Environment and Research Division

Melbourne, Australia

Environment and Research Division

Melbourne, Australia
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Plotz R.D.,Environment and Research Division | Chambers L.E.,Environment and Research Division | Finn C.K.,Deakin University
Journal of Applied Meteorology and Climatology | Year: 2014

In most countries, national meteorological services either generate or have access to seasonal climate forecasts. However, in a number of regions, the uptake of these forecasts by local communities can be limited, with the locals instead relying on traditional knowledge to make their climate forecasts. Both approaches to seasonal climate forecasting have benefits, and the incorporation of traditional forecast methods into contemporary forecast systems can lead to forecasts that are locally relevant and better trusted by the users. This in turn could significantly improve the communication and application of climate information, especially to remote communities. A number of different methodologies have been proposed for combining these forecasts. Through considering the benefits and limitations of each approach, practical recommendations are provided on selecting a method, in the form of a decision framework, that takes into consideration both user and provider needs. The framework comprises four main decision points: 1) consideration of the level of involvement of traditional-knowledge experts or the community that is required, 2) existing levels of traditional knowledge of climate forecasting and its level of cultural sensitivity, 3) the availability of long-term data-both traditional-knowledge and contemporary-forecast components, and 4) the level of resourcing available. No one method is suitable for everyone and every situation; however, the decision framework helps to select the most appropriate method for a given situation. © 2017 American Meteorological Society.

Khan U.,University of New South Wales | Tuteja N.K.,Environment and Research Division | Ajami H.,University of New South Wales | Sharma A.,University of New South Wales
Water Resources Research | Year: 2014

The computational effort associated with physically based distributed hydrological models is one of their major limitations that restrict their application in soil moisture and land surface flux simulation problems for large catchments. In this work, a new approach for reducing the computational effort associated with such models is investigated. This approach involves the formation of equivalent cross sections, designed in a manner that ensures comparable accuracy in simulating the hydrological fluxes as a fully distributed simulation. Single or multiple equivalent cross sections are formulated in each Strahler's first-order subbasin on the basis of topographic and physiographic variables representing the entire or part of the subbasin. An unsaturated soil moisture movement model based on a two-dimensional solution of the Richards' equation is used for simulating the soil moisture and hydrologic fluxes. The equivalent cross-section approach and the model are validated against observed soil moisture data in a semiarid catchment and found consistent. The results indicate that the equivalent cross-section approach is an efficient alternative for reducing the computational time of distributed hydrological modeling while maintaining reasonable accuracy in simulating hydrologic fluxes, in particular dominant fluxes such as transpiration and soil evaporation in semiarid catchments. Key Points An approach to reduce the computational time in distributed hydrological model Validation is done against observed soil moisture data Soil moisture model is based on a 2-D solution of the Richards' equation © 2014. American Geophysical Union. All Rights Reserved.

Beesley C.,Environment and Research Division | Green J.,Environment and Research Division
The Art and Science of Water - 36th Hydrology and Water Resources Symposium, HWRS 2015 | Year: 2015

The new Intensity-Frequency-Duration (IFD) design rainfall estimates provided by the Bureau of Meteorology (the Bureau) were derived for the current climate regime. The Australian Rainfall and Runoff Revision (ARR) Rainfall IFD Relationships under Climate Change Project sought to provide more definitive advice on the impacts of climate change on the new ARR2015 IFDs. However, in order to ensure that the advice for future climate regimes was relevant, the Bureau's ARR2015 IFDs served as the reference curves against which the IFD curves for the current climate produced using different regional climate models and alternative statistical methods were benchmarked. The benchmarking of the rainfall IFD curves was undertaken by comparing the rainfall IFD curves derived from the Conformal Cubic Atmospheric Model (CCAM) and Weather Research and Forecasting (WRF) climate model based simulations and those derived using the Bayesian Hierarchical Modelling (BHM) framework for the current climate to the Bureau of Meteorology's new rainfall IFD curves. In undertaking the benchmarking comparisons for both the Greater Sydney Region and the Southeast Queensland Region were made between the gridded ungauged locations rather than gauged locations. This was to be consistent with the ARR2015 IFDs which are gridded, regionalised values rather than at-site values. The benchmarking of the BHM derived rainfall IFD curves against the Bureau's ARR2015 IFDs showed that the BHM depths are most different to the ARR2015 IFD depths for the shortest and longest durations. In addition, the BHM depths are lower in the flatter and more data sparse areas and higher in areas of more topographic complexity. However, overall there was reasonable correspondence of the BHM derived rainfall IFD curves to the Bureau's ARR2015 IFDs. In contrast, the CCAM and WRF derived IFD depths showed large differences to the Bureau's ARR2015 IFDs - both positive and negative - across durations and probabilities but without any obvious causal reason for the differences. © 2015, Engineers Australia. All rights reserved.

