Climate-informed Estimation of Hydrologic Extremes for Robust Adaptation to Non-stationary Climate

Abstract

This research develops and evaluates methods to produce the next generation of intensity-duration frequency (IDF) curves and hydrologic design events relevant for engineering design at DoD installations. The research demonstrates the utility of the methods that link non-stationary statistical analyses of observed hydrometeorological extremes to climate information produced through Earth system modeling. The effort is premised on the hypothesis that the biases and other failings of GCM projections may be overcome with innovative data science-based approaches to extracting meaningful and credible signals from the same. An assessment of climate modeling methods is made in terms of their ability to inform the key climate information needs that emerge from an analysis of historical non-stationarity already realized in the observed record. The researchers evaluated the relative advantage of various climate information tailoring methods, including different dynamical downscaling techniques, in terms of their ability to provide credible climate information relevant to hydrologic extremes.

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Document Details

Document Type
Technical Report
Publication Date
Oct 10, 2019
Accession Number
AD1092331

Entities

People

  • Casey Brown
  • Jim Hall
  • Linda Mearns
  • Paul Moody
  • Upmanu Lall

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Bayesian Networks
  • Climate Change
  • Climate Change Adaptation
  • Computational Science
  • Data Mining
  • Data Science
  • Databases
  • Flood Control
  • Geography
  • Information Science
  • Meteorology
  • River Flooding
  • Storm Surges
  • Surveys

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Systems Analysis and Design
  • Wetland-Land-Environmental Management.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference