This study assesses the impact of land-use and climate change on hydrological regime of three dam watersheds (Karaj, Latian and Mamlu) in Alborz and Tehran Provinces of Iran. Daily precipitation and temperature data from CMIP6 are transiently downscaled to ten climatic stations using the LARS-WG under global warming scenarios SSP1-1.9, SSP2-4.5, and SSP5-8.5. The Cellular Automata-Markov-chain machine learning is used to simulate future land-use maps (2021–2080) by training and testing its multilayer perceptron neural network with observed land-use change during the period 1995–2015. The study area will experience warming by 0.78, 2.1, and 2.4 °C under SSP1-1.9, SSP2-4.5, and SSP5-8.5, respectively, and precipitation anomalies by +129.3, −95.6, and −54.2 mm, respectively, compared to the baseline period 1991–2014. Extreme precipitation depth will increase at all stations under SSP1-1.9. However, precipitation change depends on the storm’s return period, station, and scenario under warmer scenarios. The SWAT-predicted river flow over three watersheds will increase, compared to the baseline period, under SSP1-1.9 but decrease under SSP2-4.5 and SSP5-8.5. Among combinations of land-use and climate change scenarios, land-use scenario High under SSP1-1.9 leads to greatest annual streamflow, while no change in land-use under SSP2-4.5 results in maximum reduction in streamflow over three watersheds. Extreme flows over Karaj and Latian watersheds show no sensitivity to different land-use scenarios due to negligible land-use development in future but they still are sensitive to climate change scenarios. Meanwhile, extreme flows over Mamlu watershed show significant sensitivity to both land-use and climate change scenarios due to significant land-use change in future.

Detecting and characterizing active faults is crucial for seismic hazard assessment, especially in regions with long earthquake recurrence intervals, such as Iran. Traditional methods, including seismology and geodesy, often fail to identify active faults and assess their seismic potential. This study overcomes these limitations by integrating multi-source datasets with different temporal and spatial resolutions to investigate the North Zanjan Fault (NZF) in the western Alborz Mountains. The NZF is a reverse-dextral fault capable of generating Mw 6.5–7.0 earthquakes. The research employs high-spatial-resolution (HSR) Pleiades Tri-stereo imagery, archival aerial photographs, and RGB overlapping drone-derived imagery to analyze Quaternary geomorphic markers. Photogrammetric techniques are utilized to detect fault traces, map deformed landforms, and reconstruct displacements at multiple scales. By integrating satellite- and archive aerial-derived digital elevation models (DEMs), a detailed 1:5000 scale morpho-structural map is developed, identifying three fault segments connected by thrust fault arrays. Horizontal displacements range from 1.5 to 670 m, while vertical offsets span 1.5 to 77 m. Drone surveys at five locations provide high-resolution data (1:500 scale), revealing an average single-event oblique displacement of 1.10 m. The Sohrein site analysis indicates four Quaternary seismic events, each with an average vertical displacement of 1.30 m. These findings highlight the seismogenic potential of the NZF and demonstrate a scalable methodology for identifying previously unrecognized fault strands, ultimately contributing to improved seismic hazard assessments in the Iranian plateau and similar tectonic environments.