What question did this study set out to answer?

The aim is to enhance the understanding of groundwater recharge processes in regions with limited data and high complexity.

Addressing Data Scarcity and Structural Uncertainty in Groundwater Recharge Assessment: Refining the Recharge Model in the Chiquitano Dry Forest (Eastern Bolivia)

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Refines groundwater recharge assessment in a dry forest, indicating improved strategies for water management.

The aim is to enhance the understanding of groundwater recharge processes in regions with limited data and high complexity.
Integrated field-informed conceptual model with three numerical models: SWB-USGS, SGWB-FLDAS, and SeasonalWS.
Assessment of rainfall percolation beyond the root zone and seasonal recharge rates.
Cross-model inconsistencies were utilized to iteratively refine the conceptual model.
Approximately 16.4% of annual rainfall contributes to groundwater recharge.
Rainy-season recharge is about 6.8 times greater than dry-season recharge.
Daily-scale models (SWB-USGS) effectively capture short-lived rainfall events.

Guzmán‐Rojo et al. (2026) studied this question.

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