Serving source water for the Yellow, Yangtze and Lancang-Mekong
rivers, the Sanjiangyuan region affects 700 million people over its
downstream areas. Recent research suggests that the Sanjiangyuan region will
become wetter in a warming future, but future changes of streamflow extremes
remain unclear due to the complex hydrological processes over high-land
areas and limited knowledge of the influences of land cover change and
CO2 physiological forcing. Based on high-resolution land surface
modeling during 1979–2100 driven by the climate and
ecological projections from 11 newly released Coupled Model Intercomparison
Project Phase 6 (CMIP6) climate models, we show that different accelerating
rates of precipitation and evapotranspiration at 1.5 ∘C global
warming level induce 55 % more dry extremes over Yellow River and 138 %
more wet extremes over Yangtze River headwaters compared with the reference
period (1985–2014). An additional 0.5 ∘C warming
leads to a further nonlinear and more significant increase for both dry
extremes over Yellow River (22 %) and wet extremes over Yangtze River
(64 %). The combined role of CO2 physiological forcing and vegetation
greening, which used to be neglected in hydrological projections, is found
to alleviate dry extremes at 1.5 and 2.0 ∘C warming levels but to
intensify dry extremes at 3.0 ∘C warming level. Moreover,
vegetation greening contributes half of the differences between 1.5 and
3.0 ∘C warming levels. This study emphasizes the importance of
ecological processes in determining future changes in streamflow extremes
and suggests a “dry gets drier, wet gets wetter” condition over the
warming headwaters.
Introduction
Global temperature has increased at a rate of 0.17∘C per decade since
1970, contrary to the cooling trend over the past 8000 years (Marcott et
al., 2013). The temperature measurements suggest that 2015–2019 is the
warmest 5 years and 2010–2019 is also the warmest decade since 1850 (WMO,
2020). To mitigate the impact of this unprecedented warming on the global
environment and human society, 195 nations adopted the Paris Agreement, which
commits to “hold the increase in the global average temperature to well
below 2 ∘C above preindustrial levels and pursuing efforts to
limit the temperature increase to 1.5 ∘C”.
The response of regional and global terrestrial hydrological processes
(e.g., streamflow and its extremes) to different global warming levels has
been investigated by numerous studies in recent years (Döll et al.,
2018; Hoegh-Guldberg et al., 2018; Marx et al., 2018; Mohammed et al., 2017;
Thober et al., 2018; Xu et al., 2019; Zhang et al., 2016). In addition to
climate change, recent works reveal the importance of ecological factors
(e.g., CO2 physiological forcing and land cover change) in modulating
streamflow and its extremes. For example, increasing CO2
concentration is found to alleviate the decreasing trend of streamflow in
the future at the global scale through decreasing the stomatal conductance and
vegetation transpiration (known as CO2 physiological forcing)
(Fowler et al., 2019; Wiltshire et al., 2013; Yang et al., 2019; Zhu et al.,
2012). Contrary to CO2 physiological forcing, vegetation
greening in a warming climate is found to play a significant role in
exacerbating hydrological drought, as it enhances transpiration and dries up
the land (Yuan et al., 2018b). However, the relative contributions of
CO2 physiological forcing and vegetation greening to the changes in
terrestrial hydrology, especially the streamflow extremes, are still unknown,
and whether their combined impact differs among different warming levels
needs to be investigated.
Hosting the headwaters of the Yellow River, the Yangtze River and the
Lancang-Mekong River, the Sanjiangyuan region is known as the “Asian Water
Tower” and affects 700 million people over its downstream areas. Changes
of streamflow and its extremes over the Sanjiangyuan region not only
influence local ecosystems, environment and water resources, but also
the security of food, energy and water over the downstream areas. Both the
regional climate and ecosystems show significant changes over the
Sanjiangyuan region due to global warming (Bibi et al., 2018; Kuang and
Jiao, 2016; Liang et al., 2013; Yang et al., 2013; Zhu et al., 2016).
Historical changes of climate and ecology (e.g., land cover) are found to
cause significant reduction in mean and high flows over the Yellow River
headwaters during 1979–2005, which potentially increases drought risk over
its downstream areas (Ji and Yuan, 2018). And CO2 physiological
forcing is revealed to cause equally large changes in regional flood
extremes as the precipitation over the Yangtze and Mekong rivers (Fowler et
al., 2019). Thus the Sanjiangyuan region is a sound region to investigate
the role of climate change and ecological change (e.g., land cover change
and CO2 physiological forcing) in influencing the streamflow and its
extremes (Cuo et al., 2014; Ji and Yuan, 2018; Zhu et al., 2013). Recent
research suggests that the Sanjiangyuan region will become warmer and wetter
in the future, and extreme precipitation will also increase at the
1.5 ∘C global warming level and further intensify with a
0.5 ∘C additional warming (Li et al., 2018; Zhao et al., 2019).
