Home > Research > Publications & Outputs > Identification of diurnal, seasonal and inter-a...
View graph of relations

Identification of diurnal, seasonal and inter-annual variability across SE Asian field observations of key water cycle variables : rainfall, net radiation, total evaporation and river discharge.

Research output: Contribution to conference - Without ISBN/ISSN Abstract

Published

Standard

Identification of diurnal, seasonal and inter-annual variability across SE Asian field observations of key water cycle variables : rainfall, net radiation, total evaporation and river discharge. / Solera-Garcia, M. A.; Chappell, N.; Tych, W.

2007. Abstract from American Geophysical Union, Fall Meeting 2007, abstract #H33C-1445, United States.

Research output: Contribution to conference - Without ISBN/ISSN Abstract

Harvard

APA

Vancouver

Author

Bibtex

@conference{5e4d8ba232a44a429777848c12cb44d5,
title = "Identification of diurnal, seasonal and inter-annual variability across SE Asian field observations of key water cycle variables : rainfall, net radiation, total evaporation and river discharge.",
abstract = "The identification of periodic patterns in water cycle variables is critical to the understanding of land-atmosphere interactions, climate change and the evaluation of General Circulation Model (GCM) output. SE Asia in particular plays a very important role on the global climate because it is a large source of energy and water fluxes into the upper atmosphere. Cycle identification is carried out following the Data Based Mechanistic (DBM) philosophy, which focuses on the use of parsimonious, rigorous models which are characterised by lack of a priori assumptions, built in uncertainty analysis and final model acceptance dependent on the physical interpretation of the results. The DBM tool used here is the Unobserved Component - Dynamic Harmonic Regression (UC-DHR) model, which is a statistical method that allows the identification of variability in time series by introducing Time Variable Parameter (TVP) estimation of harmonic components. UC-DHR is not scale dependent and was thus applied to both hourly (to investigate diurnal variation) and fortnightly datasets (for intra- and inter-annual variability). The data used in the analysis has been gathered from existing catchment datasets for three regions of tropical SE Asia, namely Northern Thailand, Central Peninsular Malaysia and Northeast Borneo. These regions were chosen because they represent the hydro-climatic gradient (seasonal to equatorial) present within the tropics and because SE Asia has the most extensive set of catchment/plot studies within the humid tropics. Results show modeling tools were able to quantify the main patterns present in the observations throughout different time scales (diurnal, intra-annual and inter-annual) and the strength of the correlation pattern between the four hydro-climatic variables. The subsequent discussion focuses on the physical processes behind those patterns (e.g. diurnal variability caused by local convection due to solar heating; impact of El Ni{\~n}o Southern Oscillation on inter-annual variability of rainfall and river discharge).",
author = "Solera-Garcia, {M. A.} and N. Chappell and W. Tych",
year = "2007",
language = "English",
note = "American Geophysical Union, Fall Meeting 2007, abstract #H33C-1445 ; Conference date: 10-11-2007 Through 14-11-2007",

}

RIS

TY - CONF

T1 - Identification of diurnal, seasonal and inter-annual variability across SE Asian field observations of key water cycle variables : rainfall, net radiation, total evaporation and river discharge.

AU - Solera-Garcia, M. A.

AU - Chappell, N.

AU - Tych, W.

PY - 2007

Y1 - 2007

N2 - The identification of periodic patterns in water cycle variables is critical to the understanding of land-atmosphere interactions, climate change and the evaluation of General Circulation Model (GCM) output. SE Asia in particular plays a very important role on the global climate because it is a large source of energy and water fluxes into the upper atmosphere. Cycle identification is carried out following the Data Based Mechanistic (DBM) philosophy, which focuses on the use of parsimonious, rigorous models which are characterised by lack of a priori assumptions, built in uncertainty analysis and final model acceptance dependent on the physical interpretation of the results. The DBM tool used here is the Unobserved Component - Dynamic Harmonic Regression (UC-DHR) model, which is a statistical method that allows the identification of variability in time series by introducing Time Variable Parameter (TVP) estimation of harmonic components. UC-DHR is not scale dependent and was thus applied to both hourly (to investigate diurnal variation) and fortnightly datasets (for intra- and inter-annual variability). The data used in the analysis has been gathered from existing catchment datasets for three regions of tropical SE Asia, namely Northern Thailand, Central Peninsular Malaysia and Northeast Borneo. These regions were chosen because they represent the hydro-climatic gradient (seasonal to equatorial) present within the tropics and because SE Asia has the most extensive set of catchment/plot studies within the humid tropics. Results show modeling tools were able to quantify the main patterns present in the observations throughout different time scales (diurnal, intra-annual and inter-annual) and the strength of the correlation pattern between the four hydro-climatic variables. The subsequent discussion focuses on the physical processes behind those patterns (e.g. diurnal variability caused by local convection due to solar heating; impact of El Niño Southern Oscillation on inter-annual variability of rainfall and river discharge).

AB - The identification of periodic patterns in water cycle variables is critical to the understanding of land-atmosphere interactions, climate change and the evaluation of General Circulation Model (GCM) output. SE Asia in particular plays a very important role on the global climate because it is a large source of energy and water fluxes into the upper atmosphere. Cycle identification is carried out following the Data Based Mechanistic (DBM) philosophy, which focuses on the use of parsimonious, rigorous models which are characterised by lack of a priori assumptions, built in uncertainty analysis and final model acceptance dependent on the physical interpretation of the results. The DBM tool used here is the Unobserved Component - Dynamic Harmonic Regression (UC-DHR) model, which is a statistical method that allows the identification of variability in time series by introducing Time Variable Parameter (TVP) estimation of harmonic components. UC-DHR is not scale dependent and was thus applied to both hourly (to investigate diurnal variation) and fortnightly datasets (for intra- and inter-annual variability). The data used in the analysis has been gathered from existing catchment datasets for three regions of tropical SE Asia, namely Northern Thailand, Central Peninsular Malaysia and Northeast Borneo. These regions were chosen because they represent the hydro-climatic gradient (seasonal to equatorial) present within the tropics and because SE Asia has the most extensive set of catchment/plot studies within the humid tropics. Results show modeling tools were able to quantify the main patterns present in the observations throughout different time scales (diurnal, intra-annual and inter-annual) and the strength of the correlation pattern between the four hydro-climatic variables. The subsequent discussion focuses on the physical processes behind those patterns (e.g. diurnal variability caused by local convection due to solar heating; impact of El Niño Southern Oscillation on inter-annual variability of rainfall and river discharge).

M3 - Abstract

ER -