SERDP and ESTCP have launched a webinar series to promote the transfer of innovative, cost-effective and sustainable solutions developed through projects funded in five program areas. The webinar series targets Department of Defense and Department of Energy practitioners, the regulatory community and environmental researchers with the goal of providing cutting edge and practical information that is easily accessible at no cost.

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Webinar #86 (03/14/2019)

Determining the Temporal and Spatial Scales of Non-Stationarity in Temperature and Precipitation across the Continental United States

Dr. Daniel Feldman, Lawrence Berkeley National Laboratory

Dr. Xixi Wang, Old Dominion University

March 14, 2019

12:00 PM ET (9:00 AM PT)

Abstracts

“Charting a Path for End-Users from the CMIP Ensemble Projection to Facility-Level Risks from Non-Stationarity in temperature and Precipitation Distributions” by Dr. Daniel Feldman

As part of the most recent Coupled Model Intercomparison Project (CMIP) exercise, dozens of climate modeling centers have contributed projections for the 21^st century of temperature and precipitation at approximately 100 kilometer (km) resolution associated with future emissions scenarios. The project objective is to effectively translate this information into risks that Department of Defense (DoD) facilities will face, as non-stationarity in the distributions of, in particular, extreme temperature and precipitation can adversely impact infrastructure. Model bias correction based on historical observations and downscaling are typically used, and the range of bias-corrected, downscaled model results is interpreted as an estimate of uncertainty.

This project examined several aspects of the model bias correction process, first ensuring that the correction is not systematically biased low. The sparse network of meteorological station observations across the United States must be carefully gridded to avoid systematic underestimation of extreme precipitation. This project also examined dynamical versus statistical downscaling and will provide examples from California to illustrate how statistical methods will be problematic due to non-stationarity by the end of the 21^st century. Finally, estimating uncertainty from multiple models will be discussed, including considerations for model averaging, weighting, and down-selecting from the CMIP ensemble.

“Next-Generation Rainfall IDF Curves for the Virginian Drainage Area of Chesapeake Bay” by  Dr. Xixi Wang

The goal of this project is to develop an innovative approach for creating next-generation intensity-duration-frequency (IDF) curves that consider non-stationary rainfall and uses this information to create future IDF curves for the Virginian drainage area of Chesapeake Bay. These curves can be used as a tool for the planning, design and management of DoD’s infrastructure. To generate the IDF curves, the observed 15-minute rainfall time series for the historic periods and the projected precipitation time series by eleven pairs of general circulation models (GCM) and regional climate models (RCM) were used. In accordance with the empirical exceedance probabilities, a best distribution was chosen to create existing, projected historic, and projected future IDF curves. For a given return period, the projected historic IDF curves were compared to the existing ones to determine the lower and upper limits of the future IDF curve. The most-probable future IDF curve was determined as the average of the eleven curves responding to the GCM-RCM models. In addition, for a given duration and return period, the eleven values were used to create a probability-based IDF curve. Further, the areal precipitation time series for each eight-digit Hydrologic Cataloging Unit were used to create watershed-level future probability-based IDF curves. The presentation will describe the project’s technical approach and preliminary results.

Speaker Biographies

Dr. Daniel Feldman is an Earth Research Scientist working in the Earth and Environmental Sciences Area at the Lawrence Berkeley National Laboratory in Berkeley, California. Daniel’s research covers many aspects of atmospheric science including fundamental radiative transfer and Earth system model intercomparison. He co-led the development of the protocol and novel, error-revealing, diagnostics for the Radiative Forcing Model Intercomparison Project in the upcoming Coupled Model Intercomparison Project – Phase 6 (CMIP6). More recently, he has focused on hydroclimate science questions and developed a range of plausible downscaled climate-change scenarios for the California Department of Water Resources. Daniel is also leading research to develop the modeling tools and observations needed to understand why Earth System Models overpredict spring-time snow-melt. He has authored or co-authored 28 peer-reviewed publications on radiative transfer, remote sensing, and Earth system model evaluation. He earned a bachelor’s degree in environmental engineering science at the Massachusetts Institute of Technology in Cambridge, Massachusetts, and a master’s degree and doctoral degree in environmental science and engineering from the California Institute of Technology in Pasadena, California.

Dr. Xixi Wang is an Associate Professor of Civil and Environmental Engineering at Old Dominion University in Norfolk, Virginia. Prior to that, Xixi worked as an Assistant Professor at Tarleton State University, a Research Scientist at the University of North Dakota, as a Civil Engineer at Michael Baker Jr. Inc., and an Assistant Professor at Tsinghua University. His current research focuses on the effects of climate change and human activity on water resources, water-soil-vegetation nexus and equilibrium with changing climate, and watershed hydrology and stormwater management. Xixi served as the Principal Investigator on several research projects focused on spatiotemporal variation of precipitation as influenced by nonstationary climate. He has authored more than 80 peer-reviewed research papers, books, and book chapters, including several on precipitation trend and frequency analyses. He earned his bachelor’s and master’s degrees in hydrology and water resources engineering from Tsinghua University, and a doctoral degree in Agricultural Engineering from Iowa State University. He is a registered professional engineer in North Dakota.

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