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|Title:||Sediment flux sensitivity to climate change: A case study in the Longchuanjiang catchment of the upper Yangtze River, China|
|Keywords:||Artificial Neural Network|
the Upper Yangtze River
|Source:||Zhu, Y.-M., Lu, X.X., Zhou, Y. (2008). Sediment flux sensitivity to climate change: A case study in the Longchuanjiang catchment of the upper Yangtze River, China. Global and Planetary Change 60 (3-4) : 429-442. ScholarBank@NUS Repository. https://doi.org/10.1016/j.gloplacha.2007.05.001|
|Abstract:||Climate change may affect the sediment generation and transportation processes and the consequent sediment flux in a river. The sensitivity of suspended sediment flux to climate change in the Longchuanjiang catchment is investigated with Artificial Neural Networks (ANNs). ANNs were calibrated and validated using sediment flux data from 1960 to 1990 during which the influence from human activities was relatively stable. The established ANN is used to predict the responses of sediment flux to 25 hypothetical climate scenarios, which were generated by adjusting the baseline temperature up to -1, 1, 2 and 3 °C and by scaling the baseline precipitation by +/- 10% and +/- 20%. The results indicated when temperature remains unchanged, an increase in rainfall will lead to a rise in sediment flux; when rainfall level remains unchanged, an increase in temperature is likely to result in a decrease in sediment flux. Same percentage of changes in rainfall and temperature are likely to trigger higher responses in wetter months than in drier months. However, it is the combination of the change in temperature and rainfall that determines the change of sediment flux in a river. Higher sediment flux is expected to appear under wetter and warmer climate, when higher transport capacity is accompanied by higher erosion rate. © 2007 Elsevier B.V. All rights reserved.|
|Source Title:||Global and Planetary Change|
|Appears in Collections:||Staff Publications|
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