東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Investigating error propagation in f...

Hossain, Faisal.

Linked to FindBook

Google Book

Amazon

博客來

Investigating error propagation in flood prediction based on remotely-sensed rainfall.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Investigating error propagation in flood prediction based on remotely-sensed rainfall./
Author:	Hossain, Faisal.
Description:	277 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 4217.
Contained By:	Dissertation Abstracts International65-08B.
Subject:	Engineering, Environmental. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3144586
ISBN:	0496027824

Investigating error propagation in flood prediction based on remotely-sensed rainfall.
Hossain, Faisal.

Investigating error propagation in flood prediction based on remotely-sensed rainfall. - 277 p.

Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 4217.

Thesis (Ph.D.)--The University of Connecticut, 2004.

Quantifying hydrologic prediction uncertainty based on remotely-sensed rainfall requires understanding the error interaction between hydrologic modeling and rainfall estimation. This study attempts to address this issue. A Remote Sensing Rainfall Error Model (RSREM) is developed to characterize the error structure in rainfall. The Generalized Likelihood Uncertainty Estimation (GLUE) framework (Beven and Binley, 1992) is implemented for quantification of hydrologic modeling uncertainty. Application of the GLUE framework on radar observations from a mountainous basin demonstrated that estimates adjusted for mean-field bias and vertical reflectivity profile effects yield runoff error propagation similar to that by a dense rain gauge network. A probabilistic discharge prediction scheme developed on the basis of the GLUE framework yielded 50% less variability in prediction error of time to peak than the conventional optimum parameter set approach. In terms of satellite Passive Microwave (PM) rain estimation, the 3-hourly sampling, as anticipated during the Global Precipitation Measurement ( GPM) mission after 2009, is found to be adequate for flood prediction of weeklong events. It is further shown that the current PM sampling is associated with almost twice the flood prediction uncertainty expected from the planned 3-hourly GPM sampling.

ISBN: 0496027824Subjects--Topical Terms:

783782
Engineering, Environmental.

Investigating error propagation in flood prediction based on remotely-sensed rainfall.
LDR:03300nmm 2200289 4500 001 1842651
005 20050921082523.5
008 130614s2004 eng d
020 $a 0496027824
035 $a (UnM)AAI3144586
035 $a AAI3144586
040 $a UnM $c UnM
100 1 $a Hossain, Faisal. $3 1066534
245 1 0 $a Investigating error propagation in flood prediction based on remotely-sensed rainfall.
300 $a 277 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 4217.
500 $a Adviser: Emmanouil N. Anagnostou.
502 $a Thesis (Ph.D.)--The University of Connecticut, 2004.
520 $a Quantifying hydrologic prediction uncertainty based on remotely-sensed rainfall requires understanding the error interaction between hydrologic modeling and rainfall estimation. This study attempts to address this issue. A Remote Sensing Rainfall Error Model (RSREM) is developed to characterize the error structure in rainfall. The Generalized Likelihood Uncertainty Estimation (GLUE) framework (Beven and Binley, 1992) is implemented for quantification of hydrologic modeling uncertainty. Application of the GLUE framework on radar observations from a mountainous basin demonstrated that estimates adjusted for mean-field bias and vertical reflectivity profile effects yield runoff error propagation similar to that by a dense rain gauge network. A probabilistic discharge prediction scheme developed on the basis of the GLUE framework yielded 50% less variability in prediction error of time to peak than the conventional optimum parameter set approach. In terms of satellite Passive Microwave (PM) rain estimation, the 3-hourly sampling, as anticipated during the Global Precipitation Measurement ( GPM) mission after 2009, is found to be adequate for flood prediction of weeklong events. It is further shown that the current PM sampling is associated with almost twice the flood prediction uncertainty expected from the planned 3-hourly GPM sampling.
520 $a The GLUE and RSREM error frameworks are subsequently applied on a land surface model to study the dependency of soil moisture prediction accuracy on satellite rainfall estimation, modeling uncertainties and site characteristics. Satellite rainfall estimation uncertainty under-represented to a considerable degree the total prediction uncertainty. However this under-representation is observed to be influenced by the level of model accuracy. The study finally investigated techniques for accelerating random sampling used in error propagation studies. The Latin Hypercube Sampling technique applied to sample rainfall error structure yielded upper confidence limits (>80%) in runoff simulation consistent with the unconstrained Monte Carlo method, but with two orders fewer computational burden. A stochastic response surface method developed for accelerating sampling of model parameters within the GLUE framework is shown to reduce the computational burden (up to 70%) of the uniform Monte Carlo sampling.
590 $a School code: 0056.
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Remote Sensing. $3 1018559
690 $a 0775
690 $a 0799
710 2 0 $a The University of Connecticut. $3 1249323
773 0 $t Dissertation Abstracts International $g 65-08B.
790 1 0 $a Anagnostou, Emmanouil N., $e advisor
790 $a 0056
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3144586