Data-Driven Analysis of the Hydrologic Balance

Change in the general education curriculum at Wittenberg University shifted the focus of natural science courses from content delivery to active inquiry and redefined our approach to introductory courses in physical geology. I teach physical geology through the lens of hydrology, beginning with a field- and data-driven analysis of the hydrologic balance. Field exercises related to precipitation, soil moisture, groundwater level, and stream discharge expose students to field methods in hydrology, but they also provide opportunities for experimental design and variable control, hypothesis testing, and discovery. The focus of these exercises is on units of measure related to water quantity and flux and order of magnitude comparisons between input, output, and storage for single events as well as seasonal and annual timescales. With the relative low cost of dataloggers, class measurements can be analyzed at a point in time, but the longer term antecedent conditions can be incorporated into discussions of those data while subsequent changes can be used to verify findings or reinforce concepts. Local data are used in conjunction with real-time or historic data available on the web to examine scale effects. For example, synoptic views of precipitation for significant rainfall events occurring during the semester are compared with locally-measured precipitation in discussion of spatial variability of input and with output from subwatersheds using locally-measured stage-discharge on a stream near campus and real-time USGS gage data to examine hydrologic balance. For significant flood events, peak discharges are compared to historic peak-flow data with the added advantage of using flood forecasts from the hydrologic prediction service of the National Weather Service and local video clips of flooding available from on-line and broadcast news organizations to discuss societal impact. Scaffolding content, technique, and applications in upper-level courses allows for mastery of technique and builds confidence in data collection and analysis.