Analysis and forecasting of snow cover using ANN in Kaligandaki Basin, Nepal - Mishra, Bhogendra; Tripathi, Nitin Kumar; Babel, Mukand Singh; Taravudh Tipdecho; Government of Japan (Pathum Thani, Thailand : Asian Institute of Technology, 2011)
As Himalayas has been considered as one of the most vulnerable region in the world from the climate change point of view, it is very important to study about the trends of climate variability and snow cover area in higher mountainous regions. On the other hand, timely and accurate data are always a big problem in this kind of study. Hence, this study aims primarily to access the usability of various remote sensing data, and identifying the bias and calibration based on the observed dataset. Secondarily, trend analysis using nonparametric techniques was carried out. Finally, snow cover area has been forecasted using an artificial neural network based on HadCM A2 scenario. It is shown that remote sensing technology can detect the spatial-temporal pattern of temperature and snow cover in an inaccessible terrain of Himalayas but the precipitation data obtained from remote sensing do not seem reliable. Similarly, the MODIS products have already been ensuring in different region of the world in different ways, but the accuracy of MODIS snow products have still not ensured in the Himalayas. Therefore, MODlOAl snow product was also related with ASTER snow cover area in this study by assuming that the ASTER has 1003 accuracy. And it was found that MODlOAl has approximately 813 accuracy with respect to ASTER snow cover. Nonparametric-methods (ie. Mann-Kendall and Sens) were used to identify the trend. Increasing trends of temperature, approximately 0.03°Cyr-1 was obtained from the test whereas the mixed seasonal trend of precipitation was obtained. In general, we can conclude that, there is the increasing trend in summer and spring and decreasing trend in winter. Therefore, the flash floods and winter droughts are also increasing in the region. While talking about snow cover area, a significant negative winter and spring snow cover trend was identified. Consequently the glacier retreatment was also noticed significantly. From the analysis, it is concluded that there is clear indications that the regional warming is affecting the precipitation and snow cover area in the Himalayas. Similarly in another study, Artificial Neural Network (ANN) models were developed to predict monthly snow cover area in higher Himalayas based on the selected GCM as input. Two types of models were used to forecast, one was time series and another war normal. The accuracy of both models obviously depends on the accuracy of input climatic variables. But, the accuracy gradually goes down on increasing the lead time in NARX time series network. Whereas the accuracy remains same for GRNN till the indefinite time, but it solely depends with the accuracy of input variables. While comparing the performance of two networks NARX gives the better result for the short lead time where as GRNN, the second network, seems better for the longer lead time.
A thesis submitted in partial fulfillment of the requirements fo r the degree of Master of Science in Remote Sensing and Geographic lnfonnation Systems, School of Engineering and Technology
Thesis (M. Sci.) - Asian Institute of Technology, 2011
Keywords: Snow -- Forecasting -- Nepal -- Kaligandaki Basin