Infrastructure Engineering - Research Publications

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    An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts
    Guo, D ; Wang, QJ ; Ryu, D ; Yang, Q ; Moller, P ; Western, AW (SPRINGER, 2022-07-14)
    Abstract Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.
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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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    Toward capturing hydrologically significant connectivity in spatial patterns
    Western, AW ; Bloschl, G ; Grayson, RB (AMER GEOPHYSICAL UNION, 2001-01-01)
    Many spatial fields exhibit connectivity features that have an important influence on hydrologic behavior. Examples include high-conductivity preferred flow paths in aquifers and saturated source areas in drainage lines. Connected features can be considered as arbitrarily shaped bands or pathways of connected pixels having similar (e.g., high) values. Connectivity is a property that is not captured by standard geostatistical approaches, which assume that spatial variation occurs in the most random possible way that is consistent with the spatial correlation, nor is it captured by indicator geostatistics. An alternative approach is to use connectivity functions. In this paper we apply connectivity functions to 13 observed soil moisture patterns from the Tarrawarra catchment and two synthetic aquifer conductivity patterns. It is shown that the connectivity functions are able to distinguish between connected and disconnected patterns. The importance of the connectivity in determining hydrologic behavior is explored using rainfall-runoff simulations and groundwater transport simulations. We propose the integral connectivity scale as a measure of the presence of hydrologic connectivity. Links between the connectivity functions and integral connectivity scale and simulated hydrologic behavior are demonstrated and explained from a hydrologic process perspective. Connectivity functions and the integral connectivity scale provide promising means for characterizing features that exist in observed spatial fields and that have an important influence on hydrologic behavior. Previously, this has not been possible within a statistical framework.
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    On the computation of the quasi-dynamic wetness index with multiple-flow-direction algorithms
    Chirico, GB ; Grayson, RB ; Western, AW (AMER GEOPHYSICAL UNION, 2003-05-06)
    The quasi-dynamic wetness index, in its original development, was computed by calculating the travel time along all the possible upslope flow paths on a contour-based terrain network. In more recent applications the same approach has been extended to gridded digital elevation models with single-flow- direction algorithms. Multiple-flow-direction algorithms, although more effective in representing flow paths, have not been used because they are not practicable with the established methodology. We propose an alternative method for computing the quasi-dynamic wetness index based on the numerical integration of the linear-kinematic wave equation. This method can be applied to any of the terrain-based flow-direction algorithms currently published. The method is robust and efficient.