DRAINMOD simulation of paddy field drainage strategies and adaptation to future climate change in lower reaches of the Yangtze river basin *

2021 ◽  
Author(s):  
Ahmed Awad ◽  
Wan Luo ◽  
Jiarong Zou
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Paddy Field ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Future Climate ◽  
Future Climate Change ◽  
The Yangtze River ◽  
The Yangtze River Basin ◽  
Field Drainage

scholarly journals Projected Effects of Climate Change on Future Hydrological Regimes in the Upper Yangtze River Basin, China

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yuqian Wang ◽  
Xiaoli Yang ◽  
Mengru Zhang ◽  
Linqi Zhang ◽  
Xiaohan Yu ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Future Climate ◽  
Future Climate Change ◽  
Upper Yangtze River ◽  
The Upper Yangtze River ◽  
Hydrological Regimes

Climate change directly impacts the hydrological cycle via increasing temperatures and seasonal precipitation shifts, which are variable at local scales. The water resources of the Upper Yangtze River Basin (UYRB) account for almost 40% and 15% of all water resources used in the Yangtze Basin and China, respectively. Future climate change and the possible responses of surface runoff in this region are urgent issues for China’s water security and sustainable socioeconomic development. This study evaluated the potential impacts of future climate change on the hydrological regimes (high flow (Q5), low flow (Q95), and mean annual runoff (MAR)) of the UYRB using global climate models (GCMs) and a variable infiltration capacity (VIC) model. We used the eight bias-corrected GCM outputs from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) to examine the effects of climate change under two future representative concentration pathways (RCP4.5 and RCP8.5). The direct variance method was adopted to analyze the contributions of precipitation and temperature to future Q5, Q95, and MAR. The results showed that the equidistant cumulative distribution function (EDCDF) can considerably reduce biases in the temperature and precipitation fields of CMIP5 models and that the EDCDF captured the extreme values and spatial pattern of the climate fields. Relative to the baseline period (1961–1990), precipitation is projected to slightly increase in the future, while temperature is projected to considerably increase. Furthermore, Q5, Q95, and MAR are projected to decrease. The projected decreases in the median value of Q95 were 21.08% to 24.88% and 16.05% to 26.70% under RCP4.5 and RCP8.5, respectively; these decreases were larger than those of MAR and Q5. Temperature increases accounted for more than 99% of the projected changes, whereas precipitation had limited projected effects on Q95 and MAR. These results indicate the drought risk over the UYRB will increase considerably in the future.

The effects of projected climate change and extreme climate on maize and rice in the Yangtze River Basin, China

2020 ◽  
Vol 282-283 ◽  
pp. 107867 ◽  
Author(s):  
Xinxin Chen ◽  
Lunche Wang ◽  
Zigeng Niu ◽  
Ming Zhang ◽  
Chang'an Li ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
The Yangtze River ◽  
Extreme Climate ◽  
The Yangtze River Basin

scholarly journals Changes in Forest Net Primary Productivity in the Yangtze River Basin and Its Relationship with Climate Change and Human Activities

2019 ◽  
Vol 11 (12) ◽  
pp. 1451
Author(s):  
Fengying Zhang ◽  
Zengxin Zhang ◽  
Rui Kong ◽  
Juan Chang ◽  
Jiaxi Tian ◽  
...  
Keyword(s):  
Climate Change ◽  
Spatial Distribution ◽  
River Basin ◽  
Primary Productivity ◽  
Yangtze River ◽  
Net Primary Productivity ◽  
Yangtze River Basin ◽  
The Yangtze River ◽  
The Yangtze River Basin ◽  
Lower Yangtze

Net Primary Productivity (NPP) is a basis of material and energy flows in terrestrial ecosystems, and it is also an important component in the research on carbon cycle and carbon budget. This paper evaluated the spatial distribution pattern and temporal change trends for forest NPP simulated by the LPJ (Lund-Potsdam-Jena) model and NDVI (normalized difference vegetation index) in the Yangtze River basin from 1982 to 2013. The results revealed that: (1) the spatial distribution of the forest NPP and NDVI in the Yangtze River basin has gradually decreased from the southeast coast to the northwest. The forest NPP and NDVI in the mid-lower Yangtze were higher than that of the upper Yangtze; (2) the forest NPP and NDVI in most areas of the Yangtze River basin were positively correlated with the temperature and precipitation. Moreover, the correlations among the temperature with the forest NPP and NDVI were stronger than that of correlations among precipitation with forest NPP and NDVI. Moreover, the extreme drought event in the year of 2004–2005 led the NPP to decrease in the middle and lower Yangtze River basin; (3) human activity such as major ecological projects would have a certain impact on the NPP and NDVI. The increase in forest areas from 2000 to 2010 was larger than that from 1990 to 2000. Moreover, the increasing rate for the NDVI was higher than that of NPP, especially after the year 2000, which indicates that the major ecological projects might have great impacts on the vegetation dynamics. Moreover, more attention should be paid on the joint impacts of human activity and climate change on terrestrial NPP and NDVI.

scholarly journals Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices

Climate ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 53
Author(s):  
Heng Qian ◽  
Shi-Bin Xu
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Future Climate Change ◽  
Climate Indices ◽  
The Yangtze River ◽  
Second Mode ◽  
The Yangtze River Basin

Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual variation in AP based on daily precipitation data from 97 stations throughout the MLYRB during 1980–2015. The results show that the first leading EOF mode accounts for 30.83% of the total variation. The spatial pattern shows uniform change over the whole region. The variance contribution of the second mode is 16.13%, and its spatial distribution function shows a north-south phase inversion. Based on previous research and the physical considerations discussed herein, we include 13 climate indices to reveal the major predictors. To obtain an acceptable prediction performance, we comprehensively rank the climate indices, which are sorted according to the values of the new standardized algorithm of information flow (NIF, a causality-based approach) and correlation coefficient (a traditional climate diagnostic tool). Finally, Tropical Indian Ocean Dipole (TIOD), Arctic Oscillation (AO), and other four indicators are chosen as the final predictors affecting the first mode of AP over the MLYRB; NINO3.4 SSTA (NINO3.4), Atlantic-European Circulation E Pattern (AECE), and other four indicators are the major predictors for the second mode. In the final prediction experiment, considering the time series prediction of principal components (PCs) to be a small-sample problem, the Bayesian linear regression (BLR) model is used for the prediction. The experimental results reveal that the BLR model can effectively capture the time series trends of the first two modes (the correlation coefficients are greater than 0.5), and the overall performance is significantly better than that of the multiple linear regression (MLR) model. The prediction factors and precipitation prediction results identified in this study can be referenced to rapidly obtain climatological information for AP over the MLYRB and improve the regional prediction of AP elsewhere, which will also help policymakers prepare appropriate adaptation and mitigation measures for future climate change.

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