Description: This lecture introduces the key problems and challenges in groundwater data collection, analysis, and modeling. Groundwater data are fundamental for managing water resources, yet achieving reliable and complete assessments remains a persistent challenge. The session discusses how data gaps, measurement inconsistency, and model uncertainty limit our understanding of groundwater storage, dynamics, and quality. It also examines the limitations of both physically based and data-driven models, emphasizing how scale mismatches, sparse observations, and missing spatial context affect model performance. Special focus is given to the role of spatiotemporal data in improving groundwater assessment through better spatial interpolation, integration of remote sensing products, and machine learning-based inference. Participants will learn how incorporating spatial and temporal information enhances the accuracy, consistency, and interpretability of groundwater predictions, supporting more sustainable and evidence-based water management.
Duration: 90 minutes
Lecture Flow (content):
Outcomes: The basic understating of spatiotemporal data and challenges in its handling for trend and spatiotemporal analysis