However, how the streamflow extremes would respond to the 1.5 ∘C
warming, what an additional 0.5 ∘C or even greater warming would
cause and how much ecological factors (e.g., CO2
physiological forcing and land cover change) contribute are still unknown.
Solving the above issues is essential for assessing the climate and
ecological impact on this vital headwater region.
In this study, we investigated the future changes in the streamflow extremes
over the Sanjiangyuan region from an integrated ecohydrological perspective
by taking CO2 physiological forcing and land cover change into
consideration. The combined impacts of the above two ecological factors at
different global warming levels were also quantified and compared with the
impact of climate change. The results will help understand the role of
ecological factors in future terrestrial hydrological changes over the
headwater regions like the Sanjiangyuan and provide guidance and support
for the stakeholders to make relevant decisions and plans.
Data and methodsStudy domain and observational data
The Sanjiangyuan region is located at the eastern part of the Tibetan
Plateau (Fig. 1a), with the total area and mean elevation being
3.61 × 105 km2 and 5000 m respectively. It plays a
critical role in providing freshwater, by contributing 35 %, 20 % and
8 % to the total annual streamflow of the Yellow, Yangtze and
Lancang-Mekong rivers (Li et al., 2017; Liang et al., 2013). The source
regions of Yellow, Yangtze and Lancang-Mekong rivers each account for 46 %,
44 % and 10 % of the total area of the Sanjiangyuan, and
the Yellow River source region has a warmer climate and sparser snow cover
than the Yangtze River source region.
(a) The locations of the Sanjiangyuan region and streamflow
gauges. (b–d) The time series of annual temperature, precipitation and
growing season leaf area index averaged over the Sanjiangyuan region during
1979–2100. (e) Observed and simulated annual CO2 concentration over the
Sanjiangyuan region. Red stars on the right in (a) are two streamflow stations named
Tangnaihai (TNH) and Zhimenda (ZMD). Black, blue and red lines in (b–d) are
ensemble means of CMIP6 model simulations from the historical, SSP245 and
SSP585 experiments. Shadings show the ranges of individual ensemble members. Cyan
and brown lines in (e) are future CO2 concentration under SSP245 and
SSP585 scenarios simulated by the MAGICC7.0 model.
Monthly streamflow observations from the Tangnaihai (TNH) and the Zhimenda
(ZMD) hydrological stations (Fig. 1a), which were provided by the local
authorities, were used to evaluate the streamflow simulations. Data periods
are 1979–2011 and 1980–2008 for the Tangnaihai and Zhimenda stations, respectively. Estimations of monthly terrestrial water storage change and
its uncertainty during 2003–2014 were provided by the Jet Propulsion
Laboratory (JPL), which used the mass concentration blocks (mascons) as basis
functions to fit the Gravity Recovery and Climate Experiment (GRACE)
satellite's inter-satellite ranging observations (Watkins et al., 2015). The
Model Tree Ensemble evapotranspiration (MTE_ET; Jung et al.,
2009) and the Global Land Evaporation Amsterdam Model evapotranspiration
(GLEAM_ET) version 3.3a (Martens et al., 2017) were used to
evaluate the ET simulation.
