Abstract
Hydrologic data has traditionally been collected with permanent installations of sophisticated and accurate but expensive monitoring equipment at limited numbers of sites. Consequently, observation frequency and costs are high, but spatial coverage of the data is limited. Citizen Hydrology can possibly overcome these challenges by leveraging easily scaled mobile technology and local residents to collect hydrologic data at many sites. However, understanding of how decreased observational frequency impacts the accuracy of key streamflow statistics such as minimum flow, maximum flow, and runoff is limited. To evaluate this impact, we randomly selected 50 active United States Geological Survey streamflow gauges in California. We used 7 years of historical 15-min flow data from 2008 to 2014 to develop minimum flow, maximum flow, and runoff values for each gauge. To mimic lower frequency Citizen Hydrology observations, we developed a bootstrap randomized subsampling with replacement procedure. We calculated the same statistics, and their respective distributions, from 50 subsample iterations with four different subsampling frequencies ranging from daily to monthly. Minimum flows were estimated within 10% for half of the subsample iterations at 39 (daily) and 23 (monthly) of the 50 sites. However, maximum flows were estimated within 10% at only 7 (daily) and 0 (monthly) sites. Runoff volumes were estimated within 10% for half of the iterations at 44 (daily) and 12 (monthly) sites. Watershed flashiness most strongly impacted accuracy of minimum flow, maximum flow, and runoff estimates from subsampled data. Depending on the questions being asked, lower frequency Citizen Hydrology observations can provide useful hydrologic information.
Highlights
Background and IntroductionNatural resource managers rely on timely and accurate data to make management decisions
The goal of this paper is to evaluate the impacts of decreased observational frequency, which is a primary tradeoff of Citizen Hydrology observations, on estimates of minimum flow, maximum flow, and runoff
In addition to uncertainties in water level observations, the discussion about Citizen Hydrology should focus on understanding uncertainties in rating curves (Mason et al 2016; McMillan and Westerberg 2015; Domeneghetti et al 2012 and others), focusing on those developed from infrequent observations, or on new methods for Citizen Hydrologists to accurately observe streamflow directly
Summary
Natural resource managers rely on timely and accurate data to make management decisions. The factors leading to this decline are diverse, but include a lack of understanding of the importance of long-term streamflow data, and persistent funding challenges (Pearson 1998). This lack of information makes it difficult to know how our Environmental Management (2017) 60:12–29 water systems are changing over time and space due to natural or human activities, and to decide what management actions should be taken to either avoid or mitigate undesirable conditions in the present and future. In addition to remotely sensed stream stage and flow measurement techniques (Hirsch and Costa 2004; currently applicable to large rivers only), Citizen science appears to be a promising methodology for filling these data gaps (Sanz et al 2014; Fienen and Lowry 2012)
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