Impacts of the Three Gorges Dam on the streamflow fluctuations in the downstream region

Guo, Shuhui; Xiong, Lihua; Zeng, Ling; Cheng, Lei

doi:10.1016/j.jhydrol.2021.126480

Cited by 19 publications

(10 citation statements)

References 42 publications

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“…The GARCH model has been validated as a suitable model for simulating the second‐order moment (conditional variance) behavior of streamflow series (Guo et al., 2021). Thus, the FARIMA‐GARCH model was established to describe the daily streamflow series for all stations.…”

Section: Resultsmentioning

confidence: 99%

“…Guo et al. (2021) used the daily average

m(m=1,{{\cdots}},M)

N

years to represent the seasonality of daily scale time series, that is,

{\mu }_{i}^{m}

, with

\mathrm{M}=366

in a leap year and 365 in a normal year. The seasonal mean and standard deviation in daily scale time series are calculated as follows:

\begin{array}{c}\left\{\begin{array}{l}{\mu }_{i}^{m}=\frac{1}{N}\sum \nolimits_{n=1}^{N}{y}_{i,t}^{(n,m)}\hfill \\ {\sigma }_{i}^{m}={\left[\frac{1}{n}\sum \nolimits_{n=1}^{N}{\left({y}_{i,t}^{(n,m)}-{\mu }_{i,m}\right)}^{2}\right]}^{0.5}\hfill \end{array}\right.\end{array}

where

{y}_{i,t}^{(n,m)}(i=1,{{\cdots}},21;n=1,{{\cdots}},N;m=1,{{\cdots}},M)

represent the daily scale time series of the m th day in the n th year at the i th station.…”

Section: Methodsmentioning

confidence: 99%

“…To avoid seasonality-induced heteroscedasticity effects, the daily runoff time series were deseasonalized before modeling. Guo et al (2021) used the daily average 𝐴𝐴 𝐴𝐴(𝐴𝐴 = 1, ⋯, 𝑀𝑀) in 𝐴𝐴 𝐴𝐴 years to represent the seasonality of daily scale time series, that is, 𝐴𝐴 𝐴𝐴 𝑚𝑚 𝑖𝑖 , with 𝐴𝐴 M = 366 in a leap year and 365 in a normal year. The seasonal mean and standard deviation in daily scale time series are calculated as follows:…”

Section: De-seasonal Standardizationmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Volatility Modeling of Daily Streamflow Time Series

Wang

Song

Zhang

et al. 2023

Water Resources Research

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The analysis of hydrological series is important for water resource management and allocation, as well as energy and food security in the face of climate change (Arriagada et al., 2019;Tencaliec et al., 2015). As one of the most important indicators in hydrological analysis, providing an important source of hydrological information for water resources engineering applications (Birsan, 2015;Shiau & Hsu, 2016). However, streamflow series have strong dynamic spatiotemporal characteristics, making accurate simulation and prediction difficult. This necessitates the use of highly complex tools to model the sophisticated features of streamflow series.In general, hydrological models are classified into two types: physical models and statistical models. Specifically, physical models are complicated because they take into account a large number of variables that influence hydrological processes. Statistical models simulate hydrological changes using observational data collected beforehand. Owing to their simple implementation, statistical models are easier to use in practice (Modarres & Ouarda, 2013b). Data-driven technologies, such as artificial neural networks, support vector machines, wavelet transforms, and time series analysis (TSA) models, have been receiving increasing attention in the field of hydrology (Hadi et al., 2020;Khan et al., 2020;Parisouj et al., 2020). In the application of the aforementioned data-driven models, the development of TSA models and new methods have consistently been the main theme in hydrological modeling (Fathian, Fard, et al., 2019;Fathian et al., 2018).In particular, linear time series models have been widely used to simulate hydroclimatic time series (Hu et al., 2020;H. Wang et al., 2021). Traditional time series models, such as the autoregressive integrated moving average (ARIMA) model, have primarily been developed for simulating the first-moment behavior of time series, but they ignore the second-moment information (volatility), leading to insufficient model fitting results (Fathian, Fard, et al., 2019). In reality, hydrological time series have strong volatility associated with environmental change.

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Section: Resultsmentioning

confidence: 99%

“…Guo et al. (2021) used the daily average

m(m=1,{{\cdots}},M)

N

years to represent the seasonality of daily scale time series, that is,

{\mu }_{i}^{m}

, with

\mathrm{M}=366

in a leap year and 365 in a normal year. The seasonal mean and standard deviation in daily scale time series are calculated as follows:

\begin{array}{c}\left\{\begin{array}{l}{\mu }_{i}^{m}=\frac{1}{N}\sum \nolimits_{n=1}^{N}{y}_{i,t}^{(n,m)}\hfill \\ {\sigma }_{i}^{m}={\left[\frac{1}{n}\sum \nolimits_{n=1}^{N}{\left({y}_{i,t}^{(n,m)}-{\mu }_{i,m}\right)}^{2}\right]}^{0.5}\hfill \end{array}\right.\end{array}

where

{y}_{i,t}^{(n,m)}(i=1,{{\cdots}},21;n=1,{{\cdots}},N;m=1,{{\cdots}},M)

represent the daily scale time series of the m th day in the n th year at the i th station.…”