CMIP6 data
Here, 19 Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et
al., 2016) models which provide precipitation, near-surface temperature,
specific humidity, 10 m wind speed and surface downward shortwave and longwave
radiation at a daily timescale were first selected for evaluation. Then,
models were chosen for the analysis when the simulated meteorological
forcings (e.g., precipitation, temperature, humidity, and shortwave
radiation) averaged over the Sanjiangyuan region have the same trend signs
as the observations during 1979–2014. Table 1 shows the 11 CMIP6 models that
were finally chosen in this study. For the future projection (2015–2100), we
chose two Shared Socioeconomic Pathway (SSP) experiments: SSP585 and
SSP245. SSP585 combines the fossil-fueled development socioeconomic pathway
and 8.5 W m-2 forcing pathway (RCP8.5), while SSP245 combines the
moderate development socioeconomic pathway and 4.5 W m-2 forcing pathway
(RCP4.5) (O'Neill et al., 2016). Land cover change is quantified by the leaf
area index (LAI) as there is no significant transition between different
vegetation types (not shown) according to the Land-use Harmonization 2
(LUH2) dataset (https://esgf-node.llnl.gov/search/input4mips/, last access: 1 April 2020). For the
CNRM-CM6-1, FGOALS-g3 and CESM2, the ensemble mean of LAI simulations from
the other 8 CMIP6 models was used because CNRM-CM6-1 and FGOALS-g3 do not
provide dynamic LAI, while the CESM2 simulates an abnormally large LAI over
the Sanjiangyuan region. To avoid systematic bias in meteorological forcing,
the trend-preserved bias correction method suggested by ISI-MIP (Hempel et
al., 2013) was applied to the CMIP6 model simulations at monthly scale. The
China Meteorological Forcing Dataset (CMFD) was taken as meteorological
observations (He et al., 2020). For each month, the temperature bias in CMIP6
simulations during 1979–2014 was directly deducted. Future temperature
simulations in SSP245 and SSP585 experiments were also adjusted according to
the historical bias. Other variables were corrected using a
multiplicative factor, which was calculated using observations to divide
simulation during 1979–2014. In addition, monthly leaf area index was also
adjusted to be consistent with satellite observation using the same method
as temperature. All variables were first interpolated to a 10 km
resolution over the Sanjiangyuan region, and the bias correction was
performed for each CMIP6 model at each grid. After bias correction, absolute
changes of temperature and leaf area index and relative changes of other
variables were preserved at a monthly timescale (Hempel et al., 2013). Then,
the adjusted CMIP6 daily meteorological forcings were disaggregated into hourly time steps using the diurnal cycle ratios from the China Meteorological Forcing
Dataset.
The historical CO2 concentration used here is the same as the CMIP6
historical experiment (Meinshausen et al., 2017), while future CO2
concentration in SSP245 and SSP585 scenarios came from simulations of a
reduced-complexity carbon-cycle model, MAGICC7.0 (Meinshausen et al., 2020).
CMIP6 simulations used in this study. “His” means historical
simulations during 1979–2014 with both anthropogenic and natural forcings;
SSP245 and SSP585 represent two Shared Socioeconomic Pathways during
2015–2100. Note the CNRM-CM6-1 and CNRM-ESM2-1 do not provide r1i1p1f1
realization, so r1i1p1f2 was used instead.
The land surface model used in this study is the Conjunctive
Surface-Subsurface Process model version 2 (CSSPv2), which has been proved
to simulate the energy and water processes over the Sanjiangyuan region well
(Yuan et al., 2018a). Figure 2 shows the structure and main ecohydrological
processes in CSSPv2. The CSSPv2 is rooted in the Common Land Model (CoLM;
Dai et al., 2003), with some improvements of hydrological processes. CSSPv2
has a volume-averaged soil moisture transport (VAST) model, which solves the
quasi-three-dimensional transportation of the soil water and explicitly
considers the variability of moisture flux due to subgrid topographic
variations (Choi et al., 2007). Moreover, the Variable Infiltration Capacity
runoff scheme (Liang et al., 1994) and the hydrological properties of soil
organic matters were incorporated into the CSSPv2 by Yuan et al. (2018a), to
improve its performance in simulating the terrestrial hydrology over the
Sanjiangyuan region. Similar to CoLM and the Community Land Model (Oleson et
al., 2013), vegetation transpiration in CSSPv2 is based on Monin–Obukhov
similarity theory, and the transpiration rate is constrained by the leaf
boundary layer and stomatal conductance. Parameterization of the stomatal
conductance (gs) in CSSPv2 is
gs=mAnPCO2/Patmhs+bβt,
where m is a plant functional type-dependent parameter, An is
leaf net photosynthesis (µ mol CO2 m-2 s-1), PCO2 is the CO2 partial pressure at the leaf surface (Pa), Patm is
the atmospheric pressure (Pa), hs is the leaf surface humidity and b
is the minimum stomatal conductance (µ mol m-2 s-1), while
βt is the soil water stress function. Generally, the stomatal
conductance decreases with an increase in CO2 concentration.
Main ecohydrological processes in the Conjunctive
Surface-Subsurface Process version 2 (CSSPv2) land surface model.
First, bias-corrected meteorological forcings from CMIP6 historical
experiment were used to drive the CSSPv2 model (CMIP6_His/CSSPv2). All simulations were conducted for two cycles during 1979–2014
at a half-hourly time step and a 10 km spatial resolution, with the first cycle
serving as the spin-up. The correlation coefficient (CC) and root mean square
error (RMSE) were calculated for validating the simulated monthly
streamflow, annual evapotranspiration and monthly terrestrial water storage.
