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Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces. [ABSTRACT FROM AUTHOR]

Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the difference between the tested methods is small in the modelling experiments here (and as reported in the literature), mainly because of the substantial corrections required and inconsistent errors over time (non-stationarity). The errors remaining in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitation of RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces. [ABSTRACT FROM AUTHOR]

This paper describes a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia. Data have been archived from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN) since its inception in September 2001. The Murrumbidgee Catchment represents a range of conditions typical of much of temperate Australia, with climate ranging from semiarid to humid and land use including dry land and irrigated agriculture, remnant native vegetation, and urban areas. There are a total of 38 soil moisture-monitoring sites across the Murrumbidgee Catchment, with a concentration of sites in three subareas. The data set is composed of 0-5 (or 0-8), 0-30, 30-60, and 60-90 cm average soil moisture, soil temperature, precipitation, and other land surface model forcing at all sites, together with other ancillary data. These data are available on the World Wide Web at http ://www.oznet.org.au. [ABSTRACT FROM AUTHOR]

This paper uses the Budyko and Fu models to estimate mean annual runoff in 0.05° grid cells across southeast Australia, and assesses the estimates against observations in 323 catchments. The results show that the Budyko model can provide reasonable estimates of runoff and is suitable for data limited regions. In regions where there are some streamflow data available, the calibration of a single parameter in the Fu model gives better runoff estimates than the Budyko model and similar to regionalised rainfall-runoff models. In drier regions, the consideration of different vegetation types in the Fu model can improve the modelling results. [ABSTRACT FROM AUTHOR]

The recent drought in the southern Murray-Darling Basin has seen a larger reduction in annual runoff in many places compared to historical droughts with similar annual rainfall reductions. Several reasons have been suggested for this, including proportionally less autumn and winter rainfall, fewer high rainfall years, and increased temperatures. Using the SIMHYD daily rainfall-runoff model and scenarios of rainfall and potential evapotranspiration, we investigate the causes of the observed runoff reduction over 1997-2008 in the Campaspe river basin, an area representative of the southern Murray-Darling Basin. This method accounts for 83% of the runoff reduction, with the reduction in annual rainfall accounting for 52%. The remainder is not explained by any single hydroclimatic feature but is mostly accounted for by the combination of changes in rainfall variability outside monthly and annual time scales (15%), changed seasonality of rainfall (11%), and increased potential evapotranspiration (5%). [ABSTRACT FROM AUTHOR]

The statistical behaviour and distribution of high-resolution (6 min) rainfall intensity within the wet part of rainy days (total rainfall depth >10mm) is investigated for 42 stations across Australia. This paper compares nine theoretical distribution functions (TDFs) in representing these data. Two goodness-of-fit statistics are reported: the Root Mean Square Error (RMSE) between the fitted and observed within-day distribution; and the coefficient of efficiency for the fit to the highest rainfall intensities (average intensity of the 5 highest intensity intervals) across all days at a site. The three-parameter Generalised Pareto distribution was clearly the best performer. Good results were also obtained from Exponential, Gamma, and two-parameter Generalized Pareto distributions, each of which are two parameter functions, which may be advantageous when predicting parameter values. Results of different fitting methods are compared for different estimation techniques. The behaviour of the statistical properties of the within-day intensity distributions was also investigated and trends with latitude, Köppen climate zone (strongly related to latitude) and daily rainfall amount were identified. The latitudinal trends are likely related to a changing mix of rainfall generation mechanisms across the Australian continent. [ABSTRACT FROM AUTHOR]

Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large-scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south-east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI-ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO-MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS-AOM, MIROC-M, NCAR-PCM1, IAP and NCAR-CCSM. However, the reduction in the combined score as we move from the best- to the worst-performing GCMs is gradual, and there is no evident cut-off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study describes a daily rainfall, potential evaporation and streamflow data set compiled for the important water resources region of southeast Australia, and the application of six commonly used lumped conceptual rainfall-runoff models to estimate daily runoff across the region. The daily climate data set and the daily modelled runoff are available from 1895 to 2008 at 0.05° grid resolution across the region. The modelling exercise indicates that the rainfall-runoff models can generally be calibrated to reproduce the daily observed streamflow (for 232 catchments in the high runoff generation areas), and the regionalisation results indicate that the use of optimised parameter values from a gauged catchment nearby can model runoff reasonably well in the ungauged areas. There are differences between the six models, but they are relatively small when used to describe aggregated results across large regions. [ABSTRACT FROM AUTHOR]

