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Hydro-Meteorological Uncertainty Quantification for Water Resources Planning and Management: Advances in Synthetic Forecasting and Stochastic Watershed Models- [electronic resource]
Hydro-Meteorological Uncertainty Quantification for Water Resources Planning and Management: Advances in Synthetic Forecasting and Stochastic Watershed Models- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100117
- ISBN
- 9798379711382
- DDC
- 628
- 서명/저자
- Hydro-Meteorological Uncertainty Quantification for Water Resources Planning and Management: Advances in Synthetic Forecasting and Stochastic Watershed Models - [electronic resource]
- 발행사항
- [S.l.]: : Cornell University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(233 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Steinschneider, Scott.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Accounting for hydro-meteorological uncertainty in water resources systems analysis (WRSA) is fundamental to robust system design and operations. As water resources systems become more stressed due to factors like complex human population demands and climate change, the need for faithful representation of this uncertainty is increasingly more salient. Going forward, the challenge of adapting these systems to new hydro-meteorological regimes further underscores the importance of attempting to understand and model emergent properties of this uncertainty. Moreover, the continued evolution of water resources management and planning strategies make legacy methods of hydro-meteorological uncertainty characterization inadequate. In this study, we develop novel methodologies to address these emerging requirements for uncertainty modeling brought about both by new adaptation strategies (e.g. forecast informed operations) and the need to address anthropogenic non-stationarity in hydro-meteorological errors. We first develop a modeling approach to produce synthetic forecasts, which are emulations of hindcasts produced by computationally demanding meteorological and hydrological forecast models. This computational demand and short period of availability (~1980 to present) severely limit the utility of the native hindcasts for robust system analysis and design. Synthetic forecasts can be generated anywhere observations exist with manageable computational effort allowing for a much richer characterization of forecast uncertainty. We extend this effort to hydrologic ensemble forecasts that underpin current efforts to implement forecast informed reservoir operations (FIRO) in the western U.S. Through operational testing with the latest FIRO operations model, we show that these synthetic forecasts both faithfully replicate operational behaviors of the original hindcasts and elucidate system vulnerabilities. Finally, we address emergent properties of hydro-meteorological uncertainty through an idealized 'model-as-truth' experimental design that shows the effect of climate shifts on hydrologic uncertainty. We then develop a hybrid machine learning-statistical approach that can capture these shifts in uncertainty through model state relationships and propagate it into new simulations through a stochastic watershed model (SWM) architecture. Overall, the methodological advances forwarded in this work provide a rich suite of hydro-meteorological uncertainty modeling tools to address fundamental challenges in the critically important sphere of water resources systems adaptation.
- 일반주제명
- Hydrologic sciences.
- 키워드
- Climate change
- 키워드
- Non-stationarity
- 기타저자
- Cornell University Civil and Environmental Engineering
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798379711382
■035 ▼a(MiAaPQ)AAI30423039
■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aBrodeur, Zachary Paul.▼0(orcid)0000-0001-5242-7696
■24510▼aHydro-Meteorological Uncertainty Quantification for Water Resources Planning and Management: Advances in Synthetic Forecasting and Stochastic Watershed Models▼h[electronic resource]
■260 ▼a[S.l.]:▼bCornell University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(233 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Steinschneider, Scott.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aAccounting for hydro-meteorological uncertainty in water resources systems analysis (WRSA) is fundamental to robust system design and operations. As water resources systems become more stressed due to factors like complex human population demands and climate change, the need for faithful representation of this uncertainty is increasingly more salient. Going forward, the challenge of adapting these systems to new hydro-meteorological regimes further underscores the importance of attempting to understand and model emergent properties of this uncertainty. Moreover, the continued evolution of water resources management and planning strategies make legacy methods of hydro-meteorological uncertainty characterization inadequate. In this study, we develop novel methodologies to address these emerging requirements for uncertainty modeling brought about both by new adaptation strategies (e.g. forecast informed operations) and the need to address anthropogenic non-stationarity in hydro-meteorological errors. We first develop a modeling approach to produce synthetic forecasts, which are emulations of hindcasts produced by computationally demanding meteorological and hydrological forecast models. This computational demand and short period of availability (~1980 to present) severely limit the utility of the native hindcasts for robust system analysis and design. Synthetic forecasts can be generated anywhere observations exist with manageable computational effort allowing for a much richer characterization of forecast uncertainty. We extend this effort to hydrologic ensemble forecasts that underpin current efforts to implement forecast informed reservoir operations (FIRO) in the western U.S. Through operational testing with the latest FIRO operations model, we show that these synthetic forecasts both faithfully replicate operational behaviors of the original hindcasts and elucidate system vulnerabilities. Finally, we address emergent properties of hydro-meteorological uncertainty through an idealized 'model-as-truth' experimental design that shows the effect of climate shifts on hydrologic uncertainty. We then develop a hybrid machine learning-statistical approach that can capture these shifts in uncertainty through model state relationships and propagate it into new simulations through a stochastic watershed model (SWM) architecture. Overall, the methodological advances forwarded in this work provide a rich suite of hydro-meteorological uncertainty modeling tools to address fundamental challenges in the critically important sphere of water resources systems adaptation.
■590 ▼aSchool code: 0058.
■650 4▼aEnvironmental engineering.
■650 4▼aWater resources management.
■650 4▼aHydrologic sciences.
■653 ▼aClimate change
■653 ▼aForecast informed reservoir operations
■653 ▼aHydrologic uncertainty
■653 ▼aNon-stationarity
■653 ▼aStochastic watershed models
■653 ▼aSynthetic forecasts
■690 ▼a0775
■690 ▼a0595
■690 ▼a0388
■71020▼aCornell University▼bCivil and Environmental Engineering.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931783▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
