Infrastructure Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 108
  • Item
    No Preview Available
    The influence of spatial arrangement and site conditions on the fate of infiltrated stormwater
    Poozan, A ; Fletcher, TD ; Arora, M ; William Western, A ; James Burns, M (Elsevier BV, 2024-02-01)
  • Item
    No Preview Available
  • Item
    No Preview Available
    Quantifying the impact of the urban karst on infiltrated stormwater
    Poozan, A ; Burns, MJ ; Arora, M ; Western, AW (IWA Publishing, 2023-06-01)
    Urbanization alters the flow regime of streams, including increasing the frequency and magnitude of storm flows, along with reducing baseflows. An increasingly common management strategy is stormwater infiltration, which is thought to reduce surface runoff and recharge groundwater and thus restore lost baseflows to streams. Recent research has pointed to considerable uncertainty on the fate of infiltrated stormwater, particularly due to the presence of human-made underground infrastructure – e.g. sewer and water supply pipes and telecommunication cables. Such infrastructure is commonly housed in trenches partially filled with highly permeable material which can cause urban karst like flow conditions. We used a dynamic subsurface flow model (HYDRUS-3D) to predict the impact of the urban karst on the fate of infiltrated stormwater. The model was constructed with the presence of a sewer pipe situated between an infiltration basin and a stream. The model predicted that the impact of the urban karst on infiltrated stormwater increases with higher groundwater levels, and greater contrast between hydraulic conductivity of regional soil and gravel which surrounds the sewer pipe. Results suggest that it is important to consider the impact of the urban karst in cases where the goal of stormwater infiltration is baseflow restoration.
  • Item
    No Preview Available
    The influence of stormwater infiltration on downslope groundwater chemistry
    Arora, M ; Fletcher, TD ; Burns, MJ ; Western, AW ; Yong, CF ; Poelsma, PJ ; James, RB (SPRINGER, 2023-11)
    Stormwater infiltration basins have been used extensively around the world to restore urban hydrology towards more natural flow and water quality regimes. There is, however, significant uncertainty in the fate of infiltrated water and accompanying contaminants that depends on multiple factors including media characteristics, interactions with downslope vegetation, legacy contaminants, and presence of underground infrastructure. Understanding the influence of such factors is thus central to the design and siting of infiltration basins. An extensive field program was established to collect monthly data on ground water quality, including nutrients and major ion concentrations, in a bore network downstream of a stormwater infiltration basin in Victoria, Australia. The groundwater samples were analysed for temperature, pH, EC, turbidity, major ions (Na+, Ca2+, K+, Mg2+, Cl-, SO42-, NO3-, CO32-, HCO3-), NOx and heavy metals. The collected data were used to understand the origin and fate of water and solutes in the subsurface and their interactions with the soil matrix. The results revealed that Ca-HCO3, Na-Cl water types predominate in the study area, grouped in 3 clusters; shallow fresh groundwater in the vicinity of the basin (near basin), deep saline groundwater further downstream of the basin (near-stream) and a mid-section where rock-water interaction (Na-HCO3 water) through cation exchange control the chemistry of groundwater. The results also suggest that as the water moves downstream of the basin, it experiences significant evapotranspiration and concentration due to the presence of deep-rooted vegetation. The results suggest that while infiltration basins can remove infiltrated contaminants, the infiltrated stormwater can mobilise legacy contaminants such as nitrate. Overall, the efficacy of infiltration basins in urban regions depends substantially on the downstream vegetation, urban underground infrastructure and the presence of legacy contaminants in the soils. These all need to be considered in the design of stormwater infiltration basins.
  • Item
    Thumbnail Image
    Using Ensemble Streamflow Forecasts to Inform Seasonal Outlooks for Water Allocations in the Murray Darling Basin
    Graham, TDJ ; Wang, QJJ ; Tang, Y ; Western, A ; Wu, W ; Ortlipp, G ; Bailey, M ; Zhou, S ; Hakala, K ; Yang, Q (ASCE-AMER SOC CIVIL ENGINEERS, 2023-09-01)
    Water is a limited and highly valuable resource. In many parts of the world, water agencies allocate water according to agreed entitlement systems. The allocations are largely based on water already available in storages and rivers. Water agencies may also issue seasonal water allocation outlooks by anticipating future inflows to the storages and rivers. These outlooks are meant to assist water entitlement holders to plan for their crop planting, irrigation, and participation in water markets. Currently, these outlooks are generally based on historical inflow observations (climatology) and are often determined for a small selection of possible climatic scenarios (e.g., extreme dry, dry, average, and wet). These outlooks have large uncertainties, which require users to manage high risks themselves, leading to inefficient water use. In this study, we investigate the use of ensemble seasonal inflow forecasts to improve the production of seasonal water allocation outlooks through a case study of the Goulburn system in central Victoria, Australia. This is a complex system with active water trade both within the region and outside with the larger connected southern Murray-Darling Basin. In this case study, we integrate Australian Bureau of Meteorology's seasonal streamflow forecasts with Goulburn-Murray Water's water allocation to produce fully probabilistic water allocation outlooks. We evaluate the outlooks for three irrigation seasons from 2017 to 2020. We compare these outlooks with those produced from using inflows based on climatology only, an approach akin to the current practice of Goulburn-Murray Water. Using seasonal streamflow forecasts resulted in outlooks up to 60% (average 20%) closer to actual determinations, with uncertainty reduced by up to 65% (average 19%) Improvements were most obvious for short lead times and later in the irrigation season. This is a clear demonstration of how integration of streamflow forecasts can improve end-user products, which can lead to more efficient water use and water market participation.
