September 24, 3:00 p.m.
A special seminar in memory of Deb Swackhamer
Sources of Airborne PCBs in Schools, Homes, and Cities
Dr. Keri Hornbuckle, University of Iowa
Keri Hornbuckle is the Donald E. Bently Professor of Engineering and Professor of Civil Environmental Engineering. She is a Research Engineer at IIHR-Hydroscience Engineering, holds a secondary appointment as Professor of Occupational and Environmental Health and serves as on the faculty of the Interdisciplinary Graduate Program in Human Toxicology, all at the University of Iowa.
Past Headwaters Lecture abstracts and recordings
Assoc. Professor of Hydrology, Dept. of Earth & Environmental Sciences, University of Minnesota
February 16, 2021
Manoomin (in Ojibwe), or wild rice, is central to the culture and diet of many Native people throughout the Upper Great Lakes region. Sulfate entering our lakes and streams from mining discharges poses a threat, but it is just one factor affecting the well-being of this sacred plant and its environment. Native people who have lived with manoomin for generations understand this intimately, but Tribal views and resource rights have not been adequately incorporated into its management. This project adopts a collaborative approach that prioritizes Tribal values and knowledge. Dr. Chan Lan Chun (Asst. Professor, Dept. of Civil Engineering, Natural Resources Research Institute, University of Minnesota – Duluth) and Ed Swain were available for questions.
Kelly M. Cobourn
Associate Professor, Forest Resources & Environmental Conservation, Virginia Tech
October 30, 2020
Coupled natural-human systems (CNHS) modeling can be used to study the two-way feedbacks between human actions and ecosystem processes. The complexity of CNHS makes it challenging to characterize the pathways by which humans and natural systems influence one another, yet understanding the full set of feedbacks is critical for developing insight into system behavior over time and across a range of conditions. This talk presents a conceptual framework and corresponding empirical modeling approach to explore these feedbacks in freshwater lake catchments. The empirical methodology illustrates an integrative strategy for coupling a suite of human and natural system models that span disciplines, including soil science, hydrology, limnology, economics, and social psychology. In an application to eutrophic Lake Mendota in Wisconsin, this framework demonstrates the connections between agricultural land-management decision making, hydrologic-solute transport, aquatic nutrient cycling, and civic engagement to protect lake water quality.
John Linc Stine
Executive Director of Freshwater
December 6, 2019
Minnesota water laws, rules, programs and responsibilities have their beginnings in the late nineteenth century. Since then, barely a decade has passed when some water problem hasn’t required attention by MN lawmakers, courts, communities. The result is thousands of pages of water laws, regulations, plans and programs. In this seminar, John Linc Stine – a 40-year public servant - describes MN’s history of water governance and offer perspectives on the benefits and challenges for future water professionals.
Associate Professor, Colorado State University
October 4, 2019
Groundwater resources are increasingly relied upon for drinking water and inputs to irrigation. However, in many areas, demand for groundwater exceeds natural recharge. As management organizations seek to sustain groundwater resources, it is important to understand the tradeoffs associated with various conservation options. Economics plays an important role in such policy analysis both from the perspective of predicting behavioral responses to policy and the valuation of policy benefits. This presentation discussed the importance of linking models of human behavior and physical models of groundwater systems to understand the implications of specific groundwater management strategies over time. It also described the role of economic valuation in determining the magnitude of conservation benefits derived from specific policies. Results related to conservation policy tradeoffs were discussed in the context of the Ogallala Aquifer in the High Plains region of the US. In addition to highlighting the implications for groundwater management, future research challenges and opportunities related to hydro-economic modeling were discussed.
Petrus (Peter) J. van Oevelen
Principal Scientist, USRA
Director, International GEWEX Project office
October 5, 2018
The Water for the Food Baskets Grand Challenge is a GEWEX (Global Energy and Water EXchanges Project) led activity as part of the World Climate Research Programme. Within this grand challenge we address how to effectively incorporate aspects of the human dimension, e.g. irrigation and water management, crop rotation etc. in climate and weather modeling as well as how to link the latter to crop and vegetation modeling at regional and global scales. The human-related aspects are not just geophysically driven but in particular by socio-economical factors and hence require different expertise. Within this grand challenge, we seek to integrate the various knowledge communities to address these issues. An important aspect of modeling food and crop production is a realistic representation of the climate and weather at the appropriate scales. Hence, convection-permitting modeling provides a new and clear prospect in improving our global and regional climate forecasts and projections. This presentation gives an overview of the various aspects and rationale behind this grand challenge as well as offers an explanation on the implementation of this activity. It also will show how this is relevant to two major regional activities, Third Pole Environment in Asia and ANDEX in Latin America. The global context provides a fertile ground for knowledge increase and exchange as well as opportunities for capacity development much needed in both regions.
University of North Carolina, Chapel Hill
March 30, 2018
Harmful (toxic, hypoxia-generating, food web altering) blue-green algal or cyanobacterial blooms (CyanoHABs) are proliferating worldwide in freshwater ecosystems, where they represent a serious threat to drinking water, recreational and fishing use and overall sustainability. Nutrient (both phosphorus and nitrogen) input reductions have been prescribed to control CyanoHABs. However, climatic changes, specifically warming, increased vertical stratification, salinization, and intensification of storms and droughts, favor CyanoHABs and thus play synergistic roles in promoting CyanoHAB frequency, intensity, geographic distribution, and duration. In particular, rising temperatures cause shifts in critical nutrient thresholds at which cyanobacterial blooms can develop. From a management perspective, nutrient input reductions aimed at controlling CyanoHABs may need to be more aggressively pursued in a warmer, hydrologically more extreme world. Additional control steps that have been taken include 1) altering the hydrology to enhance vertical mixing and/or flushing and 2) decreasing nutrient fluxes from organic-rich sediments by physically oxygenating or removing the sediments or capping sediments with clay. These efforts, however, have met with mixed results and can disrupt benthic and planktonic habitats. In most instances, long-term effective eutrophication and CyanoHAB control must consider adaptive nutrient control strategies within the context of altered thermal and hydrologic regimes associated with climate change.
University of Minnesota
January 19, 2018
Water resources worldwide are coming under stress due to increasing demand from a growing population, increasing pollution, and depleting or uncertain supplies due to changing climate in which drought and floods have both become more frequent. As domains associated with Water continue to experience tremendous data growth from models, sensors, and satellites, there is an unprecedented opportunity for machine learning to help address urgent water challenges facing the humanity. This talk will examine the role of big data and machine learning can play in advancing water science, challenges faced by traditional Machine learning methods in addressing the domain of water, and some early successes.