YouTube as a crowd-generated water level archive

In view of the substantial costs associated with classic monitoring networks, participatory data collection methods can be deemed a promising option to obtain complementary data. An emerging trend in this field is social media mining, i.e., harvesting of pre-existing, crowd-generated data from social media. Although this approach is participatory in a broader sense, the users are mostly not aware of their participation in research. Inspired by this novel development, we demonstrate in this study that it is possible to derive a water level time series from the analysis of multiple YouTube videos. As an example, we studied the recent water level rise in Dahl Hith, a Saudi Arabian cave. To do so, we screened 16 YouTube videos of the cave for suitable reference points (e.g., cave graffiti). Then, we visually estimated the distances between these points and the water level and traced their changes over time. To bridge YouTube hiatuses, we considered own photos taken during two site visits. For the time period 2013-2014, we estimate a rise of 9.5m. The fact that this rise occurred at a somewhat constant rate of roughly 0.4m per month points towards a new and permanent water source, possibly two nearby lakes formed from treated sewage effluent. An anomaly in the rising rate is noted for autumn 2013 (1.3m per month). As this increased pace coincides with a cluster of rain events, we deem rapid groundwater recharge along preferential flow paths a likely cause. Despite the sacrifice in precision, we believe that YouTube harvesting may represent a viable option to gather historical water levels in data-scarce settings and that it could be adapted to other environments (e.g., flood extents). In certain areas, it might provide an additional tool for the monitoring toolbox, thereby possibly delivering hydrological data for water resources management.