Statistical downscaling of multi‐site daily rainfall in a South Australian catchment using a Generalized Linear Model

Abstract

The intention of this study was to identify a suitable Generalized Linear Model (GLM) for modelling multi‐site daily rainfall in the Onkaparinga catchment in South Australia and to examine the suitability of the model for downscaling of General Circulation Model (GCM) rainfall projections. A GLM was applied and multi‐site daily rainfall was downscaled using National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets. Nineteen large‐scale atmospheric and circulation variables were selected at first and these were eventually reduced, based on correlation with daily rainfall, to 10 final variables to be used in the model. First, logistic regression was used to identify the wet and dry days, then wet day rainfall was modelled using a gamma distribution. The model was fitted for a calibration period (1991–2010) and it was then validated over the period 1981–1990. Several summary statistics including mean, standard deviation, number of wet days, maximum rainfall amount and lag 1 and lag 2 autocorrelations were used to check the model performance. The 2.5th and 97.5th percentiles of the simulated rainfall statistics were plotted against the observed rainfall statistics and it was shown that most of the observed statistics were within these bounds. Area averaged and station wise monthly, seasonal and annual totals for observed and simulated rainfall were estimated and compared. The overall performance of the GLM to downscale rainfall was considered satisfactory. However, a few discrepancies were observed in different performance statistics. Parameterization of the model to capture the local convective variability of rainfall would increase the model performance. It was found overall that the GLM can be applied for downscaling of GCM rainfall projections for this catchment.

Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment

“…Each predictor variable was extracted and averaged from the twelve reanalysis grid points (2.5°× 2.5°) around the study area. Details of the predictor selection process are reported in Beecham et al (2014). The final selected predictor variables were air temperature at 700 hPa, geopotential height at 700 hPa and 800 hPa, relative humidity at 500 hPa, 700 hPa and 850 hPa, zonal wind component at 700 hPa and 850 hPa and meridional wind component at 500 hPa and 700 hPa.…”

“…In addition to the atmospheric and circulation variables (NCEP reanalysis variables), seasonality (using sine and cosine terms), autocorrelation and temporal dependence (using preceding days' rainfall) and spatial variation (using latitude, longitude and altitude) were also used as predictors for fitting the downscaling model. Further details of the model fitting procedure for GLIMCLIM are available in Chandler (2002) and Beecham et al (2014). The model was validated over the period 1987-2000.…”

“…Although application of GLMs for multi-site stochastic rainfall simulation is relatively new in Australia, successful application of this model in Australia is available in recent studies Frost, 2007;Frost et al, 2011). Beecham et al (2014) used GLIMCLIM to downscale multi-site daily rainfall from NCEP reanalysis outputs in the Onkaparinga catchment in South Australia for the present climate . They concluded that the model has the ability to statistically downscale GCM outputs to catchment scale rainfall.…”

Assessment on the Effect of Climate Change on Streamflow in the Source Region of the Yangtze River, China

“…The three RCPs use radiative forcing values of 2.6, 4.5 and 8.5 W/m −2 , respectively [28]. Different temporal scale outputs (daily or monthly) of GCMs have been used by various studies to assess climate change effects on hydrology [29][30][31][32]. However, some studies pointed out that the daily outputs of GCM could not be directly used [15] and monthly GCMs outputs were widely used in the northwest China [33,34].…”

Analysis of rainfall and large-scale predictors using a stochastic model and artificial neural network for hydrological applications in southern Africa

“…Recent studies include their applications for modelling drought indices under climate change scenarios (Chun et al 2013),application in semi-arid areas for identifying rainfall predictors associated with inter annual variability of rainfall series in Botswana catchment as well as for climate change assessment (Kenabatho et al 2012a(Kenabatho et al , 2012b,for agricultural impact assessment in South African catchments (Ambrosino et al 2013), in tropical African climate with high rainfall and bi-modal rainfall seasons in Ugandan catchments (Kigobe et al 2011), and more recently for downscaling General Circulation Model's rainfall projections in a South Australian catchment (Beecham et al 2014).In these applications, the GLMs were used for simulating daily rainfall at multiple sites based on site specific rainfall predictors (covariates) such as previous days' rainfall, or external predictors such as ENSO, temperature or sea-level pressure, among others.…”