Physics & Astronomy Colloquium: Prof. Alan F. Hamlet, University of Notre Dame


Location: 118 Nieuwland Science Hall

An Overview of the Indiana Climate Change Impacts Assessment, and Risk-Based Methods for Creating Hydrometeorological Design Standards in a Non-Stationary Environment

Prof. Alan F. Hamlet
Department of Civil and Environmental Engineering and Earth Sciences
University of Notre Dame

Part I. An Overview of the Indiana Climate Change Impacts Assessment
The Indiana Climate Change Impacts Assessment (INCCIA) was led by the Purdue Climate Change Research Center in collaboration with several other IN universities, including the University of Notre Dame, Indiana University Bloomington, and Ball State. Using an ensemble of 10 statistically downscaled global climate model (GCM) simulations, the climate working group for the INCCIA projected future climate change impacts on the state of Indiana (IN) for two scenarios of greenhouse-gas concentrations (a medium scenario--RCP4.5, and a high scenario--RCP 8.5) for three future time periods (2020s, 2050s, 2080s). Relative to a 1971-2000 baseline, the scenarios project substantial changes in temperature for IN, with a change in the annual ensemble mean temperature for the 2080s RCP8.5 scenario of about 5.6 °C (10.1 °F). Such changes also indicate major changes in extreme temperatures. For southern IN, the number of days with daily maximum temperatures above 35 °C (95 °F) is projected to be about 100 days per year for the 2080s RCP8.5 scenario, as opposed to an average of 5 days for the historical baseline climate. Locations in northern IN could experience 50 days per year above 35 °C (95 °F) for the same conditions. Energy demand for cooling, as measured by Cooling Degree Days (CDD), is projected to increase nearly fourfold in response to this extreme warming, but heating demand as measured by Heating Degree Days (HDD) is projected to decline by 30%, which would result in a net reduction in annual heating/cooling energy demand for consumers. The length of the growing season is projected to increase by about 30 to 50 days by the 2080s for the RCP8.5 scenario, and USDA hardiness zones are projected to shift by about two half zones throughout IN. By the 2080s, all GCM simulations for the RCP8.5 scenario show higher annual precipitation (P) over the Midwest and IN. Projected seasonal changes in P include a 25-30% increase in winter and spring precipitation by the 2080s for the RCP8.5 scenarios and a 1-7% decline in summer and fall P (although there is low model agreement in the latter two seasons). Rising temperatures are projected to result in systematic decreases in the snow-to-rain ratio from Nov-Mar. Snow is projected to become uncommon in southern IN by the 2080s for the RCP8.5 scenario, and snowfall is substantially reduced in other areas of the state. The combined effects of these changes in T, P, and snowfall will likely result in increased surface runoff and flooding during winter and spring.

Part II. Risk-Based Methods for Creating Hydrometeorological Design Standards in a Non-Stationary Environment
In a rapidly changing environment, analysis of risks associated with non-stationary hydroclimatic extremes has many important implications for resilient and sustainable water resources management, including the evaluation of risk for existing systems and the design of new infrastructure. This study develops a new risk-based analytical framework called the Non-Stationary Monte-Carlo (NSMC) to better address various problems associated with non-stationarity of hydrologic extremes. Current approaches in the literature evaluating non-stationary Probability Distributions of extremes events commonly use trend extension or multivariate analysis based on observed data, which often fail to account for larger changes projected for the future. To avoid these  problems, NSMC explicitly accounts for the projected changes in hydroclimatic extremes by analyzing the changing probability distributions of extremes for each future year based on statistically downscaled climate projections and hydrologic simulations. Using Monte Carlo techniques, NSMC generates a Super Ensemble (SE) of extremes, the statistics of which can be readily applied to various problems in non-stationary flood frequency analysis. For example, we show that the estimation of design standards based on Design Life Level (DLL) or Average Risk of Failure (ARF) metrics can be reduced to a simple look-up process of quantiles in the SE of extremes. A case study analyzing changing extreme high streamflow for the Wabash River  IN, USA) demonstrates the applicability of NSMC to real-world flood risk problems.


Dr. Alan F. Hamlet is an Associate Professor in Civil and Environmental Engineering and Earth Sciences at the University of Notre Dame. He is a specialist in the integrated computer modeling of climate, hydrologic systems, water resources systems, and ecosystems. These activities include construction of historical hydrometeorological data sets, statistical and dynamic downscaling of climate model output, large- and small-scale hydrologic modeling of surface and groundwater systems, analysis of hydrologic extremes (floods, droughts), water resources modeling (simulation and optimization of reservoir operations), and ecosystem modeling of the coastal ocean, rivers, estuaries, wetlands, and lakes. Dr. Hamlet has also participated in modeling studies of regional energy systems, agricultural systems, as well as urban heat-related deaths stemming from changing temperature and humidity regimes. The tools employed in these diverse research activities can be readily applied at local- to global-scales to suit a wide range of integrated applications analyzing both natural and human systems. Over the last 25 years Dr. Hamlet has been involved extensively in integrated modeling studies in North America (e.g. in the western, central, and southeastern U.S.) as well as at the global scale. Recent large-scale efforts include climate change impacts assessment for the entire Midwest and Great Lakes region, and scenarios supporting the Indiana Climate Change Impacts Assessment [].