Keywords
daily rainfall; spatial cross correlation; rainfall temporal and spatial patterns; fronts; climate change
Location
Session E1: Data Acquisition, Management and Processing for Sustainability Appraisal
Start Date
17-6-2014 9:00 AM
End Date
17-6-2014 10:20 AM
Abstract
Rainfall is a highly variable component of the climate system. There are substantial spatial and temporal variations in the frequency and spatial distribution of rainfall events. Little attention has been paid to the slow but ongoing variations of the spatial patterns of daily rainfall, especially over broad spatial scales. A better understanding of these variations and their long term trends would reduce uncertainty in environmental and natural resource models and improve assessment of ongoing climate change. This study examined the spatial cross-correlations of daily rainfall at 2322 high quality long run rainfall stations across Australia from 1910 to 2011, and assessed their spatial and temporal patterns. It was found that: 1) There has been a long term continuous strengthening in the spatial cross-correlation of daily rainfall across Australia over this period. Most of this strengthening has occurred since the 1970s; 2) The strengthening is more consistent in winter and spring but has occurred in all four seasons; 3) Southeastern Australia has had the most stable strengthening pattern over a broader spatial scale; 4) The strengthening suggests and increase in the broad scale coherence of daily rainfall, such as found in frontal rainfall; 5) These findings are consistent with recent reported changes in synoptic scale climatic driving processes, especially the increasing frequency of frontal systems and the decreasing frequency of storm events in the Australian region. An increase in the broad scale coherence of rainfall is likely to improve the accuracy of daily rainfall interpolation and influence dependent hydrological modelling. Interactions of data quality with the derived correlation patterns are also discussed.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Identification of spatial and temporal patterns of Australian daily rainfall under a changing climate
Session E1: Data Acquisition, Management and Processing for Sustainability Appraisal
Rainfall is a highly variable component of the climate system. There are substantial spatial and temporal variations in the frequency and spatial distribution of rainfall events. Little attention has been paid to the slow but ongoing variations of the spatial patterns of daily rainfall, especially over broad spatial scales. A better understanding of these variations and their long term trends would reduce uncertainty in environmental and natural resource models and improve assessment of ongoing climate change. This study examined the spatial cross-correlations of daily rainfall at 2322 high quality long run rainfall stations across Australia from 1910 to 2011, and assessed their spatial and temporal patterns. It was found that: 1) There has been a long term continuous strengthening in the spatial cross-correlation of daily rainfall across Australia over this period. Most of this strengthening has occurred since the 1970s; 2) The strengthening is more consistent in winter and spring but has occurred in all four seasons; 3) Southeastern Australia has had the most stable strengthening pattern over a broader spatial scale; 4) The strengthening suggests and increase in the broad scale coherence of daily rainfall, such as found in frontal rainfall; 5) These findings are consistent with recent reported changes in synoptic scale climatic driving processes, especially the increasing frequency of frontal systems and the decreasing frequency of storm events in the Australian region. An increase in the broad scale coherence of rainfall is likely to improve the accuracy of daily rainfall interpolation and influence dependent hydrological modelling. Interactions of data quality with the derived correlation patterns are also discussed.
