that the T effect would be twice as large
as the P effect at the CONUS scale. This suggests that the bias in PET model
structure is not likely to turn over the relative importance of P and T
effects as long as ET model is properly calibrated. However, the
projected decreases in R (i.e., the “Total” effects) are obviously more
severe when using Hamon PET because the positive effect of increasing
humidity is not considered.
Relative contributions of precipitation and temperature
Table 2 summarizes the relative contributions of P and T to R change
for the historical and future periods in 18 WRRs and the entire CONUS.
Historical changes in P, T, and their effects on R were tested using
PRISM climate data spanning from January 1960 to December 2010. Given the
significant spatial and temporal variability in R trend across the CONUS
(Mauget, 2003; McCabe and Wolock, 2002, 2011; Gupta et al., 2015), a
consistent breakpoint is statistically unavailable. We hereby took 1985 as
the breakpoint year for all the watersheds and evaluated the multi-decadal
mean changes from 1961–1985 (pre-change period) to 1986–2010 (post-change
period). Although the selection of different breakpoints may cause certain
deviations, the analysis can provide a comparable benchmark for exploring the
shifts in future scenarios at a multi-decadal scale. Unsurprisingly, the
results of these latest decades show the prevailing role of P in nearly all
the regions, with WRR14 being the only exception. In the future periods (from
baseline to S1–S4), however, results derived from both PM and Hamon
PET suggest that the role of T rise will surpass P and become the largest
driver in most of the regions (15–16 out of 18 WRRs) in the future.
In contrast, a larger mean contribution of P can be occasionally found in
the Atlantic coast (WRR1, 2), Pacific coast (WRR18), and the southwest
(WRR12, 15). Considering that the inconsistency among GCMs may make the
recognition of larger contributor dubious, we used Wilcoxon signed-rank test
(Gibbons and Chakraborti, 2011) to assess the statistical significance of the
difference between each pair of P and T contributions (i.e., 20 samples
from the 20 GCMs). The test results reveal high agreement among GCMs on the
prominent role of T across most regions (underlined in Table 2).
At CONUS level, the mean contributions of P and T are projected to lie
within 20–24 and 43–50 % using PM PET, and
33–40 and 55–62 % using Hamon PET, suggesting a similar
shift in the relative importance of these two key driving factors. However,
future changes in Sh, Rs, and Ws account for another
16–23, 2–7, and 1–4 % of R change
respectively, and indirectly affect the attributions to P and T. For
example, the R increase in WRR1 would be completely attributed to P
increase if Sh was not considered, and thus lead to an overestimation of P
contribution.
Comparison of multi-model averaged contributions (%) of
precipitation (P) and temperature (T) to changes in runoff in the 18
water resource regions (WRRs) and entire CONUS in the historical period
(1961–2010) and future scenarios S1 (RCP4.5/2030s), S2 (RCP4.5/2080s), S3
(RCP8.5/2030s), and S4 (RCP8.5/2080s). A larger contributor identified by
multi-model ensemble means is in bold, and a significantly larger contributor
identified by Wilcoxon signed-rank test (at 5 % significance) is in
italic.
WRRHistorical Projections based on PM PET Projections based on Hamon PET S1 S2 S3 S4 S1 S2 S3 S4 PTPTPTPTPTPTPTPTPT18893636363834383142613858405741534628017274028413039284347504950514746523603031372641303824444349385641523260483132444234629412347445442575048405857322234223442940254640573859465137606643028402742323826454154405646493758789623471951234820524057326537593265848372739234324422446385334583556296198982247204926452043375634614053335710816194718501849204635573262326033591188420421945184418473055296027582663127414352927353032273944383746384231511371182536273826352242355336563753286114305121482548204924493164366032643161157217283336323332362935524148434737491665232145244623452943345936583260385117917284228432942314244544454455347511895447294332463046305836544156395442CONUS572920452047244321503558356040553362
Relative importance of P and T in affecting runoff change across
the HUC-8 watersheds in the future scenarios of S1 (RCP4.5/2030s) (a), S2
(RCP4.5/2080s) (b), S3 (RCP8.5/2030s) (c), and S4
(RCP8.5/2080s) (d). The watersheds under larger influence of P and
T are marked with blue and red colors, respectively. The dark colors denote
the areas where 80 % or more of the 20 GCMs agree on the sign, while the
light colors denote the results of ensemble average.
Spatial distribution of the major driving factors
To further investigate the spatial pattern of future climatic controls on
annual R, we mapped the coverage of dominant driving factors (Fig. 8) and
examined its consistency with the changing trend in R at watershed scale
(Table 3). Judging by multi-model ensemble means, P and T are the largest
driving factor in 10–22 and 68–89 % of the CONUS area, respectively.
