Rainfall-Runoff Modelling Across Southeast Australia: Datasets, Models and Results

Vaze, Jai; Chiew, Francis H. S.; Perraud, Jean-Michel; Viney, Neil R.; Post, D. A.; Teng, Jin; Wang, B; Lerat, Julien; Goswami, Monomoy

doi:10.1080/13241583.2011.11465379

Cited by 74 publications

(73 citation statements)

References 10 publications

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“…Quality controlled daily streamflow records for 416 unregulated catchments were obtained from the datasets of Vaze et al (2011) (231 of 232 for south-eastern Australia), and Zhang et al (2011) (185 of 719 Australia-wide). Streamflow records for these catchments were intermittent during the period 1950-2010.…”

Section: Streamflow Datamentioning

confidence: 99%

Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles

et al. 2013

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Information about the carbon cycle potentially constrains the water cycle, and vice versa. This paper explores the utility of multiple observation sets to constrain a land surface model of Australian terrestrial carbon and water cycles, and the resulting mean carbon pools and fluxes, as well as their temporal and spatial variability. Observations include streamflow from 416 gauged catchments, measurements of evapotranspiration (ET) and net ecosystem production (NEP) from 12 eddy-flux sites, litterfall data, and data on carbon pools. By projecting residuals between observations and corresponding predictions onto uncertainty in model predictions at the continental scale, we find that eddy flux measurements provide a significantly tighter constraint on continental net primary production (NPP) than the other data types. Nonetheless, simultaneous constraint by multiple data types is important for mitigating bias from any single type.

Four significant results emerging from the multiply-constrained model are that, for the 1990–2011 period: (i) on the Australian continent, a predominantly semi-arid region, over half the water loss through ET (0.64 ± 0.05) occurs through soil evaporation and bypasses plants entirely; (ii) mean Australian NPP is quantified at 2.2 ± 0.4 (1σ) Pg C yr⁻¹; (iii) annually cyclic ("grassy") vegetation and persistent ("woody") vegetation account for 0.67 ± 0.14 and 0.33 ± 0.14, respectively, of NPP across Australia; (iv) the average interannual variability of Australia's NEP (±0.18 Pg C yr⁻¹, 1σ) is larger than Australia's total anthropogenic greenhouse gas emissions in 2011 (0.149 Pg C equivalent yr^–1), and is dominated by variability in desert and savanna regions

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Section: Streamflow Datamentioning

confidence: 99%

Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles

et al. 2013

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“…The median NSE obtained from this study is approximately 0.50 higher than that in Zhang et al (2016), which used 0.5° resolution Princeton global forcing data and is approximately 0.90 higher than that obtained from the ensemble mean of eight global hydrological models in Beck et al (2016b), which used 0.5° resolution WATCH global forcing data ERA-interim (WFDEI) meteorological data. However, the median NSE obtained from this study 20 is similar to or marginally different from that obtained in the regionalisation studies conducted in southeastern Australia (Vaze et al, 2011;Viney et al, 2009a), which all used the same SILO forcing data. Therefore, the key for improving the prediction of runoff at regional and continental scales is the quality of the forcing data.…”

Section: Quality Forcing Data Are the Key For Macro-scale Runoff Predmentioning

confidence: 45%

Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia

Zhang

2016

Preprint

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Abstract. Numerous regionalisation studies have been conducted to predict the runoff time series in ungauged catchments. However, there are few studies investigating their benefits for predicting runoff time series on a continental scale. This study uses four regionalisation approaches, including spatial proximity (SP), gridded 10 SP, integrated similarity (IS) and gridded IS, to regionalise two rainfall-runoff models (SIMHYD and Xinanjiang) for 605 unregulated catchments distributed across Australia. The SP and IS approaches are used for directly predicting catchment streamflow; the gridded SP and gridded IS approaches are used for predicting runoff at each 0.05° × 0.05° grid cell for continental Australia, which is then aggregated for each catchment.The IS and gridded IS approaches use five properties to build similarity indices, including three physical 15 properties (an aridity index, a fraction of forest ratio and the mean annual air temperature) and two rainfall indices (rainfall seasonality and the standard deviation of daily rainfall). The two rainfall-runoff models show consistent regionalisation results, and there is a marginal difference among the four regionalisation approaches in the wet and densely located catchments. However, the gridded IS approach outperforms the other three in the dry and sparsely located catchments, and it overcomes the unnatural tessellated effect obtained from the 20 gridded SP approach. Use of the gridded IS approach together with rainfall-runoff modelling for predicting runoff on a continental scale is highly recommended. Extra predictors should be included to build similarity indices in other regions, such as the high latitude northern hemisphere or high elevation regions.

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“…1, Table 1). Australian streamflow data are taken from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al, 2011). Australian rainfall and potential evaporation data are derived from the Australian Water Availability Project (AWAP) data set (Jones et al, 2009).…”

Section: Datamentioning

confidence: 99%

A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts

Wang

Bennett

2015

Hydrol. Earth Syst. Sci.

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Abstract. For streamflow forecasting, rainfall-runoff models are often augmented with updating procedures that correct forecasts based on the latest available streamflow observations of streamflow. A popular approach for updating forecasts is autoregressive (AR) models, which exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of forecasts. In using an AR model applied to raw errors, the overcorrection usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the overcorrection usually occurs when streamflow is rapidly rising. In addition, when parameters of a hydrological model and an AR model are estimated jointly, the AR model applied to normalised errors sometimes degrades the stand-alone performance of the base hydrological model. This is not desirable for forecasting applications, as forecasts should rely as much as possible on the base hydrological model, with updating only used to correct minor errors. To overcome the adverse effects of the conventional AR models, a restricted AR model applied to normalised errors is introduced. We show that the new model reduces over-correction and improves the performance of the base hydrological model considerably.

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Rainfall-Runoff Modelling Across Southeast Australia: Datasets, Models and Results

Cited by 74 publications

References 10 publications

Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles

Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles

Regionalising rainfall–runoff modelling for predicting daily runoff in continental Australia

A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts

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