Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal

Heavy precipitation events are recurrently occurring in Nepal, affecting lives and properties every year, especially in the summer monsoon season (i.e., June-September). We investigated an extreme (heavy) precipitation event of August 2014 over the West Rapti River (WRR) Basin, Nepal. First, we forced a rainfall-runoff model with ground-based (gauge) hourly rainfall data of nine stations. Second, we validated against hourly water level at an outlet of the WRR Basin. This study then evaluated the performance of different satellite-based rainfall estimates (SREs) in capturing an extreme precipitation event. We considered the use of half-hourly data of Integrated Multi-satellite Retrievals for GPM (IMERG) (Early, Late, and Final versions), spatial resolution (10 km), and hourly data of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), spatial resolution (25 km), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), spatial resolution (4 km). Also, we used 3 h data of Tropical Multi-satellite Precipitation Analysis (TMPA) product real-time (3B42RT), spatial resolution (25 km). In general, we find that all selected SREs depicted a similar pattern of extreme precipitation as shown by the gauge data on a daily scale. However, we find these products could not replicate precisely on a sub-daily scale. Overall, IMERG and TMPA showed a better performance than PERSIANN and PERSIANN-CCS. Finally, we corrected poor-performed SREs with respect to gauge data and also filled data gaps of gauge rainfall using the information of good-performed SREs. Our study reveals that there is a great challenge in local flood simulation employing SREs at high-temporal resolution in Nepal

Nepal Himalaya Offers Considerable Potential for Pumped Storage Hydropower

There is a pressing need for a transition from fossil-fuel to renewable energy to meet the increasing energy demands and reduce greenhouse gas emissions. The Nepal Himalaya possesses substantial renewable energy potential that can be harnessed through hydropower projects due to its peculiar topographic characteristics and abundant water resources. However, the current exploitation rate is low owing to the predominance of run-of-river hydropower systems to support the nation's power system. The utility-scale storage facility is crucial in the load scenario of an integrated Nepalese power system to manage diurnal variation, peak demand, and penetration of intermittent energy sources. In this study, we first identify the potential of pumped storage hydropower across the country under multiple configurations by pairing lakes, hydropower projects, rivers, and available flat terrains. We then identify technically feasible pairs from those of potential locations. Infrastructural, environmental, operational, and other technical constraints govern the choice of feasible locations. We find the flat land-to-river configuration most promising over other configurations for Nepal. Our results provide insight into the potential of pumped storage hydropower and are of practical importance in planning sustainable power systems in the Himalayas

Soda Bottle Science—Citizen Science Monsoon Precipitation Monitoring in Nepal

Citizen science, as a complement to ground-based and remotely-sensed precipitation measurements, is a promising approach for improving precipitation observations. During the 2018 monsoon (May to September), SmartPhones4Water (S4W) Nepal—a young researcher-led water monitoring network—partnered with 154 citizen scientists to generate 6,656 precipitation measurements in Nepal with low-cost (<1 USD) S4W gauges constructed from repurposed soda bottles, concrete, and rulers. Measurements were recorded with Android-based smartphones using Open Data Kit Collect and included GPS-generated coordinates, observation date and time, photographs, and observer-reported readings. A year-long S4W gauge intercomparison revealed a −2.9% error compared to the standard 203 mm (8-inch) gauge used by the Department of Hydrology and Meteorology (DHM), Nepal. We analyzed three sources of S4W gauge errors: evaporation, concrete soaking, and condensation, which were 0.5 mm day−1 (n = 33), 0.8 mm (n = 99), and 0.3 mm (n = 49), respectively. We recruited citizen scientists by leveraging personal relationships, outreach programs at schools/colleges, social media, and random site visits. We motivated ongoing participation with personal follow-ups via SMS, phone, and site visit; bulk SMS; educational workshops; opportunities to use data; lucky draws; certificates of involvement; and in certain cases, payment. The average citizen scientist took 42 measurements (min = 1, max = 148, stdev = 39). Paid citizen scientists (n = 37) took significantly more measurements per week (i.e., 54) than volunteers (i.e., 39; alpha level = 0.01). By comparing actual values (determined by photographs) with citizen science observations, we identified three categories of observational errors (n = 592; 9% of total measurements): unit (n = 50; 8% of errors; readings in centimeters instead of millimeters); meniscus (n = 346; 58% of errors; readings of capillary rise), and unknown (n = 196; 33% of errors). A cost per observation analysis revealed that measurements could be performed for as little as 0.07 and 0.30 USD for volunteers and paid citizen scientists, respectively. Our results confirm that citizen science precipitation monitoring with low-cost gauges can help fill precipitation data gaps in Nepal and other data scarce regions

