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Attributions of rainfall anomalies to weather systems and their spatio‐temporal variability in Victoria, southeast Australia are investigated with a multimethod weather type dataset and two popularly used gridded daily rainfall datasets for the period 1979–2015. The rainfall anomalies before, during and after the Millennium Drought (1997–2009) are compared to quantify the temporal variability of rainfall responses to weather type changes. The results show: (1) Three weather systems (Front, Cyclone and Thunderstorm) and their combinations contribute 89% of total rainfall; (2) Contributions of weather types to rainfall vary from month to month with winter season rainfall coming from more diverse weather types than summer rainfall; (3) The contributions of weather types to rainfall in three periods show temporal variabilities and there is a clear shift of contribution pattern after the Millennium Drought, such as front‐thunderstorm (FT) is now the largest contributor to rainfall compared with cyclone‐frontal‐thunderstorm (CFT) before and during the Millennium Drought; (4) A seasonal shift in the post‐drought period is found with higher rainfall in February and March and lower rainfall in September and October. The increased rainfall in February mainly results from Front–Thunderstorm (FT) and Thunderstorm‐only (TO), while rainfall declines in September from all weather types; (5) Several rainfall characteristics that are important for streamflow generation, such as rainfall intensity, probability of rainfall occurrence, number of rainfall days and the maximum daily rainfall, do depend on the weather types; (6) The results are similar with different rainfall datasets, but differences do exist, especially at the local scale. The conclusions of this study are drawn from an Australian case study but have implications for other regions to investigate the attributions of rainfall characteristic changes to weather systems. [ABSTRACT FROM AUTHOR]

Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile-quantile matched (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological simulations, in a case study for the State of Victoria, Australia (237 629 km2). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km2 grid point across Victoria, the GR4J rainfall-runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection. [ABSTRACT FROM AUTHOR]

Dynamical downscaling of future projections of global climate model outputs can potentially provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand for regional water resource planning processes. By explicitly modelling climate processes within and across global climate model gridcells for a region, dynamical downscaling can provide higher resolution hydroclimate projections, as well as independent (from historical timeseries) and physically plausible future rainfall timeseries for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile-quantile mapping to rainfall dynamically downscaled by NARCliM in the State of Victoria, Australia and examine the effect of this on: (i) biases both before and after bias correction in different rainfall metrics; (ii) change signals in metrics in comparison to the bias; and (iii) the effect of bias correction on wet-wet and dry-dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile-quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. This demonstrates that any quantile mapping bias correction methods are unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias correct dynamical downscaling rainfall outputs. [ABSTRACT FROM AUTHOR]

Abstract: Accurate quantification of terrestrial evapotranspiration and ecosystem productivity is of significant merit to better understand and predict the response of ecosystem energy, water, and carbon budgets under climate change. Existing diagnostic models have different focus on either water or carbon flux estimates with various model complexity and uncertainties induced by distinct representation of the coupling between water and carbon processes. Here, we propose a diagnostic model to estimate evapotranspiration and gross primary production that is based on biophysical mechanism yet simple for practical use. This is done by coupling the carbon and water fluxes via canopy conductance used in the Penman–Monteith–Leuning equation (named as PML_V2 model). The PML_V2 model takes Moderate Resolution Imaging Spectrometer leaf area index and meteorological variables as inputs. The model was tested against evapotranspiration and gross primary production observations at 9 eddy‐covariance sites in Australia, which are spread across wide climate conditions and ecosystems. Results indicate that the simulated evapotranspiration and gross primary production by the PML_V2 model are in good agreement with the measurements at 8‐day timescale, indicated by the cross site Nash–Sutcliffe efficiency being 0.70 and 0.66, R2 being 0.80 and 0.75, and root mean square error being 0.96 mm d−1 and 1.14 μmol m−2 s−1 for evapotranspiration and gross primary production, respectively. As the PML_V2 model only requires readily available climate and Moderate Resolution Imaging Spectrometer vegetation dynamics data and has few parameters, it can potentially be applied to estimate evapotranspiration and carbon assimilation simultaneously at long‐term and large spatial scales. [ABSTRACT FROM AUTHOR]

Quantifying the responses of the coupled carbon and water cycles to current global warming and rising atmospheric CO2 concentration is crucial for predicting and adapting to climate changes. Here we show that terrestrial carbon uptake (i.e. gross primary production) increased significantly from 1982 to 2011 using a combination of ground-based and remotely sensed land and atmospheric observations. Importantly, we find that the terrestrial carbon uptake increase is not accompanied by a proportional increase in water use (i.e. evapotranspiration) but is largely (about 90%) driven by increased carbon uptake per unit of water use, i.e. water use efficiency. The increased water use efficiency is positively related to rising CO2 concentration and increased canopy leaf area index, and negatively influenced by increased vapour pressure deficits. Our findings suggest that rising atmospheric CO2 concentration has caused a shift in terrestrial water economics of carbon uptake. [ABSTRACT FROM AUTHOR]