An Artificial Neural Network-Based Snow Cover Predictive Modeling in the Higher Himalayas

• Published in: Journal of mountain science Vol. 11; no. 4; pp. 825 - 837
• Main Authors: Mishra, Bhogendra, Tripathi, Nitin K., Babel, Mukand S.
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.07.2014; Springer Nature B.V
• Subjects: Climate change; Climate effects; Climatic data; Earth and Environmental Science; Earth Sciences; Ecology; Economic growth; Energy sources; Environment; Environmental impact; Geography; Global warming; Mountains; Neural networks; River basins; Snow; Snow cover; 人工神经网络; 全球大气环流; 喜马拉雅山; 基础; 积雪; 覆盖面积; 非线性自回归模型; 预测模型; Himalaya Mountains; Climate change; Global warming; Snow cover; Kaligandai river; Artificial neural network; Himalayas
• DOI: 10.1007/s11629-014-2985-5
• ISSN: 1672-6316

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.