Integrating Community Science Research and Space-Time Mapping to Determine Depth to Groundwater in a Remote Rural Region

Continuous depth to groundwater (DTG) data collection is challenging in remote regions. Community participation offers a way to both increase data collection and involves the local community in scientific projects. Local knowledge, which is often descriptive, can be difficult to include in quantitative analysis; however, it can increase scientists' ability to formulate hypotheses or identify relevant environmental processes. We show how Community Science Research can add useful descriptive information for a study based in rural Colombia. To estimate the spatiotemporal distribution of DTG, the community collected water level measurements during a wet (La Niña) year and an average year. We built one spatial and two spatiotemporal models (with and without probabilistic data) using Bayesian Maximum Entropy. Due to the inclusion of local knowledge, the spatiotemporal model with probabilistic data reduced its mean square error by a factor of 15 compared to the spatial model. Using this model, we found that 13% of the study area has a high probability of very shallow DTG (