PIAHSProceedings of the International Association of Hydrological SciencesPIAHSProc. IAHS2199-899XCopernicus PublicationsGöttingen, Germany10.5194/piahs-379-231-2018Response of streamflow to climate change in a sub-basin of the source region
of the Yellow River based on a tank modelResponse of streamflow to climate changeWuPanhttps://orcid.org/0000-0001-5696-1597WangXu-Shengwxsh@cugb.edu.cnhttps://orcid.org/0000-0001-8736-2378LiangSihailiangsh@cugb.edu.cnSchool of Water Resources and Environment, China University of Geosciences, Beijing, ChinaXu-Sheng Wang (wxsh@cugb.edu.cn) and Sihai Liang (liangsh@cugb.edu.cn)5June201837923124119December20176February201811February2018This 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://piahs.copernicus.org/articles/379/231/2018/piahs-379-231-2018.htmlThe full text article is available as a PDF file from https://piahs.copernicus.org/articles/379/231/2018/piahs-379-231-2018.pdf
Though extensive researches were conducted in the source region of the Yellow
River (SRYR) to analyse climate change influence on streamflow, however, few
researches concentrate on streamflow of the sub-basin above the Huangheyan
station in the SRYR (HSRYR) where a water retaining dam was built in the
outlet in 1999. To improve the reservoir regulation strategies, this study
analysed streamflow change of the HSRYR in a mesoscale. A tank model (TM) was
proposed and calibrated with monthly observation streamflow from 1991 to
1998. In the validation period, though there is a simulation deviation during
the water storage and power generation period, simulated streamflow agrees
favourably with observation data from 2008 to 2013. The model was further
validated by two inside lakes area obtained from Landsat 5, 7, 8 datasets
from 2000 to 2014, and significant correlations were found between the
simulated lake outlet runoff and respective lake area. Then 21 Global Climate
Models (GCM) ensembled data of three emission scenarios (SRA2, SRA1B and
SRB1) were downscaled and used as input to the TM to simulate the runoff
change of three benchmark periods 2011–2030 (2020s), 2046–2065 (2050s),
2080–2099 (2090s), respectively. Though temperature increase dramatically,
these projected results similarly indicated that streamflow shows an increase
trend in the long term. Runoff increase is mainly caused by increasing
precipitation and decreasing evaporation. Water resources distribution is
projected to change from summer-autumn dominant to autumn winter dominant.
Annual lowest runoff will occur in May caused by earlier snow melting and
increasing evaporation in March. According to the obtained results, winter
runoff should be artificially stored by reservoir regulation in the future to
prevent zero-flow occurrent in May. This research is helpful for water
resources management and provides a better understand of streamflow change
caused by climate change in the future.
Introduction
Recently, climate change (IPCC, 2007) and uncertainties of global water
resources change caused draw extensive attention all over the world (Taylor
et al., 2013; Bae et al., 2008; Lan et al., 2009). As the water tower of Asia
(Immerzeel et al., 2010), the Tibetan Plateau is sensitive to climate change
(Su et al., 2016; Li et al., 2014; Yang et al. 2012; Li et al., 2013). The
Yellow River, originates from the Tibetan Plateau, is the second longest
river of China and the fifth longest river of the world. Water resources and
runoff response to climate change of the Yellow River basin have been
extensively researched (Yang et al., 2004; Fu et al., 2004; Chang et al.,
2007), especially in the head water of the Yellow River (Wang and Cheng,
2000; Tang et al., 2008; Zheng et al., 2009, 2010; Liang et al., 2010; Lan et
al., 2010, 2013). Above Huangheyan station, two lakes which play a role as
nature reservoirs for the basin located in the topside source region of the
Yellow River (HSRYR) with area about 21 000 km2 (Liang et al., 2010;
Brierley et al., 2016). Because of water shortage and zero-flow phenomenon in
1990s, a 20 m-high Huangheyan Dam was built at the Ngoring Lake downstream,
during 1999 and early 21st century (Liang et al., 2010; Brierley et al.,
2016), to conserve enough water for the ecological environment maintenance.
Though projections of runoff change were proposed by inputting the GCMs data
to a calibrated model (Immerzeel et al., 2010; Xu et al., 2009), these
researches conducted at a largescale more than 100 000 km2 area is not
suitable for water resources management of a mesoscale basin like the HSRYR.