High consistency on their dominant roles (80 % or more of the 20 GCMs
agree on the sign) can be found in 4–7 and 21–41 % of
the CONUS, respectively. As P and T are projected to keep increasing, the
coverages of P- and T-dominant areas are also expected to expand from the
2030s to the 2080s. A directional change suggests that rising T will become
more influential in the east (WRR1–6), while P will prevail in
more watersheds across the west (WRR13–18). Although the aggregated
effect of Sh is quite close to that of P at large scales, it is only
expected to play a dominant role in several watersheds (1 % in area)
across the borders between WRR10 and WRR11 under RCP8.5/2080s.
The P-dominant areas that are mainly distributed in the southwest (WRR13,15)
and Pacific coast (WRR17,18) show clear signals of increasing R, driven by
the widespread increase in P. One the other hand, only two-thirds of the
T-dominant areas coincide with the areas of decreasing R, covering a large
part of the central CONUS (WRR7, 9, 10, 11) and a number of watersheds
scattered in the northwest (WRR14, 16, 17). Although T is also identified
as the most influential factor in the eastern regions WRR1–5, the
combined effect of the other four factors, primarily P and Sh, is projected to
exceed the T effect and lead to an increase in R.
Cross-comparison of the areal proportions (%) with different
dominant driving factors and changing directions of runoff (R) in the
future scenarios S1 (RCP4.5/2030s), S2 (RCP4.5/2080s), S3 (RCP8.5/2030s), and
S4 (RCP8.5/2080s). The areas where a variable is the largest driving factor
identified by multi-model ensemble means is marked in parentheses. The areas
where a variable is identified as the “dominant” factor are in bold. A
climate variable is identified as the “dominant” one only when 80 % or
more of the 20 GCMs agree that it is the largest driving factor of runoff
change.
* “↗” and “↘” indicate increase and
decrease in the multi-model means of runoff, respectively.
DiscussionSpatial patterns of future runoff change
This study characterizes and generalizes large-scale relationships among
changing P, T, and R despite the large geographic differences. The
coherence in the spatial dynamics of R trend and the corresponding climatic
drivers shows a rough pattern: T change dominates R decrease while P
and Sh changes dominate R increase. However, it should be interpreted with
limitations on timescale and underlying surface features. This pattern does
not hold true in all the watersheds due to the nonlinear complexity of R
response to climate change at various timescales, as well as the influence of
other watershed characteristics (e.g., topography, land use, soil property).
For example, slight decreases in annual P but increases in annual R are projected in south Texas due to the
changes in intra-annual climate variability. The role of T may also become
more positive in regions where water availability is dominated by snow
melting (Barnett et al., 2005; Lutz et al., 2014). In addition, local R can
be affected by other factors, such as land-cover evolution and the direct
effects of atmospheric composition on transpiration (Gedney et al., 2006;
Zhang et al., 2001, 2015).
The role of land cover and land use
Land cover, LAI, and soil are important controls on catchment water balance
and R sensitivity to climate change (Zhang et al., 2001; Bosch and Hewlett,
1982; Cheng et al., 2014). This study specifically focused on evaluating the
separate and combined effects of changing climates on R within a static
land cover/land use. We did not consider the potential evolution of land
cover and its interactions with water balance. We made no explicit tabulation
of the impact of land cover/land use on the R responses to climate change,
but we did incorporate it as a key factor by estimating ET with a
set of functions of climate, LAI, and soil moisture capacity and deficit.
Across the land-cover classes, the uncertainty ranges of independent
contributions of P (13–30 %) and T (39–51 %)
are relatively small compared to the ranges across WRRs (18–47 and
29–52 %). This may be because the discrepancy across different
land covers is largely offset by the different climate backgrounds across the
country. Evaluation of future land-cover change and its impact on R is beyond
the scope of this study. However, our results imply that the potential
impact of land-cover change might not be large enough to alter the relative
significance of P and T in controlling future continental water
availability.
Implications for water and land management
Our results have important implications for water and land management across
the CONUS. Water resource planning may need to prepare different management
strategies for areas facing contrasting future hydrological conditions.
Additional water storage such as reservoirs may be needed in regions
expecting more R, while inter-basin water transfer, improving water use
efficiency, and other water conservation measures such as rain harvesting,
and waste water recycling should be implemented for areas expecting water
shortages. The vast croplands across central US are likely to be threatened
by rising T and diminishing water availability for irrigation and food
production. Adaptations in cropping systems and irrigation strategy are
needed to secure food supply and increase resiliency to drought and changing
climate (Challinor et al., 2014; Teixeira et al., 2013). The drier and hotter
conditions may also result in increasing water stress, higher risks of tree
insects and disease outbreaks, and catastrophic wildfires in forests (Dale et
al., 2001) (e.g., national forests in WRR14, 16, 17) and grasslands (e.g., in
WRR10–11). Innovative land management practices such as forest
thinning and fuel management, irrigation, and planting drought-tolerant
species are vital to minimize the potential risk and vulnerability to climate
change and reduce the threats to ecosystems and society (G. Sun et al., 2015;
Grant et al., 2013; Vose et al., 2016).
