The world is faced with uncertain and dramatic changes in water movement, availability, and quality are due to human-induced stressors such as population growth, climatic variability, and land use changes. At the apex of this problem is the need to understand and predict the complex forces that control the movement and life-cycle of water, a critical component of which is stormwater runoff. Success in addressing these issues is also dependent upon educating hydrology professionals who understand the physical processes that produce stormflow and the effects that these stressors have on stormwater runoff and water quality. This dissertation addresses these challenges through methodologies that can improve the way we measure stormflow and educate future hydrology professionals.

A methodology is presented to (i) evaluate the uncertainty due to inadequate temporal sampling of stormflow data, and (ii) develop equations using regional regression analysis that can be used to select a stormflow sampling frequency of a watershed. A case study demonstrates how the proposed methodology has been applied to 25 stream gages with watershed areas ranging between 30 and 11,865 km2 within the Valley and Ridge geomorphologic region of Virginia. Results indicate that autocorrelation of stormflow hydrographs, drainage area of the catchment, and time of concentration are statistically significant predictor variables in single-variable regional regression analysis for estimating the site-specific stormflow sampling frequency under a specific magnitude of uncertainty.

Methods and resources are also presented that utilize high-frequency continuous stormwater runoff data in hydrology education to improve student learning. Data from a real-time continuous watershed monitoring station (flow, water quality, and weather) were integrated into a senior level hydrology course at Virginia Tech (30 students) and two freshman level introductory engineering courses at Virginia Western Community College (70 students) over a period of 3 years using student-centered modules. The goal was to assess student learning through active and collaborative learning modules that provide students with field and virtual laboratory experiences. A mixed methods assessment revealed that student learning improved through modules that incorporated watershed data, and that students most valued working with real-world data and the ability to observe real-time environmental conditions.