Reasonable regulation of the Huangheyan dam in the climate changing future is
important. Tank model were first proposed by Sugawara and Maruyama (1956), a
typical runoff model with a brief model structure requires less data and gets
a good performance in simulation and forecast (Franchini and Pacciani, 1991).
This study aims to establish a semi-distributed model by connecting several
typical tank models. GCMs data are input into the built and calibrated model
to assess the runoff in 21st century. This study will offer a reference for
dam regulation strategy and water resources management in the sub-basin above
Huangheyan station.
Study area background and data processingStudy area background
The HSRYR with drainage area about 21 000 km2 is a topside sub-basin
of the source region of the Yellow River above Huangheyan hydrological
station (HSRYR) which is embraced by high mountains from the north, south, to
west sides (Fig. 1). Three meteorological stations are around or inside this
area, from southwest to northeast is Qumalai, Qingshuihe and Maduo,
respectively. This is a cold and arid area with an annual mean air
temperature of -4 ∘C and annual mean precipitation of 310 mm. The
potential evapotranspiration is about 1300–1400 mm year-1 (Liang et
al., 2010).
The two major lakes, Gyaring and Ngoring, show an expansion trend in the past
decade (Duan et al., 2015). Huangheyan dam is about 20 m-height which
located at 1km away from Ngoring lake outlet (Brierley et al., 2016).
Previously, the dam had been used for hydroelectric generation and water
regulation, but recently it has only been used to store water due to low
power generation efficiency. However, if the cut off phenomenon will show
again in this area is still unknown. Notably, there is a runoff disturbed
period caused by dam construction activity and power generation during 1999
to early 21st century.
Stations distribution and study area topography.
Data processing
Monthly runoff data from 1990–2013 are obtained from the Yellow River
Conservancy Commission (YRCC). Daily precipitation, air temperature and wind
speed at 10 m height, relative humidity and sunshine duration data between
1961 and 2013 at the three meteorological stations are collected from the
China Meteorological Administration (CMA). These data are applied to derive
monthly mean air temperature (T), monthly positive accumulated
temperature (∑T+) monthly precipitation (P) and
snowfall (Ps). The potential evapotranspiration is calculated with the
FAO Penman-Monteith equation (Allen et al., 1998). 21 Global Climate Models
(GCMs) are selected as shown in Table 1. Due to uncertainty of each model
projection, mean value of 21 GCMs output data under three emission scenarios
(SRA2, SRA1B and SRB1), including shortwave flux at surface (W m-2)
(ΔRs), precipitation rate change (mm day-1) and air temperature
change(ΔT) are used as ensembled data in this study. Mean value of
respective 20C3M output precipitation rate P0′ (mm day-1) during 1961
to 1990 is used as reference value. Landsat 5, 7, 8 datasets of July or
August from 2000 to 2014 are used to extract lake area for further
validation of model distribution ability.
MethodsModel construction and mechanism
As shown in Fig. 2, snow, surface runoff and baseflow are considered in each
land surface hydrologic response unit (HRU). Parameters described in Fig. 2
and used in the following equations are shown in Table 2.
Details of selected model and respective output scenarios.
Institute IDModel nameOutput scenarios of each model BCCRBCM220C3MSRA2SRA1BSRB1CCCMACGCM3_1-T4720C3MSRA2SRA1BSRB1CNRMCM320C3MSRA2SRA1BSRB1CSIROMK320C3MSRA2SRA1BSRB1CONSECHO-G20C3MSRA2SRA1B–LASGFGOALS-G1_020C3M–SRA1BSRB1GFDLCM220C3MSRA2SRA1BSRB1GFDLCM2_120C3MSRA2SRA1BSRB1NASAGISS-AOM––SRA1BSRB1NASAGISS-EH––SRA1B–NASAGISS-ER20C3MSRA2SRA1BSRB1UKMOHADCM320C3MSRA2SRA1BSRB1UKMOHADGEM120C3MSRA2SRA1B–INMCM320C3MSRA2SRA1BSRB1IPSLCM4–SRA2SRA1BSRB1NIESMIROC3_2-HI20C3M–SRA1BSRB1NIESMIROC3_2-MED20C3MSRA2SRA1BSRB1MPIMECHAM520C3MSRA2SRA1BSRB1MRICGCM2_3_220C3MSRA2SRA1BSRB1NCARCCSM320C3MSRA2SRA1BSRB1NCARPCM20C3MSRA2SRA1B–
Snowfall and rainfall are distinguished based on critical temperature
Tc, expressed as:
Psi=0,Ti>TcPi,Ti>Tc
Snow accumulation is indicated by following equations:
hs(i)=Ti>Tc:hs(i-1)≥Msi+Esi:hs(i-1)-Msi-Esi,hs(i-1)<Msi+Esi:0,Ti≤Tc:hsi-1+Psi-Esi,hs is accumulated snow relative depth (mm) in snow water equivalent (SWE)
form.
