Quantifying the impacts of the Three Gorges Reservoir on water temperature in the middle reach of the Yangtze River

Journal Pre-proofs Research papers Quantifying the impacts of the Three Gorges Reservoir on water temperature in the middle reach of the Yangtze River Yuwei Tao, Yuankun Wang, Bruce Rhoads, Dong Wang, Lingling Ni, Jichun Wu PII: DOI: Reference:

S0022-1694(19)31211-9 https://doi.org/10.1016/j.jhydrol.2019.124476 HYDROL 124476

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

25 July 2019 6 December 2019 15 December 2019

Please cite this article as: Tao, Y., Wang, Y., Rhoads, B., Wang, D., Ni, L., Wu, J., Quantifying the impacts of the Three Gorges Reservoir on water temperature in the middle reach of the Yangtze River, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124476

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Quantifying the impacts of the Three Gorges Reservoir on water

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temperature in the middle reach of the Yangtze River

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Yuwei Taoa, Yuankun Wanga,*, Bruce Rhoadsb, Dong Wanga, Lingling Nia, Jichun

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Wua

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aKey

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Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of

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Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.

Laboratory of Surficial Geochemistry, Ministry of Education, Department of

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bDepartment

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Illinois at Urbana-Champaign, Champaign, IL, USA

of Geography and Geographic Information Science, University of

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(*Corresponding author: Yuankun Wang, [email protected])

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Abstract: The flow, sediment and temperature regimes of the Yangtze River have

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changed greatly due to the construction of the Three Gorges Reservoir (TGR).

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Although past work has focused mainly on the influence of the TGR on water and

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sediment regimes, less attention has been given to temperature effects. Water

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temperature changes have important implications for the quality of aquatic habitat and

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the health of the river ecosystem. This study investigates the impact of the TGR on

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the water temperature regime along the middle reach of the Yangtze River. To

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accurately quantify the impact of TGR on water temperature, a regression-modeling

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framework is employed to reconstruct the temporal pattern of flow and temperature

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variation along the middle reach of the river in the absence of the TGR. Based on this

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modeling, reconstructed water temperatures are compared to observed water

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temperature for the post-impounded period (period 2003-2014) to estimate the

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influence of impoundment on water temperature. Results show that the influence of

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the TGR on water temperature alteration exceeds the influence of natural factors. The

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effect of TGR, relative to unimpounded conditions, is to reduce water temperatures in

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the spring, summer, and autumn, and to increase water temperatures in the winter. The

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results of this study illustrate the pronounced effect of the TGR on the temperature

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regime of the Yangtze River and provide information that can help guide operation of

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Three Gorges Reservoir to enhance biological conservation.

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Keywords: Water temperature, Three Gorges Reservoir, Reconstruction, Yangtze

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River

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1.

Introduction

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Water temperature is a crucial physical property of rivers, having a direct impact

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on almost all ecological and biogeochemical processes, including chemical reaction

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rate, oxygen solubility, primary production and fish habitat (Caissie, 2006; Webb et

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al., 2008). The complexity of river thermal response is strongly related to large-scale

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climate changes (e.g., air temperature, precipitation and solar radiation) (Cassie, 2006;

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Rice and Jastram, 2015; Chen et al., 2016) and human activities (e.g., agricultural

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irrigation systems, power generation, dam construction etc.) (van Vliet et al., 2011;

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Ding et al., 2015).

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Large dams can influence temperature regimes of rivers by impounding water for

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prolonged periods. Reservoirs in temperate regions often stratify in response to

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changing atmospheric conditions through heat transfer at the surface and internal

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thermodynamics (Elçi, 2008). Stratification results in a warm surface (epilimnetic)

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layer and a cool bottom (hypolimnetic) layer. In view of the development of

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epilimnetic and hypolimnetic layers in reservoirs, which may have different thermal

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characteristics than the flowing river upstream of a reservoir, the impact of reservoirs

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on downstream water temperature can be important if the release of water from the

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reservoir substantially modifies natural thermal conditions suitable for native aquatic

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biota (Kedra and Wiejaczka, 2018). Previous work has mainly focused on qualitative

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evaluation of the role of reservoirs in affecting downstream thermal regimes in river

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systems (e.g., Erickson and Stefan, 2000; Steel and Lange, 2007; Olden and Naiman,

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2010; Casado et al., 2013). These studies have revealed that changes occur to all 3

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aspects of water temperature, including reduction in thermal variability, changed

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frequency and duration of temperature extremes, and weakening of air-water

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temperature interaction. Quantifying the impact of reservoirs on water temperature is

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vitally important for developing adequate strategies to minimize adverse effects of

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thermal changes on aquatic habitat.

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Temperature in a stream is the product of heat energy exchange between the

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stream and its environment, including the atmosphere and the riverbed (Risley et al.,

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2010). Air temperature is commonly used as a predictor variable for water

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temperature because it can be viewed as a surrogate for net changes in heat flux that

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affect the water surface, and also because it approximates the equilibrium temperature

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of a water course (Webb et al., 2003, 2008). Alternative modeling approaches used to

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investigate air-water temperature relationships include linear and nonlinear regression

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models, wavelet models, a time-varying coefficient, and time series models (Mohseni

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et al., 1998; Cho and Lee, 2012; Li et al., 2014; Gu et al., 2015; Rice and Jastram,

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2015; Jackson et al., 2018). Linear regression models linking water temperature and

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air temperature have been developed successfully at multiple time scales (Erickson

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and Stefan, 2000; Webb et al., 2003; Jackson et al., 2018). For example, Jackson et al.

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(2018) formulated a large-scale spatio-temporal model in which a linear function is

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used to relate maximum daily water temperature to air temperature, showing that the

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relationship between these two variables is linear. Besides air temperature, changes in

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runoff volumes are also known to affect water temperature (Langan et al., 2001;

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Webb et al., 2003; van Vliet et al., 2012). However, the relationship between 4

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discharge and water temperature remains poorly understood. A critical need exists to

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determine how air temperature and discharge influence the water temperature of

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rivers.

