東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Dynamic Vulnerability Classification...

Robinson, Bethany.

FindBook

Google Book

Amazon

博客來

Dynamic Vulnerability Classification for Water Resources Systems Under Uncertain Climate Change.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic Vulnerability Classification for Water Resources Systems Under Uncertain Climate Change./
作者:	Robinson, Bethany.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	119 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Contained By:	Dissertations Abstracts International82-03B.
標題:	Water resources management. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27999946
ISBN:	9798664727166

Dynamic Vulnerability Classification for Water Resources Systems Under Uncertain Climate Change.
Robinson, Bethany.

Dynamic Vulnerability Classification for Water Resources Systems Under Uncertain Climate Change. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 119 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2020.

This item must not be sold to any third party vendors.

Water resources planners routinely face uncertainty in supply and demand, including inter- and intra-annual weather variability as well as longer-term population changes. However, model projections of water security risks under climate change-for example, due to nonstationary mean and variance of annual streamflow, or changes in drought frequency and severity-remain highly uncertain by comparison. This uncertainty prevents analysts from knowing or agreeing on the probabilities of future scenarios, which hinders the interpretation of ensemble projections from general circulation models (GCMs). Despite this uncertainty, decisions must be made regarding infrastructure and operating policies for water supply, flood control, hydropower generation, and environmental flows. Adapting to a changing climate could be done with exact knowledge of a future probability distribution, but planning based on a range of plausible scenarios, with some indicating significant departures from the historical record, remains challenging. This dissertation advances the question of whether future system vulnerabilities due to climate change can be detected dynamically based on observations in advance of when they occur, and how adaptations can be planned in response to these detections. Experiments investigate the tradeoff between frequently triggering unnecessary action and failing to identify potential vulnerabilities. Moving from a threshold-based method to a machine learning classifier for vulnerability detection improves accuracy, especially when identifying early warning signals of future vulnerability. Results show that relatively few hydrologic feature variables can be used to predict vulnerability and apply adaptations at lead times with high true positive and true negative ratios provided that the training data contains a sufficient balance of negative and positive outcomes. Using these classifications to trigger adaptations to infrastructure and conservation shows little system benefit to additional storage, which does not increase reliability as much as either conservation measures or additional urban supply. While the classifier shows inaccuracies, more than 50% of the false positives that implemented conservation in 2050 still prevent future vulnerabilities, showing that some adaptations provide benefits in the long term even if they are not needed in the short term. In summary, this dissertation contributes a framework for identifying and testing vulnerability thresholds using hydrologic variables and machine learning techniques to determine if and when adaptations should be applied to a water resource system under uncertain climate change.

ISBN: 9798664727166Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

Adaptation

Dynamic Vulnerability Classification for Water Resources Systems Under Uncertain Climate Change.
LDR:03898nmm a2200385 4500 001 2278552
005 20210628082347.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798664727166
035 $a (MiAaPQ)AAI27999946
035 $a AAI27999946
040 $a MiAaPQ $c MiAaPQ
100 1 $a Robinson, Bethany. $3 3556931
245 1 0 $a Dynamic Vulnerability Classification for Water Resources Systems Under Uncertain Climate Change.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 119 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
500 $a Advisor: Herman, Jonathan D.
502 $a Thesis (Ph.D.)--University of California, Davis, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Water resources planners routinely face uncertainty in supply and demand, including inter- and intra-annual weather variability as well as longer-term population changes. However, model projections of water security risks under climate change-for example, due to nonstationary mean and variance of annual streamflow, or changes in drought frequency and severity-remain highly uncertain by comparison. This uncertainty prevents analysts from knowing or agreeing on the probabilities of future scenarios, which hinders the interpretation of ensemble projections from general circulation models (GCMs). Despite this uncertainty, decisions must be made regarding infrastructure and operating policies for water supply, flood control, hydropower generation, and environmental flows. Adapting to a changing climate could be done with exact knowledge of a future probability distribution, but planning based on a range of plausible scenarios, with some indicating significant departures from the historical record, remains challenging. This dissertation advances the question of whether future system vulnerabilities due to climate change can be detected dynamically based on observations in advance of when they occur, and how adaptations can be planned in response to these detections. Experiments investigate the tradeoff between frequently triggering unnecessary action and failing to identify potential vulnerabilities. Moving from a threshold-based method to a machine learning classifier for vulnerability detection improves accuracy, especially when identifying early warning signals of future vulnerability. Results show that relatively few hydrologic feature variables can be used to predict vulnerability and apply adaptations at lead times with high true positive and true negative ratios provided that the training data contains a sufficient balance of negative and positive outcomes. Using these classifications to trigger adaptations to infrastructure and conservation shows little system benefit to additional storage, which does not increase reliability as much as either conservation measures or additional urban supply. While the classifier shows inaccuracies, more than 50% of the false positives that implemented conservation in 2050 still prevent future vulnerabilities, showing that some adaptations provide benefits in the long term even if they are not needed in the short term. In summary, this dissertation contributes a framework for identifying and testing vulnerability thresholds using hydrologic variables and machine learning techniques to determine if and when adaptations should be applied to a water resource system under uncertain climate change.
590 $a School code: 0029.
650 4 $a Water resources management. $3 794747
650 4 $a Civil engineering. $3 860360
650 4 $a Environmental engineering. $3 548583
653 $a Adaptation
653 $a Climate change
653 $a CMIP5
653 $a Early warning signals
653 $a Machine learning
653 $a Time series forecasting
690 $a 0595
690 $a 0543
690 $a 0775
710 2 $a University of California, Davis. $b Civil and Environmental Engineering. $3 1671151
773 0 $t Dissertations Abstracts International $g 82-03B.
790 $a 0029
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27999946