Perera K.C.,University of Melbourne | Western A.W.,University of Melbourne | Nawarathna B.,Environment and Research Division | George B.,University of Melbourne
Agricultural and Forest Meteorology | Year: 2014

Farmers and irrigation system operators make real-time irrigation decisions based on a range of factors including short-term weather forecasts of rainfall and temperature. The simplest and oldest statistical method for forecasting daily ET0 is to use the long-term monthly mean ET0 based on historical observations. Forecasts of reference crop evapotranspiration (ET0) can be calculated from Numerical Weather Prediction (NWP) outputs and ET0 has the advantage of being more directly relevant to crop water requirements than temperature. This paper evaluates forecasts of ET0 made using the Bureau of Meteorology's operational NWP forecasts derived from the Australian Community Climate and Earth System Simulator - Global (ACCESS-G). The forecast performance for ET0 was quantified using the root mean squared error (RMSE), coefficient of determination (R2), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). Daily ET0 forecasts for lead times up to 9 days were compared against ET0 calculated using hourly observations from the 40 automatic weather stations across Australia.It was found that using NWP forecast daily ET0 was better than using the long-term monthly mean ET0 for lead times up to 6 days, beyond which the long-term monthly mean was better. The average MSSS for ET0 forecasts across all stations varied between 66% and 12% for lead times of 1-6 days, respectively. Further, it was found that forecast performance for daily ET0 was highest in autumn for tropical climates and lowest in spring for temperate climates. Errors in incoming solar radiation were the most important source of ET0 forecast error, followed by air temperature, dew point temperature and wind speed, for all lead times. Also, it was found that the forecast performances for incoming solar radiation and mean wind speed were relatively poor compared with the air and dew point temperatures. © 2014 Elsevier B.V.

Srikanthan S.,Environment and Research Division
Hydrology and Water Resources Symposium 2014, HWRS 2014 - Conference Proceedings | Year: 2014

Two methods are generally used to sample the original data for extreme events: Annual maximum series (AMS) and partial duration series (PDS). In AMS series the largest events from each year is selected, so that the series length equals the number of years of record. As the events have been sampled at fixed intervals, the return period of an event of magnitude greater or equal to a given value equals the inverse of its probability of exceedance. The PDS has all the values of the variable that exceed an a priori determined threshold. The main reason put forward for using PDS is that it uses the second, third ... largest values in a year while the AMS ignores these. Most of the literature supporting the use of PDS only considers the variance or the root mean square error of the quantile estimates and not the bias. Two simulation experiments are carried out to compare the bias and variance of the quantile estimates obtained from AMS and PDS. In simulation experiment 1, known population values of generalised Pareto distribution parameters are used and in the second simulation experiment long sequences of daily rainfall are generated. The generated sequences are then broken into sub-samples of different lengths and the quantiles for different average recurrence intervals are estimated using AMS and PDS. The bias and the standard deviation of the quantiles obtained from the replicates are compared. From the results, it is found that it is not possible to select a single value for the mean number of events per year for PDS. Since AMS gave the smallest bias for most of the cases in the second experiment and for negative k values in the first experiment, use of AMS in frequency analysis is preferred to PDS.

Cottrill A.,Environment and Research Division | Kuleshov Y.,Environment and Research Division
Australian Meteorological and Oceanographic Journal | Year: 2014

The statistical model SCOPIC (Seasonal Climate Outlook for Pacific Island Countries) has been used to produce seasonal forecasts in ten Pacific Island nations since mid-2007 to improve their seasonal forecasting capacity and to provide timely warnings to changes in rainfall. However, to date there has been no detailed hindcast validation study to compare the forecast skill from the different predictors used to produce the seasonal forecasts at different stations from across the Pacific region. Here, we compare the rainfall forecasts created by the linear discriminant analysis model within SCOPIC using the four predictors: the Southern Oscillation Index (SOI); empirical orthogonal functions of sea surface temperature anomalies (SST1&9) and the NINO3.4 and the 5VAR index. This indicates that skill varies from season to season across the Pacific, with the highest skill in the austral summer and lowest skill in the austral winter. This study using tercile hit rates and LEPS percentage scores shows the 5VAR index has slightly superior skill compared to the NINO3.4, SOI and the SST1&9 indices, but results will vary depending on the station location, analysis period and the number of months used to calculate the predictor value. © 2014 Australian Meteorological and Oceanographic Journal.

Peterson T.J.,University of Melbourne | Western A.W.,University of Melbourne | Argent R.M.,Environment and Research Division
Water Resources Research | Year: 2014