One approach to predicting streamflow in an ungauged catchment is to select an ensemble of hydrological models previously identified for similar gauged catchments, where the similarity is based on some combination of important physical catchment attributes. The focus of this paper is the identification of catchment attributes and optimization of a similarity measure to produce the best possible ungauged streamflow predictions given a data set and a conceptual model structure. As a case study, the SimHyd rainfall-runoff model is applied to simulate monthly streamflow in 184 Australian catchments. Initial results show that none of 27 catchment attributes can be safely said to consistently give a better ensemble of models than random selection when used independently of other attributes. This is contrary to prior expectations and indicates the sparseness of information within our database of catchments, the importance in this case of prior knowledge for defining important attributes, and the potential importance of combining multiple attributes in order to usefully gauge similarity. Seven relatively independent attributes are then selected on the basis of prior knowledge. The weight with which each of these attributes contributes to the similarity measure is optimized to maximize streamflow prediction performance across a set of 95 catchments. The other 89 catchments are used to independently test the accuracy of streamflow predictions. Using the optimal set of weights led to marked improvement in the accuracy of predictions, showing that the method, while inferior to local calibration, is superior to alternative methods of model regionalization based on regression and spatial proximity. However, there is evidence of nonuniqueness in the optimal solution and the possibility that the attribute weights are somewhat dependent on the catchments used. [ABSTRACT FROM AUTHOR]

This paper describes the modeling of climate change impact on runoff across southeast Australia using a conceptual rainfall-runoff model SIMHYD and presents the results and assesses the robustness of the modeling approach. The future climate series is obtained by scaling the historical series, informed by 15 global climate models (GCMs), to reflect a 0.9°C increase in global average surface air temperature, using a daily scaling method that considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as in the daily rainfall distribution. The majority of the modeling results indicate that there will be less runoff in southeast Australia in the future. However, there is considerable uncertainty, with the results ranging from a 17% decrease to a 7% increase in the mean annual runoff averaged across the study area for the 0.9°C global warming. The model assessments indicate that the modeling approach is generally robust and can be used to estimate the climate impact on runoff. The modeled mean annual runoff is generally within 10-20% of the observed runoff. The modeling results for an independent test period are only slightly poorer than the calibration period, indicating that a satisfactorily calibrated rainfall-runoff model can be used to estimate runoff for another climate period. The modeled impact on various runoff characteristics as estimated by two rainfall-runoff models investigated here differ by less than 10%, which is relatively small compared to the range of modeled runoff results using rainfall projections from different GCMs. [ABSTRACT FROM AUTHOR]

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data. [ABSTRACT FROM AUTHOR]

Many physical-based models of surface and groundwater hydrology are constructed without the possibility of multiple stable states for the same parameter set. For such a conceptualization, at the cessation of a transient hydrological disturbance of any magnitude the model will return to the same stable state and thus show an infinite resilience. To highlight and falsify this assumption, a numerical distributed ecohydrological model (coupled hillslope Boussinesq-vertically lumped vadose zone) is presented, in which qualitatively different steady state water table elevations exist for the same parameter set. The multiple steady states are shown to emerge from a positive feedback arising from a reduction in leaf area index (LAI) and thus transpiration, as a saline water table approaches the surface. Limit cycle continuation is also undertaken to quantify the state-space location of the threshold (repellor) between the steady states (attractors) and quantify the resilience. While the model is biophysically simple, it is sufficiently complex to challenge this potentially significant assumption within water resource planning. [ABSTRACT FROM AUTHOR]

