Infrastructure Engineering - Research Publications

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    A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments
    Liu, S ; Ryu, D ; Webb, JA ; Lintern, A ; Guo, D ; Waters, D ; Western, AW (ELSEVIER SCI LTD, 2021-05-14)
    Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.
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    Cover Image, Volume 8, Issue 2
    Kattel, G ; Reeves, J ; Western, A ; Zhang, W ; Jing, W ; McGowan, S ; Cuo, L ; Scales, P ; Dowling, K ; He, Q ; Wang, L ; Capon, S ; Pan, Z ; Cui, J ; Zhang, L ; Xiao, L ; Liu, C ; Zhang, K ; Gao, C ; Tian, Z ; Liu, Y (Wiley, 2021-03)
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    Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery
    Gao, Z ; Guo, D ; Ryu, D ; Western, AW (MDPI, 2022-02-01)
    Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort.
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    Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems
    Guo, D ; Parehkar, A ; Ryu, D ; Wang, QJ ; Western, AW (MDPI, 2022-03-01)
    Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
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    Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
    Yang, Q ; Wang, QJ ; Western, AW ; Wu, W ; Shao, Y ; Hakala, K (COPERNICUS GESELLSCHAFT MBH, 2022-02-18)
    Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.
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    Healthy waterways and ecologically sustainable cities in Beijing-Tianjin-Hebei urban agglomeration (northern China): Challenges and future directions
    Kattel, G ; Reeves, J ; Western, A ; Zhang, W ; Jing, W ; McGowan, S ; Cuo, L ; Scales, P ; Dowling, K ; He, Q ; Wang, L ; Capon, S ; Pan, Z ; Cui, J ; Zhang, L ; Xiao, L ; Liu, C ; Zhang, K ; Gao, C ; Tian, Z ; Liu, Y (WILEY, 2020-12-13)
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    The politicisation of science in the Murray-Darling Basin, Australia: discussion of 'Scientific integrity, public policy and water governance'
    Stewardson, MJ ; Bond, N ; Brookes, J ; Capon, S ; Dyer, F ; Grace, M ; Frazier, P ; Hart, B ; Horne, A ; King, A ; Langton, M ; Nathan, R ; Rutherfurd, I ; Sheldon, F ; Thompson, R ; Vertessy, R ; Walker, G ; Wang, QJ ; Wassens, S ; Watts, R ; Webb, A ; Western, AW (Taylor & Francis, 2021-10-30)
    Many water scientists aim for their work to inform water policy and management, and in pursuit of this objective, they often work alongside government water agencies to ensure their research is relevant, timely and communicated effectively. A paper in this issue, examining 'Science integrity, public policy and water governance in the Murray-Darling Basin, Australia’, suggests that a large group of scientists, who work on water management in the Murray-Darling Basin (MDB) including the Basin Plan, have been subject to possible ‘administrative capture'. Specifically, it is suggested that they have advocated for policies favoured by government agencies with the objective of gaining personal benefit, such as increased research funding. We examine evidence for this claim and conclude that it is not justified. The efforts of scientists working alongside government water agencies appear to have been misinterpreted as possible administrative capture. Although unsubstantiated, this claim does indicate that the science used in basin water planning is increasingly caught up in the politics of water management. We suggest actions to improve science-policy engagement in basin planning, to promote constructive debate over contested views and avoid the over-politicisation of basin science.
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    Modelling the interaction between vegetation and infiltrated stormwater
    Poozan, A ; William Western, A ; James Burns, M ; Arora, M (Elsevier, 2022-04-01)
    A major problem associated with sealing native soils with impervious surfaces in urban areas is reduced groundwater recharge. This in turn reduces stream baseflows which has serious implications for freshwater ecosystems. To address this problem, the use of stormwater infiltration systems is becoming increasingly common worldwide. There is, however, substantial uncertainty on the fate of infiltrated stormwater and its interactions with downslope vegetation. This study aimed to investigate the role of vegetation on the amount of infiltrated stormwater reaching the stream. A model using MIKE SHE was constructed, calibrated, and validated based on a real infiltration system which features extensive vegetation between the site of stormwater infiltration and the stream. We then used the calibrated model to predict the amount of infiltrated stormwater reaching the stream in the absence of vegetation. We also predicted the impact of infiltrated stormwater on the evapotranspiration downslope of the system. The results showed that the performance of the model was satisfactory, and the model captured the overall groundwater dynamic very well. The amount of infiltrated stormwater reaching the stream increased by about 17 percent in the absence of vegetation. The model also predicted that evapotranspiration would be 13 percent lower in the warmer months if stormwater was not infiltrated upslope. The results suggest that the choice of location of infiltration systems should consider the potential of vegetation to intercept infiltrated water and impact on achievement of the design objectives, which, in this case, included restoring baseflow. Where increasing the baseflows is not a priority, the increased evapotranspiration afforded by stormwater infiltration could provide important microclimate benefits.
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    Impacts of stormwater infiltration on downslope soil moisture and tree water use
    Western, AW ; Arora, M ; Burns, MJ ; Bonneau, J ; Thom, JK ; Yong, CF ; James, RB ; Poelsma, PJ ; Fletcher, TD (IOP Publishing Ltd, 2021-10-01)
    Abstract Infiltration of stormwater is a widely used strategy to mitigate the flooding and environmental risks that come from urban runoff and conventional urban drainage. An understanding of the fate of this infiltrated water is required for rigorous design. Principal design objectives are typically to restore more natural hydrology in order to protect receiving waters from pollution and hydrologic change. Without such understanding there is also a risk of unforeseen impacts on nearby infrastructure and urban vegetation. We sought to understand the pathways and fate of water from a stormwater infiltration basin. To trace water, we used a combination of water table monitoring and isotopic composition analysis in the infiltration basin, as well as in rainfall, soil water, the shallow groundwater, and in vegetation upslope and downslope of the basin. We also measured tree water use directly using sap flow sensors. The infiltration basin was shown to increase the availability of water downslope, allowing trees to maintain elevated levels of water use during dry periods with high energy demand. In contrast, water limitation upslope saw substantial seasonal reductions in tree water use. The soil water isotopic composition demonstrated significant differences from upslope to downslope, with downslope water being more reflective of rainfall, while the upslope water used by the trees was more depleted. The results paint a picture of stormwater infiltration being a significant source of lateral flow, while trees are a significant sink of lateral flow emanating from the basin. This finding suggests that stormwater infiltration could be used as a strategy to support the health and growth of urban trees. Urban trees have demonstrated benefits for human health and comfort, particularly in a warming climate. It also suggests that stormwater infiltration may not always recharge groundwater and provide baseflow in receiving waters, being instead taken up by vegetation. These findings should be considered in the siting of stormwater infiltration systems, to ensure that the objectives they were designed for are actually met.
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