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Three Gorges Reservoir (TGR), the largest water control project in the world,

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provides numerous benefits that contribute to economic prosperity and social

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well-being. On the other hand, the TGR has substantially altered the hydrological and

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thermal regimes of the Yangtze River by changing the amount and timing of flow and

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by producing thermal stratification within the reservoir that results in the release of

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hypolimnetic water (Wang et al., 2012; Chen et al., 2016; Long et al., 2016; Cai et al.,

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2018). It also markedly affects the behavior and distributions of aquatic species (Long

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et al., 2016; Wang et al., 2017). For these reasons, water temperature variations

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downstream associated with the TGR have been of considerable interest. However,

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previous analysis attributed differences of water temperature downstream and

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upstream of the reservoir to operation of the TGR, without considering effects of

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other factors unrelated to the TGR (e.g., changing climate condition) on changes in

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downstream water temperature (Zou et al., 2011; Long et al., 2016). To fully assess

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the effects of TGR on water temperature, reconstruction of water temperatures from

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meteorological and hydrological data in the absence of the reservoir is necessary. As

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the basis for reconstruction, natural river flow without the reservoir needs to be

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simulated using a general regression neural network (GRNN) which outperforms

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other neural network methods (Kim et al., 2013; Tafur et al., 2014). The main objective of this study is to determine the extent to which construction 5

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of the TGR has changed water temperatures within the Yangtze River downstream of

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the reservoir. To achieve this objective, the study: (1) develops a predictive regression

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model of water temperature in the river based on air temperature and discharge; (2)

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uses the model to reconstruct water temperatures for the post-impoundment period in

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the absence of impoundment; and (3) assesses the influence of the TGR on water

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temperatures by comparing observed and predicted values of water temperature in the

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post-impoundment period. Quantitative assessment of the impact of the reservoir on

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river water temperature informs future science-based management of the reservoir

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aimed at minimizing adverse ecological effects.

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2.

Study area and data

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The Yangtze River is the longest river in Asia and the third longest river in the

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world. It passes through its source in Qinghai Province eastward to the East China Sea

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at Shanghai. The river is about 6300 km long and its catchment covers 1,800,000 km2

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(Xu et al., 2006). The basin includes zones of subtropical and temperate climate (Li et

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al., 2011). The TGR is located along the main stream of Yangtze River between

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Chongqing and Yichang (Figure 1). The TGR started to impound water in 2003 and

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became fully operational in 2009. The reservoir has a water storage capacity of 39.3

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km3 - about 4.5% of Yangtze's annual discharge (Wang et al., 2017). The TGR is

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operated to accommodate multiple needs, including flood control, irrigation, and

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power generation, with amounts of released water varying seasonally (Cai et al.,

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2018).

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Yichang hydrological station, located 44 km downstream of TGR, provided 6

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information on discharge and water temperature below the reservoir (Figure 1).

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Discharge data for Cuntan and Wulong hydrological stations located in the upper

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Yangtze River, were used to reconstruct time series of natural discharge in the

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absence of the TGR at Yichang since 2003. Daily water temperature at Yichang and

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discharge at the three hydrological stations for the pre-TGR (1952-2002) and

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post-TGR (2003-2015) periods were provided by the Yangtze River Water Resources

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Commission. Daily air temperature data for 1956 to 2015 at Yichang meteorological

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station were obtained from the National Meteorological Information Center

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(http://data.cma.cn/).

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Insert Figure 1 here.

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3.

Research Design and methods

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The research design to address the main objective of the study involves several

136

sequential components (Figure 2). First, observed daily data on water temperature

137

(WT), air temperature (Ta), and discharge (Q) were used to develop a set of

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multivariate regression models predicting water temperature on the basis of air

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temperature and discharge. Although past work has indicated that multivariate

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relations between water temperature and independent variables can be nonlinear

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(Mohseni et al., 1998), the use of multivariate linear regression analysis is justified in

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this study given linear relationships between water temperature and air temperature

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and between water temperature and discharge (Table 1). The general model has the

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form:

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WT (t )   0   1Ta (t  l )   2 Q (t ) 7

(1)

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where β0, β1, and β2 are regression coefficients, t is time, and l is a time lag. The need

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to include a lag effect for air temperature reflects the tendency for water temperature

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variations to be delayed relative to air temperature fluctuations at daily time scales

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(Erickson and Stefan, 2000; Webb et al., 2003). The root mean squared error (RMSE)

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is used to evaluate how well the regression model fits the data:

RMSE  151

(yˆ t  yt ) 2  n

(2)

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where yˆ t is the simulated value; yt is the observed value and n is the sample size.

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Insert Table 1 and Figure 2 here

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Linear multivariate water temperature regression models were estimated for each

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month with different time lags based on daily air temperature and discharge (Table

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2). A daily time scale was chosen because compared to monthly or seasonal time

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scales, exploratory regression analysis based on daily data yielded the lowest RMSE.

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Separate sets of monthly models were fitted to data for periods before (1983-2002)

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and after (2003-2014) the construction of the TGR. WTsim,pre-TGR represents estimates

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of the water temperature derived from the linear water temperature regression models

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in the pre-TGR period (1983-2002). The corresponding coefficients of these models

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are β0,pre-TGR, β1,pre-TGR and β2,pre-TGR. Similarly, values of WTsim,post-TGR are post-TGR

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estimates of water temperature associated with coefficients β0,post-TGR, β1,post-TGR and

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β2,post-TGR. The optimal time lag was determined by varying the lag and selecting the

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value that produced the lowest RMSE.

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Insert Table 2 here

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The second step in the methodology involves reconstructing what the natural 8

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variation in discharge at Yichang station would have been since 2003 in the absence

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of the TGR. This step in the analysis is necessary so that the influence of discharge on

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water temperature in the post-impoundment period (2003-2013) in the absence of

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impoundment can be estimated. To accomplish this task, a general regression neural

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network (GRNN) model was generated using discharge data for Cuntan and Wulong

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hydrological stations, which represent inflows of the TGR and are major components

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of discharge at Yichang.

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GRNN is a one-pass neural network learning algorithm (Specht, 1991).

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Consistent with the basic principle of all neural networks, GRNN requires training

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data to train itself based on input-output mapping. After a trained model is developed,

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it can be used to estimate outputs for test data. The basic equation associated with

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GRNN is: yi 

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 y K ( x, x )  K ( x, x ) k

k

(3)

k

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where yi the output or prediction, yk is an activation weight, x is the input, and xk is the

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training sample. The activation function K(x, xk) is:

K ( x, xk )  e

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 d2   i 2   2   

(4)

d k  ( x  xk )T ( x  xk ) and  is a spread constant. Optimization involves

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where

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determining the value of  that minimizes the mean square error when outputs are

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predicted based on the model developed for the training data.

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To apply GRNN on a daily basis, a lag effect is incorporated because discharge

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variations at Yichang tend to lag behind those at Cuntan and Wulong by about two 9

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days on average. For model development, 80% of the data prior to dam construction

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were used for training (1952-1991) and 20% (1992-2002) were used for testing

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(Tayfur et al., 2014). The performance of the GRNN model is assessed using the

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Nash-Sutcliffe efficiency coefficient (NSE):

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 NSE  1  

n

t 1 n

(yˆ t  yt ) 2

t 1

(yt  y) 2

(5)

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where yˆ t is the simulated value; yt is the observed value and 𝑦 is the mean value of

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observed data. An NSE of 1.0 corresponds to a perfect match between simulated and

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observed values; the closer NSE is to 1.0 the more accurate the model is. If NSE >

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0.5, forecasting is feasible (Moriasi et al., 2007).

