Attribution of decreasing annual and autumn inflows to the Three Gorges Reservoir, Yangtze River: Climate variability, water consumption or upstream reservoir operation?

Journal Pre-proofs Research papers Attribution of decreasing annual and autumn inflows to the Three Gorges Reservoir, Yangtze River: Climate variability, water consumption or upstream reservoir operation? Wenpeng Wang, Yuelong Zhu, Sifang Dong, Stefan Becker, Yuanfang Chen PII: DOI: Reference:

S0022-1694(19)30915-1 https://doi.org/10.1016/j.jhydrol.2019.124180 HYDROL 124180

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

14 February 2019 2 August 2019 24 September 2019

Please cite this article as: Wang, W., Zhu, Y., Dong, S., Becker, S., Chen, Y., Attribution of decreasing annual and autumn inflows to the Three Gorges Reservoir, Yangtze River: Climate variability, water consumption or upstream reservoir operation?, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124180

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Attribution of decreasing annual and autumn inflows to the Three Gorges

Reservoir,

Yangtze

River:

Climate

variability,

water

consumption or upstream reservoir operation?

Wenpeng Wanga,b, , Yuelong Zhua, Sifang Dongc, Stefan Beckerd, Yuanfang Chene

aCollege

of Computer and Information, Hohai University, Nanjing, 210098,

China. bState

Key Laboratory of Hydrology-Water Resources and Hydraulic

Engineering, Nanjing, 210098, China cWater

Resources Management Center, Ministry of Water Resources,

Beijing, 100053, China. dRamapo

College of New Jersey, Mahwah, NJ 07430, USA.

eCollege

of Hydrology and Water Resources, Hohai University, Nanjing,

210098, China.

1 INTRODUCTION Attribution of past changes in river streamflow associated with alteration in climate condition and human activities has recently attracted several scholars’ attention (Dey and Mishra, 2017; Roderick and Farquhar, 2011; Wang, 2014; Wu et al., 2017). These researches are mainly motivated by the consensus that decomposition of streamflow changes shed light on the hydrological response to climatic or human’s changes in the catchment. However, whether the magnitude and timing of streamflow fluctuate beyond the envelope of historical variability has still remained unclear, and if so, why,



Corresponding author E-mail: [email protected] . 1

is very relevant for framing adaptation strategies in terms of regional water resources planning and management. It was reported that several major rivers in China have experienced downward trends of streamflow (Li et al., 2017; Wang et al., 2015a; Xu et al., 2014; Zhai and Tao, 2017), and the Yangtze River, that is the largest river in China, was even no exception. Its annual discharge has monotonically decreased by 11% over the industrial period (1865–2008) at the most seaward gauging station (Yang et al., 2010). This negative change has been a serious challenge, accompanying with provision of sustainable water resources for human consumption, food production, electricity generation, etc. The Three Gorges Reservoir (TGR), which is fed by the runoff from almost the entire upper catchment of the Yangtze River, indeed suffers from reduction of inflow. At present, the TGR is the largest hydropower project worldwide, and it is also a strategic and ambitious contribution to meet the growing electricity demands of China territory, accounting for 14% of China’s total hydropower generation (IHA, 2013). However, the TGR has failed to accomplish its designed electricity generation twice in four years due to lower inflow than that in a normal year since it was entered into fulloperation during 2012 to 2015 (CTG, 2013-2016). While the side effects of its operation on altering the downstream flow regime has greatly attracted public attention, e.g. aggravating downstream hydrological droughts (Li et al., 2013), extremely low water levels (Lai et al., 2014), and environmental flow deficit (Wang et al., 2018) during the period of water impoundment, little attention has been paid to inflow variations and their causes from the upstream area. Hence, such information may realize the development of adaptive measures to mitigate a possible decline in hydropower generation caused by inflow variations. Both climate variability and human activities play crucial role in the change of hydrological regime. In recent decades, human activities have become increasingly extensive in the upper Yangtze River, especially after 2

Chinese economic reform in 1978. The booming economy inevitably increased water demands to meet the needs of households, manufacturing, electricity and food production, etc., which directly altered temporal distribution of streamflow via water withdrawal and reservoirs regulation. Nilsson et al. (2005) categorized the Yangtze as one of the 104 world’s large river systems that have been strongly affected by constructed dams according to the extent of channel fragmentation and flow regulation. In particular, in the upper Yangtze River, total storage capacity of large reservoirs may rise up to 61% of annual runoff, in case of on-time completion of all dams whose are under planning and construction(Zhang et al., 2012). Alcamo et al. (2003b) presented a “business-as-usual” scenario of continuing demographic, economic, and technological trends up to 2025, including the Yangtze River. They estimated that the growth of water demand would outweigh the assumed improvement in water-use efficiency, indicating higher pressure on available water. Along with intensified human activities, changes in climatic variables have also been detected in the upper Yangtze River. The basin was found to be dominated by an upward temperature trend as well as a downward precipitation trend (Chen et al., 2014; Zhang et al., 2005). Against the backdrop of global warming, the mean annual temperature has increased by 0.8 ℃, while precipitation has decreased by 3% over the period of 1955-2014 (Chen et al., 2016). This warming and drier climate may similarly continue for another decade until 2030 based on processed global climate model outputs in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Sun et al., 2015). Some efforts have been made to identify major causes for streamflow decreases. However, the conclusions were spatially inconsistent in different sub-catchments within the Yangtze River basin. For example, in one hand, Yang et al. (2015) attributed 61% of runoff decline for the entire river basin during post-TGR decade (2003-2012) to a reduction in precipitation, when that is relative to the pre-TGR decade (1993-2002). This is also the case in 3

