High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations.

Like other cities in the Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Rivers are negatively impacted by uncontrolled settlements in flood-prone areas, lowering permeability, decreasing channels widths, and waste blockage. All these issues, along with more extreme rain events during the monsoon due to climate change, have led to increased flooding in Bhaktapur, especially by the Hanumante River. For a better understanding of flood risk, the first step is a return level analysis. For this, historical data are essential. Unfortunately, historical records of water levels are non-existent for the Hanumante River. We measured water levels and discharge on a regular basis starting from the 2019 monsoon (i.e., June). To reconstruct the missing historical data needed for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ). By employing SEJ, we were able to reconstruct historical water level data. Expert assessments were validated using the limited data available. Based on the reconstructed data, it was possible to estimate the return periods of extreme water levels of the Hanumante River by fitting a Generalized Extreme Value (GEV) distribution. Using this distribution, we estimated that a water level of about 3.5 m has a return period of ten years. This research showed that, despite considerable uncertainty in the results, the SEJ method has potential for return level analyses.

Urban mountain catchments are highly vulnerable to erosion and sedimentation due to steep terrain, intense rainfall, and rapid land-use change at the urban fringe. However, event-scale sediment transport remains poorly understood in these regions, particularly in data-scarce areas such as the Himalayas. This study presents the first high-frequency, event-based analysis of suspended sediment transport in the Nakkhu River, a rapidly urbanizing catchment in Kathmandu Valley, Nepal. Using optical backscatter sensors and targeted field sampling during the 2023 monsoon, we analysed how rainfall, antecedent moisture, and human disturbance shaped sediment responses. Sediment transport was highly episodic, with two extreme storms accounting for nearly half of the seasonal suspended sediment load. Analysis of SSC–discharge hysteresis patterns revealed event-specific variability shaped by rainfall intensity, antecedent conditions, and hydrologic connectivity. Peak SSC often lagged peak discharge during low-flow events, suggesting upstream sediment sources. In contrast, high-intensity storms produced rapid sediment delivery, likely from hillslopes, mining zones, and in-channel deposits. Low-frequency (daily) monitoring underestimated sediment loads by approximately 30 % compared to 30-minutes interval data. This study provides the first 30-minute interval event-scale analysis of suspended sediment transport in Kathmandu Valley, revealing how sediment responses vary across the monsoon season in relation to rainfall intensity, discharge dynamics, and antecedent conditions. These insights, including lagged sediment peaks during low-flow events and rapid sediment flushing during intense storms, highlight the value of sub-hourly monitoring for capturing within-event variability and identifying short-lived sediment sources in urban mountain watersheds.

Urban mountain catchments are highly vulnerable to erosion and sedimentation due to steep terrain, intense rainfall, and rapid land-use change at the urban fringe. However, event-scale sediment transport remains poorly understood in these regions, particularly in data-scarce areas such as the Himalayas. This study presents the first high-frequency, event-based analysis of suspended sediment transport in the Nakkhu River, a rapidly urbanizing catchment in Kathmandu Valley, Nepal. Using optical backscatter sensors and targeted field sampling during the 2023 monsoon, we analysed how rainfall, antecedent moisture, and human disturbance shaped sediment responses. Sediment transport was highly episodic, with two extreme storms accounting for nearly half of the seasonal suspended sediment load. Analysis of SSC–discharge hysteresis patterns revealed event-specific variability shaped by rainfall intensity, antecedent conditions, and hydrologic connectivity. Peak SSC often lagged peak discharge during low-flow events, suggesting upstream sediment sources. In contrast, high-intensity storms produced rapid sediment delivery, likely from hillslopes, mining zones, and in-channel deposits. Low-frequency (daily) monitoring underestimated sediment loads by approximately 30 % compared to 30-minutes interval data. This study provides the first 30-minute interval event-scale analysis of suspended sediment transport in Kathmandu Valley, revealing how sediment responses vary across the monsoon season in relation to rainfall intensity, discharge dynamics, and antecedent conditions. These insights, including lagged sediment peaks during low-flow events and rapid sediment flushing during intense storms, highlight the value of sub-hourly monitoring for capturing within-event variability and identifying short-lived sediment sources in urban mountain watersheds.