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In step three, the set of monthly regressions models for the pre-dam period were

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used to estimate values of WT in the absence of the TGR (WTnat,post-TGR) based on

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actual measured air temperatures and the predicted values of discharge derived from

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the GRNN model (Qna)

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WTnat,post-TGR(t)=β0,pre-TGR+ β1,pre-TGR Ta,post-TGR(t-l)+ β2,pre-TGR Qna(t)

(6)

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In the final step, the contributions to the total change in average water

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temperature (ΔTOT) of the TGR (ΔTGR), of natural changes in air temperature and

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discharge (ΔNC) and of changes associated with other factors, such as land use change,

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other dams, and industrial activities (Δβ0) were determined from the model results.

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These changes can be computed as:

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ΔTOT = WTobs,post-TGR- WTobs,pre-TGR

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= Δβ0+(WTnat,post-TGR- WTsim,pre-TGR)+ (WTsim,post-TGR- WTnat,post-TGR-Δβ0) +ε 10

=Δβ0+ ΔNC + ΔTGR + ε

210 211

Δβ0= β0,post-TGR-β0,pre-TGR

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ΔNC= WTnat,post-TGR- WTsim,pre-TGR

(7) (8)

=β1,pre-TGR*( Ta,post-TGR - Ta,pre-TGR) + β2,pre-TGR*( Qna,post-TGR –Qpre-TGR)

213 214

= β1,pre-TGR*ΔTa +β2,pre-TGR*ΔQnat

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ΔTGR= WTsim,post-TGR- WTnat,post-TGR-Δβ0

(9)

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=Ta,post-TGR(t-l pre-TGR )*Δβ1 + ΔTa,lag effect*β1,post-TGR +

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changed air-water temperature interaction

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Qna,pre-TGR*Δβ2 +β2,post-TGR*ΔQTGR

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changed pattern of discharge

(10)

Mean residual error (ε) associated with the linear regression models is zero.

220 221

4.

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4.1. Basic trends in air temperature, water temperature, and discharge

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4.1.1 Inter-annual variation

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Results

Since 1956 mean annual air temperature at Yichang has varied between about 16℃

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and 18℃. A slight downward trend is evident between 1956 and 1984, whereas an

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upward trend occurred between 1985 and 2006 (Figure 3). Yichang experienced an

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extreme hot spell and drought during the summer of 2006 (Wu et al., 2012), followed

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by a slight downward trend in annual air temperature between 2007 and 2015.

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The annual water temperature variation at Yichang generally exhibits a similar

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pattern to air temperature. Between 1956 and 1973, annual water temperature

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increased slightly, followed by a slight decrease from 1974 to 1985. A sustained 11

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increasing trend is evident from 1986-2006 at an average rate of 0.04 ℃ per year. As

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with air temperature, the maximum of water temperature occurred in 2006. The

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extreme drought in the summer of 2006 apparently affected both air and water

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temperature at Yichang. This similarity in air and water temperature trends verifies

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the close correlation between the two variables, which is related to the influence of

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solar radiation on both water and air temperature (Brown, 1969; Moore, 1967). Since

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impoundment in 2003, the mean annual water temperature has risen from 18.04 ℃ to

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18.86 ℃.

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Discharge at Yichang has exhibited a slow decline since 1956. Annual discharge

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from 1956-2002 was 13656 m3/s, decreasing to 12668 m3/s after the impoundment of

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TGR, a reduction of 7.2%.

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Insert Figure 3 here.

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4.1.2. Intra-annual variation

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As seen in Figure 4 (a), monthly air temperature is almost consistent before and

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after the construction of TGR, with the differences varying from -0.19 ℃ to 1.42 ℃.

247

While monthly water temperature in the heating period from March to June has

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decreased 0.84 ℃ to 2.39 ℃ after impoundment and it has warmed slightly to 17.77 ℃

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in the cooling period from September to February (Figure 4 (b)). Overall, the water

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temperature at Yichang became less variable after impoundment and the lowest water

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temperature shifted from January and February to February and March.

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River discharge changes greatly at intra-annual time scales. Because 56% of the

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storage capacity of the Three Gorges reservoir is used for regulation of water, 12

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discharges vary in conjunction with the schedule of regulated releases of water.

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Discharges in April, May and December between 2003 and 2014, the period of

256

impoundment, are nearly the same as those before impoundment (Figure 4 (c)). From

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June to November, post-impoundment discharges have decreased compared to mean

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monthly discharges from 1956 to 2002. During every flood season from mid-June to

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late-September, the reservoir is operated to impound floodwaters and control the

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discharged flow (Zheng, 2016). After the flood season (early October) TGR impounds

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water to increase the reservoir water level gradually to a surface elevation of 175 m.

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During the low-water season from January to March, TGR releases water to sustain

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power generation and navigation in the reservoir area; thus, the mean monthly

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discharge has increased to 5195 m3/s - 5597 m3/s higher than pre-TGR period.

265

Insert Figures 4 here.

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4.2. Performance of regression models

267

Values of RMSE for linear regression models for each month in the pre-TGR

268

period range from 0.71 ℃ to 1.21 ℃ (Table 2). Regression models for the post-TGR

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period also perform well with RMSE ranging from 0.71 ℃ to 1.67 ℃. The optimal

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time lags between water temperature and air temperature range from 2 to 30 days. The

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lag days for May and November are equal to 30, indicating that the relations between

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the water temperatures of virtually all days in those months are strongly influenced by

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air temperatures from the preceding month. The average optimal lag is slightly longer

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(14 days) for the post-dam set of models than for the pre-dam set of models (8 days)

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(Table 2), consistent with the previous study that water temperature responded more 13

276

slowly to air temperature specifically after dam construction (Kędra and Wiejaczka,

277

2016). Predicted patterns of water temperature over the year for both pre- and

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post-impoundment match closely the observed pattern (Figure 5), consistent with the

279

lack of model bias ( =0).

280

Insert Table 2 here

281

Insert Figure 5 here.

282

Optimization of the GRNN model yielded a nondimensional spread value () of

283

0.03. NSE value for discharge reached 0.89 higher than the satisfactory level of 0.5

284

and the model performance could be described as very good (Moriasi et al., 2007).

285

The result indicates the model accurately represents variation in the natural discharge

286

at Yichang without the effects of impoundment. As expected, the model predicts that

287

since 2003 peak discharges would be greater and minimum discharges would be less

288

than observed values of discharge if the flow was not regulated by the TGR (Figure

289

6).

290

Insert Figure 6 here.

291

To evaluate uncertainties that may be propagated to reconstructed water

292

temperatures based on the use of simulated discharges, data for the pre-dam period

293

(1983-2002) were divided into subsets for training (1983-1996) and testing

294

(1997-2002) periods. Models fitted to data for the training period were then used to

295

estimate water temperatures for the testing period based on predicted discharges and

296

data on air temperature. Reconstructed water temperatures for the testing period

297

(1997-2002) were then compared to corresponding observed water-temperatures. 14

298

Relative errors in predicted monthly averaged water temperatures ranged from -8.3%-

299

1.6% with an average of -3.5%. The NSE for the testing period was 0.99 with an

300

RMSE of only 0.68 ℃. These results indicate that reconstruction of water temperature

301

using simulated discharges is highly accurate and that uncertainties associated with

302

this method have little effect on subsequent analysis.