  • Item
    No Preview Available
    Training sample selection for robust multi-year within-season crop classification using machine learning
    Gao, Z ; Guo, D ; Ryu, D ; Western, AW (Elsevier BV, 2023-07-01)
    Within-season crop classification using multispectral imagery is an effective way to generate timely crop maps that can support water and crop management; however, developing such models is challenging due to limited satellite imagery and ground truth data available during the season. This study investigated ways to optimize the use of multi-year samples in a within-season crop classification model, aiming to enable accurate within-season crop mapping across years. Our study focused on classifying field-scale corn/maize, cotton, and rice in south-eastern Australia from 2013 to 2019. The crop classification model was based on the random forest and support vector machine algorithms applied to Landsat 8 multispectral bands. We designed four experiments to understand the influences of training sample selection on model accuracy. Specifically, we analyzed how the within-season classification accuracies are affected by 1) training sample size; 2) proportions of classification classes; 3) the inclusion of a non-crop class (e.g., fallow land) in the training sample, and 4) training samples collected from different years. We found that 1) the training sample size should be sufficiently large to ensure within-season classification accuracy; 2) using training samples for each crop type in proportion to their occurrence within the landscape results in more accurate multi-year classification; 3) the inclusion of the non-crop class can reduce the accuracy with which crop types are distinguished, so the proportion of the non-crop class should be maintained at a relatively low level, and 4) predicting the current year with training samples from previous years can lead to a minor decline in accuracy compared to using samples only from the current year. These training sample settings were adopted to develop a final model. We found that the model accuracy continues to improve as more input imagery is added as the cropping season progresses, with a rapid rate of initial improvement which then slows. December, the third month of the summer growing season, is the earliest time that reliable maps were generated, with an overall accuracy of 86 % and user's accuracies for all crops exceeding 80 %. Our proposed experiments are robust and transferable to other regions and seasons to assist the development of within-season crop maps, and can thus be valuable tools to support agricultural management.
  • Item
    No Preview Available
    Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis
    Hao, S ; Ryu, D ; Western, A ; Perry, E ; Bogena, H ; Franssen, HJH (ELSEVIER SCI LTD, 2021-12)
    CONTEXT: Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. OBJECTIVE: This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. METHODS: We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. RESULTS AND CONCLUSIONS: Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. SIGNIFICANCE: This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.
  • Item
    Thumbnail Image
    Modelling electrical conductivity variation using a travel time distribution approach in the Duck River catchment, Australia
    Riazi, Z ; Western, AW ; Bende-Michl, U (WILEY, 2022-11)
    Abstract Solute dynamics depend strongly on hydrologic flow paths and transit times within catchments. In this paper, we use a travel time tracking method to simulate stream salinity (as measured by electrical conductivity) in the Duck River catchment, NW Tasmania, Australia. The study couples storage selection function transit time modelling with two alternate approaches to model electrical conductivity (EC). The first approach assumes the catchment has a cyclic salt balance (i.e., rainfall source, stream flow sink) that is in dynamic equilibrium and evapoconcentration of salt is the only process changing concentration. The second approach assumes that the salinity of water in catchment storages is a function of water age in those stores, without explicitly simulating salt mass balance processes. The paper compares these alternate approaches in terms of EC simulation performance, simulated stream water age distributions, and simulated storage age distributions. A split sample calibration‐validation analysis was conducted using the 2008 and 2009 water years. Both EC simulation approaches reproduced stream EC variations very well under both calibration and validation. The simulations using the age‐related EC simulation approach produced less biased results and, consequently, higher model coefficient of efficiency for validation periods. This approach also produced more consistent model parameter estimates between periods. There were systematic differences in the resultant age distributions between models, particularly for the solute balance‐based simulations where parameters (catchment storage size) changed more between the two calibration periods. The effect of time varying versus static storage selection functions were compared, with clear evidence that time varying storage selection functions with parameters linked to catchment conditions (flow) are essential for adequate simulation of EC dynamics during flow events.
  • Item
    Thumbnail Image
    Explaining changes in rainfall-runoff relationships during and after Australia's Millennium Drought: a community perspective
    Fowler, K ; Peel, M ; Saft, M ; Peterson, TJ ; Western, A ; Band, L ; Petheram, C ; Dharmadi, S ; Tan, KS ; Zhang, L ; Lane, P ; Kiem, A ; Marshall, L ; Griebel, A ; Medlyn, BE ; Ryu, D ; Bonotto, G ; Wasko, C ; Ukkola, A ; Stephens, C ; Frost, A ; Weligamage, HG ; Saco, P ; Zheng, H ; Chiew, F ; Daly, E ; Walker, G ; Vervoort, RW ; Hughes, J ; Trotter, L ; Neal, B ; Cartwright, I ; Nathan, R (COPERNICUS GESELLSCHAFT MBH, 2022-12-06)
    Abstract. The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.
  • Item
    Thumbnail Image
    Comparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model
    Tao, L ; Ryu, D ; Western, A ; Lee, S-G (MDPI, 2022-08)
    X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3/cm3 and 0.032 cm3/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.