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There Are 505 Stations with More Than 30 Years of Data with Less Than 10

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There Are 505 Stations with More Than 30 Years of Data with Less Than 10 Impact of Climate Variability and Climate Change on Rainfall Extremes in Western Sydney and Surrounding Areas Report to the Upper Parramatta River Catchment Trust on Research Activities in Financial Year 2004-05 Debbie Abbs, Bryson Bates, Santosh Aryal CSIRO Climatic Extremes Research Group November 2004 1 Introduction The CSIRO Climatic Extremes research group (CER) is investigating rainfall intensity-frequency-duration (IFD) and depth-area curves for western Sydney and surrounding areas for present day and projected future conditions in collaboration with the Upper Parramatta River Catchment Trust and its partners. The other objectives of the project are: • Assess the impact of decadal-scale climate fluctuations on rainfall frequency characteristics (subsidiary). • Obtain a “broad brush” understanding of the likely changes in average and extreme rainfall under enhanced greenhouse conditions (core). • Quantify the likely future changes to the rainfall frequency characteristics of the study area due to global warming (core). 2 Study Area The study area has a homogeneous climate roughly bounded by longitudes 149.5º to 153º E; and latitudes 31.5º to 36ºS. The focus is on the coastal drainage areas of this region only (i.e., the SW corner of the region defined above would be ignored completely). The river basins that could be included in the study are shown in Fig. 1: Figure 1. Location of study area. 3 Scope of the Report The proposed work plan consists of four components couched within a three-year timeframe. The objectives of Components 1 to 3 are to provide information on rainfall IFD and depth-area curves for durations less than 6 hr for present day and projected future conditions, respectively. This information is critical for flood design applications. Component 1 will develop a statistical model of extreme rainfall events for the study area, and will provide a list of candidate extreme events that can be explored using high resolution downscaling. The main task undertaken in Component 1 is the statistical analysis of observations at daily rain gauges and recording rain gauges (pluviometers) within and adjacent to the study area. The following steps are involved: 1.1 Clean rainfall data set prepared, 1.2 Spatial model(s) developed and applied to available data for durations of less than 1 hour to 72 hours, 1.3 Initial characterisation of IFD and depth-area curves for present day conditions, 1.4 Comparison of IFD and depth-area curves with those obtained from high resolution dynamical downscaling. Step 1.1 is a major activity in terms of length of time involved. Section 4 of this report is concerned with the progress made with Step 1.1. Component 4 of the project is the high resolution dynamical downscaling of extreme rainfall events for the current climate and the climates of 2030 and 2070. This component is being undertaken as a joint project with the AGO. The tasks are: 4.1 Identification of candidate cases, 4.2 High-resolution simulation of extreme rainfall events at a grid spacing of approximately 5km, 4.3 Analysis of downscaled events and preparation of IFD and depth-area curves for durations of less than 1 hour to 72 hours. Some results from 4.1 are presented in Section 5. 4 Description of Rainfall data Data mainly from the Bureau of Meteorology’s (BOM) rainfall network is used in this project. This network includes over 6000 stations nation-wide, all of which record daily rainfall using standardised equipment and observing protocols. The observations are made at 0900 hours each day, with the 24 hour rainfall total being recorded against the day of observation. Mostly these gauges are operated by volunteers, often at workplaces like post offices, local government offices and farms. As a consequence, the quality of the data is quite variable, even for different time periods at a single station. Some of the data quality issues have been discussed by Lavery et al. (1992) and Viney and Bates (2004). The data quality is affected by a number of reasons including observer’s inconsistencies and exposure changes (changes in the height or structure of the gauge; changes in the windfield associated with growing trees or the construction of nearby buildings). Missing observations in the rainfall records arise from two main sources. The first appears to relate to communication and data management issues. The second source of missing data occurs when observers are absent from the station or otherwise unable to observe the gauge for a period of one or more days. When the observer returns to the gauge it contains rainwater that potentially fell over a period of two or more days. In these circumstances the observer records the accumulation period as well as the rainfall amount, which is entered against the date of observation. Viney and Bates (2004) have discussed the issues related to these “untagged accumulations”. 