High Mountain Plateau Margin Critical Zone Observatory, Kaligandaki River Nepal

International audienceMountains are hotspots for earth surface processes, with very fast erosion rates, mass movements, catastrophic flooding and enhanced geochemical weathering rates. These landscapes respond quickly to external forcing by tectonics and/or climate. As a consequence, the hazard potential in mountains is very high, and mountains produce a wide range of large catastrophes which often have wide-reaching impacts on infrastructure and human lives. Furthermore, mountains can be considered as the water towers of the world, as they are very effective at harvesting water from the atmosphere, storing it, and redistributing it to the adjacent lowlands. The key role of mountain regions can be extended endlessly to other disciplines such as ecology, climatology, social sciences and so forth. Yet, despite their importance, high mountains remain inaccessible and notoriously understudied. High elevation terrains are only lightly covered by monitoring systems, with elevations >2500 m asl. widely underrepresented in global monitoring networks (Shahgedanova et al., 2021). The Himalayan mountains are particularly poorly covered by coordinated monitoring observatories.In this contribution we present the set up and overview results of the ~last 10 years of integrated critical zone monitoring in the Kaligandaki Catchment in the central Himalayas in Nepal.Motivated by fundamental research questions on coupled surface process and the high mountain water cycle in the Himalayan mountain range, we began observation in the Kaligandaki Catchment with two major stations for climatological and hydrological monitoring that have operated continuously over the past 10 years. At each location trained personal conducted manual river water sampling for river water geochemistry and suspended sediment monitoring as well as water discharge and bulk meteorological parameters. These observations were complemented by targeted short-term deployments and field sampling campaigns to cover the full spatial extent as well as the seasonal variability. Research question range from organic carbon export, climate and erosion feedback as well as water pathways in high mountains to large mass-movements and intramountain sediment storage and feedbacks with landscape evolution.Our findings from the past 10 years of monitoring motivate the development of a more substantial observatory in the Kaligandaki catchment, which is particularly suited as a critical zone observatory in the Himalayas. The Kaligandaki is a trans-Himalayan river that connects the Tibetan Plateau through the Himalaya to the low elevation foreland. The river crosses distinct climatological, ecological, tectonic, and geomorphic zones, including the arid high elevation plateau, the rapidly uplifting high Himalaya and monsoon precipitation maxima, and the middle hills. The river corridor is highly prone to flood and landslide hazards, and is experience increasing development and human impact, particularly road construction and hydropower. In addition, the river basin is highly sensitive to changing precipitation patterns, which have brought anomalous rainfall and flooding in recent years, and to changing melting patterns, which affect water resources. Together with local partners and the international research community we are proposing this unique catchment as potential integrated mountain critical zone observatory in order to close the monitoring gap in the highest mountain range on Earth

Evaluating Magnitude Agreement and Occurrence Consistency of CHIRPS Product with Ground-Based Observations over Medium-Sized River Basins in Nepal

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), while the false alarm ratio was significant (>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

Return level analysis of the hanumante river using structured expert judgment: A reconstruction of historical water levels

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.</p

Citizen science flow-an assessment of simple streamflow measurement methods

Wise management of water resources requires data. Nevertheless, the amount of streamflow data being collected globally continues to decline. Generating hydrologic data together with citizen scientists can help fill this growing hydrological data gap. Our aim herein was to (1) perform an initial evaluation of three simple streamflow measurement methods (i.e., float, salt dilution, and Bernoulli run-up), (2) evaluate the same three methods with citizen scientists, and (3) apply the preferred method at more sites with more people. For computing errors, we used midsection measurements from an acoustic Doppler velocimeter as reference flows. First, we (authors) performed 20 evaluation measurements in headwater catchments of the Kathmandu Valley, Nepal. Reference flows ranged from 6.4 to 240 L s−1. Absolute errors averaged 23 %, 15 %, and 37 % with average biases of 8 %, 6 %, and 26 % for float, salt dilution, and Bernoulli methods, respectively. Second, we evaluated the same three methods at 15 sites in two watersheds within the Kathmandu Valley with 10 groups of citizen scientists (three to four members each) and one “expert” group (authors). At each site, each group performed three simple methods; experts also performed SonTek FlowTracker midsection reference measurements (ranging from 4.2 to 896 L s−1). For float, salt dilution, and Bernoulli methods, absolute errors averaged 41 %, 21 %, and 43 % for experts and 63 %, 28 %, and 131 % for citizen scientists, while biases averaged 41 %, 19 %, and 40 % for experts and 52 %, 7 %, and 127 % for citizen scientists, respectively. Based on these results, we selected salt dilution as the preferred method. Finally, we performed larger-scale pilot testing in week-long pre- and post-monsoon Citizen Science Flow campaigns involving 25 and 37 citizen scientists, respectively. Observed flows (n=131 pre-monsoon; n=133 post-monsoon) were distributed among the 10 headwater catchments of the Kathmandu Valley and ranged from 0.4 to 425 L s−1 and from 1.1 to 1804 L s−1 in pre- and post-monsoon, respectively. Future work should further evaluate uncertainties of citizen science salt dilution measurements, the feasibility of their application to larger regions, and the information content of additional streamflow data.Water Resource

