Development of flood regressions and climate change scenarios to explore estimates of future peak flows

Abstract

A new Web-based application, titled "Application of Flood Regressions and Climate Change Scenarios To Explore Estimates of Future Peak Flows," has been developed by the U.S. Geological Survey, in cooperation with the New York State Department of Transportation, that allows a user to apply a set of regression equations to estimate the magnitude of future floods for any stream or river in New York State (exclusive of Long Island) and the Lake Champlain Basin in Vermont. The regression equations that are the basis of the current application were developed in previous investigations by the U.S. Geological Survey (USGS) and are described at the USGS StreamStats Web sites for New York (

Examining Observed Rainfall, Soil Moisture, and River Network Variabilities on Peak Flow Scaling of Rainfall‐Runoff Events with Implications on Regionalization of Peak Flow Quantiles

“…Multiple studies have focused on understanding the physical mechanisms that control α and θ , with respect to geomorphologic features and rainfall properties in a regional domain (e.g., Ayalew et al, ; Gupta et al, ; Jothityangkoon & Sivapalan, ; Kroll et al, ; Mandapaka et al, ; Mantilla et al, ; Medhi & Tripathi, ). Understanding the factors governing α and θ is crucial for the development of regional equations for estimating peak flows at ungauged sites, as well as the increasing need to understand how to adapt these regression parameters in future climatic scenarios (Burns et al, ; Gupta et al, ).…”

Explaining the physics behind regional peak flow equations using the scaling theory of floods and river network descriptors

“…Two scaling parameters control the change of peak flows: the intercept (α) and the scaling exponent (ϴ). Basically, an understanding of the factors governing and is crucial for the development of regional equations to estimate peak flows at ungauged sites, and to meet our increasing need to understand how to adapt these regression parameters in future climatic scenarios (Burns, et al, 2015;Gupta et al, 2007). Fuller (1914) was the first to connect the power-scaling structure to a statistical framework for peak flow data for the United States; however, significant questions have emerged, and many remain unanswered with regard to the physical controls and hydrologic variables governing power law-scaling structure in peak flows.…”