Infrastructure Engineering - Research Publications
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ItemComparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model(MDPI, 2022-08-01)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.
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ItemAn analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts(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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ItemA Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments( 2021-01-12)Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programs have collected a large amount of data that provide a good basis to investigate the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provides an opportunity to further our understanding of water quality dynamics in sub-tropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of: 1) identifying the influential environmental drivers of temporal variation in flow event concentrations; and 2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g., runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations, as well as between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.
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ItemTowards Understanding Evapotranspiration Shifts Under a Drying Climate(Copernicus, 2022-03-28)Around 60 percent of terrestrial precipitation on the global average transforms into evapotranspiration. However, reliable estimation of actual evapotranspiration (AET) is challenging as it depends on multiple climatic and biophysical factors. Despite developments such as remotely sensed AET products, AET responses to prolonged drought is still poorly understood. Therefore, this study focuses on understanding long-term changes and variability of AET prior to and during the Millennium Drought in Victoria, Australia. We also investigate the capability of commonly used rainfall-runoff models to simulate AET under multiyear droughts. Therefore, we employ simple sensitivity analysis to examine four different water balance approaches between pre-drought and drought periods in six different study catchments in Victoria. The first water balance approach is the simplest long-term water balance approach, partitioning long-term precipitation into evapotranspiration and runoff. The second water balance approach adopts a long-term change in storage to the water balance during the Millennium Drought by employing regional-scale change in GRACE estimates derived from Fowler et al. (2020). The third and fourth water balances are based on simulations from SIMHYD and SACRAMENTO. Surprisingly, the adoption of long-term change in storage during the Millennium Drought indicates that the annual rates of pre-drought AET were largely maintained throughout the drought; i.e. the rate was relatively constant with time. This suggests that AET gets priority over streamflow following a drying shift in precipitation partitioning; resulting in a relatively constant AET under multiyear drought. In contrast, the rainfall-runoff models underestimated AET during the drought compared to both water balance approaches. These results broadly acknowledge the need for model improvements to provide more realistic AET estimates under future drying climates and provide a new perspective on recent hydrological phenomena such as changing rainfall-runoff relationships in these regions. Furthermore, this sensitivity analysis was augmented and confirmed by a regional-scale water balance approach.
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ItemExplaining changes in rainfall-runoff relationships during and after Australia's Millennium Drought: a community perspective( 2022-04-20)The Millennium Drought lasted more than a decade, and is notable for causing persistent shifts in the relationship between rainfall and runoff in many south-east 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 a range of possible process explanations of flow response, and then evaluates these hypotheses against available evidence. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (eg. why was the Millennium Drought different to previous droughts?) and spatially (eg. why did rainfall-runoff relationships shift in some catchments but not in 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, reduced recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and interception 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 understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.
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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, 2021-05-14)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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ItemIntroducing long-term trends into subseasonal temperature forecasts through trend-aware postprocessing(WILEY, 2022-01-06)Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend-aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30-year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30-year trends is to robustly represent long-term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20-year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day-of-year climatology, raw week-1 forecasts reproduce temperature trends of the 20-year observations in many regions while raw week-4 forecasts do not exhibit the 20-year observed trends. After trend-aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week-1 forecasts show apparent trends consistent with the 20-year observations, as the calibration transfers forecast skill and embeds the 20-year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week-4 forecasts exhibit the 30-year observed trends, as the calibration reverts the forecasts to the 30-year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend-aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend-aware method can be applied to deliver high-quality subseasonal forecasts and support decision-making in a changing climate.