303

4.3. Assessing the impact of TGR on water temperature

304

The sets of linear water temperature regression models, combined with the

305

prediction of discharge in the absence of TGR at Yichang in 2003-2013 using the

306

GRNN model, provides the basis for accurately quantifying the separate contributions

307

of natural changes versus reservoir-related changes on water temperature at Yichang

308

after the TGR. Seasonal and annual statistics were generated using equation (7) based

309

on the observed and simulated water temperatures (Table 3). Results reveal that total

310

annual change in water temperature at Yichang attributable to TGR (TGR) is larger

311

than change attributable to natural factors (ΔNC). Overall, the annual water

312

temperature has increased by 0.67 ℃ since impoundment. Of this total change, only

313

0.16 ℃ of warming is attributable to natural changes in air temperature and discharge

314

(ΔNC). On the other hand, reservoir operation has independently decreased water

315

temperature by 0.65 ℃ (TGR). Other factors not explicitly accounted for by the model

316

(0) have produced the greatest change, resulting in warming of 1.16 ℃.

317

Insert Tables 3 here.

318

The results also show that the relative impact of the TGR on water temperature

319

varies seasonally. Seasonal impacts of TGR differ significantly (p-value < 0.01) from 15

320

those of natural factors based on a Student’s t test. Absolute differences of changes in

321

water temperature attributable to TGR are 2.78 - 16.83 times those attributable to

322

natural factors. During relatively warm parts of the year (March to November) the

323

TGR has a cooling effect ranging from -0.57 ℃ to -1.29 ℃ on water temperature. This

324

effect is consistent with release of water stored within the reservoir that, because of

325

the high thermal inertia of the stored water, is cooler than expected based on air

326

temperature. Similarly, during the winter (December to February), the TGR has a

327

slight warming effect (0.22 ℃), perhaps reflecting the release of slightly warmer

328

reservoir water compared to the water temperature that would occur if this

329

temperature was strongly controlled by air temperature (Cai et al., 2018). The cooling

330

effect is greatest in the spring (March to May) (-1.29 ℃) and during this season the

331

release of cool water from the TGR has had the greater impact on water temperature

332

relative to natural changes in air temperature or discharge. Other factors reflected in

333

Δβ0 have led to a significant warming of water temperature in autumn and winter.

334

Using equation (10), the contributions of changes in air-water temperature

335

interaction and of changes in discharge to changes in water temperature can be

336

determined. The construction of TGR has resulted in substantial changes in nearby

337

land use, land cover, topography, and evaporation (Wu et al., 2012), which has in turn

338

affected the sensitivity of water temperature to air temperature (Erickson and Stefan,

339

2000; Langan et al., 2001; Webb et al., 2008). Moreover, water storage within the

340

TGR and thermal inertia associated with storage also affect the relationship between

341

air temperature and water temperature. As shown in Table 4, changes in air-water 16

342

temperature interaction following the construction of the TGR have a cooling effect

343

ranging from -0.26 ℃ to -0.94 ℃ in all four seasons. The largest decrease (-2.1 ℃)

344

occur in August, when the water in the river should be quite warm. TGR regulates the

345

amount and timing of water releases for flood prevention, power generation and

346

navigation throughout the year, which can change the relationship between water

347

temperature and discharge, especially given thermal inertia effects of stored water.

348

The results indicate that the altered pattern of discharge exerted different effects on

349

water temperature in the four seasons – a cooling effect in spring and autumn and a

350

warming effect in summer and winter. In March, the changed pattern of flow has its

351

maximum independent effect on water temperature – producing cooling of -3.54 ℃

352

compared to the independent effect of predicted unregulated discharges. At this time

353

of year, flow within the river may normally be warming, but thermal inertial of

354

relatively cool reservoir water may promote cooling.

355

Insert Tables 4 here.

356

TGR experienced three operation stages which may contribute to impacts on

357

water temperature at Yichang through changes in the water volume stored in the

358

reservoir, and hence in its thermal inertia (Cai et al., 2018). In June 2003, the water

359

level of the TGR was raised to an elevation of 135 m and power generation began

360

with the water being retained by a cofferdam. The water level fluctuated seasonally

361

between 136 and 143 m until 2006 (initial stage). From October 2006 to October 2008

362

(transitional stage), the water level rose to between 145 and 156 m and preliminary

363

operations began. By November 2008 (standard normal stage), the level of the 17

364

reservoir reached its normal level of 175 m. Contributions related to TGR vary during

365

different operational stages (Table 5). The results for standard normal stage are

366

roughly consistent with effects throughout the period from 2003-2014. The cooling

367

effect of TGR became enhanced in summer as the stage increased, while warming

368

trends developed in autumn and winter as the water level in the reservoir rose. The

369

clear variations in ΔTGR during different operation stages of TGR reinforce the

370

assumption that TGR brought about significant impacts on water temperature

371

behavior at Yichang.

372

Insert Tables 5 here.

373

5.

Discussion

374

The framework developed and implemented in this study provides an improved

375

method for determining the effects of natural factors versus reservoir impoundment on

376

water temperature. Specifically, it isolates the separate contributions of natural factors

377

versus impoundment using a water temperature regression model that captures the

378

changes induced by varying external conditions. This research has refined the analysis

379

of the effects of dams on water temperature variations by accounting for factors other

380

than the presence of the dam that could produce differences in water-temperature

381

characteristics before and after impoundment.

382

discharge, other factors, such as sewage discharges, land use changes, and industrial

383

pollution, can influence water temperature (Cai et al., 2018). These other influences

384

are accounted for in the modeling by changes in the coefficient β0. The findings of

385

this study reveal that these other factors have had the most important influence on 18

Besides air temperature and

386

water temperature in the river below the TGR which needs further work to identify

387

the way in which these factors influence water temperature. However, the effect of the

388

TGR exceeds the effect related to natural factors with the TGR producing net cooling

389

during all but the coldest parts of the year.

390

Past studies have revealed that reservoirs can substantially change hydrological

391

and thermal regimes downstream and that this impact can extend for tens to hundreds

392

of kilometers (Petts, 1986; Richter et al., 1998; Soja and Wiejaczka, 2014). This study

393

confirms that the construction of the TGR has had an important independent effect on

394

the water temperature regime of the Yangtze River downstream of the TGR,

395

consistent with previous findings. The findings are also consistent with results of

396

previous work indicating that natural synchronization between air temperature and

397

water temperature has changed since initiation of reservoir operation (Kedra and

398

Wiejaczka 2018). The analysis in this study supports the conclusion that the

399

interaction between air temperature and water temperature has weakened following

400

dam construction. Past work has not considered explicitly the linkage between water

401

temperature and discharge, whereas the findings of this study show that the

402

connection between discharge and water temperature is also affected by the operation

403

of the TGR.

