Academic literature on the topic 'Streamflow Victoria Acheron River'

Life history studies were carried out for populations of six species of Leptoceridae. Study sites were a permanent river (Acheron), a temporary river (Lerderderg) and a permanent lake (Monash University). Life histories varied in degree of synchrony of larval development, in length of adult emergence period, and from bi- to semivoltine. Oviposition requirements of adults were found to be the major factor influencing synchrony of the life history of one species. Life history features did not ameliorate the effects of the severe drought of 1982-83, nor did drought result in large changes in life histories.

The activity of benthic invertebrates was monitored in the water column of slowly flowing backwaters of the Acheron River during summer. Samples were taken throughout 24 h on two occasions, and densities of fauna were compared with densities in drift samples taken concurrently in the main channel. Drift densities were generally higher than those in backwaters, but not by orders of magnitude. Also, drift densities displayed significant die1 variation, whereas densities in backwaters did not consistently show such a pattern. Species composition generally differed between the two habitats. This brief study demonstrates that benthic invertebrates do swim in the water column of stream backwaters and that they may use this opportunity for colonization.

Abstract Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.

Climate change has significant effects on societies and ecosystems. Due to the strong link between climate and the hydrological cycle, water resources is one of the most affected fields by climate change. It is of great importance to investigate climate change effects on streamflows by producing future streamflow projections under different scenarios to create adaptation measures and mitigate potential impacts of climate change. The Upper Campaspe Catchment (UCC), located at North Central Victoria in Australia, is a significant catchment as it provides a large portion of total inflow to the Lake Eppalock Reservoir, which supplies irrigation to the Campaspe Irrigation district and urban water to Bendigo, Heathcote, and Ballarat cities. In this study, climate change effects on monthly streamflows in the UCC was investigated using high resolution future climate data from CSIRO and MIROC climate models in calibrated IHACRES hydrological model. The IHACRES model was found to be very successful to simulate monthly streamflow in UCC. Remarkable streamflow reductions were projected based on the climate input from both models (CSIRO and MIROC). According to the most optimistic scenario (with the highest projected streamflows) by the MIROC-RCP4.5 model in near future (2035–2064), the Upper Campaspe River will completely dry out from January to May. The worst scenario (with the lowest streamflow projection) by the CSIRO-RCP8.5 model in the far future (2075–2104) showed that streamflows will be produced only for three months (July, August, and September) throughout the year. Findings from this study indicated that climate change will have significant adverse impacts on reservoir inflow, operation, water supply, and allocation in the study area.

Abstract. Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services, such as freshwater supply. Streamflow variability and temporal evolution are impacted by the presence of lakes in the river network; therefore, any change in the lake state can induce a modification of the regional hydrological regime. Despite the importance of the impact of lakes on hydrological fluxes and the water balance, a representation of the mass budget is generally not included in climate models and global-scale hydrological modeling platforms. The goal of this study is to introduce a new lake mass module, MLake (Mass-Lake model), into the river-routing model CTRIP to resolve the specific mass balance of open-water bodies. Based on the inherent CTRIP parameters, the development of the non-calibrated MLake model was introduced to examine the influence of such hydrological buffer areas on global-scale river-routing performance. In the current study, an offline evaluation was performed for four river networks using a set of state-of-the-art quality atmospheric forcings and a combination of in situ and satellite measurements for river discharge and lake level observations. The results reveal a general improvement in CTRIP-simulated discharge and its variability, while also generating realistic lake level variations. MLake produces more realistic streamflows both in terms of daily and seasonal correlation. Excluding the specific case of Lake Victoria having low performances, the mean skill score of Kling–Gupta efficiency (KGE) is 0.41 while the normalized information contribution (NIC) shows a mean improvement of 0.56 (ranging from 0.15 to 0.94). Streamflow results are spatially scale-dependent, with better scores associated with larger lakes and increased sensitivity to the width of the lake outlet. Regarding lake level variations, results indicate a good agreement between observations and simulations with a mean correlation of 0.56 (ranging from 0.07 to 0.92) which is linked to the capability of the model to retrieve seasonal variations. Discrepancies in the results are mainly explained by the anthropization of the selected lakes, which introduces high-frequency variations in both streamflows and lake levels that degraded the scores. Anthropization effects are prevalent in most of the lakes studied, but they are predominant for Lake Victoria and are the main cause for relatively low statistical scores for the Nile River However, results on the Angara and the Neva rivers also depend on the inherent gap of ISBA-CTRIP process representation, which relies on further development such as the partitioned energy budget between the snow and the canopy over a boreal zone. The study is a first step towards a global coupled land system that will help to qualitatively assess the evolution of future global water resources, leading to improvements in flood risk and drought forecasting.

