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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 (<0.1 m) during an average year, whereas during La Niña, this area increases to 56%. The difference in shallow DTG between the average and wet year implies that after reaching a precipitation threshold, the study region may lose its flow regulation capacity, contributing to flooding during extreme precipitation events. Our approach presents a method to incorporate local knowledge in data‐driven models by combining qualitative and quantitative information. Plain Language Summary: Groundwater is a key source of water supply in many regions, supporting crop yields and maintaining water levels in rivers and wetlands. In unconfined aquifers, groundwater may reach the surface during wet periods, contributing to overland flow and intensifying erosion. Identifying groundwater level changes helps to establish water and land management activities. However, continuous depth to groundwater (DTG) data collection, essential for identifying groundwater level changes, is challenging in remote rural areas. We show how Community Science Research, an approach involving active community participation, added crucial information to a statistical model to represent shallow aquifer's groundwater levels in Colombia. The community collected DTG during an extreme wet year and an average year in a middle‐low‐elevation watershed. We created DTG maps using three statistical models. DTG is better represented by the model that combines descriptive observations with DTG measurements. We also created a map with the probability that the groundwater is near the surface and showed that the area was much larger in the wet year than during the average year. This difference implies that after the watershed receives a lot of precipitation, its flow regulation capacity decreases, which is threatened by land‐use activities. Key Points: We built one spatial and two spatiotemporal models using Bayesian Maximum Entropy to estimate Depth to Groundwater (DTG) in a rural regionThe addition of probabilistic data, based on local knowledge collected through Community Science Research, improved the space‐time modelThe watershed's portion more likely to have shallow DTG increased from 13% in an average year to 56% in a La Niña year [ABSTRACT FROM AUTHOR]