Comparing hydrological modelling, linear and multilevel regression approaches for predicting baseflow index for 596 catchments across Australia

Abstract. Estimating baseflow at a large spatial scale is critical for water balance budget, water resources management, and environmental evaluation. To predict baseflow index (BFI, the ratio of baseflow to total streamflow), this study introduces a multilevel regression approach, which is compared to two traditional approaches: hydrological modelling (SIMHYD, a simplified version of the HYDROLOG model, and Xinanjiang models) and classic linear regression. All of the three approaches were evaluated against ensemble average estimates from four well-parameterised baseflow separation methods (Lyne–Hollick, UKIH (United Kingdom Institute of Hydrology), Chapman–Maxwell and Eckhardt) at 596 widely spread Australian catchments in 1975–2012. The two hydrological models obtain BFI from three modes: calibration and two regionalisation schemes (spatial proximity and integrated similarity). The classic linear regression estimates BFI using linear regressions established between catchment attributes and the ensemble average estimates in four climate zones (arid, tropics, equiseasonal and winter rainfall). The multilevel regression approach not only groups the catchments into the four climate zones, but also considers variances both within all catchments and catchments in each climate zone. The two calibrated and regionalised hydrological models perform similarly poorly in predicting BFI with a Nash–Sutcliffe Efficiency (NSE) of −8.44 ~ −2.58 and an absolute percenrate bias (Bias) of 81 146; the classic linear regression is intermediate with the NSE of 0.57 and bias of 25; the multilevel regression approach is best with the NSE of 0.75 and bias of 19. Our study indicates the multilevel regression approach should be used for predicting large-scale baseflow index such as Australian continent where sufficient catchment predictors are available.