1). This is evident in the time series for rainfall averaged over the SWWA region defined as southwest of a line connecting 30° S, 115° E and 35° S, 120° E (Fig. 1). Fig. 3a shows the long-term (1911–2013) time series of SWWA annual rainfall values as provided by the Bureau of Meteorology (http://www.bom.gov.au/climate/change). The rainfall decline is characterized by an absence of values above 800 mm after 1965 with only 400 mm recorded in 2010 – the lowest value on record. At the same time, SWWA annual mean
temperatures have exhibited a positive trend of about +0.8 °C per century with 2011 being the warmest year on record (Fig. 3b). We also consider the results for simulated SWWA rainfall from climate model simulations which attempt to account for past and projected factors which affect global and regional climate. Specifically, we analyze the results from the Coupled Model Intercomparison Project-Phase check details Five (CMIP5) which involves a range of experiments based on uniform inputs for atmospheric greenhouse gas, aerosol EX 527 and ozone concentrations (Taylor et al., 2012). These include “historical” (1850–2005) runs which are forced by observed atmospheric composition changes and changes in land cover, and “projection” (2006–2100)
runs forced with specified concentrations (referred to as “representative concentration pathways” or, RCPs). The projections of interest here are those which involve the relatively high RCP8.5 emissions scenario. We have analyzed a total of 38 model results (one run per model) that were available at the time of the study (see Table A1). In this section we investigate simple linear relationships between observed total inflows and both observed SWWA annual rainfall and annual mean temperature. The direct effect of rainfall is quite
clear but, in order to identify the role of temperature, we firstly remove the direct effect of rainfall on Nintedanib (BIBF 1120) inflows and then correlate temperature with the inflow residuals. Secondly, in order to assess the statistical significance of the relationship, we remove the effect of long term trends in temperature and residual inflow data by considering only first-order difference values. A plot (Fig. 4a) of total inflows versus SWWA annual rainfall (1911–2013) reveals a significant (p < 0.01) linear fit (correlation coefficient r = +0.80) that can explain 63% of the total variance in the data. This is particularly useful since it indicates that interannual rainfall changes at the relatively large (i.e. SWWA) scale are relevant to changes that take place at the relatively small (i.e. catchment) scales. This implies that, while often desirable, it may not be necessary to downscale coarse, large scale climate model results in order to make estimates of impacts at smaller scales.