Spatiotemporal changes in early human land use during the Holocene throughout the Yangtze River Basin, China

The contribution of early human activity to the increase in atmospheric CH4 content during the middle to late-Holocene is still debated. The quantitative reconstruction of past changes in land use by early rice agriculture is a key to resolving the issue, because large uncertainties still exist in current prehistoric land use estimates, owing to a lack of direct records. In this study, we used the combination of archaeological data (the area and distribution of archaeological sites) and an improved prehistoric land use model (PLUM) to determine the spatiotemporal changes in land use by rice agriculture throughout the Yangtze River Valley, China, which was the origin and centre of the development of rice cultivation. The results indicate that the area devoted to rice agriculture increased during 10–2 ka BP, and that a significant increase occurred at ~5 ka BP accompanied by a spatial expansion from the northern part of the valley to the entire valley. However, the rice land use area decreased slightly during 4–3 ka BP but then increased after 3 ka BP. We estimate that the CH4 emissions from the rice cultivated area in the Yangtze River Valley increased from ~0.001 (±0.001) to ~1.3 (±0.6) Tg/year during 10–2 ka BP, and the resulting atmospheric CH4 concentrations increased from ~0.004 (±0.002) to ~4.1 (±2.0) ppb, which accounted for 3 (±2)–9 (±5) % of the ‘anomalous atmospheric CH4 increase’ during 5–2 ka BP.

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Spatial–Temporal Variation of Cropping Patterns in Relation to Climate Change in Neolithic China

Atmosphere ◽

10.3390/atmos11070677 ◽

2020 ◽

Vol 11 (7) ◽

pp. 677 ◽

Cited By ~ 1

Author(s):

Ruo Li ◽

Feiya Lv ◽

Liu Yang ◽

Fengwen Liu ◽

Ruiliang Liu ◽

...

Keyword(s):

Climate Change ◽

Yangtze River ◽

Yellow River ◽

Yangtze River Valley ◽

River Valley ◽

Neolithic Period ◽

The Yangtze River ◽

Rice Agriculture ◽

Regional Patterns ◽

Cropping Patterns

The Neolithic period witnessed the start and spread of agriculture across Eurasia, as well as the beginning of important climate changes which would take place over millennia. Nevertheless, it remains rather unclear in what ways local societies chose to respond to these considerable changes in both the shorter and longer term. Crops such as rice and millet were domesticated in the Yangtze River and the Yellow River valleys in China during the early Holocene. Paleoclimate studies suggest that the pattern of precipitation in these two areas was distinctly different. This paper reviews updated archaeobotanical evidence from Neolithic sites in China. Comparing these results to the regional high-resolution paleoclimate records enables us to better understand the development of rice and millet and its relation to climate change. This comparison shows that rice was mainly cultivated in the Yangtze River valley and its southern margin, whereas millet cultivation occurred in the northern area of China during 9000–7000 BP. Both millet and rice-based agriculture became intensified and expanded during 7000–5000 BP. In the following period of 5000–4000 BP, rice agriculture continued to expand within the Yangtze River valley and millet cultivation moved gradually westwards. Meanwhile, mixed agriculture based on both millet and rice developed along the boundary between north and south. From 9000–7000 BP, China maintained hunting activities. Subsequently, from 7000–6000 BP, changes in vegetation and landscape triggered by climate change played an essential role in the development of agriculture. Precipitation became an important factor in forming the distinct regional patterns of Chinese agriculture in 6000–4000 BP.

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Improving the regional model forecasting of persistent severe rainfall over the Yangtze River Valley using the spectral nudging and update cycle methods: a case study

Atmospheric Science Letters ◽

10.1002/asl.731 ◽

2017 ◽

Vol 18 (2) ◽

pp. 96-102 ◽

Cited By ~ 4

Author(s):

Yanfeng Zhao ◽

Donghai Wang ◽

Jianjun Xu

Keyword(s):

Yangtze River ◽

Regional Model ◽

Yangtze River Valley ◽

River Valley ◽

The Yangtze River ◽

Spectral Nudging

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Impacts of land use change on thermodynamic and dynamic changes of precipitation for the Yangtze River Basin, China

International Journal of Climatology ◽

10.1002/joc.7037 ◽

2021 ◽

Author(s):

Qian Lin ◽

Jie Chen ◽

Wei Li ◽

Kailin Huang ◽

Xuezhi Tan ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

River Basin ◽

Yangtze River ◽

Yangtze River Basin ◽

The Yangtze River ◽

Dynamic Changes ◽

The Yangtze River Basin

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Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Water ◽

10.3390/w13223294 ◽

2021 ◽

Vol 13 (22) ◽

pp. 3294

Author(s):

Chentao He ◽

Jiangfeng Wei ◽

Yuanyuan Song ◽

Jing-Jia Luo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Yangtze River ◽

Climate Models ◽

Summer Precipitation ◽

Yangtze River Valley ◽

River Valley ◽

Training Data ◽

The Yangtze River ◽

Learning Methods

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.

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