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Climate Informed Modeling of Precipi...

Armal, Saman.

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Climate Informed Modeling of Precipitation and Its Application to Regional Analysis of Water Resources.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Climate Informed Modeling of Precipitation and Its Application to Regional Analysis of Water Resources./
作者:	Armal, Saman.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	134 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=27960454
ISBN:	9798664761801

Climate Informed Modeling of Precipitation and Its Application to Regional Analysis of Water Resources.
Armal, Saman.

Climate Informed Modeling of Precipitation and Its Application to Regional Analysis of Water Resources. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 134 p.

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

Thesis (Ph.D.)--The City College of New York, 2020.

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

The climate non-stationarity poses a challenge to regional water resources and raises the question that how and where the impact of internal mechanism interacts with forcing of anthropogenic climate change, on timescales of a few decades and spatial scales smaller than continental. The water community relies on the application of GCMs to account for future uncertainty. However, these models are limited in replicating the regional variability. Moreover, the bias correction methods conventionally adopted to calibrate the models' output are not reliable in preserving the non-stationarity assumption. As a mean of addressing these issues, this dissertation proposes a framework to integrate a climate-informed weather generator with a regional water analysis, to include the climate non-stationarity in the simulation of precipitation, and use the design to provide a dynamically updated framework for water management. This goal is achieved in three stages: (1). Analysis of space-time structure of trends in the extremes and how they relate to climate change and natural variability, (2). Creating a climate-informed weather generator that captures these climate connections into the simulation of precipitation. (3). Developing a physically-based regional hydrologic modeling framework to integrate climate, weather, and basin characteristics and provide dynamically updated water resources analysis. The Delaware River Basin is used as a testbed in this investigation. The Dissertation initiates with a hypothesis-based analysis in a Bayesian learning framework to attribute the trend in annual extremes to climate change forcing (using global near-surface temperature as a proxy) and natural processes of the ocean-atmospheric interactions' driven oscillations. This new climate knowledge is used as large-scale driver for the weather generator that simulates the regional weather variables. From a climate risk management perspective, there is a need to link the climatic events with large-scale drivers and regional forcing. Given a prototypical space-time rainfall pattern for events or for a year, different rainfall inducing mechanisms are involved in rainfall generator to simulate the usable characteristics of daily precipitation. Finally, to identify the climate associated risks, the output of the stochastic weather generator is coupled with SWAT hydrological model to enable the analysis of the Delaware water system. This investigation improves the literature by introducing an adaptation strategy to non-stationarity in climate that requires an understanding of the influence of anthropogenic forcing and natural climate variability on the occurrence of extremes in a unified framework. It provides an innovative methodology that relies on systematic learning to explore the time-space structure of the trends in extreme events and how they relate to large-scale climate and atmospheric variables. Next, it proposes a stochastic modeling strategy that includes the drivers to simulate near-term climate conditions. Finally, the application of weather generator on the regional analysis of water resources provides an integrated solution to address the regional complexity in connection with climate drivers. This framework will enable a robust means of informing decisions for near-term range and assessing risks to infrastructure related to climatic events. The final product serves as a hydrologic consistent model for achieving an advance warning for the end-user community.

ISBN: 9798664761801Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

Climate variability

Climate Informed Modeling of Precipitation and Its Application to Regional Analysis of Water Resources.
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