Typical tank model constructions, panels (a) and
(b) indicate land surface HRU and lake HRU, respectively.
Parameters and descriptions for the built model.
Model constructionsParametersUnitDescriptionsSnow modelPmmPrecipitation(Eqs. 1, 2, 3, 4, 5, 10)PsSnowfall in SWE formPlRainfallhsSnow depth in SWE formMspPotential snowmelt in SWE formMsSnowmelt in SWE formEspPotential snow sublimation in SWE formEsSnow sublimation in SWE formαmm (∘C)-1 day-1Degree-day factorβSnow sublimation coefficientSurface RunoffqmmSurface runoff(Eqs. 6, 11, 14)HWater depth of surface tankElsLand surface actual evaporationh0Critical value for surface runoff generationh1Critical value for peak runoffk2–Land surface evaporation conversion coefficientb1Runoff coefficient for normal runoff generationb2Runoff coefficient for peak valueInfiltration and BaseflowFmmInfiltration water(Eqs. 7, 8, 12)hgWater depth of underground tankqgBase flowh2Critical value for base flow generationb0–Infiltration coefficientdBase flow coefficientLake modelqlmmLake outlet runoff(Eqs. 9, 13, 14)ElLake water evaporationHlWater depth of lake tankhl1Critical value for lake outlet runoff generationhl2Lake outlet runoff coefficient for peak valuek2–Water evaporation conversion coefficientc0Lake outlet runoff coefficient for normal runoff generationc1Lake outlet runoff coefficient for peak valueCommon ParametersT+∘CPositive temperatureTcCritical temperature for meltimonthTime step indicator
Snowmelt is calculated by Eq. (3)
Msi=Ti>Tc:hsi-1≥Mspi+Espi:Mspi,hs(i-1)<Mspi+Espi:hs(i-1),Ti≤Tc:0Msp(i) is potential snowmelt water as indicated in Eq. (4). And
the potential snow sublimation is given by Eq. (5):
Mspi=α⋅∑T+-TcEspi=β⋅∑T+
Surface runoff is calculated by Eq. (6) based on different water depth.
q(i)=0,H(i-1)<0b1⋅H(i-1),0<H(i-1)≤h0b1⋅H(i-1)+b2⋅Hi-1-h0,H(i-1)>h0
Surface water infiltration is indicated as follow:
F(i)=b0×H(i)
Baseflow is given by Eqs. (8) and (9):
qg(i)=d⋅hgi-h2,hg(i)>h20,hg(i)>h2
As Fig. 2 indicated lake HRU plays a role as a regulate reservoir which
outlet runoff is calculated by following equations:
ql(i)=Ti>Tc:hl(i-1)<hl1:0,hl1≤hl(i-1)<hl2:c0hl(i-1)-hl1,hl2≤hl(i-1):c0+hl(i-1)-hl1+c1hl(i-1)-hl2,)Ti≤Tc:hl(i-1)<hl1:0,hl(i-1)≥hl1:c0hl(i-1)-hl1,
Snow accumulated depth, water depth of each step can be calculated by
solving following equation by implicit finite difference method.
Time-varying values of snow depth, land surface water depth, baseflow and
lake water depth are given by Eqs. (10), (11), (12) and (13)
respectively:
dhsdt=Ps-Es-MsdHdt=Pl+Ms-Els-F+qin-qdhgdt=F-qgdHldt=P-El+qin-ql
Land surface actual evaporation and lake water evaporation are calculated
from:
E=Els=k1⋅E0El=k2⋅E0
Then total outlet runoff is transformed into m3 s-1 unit.
Model performance evaluation
Volume difference (Dv), and Nash–Sutcliffe efficiency (R2) are used
to evaluate the model performance defined as following:
R2=1-∑i=1nQi-Qi′2∑i=1nQi-Q‾Dv=VR-VR′VR⋅100%R2 is Nash–Sutcliffe efficiency Qi is observed monthly runoff,
Qi′ simulated monthly runoff, Q‾ is observed annual mean
runoff, n is the total number of simulated months. Dv is volume
difference between observed runoff and simulated runoff. VR indicates
total observed runoff volume. VR′ indicates total simulated runoff
volume.
