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Climate-informed Estimation of Hydrologic Extremes for Robust Adaptation to Non-stationary Climate.
| 题名： | Climate-informed Estimation of Hydrologic Extremes for Robust Adaptation to Non-stationary Climate. |
| 作者： | Brown, C.; Mearns, L.; Lall, U.; Moody, P.; Hall, J. |
| 关键词： | Climate, Estimates, Hydrology, Military facilities, Statistical analysis, Uncertainty, Case studies, Precipitation, Models, Artificial neural networks, Floods, Flood control, Infrastructure, Snow, Data set, Sea surface temperature, Climate change, Drainage basins, Climate-informed, Hydrologic extremes, Robust adaptation, Non-stationary climate, Idf (intensity-duration frequency), Idf curves, Hydrologic design events, Dann (deep artificial neural networks), Variability, Streamflow, Gcm (global circulation models), Missouri river basin, Rcm (regional climate models), Ohio river basin, Mississippi river basin |
| 摘要： | 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. |
| 报告类型： | 科技报告 |
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