Development of Groundwater Levels Dataset for Chile since 1970.

Access to accurate spatio-temporal groundwater level data is crucial for sustainable water management in Chile. Despite this importance, a lack of unified, quality-controlled datasets have hindered large-scale groundwater studies. Our objective was to establish a comprehensive, reliable nationwide groundwater dataset.

Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts.

Hydrological models are often set up to provide specific forecasts of interest. Owing to the inherent uncertainty in data used to derive model structure and used to constrain parameter variations, the model forecasts will be uncertain. Additional data collection is often performed to minimize this forecast uncertainty.

Revisiting the Relationship Between Data, Models, and Decision-Making.

We hydrologists can do a better job of supporting water-resources decision-making. I will argue that we can do this by recognizing that decision makers use qualitative, multiple-narrative approaches. So, rather than providing single-model predictions with quantitative uncertainties, we should develop teams of rival models that inform decision makers about what is known, what is possible, and what is unknown.

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences.