Modelled estimates of dissolved inorganic nitrogen exported to the Great Barrier Reef lagoon

McCloskey, G.; Baheerathan, R.; Dougall, C.; Ellis, R H; Bennett, F.R.; Waters, David; Darr, S.; Fentie, B.; Hateley, Louise; Askildsen, M.

doi:10.1016/j.marpolbul.2021.112655

Cited by 20 publications

(4 citation statements)

References 73 publications

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“…The Wet Tropics region comprises 5% of the total GBR catchment area, however, this region contributes a large proportion of total nitrogen loads (TN~31%) exported to the GBR lagoon 49 , 50 . The Johnstone and Tully catchments, collectively, cover ~19% of the Wet Tropics region (~4,532 km 2 ) and have an average annual rainfall of ~3,500 mm (Fig.…”

Section: Methodsmentioning

confidence: 99%

The origin of suspended particulate matter in the Great Barrier Reef

Bahadori,

Chen,

Lewis

et al. 2023

Nat Commun

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River run-off has long been regarded as the largest source of organic-rich suspended particulate matter (SPM) in the Great Barrier Reef (GBR), contributing to high turbidity, pollutant exposure and increasing vulnerability of coral reef to climate change. However, the terrestrial versus marine origin of the SPM in the GBR is uncertain. Here we provide multiple lines of evidence (13C NMR, isotopic and genetic fingerprints) to unravel that a considerable proportion of the terrestrially-derived SPM is degraded in the riverine and estuarine mixing zones before it is transported further offshore. The fingerprints of SPM in the marine environment were completely different from those of terrestrial origin but more consistent with that formed by marine phytoplankton. This result indicates that the SPM in the GBR may not have terrestrial origin but produced locally in the marine environment, which has significant implications on developing better-targeted management practices for improving water quality in the GBR.

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Section: Methodsmentioning

confidence: 99%

The origin of suspended particulate matter in the Great Barrier Reef

Bahadori,

Chen,

Lewis

et al. 2023

Nat Commun

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“…the framework for evaluating and tracking progress towards achieving GBR water quality targets outlined in the Reef 2050 Water Quality Improvement Plan (Queensland, 2018). The models have been described in detail elsewhere (McCloskey et al, 2021a(McCloskey et al, , 2021b, but essentially DIN is modelled two ways in the Pioneer catchment model. Agricultural Production Systems Simulator (APSIM) (Shaw et al, 2013) paddock scale agricultural models are used to estimate DIN generation and transport into the wider catchment from sugarcane landuse areas.…”

Section: Catchment Water Quality Modelmentioning

confidence: 99%

Assimilating modelled dissolved inorganic nitrogen loads to monitored data using component-wise iterative ensemble Kalman inversion

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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Understanding the fate of dissolved inorganic nitrogen (DIN) sourced from land-based runoff is an important element in the development of strategies for managing water quality on the Great Barrier Reef (GBR). Direct measurement of the generation and transport of DIN from the landscape is impractical at large scale so most of the burden is deferred to water quality models (WQMs) that simulate catchment processes. Although WQMs are subject to uncertainty as most environmental models are, until recently WQMs designed to quantify nutrient, sediment and pesticide loads being discharged into the GBR lagoon have been deterministically calibrated with no reference to uncertainty. To properly assess the reliability and performance of WQMs they must be able to produce probabilistic predictions that correctly represent model uncertainty and Bayesian inference is an appealing framework for achieving this.Although the principles of Bayesian inference can be succinctly expressed as Bayes' theorem, 𝑓(M, |d) ∝ 𝑓(d|M, )𝑓(M, ), where M represents the model parameters,  are parameters of the likelihood probability density function (pdf) and d some measured data, its application for WQMs can be challenging. In all but the most trivial cases, there is no practical way of symbolically deriving the posterior 𝑓(M, |d). In the absence of an analytical representation of the posterior, one may resort to Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods for sampling from the posterior. Although these stochastic processes are a tour de force that can yield high quality results, they require a large number of model evaluations to converge to an equilibrated solution.Iterative ensemble Kalman inversion (IEKI) encompasses a family of algorithms, such as the ensemble randomized maximum likelihood method and ensemble smoother with multiple data assimilations that offer an efficient method for gradually propagating error statistics via an ensemble of model outputs to connect samples from the prior to an ensemble of posterior samples. The major advantage of these schemes is that they consume a fraction of the model evaluations that would be required otherwise and scale favorably with the dimensionality of the inverse problem. A major drawback of the ensemble methods in the current context is that in their formulation, they require that the likelihood 𝑓(d|M, ) is Gaussian and that the covariance, , is known. Unfortunately, measurement error statistics for water quality measurements used to inform the WQM calibration are difficult to derive and quite often unavailable. On the other hand, MCMC and SMC methods can infer the parametric likelihood given the data.The authors recently introduced the component-wise IEKI (CW-IEKI) which hybridizes MCMC and IEKI by iteratively updating the model parameters, M, using an ensemble step followed by an MCMC step to adjust the likelihood parameters, . Since the MCMC step requires no further model evaluations, the algorithm offers efficiency comparable with conventional ensemble method...

