A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments

Ghahramani, Afshin; Freebairn, D. M.; Sena, D.R.; Cutajar, Justin L.; Silburn, D. M.

doi:10.1016/j.envsoft.2020.104733

Cited by 7 publications

(7 citation statements)

References 57 publications

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“…Further model refinement could focus on constraining the upper simulation values throughout the model. A qualitative "reasonable fit" visual inspection has been shown to be an effective means of assessing model performance using diverse incomplete data sets (Ghahramani et al, 2020). Ghahramani et al (2020) found that the ranking of confidence in model predictions between determinands was related to data availability as much as to the model itself, with pesticide simulations performing less well than those for hydrology, sediments, and phosphorus.…”

Section: Model Validationmentioning

confidence: 99%

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

Troldborg

Gagkas

Vinten

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km2) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

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Section: Model Validationmentioning

confidence: 99%

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

Troldborg

Gagkas

Vinten

et al. 2022

Hydrol. Earth Syst. Sci.

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“…The program uses monitoring and modelling tools at the paddock, catchment and marine scale to enable reporting in the short-to-medium term (Waterhouse 2018). The findings from studies such as the Brigalow Catchment Study are extrapolated across subcatchments by using models such as HowLeaky and APSIM (Keating et al 2003;Ghahramani et al 2020). The outputs are then aggregated and routed to the basin outlet by using the Great Barrier Reef Dynamic SedNet model (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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“…Unlike deterministic modelling approaches that use quantitative parameters and initial conditions to simulate outputs [24], BBNs use probabilistic expressions to characterize the strength of the relationships between variables [25,26]. This means that BBNs can incorporate both quantitative and qualitative information, as well as information of variable quality, such as subjective assessments (e.g., expert opinion) of the probability that a particular outcome will occur, where data may be limiting.…”

Section: Bayesian Belief Network Modelsmentioning

confidence: 99%

“…A BBN model that is established from expert opinion and the published literature can be improved by incorporating additional available datasets to calibrate the CPTs [56,57] as new data become available over time. The Dirichlet distribution for CPT columns intuitively interprets the combined data (expert beliefs and observed data) [58]; however, considering the small number of available datasets, we applied a manual calibration [24]. To do this, the CPTs of the initial expert-elicited BBN model were updated, where possible, from the available spoil characteristics and environmental covariates using the Lake Lindsay site data collected 18 months post treatment (Figure 5).…”

Section: Updating Of Cptsmentioning

confidence: 99%

A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil

et al. 2021

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Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand.

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A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments

Abstract: A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments, Environmental Modelling and Software (2020), doi:

Cited by 7 publications

References 57 publications

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

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

A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil

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