Hanjiang, a tributary of the Yangtze, where climate variation accounted for 84-90% of the runoff reduction in 1991-2006 compared with the period of 1951-1989 (Liu et al., 2012). On the other hand, Zhang et al. (2015) reported that human activities have become dominant driving forces in the upper and middle Yangtze River since 1961. Hence, specific investigations on the inflow variations regarding the TGR and their causes are still indispensable. In an attempt made to differentiate the influence of climate variability and human activities on streamflow variations, hydrological modeling, mass balance, and regression approach have been already utilized (Ahn and Merwade, 2014; Wu et al., 2017). These approaches normally share a common rationale that a baseline or a near-pristine period can be determined with negligible human activities, so that an empirical or a physically-based hydrological model could be set up on the basis of runoff observations to estimate climate-induced variations of streamflow. The discrepancy between the observed variation of streamflow and the climate-induced variation is attributed to human activities. However, the majority of rivers have a long history of human activities that affect their streamflow patterns, in particular, water abstraction. The absolute ignorance of human activities during the baseline period will over- or under- estimate human influences, depending on how pre-inflection human activities affect streamflow (Dey and Mishra, 2017; Wang, 2014; Zhang et al., 2016). Moreover, disentangling of two major impacts of direct human activities, i.e. water consumption and reservoir regulation, is of great significance for policy makers (Wada et al., 2016). Therefore, this study recommended a stepwise procedure to estimate absolute streamflow variation caused by climate variability, water consumption, and reservoir regulation. It was also attempted to back-calculate regional water-uses over the entire study period by the use of a global water assessment model called “WaterGAP 2” (Doll et al., 2003). By excluding human influences during the baseline period, natural runoff process was reconstructed based on a lumped hydrological model. In addition, a generic 4

reservoir operation model was applied to simulate water storage increments of upstream reservoirs for an impact period with dramatic dam construction. Consequently, the annual and seasonal inflow variations induced by climate variability could be quantified, involving water consumption and upstream reservoir operation between the baseline and the impact period. The practical purpose of the present study was to identify dominant factors causing decline in annual and autumn inflow to the TGR. The procedure proposed for quantifying the role of climate and direct human impacts on streamflow can potentially be applied in catchments along with multi-reservoirs and nonnegligible water abstraction. 2 REGIONAL SETTING AND DATASETS 2.1 The upper Yangtze River basin The main course of Yangtze River originates from the Qinghai-Tibet Plateau and flows 6300 km eastward to the East China Sea. Its upper basin lies in the transition zone from the West-high alpine terrain to the East-low plain, in which the corresponding elevation ranges from 5000 m above sea level at headwater section to below 500 m near Yichang, a hydrological gauging station dividing Yangtze River into the upper and lower sections. The TGR (111.0E, 30.82N) has been located at the downstream end of the upper Yangtze River basin (see Fig. 1), and it receives discharge from a drainage area of 1.006 Mkm2, with mean annual total streamflow of about 427 Gm3 (1961-2015), which accounted for approximately 50% of the Yangtze River’s total streamflow. Fig. 1 As a result of the abundant water resources and steep river slopes, the upper Yangtze River has a tremendous potential for hydropower generation. Six tributaries (Jinshajiang River, Yalongjiang River, Daduhe River, Minjiang River, Jialingjiang River, and Wujiang River) in addition to the up reaches of Yangtze River are extensively used for hydropower exploitation (Huang and Yan, 2009). The TGR is the first reservoir, providing carryover 5

storage over multiple seasons in the main reaches of Yangtze, accompanying with impressively large storage capacity (39.3 Gm3) by global standards. However, in terms of hydrology, the TGR can only store 9% and also regulate 4% of the mean annual total inflow, thus its hydropower potential is firmly dependent on the allocation of seasonal inflows, which is highly uneven as a consequence of the monsoon climate, dominating the majority of the basin. Annual-

and

monthly-mean

of

precipitation,

potential

evapotranspiration (PET) for the upper Yangtze River, and the observed inflow to the TGR are listed in Table 1. In a normal year, the subtropical monsoon transports a huge amount of atmospheric moisture from the East and South China Sea to the basin during April to October. Thus, the inflow is mainly concentrated on mid-summer and early-autumn, from July to September, accounting for nearly one half of the total rate per year. However, the TGR does not store water until the period of mid-autumn (October) to reserve capacity for attenuating summer floods. Thus, the hydropower potential of TGR is particularly sensitive to the inflow variations in autumn. Table 1 2.2 Major human activities in the basin Socioeconomic development. After China stabilized around 1960s, major socioeconomic indicators of the basin, such as gross domestic product (GDP), population, electricity production, as well as effective irrigation area (EIA) have continuously increased for over more than half a century. According to the census, as shown in Fig. 2a, regional population has been doubled to 173 million within 55 years, even though the rate of population growth remarkably slowed down after the 1980s, when one-child policy was implemented. To sustain this population growth, the EIA for food supply has increased by 2.4 times, from 21×103 km2 in 1961 to 51×103 km2 in 2015 (see Fig. 2b). As a measure of people’s productive activity, the rate of GDP growth is impressive as well. It is noteworthy that the annual growth rate of GDP in China was continually higher than 6.5% from the 1990s onward. In 6

addition, the growth of electricity production was in consistent with the GDP rate. The rapid increase in the above-mentioned factors implies more extensive water-use activities and non-negligible growth of freshwater consumption abstracted from river network. Reservoir construction. The storage delays in reservoirs not only redistribute seasonal streamflow, but also result in water loss through increasing water surface evaporation as well as initial filling of reservoirs. Over the period of 1990-2015, several construction projects related to large dams were completed or being constructed in the basin. Their main utilizations were hydropower generation, flood control, and irrigation. Up to 2015, the basin upstream of the TGR involved 35 large reservoirs with an installed power generation capacity in excess of 300 MW in operation. The cumulative total and regulated storage capacity had reached 68 and 35 Gm3, respectively, which are about twice the ones of the TGR, as illustrated in Fig. 2c. Fig. 2 2.3 Datasets Data applied in the analyses consist of three categories: reservoir information, hydrometeorological observations, and water-use and socioeconomic data. General information belonged to 36 large reservoirs including the TGR was extracted from the “4th National Survey on Hydroelectric Resources” published in 2005 and Almanac of China’s Water Power (19612015). Data belonged to each reservoir described geographical location, year of initial operation, catchment area, riverbed slope, and total storage capacity (see Table S1). According to the location of 36 large reservoirs and 5 hydrological stations (Yichang is not included), the upper Yangtze River was divided into 41 sub-basins, as shown in Fig. 3. Monthly discharge data at five gauging stations, including Pingshan, Gaochang, Fushun, Beibei and Wulong, in the upper Yangtze River basin were collected to calibrate the water balance model for major tributaries of 7

