Muhammad Saifullah

By Muhammad Saifullah, Shiyin Liu, Muhammad Adnan, Muhammad Ashraf, Muhammad Zaman, Sarfraz Hashim and Sher Muhammad

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

By Muhammad Adnan, Rana Muhammad Adnan, Shiyin Liu, Muhammad Saifullah, Yasir Latif and Mudassar Iqbal

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

By Muhammad Waqas, Muhammad Saifullah, Sarfraz Hashim, Mohsin Khan and Sher Muhammad

The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981–2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning.

Part of the book: Weather Forecasting

By Muhammad Saifullah, Muhammad Adnan, Muhammad Arshad, Muhammad Waqas and Asif Mehmood

Climate change has a major impact on crop yield all over the world. Pakistan is one of the major affected countries by climate change. The agrometeorology indices were determined for the South Punjab region, which is a hot spot for climate change and food security. This region is rich in agriculture, but crop yield relationship is estimated with agrometeorology indices (AMI). Temperature stress (33°C), average diurnal temperature range (12°C), Average accumulative growing degree days (1303°C), phototemperature (27°C) and nyctotemperature (21°C) indices were determined for Multan. The variation in diurnal temperature was found at 0.39 for Bahawalpur region and similar variation was observed in growing degree days, which is 0.11 more than the diurnal temperature range. The extreme of these indices which influence the crop yield was found in May and June. The cropping period from sowing to harvest varied due to climate change and cause to decrease in the yield of the crop. The indices are regarded as crop performance indicators. So, policymakers and agricultural scientists should take necessary measures to mitigate such kinds of challenges.

Part of the book: Challenges in Agro-Climate and Ecosystem