Chapters authored
Risks of Glaciers Lakes Outburst Flood along China Pakistan Economic Corridor
The China-Pakistan Economic Corridor (CPEC) passes through the Hunza River basin of Pakistan. The current study investigates the creation and effects of end moraine, supra-glacial, and barrier lakes by field visits and remote sensing techniques along the CPEC in the Hunza River basin. The surging and moraine type glaciers are considered the most dangerous type of glaciers that cause Glacial Lake Outburst Floods (GLOFs) in the study basin. It can be concluded from the 40 years observations of Karakoram glaciers that surge-type and non-surge-type glaciers are not significantly different with respect to mass change. The recurrent surging of Khurdopin Glacier resulted in the creation of Khurdopin Glacial Lake in the Shimshal valley of the Hunza River basin. Such glacial lakes offer main sources of freshwater; however, when their dams are suddenly breached and water drained, catastrophic GLOFs appear and pose a great threat to people and infrastructure in downstream areas. This situation calls for an in-depth study on GLOF risks along the CPEC route and incorporation of GLOF for future policy formulation in the country for the CPEC project so that the government may take serious action for prevention, response to GLOFs, and rehabilitation and reconstruction of the areas.
Part of the book: Glaciers and the Polar Environment
Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models
Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools.
Part of the book: Weather Forecasting