The following dataset is related to the paper: Integrating community science research and space-time mapping to determine depth to groundwater in a remote rural region. It contains the results of the modeling approach that combines space-time depth to groundwater values with probabilistic depth to groundwater data. Abstract [From the paper]: We show how Community Science Research, an approach in which research projects are built with active community participation, added essential descriptive information to a statistical model to represent groundwater levels of a shallow aquifer located in Colombia. The community collected depth to groundwater during an extreme wet year and an average year in the watershed's middle-low-elevation areas. We used the data to map depth to groundwater using three statistical models, each with different configurations. Depth to groundwater is better represented by the model that incorporates qualitative data into quantitative observations. We map the probability of shallow depth to groundwater to identify how the aquifer responds to precipitation and show that in the wet year, the area of shallow depth to groundwater considerably increases compared to the average year. This difference implies that after the watershed receives an excess in precipitation, its flow regulation capacity can decrease, which is further threatened by local land-use activities.

{"references": ["Gomez et al 2020 [Submission process] Integrating community science research and space-time mapping to determine depth to groundwater in a remote rural region"]}