The companion paper showed that multiple steady state groundwater levels can exist within a hill-slope Boussinesq-vegetation model under daily stochastic forcing. Using a numerical limit-cycle continuation algorithm, the steady states (henceforth attractors) and the threshold between them (henceforth repellor) were quantified at a range of saturated lateral conductivity values, ksmax. This paper investigates if stochastic daily forcing can switch the catchment between both of the attractors. That is, an attractor may exist under average forcing conditions but can stochastic forcing switch the catchment into and out of each of the attractor basins-; i.e., making the attractor emerge. This was undertaken using the model of the companion paper and by completing daily time-integration simulations at six values of the saturated lateral hydraulic conductivity, ksmax; three having two attractors and three having only a deep water table attractor. By graphically analyzing the simulations, and comparing against simulations from a model modified to have only one attractor, multiple attractors were found to emerge under stochastic daily forcing. However, the emergence of attractors was significantly more subtle and complex than that suggested by the companion paper. That is, an attractor may exist but never emerge; both attractors may exist and both may emerge but identifying the switching between attractors was often ambiguous; and only one attractor may exist and but a second temporary attractor may exist and emerge during periods of high precipitation. This subtle and complex emergence of attractors was explained using continuation analysis of the climate forcing rate, and not a model parameter such as ksmax. It showed that the temporary attractor existed over a large range of ksmax values and this suggests that more catchments may have multiple attractors than suggested by the companion paper. By combining this continuation analysis with the time-integration simulations, hydrological signatures indicative of a switch of multiple attractors were proposed. These signatures may provide a means for identifying actual catchments that have switched between multiple attractors. Key Points Stochastic daily forcing can switch a catchment to both attractors Emergence of attractors differs significantly from the existence of attractors Switching between attractor basins can be subtle and difficult to identify © 2014. American Geophysical Union. All Rights Reserved.

Nair S.,University of Melbourne | George B.,University of Melbourne | Malano H.M.,University of Melbourne | Arora M.,University of Melbourne | Nawarathna B.,Environment and Research Division
Resources, Conservation and Recycling | Year: 2014

Water supply and wastewater services incur a large amount of energy and GHG emissions. It is therefore imperative to understand the link between water and energy as their availability and demand are closely interrelated. This paper presents a literature review and assessment of knowledge gaps related to water-energy-greenhouse gas (GHG) nexus studies in an urban context from an 'energy for water' perspective. The review comprehensively surveyed various studies undertaken in various regions of the world and focusing on individual or multiple subsystems of an urban water system. The paper also analyses the energy intensity of decentralized water systems and various water end-uses together with the major tools and models used. A major gap identified from this review is the lack of a holistic and systematic framework to capture the dynamics of multiple water-energy-GHG linkages in an integrated urban water system where centralized and decentralized water systems are combined to meet increased water demand. Other knowledge gaps identified are the absence of studies, peer reviewed papers, data and information on water-energy interactions while adopting a 'fit for purpose water strategy' for water supply. Finally, based on this review, we propose a water-energy nexus framework to investigate 'fit-for-purpose' water strategy. © 2014 Elsevier B.V. All rights reserved.

Perera K.C.,University of Melbourne | Western A.W.,University of Melbourne | Nawarathna B.,Environment and Research Division | George B.,University of Melbourne | George B.,International Center for Agricultural Research in the Dry Areas
Agricultural Water Management | Year: 2015

Estimates from the FAO Penman-Monteith (FAO-PM) and the standardized ASCE Penman-Monteith (ASCE-PM) hourly and daily reference evapotranspiration (ET0) equations were compared at daily scale, based on the hourly climate data collected from forty (40) geographically and climatologically diverse Automatic Weather Stations (AWS) across the Australian continent. These locations represent 23 agricultural irrigation areas in tropical, arid and temperate climates. The aims of this paper are to: compare the effects of different methods of estimating Clear-sky-radiation-(Rso); compare sum-of-hourly and daily ET0; compare the results of aggregation of hourly ET0 over 24h compared with daylight hours; and examine the impact of seasonality and climate type. At selected AWS locations, the hourly ET0 was calculated using the hourly FAO-PM and the ASCE-PM equations and then summed to derive daily ET0 (reported as ET0,soh). This was compared against the daily ET0 values, calculated using the corresponding daily equation (reported as ET0,daily). Using Rso calculated following the "complex" approach improves the agreement between ET0,soh and ET0,daily of both hourly equations, compared with the "simple" approach. Better agreement between ET0,soh and ET0,daily estimates for the FAO-PM and ASCE-PM were found, when the hourly values are aggregated over 24h rather than over daylight hours. The average ratio between ET0,soh and ET0,daily for the FAO-PM and ASCE-PM equations is 0.95 and 1.00, respectively. The range of the former is 0.90-0.98 and that of the latter is 0.96-1.04. There was very strong correlation between the two hourly equations at the daily time step: on average 0.997, with a range of 0.993-0.998. The results imply that the ASCE-PM hourly equation's daily ET0 values are higher than those of FAO-PM, which can be explained by the difference in the treatment of surface resistances. Better agreements between ET0,soh and ET0,daily values for winter, spring and autumn were found for the FAO-PM version, while during summer, the ASCE-PM version showed better agreement. The best agreement between the hourly and daily results for the FAO-PM version was found in temperate climates and the ASCE-PM version showed best agreement in the tropical and arid climates. © 2014 Elsevier B.V.

Peel M.C.,University of Melbourne | Srikanthan R.,Environment and Research Division | McMahon T.A.,University of Melbourne | Karoly D.J.,University of Melbourne
Hydrology and Earth System Sciences | Year: 2015

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015-2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould-Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable - these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified. © Author(s) 2015.

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