Climate Variability Masked Greening Effects on Water Yield in the Yangtze River Basin during 2001‐2018

2021 ◽  
Author(s):  
Jiehao Zhang ◽  
Yulong Zhang ◽  
Ge Sun ◽  
Conghe Song ◽  
Jiangfeng Li ◽  
...  
Keyword(s):  
Climate Variability ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Water Yield ◽  
The Yangtze River ◽  
The Yangtze River Basin

scholarly journals Identifying the drivers of water yield ecosystem service: A case study in the Yangtze River Basin, China

2021 ◽  
Vol 132 ◽  
pp. 108304
Author(s):  
Xiu Zhang ◽  
Guanshi Zhang ◽  
Xie Long ◽  
Qi Zhang ◽  
Dongsheng Liu ◽  
...  
Keyword(s):  
River Basin ◽  
Ecosystem Service ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Water Yield ◽  
The Yangtze River ◽  
The Yangtze River Basin

scholarly journals Drought Characteristics and Its Response to the Global Climate Variability in the Yangtze River Basin, China

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 13 ◽  
Author(s):  
Tao Huang ◽  
Ligang Xu ◽  
Hongxiang Fan
Keyword(s):  
Climate Variability ◽  
River Basin ◽  
Yangtze River ◽  
Southern Oscillation ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Distribution Model ◽  
Joint Probability Distribution ◽  
The Yangtze River ◽  
The Yangtze River Basin

The frequent occurrence of drought events in humid and semi-humid regions is closely related to the global climate variability (GCV). In this study, the Standard Precipitation Evapotranspiration Index (SPEI) was taken as an index to investigate the drought in the Yangtze River Basin (YRB), a typical humid and semi-humid region in China. Furthermore, nine GCV indices, such as North Atlantic Oscillation (NAO) were taken to characterize the GCV. Correlation analysis and a joint probability distribution model were used to explore the relationship between the drought events and the GCV. The results demonstrated that there were six significant spatiotemporal modes revealed by SPEI3 (i.e., seasonal drought), which were consistent with the distribution of the main sub basins in the YRB, indicating a heterogeneity of drought regime. However, the SPEI12 (i.e., annual drought) can only reveal five modes. Precipitation Indices and El Niño/Southern Oscillation (ENSO) Indices were more closely related to the drought events. A causal relationship existed between ENSO precipitation index (ESPI), NAO, East Central Tropical Pacific Sea Surface Temperature (Nino3.4) and Northern Oscillation Index (NOI) and drought in the YRB, respectively. Drought events were most sensitive to the low NAO and high NOI events. This study shows a great significance for the understanding of spatiotemporal characteristics of meteorological drought and will provide a reference for the further formulation of water resources policy and the prevention of drought disasters.

Phenological development and grain yield of canola as affected by sowing date and climate variation in the Yangtze River Basin of China

2012 ◽  
Vol 63 (5) ◽  
pp. 478 ◽  
Author(s):  
Sha Wang ◽  
Enli Wang ◽  
Fei Wang ◽  
Liang Tang
Keyword(s):  
Climate Variability ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Sowing Date ◽  
The Yangtze River ◽  
Phenological Stages ◽  
Sowing Time ◽  
Sowing Dates ◽  
The Yangtze River Basin

Canola is a major oil crop in the Yangtze River Basin of China, and its yield and oil content vary significantly from year to year due to changes in sowing time and inter-annual climate variability. However, there have been no studies to quantify the impacts of sowing time and climate variability. Experimental data to analyse the response of canola growth to sowing date are limited to a few seasons; however, combining these data with modelling provides an efficient means to study the impact of sowing date and historical climate variation. The APSIM-Canola model was calibrated and tested using data from three field experimental sites in the Yangtze River Basin. These experiments included different cultivars and sowing dates, and recorded major phenological stages, biomass, and grain yield. After calibration of the phenological parameters and maximum harvest index, the model was able to simulate the onset of phenological stages with different sowing dates, and to explain 75% of the variation in biomass and yield caused by late sowings. However, the model overestimated canola yield under late sowing dates. The results revealed that canola yield declined linearly with late sowing time, mainly due to shortened vegetative growth stages, and varied significantly due to inter-annual climate variability. The yield potential at the study region is ~3 t/ha, on average. However, this potential cannot be achieved in the rice–canola double-cropping system due to later sowing time after rice harvest in mid–later October. Transplanting canola may still be an effective measure against the constraint of season length to achieving higher yields of both rice and canola.

scholarly journals Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 731
Author(s):  
Zhuoqing Hao ◽  
Jixia Huang ◽  
Yantao Zhou ◽  
Guofei Fang
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Spatiotemporal Pattern ◽  
Pinewood Nematode ◽  
The Yangtze River ◽  
Pine Wilt ◽  
The Yangtze River Basin

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.

scholarly journals Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

2021 ◽  
Vol 13 (15) ◽  
pp. 3023
Author(s):  
Jinghua Xiong ◽  
Shenglian Guo ◽  
Jiabo Yin ◽  
Lei Gu ◽  
Feng Xiong
Keyword(s):  
River Basin ◽  
Hydrodynamic Model ◽  
Yangtze River ◽  
Large Scale ◽  
Yangtze River Basin ◽  
The Yangtze River ◽  
Ecosystem Monitoring ◽  
Catastrophic Flood ◽  
Basin Scale ◽  
The Yangtze River Basin

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

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