the basin (see Fig. 1). On the main reaches between the TGR and Yichang station, there was indeed a reservoir being operated, named Gezhouba. However, it was a run-of-river hydroelectric project without a notable impact on seasonal streamflow. Consequently, historical records at Yichang station were served as a practical estimate of virtual inflow to the TGR before the dam being constructed in June 2003. Afterwards, observed inflow to the TGR was compiled from China Three Gorges Corporation, as a Chinese stateowned power company. Monthly observations of meteorological data belonging to 75 stations are plotted in Fig. 3, which acquired from the “National Climate Center of China Meteorological Administration”. Daily PET was determined for each station using the Penman-Monteith method. Monthly PET was computed by accumulating the daily values. General description of the Penman-Monteith method was given in Supplementary evidences. The Thiessen Polygon method was used to calculate basin-wide precipitation and PET data over the time as well. The values of population, EIA, and electricity production were gathered from the Provincial Statistical Yearbooks of 9 provinces in mainland China (Qinghai, Tibet, Sichuan, Yunnan, Chongqing, Gansu, Shaanxi, Guizhou, and Hubei), describing upstream tributaries of the TGR. Assume that population, EIA, and electricity production are evenly distributed within each province, then their basin-wide value can be derived from multiplying the density by the area of each province located in the basin. It was herein attempted to apply China’s GDP per capita (constant 2005 US$) derived from World Bank national accounts data (http://geodata.grid.unep.ch) as a surrogate for basinwide value. Annual national water withdrawal data were collected on the basis of China Statistical Yearbooks published since 2000 for calibrating industrial and domestic use models. Data on regional total water withdrawal and consumption in the upper Yangtze River were collected from Changjiang and Southwest River Water Resources Bulletin (1998-2015), which serve to 8

cross check on the total water withdrawal simulation. The period of study and analysis includes the baseline period (19611990) and the impact period (1991-2015). The year of 1990 is a milestone with regard to human activities in the region, as it is the year from which on economic growth and dam construction began to accelerate. In addition, the TGR was designed before 1990, thus understanding of main causes of inflow variations after the design period is crucial to design an optimal reservoir operation strategy. Fig. 3 3 METHODS 3.1 Statistical analysis for detecting abrupt changes The

Pettitt

test

was

used

to

identify

abrupt

changes

in

hydrometeorological variables. The test is widely used in the stochastic analysis to detect abrupt changes in the mean distribution of the variable of interest due to its robustness for data distribution skewness under the null hypothesis of no change (Rougé et al., 2013; Xie et al., 2014). The Mann-Whitney U test was utilized for testing two samples (before and after the change-point) obtained from the same distribution, in which a change-point was selected, maximizing the statistic. The Pettitt’s statistics

U ,n were derived from the rank-based comparison between observations with length of n before and after a specific time  , which could be defined as follows 

U , n  

 sgn  x n

i 1 j  1

j

 xi 

(1)

where sgn    1 if   0 , 0 if   0 , and -1 if   0 . Once a potential change-point is specified, e.g. the year of 1990, the confidence probability of change can be quantified as



p  1  2 exp 6U2, n

n

2

 n3 



(2)

Given a critical level of confidence 1   that usually equals 95%, if 9

p  1   , the null hypothesis is rejected and it can be inferred that a significant change has occurred at the point  . The

Pettitt

test

may

lead

to

misleading

results

when

hydrometeorological data are auto-correlated. To overcome this weakness, we followed the TFPWcu procedure, i.e. Trend-free Prewhitening along with residuals correction and unbiased estimation of autocorrelation coefficient, to mitigate the adverse effect of autocorrelation on assessing the confidence probability of change (Serinaldi and Kilsby, 2016). The original data was whitened via a short-term persistence process (lag-one autoregressive AR1) and a long-term persistence process (fractional Gaussian noise FGN), respectively, before implementing the Pettitt test. Significant changes were reported only if the AR(1)-based and FGN-based prewhitening approaches reached an agreement (see Table S2). 3.2 Quantitative assessment of inflow variations 3.2.1 General framework for quantifying the attribution to inflow changes To differentiate and quantify the relative importance of major factors, influencing inflow, the water balance equation needs to be developed, that is described as,

ITGR  Qnat  Quse  Sres

(3)

where ITGR is the monthly total inflow to the TGR, Qnat and Quse denote the natural runoff and the water consumption in the basin, respectively, and

Sres represents the monthly increments in water storage of upstream reservoirs and it is positive, while more water is stored in the reservoirs at the end of the month than at the beginning. The equation was established regarding the basic assumption that climate variability, water consumption, and upstream reservoir operation were only drivers for monthly inflow to the TGR. Thus, the variations in natural runoff, water consumption, and water storage increments of upstream 10

reservoirs were accurately simulated and compared, so as to assess their relative contributions to total inflow changes. The procedure for calibrating and validating models to reproduce historical variations of the three mentioned impact factors are outlined as follows: Step 1: Water-use reconstruction. The water-use model was calibrated based on water-use records (2000-2015), and then was applied to reconstruct water-uses over the whole study period (1961-2015). Step 2: Natural runoff simulation. The baseline period (1961-1990) was divided into calibration (1961-1980) and validation (1981-1990) phases to calibrate and validate the hydrological model parameters. During the baseline period, only 3 out of 35 reservoirs were in operation. Their cumulative total storage capacities take less than one percent of the annual inflow to the TGR (see Fig. 2c). Consequently, the differences between the observed inflow and the simulated water consumption were calculated to present reliable references for calibrating hydrological model, so as to create natural runoff for the entire study period. Step 3: Reservoir regulation estimation. The reservoir operation model was calibrated against the estimated monthly water storage increments of upstream reservoirs ( Sres ) in the impact period. The estimated Sres were calculated by subtracting the simulated water consumption and natural runoff obtained from the observed inflow. If the model was capable of reproducing these estimation values, climate variability, water consumption, and upstream reservoir operation would be taken as the major factors into account, influencing monthly inflow variations. Otherwise, we might suspect that there were other effective factors which were not considered by the aforementioned water balance equation (Eq.3). We should not overlook that model validation is also an essential procedure for water-use model and reservoir operation model. With regard to water-use model, water withdrawal in each sector was calibrated according to 11

