Abstract
Citizen science, as a compliment 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.
Highlights
Precipitation is the main terrestrial input of the global water cycle; without it, our springs, streams, lakes, and communities would gradually disappear
Measured precipitation amounts were linearly correlated for the three precipitation ranges, but the correlation decreased in strength as total precipitation decreased (Figure 5)
There was no increase in data accuracy with increases in cost per observation (CPO), efforts to minimize CPO do not appear to systematically lower performance ratios (PRs)
Summary
Precipitation is the main terrestrial input of the global water cycle; without it, our springs, streams, lakes, and communities would gradually disappear. Observing, and understanding, precipitation variability over space and time is fraught with difficulty and uncertainty. Because of these challenges, there are persistent, but spatially heterogeneous, precipitation data gaps that need to be addressed (Kidd et al, 2017). Accuracy is a primary concern, even for common precipitation measurement methods (Krajewski et al, 2003; Villarini et al, 2008) including: manual and automatic gauges, radar, and satellite remote sensing. Precipitation radars can provide meaningful data between gauges, but are subject to errors from beam blockage, range effects, and imperfect relationships between rainfall and backscatter (Kidd et al, 2017). There remain precipitation data gaps and uncertainties that need to be filled
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