The Kling–Gupta efficiency (KGE; Gupta et al., 2009), which is widely used in
streamflow evaluations, was also calculated. The above metrics were calculated as
follows:
2CC=∑i=1n(xi-x‾)(yi-y‾)∑i=1n(xi-x‾)2∑i=1n(yi-y‾)23RMSE=∑i=1n(xi-yi)2n4KGE=1-(1-CC)2+1-σxσy2+1-x‾y‾2,
where xi and yi are observed and simulated variables in a
specific month (or a specific year) i, respectively, and x‾ and y‾
are the corresponding monthly/annual means during the evaluation period n.
The σx and σy are standard deviations for observed
and simulated variables respectively. The correlation coefficient represents
the correlation between simulation and observation, while the RMSE is the
simulated error. The KGE ranges from negative infinity to 1, and model
simulations can be regarded as satisfactory when the KGE is larger than 0.5
(Moriasi et al., 2007).
Second, bias-corrected meteorological forcings in SSP245 and SSP585 were
used to drive CSSPv2 during 2015–2100 with dynamic LAI and CO2
concentration (CMIP6_SSP/CSSPv2). Initial conditions of
CMIP6_SSP/CSSPv2 came from the last year in
CMIP6_His/CSSPv2.
Then, the second step was repeated twice by fixing the monthly LAI
(CMIP6_SSP/CSSPv2_FixLAI) and mean CO2
concentration (CMIP6_SSP/CSSPv2_FixCO2) at
2014 level. The difference between CMIP6_SSP/CSSPv2 and
CMIP6_SSP/CSSPv2_FixLAI is regarded as the net
effect of land cover change, and the difference between CMIP6_SSP/CSSPv2 and CMIP6_SSP/CSSPv2_FixCO2 is
regarded as the net effect of CO2 physiological forcing.
Warming level determination
A widely used time-sampling method was adopted to determine the periods of
different global warming levels (Chen et al., 2017; Döll et al., 2018;
Marx et al., 2018; Mohammed et al., 2017; Thober et al., 2018). According to
the HadCRUT4 dataset (Morice et al., 2012), the global mean surface
temperature increased by 0.66 ∘C from the preindustrial era
(1850–1900) to the reference period defined as 1985-2014. Then, starting
from 2015, 30-year running mean global temperatures were compared to those
of the 1985–2014 period for each global climate model (GCM) simulation. And the 1.5 ∘C/2.0 ∘C/3.0 ∘C warming period is defined as the
30-year period when the 0.84 ∘C/1.34 ∘C/2.34 ∘C global warming, compared with the reference period (1985–2014), is first
reached. The median years of identified 30-year periods, referred to as
“crossing years”, are shown in Table 2.
Determination of “crossing years” for the periods reaching 1.5, 2
and 3 ∘C warming levels for different GCM and SSP combinations.
Models1.5 ∘C warming level 2.0 ∘C warming level 3.0 ∘C warming level SSP245SSP585SSP245SSP585SSP245SSP585ACCESS-ESM1-5202420232037203420702052BCC-CSM2-MR2026202320432034Not found2054CESM2202420222037203220692048CNRM-CM6-1203220282047203920752055CNRM-ESM2-1203020262049203920752058EC-Earth3-Veg202820232044203520722053FGOALS-g32033203220632046Not found2069GFDL-CM4202520242038203620732053INM-CM5-02031202720592038Not found2063MPI-ESM1-2-HR2032203020552044Not found2066MRI-ESM2-0202420212038203020742051Definition of dry and wet extremes and robustness assessment
In this research, the standardized streamflow index (SSI) was used to define
dry and wet extremes (Vicente-Serrano et al., 2012; Yuan et al., 2017). The
July–August–September (JAS) mean streamflow for each year of the reference
period was first collected and used to fit a gamma distribution:
f(x,β,α)=βαΓ(α)xα-1e-βx,
where x is the streamflow, and α and β are parameters.
Then the fitted distribution was used to standardize the JAS mean streamflow
in each year (i) during both the reference and projection periods as
6SSIi=Z-1(F(xi))7F(xi)=∫0xif(x,β,α)dx,
where Z-1 is the inverse cumulative distribution function of
the normal distribution, and F(x) is the cumulative distribution
function of the gamma distribution. Here, dry and wet extremes were defined
as SSIs smaller than -1.28 (a probability of 10 %) and larger than 1.28
respectively.
The relative changes in frequency of dry/wet extremes between the reference
period and different warming periods were first calculated for each GCM
under each SSP scenario, and the ensemble means were then determined for
each warming level. To quantify the uncertainty, the above calculations were
repeated using bootstrapping 10 000 times, and 11 GCMs were resampled
with replacement during each bootstrap (Christopher et al., 2018). The 5 %
and 95 % percentiles of the total 10 000 estimations were finally taken as
the 5 %–95 % uncertainty ranges.