national statistics, while their totality was verified in reference to total water withdrawal documented in regional statistics. As actual reservoir operation data are scattered over independent companies, which can hardly be gathered, we resorted to the cumulative storage capacity of these reservoirs as a surrogate for reflecting the general impact of reservoir operation. The growth of cumulative storage capacity over time was applied to verify the output of reservoir operation model, i.e., cumulative water storage increments of upstream reservoirs. 3.2.2 Water-use model The water-use model of WaterGAP2 was used to simulate water withdrawal in major water-use sectors (e.g., domestic, industry, and irrigation). Water consumption was considered as the part of the withdrawn water that was consumed through evapotranspiration, incorporating into manufacture, crops and humans. The water consumption was estimated ( Quse , Eq.3) by applying the multi-year average value of consumptive-uses ratio, i.e. 45%, which was collected from the Changjiang and Southwest River Water Resources Bulletin (1998-2015). The mentioned model could estimate withdrawn water based on the water-use intensity in each sector and corresponding driving force. The driving forces of water-use refer to population in the domestic sector, electricity generation in the industrial sector, and EIA in the irrigation sector. The domestic and industrial water intensities were modeled by considering variations in structure and technology of water-use. According to the data describing global water-use trends, Alcamo et al. (2003a) suggested a sigmoid curve to describe the structural water-use intensity in the domestic sector (m3/person) and a hyperbolic curve to describe the structural water-use intensity in the industrial sector (m3/MWh), denoted by DSWI and ISWI , respectively

DSWI  DSWI min  DSWI max 1  exp  rdom GDP 2   (4) 12

ISWI  1  rind  GDP  GDPmin    ISWI min

(5)

where GDP is the per capita annual GDP (US$/year), rdom and rind are dimensionless curve parameters that require calibration. Further accounting for technological change, improving water-use efficiency, and DSWI and

ISWI can be computed by DWI  DSWI  1   dom  IWI  ISWI  1  ind 

t  t0

(6)

t  t0

(7)

where dom and ind are the annual rates of decrease in structural wateruse intensity (%/year), t0 is the time when technological change is taken into account. Here, it was attempted to assume t0  1980 , dom and ind equal 0.6% that is consistent with the reports for hindcasting and projecting of global water-uses published by Florke et al. (2013) and Wada et al. (2016). The irrigation water intensity is the irrigation of water requirement per unit of crop land (mm/km2). It is computed for each month of the growing season as the difference between the crop water requirement and the available soil moisture supplied by precipitation. IRWI   kc E pet  Pavail  irri

(8)

where irri is the ratio of irrigation water consumption over withdrawal. In absence of detail data on measuring improvement in irrigation water-use efficiency, it could be assumed that irri would be the same as the basinwide consumptive-uses ratio, i.e. 45%. Pavail is the effective precipitation available to the crop that was estimated on the basis of the United States Department of Agriculture (USDA) Soil Conservation Service method (SCS).

Epet denotes the PET. The non-dimensional crop coefficient kc was set for the five growing months as: 0.1, 1.1, 1.1, 1.2, 0.8 for paddy situations (Doll and Siebert, 2002). We assumed the cropping pattern to be paddy rice over the effective irrigation area, because the basin is among the major paddy rice 13

cropping areas of the country. The growing season was determined using a rule-based

algorithm

incorporating

the

site-specific

meteorological

conditions (Doll and Siebert, 2002). Sectoral water intensities multiplied by driving forces yielded the withdrawn water. Both of the domestic, industrial and irrigation water withdrawals were calculated in each sub-basin, and then aggregated to the whole basin scale. 3.2.3 Monthly hydrological model For assessing hydrological consequences of climate variability, a lumped monthly rainfall-runoff model was employed, which initially proposed by Xiong and Guo (2002; 1999), and that is appropriate for using in the humid and semi-humid region of China. This two-parameter model, namely Xiong Model, considers runoff as a single component. It offers parsimonious yet refined structure to describe monthly runoff generation process and simplifies model’s calibration. Its high-efficiency in reproducing historical monthly runoff has been proved in more than 100 basins in China. Multi-model comparison results revealed that two-parameter models (e.g., Xiong Model) are perfect to achieve a comparable or even better performance than complex models, reflecting that the objective is proper, e.g. construction of monthly natural runoff that is driven by historical or projected variations of climatic variables (Bai et al., 2015). The structure of model consists of two fundamental functions for simulating actual evapotranspiration ( Eact ) and natural runoff ( Qnat , Eq.3) at the t-th month, which are given as Eact ,t  C  E pan ,t tanh  Pt E pan ,t 

(9)

Qnat ,t   S soil ,t 1  Pt  Eact ,t  tanh  S soil ,t 1  Pt  Eact ,t  SC 

S soil ,t =S soil ,t 1  Pt  Eact ,t  Qnat ,t

(10)

(11)

where climatic inputs precipitation ( P ) and pan evaporation ( E pan ) determine water-limited and energy-limited conditions for the model, 14

respectively. Without changing physical meaning, PET was adopted instead of pan evaporation as the energy boundary to restrict the actual evapotranspiration. The quantity of the remaining water in the soil after the evaporation losses is determined by the term

