Intra-annual Distribution of Streamflow and Individual Impacts of Climate Change and Human Activities in the Dongijang River Basin, China

Tu, Xin; Singh, Vijay P.; Chen, Xiaohong; Chen, Lu; Zhang, Qiang; Zhao, Yong

doi:10.1007/s11269-015-0963-5

Cited by 30 publications

(9 citation statements)

References 49 publications

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“…Fluvial streamflow variations are the result of the combined influence of climate change and human activities, such as land use and land cover changes (e.g. Zhang et al, 2012;Tu et al, 2015), and the influence of these two factors can even surpass that of increasing CO 2 on streamflow changes (Piao et al, 2007). Meanwhile, amplification of precipitation extremes as a result of warming climate (Allan and Soden, 2008;Zhang et al, 2013;Li et al, 2015) and intensifying human activities, such as urbanization (Shi et al, 2013), greatly alters hydrological processes at different spatial scales, which will have critical impacts on streamflow variations and the consequent amount and variability of water resources (e. g. Milly et al, 2005) and basin-scale water resources management.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China

Zhang

Liu

Singh

et al. 2016

Hydrological Processes

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106

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Abstract:Variations in streamflows of five tributaries of the Poyang Lake basin, China, because of the influence of human activities and climate change were evaluated using the Australia Water Balance Model and multivariate regression. Results indicated that multiple regression models were appropriate with precipitation, potential evapotranspiration of the current month, and precipitation of the last month as explanatory variables. The NASH coefficient for the Australia Water Balance Model was larger than 0.842, indicating satisfactory simulation of streamflow of the Poyang Lake basin. Comparison indicated that the sensitivity method could not exclude the benchmark-period human influence, and the human influence on streamflow changes was overestimated. Generally, contributions of human activities and climate change to streamflow changes were 73.2% and 26.8% respectively. However, human-induced and climate-induced influences on streamflow were different in different river basins. Specifically, climate change was found to be the major driving factor for the increase of streamflow within the Rao, Xin, and Gan River basins; however, human activity was the principal driving factor for the increase of streamflow of the Xiu River basin and also for the decrease of streamflow of the Fu River basin. Meanwhile, impacts of human activities and climate change on streamflow variations were distinctly different at different temporal scales. At the annual time scale, the increase of streamflow was largely because of climate change and human activities during the 1970s-1990s and the decrease of streamflow during the 2000s. At the seasonal scale, climate change was the main factor behind the increase of streamflow in the spring and summer season. Human activities increase the streamflow in autumn and winter, but decrease the streamflow in spring. At the monthly scale, different influences of climate change and human activities were detected. Climate change was the main factor behind the decrease of streamflow during May to June and human activities behind the decrease of streamflow during February to May. Results of this study can provide a theoretical basis for basin-scale water resources management under the influence of climate change and human activities.

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Section: Introductionmentioning

confidence: 99%

Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China

Zhang

Liu

Singh

et al. 2016

Hydrological Processes

Self Cite

106

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“…Take the Dongjiang River at the Pearl River Estuary, for example; Yang () shows that its mean runoff is 744 m 3 /s, at which the invariant sections are approximately 11.0 and 19.0 km from the river mouth for the north branch and south branch, respectively. Xie et al () and Tu et al () show that RV decreases in recent decades. Hence, under this trend, for the north branch (south branch), the P s is expected to increases downstream from the 11.0‐km (19.0‐km) section and decreases upstream from this section.…”

Section: Discussionmentioning

confidence: 94%

“…Especially in dry seasons, the influence is much stronger (Chen et al, ). Meanwhile, the runoff variability (RV) has also changed in river basins such as the Pearl River Estuary, Poyang Lake Basin, Songhua River, and some rivers in the northwest United States (Gu et al, ; Lin et al, ; Luce & Holden, ; Tu et al, ; Xie et al, ). However, the effect of changes in RV on the saltwater intrusion remains unknown, and a detailed quantitative analysis is needed.…”

Section: Introductionmentioning

confidence: 99%

Effect of Runoff Variability and Sea Level on Saltwater Intrusion: A Case Study of Nandu River Estuary, China

Zhang

Sheng

et al. 2018

Water Resources Research

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Saltwater intrusion exits in estuary areas. Significant change in runoff variability (RV) has been detected in many basins, but limited research has investigated this change's effect on downstream saltwater intrusion. Using measured time series, this study has statistically detected a significant trend of increased RV and sea level (SL) rise in Nandu River Estuary, China, and analyzed their effect on the saltwater intrusion combining the Finite Volume Community Ocean Model and Monte Carlo method. The Mann-Kendall test and Theil-Sen estimator show that the yearly variance of the log-transformed runoff and mean SL have significantly increased and are expected to increase by approximately 50% and 0.2 m by the 2060s, respectively, and 101% and 0.4 m by the 2100s. SL rise increases the salinity-exceeding rate (salinity > 0.45‰, the standard for drinking water), and the increase in middle channels are higher than that of upstream and downstream channels. With an increased RV, the model-predicted salinity-exceeding rate decreases at downstream and increases at upstream channels, respectively. In our specific site, the location with a constant salinity-exceeding rate is predicted to move downstream, and the maximum increase of salinity-exceeding rate will reach 13.3% and 19.1% in the 2060s and 2100s, respectively, at the middle channel section. The results are validated in Yangtze River Estuary and are generalized to other estuaries to determine the change pattern of salinity-exceeding rate. Moreover, a lower RV helps to extend the channel length with fresh water available and can be used as a management tool.

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“…The watershed belongs to a subtropical maritime monsoon climate zone characterized by high temperatures, high humidity and abundant water. The rainfall of the region is mainly affected by frontal rain and typhoon rain and is characterized by large magnitudes, high intensities, a long flood season and uneven distribution in time and space, which cause great pressure on river basin flood control [50]. Although the Baipenzhu Reservoir has a relatively large storage capacity, in the event of a large flood, if the inflow flood of the reservoir cannot be accurately predicted, a large amount of water will still be discarded.…”

Section: Baipenzhu Reservoir Basinmentioning

confidence: 99%

Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model

Chen

Xing

et al. 2021

Water

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For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected as the study case, and an inflow flood forecast scheme was designed for the reservoir by a physically based distributed hydrological model, the Liuxihe model. The results show that the Liuxihe model has strong applicability for flood forecasting in the studied reservoir basin and that the simulation results are very accurate. This study also found that the use of different Digital Elevation Model (DEM) data sources has a certain impact on the structure of the Liuxihe model, but the constructed models can both simulate the inflow flood process of the Baipenzhu Reservoir well. At the same time, the Liuxihe model can reflect the spatial variation in rainfall well, and using the Particle swarm optimization (PSO) algorithm to optimize the initial model parameters can greatly reduce the uncertainty of the model forecasts. According to China’s hydrological information forecast standards, the Liuxihe model forecast schemes constructed by the two data sources are rated as Grade A and can be used for real-time flood forecasting in the Baipenzhu Reservoir basin.

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Intra-annual Distribution of Streamflow and Individual Impacts of Climate Change and Human Activities in the Dongijang River Basin, China

Cited by 30 publications

References 49 publications

Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China

Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China

Effect of Runoff Variability and Sea Level on Saltwater Intrusion: A Case Study of Nandu River Estuary, China

Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model

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