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Improving Reservoir Management with ...

Mukhopadhyay, Sudarshana.

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Improving Reservoir Management with Sub-Seasonal to Seasonal Streamflow Forecast.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving Reservoir Management with Sub-Seasonal to Seasonal Streamflow Forecast./
作者:	Mukhopadhyay, Sudarshana.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	153 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
Contained By:	Dissertations Abstracts International81-11B.
標題:	Civil engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27919740
ISBN:	9781658454063

Improving Reservoir Management with Sub-Seasonal to Seasonal Streamflow Forecast.
Mukhopadhyay, Sudarshana.

Improving Reservoir Management with Sub-Seasonal to Seasonal Streamflow Forecast. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 153 p.

Source: Dissertations Abstracts International, Volume: 81-11, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2020.

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

Regional water management that aims at sustaining water supply and irrigation, mitigating flood and drought impacts and meeting hydroelectric demands, critically depends on streamflow variability over multiple time scales. Thus, for efficient water management, reliable streamflow forecasts across different time scales is of utmost importance. From an operational perspective, sub-seasonal to seasonal (S2S) streamflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g. irrigation and municipal supply) and to meet the end-of-the-season (typically 12 weeks) target storage. In the present study, I systematically analyze different aspects of regional water management and the utility of S2S streamflow forecasts in improving reservoir management. With the objective of understanding the inuence of high frequency climate modes on S2S scale water availability in the upper Tennessee region, I first developed a S2S streamflow forecast model using Non-Homogeneous Hidden Markov Model (NHMM). The hidden states in the NHMM represents the underlying weather condition or regional moisture transport, that depends on high frequency atmospheric variability patterns such as Madden Julian Oscillation (MJO), Pacific/North American (PNA) and North Atlantic Oscillation (NAO) at S2S time scale. The findings of this work led to two different approaches of reservoir system analysis. In the first approach, I compared equivalent reservoir models - from both power and water systems perspective, with a multi-reservoir network model for a USACE operated cascade system in Savannah, GA. Here I analyzed different equivalent reservoir models for a cascade of three reservoirs, that are operated in series. I generalized my findings for different reservoir system configuration and streamflow forecasts with varied predictive skill. In the second approach, I implemented a multi-reservoir water allocation network model considering 28 reservoirs operated by Tennessee Valley Authority (TVA), for flood control and hydropower generation. The 28 reservoir simulation model is coupled with an iterative linear programming (ILP) framework, to optimize water allocation as the 28 reservoir system's sensitivity of unit change in reservoir storage to unit change in release has a quasi linear dependence. I focus on different historical drought episodes as well as flood events to understand the utility of monthly streamflow forecasts for a multi-reservoir system under different operating policies. The proposed ILP methodology can be used for analyses of reservoir management and regulatory policies, under existing as well as potential operational constraints and climatic conditions. The simulation-optimization model is implemented with historical streamflow as well as with climate-informed stochastic streamflow forecasts to understand the utility of probabilistic streamflow forecasts in a multi-reservoir multipurpose system management.

ISBN: 9781658454063Subjects--Topical Terms:

860360
Civil engineering.

Improving Reservoir Management with Sub-Seasonal to Seasonal Streamflow Forecast.
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