404

It may be feasible to produce river water temperatures sufficiently close to

405

natural temperatures using adaptive management strategies designed to restore

406

environmental river flows (Gu et al., 1999; Richter and Thomas, 2007). For that

407

purpose, monitoring river flow and air temperature downstream of dams and 19

408

quantifying the respective contributions of these factors to thermal regime are

409

necessary to facilitate modeling of future scenarios based on modification of reservoir

410

outflows. Different from other statistical methods for water temperature

411

reconstruction that seek to quantify the separate contributions of climate and human

412

interventions (e.g., air2stream model), this study has used a linear regression model to

413

isolate the effects of changes in air-water temperature interaction and the pattern of

414

discharge produced by dam construction on the river water temperature. This

415

approach leads to an improved understanding of the mechanisms by which reservoirs

416

influence thermal regimes.

417

Dams change natural stream temperatures and water quality which in turn affect

418

the existing aquatic populations and species composition (Carron and Rajaram, 2001).

419

Downstream changes in water temperature related to impoundment can alter the

420

abundance of warm-water versus cold-water fish species, thereby changing the

421

characteristics of fish communities (Lessard and Hayes 2003). In the Yangtze River,

422

changes in temperature have affected the spawning of four major Chinese carp and

423

Chinese sturgeon (Zhang et al., 2016). Changes in thermal regime produced by dams

424

have led to declines in adult abundance of Chinese sturgeon and in the breeding

425

activity of these fish (Huang and Wang, 2018). Optimization of the operating rules of

426

the TGR may be necessary to minimize negative impacts of thermal regime changes

427

on the river ecosystem and the modeling approach presented here can contribute to

428

this goal.

429

6.

Conclusions 20

430

This study has reconstructed water temperature in the absence of the Three

431

Gorges Reservoir to examine the separate impacts of natural change in air temperature

432

and discharge versus dam construction on the water temperature of the middle reach

433

of the Yangtze River following impoundment. The results reveal that the TGR has

434

had a greater impact on water temperature than natural changes in air temperature and

435

discharge. However, factors not explicitly accounted for by the model (0) have

436

produced the greatest warming effect, greater than the effects of the TGR or of

437

changes in air temperature and discharge. The reservoir acts as a source of cold water

438

in spring, summer and autumn and a warm source in winter. The enhanced impacts of

439

TGR as the operation stage of TGR was completed confirm that changes of water

440

temperature can be attributed primarily to the TGR.

441

By reliably identifying the independent influence of the TGR on water

442

temperature, this study provides useful information on the extent to which the

443

reservoir has altered an important characteristic of the river ecosystem. The

444

information can also be used to assess the long-term impact of the TGR on thermal

445

regime and to guide river conservation planning strategies aimed at mitigating the

446

impact of impoundment on the ecosystem of the Yangtze River.

447

Acknowledgments

448

This study was supported by the National Key Research and Development

449

Program of China (2017YFC1502704, 2016YFC0401501), and the National Natural

450

Science Fund of China (51679118, 41571017, and 91647203), and Jiangsu

451

Province"333 Project" (BRA2018060). 21

452 453 454 455 456 457 458 459

22

460

References

461

Brown, G.W., 1969. Predicting temperatures of small streams. Water Resour.

462

Res. 5(1), 68-75. https://doi.org/10.1029/WR005i001p00068

463

Cai, H.Y., Piccolroaz, S., Huang, J.Z., Liu, Z.Y., Liu, F., Toffolon, M., 2018.

464

Quantifying the impact of the Three Gorges Dam on the thermal dynamics of the

465

Yangtze

466

https://doi.org/10.1088/1748-9326/aab9e0

467 468

River.

Environ.

Res.

Lett.

13(0540165).

Caissie, D., 2006. The thermal regime of rivers: a review. Freshw. Biol. 51(8), 1389-1406. https://doi.org/10.1111/j.1365-2427.2006.01597.x

469

Cao, G.J., Hui, E.Q., Hu, X.E., 2012. Analysis of the vertical structure of water

470

temperature in the vicinity area of Three Gorges Dam since the Three Gorges

471

Reservoir impounds. J. Hydraul. Eng. 43(10), 1254-1259. (in Chinese)

472

Carron, J.C., Rajaram, H., 2001. Impact of variable reservoir releases on management

473

of downstream water temperatures. Water Resour. Res. 37(6), 1733-1744.

474

https://doi.org/10.1029/2000WR900390

475

Casado, A., Hannah, D. M., Peiry, J. L., Campo, A. M., 2013. Influence of

476

dam-induced hydrological regulation on summer water temperature: sauce grande

477

river, argentina. Ecohydrology, 6(4), 523-535. https://doi.org/10.1002/eco.1375

478

Chen, J., Finlayson, B. L., Wei, T.Y., Sun, Q.L., Webber, M., Li, M.T., Chen, Z.Y.,

479

2016. Changes in monthly flows in the Yangtze River, China-With special

480

reference

481

https://doi.org/10.1016/j.jhydrol.2016.03.008

to

the

Three

Gorges

23

Dam. J.

Hydrol. 536,

293-301.

482

Chen, L., Singh, V.P., Guo, S.L., Zhou, J.Z., Ye, L., 2014. Copula entropy coupled

483

with artificial neural network for rainfall-runoff simulation. Stoch. Environ. Res.

484

Risk Assess. 28(7), 1755-1767. https://doi.org/10.1007/s00477-013-0838-3

485

Cho, H. Y., and K. H. Lee, 2012, Development of an air–water temperature

486

relationship model to predict climate-induced future water temperature in estuaries,

487

J.

488

https://doi.org/10.1061/(ASCE)EE.1943-7870.0000499

Environ.

Eng.,

138,

570–577.

489

Ding, J., Jiang, Y., Fu, L., Liu, Q., Peng, Q.Z, Kang, M.Y., 2015. Impacts of land use

490

on surface water quality in a subtropical river basin: a case study of the Dongjiang

491

River

492

https://doi.org/10.3390/w7084427

493 494 495

Basin,

Southeastern

China. Water. 7(8),

4427-4445.

Elçi, Ş., 2008. Effects of thermal stratification and mixing on reservoir water quality. Limnology, 9(2), 135–142. https://doi.org/10.1007/s10201-008-0240-x Erickson, T.R., Stefan, H.G., 2000. Linear air/water temperature correlations for

496

streams

during

open

water

periods,

J.

Hydrol.

497

https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(317)

Eng.

5(3),

317-322.

498

Frost, F., Karri, V., 1999. Performance comparison of BP and GRNN models of the

499

neural network paradigm using a practical industrial application. International

500

Conference

501

https://doi.org/10.1109/ICONIP.1999.844684

on

Neural

Information

Processing.

IEEE.