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“…Study data were also used to calibrate and validate the HowLeaky water balance submodel and to design, calibrate and validate the phosphorus and pesticide submodels (Thornton et al 2007;Robinson et al 2011;Shaw et al 2011). Within the Paddock to Reef Integrated Monitoring, Modelling and Reporting Program, data from the existing project have been used to model runoff, erosion, dissolved inorganic nitrogen and atrazine loss from cropping (Ghahramani et al 2020), to estimate erosion from grazed hillslopes and to validate remotely sensed ground-cover inputs to the model (Dougall and McCloskey 2017), and to calibrate and validate estimates of both erosion and nutrient loss estimations (McCloskey et al 2021a(McCloskey et al , 2021b.…”

Section: How Representative Is the Brigalow Catchment Study Of The Fi...mentioning

confidence: 99%

Leichhardt, land clearing and livestock: the legacy of European agriculture in the Brigalow Belt bioregion of central Queensland, Australia

Thornton¹,

Elledge²

2022

Anim. Prod. Sci.

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Context The Brigalow Belt bioregion of central Queensland has been extensively developed for agriculture since exploration by Leichhardt in 1844. About 4.5 million hectares of vegetation dominated by brigalow (Acacia harpophylla) was cleared as part of the Land Development Fitzroy Basin Scheme, which commenced in 1962. When the Vegetation Management Act 1999 commenced, 93% of brigalow woodland had been cleared. Grazing is the dominant land use in the Fitzroy Basin, with 2.6 million cattle over 11.1 million hectares (72% of the catchment area). This is the largest cattle herd in any natural resource management region in Australia, accounting for 25% of the state herd and 11% of the national herd. Aims The Fitzroy Basin, Queensland’s largest coastal catchment, drains directly to the Great Barrier Reef, and as reef health continues to decline, there has been increased focus on the impacts of land-use change and grazing management on hydrology and runoff water quality. The Brigalow Catchment Study sought to determine the impact of land clearing, land-use change and land management on hydrology, soil fertility, water quality and animal production in the Fitzroy Basin. Methods The study is a paired, calibrated catchment study. Catchment hydrology, soil fertility, water quality and agricultural productivity were monitored before and after land clearing and land-use change. Key results The Brigalow Catchment Study has shown that clearing brigalow for grazing in the Fitzroy Basin doubled runoff, increased peak runoff rate by 50% and increased total suspended solid loads by 80%. Soil fertility and pasture productivity also declined under grazing compared with brigalow. Overgrazing exacerbated these results, as failure to reduce stocking rate with reduced pasture productivity more than tripled runoff, peak runoff rate and total suspended solid load compared with conservatively grazed pasture. Conclusions This study demonstrates the impacts of land-use change and land management on hydrology, soil fertility and water quality. The long-term data records are a model in their own right, capable of answering land-use and land-management questions beyond the initial study scope. Implications Sustainable grazing management should consider the production limitations of depleted soil and pasture resources to minimise land degradation.

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Modelled estimates of dissolved inorganic nitrogen exported to the Great Barrier Reef lagoon

Cited by 20 publications

References 73 publications

The origin of suspended particulate matter in the Great Barrier Reef

The origin of suspended particulate matter in the Great Barrier Reef

Assimilating modelled dissolved inorganic nitrogen loads to monitored data using component-wise iterative ensemble Kalman inversion

Leichhardt, land clearing and livestock: the legacy of European agriculture in the Brigalow Belt bioregion of central Queensland, Australia

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