S

soil , t 1

 Pt  Eact ,t  , with

S soil ,t -1 being soil water content at the end of the  t 1 -th month. Soil water content at the end of the next month ( Ssoil ,t ) is calculated according to the water conservation law (Eq. 11). The evaporation coefficient ( C ) and the field capacity ( SC ) are model parameters, which were calibrated based on two assessment criterion, including Nash-Sutcliffe Efficiency (NSE) and Water Balance Error (WBE). The ranges and optimized values of the model parameters are given in Table 2. Table 2 3.2.4 Reservoir operation model Generic reservoir operation models are often adopted solutions to comprehensively assess the large-scale hydrologic alterations caused by multi-reservoirs using limited information including reservoir specifications, inflow generation, and water demand (Doll et al., 2009; Ehsani et al., 2016). In this study, the algorithm developed by Hanasaki et al. (2006) was used to simulate the process of reservoir outflows. The algorithm presents a representative operation scheme diverting excess water from the wet season to the dry season in addition to fulfilling downstream water requirements. This scheme does not necessary be the same as the actual operation rules at each reservoir, however, it reflects the general principle of reservoir regulation. As such, the monthly release ( R ) is parameterized as  0.5 2 k R  1   0.5 2  I 0    0.5  y p    res  (12) R k R 0.5       y p

where,  denotes the total storage capacity to mean total annual inflow ratio, ky is the release coefficient reflecting initial storage in the y th 15

operational year, Ires is the monthly inflow, and R p is the provisional monthly release accounting for mean annual inflow and fluctuations in downstream water requirements. Herein, downstream refers to the downward area to the next reservoir, or if there was no further reservoir, to the outlet of the basin. Apart from Hanasaki et al.’s scheme (2006), evaporation losses were regarded from water surface of large reservoirs. The difference between the potential and actual evapotranspiration was added to the water balance of reservoirs, thus taking into account the landscape change from land to open water body. Reservoir evaporation is directly proportional to reservoir surface area. However, this requires establishing a geometrical relationship to translate reservoir storage volume to surface area. For that purpose, it was attempted to select a tetrahedron shape with isosceles triangles proposed by Fekete et al. (2010) to compute the reservoir surface area as a function of the storage. The monthly increments in water storage of upstream reservoirs ( Sres ), as previously defined in Eq. (3), was then calculated according to the water balance of reservoirs, which is expressed as. S res    I res  Eres    R   upstream    S S res  S res  Stotal  dead

where the term



(13)

indicates integration over the basin upstream of the

upstream

TGR, Eres denotes the increased evaporation losses, S res is water content in reservoirs at the beginning of the month. The reservoir storage may vary from dead storage ( Sdead ) to total storage ( Stotal ). Herein,



is a

dimensionless coefficient for correcting monthly releases, and it is used to account for the impact of human’s decisions on monthly releases. After calibration, it was revealed that  was in the range between 0.5 and 2.5 among different months. With a  below 0.5, no more inflow can be stored 16

as the reservoir has been filled, while with a  above 2.5, no more release is produced as the reservoir is nearly empty. Therefore, if  is inside the range of 0.5-2.5, that may prevent the reservoir operation model from reproducing monthly releases in an unrealistic manner. 4 RESULTS AND DISSCUSSION 4.1 The observed inflow variations, precipitation, and PET The Pettitt test was utilized to evaluate the confidence probability of abrupt changes in the mean value of annual and seasonal inflows, precipitation, and PET from the baseline period to the impact period. Figure 4 depicts the computed magnitude, percentage and probability of the variations. In comparison with the baseline period, annual inflow was 27Gm3 (6%) lower during the impact period. This magnitude of reduction accounts for nearly 70% of total storage capacity of the TGR that has practical significance for hydropower generation. Considering the intra-annual variability, seasonal inflows were stable in spring (March-May), and became slightly lower in summer (June-August) and higher in winter (DecemberFebruary). Significant inflow variations occurred in autumn (SeptemberNovember) with a decrease of 24Gm3 (16%). According to the variations were shown in climatic variables, seasonal precipitation did not remarkably change during spring and summer. However, it became slightly larger in winter and significantly smaller in autumn. Autumn precipitation decreased by 19mm (9%). Annual precipitation declined insignificantly by 18mm (2%). Changes in PET were positive in all seasons. Rising PET neutralized increase of precipitation in winter and emphasized the aggravation of water loss in autumn. Autumn PET rose insignificantly by 4mm (2%). Qualitatively, the increased PET and decreased precipitation were consistent with annual and autumn inflow reductions. However, to find out whether climatic impact has dominated inflow changes, it was attempted to resort to the numerical simulation results. 17

Fig. 4 Figure 5 presents the simulated total inflow as the summation of natural runoff and water consumption for the baseline period, and by integrating monthly increments in upstream reservoir storage for the impact period. Calibration of the hydrological model was carried out from 1961 to 1980 and validation was from 1981 to 1990. It can be clearly seen that the simulated total inflow was well aligned with the observations in the baseline period. The NSE coefficient in the calibration and validation phases is 93.7% and 95.7% on the monthly time scale, respectively. The WBE coefficient for these two phases is 2.0% and -1.0%, respectively. By further involving the effects of reservoir operation, the simulated total inflow excellently reproduces historical variations for the later impact period with nearly the same NSE (93.1%), and a better WBE (0.9%). Accordingly, it is feasible to reliably investigate variations in each factor in order to assess their impacts on total inflow variations. Fig. 5 4.2 Variations in water-use Water-use hindcasting presents widespread increases in domestic, industrial, and irrigation water withdrawals driven by growing population and rising prosperity. The modelled temporal variations of sectoral water-uses over the basin during the past 55 years are shown in Fig. 6. The overall regional water-uses increased by 9.3Gm3 between the baseline and the impact period. Specifically, regional domestic, industrial, and irrigation water-uses increased by 2.7Gm3, 0.4Gm3, and 6.2Gm3, respectively. Since there was no regional dataset reporting sectoral water-uses, the domestic and industrial water-use models were calibrated based on input data derived from national statistical values that were rescaled to the upper Yangtze River according to the density of population and electricity production. The simulated domestic and industrial water withdrawal achieved a satisfactory agreement with the national statistics. Regional total water 18