502

Gu, R., McCutcheon, S., Chen, C.J., 1999. Development of weather dependent flow

503

requirements for river temperature control. Environ. Manag. 24, 529–540. 24

504

https://doi.org/10.1007/s002679900252

505

Gu, C., Anderson, W. P., Colby, J. D., Coffey, C. L., 2015. Air-stream temperature

506

correlation in forested and urban headwater streams in the southern appalachians.

507

Hydrol. Processes. 29(6), 1110-1118. https://doi.org/10.1002/hyp.10225

508

Huang, Z.L., Wang, L.H., 2018. Yangtze dams increasingly threaten the survival of

509

the

Chinese

Sturgeon.

Current

510

https://doi.org/10.1016/j.cub.2018.09.032

Biol.

28,

3640-3647.

511

Islam, M.N., Liong, S.-Y., Phoon, K.K., Liaw, C.-Y., 2001. Forecasting of river flow

512

data with a general regression neutral network. IAHS-AISH Publication. (272),

513

285-590.

514

Jackson, F. L., Fryer, R. J., Hannah, D. M., Millar, C. P., Malcolm, I. A., 2018. A

515

spatio-temporal statistical model of maximum daily river temperatures to inform the

516

management of Scotland’s Atlantic salmon rivers under climate change. Sci. Total

517

Environ. 612, 1543-1558. http://dx.doi.org/10.1016/j.scitotenv.2017.09.010Jiang,

518

B., Wang, F.S., Ni, G.H., 2018. Heating impact of a tropical reservoir on

519

downstream water temperature: A case study of the Jinghong Dam on the Lancang

520

River. Water, 10(951). https://doi.org/10.3390/w10070951

521

Karpik, S.R., Raithby, G.D., 1990. Laterally averaged hydrodynamics model for

522

reservoir

predictions.

J.

Hydraul.

Eng.

523

https://doi.org/10.1061/(ASCE)0733-9429(1990)116:6(783)

116,

783–798.

524

Kedra, M., Wiejaczka, L., 2018. Climatic and dam-induced impacts on river water

525

temperature: Assessment and management implications. Sci. Total Environ. 626, 25

526

1474-1483. https://doi.org/10.1016/j.scitotenv.2017.10.044

527

Kim, S., Shiri, J., Kisi, O., Singh, V.P., 2013. Estimating daily pan evaporation using

528

different data-driven methods and lag-time patterns. Water Resour. Manage 27,

529

2267–2286. https://doi.org/10.1007/s11269-013-0287-2

530

Langan, S.J., Donaghy, M.J., Youngson, A.F., Hay, D.W., Soulsby, C., 2001.

531

Variation in river water temperatures in an upland stream over a 30-year

532

period. Sci.

533

195-207.https://doi.org/10.1016/S0048-9697(00)00659-8

Total

Environ. 265(1),

534

Lessard, J. A. L., Hayes, D. B., 2003. Effects of elevated water temperature on fish

535

and macroinvertebrate communities below small dams. River Res. Appl. 19(7),

536

721-732. https://doi.org/10.1002/rra.713

537

Li, H., Deng, X., Kim, D.Y., Smith, E.P., 2014. Modeling maximum daily

538

temperature using a varying coefficient regression model. Water Resour. Res.

539

50(4), 3073-3087. https://doi.org/10.1002/2013WR014243

540

Li, Q.F., Yu, M.X., Lu, G.B., Cai, T., Bai, X., Xia, Z.Q., 2011. Impacts of the

541

Gezhouba and Three Gorges reservoirs on the sediment regime in the Yangtze

542

River,

543

10.1016/j.jhydrol.2011.03.043

China.

J.

Hydrol.

403(4-3),

224-233.

https://doi.org/

544

Liu, D.F., Wang, D., Singh, V.P., Wang, Y.K., Wu, J.C., Wang, L.C., et al., 2017.

545

Optimal moment determination in pome-copula based hydrometeorological

546

dependence

547

https://doi.org/10.1016/j.advwatres.2017.04.016

modelling.

Adv.

Water

26

Resour.

105,

28-50.

548

Long, L.H., Xu, H., Ji, D.B., Cui, Y.J., Liu, D.F., Song, L.X., 2016. Characteristic of

549

the water temperature lag in Three Gorges Reservoir and its effect on the water

550

temperature

551

https://doi.org/10.1007/s12665-016-6266-1

552

structure

of

tributaries. Environ.

weekly

554

https://doi.org/10.1029/98wr01877

556

Sci. 75(22),

1459.

Mohseni, O., Stefan, H. G., Erickson, T. R., 1998. A nonlinear regression model for

553

555

Earth

stream

temperatures.

Water

Resour.

Res.

34(10),

2685-2692.

Moore, A.M., 1967. Correlation and analysis of water-temperature data for oregon streams. Br. J. Surg. 73(2), 118-120. https://doi.org/10.1002/bjs.1800730213

557

Moriasi, D.N., Arnold, J.G., Liew, M.W.V., Bingner, R.L., Harmel, R.D., Veith, T.L.,

558

2007. Model evaluation guidelines for systematic quantification of accuracy in

559

watershed simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/

560

10.13031/2013.23153

561

Olden, J.D., Naiman, R.J., 2010. Incorporating thermal regimes into environmental

562

flows assessments: Modifying dam operations to restore freshwater ecosystem

563

integrity.

564

https://doi.org/10.1111/j.1365-2427.2009.02179.x

565 566 567

Freshw.

Biol.

55,

86–107.

Petts, G.E., 1986. Water quality characteristics of regulated rivers. Prog. Phys. Geogr. 10, 492–516. Rice, K.C., Jastram, J.D., 2015. Rising air and stream-water temperatures in

568

Chesapeake

Bay

region,

USA. Climatic

569

https://doi.org/10.1007/s10584-014-1295-9 27

Change. 128(1-2),

127-138.

570

Richter, B.D., Baumgartner, J.V., Braun, D.P., Powell, J., 1998. A spatial assessment

571

of hydrologic alteration within a river system. Regul. Rivers Res. Manag. 14, 329–

572

340.

573 574

Richter, B.D., Thomas, G.A., 2007. Restoring environmental flows by modifying dam operations. Ecol. Soc. 12 (1), 12. https://doi.org/10.5751/es-02014-120112

575

Risley, J.C., Constantz, J., Essaid, H., Rounds, S., 2010. Effects of upstream dams

576

versus groundwater pumping on stream temperature under varying climate

577

conditions. Water

578

https://doi.org/10.1029/2009WR008587

Resour.

Res. 46(W06517).

579

Soja, R., Wiejaczka, Ł., 2014. The impact of a reservoir on the physicochemical

580

properties of water in a mountain river. Water Environ. J. 28, 473–482.

581

https://doi.org/10.1111/wej.12059

582 583

Specht, D.F., 1991. A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576. https://doi.org/10.1109/72.97934

584

Tayfur, G., Zucco, G., Brocca, L., Moramarco, T., 2014. Coupling soil moisture and

585

precipitation observations for predicting hourly runoff at small catchment scale. J.