This paper shows the feasibility of using steady state water balances of gauged catchments to calibrate a spatially explicit evaporation model and then applying this to estimate mean annual runoff for 120 gauged catchments in the Murray-Darling Basin (MDB) of Australia from 2001 to 2005. We used remotely sensed leaf area indices from the Moderate Resolution Imaging Spectrometer (MODIS) mounted on the polar-orbiting Terra satellite with the Penman-Monteith equation, gridded meteorology, and a two-parameter biophysical model for surface conductance ( Gs) to estimate 8-day average evaporation at 1-km resolution. Parameters for the G s model were optimized using steady state water balance estimates (precipitation minus runoff) in the gauged catchments in three precipitation zones of the MDB, and the calibrated evaporation model was then used to estimate evaporation ( ERS) and runoff from gauged and ungauged catchments in the MDB. Mean annual calibrated estimates of ERS compared well with water balance estimates, indicated by a root-mean-square error (RMSE) of 78.6 mm/a and the Nash-Sutcliffe coefficient of efficiency ( CE) of 0.68. Reasonable agreement was obtained between the estimated mean annual runoff ( RRS) (rainfall minus ERS), and the measured runoff (RMSE = 71.0 mm/a and CE = 0.75). Cross validation showed that estimated ERS and RRS were almost as good as the calibrated ones. Furthermore, RRS has an accuracy similar to that of a seven-parameter conceptual rainfall-runoff model in the gauged catchments. The results show that the evaporation model can be easily applied to estimate steady state evaporation and runoff and that ERS can be used with rainfall-runoff models to improve accuracy of estimated runoff in ungauged catchments. [ABSTRACT FROM AUTHOR]

Mean annual evapotranspiration from a catchment is determined largely by precipitation and potential evapotranspiration; characteristics of the catchment (e.g., soil, topography, etc.) play only a secondary role. It has been shown that the ratio of mean annual potential evapotranspiration to precipitation (referred as the index of dryness) can be used to estimate mean annual evapotranspiration by using one additional parameter. This study evaluates the effects of climatic and catchment characteristics on the partitioning of mean annual precipitation into evapotranspiration using a rational function approach, which was developed based on phenomenological considerations. Over 470 catchments worldwide with long-term records of precipitation, potential evapotranspiration, and runoff were considered, and results show that model estimates of mean annual evapotranspiration agree well with observed evapotranspiration taken as the difference between precipitation and runoff. The mean absolute error between modeled and observed evapotranspiration was 54 mm, and the model was able to explain 89% of the variance with a slope of 1.00 through the origin. This indicates that the index of dryness is the most significant variable in determining mean annual evapotranspiration. Results also suggest that forested catchments tend to show higher evapotranspiration than grassed catchments and their evapotranspiration ratio (evapotranspiration divided by precipitation) is most sensitive to changes in catchment characteristics for regions with the index of dryness around 1.0. Additionally, a stepwise regression analysis was performed for over 270 Australian catchments where detailed information of vegetation cover, precipitation characteristics, catchment slopes, and plant available water capacity was available. It is shown that apart from the index of dryness, average storm depth, plant available water capacity, and storm arrival rate are also significant. [ABSTRACT FROM AUTHOR]

Investigates the nutrient contribution from leaf litter in urban waterways. Nutrients in leaf litter; Variables from the 10 cleanouts of the continuous deflective separation (CDS) device; Management implications.

BSTRACT: Estimates of the upper constraint on actual evapotranspiration are required as input data in the majority of rainfall-runoff models. This paper compares and discusses the applicability of Penman's potential evapotranspiration estimates and Morton's wet environment evapotranspiration estimates in rainfall-runoff modeling applications. Morton's wet environment evapotranspiration depends only on the atmospheric variables and is the estimate of evapotranspiration that would occur when water supply is not limiting. It is a conceptually more correct representation of the upper constraint on actual evapotranspiration compared to Penman's potential evapotranspiration which is dependent on the water supply to the soil-plant surfaces. Although Penman's potential evapotranspiration and Morton's wet environment evapotranspiration are two different quantities, comparison of the two estimates using data from different climatic regions throughout Australia indicate that they provide similar magnitudes of the upper limit of actual evapotranspiration at moderate climatic conditions when reliable estimates are required in rainfall-runoff models. The two estimates can therefore be used interchangeably in rainfall-runoff modeling applications. [ABSTRACT FROM AUTHOR]

This paper presents a simple approach for estimating long-term runoff and diffuse pollution loads in urban catchments, and discusses conceptual modelling methods for simulating daily runoff and pollution loads. The modelling results for several catchments in Australian capital cities are presented. The study indicates that long-term and dailyrunoff can be estimated reasonably accurately using simple approaches. However, the water quality characteristic can vary considerably between catchments, and in the absence of data, the models can only provide a guide to the probable range of diffuse pollution load generated from a catchment. [ABSTRACT FROM AUTHOR]