ResultsTerrestrial hydrological changes at different warming levels
As shown in Fig. 1b–e, observations (pink lines) show that the annual
temperature, precipitation and growing season LAI increase at the rates of
0.63 ∘C per decade (p=0), 16.9 mm per decade (p=0.02) and 0.02 m2 m-2 per decade (p=0.001) during 1979–2014 respectively. The
ensemble means of CMIP6 simulations (black lines) can generally capture the
historical increasing trends of temperature (0.30 ∘C per decade,
p=0), precipitation (7.1 mm per decade, p=0) and growing season LAI (0.029 m2 m-2 per decade, p=0), although the trends for precipitation and
temperature are underestimated. In 2015–2100, the SSP245 scenario (blue
lines) shows continued warming, wetting and greening trends, and the trends
are larger in the SSP585 scenario (red lines). The CO2 concentration
also keeps increasing during 2015–2100 and reaches 600 and 1150 ppm
in 2100 for the SSP245 and SSP585 scenarios respectively. Although the
SSP585 scenario reaches the same warming levels earlier than the SSP245
scenario (Table 2), there is no significant difference between them in the
meteorological variables during the same warming period (not shown). Thus,
we do not distinguish SSP245 and SSP585 scenarios at the same warming level
in the following analysis.
Evaluation of model simulations. (a–b) Observed and simulated
monthly streamflow at the Tangnaihai (TNH) and Zhimenda (ZMD) hydrological
stations, with the climatology shown in the upper-right corner. (c–d) Evaluation of the simulated monthly terrestrial water storage anomaly (TWSA)
and annual evapotranspiration (ET) averaged over the Sanjiangyuan region.
Red lines are the CSSPv2 simulation forced by observed meteorological forcing.
Blue lines represent ensemble means of 11 CMIP6_His/CSSPv2
simulations, while gray shadings in (a)–(b) and blue shadings in (c)–(d) are
ranges of individual ensemble members. Pink shading in (c) is GRACE
satellite observations. The black line and black shading in (d) are ensemble
mean and ranges of GLEAM_ET and MTE_ET
datasets.
Performance of CSSPv2 model simulations driven by the observed
meteorological forcing (CMFD/CSSPv2) and the bias-corrected CMIP6 historical
simulations (CMIP6_His/CSSPv2). The metrics include
the correlation coefficient (CC), root mean square error (RMSE) and
Kling–Gupta efficiency (KGE). The KGE is only used to evaluate streamflow.
VariablesExperimentsCCRMSEKGEMonthly streamflow at TNH stationCMFD/CSSPv20.95165 m3 s-10.94CMIP6_His/CSSPv20.76342 m3 s-10.71Monthly streamflow at ZMD stationCMFD/CSSPv20.93169 m3 s-10.91CMIP6_His/CSSPv20.82257 m3 s-10.81Monthly terrestrial water storage anomaly over the Sanjiangyuan regionCMFD/CSSPv20.722 mm per month–CMIP6_His/CSSPv20.424 mm per month–Annual evapotranspiration over the Sanjiangyuan regionCMFD/CSSPv20.8714 mm yr-1–CMIP6_His/CSSPv20.4713 mm yr-1–
Figure 3 and Table 3 show the evaluation of model simulation. Driven by
observed meteorological and ecological forcings, the CMFD/CSSPv2 simulates
monthly streamflow over the Yellow and Yangtze River headwaters quite well.
The Kling–Gupta efficiencies of CMFD/CSSPv2 simulated monthly streamflow are
0.94 and 0.91 over Tangnaihai (TNH) and Zhimenda (ZMD) stations,
respectively. The simulated monthly terrestrial water storage anomaly (TWSA)
during 2003–2014 in CMFD/CSSPv2 also agrees with the GRACE satellite
observation and captures the increasing trend. For the interannual
variations of evapotranspiration, CMFD/CSSPv2 is consistent with the
ensemble mean of the GLEAM_ET and MTE_ET
products, and the correlation coefficient and root mean square error (RMSE)
during 1982–2011 are 0.87 (p<0.01) and 14 mm yr-1 respectively.
This suggests the good performance of the CSSPv2 in simulating the
hydrological processes over the Sanjiangyuan region. Although meteorological
and ecological outputs from CMIP6 models have coarse resolutions
(∼ 100 km), the land surface simulation driven by bias-corrected CMIP6 results (CMIP6_His/CSSPv2) also captures the
terrestrial hydrological variations reasonably well. The Kling–Gupta
efficiency of the ensemble mean streamflow simulation reaches up to
0.71–0.81, and the ensemble mean monthly TWSA and annual evapotranspiration generally agree with
observations and other reference data (Fig. 3c–d).