4.1 Data selection and screening The analysis of rainfall extremes requires rainfall records of high quality. Because we are considering temporal trends in extreme behaviour, we require long records whose quality is consistent in time. Because we are assessing spatial patterns, we require this consistency to extend from site to site. The geographical domain chosen for the rainfall data set encompasses the Upper Paramatta River (UPR) catchment (Figure 1) and adjacent areas within about 50 km of the catchment boundary. In total, 1937 rainfall stations are located within this region. However, many of these stations have quite short or discontinuous records, while others have been decommissioned altogether. This project requires reasonably lengthy and reasonably complete records. Unfortunately, the further back in time one looks, the fewer complete (or nearly complete) records are available, so compromises are needed between completeness and length. It was decided to accept only those gauges that had operated continuously (with allowances for short discontinuities) for more than 50 years until 2003. There are 265 BOM stations that have greater than 50 years of data with less than 10% data missing. Out of these 265 stations 169 stations have records until 2003. They are listed in Table 1 and depicted in Figure 2. This set of stations provides reasonable coverage of the UPR catchment with some gaps in some areas such as the Lithgow region, mountainous regions west of Taree, and areas west of Ulladulla in Morton National Park (Fig. 2). Out of 169 stations, the following six are the automating weather stations with pluviometer data: 66137 Bankstown Airport AWS 70014 Canberra Airport 61087 Gosford (Narara Research Station) AWS 61250 Paterson (Total AWS) 66062 Sydney (Observatory Hill) 66037 Sydney Airport AMO We have not analysed rainfall data in detail from these BOM stations, however we have looked at the patched point data (PPD) in more detail. The following section describes the data analysis from the PPD. 4.2 The Patched Point Data Set The PPD is a dataset containing daily rainfall, minimum and maximum temperatures, radiation, evaporation and vapour pressure. It combines original Australian BOM measurements for a particular meteorological station with infilling of any gaps in the record using interpolation methods (see http://www.nrm.qld.gov.au/silo/ppd for further details). In the UPR catchment region there are more than 360 stations with patched data. All these stations have complete set of rainfall and other data either directly observed or interpolated from nearby stations. Out of 169 BOM stations listed in Table 1 only 127 stations are in the PPD set. A preliminary screening of the data from PDP stations showed that 54 stations have fewer than 200 days of missing observations altogether between 1950 and 2003. 41 of these stations also belong to the list of 169 Bureau of Meteorology stations given in Table 1. 4.3 Future Tasks We will acquire data from remaining 128 (169-41) BOM stations and carry out a detailed analysis to further examine the data quality. The data will be scrutinised for gaps and only those with an acceptable amount of missing data will be used for further processing. We will fill the any gaps in the observed data. The data will also be examined for any trends and inconsistencies. Underlying trends of any station will be compared with other nearby stations to detect if the trends are widespread or localised. The localised trend can be the result of local change in observation practices or setting of the stations. These can be detected by performing the double mass curve analysis. Table 1 List of rainfall stations with greater than 50 years of rainfall data with less than 10% missing. Percent Years of data Station Start Finish of available No. Station name Longitude Latitude year year record data 55006 Blackville Post Office 150.2350 -31.6414 1886 2004 114.2 96 55014 Curlewis Post Office 150.2682 -31.1168 1904 2004 93.8 94 55017 Premer (Eden Moor) 149.7762 -31.5711 1887 2004 106.7 91 55018 Mullaley (Garrawilla) 149.6456 -31.1711 1884 2004 109.4 91 55024 Gunnedah Scs 150.2687 -31.0261 1948 2004 56.7 100 55025 Willow Tree (Highlands) 150.6730 -31.7928 1915 2004 84.1 94 55036 Caroona (West Mooki) 150.4226 -31.4140 1925 2004 72.4 92 55037 Pine Ridge (Mooki Springs) 150.3986 -31.5077 1886 2004 111.4 94 55038 Mullaley Post Office 149.9114 -31.0976 1899 2004 97.6 93 55039 Spring Ridge (Murrumbah) 150.2090 -31.3753 1922 2004 82.6 100 55043 Willow Tree (Parraweena) 150.4135 -31.7118 1934 2004 67.7 96 55045 Curlewis (Pine Cliff) 150.0270 -31.1804 1903 2004 101.4 100 55046 Pine Ridge (Billabong) 150.4296
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