There are five HRUs included in this study, land surface HRUs: HRU 1, HRU 2
and HRU 3. Two Lakes HRUs: HRU G and HRU N. Each HRU is represented by one
typical tank model, the model connection is based on flow direction as shown
in Fig. 3.
GCMs data downscaling
There are several normally used downscaling methods for GCMs data:
statistical downscaling method, dynamic downscaling method and delta method
(Xu et al., 2009). But different method has different shortage and advantage
(Fowler et al. 2007). Delta method is a simple method commonly applied in
hydrological studies of climate change (Merritt et al., 2006). In this study
delta method is selected instead of complex spatial downscaling mothed.
Precipitation is projected by:
P=P0⋅1+ΔP/P0′P0 (mm) is observed monthly precipitation of pre-change period
(1961–1990), P0′ reference precipitation rate (mm day-1) of period
1961–1990 obtained from mean value of GCMs under scenario 20C3M, ΔP (mm day-1) is the mean value of 21 GCMs projected change of precipitation
rate under respective scenarios.
Connection of typical tank models in built model.
Relationship between monthly mean temperature and monthly positive
accumulated temperature (1961–2013). Panels (a, b, c) indicate
Qumalai, Qingshuihe and Maduo station, respectively.
For temperature projection:
T=T0+ΔT⋅∘CKT0 (∘C) is monthly pre-change value of temperature (mean value of
1961–1990), ΔT(K) is the projection change of temperature.
Rs=ΔRs×864001000000+Rs0Rs0 (MJ (m2× day)-1) is monthly mean value of pre-change
period (1961–1990) obtained from FAO Penman-Monteith equation (Allen et al.,
1998). ΔRs (W m-2) is mean value of 21 GCMs projected
shortwave flux change at surface under respective scenarios.
Comparison between simulation and observation runoff.
Projection of ∑T+ and E0
Monthly accumulated positive temperature ∑T+ is significantly
correlated with monthly mean temperature as shown in Fig. 4. By
using the piecewise equation shown in Fig. 4, ∑T+ can be obtained
from monthly mean temperature (T). Potential evaporation(E0) is
calculated by linear correlation with projected monthly mean temperature,
and solar radiation (Table 4).
Lakes expansion and area change shown in panels (a) and
(b), respectively.
Relationship between lake area and respective outlet runoff.
Results and discussionsResults of simulation
Monthly comparisons between the simulated runoff and observed runoff in the
calibration period (1990–1998) and validation period (2000–2013) are shown
in Fig. 5. As the red line in Fig. 5a and red points in Fig. 5c
indicated, the runoff is obviously disturbed during dam construction and
hydropower generation period. After disturbed period, simulated runoff
variation agrees favourably with the observation runoff except slight
overestimation (Fig. 5a and b). The model performance in calibration
period: R2: 0.84, Dv: -0.5 %, in period 2008–2013: R2: 0.67, Dv: -24.88 %. Generally model performance is very good
if R2 > 0.75, satisfactory if 0.36 < R2 < 0.75, and unsatisfactory if R2 < 0.36 (Nash and
Sutcliffe, 1970; Krause et al., 2005; Moriasi et al., 2007). The Dv is
about -25 % which indicates overestimation. But it is reasonable, because
there will be more water conserved in the reservoir after the a 20 m-height
dam built.
Projected climatic elements of the three periods.
Projected runoff of different scenarios.
Lakes expansion and relationship with respective outlet runoff
Figure 6 indicates variation of the lake area (Fig. 6b), in the past decade
expansion of Ngoring lake is more obvious than Gyaring lake (Fig. 6a). The
two lakes show a similar change pattern, lake area shrank at first
(2000–2004) and then expand (2004–2012). It is caused by water resources
change in this area, the last zero-flow phenomenon happened in 2004 (Chang
et al., 2007), and runoff rise again from 2004 to 2012. Though this model is
only calibrated by runoff of Huangheyan station, simulated outlet runoff of
the two lakes is significantly correlated with the lake area change (Fig. 7)
that indicate a distribution characteristic of this model.