Box plots of relative changes of regional mean precipitation,
evapotranspiration (ET), ratio of transpiration to evapotranspiration
(T/ ET), total runoff and terrestrial water storage (TWS) at different global
warming levels. The reference period is 1985–2014, and annual (ANN) and seasonal
(winter: DF, spring: MAM, summer: JJA and autumn: SON) results are all
shown. Boxes show 25th to 75th ranges among 22 CMIP6_SSP/CSSPv2 simulations, while lines in the boxes are median values.
Figure 4 shows relative changes of terrestrial hydrological variables over
the Sanjiangyuan region at different warming levels. The ensemble mean of
the increase in annual precipitation is 5 % at 1.5 ∘C warming
level, and additional 0.5 and 1.5 ∘C warming will
further increase the wetting trends to 7 % and 13 % respectively. Annual
evapotranspiration experiences significant increases at all warming levels,
and the ensemble mean increases are 4 %, 7 % and 13 % at 1.5, 2.0 and
3.0 ∘C warming levels respectively. The ratio of transpiration to
evapotranspiration also increases significantly, indicating that vegetation
transpiration increases much larger than the soil evaporation and canopy
evaporation. Although annual total runoff has larger relative changes than
evapotranspiration (6 %, 9 % and 14 % at 1.5, 2.0 and 3.0 ∘C
warming levels respectively), the uncertainty is large as only 75 % of the
models show positive signals, which may be caused by large uncertain changes
during summer and autumn seasons. The terrestrial water storage (TWS), which
includes foliage water, surface water, soil moisture and groundwater, shows
a slightly decreasing trend at the annual scale, suggesting that the increasing
precipitation in the future becomes extra evapotranspiration and runoff
instead of recharging the local water storage. The accelerated terrestrial
hydrological cycle also exists at the seasonal scale, as the seasonal changes
are consistent with the annual ones.
Changes of streamflow and its extremes at the outlets of the
headwater regions of the Yellow River and the Yangtze River, i.e.,
Tangnaihai gauge and Zhimenda gauge. (a) Simulated monthly streamflow over
the Yellow River during the reference period (1985–2014) and the periods
with different global warming levels. Solid lines represent ensemble means,
while shadings are ranges of individual ensemble members. (b) Percent
changes in the frequency of dry and wet extremes in July–September at different
warming levels. Colored bars are ensemble means, while error bars are
5 %–95 % uncertainty ranges estimated using bootstrapping
10 000 times. Panels (c) and (d) are the same as (a) and (b) but for the
Yangtze River.
Changes in streamflow extremes at different warming levels
Although the intensified terrestrial hydrology induces more streamflow over
the headwater region of Yellow River during winter and spring months,
streamflow does not increase and even decreases during the flood season
(July–September; Fig. 5a). Figure 5b shows the changes of streamflow dry
extremes over the Yellow River source region at different warming levels,
with the error bars showing estimated uncertainties. The frequency of
streamflow dry extremes over the Yellow River is found to increase by 55 %
at 1.5 ∘C warming level (Fig. 5b), but the uncertainty is larger
than the ensemble mean. However, the dry extreme frequency will further
increase to 77 % and 125 % at the 2.0 and 3.0 ∘C warming
levels, and the results become significant (Fig. 5b). No statistically
significant changes are found for the wet extremes at all warming levels
over the Yellow River headwater region, as the uncertainty ranges are larger
than the ensemble means.
Over the Yangtze River headwater region, streamflow increases in all months
at different warming levels (Fig. 5c). The frequency of wet extremes
increases significantly by 138 %, 202 % and 232 % at 1.5, 2.0 and
3.0 ∘C warming levels (Fig. 5d), suggesting a higher risk of
flooding. Although the frequency of dry extremes also tends to decrease
significantly by 35 %, 44 % and 34 % at the three warming levels, the
changes are much smaller than those of the wet extremes. Moreover,
contributions from climate change and ecological change are both smaller
than the uncertainty ranges (not shown), suggesting that their impacts on
the changes of dry extremes over the Yangtze River headwater region are not
distinguishable. Thus, we mainly focus on the dry extremes over the Yellow River and the wet extremes over the Yangtze River in the following analysis.
Probability density functions (PDFs) of regional mean rainfall,
evapotranspiration (ET) and their difference over the headwater regions of
Yellow River (YER) and Yangtze River (YZR) during flooding seasons
(July–September) for the reference period (1985–2014) and the periods with
1.5, 2.0 and 3.0 ∘C global warming levels. Shadings show the
5 %–95 % uncertainty ranges.
