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Land use and land cover change (LULCC) is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image 2001, 2014 and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the Support Vector Machine (SVM) algorithm and the Markov Chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318 and KlocationStrata = 0.9320, showing a good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001-2021, the significant increase of Agricultural land (14317 ha) and Barren area (9063 ha), and the sharp decline of Grassland (26215 ha) and Forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that Forest cover will continue to decrease from 29.46% (50720.2667 ha) in 2021 to 25.67% of area (44207.78694 ha) in 2040, as well as, unceasing expansion of Barren area, Agricultural land and Built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.

Flash floods are the most dangerous kinds of floods because they combine the destructive power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of the ground to absorb it. The main aim of this study is to generate flash flood maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in the river’s floodplain between the Jhelum River and Chenab rivers. A total of eight flash flood-causative physical parameters are considered for this study. Six parameters are based on remote sensing images of the Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), and Sentinel-2 Satellite, which include slope, elevation, distance from the stream, drainage density, flow accumulation, and land use/land cover (LULC), respectively. The other two parameters are soil and geology, which consist of different rock and soil formations, respectively. In the case of AHP, each of the criteria is allotted an estimated weight according to its significant importance in the occurrence of flash floods. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of drainage density was given the highest weight. The analysis shows that a distance of 2500 m from the river has values of FR ranging from 0.54, 0.56, 1.21, 1.26, and 0.48, respectively. The output zones were categorized into very low, low, moderate, high, and very high risk, covering 7354, 5147, 3665, 2592, and 1343 km2, respectively. Finally, the results show that the very high flood areas cover 1343 km2, or 6.68% of the total area. The Mangla, Marala, and Trimmu valleys were identified as high-risk zones of the study area, which have been damaged drastically many times by flash floods. It provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.

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