Infrastructure Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 23
-
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.
-
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.
-
ItemIn-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia(Copernicus Publications, 2021-03-03)<p>Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User&#8217;s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.</p><p>Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest</p>
-
ItemUsing short-term ensemble weather forecast to evaluate outcomes of irrigation(Copernicus Publications, 2021-03-03)<p>Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed 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 such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.</p><p>This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation; these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.</p>
-
ItemImproving the representation of cropland sites in the Community Land Model (CLM) version 5.0(Copernicus Publications, 2021-03-04)<p>The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding the two crop functional types (CFT) for sugar beet and potatoes to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity to reduce erosion and improve soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop specific parameterization, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes (RMSE reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes and seasonal cycle of LAI, and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water and energy fluxes.</p>
-
ItemSatellite cell-specific ablation of Cdon impairs integrin activation, FGF signalling, and muscle regeneration.(Wiley, 2020-08)BACKGROUND: Perturbation in cell adhesion and growth factor signalling in satellite cells results in decreased muscle regenerative capacity. Cdon (also called Cdo) is a component of cell adhesion complexes implicated in myogenic differentiation, but its role in muscle regeneration remains to be determined. METHODS: We generated inducible satellite cell-specific Cdon ablation in mice by utilizing a conditional Cdon allele and Pax7 CreERT2 . To induce Cdon ablation, mice were intraperitoneally injected with tamoxifen (tmx). Using cardiotoxin-induced muscle injury, the effect of Cdon depletion on satellite cell function was examined by histochemistry, immunostaining, and 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay. Isolated myofibers or myoblasts were utilized to determine stem cell function and senescence. To determine pathways related to Cdon deletion, injured muscles were subjected to RNA sequencing analysis. RESULTS: Satellite cell-specific Cdon ablation causes impaired muscle regeneration with fibrosis, likely attributable to decreased proliferation, and senescence, of satellite cells. Cultured Cdon-depleted myofibers exhibited 32 ± 9.6% of EdU-positive satellite cells compared with 58 ± 4.4% satellite cells in control myofibers (P < 0.05). About 32.5 ± 3.7% Cdon-ablated myoblasts were positive for senescence-associated β-galactosidase (SA-β-gal) while only 3.6 ± 0.5% of control satellite cells were positive (P < 0.001). Transcriptome analysis of muscles at post-injury Day 4 revealed alterations in genes related to mitogen-activated protein kinase signalling (P < 8.29 e-5 ) and extracellular matrix (P < 2.65 e-24 ). Consistent with this, Cdon-depleted tibialis anterior muscles had reduced phosphorylated extracellular signal-regulated kinase (p-ERK) protein levels and expression of ERK targets, such as Fos (0.23-fold) and Egr1 (0.31-fold), relative to mock-treated control muscles (P < 0.001). Cdon-depleted myoblasts exhibited impaired ERK activation in response to basic fibroblast growth factor. Cdon ablation resulted in decreased and/or mislocalized integrin β1 activation in satellite cells (weak or mislocalized integrin1 in tmx = 38.7 ± 1.9%, mock = 21.5 ± 6%, P < 0.05), previously linked with reduced fibroblast growth factor (FGF) responsiveness in aged satellite cells. In mechanistic studies, Cdon interacted with and regulated cell surface localization of FGFR1 and FGFR4, likely contributing to FGF responsiveness of satellite cells. Satellite cells from a progeria model, Zmpste24-/- myofibers, showed decreased Cdon levels (Cdon-positive cells in Zmpste24-/- = 63.3 ± 11%, wild type = 90 ± 7.7%, P < 0.05) and integrin β1 activation (weak or mislocalized integrin β1 in Zmpste24-/- = 64 ± 6.9%, wild type = 17.4 ± 5.9%, P < 0.01). CONCLUSIONS: Cdon deficiency in satellite cells causes impaired proliferation of satellite cells and muscle regeneration via aberrant integrin and FGFR signalling.