Different changes of streamflow extremes over the Yellow and Yangtze rivers
can be interpreted from different accelerating rates of precipitation and
evapotranspiration. Figure 6 shows probability density functions (PDFs) of
precipitation, evapotranspiration and their difference (P-ET, i.e., residual
water for runoff generation) during the flood season. Over the Yellow River,
PDFs of precipitation and evapotranspiration both shift to the right against
the reference period, except for the precipitation at 1.5 ∘C
warming level. However, the increasing trend of evapotranspiration is
stronger than that of precipitation, leading to a left shift for the PDF of
P- ET. Moreover, increased variations of precipitation and
evapotranspiration, as indicated by the increased spread of their PDFs, also
lead to a larger spread of PDFs of P- ET. The above two factors together
induce a heavier left tail in the PDF of P- ET for the warming future than
the reference period (Fig. 6e). The probability of P- ET < 80 mm
increases from 0.1 during the historical period to 0.11, 0.13 and 0.16 at 1.5,
2.0 and 3.0 ∘C warming levels, respectively. This indicates a
higher probability of less water left for runoff generation at different
warming levels, given little change in TWS (Sect. 3.1). Moreover, Fig. 6e also shows little change in the right tails of the PDF of P- ET as
probability for P- ET > 130 mm stays around 0.1 at different warming
levels, suggesting little change in the probability of high residual water.
This is consistent with the insignificant wet extreme change over the Yellow River. Over the Yangtze River, however, intensified precipitation is much
larger than the increased evapotranspiration, leading to a systematic
rightward shift of the PDF of P- ET (Fig. 6b, d and f). Thus both the
dry and wet extremes show significant changes over the Yangtze River.
(a–b) Influences of climate change, CO2 physiological forcing
and land cover change on relative changes in frequency of the dry and wet
extremes in July–September at different global warming levels for the
headwater regions of Yellow River and Yangtze River. (c–d) Changes of dry
and wet extremes under additional warming of 0.5 and
1.5 ∘C with the consideration of different factors. All the
changes are relative to the reference period (1985–2014). Ensemble means are
shown by colored bars, while the 5 %–95 % uncertainty ranges
estimated using bootstrapping 10 000 times are represented by error
bars.
Influences of land cover change and CO2 physiological forcing
Figure 7a–b show the changes of streamflow extremes (compared with the
reference period) induced by climate and ecological factors. Although the
contribution from climate change (red bars in Fig. 7a–b) is greater than
the ecological factors (blue and cyan bars in Fig. 7a–b), influences
of CO2 physiological forcing and land cover change are nontrivial. CO2 physiological forcing tends to alleviate dry extremes (or increase
wet extremes), while land cover change plays a contrary role. Over the
Yellow River, the combined impact of the two ecological factors (sum of blue
and cyan bars) reduces the increasing trend of dry extremes caused by
climate change (red bars) by 18 %–22 % at 1.5 and 2.0 ∘C warming levels, while it intensifies the dry extremes by 9 % at
3.0 ∘C warming level. This can be interpreted from their
contributions to the evapotranspiration, as the enhancement effect of the
increased LAI on ET is weaker than the suppression effect of CO2
physiological forcing at 1.5 and 2.0 ∘C warming levels while
stronger at 3.0 ∘C warming level (not shown). Over the Yangtze River, similarly, the combined effect of land cover and CO2 physiological
forcing increases the wet extremes by 9 % at 1.5 ∘C warming
level, while it decreases the wet extremes by 12 % at 3.0 ∘C warming
level.
In addition, Fig. 7c and d show that the combined impact of CO2
physiological forcing and land cover change also influences the differences
between different warming levels. Over the Yellow River, climate change
increases dry extremes by 26 % from 1.5 to 2.0 ∘C warming level
and by 40 % from 1.5 to 3.0 ∘C warming level (red bars in
Fig. 7c). After considering the two ecological factors (pink bars in
Fig. 7c), the above two values change to 22 % and 70 % respectively, and
the difference between 1.5 and 3.0 ∘C warming levels becomes
significant. For the wet extreme over the Yangtze River (Fig. 7d), the
climate-change-induced difference between 1.5 and 2.0 ∘C warming
levels is decreased by 16 % after accounting for the two ecological
factors. And this decrease reaches up to 49 % for the difference between
1.5 and 3.0 ∘C warming levels. We also compared the scenarios when
CO2 physiological forcing and land cover change are each combined with
climate change (blue and cyan bars in Fig. 7c–d), and the
results show land cover change dominates their combined influences on
the difference between different warming levels.