withdrawal, published by Changjiang and Southwest River Water Resources Bulletin, was not incorporated into water-use modelling. Thus, it provided a reference to validate the performance of water-use model. Figure 6 shows that the model outcomes reproduced the increasing water-use trend that has been recorded between 1998 and 2015. During 1998-2015, the multiyear average value of simulated total water withdrawal was estimated to be 6% lower than the recorded data. This underestimation can be ascribed to some minor wateruse sectors, which were not explicitly considered in this study. Despite that, the current simulation results well reflected the development of major sectoral water-uses. To be specific, there was a flat increase of domestic water withdrawal between 1975 and 1990 owing to introduction of efficiency improvement (see green line in Fig. 6). An increase was apparent again since 1990, mainly related to rising population and growing domestic structural water-use intensity, which may attribute to increasing demands for household water-use appliances. The household water-use demands would eventually saturate with the growth in income, as domestic water-uses were stabilized around 2010. Water withdrawn for industrial purposes peaked in 1980 reaching 13Gm3 per year, followed by a decline until 2000, although the electricity production steadily rose during this time period (see purple line in Fig. 6). After that, industrial water withdrawal gradually recovered to 11 Gm3 in 2015. Both decreasing the structural water-use intensity, as well as improving water-saving technologies slowed down the corresponding trend and even decreased industrial water withdrawal. Along with economic booming which began since 1980s, China encountered with more serious water stresses and strived for resource-efficient industry. From a legal perspective, the country implemented its first edition of national water law in 1988, followed by several specific policies addressing water conservation, water pollution prevention, etc. The developed legal framework significantly promoted improvement of water recycling technology within industrial factories. The 19

allocation of limited water resources as well as stringent water-use efficiency standards pushed local governments to reduce water-intensive industries. The increasing trend of irrigation water requirement was generally dominated by EIA (see blue line in Fig. 6). The strong fluctuation of irrigation water withdrawal was driven by climatic variability. The large scale of water withdrawals coincided with dry climate and vice versa. Fig. 6 Total water withdrawal showed upward variations in all seasons in terms of percentage of variations, while annual domestic and industrial water withdrawal were evenly allocated within one year (see Fig. 7). The increases in major crop growing season, mainly in summer, were marked. Fig. 7 The spatial distribution of variations of total water withdrawal in subbasins, as displayed in Fig. 8a, highlights the dominant regions experiencing intensified water-use activities, mainly in the southern and eastern part of the basin. Previous investigation on PET by using various trend detection methods have shown consistent agreement on finding major upward trends concentrated on the southern region (Wang et al., 2015b). The increased evapotranspiration led to more irrigation water requirements, which explained the considerable water-use increments in this area. The eastern region is a very densely populated area, e.g. Chongqing megacity, and is very appropriate for agricultural activities, e.g. Sichuan basin. The high total water withdrawal is driven by high population as well as cropping density. Fig. 8 4.3 Variations in natural runoff By means of monthly hydrological modelling, it is ideal to obtain the temporal variations of natural inflow to the TGR (see Fig. 7), as well as the spatial distribution of natural runoff variations in the basin (see Figs. 8b and c). It is noteworthy that both of them enhanced the policy makers’ understanding regarding the natural hydrological response to climate 20

variability. According to simulated natural inflow, all seasonal inflows became lower. The negative change in autumn was significant, being assorted with the variation in precipitation. In winter, the magnitude of rising PET outweighed the precipitation increases, leading to downward changes of natural inflow. The observed inflow (Fig. 4a) and the simulated natural inflow (Fig. 7) have close downward variation at the annual time scale (27Gm3 and 21Gm3). However, this concurrence did not happen in different seasons. In the lowflow seasons (spring and winter), additional water compensated for natural inflow losses so that more water entered into the TGR during the impact period than the baseline period in practice. In the high-flow seasons (summer and autumn), the observed inflow declined by 6Gm3 and 24Gm3, obviously exceeding variations in natural inflow (2Gm3 and 15Gm3). Both of these values demonstrate that non-climatic factors have also apparently reallocated seasonal inflows to the TGR. Another notable issue is the absence of considerable increasing trends in natural inflow in the main flooding of summer that could be expected based on observed precipitation changes (Jiang et al., 2008; Jiang et al., 2007) and the corresponding natural runoff (Xu et al., 2008) which was based on 19612000 datasets. The trends greatly weakened when the time series were extended to 1961-2015 in the present study. It casts doubt on whether the upper Yangtze River has been experienced more severe flood hazards in recent decades concerning that one of the major tasks of the TGR is flood attenuation. To answer this question, it requires a further study with an emphasis on investigating variations in frequency and intensity of flood hazards. Herein, it is worth mentioning that the intensification of floods is not necessarily associated with the larger amount of seasonal mean value. In recent decades, the regime of floods might have been characterized by higher intensity along with shorten duration as it was found in precipitation in several parts of the Yangtze River (Zhang et al., 2013). 21

Regarding the spatial distribution of annual natural runoff (see Fig. 8b), decreases were mainly concentrated on the plain area of Sichuan basin (eastern region) with an area over the upper reaches of Jialingjiang River (north-eastern region) where the percentage of variation exceeded -15%. However, in autumn (see Fig. 8c), the area of decreases were more widespread, particularly significant in the area around the upper reaches of Yangtze River and the Wujiang River (hachured areas). These are economically highly developed regions in western China with megacities and large irrigation areas. More noteworthy is, these regions also suffered from the most significant intensification of water-use activities of the basin, as it was identified in Fig. 8a. Addressing autumn water shortages could become a consequent issue if natural runoff decreases continue. 4.4 Water storage increments of upstream reservoirs in the impact period As for evaluating inflow variation regulated by upstream reservoirs, the simulated water storage increments of upstream reservoirs required verification. The cumulative annual and autumn increments in reservoir water storage were correlated with the cumulative capacity of large reservoirs upstream of the TGR, shown in Fig. 9. The strong linear correlations along with extremely low significance levels indicated that the simulated impact of reservoir operation approximated the real world cases. Fig. 9 The upstream reservoirs affected inflow to the TGR by transferring water among seasons along with raising evaporation losses as well as impounding reservoirs. The multiyear average value of seasonal increments in upstream reservoir storages clearly reflected their general principle of operation, as shown in Fig. 10. That is, from the previous winter to the next Spring, the stored water were gradually released to generate electricity and satisfy water demand, and meanwhile the water level of reservoir was kept as high as possible to gain sufficient water head for hydraulic generator as well as maintaining navigation conditions. During the flood season of summer, 22

