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Climate Change Assessment in Columbi...

Qin, Yueyue.

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Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation./
Author:	Qin, Yueyue.
Description:	95 p.
Notes:	Source: Masters Abstracts International, Volume: 54-06.
Contained By:	Masters Abstracts International54-06(E).
Subject:	Environmental engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1593340
ISBN:	9781321899283

Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation.
Qin, Yueyue.

Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation. - 95 p.

Source: Masters Abstracts International, Volume: 54-06.

Thesis (M.S.)--Portland State University, 2015.

The multi downscaled-scenario products allow us to better assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. In the present study, we have used multi-modelled statistically downscaled ensemble of precipitation and temperature variables. The dataset used is multi-model ensemble of 10 Global Climate Models (GCMs) downscaled product from CMIP5 daily dataset, using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble PDFs of both precipitation and temperature is evaluated for summer (dry) and winter (wet) periods for 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. Results have indicated that the probabilistic distribution helps remove the limitations on marginal distributions of variables in question and helps in better prediction. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends are varied, but certainly positive, for summer and winter time scales based on sub-basins. Dry season, generally, is indicating towards higher positive changes in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated by the joint precipitation and temperature, also indicates varied degree and forms during dry season whereas the wet season is rather constant across all the sub-basins.

ISBN: 9781321899283Subjects--Topical Terms:

548583
Environmental engineering.

Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation.
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100 1 $a Qin, Yueyue. $3 3276824
245 1 0 $a Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation.
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500 $a Source: Masters Abstracts International, Volume: 54-06.
500 $a Adviser: Hamid Moradkhani.
502 $a Thesis (M.S.)--Portland State University, 2015.
520 $a The multi downscaled-scenario products allow us to better assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. In the present study, we have used multi-modelled statistically downscaled ensemble of precipitation and temperature variables. The dataset used is multi-model ensemble of 10 Global Climate Models (GCMs) downscaled product from CMIP5 daily dataset, using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble PDFs of both precipitation and temperature is evaluated for summer (dry) and winter (wet) periods for 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. Results have indicated that the probabilistic distribution helps remove the limitations on marginal distributions of variables in question and helps in better prediction. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends are varied, but certainly positive, for summer and winter time scales based on sub-basins. Dry season, generally, is indicating towards higher positive changes in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated by the joint precipitation and temperature, also indicates varied degree and forms during dry season whereas the wet season is rather constant across all the sub-basins.
590 $a School code: 0180.
650 4 $a Environmental engineering. $3 548583
650 4 $a Climate change. $2 bicssc $3 2079509
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710 2 $a Portland State University. $b Civil and Environmental Engineering. $3 3169102
773 0 $t Masters Abstracts International $g 54-06(E).
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