Population growth and population inflow from other regions has caused urbanization which altered land use land cover (LULC) in the lower Himalayan regions of Pakistan. This LULC change increased the land surface temperature (LST) in the region. LULC and LST changes were assessed for the period of 1990–2017 using Landsat data and the support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The accuracy of the CA-ANN model was validated using simulated and classified images of 2017 with correctness value of 70% using validation modules in QGIS. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation. LST acquired for this period was then modeled for 2032 and 2047 using urban indices (UI) and linear regression analysis. The SVM land cover classification results showed a 5.75% and 4.22% increase in built-up area and bare soil respectively, while vegetation declined by 9.88% during 1990–2017. The results of LST for LULC classes showed that the built-up area had the highest mean LST as compared to other classes. The future projection of LULC and LST showed that the built-up area may increase by 12.48% and 14.65% in 2032 and 2047, respectively, of the total LULC area which was ~11% in 2017. Similarly, the area with temperature above 30 °C could be 44.01% and 58.02% in 2032 and 2047, respectively, of the total study area which was 18.64% in 2017. This study identified major challenges for urban planners to mitigate the urban heat island (UHI) phenomenon. In order to address the UHI in the study area, an urban planner might focus on urban plantation and decentralization of urban areas.

Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections.

Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km2 from 25.37 km2; the built-up could increase to 625.16 km2 from 168.29 km2; the barren land could decrease to 137.53 km2 from 514.13 km2; the shrub and grassland could decrease to 297.68 km2 from 539.46 km2; the sparse vegetation could decrease to 297.68 km2 from 539.46 km2; and the dense vegetation could increase to 409.65 km2 from 191.51 km2. The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R2) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources.