Impact of Three Gorges Reservoir Water Impoundment on Vegetation–Climate Response Relationship

Tian, Mengqi; Zhou, Jianzhong; Jia, Benjun; Lou, Sijing; Wu, Honglu

doi:10.3390/rs12172860

Cited by 16 publications

(9 citation statements)

References 70 publications

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“…As is widely known, water changes during plant growth are among the most important monitoring parameters in plant research [ 39 ]. Open water land cover indicates that such areas provide suitable temperatures and water for organisms inhabiting the surrounding environment within a certain space and time range [ 38 , 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

Using Consensus Land Cover Data to Model Global Invasive Tree Species Distributions

Zhang

Wang

Wan

2022

Plants

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Invasive tree species threaten ecosystems, natural resources, and managed land worldwide. Land cover has been widely used as an environmental variable for predicting global invasive tree species distributions. Recent studies have shown that consensus land cover data can be an effective tool for species distribution modelling. In this paper, consensus land cover data were used as prediction variables to predict the distribution of the 11 most aggressive invasive tree species globally. We found that consensus land cover data could indeed contribute to modelling the distribution of invasive tree species. According to the contribution rate of land cover to the distribution of invasive tree species, we inferred that the cover classes of open water and evergreen broadleaf trees have strong explanatory power regarding the distribution of invasive tree species. Under consensus land cover changes, invasive tree species were mainly distributed near equatorial, tropical, and subtropical areas. In order to limit the damage caused by invasive tree species to global biodiversity, human life, safety, and the economy, strong measures must be implemented to prevent the further expansion of invasive tree species. We suggest the use of consensus land cover data to model global invasive tree species distributions, as this approach has strong potential to enhance the performance of species distribution modelling. Our study provides new insights into the risk assessment and management of invasive tree species globally.

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Section: Discussionmentioning

confidence: 99%

Using Consensus Land Cover Data to Model Global Invasive Tree Species Distributions

Zhang

Wang

Wan

2022

Plants

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“…The Three Gorges Reservoir is a national strategic freshwater resource and an important ecological barrier in the upper reaches of the Yangtze River (Cheng et al, 2015). The reservoir was impounded for the first time in 2003, and the water level in front of the dam was 135 m. In 2006, the impoundment water level reached 156 m. In 2008, a 175-m experimental impoundment was commenced (Tian et al, 2020). The inflow flow forecast of the Three Gorges Reservoir is vital for optimizing scheduling to make correct decisions regarding production and operation.…”

Section: Study Areamentioning

confidence: 99%

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

et al. 2021

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Hydrological series data are non-stationary and nonlinear. However, certain data-driven forecasting methods assume that streamflow series are stable, which contradicts reality and causes the simulated value to deviate from the observed one. Ensemble empirical mode decomposition (EEMD) was employed in this study to decompose runoff series into several stationary components and a trend. The long short-term memory (LSTM) model was used to build the prediction model for each sub-series. The model input set contained the historical flow series of the simulation station, its upstream hydrological station, and the historical meteorological element series. The final input of the LSTM model was selected by the MI method. To verify the effect of EEMD, this study used the Radial Basis Function (RBF) model to predict the sub-series, which was decomposed by EEMD. In addition, to study the simulation characteristics of the EEMD-LSTM model for different months of runoff, the GM(group by month)-EEMD-LSTM was set up for comparison. The key difference between the GM-EEMD-LSTM model and the EEMD-LSTM model is that the GM model must divide the runoff sequence on a monthly basis, followed by decomposition with EEMD and prediction with the LSTM model. The prediction results of the sub-series obtained by the LSTM and RBF exhibited better statistical performance than those of the original series, especially for the EEMD-LSTM. The overall GM-EEMD-LSTM model performance in low-water months was superior to that of the EEMD-LSTM model, but the simulation effect in the flood season was slightly lower than that of the EEMD-LSTM model. The simulation results of both models are significantly improved compared to those of the LSTM model.

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“…A residual analysis approach was used to separate changes in the NDVI caused by human activities from those resulting from climatic variations [4,5,42]. As described by Herrmann and Wessels [43,44], the NDVI residuals were calculated for each pixel.…”

Section: Residual Analysismentioning

confidence: 99%

“…Dynamic changes in vegetation comprise an important research topic in the context of current global climate warming [3]. During the past half century, global climate change and human disturbances have become the main drivers of ecosystem change, and have had a huge impact on vegetation ecosystems [4][5][6]. Therefore, understanding vegetation changes and their internal relationships with climatic factors and human activities can help us to predict the evolution of ecosystems under conditions of future climate change, and provide a basis for ecological sustainability, ecosystem protection, and management planning [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of satellite data, many methods have been used to research vegetation change via NDVI. Tian et al analyzed the correlations among climatic factors (temperature and precipitation) and the NDVI in the Three Gorges Reservoir Region, China, and the surrounding area, by using a partial correlation coefficient method [4]. Piao et al also used a partial correlation coefficient method to reveal evidence of a weakening relationship between interannual temperature variability and northern vegetation activity [28].…”

Section: Introductionmentioning

confidence: 99%

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Vegetation Changes in Response to Climatic Factors and Human Activities in Jilin Province, China, 2000–2019

Zhao

Wang

et al. 2021

Sustainability

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Dynamic change in vegetation is an integral component of terrestrial ecosystems, which has become a significant research area in the current context of global climate warming. Jilin Province in northeast China is an ecologically fragile area, and there is an urgent need to understand its vegetation changes and responses to both climatic factors and human activities. The normalized difference vegetation index (NDVI) was used to analyze trends in vegetation growth, and indicated significant growth overall. The NDVI of different vegetation cover types is increasing, indicating that the vegetation is continuously greening, and in descending order, the growth trends were grassland (0.0035/year) > permanent wetland (0.0028/year) > cropland (0.0027/year) > forest land (0.0022/year) > barren land (−0.0001/year). Grassland and cropland vegetation types included the most severely degraded areas, with fluctuating NDVI values. Precipitation was the main positive controlling climatic factor of NDVI in the western regions of the study area, while average temperature was the main factor in the eastern regions. Precipitation was the main climatic control factor for grassland and cropland, while forest land was limited by precipitation and average temperature. Barren land and permanent wetland were slightly negatively correlated with precipitation. From 2000 to 2019, the residual values for NDVI increased from −0.0121 to 0.0116, and the impact of human activities on vegetation changed from negative to positive. By 2019, the proportion of positively affected zones was as high as 94.01%, and the negatively affected zones were mainly distributed across transitional areas of cropland and grassland, and urban and built-up land and forest land.

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Impact of Three Gorges Reservoir Water Impoundment on Vegetation–Climate Response Relationship

Cited by 16 publications

References 70 publications

Using Consensus Land Cover Data to Model Global Invasive Tree Species Distributions

Using Consensus Land Cover Data to Model Global Invasive Tree Species Distributions

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

Vegetation Changes in Response to Climatic Factors and Human Activities in Jilin Province, China, 2000–2019

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