Hydrography Seminar Series, Seminar 6 (Main) - June, 2016

Abstract - Hydrologic models are required for flood forecasting, flood plain mapping, water quality assessments, river restoration, setting environmental flows and land management, among others. A grand challenge identified by the National Research Council (2001) is better hydrologic forecasting that quantifies the effects and consequences of land surface change on hydrologic processes and conditions. Advancing the capability for hydrologic prediction requires new models that take advantage of new information and process understanding enabled by new technology. Distributed hydrologic modeling is motivated on the premise that better results will be obtained from modeling that takes advantage of better, more detailed information. High resolution hydrography and elevation data are key input data sources used by hydrologic modeling where there have been significant recent strides in enhancing their resolution and detail. This presentation will examine opportunities and challenges that arise from efforts to improve hydrologic modeling based on higher resolution data. Hydrologic processes are different on hillslopes and in streams, yet identifying the scale, where the transition from hillslope to channel processes occurs, “where do streams begin” if you will, remains a challenge. Improvements in the resolution of the national hydrography dataset has, over the years, provided considerable more detail on where streams begin. For models to take advantage of this information it needs to be framed in a way such that it is consistent with other information being used, notably elevation datasets, but also land cover and water management (dams, diversions, irrigation canals etc.) and built environment infrastructure (roads, culverts, stream crossings, drain points etc.). This presentation will address, from my personal perspective, some of the factors involved in using hydrography data in hydrologic modeling related to it being organized in a way that facilitates automated processing and integration of information from multiple sources, that I feel are important to achieve the promise of better models through reliance on more detailed distributed information.

Biography – David Tarboton is a professor of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University. He received his Sc.D. and M.S. in Civil Engineering (Water Resources and Hydrology) from the Massachusetts Institute of Technology and his B.Sc Eng in Civil Engineering from the University of Natal in South Africa. His research focuses on advancing the capability for hydrologic prediction by developing models that take advantage of new information and process understanding enabled by new technology. He has developed a number of models and software packages including the TauDEM hydrologic terrain analysis and channel network extraction package, distributed as a free plug in to ArcGIS and Utah Energy Balance snowmelt model. He is lead on the National Science Foundation HydroShare project for the development of a collaborative environment for sharing hydrologic data and models. He teaches Hydrology and Geographic Information Systems in Water Resources.