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Understanding the Influence of the A...

Knighton, James O'Neil.

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Understanding the Influence of the Atmosphere and Land Surface Conditions on Flood Risk using Probabilistic Hydrology in the Context of Mechanistic Hydrologic Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding the Influence of the Atmosphere and Land Surface Conditions on Flood Risk using Probabilistic Hydrology in the Context of Mechanistic Hydrologic Models./
Author:	Knighton, James O'Neil.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	168 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Hydrologic sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13878891
ISBN:	9781392248577

Understanding the Influence of the Atmosphere and Land Surface Conditions on Flood Risk using Probabilistic Hydrology in the Context of Mechanistic Hydrologic Models.
Knighton, James O'Neil.

Understanding the Influence of the Atmosphere and Land Surface Conditions on Flood Risk using Probabilistic Hydrology in the Context of Mechanistic Hydrologic Models. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 168 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--Cornell University, 2019.

This item must not be added to any third party search indexes.

The emergence of large-scale hydrologic datasets, data analytic techniques, and mechanistic hydrologic models has supported the advancement of flood risk analysis. Traditionally, the study of hydrologic extremes has centered on observations made within the stream channel. Recent research has highlighted the importance of understanding the aspects of the atmosphere, land surface, and river network that impart their unique fingerprint on floods. In this dissertation I first review the critical aspects of sub-daily precipitation in the context of a stochastic weather generator. Next, we consider the direction of influence of the land surface on hydrologic extremes in conjunction with changes to climatic forcing. Finally, we propose a framework for flood risk analysis from the perspective of flood inducing meteorological events.

ISBN: 9781392248577Subjects--Topical Terms:

3168407
Hydrologic sciences.

Understanding the Influence of the Atmosphere and Land Surface Conditions on Flood Risk using Probabilistic Hydrology in the Context of Mechanistic Hydrologic Models.
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100 1 $a Knighton, James O'Neil. $3 3434381
245 1 0 $a Understanding the Influence of the Atmosphere and Land Surface Conditions on Flood Risk using Probabilistic Hydrology in the Context of Mechanistic Hydrologic Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 168 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Walter, Michael.
502 $a Thesis (Ph.D.)--Cornell University, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a The emergence of large-scale hydrologic datasets, data analytic techniques, and mechanistic hydrologic models has supported the advancement of flood risk analysis. Traditionally, the study of hydrologic extremes has centered on observations made within the stream channel. Recent research has highlighted the importance of understanding the aspects of the atmosphere, land surface, and river network that impart their unique fingerprint on floods. In this dissertation I first review the critical aspects of sub-daily precipitation in the context of a stochastic weather generator. Next, we consider the direction of influence of the land surface on hydrologic extremes in conjunction with changes to climatic forcing. Finally, we propose a framework for flood risk analysis from the perspective of flood inducing meteorological events.
590 $a School code: 0058.
650 4 $a Hydrologic sciences. $3 3168407
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710 2 $a Cornell University. $b Biological and Environmental Engineering. $3 3177115
773 0 $t Dissertations Abstracts International $g 80-12B.
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793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13878891