most reservoirs were operated at a low-flood control level to reserve enough spaces for flood peak attenuation, while some of them began to impound floodwaters in advance. In autumn, the regulated water storages were again filled in preparation for the coming low flow season. Collectively, the amount of water impounded in the wet season exceeded the amount of water released in the dry season. Over the impact period, around 2 Gm3 inflow to the TGR on multi-year average were annually lost due to the combined effects of increasing evaporation and the initial filling of reservoirs. Fig. 10 From a regional perspective, the total reservoir storage capacity of Jinshajiang and Yalongjiang Rivers was obviously larger than other tributaries. In autumn, the reservoirs have retained 3.8 Gm3 water on multiyear average, which almost reached sum of others in the basin (see Fig. 11). To sustain the planning projects that divert water from Yalongjiang River to northwest China and from Jinshajiang River to central Yunnan province, more reservoirs will be built in this sub-basin in preparation for storing an additional 20.4 Gm3 water annually. These reservoirs will act to further reduce inflow to the TGR in the coming decades. Fig. 11 4.5 Main causes of annual and autumn inflow reductions Herein, we discuss the attribution for decreasing annual and autumn inflow. Table 3 shows the magnitude of annual and seasonal variations. During the baseline period in which the effect of reservoir operation was minor, the simulated total inflows (natural inflow less water consumption) were very close to the observations. Simulations slightly overestimated total inflow by 0.7% (1Gm3) in autumn, and 0.9% (4Gm3) on mean annual statistic, where the errors were much smaller than the magnitude of reductions observed over the impact period. Furthermore, the observed variations in total inflow were well replicated by the summation of simulated variations in natural inflow, water consumption, as well as water storage increments of 23

upstream reservoirs. It reinforces confidence in the methods applied to properly quantify the contribution of three impact factors. Table 3 Figure 12 further helps illustrate a relative comparison between cumulative inflow gains and losses over the impact period. One general point becomes very clear that climate variability dominated annual and autumn inflow reduction. Meanwhile, the fact deserved explicit emphasis that human activities were obviously enhanced, even though their contributions are still complementary. Fig. 12 According to the numerical simulation results, the drier climate was responsible for 78% of the annual inflow decrease, while the effects of water consumption and upstream reservoir operation were accounted for 15% and 7%, respectively. To be more specific, the percentage due to sectoral water consumption was 10% for irrigation, 4% for domestic, and 1% for industrial sectors (see Fig. 13a). In autumn, the drier climate accounted for 63% of the inflow reduction. Around 33% of the decrease arose from water impoundment from upstream reservoirs, while 4% of which can be attributed to the increased water consumption (see Fig. 13b). For other seasons, human activities contributed to a comparable or greater share of inflow variations, particularly in winter when water released from upstream reservoirs was the main reason for inflow enhancement. However, it is noteworthy that the magnitude of inflow variations in these seasons were remarkably lower than that in autumn. Fig. 13 4.6 Uncertainties of the attribution results We should clearly pointed out that there are uncertainties associated with numerical modelling for the attribution results. We did not include considerations for small reservoirs in the framework of our reservoir operation model. The majority of these reservoirs were lack of actual 24

operation data and were run-of-river projects that had minor effects on seasonal streamflow. The water-use model can be improved by introducing the spatial distribution of population, electricity generation, effective irrigation area, and cropping pattern with higher resolution. Future work requires more reliable and multi-source data involved in the reservoir operation model to improve the simulation accuracy. The effect of man-made land-use and land-cover changes (LUCCs) were omitted in the simplification of lumped hydrological model. LUCCs is a complex process, which is affected by both natural variability, socioeconomic development and water policy. The predominant land-use change was farmland. The diminishing farmland contributed to the recovery of forest vegetation and the expansion of urban land. The afforestation program increased vegetation cover and can reduce annual water yield. The urbanization program increased impervious area and would lead to an increase in the runoff yield. Meanwhile, the government set the minimum amount of arable land to ensure food security. Based on the present study, more comprehensive analyses on identifying the individual and combined effects of LUCCs on local climate and runoff generation, can assist in refining the attribution of hydrological changes. 5 CONCLUSIONS The TGR is a major alternative energy source, serving to supply growing electricity demand of China. Nevertheless, inflow reductions after the design period (1961-1990) have recently reduced its capability to fulfil the designed electricity generation. In the following decades (1991-2015), the TGR has lost 6% of annual inflow, accounting for approximately 70% of its total storage capacity. During the main season of water impoundment, inflow in autumn significantly reduced by 16%. This study was aimed at identifying inflow variations that have occurred between the baseline period (1961-1990) and the impact period (1991-2015) in association with climate variability, water consumption, and upstream 25

reservoir operation. It could be concluded that the drier climate was the main cause for decreasing annual and autumn inflow. Although the rising water consumption was impressive, however, the magnitude of its increment has not prevailed climate-induced variation. Upstream reservoir operation cannot be cited as the major driving force for decreasing autumn inflow either. Around one half upstream large reservoirs have just put into practice in the recent 5 years (2011-2015), that cannot properly explain previous inflow variations. Specifically, the relative contribution rates of climate variability, water consumption, and upstream reservoir operation, aiming to decrease annual inflow are 78%, 15%, and 7%, respectively, while those rates, leading to reduce autumn inflow are 63%, 4%, and 33%, respectively. The climatic impact is still a primary consideration for framing adaptive operation strategy of the TGR in response to negative consequences of inflow variations, despite intensified human activities. Given that autumn inflow reduction is significant and is often attributed to the drier climate that are plausible to last for another decade, it is recommended to move the starting time of TGR water impoundment ahead to early autumn to reach normal water storage in-time. Among water consumption sectors, retrofitting of irrigation system with modernized water-saving technology is an efficient approach to mitigate inflow losses because of a large amount of irrigation water consumption in the total amount of consumptive water. Besides, the upstream reservoirs should be operated in an adaptive manner to avoid retaining autumn inflow simultaneously. The study suggested a stepwise procedure with incorporation of wateruse, and hydrological and reservoir operation models to identify the main reason for streamflow variation. This approach offers merits in two respects. First, it explicitly considers water consumption on the baseline period rather than arbitrarily ignores its effect. Secondly, major human activities, including water consumption and reservoir regulation are quantitatively isolated, so they could be well managed regarding each property. Furthermore, the 26