Abstract. The Hydrologic Reference Stations is a network of 467 high-quality streamflow gauging stations across Australia that is developed and maintained by the Bureau of Meteorology as part of an ongoing responsibility under the Water Act 2007. The main objectives of the service are to observe and detect climate-driven changes in observed streamflow and to provide a quality-controlled dataset for research. We investigate trends and step changes in streamflow across Australia in data from all 467 streamflow gauging stations. Data from 30 to 69 years in duration ending in February 2019 were examined. We analysed data in terms of water-year totals and for the four seasons. The commencement of the water year varies across the country – mainly from February–March in the south to September–October in the north. We summarized our findings for each of the 12 drainage divisions defined by Australian Hydrological Geospatial Fabric (Geofabric) and for continental Australia as a whole. We used statistical tests to detect and analyse linear and step changes in seasonal and annual streamflow. Monotonic trends were detected using modified Mann–Kendall (MK) tests, including a variance correction approach (MK3), a block bootstrap approach (MK3bs) and a long-term persistence approach (MK4). A nonparametric Pettitt test was used for step-change detection and identification. The regional significance of these changes at the drainage division scale was analysed and synthesized using a Walker test. The Murray–Darling Basin, home to Australia's largest river system, showed statistically significant decreasing trends for the region with respect to the annual total and all four seasons. Drainage divisions in New South Wales, Victoria and Tasmania showed significant annual and seasonal decreasing trends. Similar results were found in south-western Western Australia, South Australia and north-eastern Queensland. There was no significant spatial pattern observed in central nor mid-west Western Australia, with one possible explanation for this being the sparse density of streamflow stations and/or the length of the datasets available. Only the Tanami–Timor Sea Coast drainage division in northern Australia showed increasing trends and step changes in annual and seasonal streamflow that were regionally significant. Most of the step changes occurred during 1970–1999. In the south-eastern part of Australia, the majority of the step changes occurred in the 1990s, before the onset of the “Millennium Drought”. Long-term monotonic trends in observed streamflow and its regional significance are consistent with observed changes in climate experienced across Australia. The findings of this study will assist water managers with long-term infrastructure planning and management of water resources under climate variability and change across Australia.

A rigorous evaluation of future hydro-climatic changes is necessary for developing climate adaptation strategies for a catchment. The integration of future climate projections from general circulation models (GCMs) in the simulations of a hydrologic model, such as the Soil and Water Assessment Tool (SWAT), is widely considered as one of the most dependable approaches to assess the impacts of climate alteration on hydrology. The main objective of this study was to assess the potential impacts of climate alteration on the hydrology of the Yarra River catchment in Victoria, Australia, using the SWAT model. The climate projections from five GCMs under two Representative Concentration Pathway (RCP) scenarios—RCP 4.5 and 8.5 for 2030 and 2050, respectively—were incorporated into the calibrated SWAT model for the analysis of future hydrologic behaviour against a baseline period of 1990–2008. The SWAT model performed well in its simulation of total streamflow, baseflow, and runoff, with Nash–Sutcliffe efficiency values of more than 0.75 for monthly calibration and validation. Based on the projections from the GCMs, the future rainfall and temperature are expected to decrease and increase, respectively, with the highest changes projected by the GFDL-ESM2M model under the RCP 8.5 scenario in 2050. These changes correspond to significant increases in annual evapotranspiration (8% to 46%) and decreases in other annual water cycle components, especially surface runoff (79% to 93%). Overall, the future climate projections indicate that the study area will become hotter, with less winter–spring (June to November) rainfall and with more water shortages within the catchment.

Streamflow data are critical for water resource investigations, and their development projects. However, the scarcity of such data, particularly measured streamflow through streamflow gauges, constitutes a serious impediment to the successful implementation of development projects. In the absence of such measured streamflow data, streamflow estimation using measured meteorological data represents a viable alternative. Nevertheless, this alternative is not always possible due to the unavailability of required meteorological data. In the face of such data limitations, many have advocated the use of remote sensing (RS) data to estimate streamflow. The aim of this study was to generate daily streamflow time series data using remote sensing data through catchment process modelling and statistical modelling.