Sudeep Duwal

Shallow groundwater level monitoring is crucial for the sustainable management of water, particularly in rapidly growing cities like the Kathmandu Valley (Valley). Engaging local communities as citizen scientists (CS) offers cost-effective data collection and creates awareness about groundwater status and management practices. This study investigates the performance of citizen scientists (regularity and data submission error) in groundwater level monitoring and evaluating the groundwater fluctuation trend in the Valley. During 2020, 68% of the groundwater CS was categorized as regular. In 2021 and 2022, regularity increased to 91% compared to the previous years, and regularity decreased in 2023 and increased in 2024. Meanwhile, a rise in the percentage of CS sending incorrect data was observed. Despite these fluctuations, the overall error percentage was very low (<8%), highlighting the reliability of the CS-collected data. The monthly groundwater level was recorded from 2020 to 2024 by citizen scientists mobilized across the Valley. Groundwater levels were shallower during the monsoon and post-monsoon months, while in pre-monsoon and winter, the levels were observed to be deeper. The study demonstrates the reliability of CS-collected data and highlights the seasonal trends in the shallow groundwater wells in the Valley, while also demonstrating the power of citizen science in improving the monitoring and sustainable management of groundwater.

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.