models can lay a groundwork for projections of regional water availability and demand balances for the next studies. The proposed procedure can be extended to more catchments along with prevailing reservoir regulation and water abstraction, and it is expected to provide more information for management of regional water resources being resilient to the change of streamflow pattern. ACKNOWLEDGEMENTS This research was supported by National Key R&D Program of China (2017YFC0405805), the National Natural Science Foundation of China (41701015),

and

the

Postdoctoral

Science

Foundation

of

China

(2018M632222). We sincerely appreciate editors and anonymous reviewers for their insightful advices on an earlier version of this manuscript. REFERENCES

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Figure Captions: Fig. 1 Sketch map of the upper Yangtze River basin showing 6 hydrological stations and 36 large reservoirs considered in this study. Fig. 2 Variations in the rates of (a) population and GDP, (b) effective irrigation area and annual electricity in the upper Yangtze River, and (c) cumulative capacity and number of large reservoirs (installed capacity ≥ 300 MW) upstream of the TGR. Fig. 3 Sub-basin division and delineation of the upper Yangtze River basin. Fig. 4 Change detection results of observations (1991-2015 versus 19611990): inflow, precipitation, and PET. The downward arrow indicates significant changes with the confidence probability of 95%. Fig. 5 Performance of the simulated monthly total inflow for the baseline and impact periods. Fig. 6 Evolution of water withdrawal: total, domestic, industrial, and irrigation. Simulated data compared with published data extracted from national and regional bulletins. The horizontal dotted lines represent the averages of the corresponding period. * denotes that significant variations were detected at 95% confidence interval. Fig. 7 Change detection results of simulations (1991-2015 versus 1961-1990): total water withdrawal over the basin, and natural inflow to the TGR. 35

Fig. 8 Spatial distribution of detecting the changes of simulation results: (a) annual total water withdrawal, (b) annual natural runoff, and (c) autumn natural runoff. The hachured areas are where the changes are statistically significant. Fig. 9 Correlations between simulated cumulative increments in reservoir water storage and actual cumulative capacity of large reservoirs upstream of the TGR. r is the linear correlation coefficient and p is its significance level. Fig. 10 Means of simulated annual and seasonal increments in reservoir water storage upstream of the TGR (1991-2015). Fig. 11 Mean values of simulated seasonal increments in reservoir water storage in autumn and cumulative total storage capacity of reservoirs in major tributaries (1991-2015). Fig. 12 Evolution of cumulative inflow gains and losses in the period of 19912015: (a) annual, (b) autumn. The benchmark values to evaluate inflow losses induced by climate variability, water consumption, and upstream reservoir operation were mean values of natural inflow, water consumption, and storage increments over the baseline period (1961-1990), respectively. Fig. 13 Relative contribution of different impact factors in (a) decreased annual inflow, (b) decreased inflow in autumn (1991-2015 versus 1961-1990).

36

Table Captions: Table 1 Annual- and monthly-mean of precipitation, PET, and the observed inflow from 1961 to 2015. Table 2 Parameters ranges and optimized values for monthly hydrological model. Table 3 Magnitude of annual and seasonal variations in observed inflow and simulated impact factors (Gm3).

37

Table 1 Annual- and monthly-mean of precipitation, PET, and the observed inflow from 1961 to 2015. Precipitation (mm)

PET (mm)

Observed Inflow (Gm3)

January

9

49

12

February

11

61

10

March

23

93

12

April

48

114

18

May

88

129

30

June

136

123

46

July

162

131

78

August

141

125

69

September

113

94

65

October

58

72

46

November

21

53

25

December

9

45

16

Annual

819

1089

427

38

Table 2 Parameters ranges and optimized values for monthly hydrological model. Optimized parameters for sub-basins of tributaries Yangt Range s(1)

Jinshajia

Daduh

ng &

e&

Tuojia

Jialingji

Wujia

River

Yalongji

Minjia

ng

ang

ng

Up

ang

ng

ze

Reach es

[0.25

C

0.76

0.53

0.78

0.79

0.74

0.76

900

800

850

550

500

1250

1.25]

SC [300 (m 2000] m) (1) Parameter

ranges are defined with reference to calibration results in the

humid and semi-humid regions of China, given by Xiong and Guo (1999).

39

Table 3 Magnitude of annual and seasonal variations in observed inflow and simulated impact factors (Gm3). Mean values over

Variations over impact period

baseline period (1961-

(1991-2015)

1990) Storag e Natu Obser ved inflow

ral inflo

Water Consum ption × (-1)

Natu Obser ved inflow

w

ral inflo

Water Consum ption × (-1)

w

Increm ents of Upstre am Reserv oirs ×(1)

Sprin 60

62

-2

~0

-3

~0

3

197

206

-6

-6

-2

-3

-1

147

150

-2

-24

-15

-1

-8

36

38

-2

3

-1

~0

4

440

456

-12

-27

-21

-4

-2

g Sum mer Autu mn Wint er Annu al

40

Annual and autumn inflow to the TGR reduced by 6% and 16% in the post-design period.

A stepwise procedure for attributing streamflow changes is suggested.

Major human impacts including water consumption and reservoir operation are isolated.

Drier climate primarily caused inflow decreases despite intensified human impacts.

41

DECLARATION OF INTEREST The authors declare that they have no conflict of interest.

42