586

Hydrol. 510, 363-371. https://doi.org/10.1016/j.jhydrol.2013.12.045

587

Toffolon, M., Piccolroaz, S., 2015. A hybrid model for river water temperature as a

588

function of air temperature and discharge. Environ. Res. Lett. 10(11).

589

https://doi.org/10.1088/1748-9326/10/11/114011

590

van Vliet, M.T.H., Ludwig, F., Zwolsman, J.J.G., Weedon, G.P., Kabat, P., 2011.

591

Global river temperatures and sensitivity to atmospheric warming and changes in 28

592

river

flow. Water

Resour.

593

https://doi.org/10.1029/2010WR009198

Res. 47(2),

247-255.

594

van Vliet, M.T.H., Yearsley, J.R., Franssen, W.H.P., Ludwig, F., Haddeland, I.,

595

Lettenmaier, D.P., Kabat, P., 2012. Coupled daily streamflow and water

596

temperature modelling in large river basins. Hydrol. Earth Syst. Sci. 16, 4303–

597

4321. https://doi.org/10.5194/hess-16-4303-2012

598

Wang, Y.K., Xia, Z.Q., Wang, D., 2012. A transitional region concept for assessing

599

the effects of reservoirs on river habitats: a case of Yangtze River, China.

600

Ecohydrol. 5(1): 28-35. https://doi.org/10.1002/eco.186

601

Wang, Y.K., Wang, D., Lewis, Q.W., Wu, J.C., Huang, F., 2017. A framework to

602

assess the cumulative impacts of dams on hydrological regime: a case study of the

603

Yangtze River. Hydrol. Processes. 31, 3045-3055. https://doi.org/10.1002/hyp.11239

604

Ward, J.V., 1985. Thermal characteristics of running waters. Hydrobiologia. 125(1),

605

31–46. https://doi.org/10.1007/BF00045924

606

Webb, B.W., Clack, P.D., Walling, D.E., 2003. Water-air temperature relationships in

607

a Devon river system and the role of flow. Hydrol. Processes. 17(15), 3069-3084.

608

https://doi.org/10.1002/hyp.1280

609

Webb, B.W., Hannah, D.M., Moore, R.D., Brown, L.E., Nobilis, F., 2008. Recent

610

advances in stream and river temperature research. Hydrol. Processes. 22(7),

611

902-918. https://doi.org/10.1002/hyp.6994

612

Wu, J., Gao, X.J., Giorgi, F., Chen, Z.H., Yu, D.F., 2012. Climate effects of the Three

613

Gorges Reservoir as simulated by a high resolution double nested regional climate 29

614

model. Quat. Int. 282, 27-36. https://doi.org/10.1016/j.quaint.2012.04.028

615

Xu, C.Y., Gong, L.B., Jiang, T., Chen, D.C, Singh, V.P., 2006. Analysis of spatial

616

distribution and temporal trend of reference evapotranspiration and pan evaporation

617

in Changjiang (Yangtze River) catchment. J. Hydrol. (Amsterdam). 327(1-2), 0-93.

618

https://doi.org/10.1016/j.jhydrol.2005.11.029

619

Yu, Z.Z., Wang, L.L., 2011. Factors influencing thermal structure in a tributary bay of

620

Three

621

https://doi.org/10.1016/s1001-6058(10)60130-8

622 623

Gorges

reservoir.

J.

Hydrodynam.

23(4),

407-415.

Zeng, X., 1990. Fishery Resources of the Yangtze River Basin. – Marine Press, Beijing, China.

624

Zhang, H., Wu, J.M., Wang, C.Y., Du, H., Liu, Z.G., Shen, L., Chen, D., Wei, Q.W.,

625

2016. River temperature variations and potential effects on fish in a typical Yangtze

626

River reach: implications for management. Appl. Ecol. Environ. Res. 14(4),

627

553-567. https://doi.org/10.15666/aeer/1404_553567

628 629

Zheng, S.R., 2016. Reflections on the Three Gorges Project since its operation. Eng. 2(4), 389-397. https://doi.org/10.1016/j.eng.2016.04.002

630

Zheng, T.G., Mao, J.Q., Dai, H.C., Liu, D.F., 2011. Impacts of water release

631

operations on algal blooms in a tributary bay of three gorges reservoir. Sci. China

632

Ser. E. 54(6), 1588-1598. https://doi.org/10.1007/s11431-011-4371-7

633

Zou, Z.H., Lu, G.B., Li, Q.F., Xia, Z.Q., Bing, J.P., 2011. Water temperature change

634

caused by large-scale water projects on the Yangtze River mainstream. J.

635

Hydroelectr. Eng. 30(5), 139-144. https://doi.org/10.1080/00405000.2010.522047 30

636

(in Chinese)

637 638 639

31

640

List of Tables

641

Table 1. Linear correlation coefficients*

642

Table 2. Parameters for the linear water temperature regression models*

643

Table 3. The contributions to water temperature at Yichang station in the post-TGR

644

period compared to the pre-TGR period*

645

Table 4 Refinement of contributions related to natural factors and TGR in the

646

post-TGR period compared to the pre-TGR period

647

Table 5. The contributions related to TGR during different operation stages of TGR

648 649

32

650

List of Figures

651

Figure 1. Location of hydrological stations and the Three Gorges Reservoir in the

652

Yangtze River

653

Figure 2. Flowchart of the regression-modeling framework

654

Figure 3. Variations of annual air temperature, discharge, water temperature at

655

Yichang station*

656

Figure 4. Monthly changes at Yichang station before and after building the TGR

657

Figure 5. Comparison between observed and simulated water temperature at Yichang

658

station in the pre-TGR and post-TGR periods

659

Figure 6. Comparison between observed and simulated discharge at Yichang station

660

in the post-TGR period

661 662

33

Table 1 Linear correlation coefficients*

663

Correlation coefficient Pearson

Air-water temperature pre-TGR 0.93 (High)

post-TGR 0.73 (Middle)

Discharge-water temperature pre-TGR 0.81 (High)

post-TGR 0.72 (Middle)

664

*Note: Three levels for traditional correlations: High (> 0.8); Middle (0.6-0.8); Low

665

(< 0.6) (Liu et al., 2017).

666 667

34

668

Table 2 Parameters for the linear water temperature regression models* l for lag (days)

669

β0

RMSE (℃)

β2 (10-5)

β1

Month

preTGR

postTGR

preTGR

postTGR

preTGR

postTGR

difference

preTGR

postTGR

difference

preTGR

postTGR

difference

Jan.

18

24

0.71

1.09

6.44

7.46

1.02

0.16

0.17

0.01

62.84

89.20

26.36

Feb.

3

8

0.82

0.79

7.61

8.95

1.34

0.18

0.08

-0.10

34.13

40.92

6.79

Mar.