-
ItemMitochondrial Quality Control in the Heart: New Drug Targets for Cardiovascular Disease.(The Korean Society of Cardiology, 2020-05)Despite considerable efforts to prevent and treat cardiovascular disease (CVD), it has become the leading cause of death worldwide. Cardiac mitochondria are crucial cell organelles responsible for creating energy-rich ATP and mitochondrial dysfunction is the root cause for developing heart failure. Therefore, maintenance of mitochondrial quality control (MQC) is an essential process for cardiovascular homeostasis and cardiac health. In this review, we describe the major mechanisms of MQC system, such as mitochondrial unfolded protein response and mitophagy. Moreover, we describe the results of MQC failure in cardiac mitochondria. Furthermore, we discuss the prospects of 2 drug candidates, urolithin A and spermidine, for restoring mitochondrial homeostasis to treat CVD.
-
ItemZNF746/PARIS overexpression induces cellular senescence through FoxO1/p21 axis activation in myoblasts.(Springer Science and Business Media LLC, 2020-05-12)Various stresses, including oxidative stress, impair the proliferative capacity of muscle stem cells leading to declined muscle regeneration related to aging or muscle diseases. ZNF746 (PARIS) is originally identified as a substrate of E3 ligase Parkin and its accumulation is associated with Parkinson's disease. In this study, we investigated the role of PARIS in myoblast function. PARIS is expressed in myoblasts and decreased during differentiation. PARIS overexpression decreased both proliferation and differentiation of myoblasts without inducing cell death, whereas PARIS depletion enhanced myoblast differentiation. Interestingly, high levels of PARIS in myoblasts or fibroblasts induced cellular senescence with alterations in gene expression associated with p53 signaling, inflammation, and response to oxidative stress. PARIS overexpression in myoblasts starkly enhanced oxidative stress and the treatment of an antioxidant Trolox attenuated the impaired proliferation caused by PARIS overexpression. FoxO1 and p53 proteins are elevated in PARIS-overexpressing cells leading to p21 induction and the depletion of FoxO1 or p53 reduced p21 levels induced by PARIS overexpression. Furthermore, both PARIS and FoxO1 were recruited to p21 promoter region and Trolox treatment attenuated FoxO1 recruitment. Taken together, PARIS upregulation causes oxidative stress-related FoxO1 and p53 activation leading to p21 induction and cellular senescence of myoblasts.
-
ItemGrowth differentiation factor 15 protects against the aging-mediated systemic inflammatory response in humans and mice.(Wiley, 2020-08)Mitochondrial dysfunction is associated with aging-mediated inflammatory responses, leading to metabolic deterioration, development of insulin resistance, and type 2 diabetes. Growth differentiation factor 15 (GDF15) is an important mitokine generated in response to mitochondrial stress and dysfunction; however, the implications of GDF15 to the aging process are poorly understood in mammals. In this study, we identified a link between mitochondrial stress-induced GDF15 production and protection from tissue inflammation on aging in humans and mice. We observed an increase in serum levels and hepatic expression of GDF15 as well as pro-inflammatory cytokines in elderly subjects. Circulating levels of cell-free mitochondrial DNA were significantly higher in elderly subjects with elevated serum levels of GDF15. In the BXD mouse reference population, mice with metabolic impairments and shorter survival were found to exhibit higher hepatic Gdf15 expression. Mendelian randomization links reduced GDF15 expression in human blood to increased body weight and inflammation. GDF15 deficiency promotes tissue inflammation by increasing the activation of resident immune cells in metabolic organs, such as in the liver and adipose tissues of 20-month-old mice. Aging also results in more severe liver injury and hepatic fat deposition in Gdf15-deficient mice. Although GDF15 is not required for Th17 cell differentiation and IL-17 production in Th17 cells, GDF15 contributes to regulatory T-cell-mediated suppression of conventional T-cell activation and inflammatory cytokines. Taken together, these data reveal that GDF15 is indispensable for attenuating aging-mediated local and systemic inflammation, thereby maintaining glucose homeostasis and insulin sensitivity in humans and mice.
-
ItemDependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard(MDPI, 2021-07)Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.