Results of projection
Change of projected ΔP (%), ΔE0 (%), ΔRs (W m-2) and ΔT (∘) in the three
respective periods under scenarios SRA2, SRA1B and SRB1 are displayed in
Fig. 8. Three scenarios similarly indicate that temperature and precipitation
will increase, but radiation will decrease in the future. Temperature and
precipitation is projected to increase for all seasons but with largest
increase appearing in winter and spring. Radiation is projected to decease
all the time but with largest decrease appearing in spring. Impacted by
change of temperature and radiation, potential evapotranspiration is
projected to decrease in summer and winter but increase in spring and autumn.
Though projected variations under different scenarios show a similar
seasonality, the projected increment and decrement are different resulted
from different emission scenarios. Radiation and temperature projected by
SRA2 show the largest change among the three scenarios, but precipitation and
potential evapotranspiration projected by SRB1 show a largest change compare
to the other two scenarios. Variables projected by SRA1B show a moderate
change. These are resulted from different scenarios setting different future
greenhouse gas emissions accompanied by storylines of social, economic and
technological development (Parry and Cox, 2007).
Projected runoff in 2011–2030 (2020s), 2046–2065 (2050s), 2080–2099 (2090s)
under the three scenarios SRA2, SRA1B and SRB1 are shown in Fig. 9a, b
and c, respectively. There are two dash line, blue and purple, indicate
pre-change period (1961–1990) and low-flow (1990s) period, respectively.
Results in Fig. 9 similarly indicated that runoff shows an increase trend in
the long term. Increasing runoff is mainly caused by dramatically increase
precipitation. Projected runoff of SRA1B and SRB1 are lower than SRA2 but
showed a similar trend as SRA2 in the three periods (Fig. 9c). Runoff is
projected to increase eventually in the future but won't return to a
pre-change level. As indicated by results of SRA2 (Fig. 9b), runoff of
August to December will reach to a pre-change level in 2090s, but runoff of
January to July in 2090s is lower than pre-change period. Annual runoff is
projected to return in 2090s, but runoff in spring and summer is projected
to be lower than pre-change period. Water resources distribution is
projected to change from summer-autumn dominant to autumn winter dominant.
Temperature increase in winter-spring and potential evapotranspiration
increase in spring cause earlier snowmelt runoff and increase
evapotranspiration in spring. Annual lowest runoff is projected to occur in
May as different from the winter months, as seen in the 1990s (Fig. 9).
∗ Coefficient for frozen lake water seepage in winter
(T < 0).
Regression Model for potential evaporation projection.
StationsInterceptMonthlyRsSignificantR2TFQumalai0.1000.0650.12600.99Qingshuihe0.2380.0620.11200.99Maduo0.2140.0670.11800.99Will zero-flow show again?
The last zero-flow phenomenon occurred in 2004 January to March (Chang et
al., 2007). In the future, precipitation is projected to increase in winter
and earlier snow melting will occur which result in higher winter runoff
than before (Fig. 8), the zero flow won't occur in winter again. And there
is no zero-flow phenomenon in projected results of different periods under
the three scenarios. Due to increase temperature in winter, frozen water
resources will release earlier than before which will cause less melt water
recharge in May runoff and a relative lower runoff appearing in May. If the
dam impacts are taken into consideration, zero-flow phenomenon is possible
show again in May in the warming future. To prevent appearing of zero-flow
phenomenon, artificially regulate the dam to store water in winter is
imperative.
Limitation remarks
Due to low people density (0.34 person km-2) (Liang et al., 2010) and
difficulty of estimating dam construction and hydropower generation impacts,
observed runoff used in this study was not naturalized first. However, this
model is well calibrated by undisturbed runoff from 1990–1998 and then
validated by 2008–2013 runoff and lake area obtained from Landsat datasets.
Only two variables are involved in E0 projection in this study, but as
shown in Table 3, a strong relationship existed between Rs and T. For ∑T+ projection, when daily air temperature is positive for in the whole
month, monthly positive accumulated temperature should be calculated from:
∑T+=days⋅T
But the deviation is negligible, the results will be same as which obtained
the linear equation of piecewise equations in Fig. 4.
Frozen ground is widely distributed in this area (Jin et al., 2010) which
degradation in the warming future will impact runoff generation (Hayashi et
al., 2003; Shanley and Chalmers, 1999; Wang et al., 2009). But few conception
models consider impacts of frozen ground and its degradation on runoff
generation. It needs a further and deeply study to conceptualize the physical
process into conception model.