Section: Methodsmentioning

confidence: 99%

Section: De-seasonal Standardizationmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Volatility Modeling of Daily Streamflow Time Series

Wang

Song

Zhang

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…In this process, the capacity and water regulation effect of the reservoir were enhanced, and its regulatory impact on the hydrology of the main stream was gradually highlighted. The maximum water level and storage capacity can reach 175 m and 39.3 km 3 , respectively (Guo et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

Changes in Dissolved Inorganic Carbon Across Yangtze River Regulated by Dam and River‐Lake Exchange

Zhao,

Wang,

et al. 2023

Global Biogeochemical Cycles

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The boom in dam construction and continuous river‐lake exchange has had a profound impact on the transmission and transformation of riverine dissolved inorganic carbon (DIC). An in‐depth understanding of the change mechanisms of DIC concentrations and sources driven by dam operation and lake recharge is crucial for regulating greenhouse gas emissions and evaluating the impact of DIC on the global carbon cycle. This study investigated dam‐ and lakes‐driven DIC via the concentration and δ13C of DIC, combined with anions, cations, δD and δ18O in the main stream of the Yangtze River. DIC showed a decreasing trend from upper reach (2,262.31 ± 113.69 μmol kg−1) to lower reach (1,771.61 ± 89.36 μmol kg−1). Carbonate dissolution proportion (from 36.45% to 28.44%) and atmospheric CO2 proportion (from 37.51% to 22.94%) of DIC decreased from upper reach to lower reach, whereas soil CO2 proportion of DIC (from 26.01% to 48.63%) increased. The control of dam operation on DIC biogeochemical process was revealed from different time scales. From the perspective of short‐term seasonal changes (from 2020 to 2021), the mineralization of organic matter in the dry season strengthened CO2 degassing and calcite precipitation, reducing the DIC and increasing the proportion of soil CO2. Meanwhile, longer periods of runoff retention provided sufficient time for water–rock reactions in the wet season and increased the DIC and carbonate dissolution source in the reservoir area. On a long‐term scale (from 2009 to 2021), a decrease in pH driven by sediment mineralization contributed to an annual increase in DIC in the reservoir. The flow of lakes mixed into the mainstream was revealed by the enrichment of δ18O, and river‐communicating recharge decreased the DIC and carbonate dissolution source. We show that dam operation and lake inflow change DIC concentrations and sources and therefore need to be considered in the transmission and transformation processes of DIC in the river‐ocean continuum.

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“…Similarly, the government in its PQ responses has declined to study the impacts of the interlinking of rivers in other countries (e.g. Gupta & van der Zaag, 2008;Sowers et al, 2011;Guo et al, 2021) or to study the hydrologic response of river systems to river channelization, flood control and river management for stream bank erosion (e.g., XV-2591). These omissions suggest reluctance on the part of the Government to take a science-based approach to assess the consequences of its 'development' schemes.…”

Section: Science and Perspectives Of Law-policymakersmentioning

confidence: 99%

Identifying evolving priorities in national river governance from Parliamentary Questions

2022

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Rivers and their surrounding land provide resources and services that result in trade-offs requiring legislative and institutional interventions, for which the perspectives of law-policymakers and their societal values and political expediencies play a major role in decision-making. This study critically evaluates the evolving land–river governance in large democratically elected governments with complex developmental priorities and how law-policymakers’ recognition of emerging issues of river management influences common pool environmental resources governance. We selected an emerging economy – India – and analysed Parliamentary Questions (PQs) between 1999 and 2020 using a mixed method approach. Conflicting priorities over economic development, pollution management, socio-cultural values and inter-state–centre issues shape the evolving priorities of land–river governance. A declining focus on large-scale dams coupled with increasing attention on inter-basin water transfers, river conservation and pollution abatement, demonstrates an evolution away from the earlier narrow view of rivers for irrigation and hydropower. Our analysis demonstrates how the priorities of law-policymakers and political expedience play a critical role in river governance and thereby provide important insights into common pool environmental resources governance for sustainable development while also identifying important knowledge gaps and suggesting scopes for interdisciplinary studies.

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Impacts of the Three Gorges Dam on the streamflow fluctuations in the downstream region

Cited by 19 publications

References 42 publications

Stochastic Volatility Modeling of Daily Streamflow Time Series

Stochastic Volatility Modeling of Daily Streamflow Time Series

Changes in Dissolved Inorganic Carbon Across Yangtze River Regulated by Dam and River‐Lake Exchange

Identifying evolving priorities in national river governance from Parliamentary Questions

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