Conclusions and discussion
This study investigates changes of streamflow extremes over the Sanjiangyuan
region at different global warming levels through high-resolution land
surface modeling driven by CMIP6 climate simulations. The terrestrial
hydrological cycle under global warming of 1.5 ∘C is found to
accelerate by 4%–6 % compared with the reference period of
1985–2014, according to the relative changes of precipitation,
evapotranspiration and total runoff. The terrestrial water storage, however,
shows a slight but significant decreasing trend as increased
evapotranspiration and runoff are larger than the increased precipitation.
This decreasing trend of terrestrial water storage in the warming future is
also found in six major basins in China (Jia et al., 2020). Although
streamflow changes during the flood season have a large uncertainty, the
frequency of wet extremes over the Yangtze River will increase significantly
by 138 % and that of dry extremes over the Yellow River will increase by
55 % compared with that during 1985–2014. With an
additional 0.5 ∘C warming, the frequency of dry and wet extremes
will increase further by 22 %–64 %. If global warming is
not adequately managed (e.g., to reach 3.0 ∘C), wet extremes over
the Yangtze River and dry extremes over the Yellow River will increase by
232 % and 125 %. The changes from 1.5 to 2.0 and 3.0 ∘C are
nonlinear compared with those from the reference period to 1.5 ∘C,
which are also found for some fixed-threshold climate indices over
Europe (Dosio and Fischer, 2018). It is necessary to cap global warming
at 2 ∘C or an even lower level to reduce the risk of wet and dry
extremes over the Yangtze and Yellow rivers.
This study also shows the nontrivial contributions from land cover change
and CO2 physiological forcing to the extreme streamflow changes,
especially at 2.0 and 3.0 ∘C warming levels. CO2
physiological forcing is found to increase streamflow and reduce the dry
extreme frequency by 14 %–24 %, which is consistent with
previous findings that CO2 physiological forcing would increase
available water and reduce water stress at the end of this century
(Wiltshire et al., 2013). However, our results further show that the drying
effect of increasing LAI on streamflow will exceed the wetting effect of
CO2 physiological forcing at 3.0 ∘C warming level (during
2048–2075) over the Sanjiangyuan region, making a reversion
in the combined impacts of CO2 physiological forcing and land cover.
Thus it is vital to consider the impact of land cover change in the
projection of future water stress, especially in high warming scenarios.
Moreover, about 43 %–52 % of the extreme streamflow changes
between 1.5 and 3.0 ∘C warming levels are attributed to the
increased LAI. Considering the LAI projections from different CMIP6 models
are induced by climate change, it can be inferred that the indirect
influence of climate change (e.g., through land cover change) has the same
and even larger importance for the changes of streamflow extremes between 1.5
and 3.0 ∘C or even higher warming levels, compared with the direct
influence (e.g., through precipitation and evapotranspiration). Thus, it is
vital to investigate the hydrological cycle and its extreme changes among different
warming levels from an ecohydrological perspective instead of focusing on
climate change alone.
Although we used 11 CMIP6 models combined with two SSP scenarios to reduce
the uncertainty of future projections caused by GCMs, using a single land
surface model may result in uncertainties (Marx et al., 2018). However,
considering the good performance of the CSSPv2 land surface model over the
Sanjiangyuan region and the dominant role of GCMs' uncertainty (Zhao et al.,
2019; Samaniego et al., 2017), uncertainty from the CSSPv2 model should have
limited influence on the robustness of the result.
Data availability
The CMIP6 data can be downloaded from the Earth System Grid Federation website at https://esgf-node.llnl.gov/search/cmip6/ (Cinquini et al., 2014). The CMFD data are provided by the National Tibetan Plateau Data Center (10.11888/AtmosphericPhysics.tpe.249369.fle, He and Yang, 2018). The JPL GRACE mascons data can be found at https://grace.jpl.nasa.gov/ (Wiese et al., 2019). The land surface model simulation data are available upon request.
Author contributions
XY conceived and designed the study. PJ conducted the simulations and performed the analyses. PJ and XY wrote the paper. FM and MP provided critical insights into the results' interpretation and revised the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the World Climate Research Programme's Working
Group on Coupled Modelling and the National Tibetan Plateau Data Center for providing the data. We also thank the editor Fuqiang Tian and the two anonymous referees for their helpful comments.
Financial support
This research has been supported by the Ministry of Science and Technology of the People's Republic of China (grant no. 2018YFA0606002) and the National Natural Science Foundation of China (grant nos. 41875105 and 91547103).
Review statement
This paper was edited by Fuqiang Tian and reviewed by two anonymous referees.
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