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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-737
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Dec 2017

Research article | 21 Dec 2017

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

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

Junlong Zhang1,2, Yongqiang Zhang1, Jinxi Song2,3, Lei Cheng1, Rong Gan1, Xiaogang Shi4, Zhongkui Luo5, and Panpan Zhao6 Junlong Zhang et al.
  • 1CSIRO Land and Water, GPO Box 1700, ACTON 2601, Canberra, Australia
  • 2Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
  • 3State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Yangling 712100, China
  • 4Lancaster Environment Centre, Lancaster University, Lancaster, UK, LA1 4YQ
  • 5CSIRO Agriculture Flagship, GPO Box 1666, ACTON 2601, Canberra, Australia
  • 6State Key Laboratory of Hydrology-Water Resource and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

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 81146; 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.

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Short summary
Estimating baseflow is critical for water balance budget, water resources management, and environmental evaluation. To predict baseflow index (the ratio of baseflow to total streamflow), this study introduces a new method, multilevel regression approach for predicting baseflow index for 596 Australian catchments, which outperformed two traditional methods: linear regression and hydrological modelling. Our results suggest that it is very promising to use this method to other parts of world.
Estimating baseflow is critical for water balance budget, water resources management, and...
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