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A Forecasting Framework for Complex ...

Palash, Khan Md Wahid.

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A Forecasting Framework for Complex Systems: Reframing of Flood Forecasting from Local to Global Scales with Requisite Simplicity.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Forecasting Framework for Complex Systems: Reframing of Flood Forecasting from Local to Global Scales with Requisite Simplicity./
Author:	Palash, Khan Md Wahid.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	314 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Contained By:	Dissertations Abstracts International81-05B.
Subject:	Water resources management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22618906
ISBN:	9781392713020

A Forecasting Framework for Complex Systems: Reframing of Flood Forecasting from Local to Global Scales with Requisite Simplicity.
Palash, Khan Md Wahid.

A Forecasting Framework for Complex Systems: Reframing of Flood Forecasting from Local to Global Scales with Requisite Simplicity. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 314 p.

Source: Dissertations Abstracts International, Volume: 81-05, Section: B.

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

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

A short (3 to 5-day) to mid-range (6 to 10-day) flood forecasting lead time is desired to increase the flood response and preparedness in flood-prone regions across the world. The major limitation of providing accurate flood forecasts of such lead times is mainly associated with system complexity (e.g., nonlinear interactions with feedback among system elements) and modeling complexity (e.g., uncertainty in precipitation measurement, scale mismatch between model equations and heterogeneity of variables and processes). To generate a perfect (or nearly perfect) model of such a system, one has to model everything (or nearly everything). Yet, as Lorenz (1963) aptly pointed out, in a nonlinear system with feedback, approximate accurate representation of the present does not guarantee accurate forecast of the future due to sensitivity to the initial conditions. How do we, then, understand, model, and manage such a complex system? The main objective of this doctoral research is, therefore, to explore and examine the utility of framing flood forecasting as a complex system problem. To achieve this objective, a data-driven simple flood forecasting scheme using the notion of requisite simplicity with a contextual and mechanistic understanding of river basin hydrology and regional hydrometeorological conditions is proposed. Applying requisite simplicity in this context involves identifying key variables and processes of rainfall-runoff, flow travel time, flood propagation along the river, and developing a mechanism to track their evolution, interconnections, feedback and system behavior. The developed data-based model-for its simplicity in model structure, utilization of easily available data, and more importantly, ease of making it operational for real-time applications-is named ReqSim (Requisitely Simple) flood forecasting model. The ReqSim model shows great promise to provide short (3 to 5-day) to mid-range (6 to 10-day) deterministic flood forecasts for medium to large river basins around the world. It also shows the potential of providing accurate forecasts for local scales by using outputs of the regional or global scale models. Findings suggest that models with requisite simplicity-relying on ﬂow persistence, aggregated upstream rainfall, and travel time-can provide reliable ﬂood forecasts comparable to relatively more complicated methods for up to 10-day lead time. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and ﬂow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.

ISBN: 9781392713020Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

Complexity science

A Forecasting Framework for Complex Systems: Reframing of Flood Forecasting from Local to Global Scales with Requisite Simplicity.
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245 1 0 $a A Forecasting Framework for Complex Systems: Reframing of Flood Forecasting from Local to Global Scales with Requisite Simplicity.
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300 $a 314 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
500 $a Advisor: Islam, Shafiqul.
502 $a Thesis (Ph.D.)--Tufts University, 2019.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a A short (3 to 5-day) to mid-range (6 to 10-day) flood forecasting lead time is desired to increase the flood response and preparedness in flood-prone regions across the world. The major limitation of providing accurate flood forecasts of such lead times is mainly associated with system complexity (e.g., nonlinear interactions with feedback among system elements) and modeling complexity (e.g., uncertainty in precipitation measurement, scale mismatch between model equations and heterogeneity of variables and processes). To generate a perfect (or nearly perfect) model of such a system, one has to model everything (or nearly everything). Yet, as Lorenz (1963) aptly pointed out, in a nonlinear system with feedback, approximate accurate representation of the present does not guarantee accurate forecast of the future due to sensitivity to the initial conditions. How do we, then, understand, model, and manage such a complex system? The main objective of this doctoral research is, therefore, to explore and examine the utility of framing flood forecasting as a complex system problem. To achieve this objective, a data-driven simple flood forecasting scheme using the notion of requisite simplicity with a contextual and mechanistic understanding of river basin hydrology and regional hydrometeorological conditions is proposed. Applying requisite simplicity in this context involves identifying key variables and processes of rainfall-runoff, flow travel time, flood propagation along the river, and developing a mechanism to track their evolution, interconnections, feedback and system behavior. The developed data-based model-for its simplicity in model structure, utilization of easily available data, and more importantly, ease of making it operational for real-time applications-is named ReqSim (Requisitely Simple) flood forecasting model. The ReqSim model shows great promise to provide short (3 to 5-day) to mid-range (6 to 10-day) deterministic flood forecasts for medium to large river basins around the world. It also shows the potential of providing accurate forecasts for local scales by using outputs of the regional or global scale models. Findings suggest that models with requisite simplicity-relying on ﬂow persistence, aggregated upstream rainfall, and travel time-can provide reliable ﬂood forecasts comparable to relatively more complicated methods for up to 10-day lead time. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and ﬂow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.
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