Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableShifts in stream salt loads during and after prolonged droughts(WILEY, 2023-06)Abstract It has been widely assumed that after prolonged droughts, catchment runoff recovers to pre‐drought levels. This assumption has recently been evaluated and challenged using empirical observations. However, water quality response and recovery, or otherwise, during and after prolonged droughts remains an open question. Answering this question potentially identifies any changes in catchment hydrological processes and water balance (e.g., the proportion of groundwater contribution to streamflow), thus informing the mechanisms for runoff non‐recovery after prolonged drought. Water quality responses to drought can also inform any long‐term water quality changes beyond what is observable from trend analyses. Here stream salt load changes were investigated using hidden Markov models (HMMs), where monthly rainfall was included as a predictor of stream salt loads. Monthly riverine salt fluxes at eight sites in Victoria (Australia) were examined before, during and after a prolonged drought in South‐East Australia—the Millennium Drought. Two‐state models, where salt loads varied between ‘normal’ and ‘low’ states, were found to better predict in‐stream salt loads compared to single‐state models. The results showed that catchments shifted to a low salt load state generally after the catchment changed to a low runoff state. As groundwater is understood to be the major source of salts in these catchments, this suggests that reductions in groundwater flow into rivers occur as a result of the shift to a lower runoff state. Understanding how readily water quality in catchments shift to different states during and after prolonged droughts enables appropriate catchment management based on our understanding of changes to catchment hydrology.
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ItemDetecting and explaining long-term changes in river water quality in south-eastern Australia(WILEY, 2022-11)Abstract Understanding the temporal changes in river water quality is important for catchment water quality management. This study aims to detect and attribute long‐term trends and abrupt changes in river water quality. We used 26 years of water quality data (1994–2020) collected from 102 river monitoring sites across Victoria, south‐eastern Australia. We analysed six water quality constituents that are of key concerns for Australian catchment management, namely: electrical conductivity (EC), total suspended solids, nitrate‐nitrite, total Kjeldahl nitrogen, total phosphorous and filtered reactive phosphorus. To detect trends and abrupt changes in water quality at each site, a Bayesian ensemble modelling approach was applied, namely, the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST). To explain water quality trends, we then built multivariate regressions to link water quality with streamflow and seasonality, and then compared alternative model structures with and without a change in the regression relationships informed by the changes detected by BEAST. Among the six constituents studied, EC shows the most distinct systematic trends, with 21 sites having a significant increase followed by a non‐significant trend; within the 21 sites, 14 had a significant change point in EC around Year 2010. The regression analyses between water quality and streamflow suggested that the observed systematic change in EC could be largely related to reduced streamflow during the Millennium drought, which greatly impacted the climate and hydrology of south‐eastern Australia over the first decade of 2000. The results of this study can help inform the design of effective mitigation strategies and avoid further degradation of water quality across Victoria. Besides, our trend analysis and attribution approaches are applicable to water quality time series in other regions for robust trend analysis and change point detection.
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ItemNo Preview AvailableDiverging projections for flood and rainfall frequency curves(ELSEVIER, 2023-05)
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ItemNo Preview AvailableTraining sample selection for robust multi-year within-season crop classification using machine learning(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.
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ItemUnderstanding event runoff coefficient variability across Australia using the hydroEvents R package(WILEY, 2022-04)Abstract Identification and pairing of hydrologic events form the basis of various analyses, from identifying events for the calibration of hydrologic models, to calculation of event runoff coefficients for catchment characterization. Despite this, there is no unified approach for identifying hydrologic events. Here, using the R package, hydroEvents (https://CRAN.R-project.org/package=hydroEvents), we compare multiple methods of extracting and pairing hydrologic events focussing on the relationship between rainfall and runoff. We find the four common analytical approaches used to identify runoff events—based on either event threshold, local maxima/minima, or proportion of baseflow contribution, give similar results. However, when rainfall events are paired to runoff, the type of algorithm and the direction of pairing (either from rainfall to runoff, or runoff to rainfall) make a considerable difference to the final event pairs identified and resulting analyses. Here, we demonstrate the value of automated event extraction and pairing algorithms for large‐sample hydrology analysis by calculating event runoff coefficients across Australia. Our results show that climatology is a key driver of catchment rainfall‐runoff response with much of Australia dominated by excess rainfall runoff generation. However, our results also show that the variability due to pairing method can introduce a variability equal to that of the climatology due to biasing the runoff mechanism within the sample. With this analysis we demonstrate the importance of systematic and consistent approaches to hydrologic characterization when identifying and pairing hydrological events.
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ItemAn analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts(SPRINGER, 2023-01)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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ItemA multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments(ELSEVIER SCI LTD, 2021-11-01)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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ItemEnhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery(MDPI, 2022-02)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.