Conclusions
In this study, the built model connected several typical tank models showed
a good performance in runoff simulation and projection. And it can be used
as a semi-distribution model proved by significantly correlation with the
lake area change. Different projections obtained by different scenarios that
improve uncertainties of water resources change in the future, but there are
similarities:
Compared to low-flow period (1990s), the projected runoff
shows a recovery trend in the long term.
Runoff distribution will change from summer-autumn dominant to
autumn-winter dominant. In the future, annual lowest runoff will occur in
May rather than in the winter.
Though there is no zero-flow showed among these projections, but
zero-flow is possible to occur in May by considering dam impacts. Reservoir
regulation strategies should be made for storing winter runoff to prevent
zero-flow phenomenon in May.
This study is helpful for water resources management and provides a better
understand of streamflow change caused by climate change in the future
GCMs data are obtained from IPCC Data Distribution Centre:
http://www.ipcc-data.org/sim/gcm_clim/SRES_AR4/index.html.
The availability of other data are mentioned in Sect. 2.2.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Innovative water
resources management – understanding and balancing interactions between
humankind and nature”. It is a result of the 8th International Water
Resources Management Conference of ICWRS, Beijing, China, 13–15 June 2018.
Acknowledgements
This research was supported by the China Geological Survey from 2000, the
National Natural Science Foundation of China (41330634, 41072191, 91125011)
and the Fundamental Research Funds for Central Universities. The authors are
grateful to the two reviewers for their insightful and constructive comments
which have greatly improved the quality of the paper.
Edited by: Wenchao Sun
Reviewed by: Shaowei Ning and one anonymous referee
References
Allen, R., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration
– Guidelines for computing crop water requirements, FAO Irrigation and
Drainage Paper no. 56, Rome, Italy, 1998.
Bae, D. H., Jung, I. W., and Chang, H.: Long-term trend of precipitation and
runoff in korean river basins, Hydrol. Process., 22, 2644–2656, 2008.Brierley, G. J., Li, X., Cullum, C., and Gao, J.: Landscape and ecosystem
diversity, dynamics and management in the Yellow River source zone, Springer
Geography, available at:
https://link.springer.com/book/10.1007/978-3-319-30475-5 (last access:
28 February 2018), 2016.
Chang, G., Li, L., Zhu, X., Wang, Z., Xiao, J., and Li, F.: Influencing
factors of water resources in the source region of the Yellow River, J.
Geogr. Sci., 17, 131–140, 2007.
Duan, S., Fan, S., Cao, G., Liu, X., and Sun, Y.: The changing features and
cause analysis of the lakes in the source regions of the yellow river from
1976 to 2014, J. Glaciol. Geocryol., 37, 745–756, 2015.
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change
modelling to impacts studies: recent advances in downscaling techniques for
hydrological modelling, Int. J. Climatol., 27, 1547–1578, 2007.
Franchini, M. and Pacciani, M.: Comparative analysis of several conceptual
rainfall-runoff models, J. Hydrol., 122, 161–219, 1991.
Fu, G., Chen, S., Liu, C., and Shepard, D.: Hydro-climatic trends of the
yellow river basin for the last 50 years, Climatic Change, 65, 149–178,
2004.
Hayashi, M., Kamp, G. V. D., and Schmidt, R.: Focused infiltration of
snowmelt water in partially frozen soil under small depressions, J. Hydrol.,
270, 214–229, 2003.
Immerzeel, W. W., van Beek, L. P., and Bierkens, M. F.: Climate change will
affect the asian water towers, Science, 328, 1382–1385, 2010.
IPCC: Climate change 2007: The Physical Science Basis, in: Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, Cambridge, UK, 996 pp.,
2007.
Jin, H. J., Wang, S. L., Lan-Zhi, L., Rui-Xia, H. E., Chang, X. L., and Luo,
D. L.: Features and degradation of frozen ground in the sources area of the
yellow river, China, J. Glaciol. Geocryol., 49, 5522–5529, 2010.Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different
efficiency criteria for hydrological model assessment, Adv. Geosci., 5,
89–97, 10.5194/adgeo-5-89-2005, 2005.
Lan, C., Lettenmaier, D. P., Alberti, M., and Richey, J. E.: Effects of a
century of land cover and climate change on the hydrology of the puget sound
basin, Hydrol. Process., 23, 907–933, 2009.
