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Congratulations to the 2023 Abdel El-Shaarawi Early Investigator Award Abhirup Datta,Johns Hopkins University, USA!

For key contributions to theoretical, methodological and applied statistics and machine learning approaches for the analysis of spatially and temporally oriented data and their applications to the environmental sciences and public health; for prolific open-access and publicly available software development; for being a role model in advising and mentoring of students and junior colleagues; and for synergistic activities related to TIES and the broader environmental statistics community. 

Harnessing the power of interdisciplinary expertise to solve modern environmental problems

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11:00 am (CT)


Karen Ryberg

U.S. Geological Survey Dakota Water Science Center

Deputy Director

Issues and Advancements in Flood-Frequency Analysis—What is the 100-Year Flood, and Will It Be the Same in the Future?

Abstract: Flood-frequency analysis provides annual exceedance probabilities for given flood magnitudes, providing recurrence intervals for flood magnitudes, such as estimates of the so-called 100-year flood. Flood-frequency analysis is essential for flood insurance studies, floodplain management, and transportation infrastructure design. The conventional assumptions for performing flood-frequency analysis in the United States are that the annual time series of peak streamflow at a streamgage is a representative sample of independent, identically distributed (IID) events and that the floods can be modeled using a statistical distribution, usually a three-parameter log Pearson Type III distribution. Advancements in flood-frequency analysis provide for the inclusion of historical and paleoflood interval estimates, as well as the inclusion of left- or right-censored flood estimates. However, in recent decades, land-use change and climate change, as well as a better understanding of long-term hydroclimatic persistence and natural climatic shifts, have challenged the IID assumption. The U.S. Federal guidelines for flood-frequency analysis call for the development of new methods for determining dynamic flood-frequency models that vary with time and can incorporate physical processes (that change the distribution of peak streamflow or result in dependence). Such models could include explanatory variables such as meteorological observations, climate indices, or changing watershed characteristics. Many methods have been suggested, but the hydrologic community has yet to converge on a new method. This presentation will describe standard flood-frequency analysis, common situations that violate the underlying assumptions, and the results of Monte Carlo experiments to evaluate the utility of proposed methods for modeling changes in annual peak streamflow.

Bio: Dr. Karen Ryberg has spent much of her career as Research Statistician in the Dakota Water Science Center of the U.S. Geological Survey, in Bismarck, North Dakota. Her research interests the effect of climate variability and change on streamflow and water quality; data mining; and outlier detection; include water-quality trend analysis, particularly pesticide trend analysis in surface water; Ryberg is also involved in statistical education within the USGS and the greater water resources community. Ryberg recently became Deputy Director of the Dakota Water Science Center.

Joint RSS Environmental Stats Section (ESS) and TIES speaker


Hayley Fowler, Newcastle University