Uncertainties and caveats
Considerable uncertainty lies in the projection of future climate changes
from the 20 GCMs. The uncertainty ranges under both RCP4.5 and RCP8.5 show
significant expansions over time from the 2030s to the 2080s. In particular, the
large uncertainty in predicting future P may substantially compromise the
reliability in evaluating either R change or the roles of P and T (Karl
and Riebsame, 1989; Piao et al., 2010). Although the results allow us to draw
some conclusions on the general patterns, uncertainties are large and vary
differently across space and time. There are certain limitations in this
evaluation that should be noted when interpreting the results. First, we did
not incorporate other sources of uncertainty, such as the methodology of
downscaling (Duan and Mei, 2014a; Chen et al., 2011), and structure and
parameters of hydrologic model (Jung et al., 2012). Although the selections
of GCM and emission scenario are more likely to be the largest sources of
uncertainty in hydro-climatic modeling (Kay et al., 2009; Wilby and Harris,
2006; Duan and Mei, 2014b), the other sources may also affect the results to
different extents. The roles of uncertainties from different sources can be
particularly equivocal when investigating seasonal/monthly variability and
extreme events (Bosshard et al., 2013; Giuntoli et al., 2015; Bae et
al., 2011; Kay et al., 2009). Second, we focused on the independent effects
of potential climate changes, while assuming that the inter-relationship among the
meteorological variables and water-balance components remains the same as in
historical periods. In future studies, improved climate datasets and better
representation of the physical mechanisms of climatic factors (e.g.,
radiation: Bohn et al., 2013; wind speed: McVicar et al., 2012) are needed to
reduce uncertainties.
Conclusions
This study evaluates the relative roles of precipitation and air
temperature, as well as solar radiation, wind speed, and air humidity, in
altering annual runoff across the CONUS based on a large ensemble of
simulations using data from both historical measurements and CMIP5 GCM
projections. Despite the large uncertainty and spatial variability involved
in the results, two robust conclusions can be drawn at the CONUS and regional
scales on a multi-decadal basis. First, the role of temperature will outweigh
that of precipitation in a continued warming future in the 21st century, in
spite of the fact that precipitation has been the primary control of runoff variation
during the latest decades. The projections from 20 climate models suggest a
high degree of consistency on the increasing trends in both precipitation and
temperature, but the negative effect of temperature is expected to exceed the
positive effect of precipitation on runoff change in most regions. Over the
entire CONUS, temperature is projected to be the largest contributor
(43–50 %), followed by precipitation (20–24 %),
humidity (16–23%), solar radiation (2–7 %), and wind
speed (1–4 %). Spatially, precipitation is likely to be the
dominant driving factor for runoff increase across the Pacific coast and the
southwest, while temperature will be more influential in the central CONUS
and parts of the northwest and cause runoff decreases.
Second, increasing humidity is expected to partially offset the additional
evaporative demand caused by warming, and consequently enhance runoff widely
across the country. Although the rising temperature is projected to be the
largest control of runoff change in the eastern CONUS, the combined effects
of increasing humidity and precipitation will surpass the detrimental effects
of warming and result in a hydrologically wetter future. This study also
raises concern on the choice of PET method. It has been well acknowledged in
hydrometeorological communities that temperature-based PET methods tend to be
oversensitive to temperature change. Our results further demonstrate that the
main risk of using temperature-based PET is overlooking the effects of other
changing climatic variables (mainly humidity in this case), which have not
been as widely measured as temperature and are relatively understudied,
rather than overestimating the effects of temperature.
All data used in the analysis and conclusions in this paper
are available in the references.
KD and GS designed the study. KD performed the analysis.
EC, HA, SS, DC, and XZ helped collect the data. All authors contributed to
the interpretation of the results. KD wrote the initial draft, and GS, PC,
SM, and YZ refined the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the National Science Foundation EaSM program
(AGS-1049200) awarded to North Carolina State University, and the Eastern
Forest Environmental Threat Assessment Center (EFETAC), USDA Forest Service,
Raleigh, NC. The MACAv2-LIVNEH dataset was produced under Northwest
Climate Science Center (NW CSC) US Geological Survey grant number G12AC20495.
Partial support was provided by the Natural Science Foundation of Jiangsu
Province, China (BK20151525), and the Pine Integrated Network: Education,
Mitigation, and Adaptation project (PINEMAP), which is a Coordinated
Agricultural Project funded by the USDA National Institute of Food and
Agriculture (award #2011-68002-30185). The authors would like to give
special thanks to Dennis Lettenmaier, Paul CD Milly, William Farmer, Brian
Finlayson, and two anonymous reviewers for their valuable comments and
suggestions.
Edited by: Jan Seibert
Reviewed by: Brian Finlayson and one anonymous referee
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