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Current hydrologic design of infrastructure relies on historical precipitation-based intensity-duration-frequency (PREC-IDF) curves. The PREC-IDF curves ignore snow processes by assuming that precipitation is in the form of rain, immediately available for the rainfall-runoff process. In snow-dominated regions where snowmelt and rain-on-snow (ROS) events are the dominant flood-generating mechanisms, the PREC-IDF design approach can lead to substantial underestimation or overestimation of design floods and associated infrastructure. Given the potential impacts of climate change, the historical PREC-IDF curves further increase infrastructure development risks because structures designed to meet traditional criteria could be underdesigned under future climate conditions. The overarching research objective of this project was to develop a new hydrologic design tool—the next-generation IDF (NG-IDF) curve—that characterizes both snow process and nonstationary climate conditions, to better inform decisions regarding U.S. Department of Defense (DoD) long-lived infrastructure.
This project used physics-based modeling and data analysis to develop and demonstrate a scientifically defensible methodology for creating NG-IDF curves that directly consider intense precipitation events and snowmelt under conditions of climate nonstationarity. Under this project, the team (1) proposed NG-IDF curves using a mass balance approach, (2) demonstrated NG-IDF curve benefits in estimating design floods using Snow Telemetry (SNOTEL) observations, (3) enhanced the Distributed Hydrology Soil and Vegetation Model (DHSVM) for modeling snow-vegetation interaction, (4) developed DHSVM regional snow parameters for large-domain hydrological applications, (5) extended NG-IDF curves from at-site observations to the Contiguous United States (CONUS) using well-validated DHSVM simulations, and (6) explored both ensemble statistically and dynamically downscaled general circulation model (GCM) data for constructing NG-IDF curves under nonstationary climate conditions.
The team's research has developed a new, robust hydrologic design tool—NG-IDF curves—to enhance hydrologic design and provide a consistent IDF design approach for both rainfall- and snow-dominated regions. Using SNOTEL observations, the team has shown that PREC-IDF curves can substantially underestimate design floods by up to 324%, leading to a potentially high risk of infrastructure failure in snow dominated locations. A key challenge in wide adoption of the NG-IDF tool by practical engineers is the limited availability of observed snow data in space and over time. To overcome this limitation, the team developed spatially continuous NG-IDF curves at 6 kilometer resolution across the CONUS, using the well-validated DHSVM. The team validated the NG-IDF tool at both hillslope and basin scales and demonstrated its accuracy and robustness in estimating design floods. The team used both ensemble statistically and dynamically downscaled GCM data to developed NG-IDF curves for future climate conditions. The team archived NG-IDF products for both historical and future climates, and they can be directly used as input to a single event hydrologic model to produce flood estimates.
This project directly addressed DoD infrastructure planning and management decisions to update hydrologic design to assist in the management of design risks encountered under changing climate conditions. In particular, this project developed improved understanding of and responses to changes in the timing and intensity of rainfall- and snowmelt-based runoff events, and provided a new, robust hydrologic design tool—NG-IDF curves—for potential use by practicing engineers and planners.