Soda Bottle Science—Citizen Science Monsoon Precipitation Monitoring in Nepal

Citizen science, as a complement to ground-based and remotely-sensed precipitation measurements, is a promising approach for improving precipitation observations. During the 2018 monsoon (May to September), SmartPhones4Water (S4W) Nepal—a young researcher-led water monitoring network—partnered with 154 citizen scientists to generate 6,656 precipitation measurements in Nepal with low-cost (&lt;1 USD) S4W gauges constructed from repurposed soda bottles, concrete, and rulers. Measurements were recorded with Android-based smartphones using Open Data Kit Collect and included GPS-generated coordinates, observation date and time, photographs, and observer-reported readings. A year-long S4W gauge intercomparison revealed a −2.9% error compared to the standard 203 mm (8-inch) gauge used by the Department of Hydrology and Meteorology (DHM), Nepal. We analyzed three sources of S4W gauge errors: evaporation, concrete soaking, and condensation, which were 0.5 mm day−1 (n = 33), 0.8 mm (n = 99), and 0.3 mm (n = 49), respectively. We recruited citizen scientists by leveraging personal relationships, outreach programs at schools/colleges, social media, and random site visits. We motivated ongoing participation with personal follow-ups via SMS, phone, and site visit; bulk SMS; educational workshops; opportunities to use data; lucky draws; certificates of involvement; and in certain cases, payment. The average citizen scientist took 42 measurements (min = 1, max = 148, stdev = 39). Paid citizen scientists (n = 37) took significantly more measurements per week (i.e., 54) than volunteers (i.e., 39; alpha level = 0.01). By comparing actual values (determined by photographs) with citizen science observations, we identified three categories of observational errors (n = 592; 9% of total measurements): unit (n = 50; 8% of errors; readings in centimeters instead of millimeters); meniscus (n = 346; 58% of errors; readings of capillary rise), and unknown (n = 196; 33% of errors). A cost per observation analysis revealed that measurements could be performed for as little as 0.07 and 0.30 USD for volunteers and paid citizen scientists, respectively. Our results confirm that citizen science precipitation monitoring with low-cost gauges can help fill precipitation data gaps in Nepal and other data scarce regionsWater Resource

Streams, sewage, and shallow groundwater: stream-aquifer interactions in the Kathmandu Valley, Nepal

The Kathmandu Valley in Nepal is facing a water quantity and quality crisis due to rapid urbanization and haphazard water and wastewater planning and management. Annually, groundwater extractions in the Kathmandu Valley exceed capture, resulting in groundwater table declines. Streams are often important sources of recharge to (or destination of discharges from) aquifers. However, stream-aquifer interactions in the Kathmandu Valley are poorly understood. To improve this understanding, we performed topographic surveys of water levels, and measured water quality, in streams and adjacent hand-dug wells (shallow aquifer). In pre-monsoon, 12% (2018) and 44% (2019) of wells had water levels higher than adjacent streams, indicating mostly a loss of stream water to the aquifer. However, in post-monsoon, 69% (2018) and 70% (2019) of wells had water levels higher than adjacent streams, indicating that monsoon rainfall contributes to shallow aquifer recharge which, at least temporarily, causes streams to transition from losing to gaining. Concentrations of all water quality parameters (electrical conductivity, ammonia, alkalinity, and hardness) were higher in the pre-monsoon compared to post-monsoon in both streams and wells. There was no recurring trend in water level difference longitudinally from upstream to downstream. However, water quality in streams and wells depleted from upstream to downstream. While we clearly observed seasonal refilling of the shallow aquifer, the role of the deep aquifer in seasonal storage processes deserve future research attention.Ship Design, Production and OperationsGeo-engineeringWater Resource

Return level analysis of the hanumante river using structured expert judgment: A reconstruction of historical water levels

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.Support Hydraulic EngineeringWater ResourcesApplied Probabilit