Lan, C., Zhang, Y., Gao, Y., Hao, Z., and Cairang, L.: The impacts of climate
change and land cover/use transition on the hydrology in the upper yellow
river basin, China, J. Hydrol., 502, 37–52, 2013.
Lan, Y., Zhao, G., Zhang, Y., Wen, J., Liu, J. Q., and Hu, X.: Response of
runoff in the source region of the yellow river to climate warming, Quatern.
Int., 226, 60–65, 2010.
Li, B., Yu, Z., Liang, Z., and Acharya, K.: Hydrologic response of a high
altitude glacierized basin in the central tibetan plateau, Global Planet.
Change, 118, 69–84, 2014.
Li, F., Zhang, Y., Xu, Z., Teng, J., Liu, C., Liu, W., and Mpelasoka, F. The
impact of climate change on runoff in the southeastern Tibetan Plateau, J.
Hydrol., 505, 188–201, 2013.
Liang, S., Ge, S., Wan, L., and Zhang, J.: Can climate change cause the
yellow river to dry up?, Water Resour. Res., 46, 228–236, 2010.
Merritt, W. S., Alila, Y., Barton, M., Taylor, B., Cohen, S., and Neilsen,
D.: Hydrologic response to scenarios of climate change in subwatersheds of
the Okanagan basin, British Columbia, J. Hydrol. 326, 79–108, 2006.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, Am. Soc. Agric. Biol.
Eng., 50, 885–890, 2007.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models: Part I. A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Parry, M. L. and Cox, B. S.: Technical Summary, in: Climate Change 2007:
Impacts, Adaptation and Vulnerability. Contribution of Working Group II to
the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change, Cambridge University Press, Cambridge, UK, 23–789, 2007.
Shanley, J. B. and Chalmers, A.: The effect of frozen soil on snowmelt runoff
at Sleepers River, Vermont, Hydrol. Process., 13, 1843–1857, 1999.
Su, F., Zhang, L., Ou, T., Chen, D., Yao, T., Tong, K., and Qi, Y.:
Hydrological response to future climate changes for the major upstream river
basins in the Tibetan Plateau, Global Planet. Change, 136, 82–95, 2016.
Sugawara, M. and Maruyama, F.: A method of revision of the river discharge by
means of a rainfall model. Collection of research papers about forecasting
hydrologic variables, The Geosphere Research Institute of Saitama University,
Saitama, Japan, 14–18, 1956.
Tang, Q., Oki, T., Kanae, S., and Hu, H.: Hydrological cycles change in the
yellow river basin during the last half of the twentieth century, J. Climate,
21, 1790–1806, 2008.
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y.,
Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., and Konikow,
L.: Ground water and climate change, Nature Clim. Change, 3, 322–332, 2013.
Wang, G. and Cheng, G.: Eco-environmental changes and causative analysis in
the source regions of the yangtze and yellow rivers, China, Environmentalist,
20, 221–232, 2000.
Wang, G. X., Hu, H. C., and Li, T. B.: The influence of freeze-thaw cycles of
active soil layer on surface runoff in a permafrost watershed, J. Hydrol.,
375, 438–449, 2009.
Xu, Z. X., Zhao, F. F., and Li, J. Y.: Response of streamflow to climate
change in the headwater catchment of the yellow river basin, Quatern. Int.,
208, 62–75, 2009.Yang, D., Li, C., Hu, H., Lei, Z., Yang, S., Kusuda, T., Koike, T., and
Musiake, K.: Analysis of water resources variability in the Yellow River of
China during the last half century using historical data, Water Resour. Res.,
40, 308–322, 2004.
Yang, T., Hao, X., Shao, Q., Xu, C. Y., Zhao, C., Chen, X., and Wang, W.:
Multi-model ensemble projections in temperature and precipitation extremes of
the tibetan plateau in the 21st century, Global Planet. Change, 80–81,
1–13, 2012.
Zheng, H., Lu, Z., Zhu, R., Liu, C., Sato, Y., and Fukushima, Y.: Responses
of streamflow to climate and land surface change in the headwaters of the
yellow river basin, Water Resour. Res., 45, 641–648, 2009.
Zheng, H., Zhang, L., Liu, C., Shao, Q., and Fukushima, Y.: Changes in stream
flow regime in headwater catchments of the yellow river basin since the
1950s, Hydrol. Process., 21, 886–893, 2010.