Guest Presentation by Dr. Steven R. Fassnacht

Tuesday, February 5, 2019 1:30 pm - 2:30 pm EST (GMT -05:00)

We are proud to have Dr. Steven R. Fassnacht, a professor of Snow Hydrology from Colorado State University, visit the University of Waterloo to present his research. Please join us in EV1-221 at 1:30 pm on Tuesday, February 5th, 2019 for his talk titledIntegration of Multi-disciplinary Data Sources to Understand Hydro-Climatic Change”.

Title: “Integration of Multi-disciplinary Data Sources to Understand Hydro-Climatic Change

Abstract:


      Across the globe, station-based data are analyzed to estimate the rate of change in temperature, precipitation, snowpack, and other hydro-meteorological variables. However, in mountainous terrain and in sparsely populated regions, such as Mongolia, stations are few and far between, leaving significant gaps in station-derived precipitation patterns across space and over time. We are combining social and physical datasets to better understand variability and uncertainty. Several datasets were analyzed.

     We used observations of Mongolian herders, who live on the land and observe nature and its changes across the landscape, as well as individuals who live on the Front Range of Colorado. Both groups provided their observations of changes in climate. These observation of changes were based on a closed ended questionnaire and open ended questions, and were summarized using the Potential for Conflict Index (PCI2). This statistical tool computes the mean response and the consensus amongst responses. We compared these datasets to station-based trends computed with the Mann-Kendall significance and Theil-Sen’s rate of change tests. 
    
     We found a variety of results. For the Mongolian data, the herder had similar responses for temperature and precipitation changes, but not seasonality. In Colorado, people tended to have a poor understanding of climatic conditions and changes, yet women had a better understanding than men. We also used the PCI2 tool to evaluate uncertainty. There was similar uncertainty among the herders and the station data for some variables and in some locations. This combination of datasets provides a new window into both change and variability of climate.