14

2

1.10

0.71

9.78

13.41

3.63

0.23

0.08

-0.15

19.48

-45.52

-65.00

Apr.

4

2

1.21

1.33

11.41

8.72

-2.69

0.28

0.28

0.00

15.50

12.11

-3.39

May.

8

30

1.05

1.67

16.30

13.27

-3.03

0.23

0.25

0.02

1.84

15.55

13.71

Jun.

8

12

0.81

0.94

19.75

18.8

-0.95

0.18

0.17

-0.01

-2.17

0.77

2.94

Jul.

7

13

0.80

0.87

21.11

19.99

-1.12

0.21

0.2

-0.01

-6.64

-2.57

4.07

Aug.

7

18

0.82

0.77

22.17

24.55

2.38

0.20

0.12

-0.08

-7.69

-9.69

-2.00

Sep.

9

18

0.95

1.05

18.19

21.02

2.83

0.27

0.19

-0.08

-6.87

-7.31

-0.44

Oct.

9

2

0.99

0.81

15.45

19.13

3.68

0.23

0.18

-0.05

-0.34

-3.91

-3.57

Nov.

2

30

0.82

1.05

12.95

14.66

1.71

0.18

0.22

0.04

15.57

5.19

-10.38

Dec.

9

11

0.92

1.07

7.76

12.82

5.06

0.20

0.18

-0.02

55.42

33.39

-22.03

*Note：β0, β1 and β2 are regression coefficients.

35

670

Table 3 The contributions to water temperature at Yichang station in the post-TGR

671

period compared to the pre-TGR period* Period Mar.-May (Spring) Jun.-Aug. (Summer) Sep.-Nov. (Autumn) Dec.-Feb. (Winter) Annual

ΔTOT (℃)

Δβ0 (℃)

ΔNC (℃)

ΔTGR (℃)

-1.75

-0.70

0.24

-1.29

-0.15

0.10

0.32

-0.57

1.86

2.74

0.06

-0.95

2.72

2.47

0.03

0.22

0.67

1.16

0.16

-0.65

672

*Note:

673

ΔTOT: the total change in water temperature before and after the TGR operation

674

Δβ0: the contribution related to other factors, such as land use changes, industrial

675

facilities and sewage discharges.

676

ΔNC: the contribution related to changes in the natural factors (air temperature and

677

discharge).

678

ΔTGR: the contribution ascribed to the TGR impacts.

679 680 681

36

682

Table 4 Refinement of contributions related to natural factors and TGR in the post-TGR period compared to the pre-TGR period ΔNC (℃) Period Mar.-May (Spring) Jun.-Aug. (Summer) Sep.-Nov. (Autumn) Dec.-Feb. (Winter) Annual

ΔTGR (℃)

ΔTa(℃)

ΔQna(℃)

0.25 0.13

Changed air-water temperature interaction(℃)

Changed pattern of discharge(℃)

Total(℃)

Δβ1(℃)

ΔTa,lag effect(℃)

Total(℃)

Δβ2(℃)

ΔQTGR (℃)

-0.01

-0.63

-0.42

-0.21

-0.67

-0.68

0.01

0.19

-0.94

-0.82

-0.12

0.37

0.37

0.00

0.07

0.00

-0.48

-0.83

0.35

-0.46

-0.53

0.07

-0.01

0.03

-0.26

-0.27

0.02

0.47

0.13

0.34

0.11

0.05

-0.77

-0.59

-0.18

0.12

0.01

0.11

683 684 685 686

37

687

Table 5 The contributions related to TGR during different operation stages of TGR Period Mar.-May (Spring) Jun.-Aug. (Summer) Sep.-Nov. (Autumn) Dec.-Feb. (Winter)

Initial stage (℃)

Transitional stage (℃)

Standard normal stage (℃)

-1.30

-1.39

-1.30

-0.53

-0.52

-0.62

-1.11

-0.88

-0.87

-0.24

-0.24

0.56

688 689 690

38

691

692 693

Figure 1 Location of hydrological stations and the Three Gorges Reservoir in the

694

Yangtze River

695 696 697

39

698 699

Figure 2 Flowchart of the regression-modeling framework

700 701

40

Discharge Air temperature Water temperature

20

38000

18

28000 23000

16

18000

Discharge (m3/s)

Temperature (℃)

33000

13000 14

8000 1955

702

1965

1975

1985 Year

1995

2005

2015

703

Figure 3 Variations of annual air temperature, discharge and water temperature at

704

Yichang station*

705

Note*: dashed lines refer to the 7-years moving average of annual variations.

706 707 708 709

41

1983-2002 2003-2015 Difference

30.00

Air temperature (℃)

25.00 20.00 15.00 10.00 5.00 0.00 1

-5.00

710

2

3

4

5

6

7

8

9

10

11

12

11

12

Month

(a) Air temperature

711

Water temperature (℃)

25.00

1983-2002 2003-2015 Difference

20.00 15.00 10.00 5.00 0.00 1

712

2

3

4

5

6

-5.00

7

8

9

10

Month

(b) Water temperature

713 50000

1956-2002 2003-2014

40000

Discharge (m3/s)

Difference 30000 20000 10000 0 1

2

3

4

5

-10000

716

7

8

9

10

11

12

Month

714 715

6

(c) Discharge Figure 4 Monthly changes at Yichang station before and after building the TGR 42

28

WTobs ( Post-TGR)

WTsim ( Post-TGR)

WTobs ( Pre-TGR)

WTsim ( Pre-TGR)

Water temperatrue (℃)

24

20

16

12

8 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Time

717 718

Figure 5 Comparison between observed and simulated water temperature at Yichang

719

station in the pre-TGR and post-TGR periods

720

43

Simulated

Observed

60000

50000

Discharge (m3/s)

40000

30000

20000

10000

0 2003/1/1

2005/1/1

2007/1/1

721

2009/1/1 Time

2011/1/1

2013/1/1

722

Figure 6 Comparison between observed and simulated discharge at Yichang station

723

in the post-TGR period

724 725 726 727 728 729

Yuwei Tao: Methodology, Formal analysis, Writing - Original Draft, Writing -

730

Review & Editing

731

Yuankun Wang: Conceptualization, Writing - Review & Editing, Supervision

732

Bruce Rhoads: Validation, Formal analysis, Writing - Review & Editing

733

Dong Wang: Writing - Review & Editing, Project administration

734

Lingling Ni: Data Curation, Investigation

735

Jichun Wu: Project administration

736 737

Highlights 44

738 739 740 741 742

 A framework for quantifying reservoir effects on water temperature in river is developed  Impact of the Three Gorges Reservoir on water temperature in the Yangtze River is isolated

743

 TGR operation led to the warming effects in winter and cooling effects in spring

744

 Influence of TGR on water temperature exceeds the influence of natural factors

745 746 747 748

Declaration of interests

749 750 751

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

752 753 754

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

755 756 757 758 759

45