A Simulation-Based Assessment of Irrigated Green Firebreaks for Reducing Fire Spread and Intensity in Wildland-Urban Interface Landscapes: Noosa Shire, Australia

Jady Damien Smith; Anthony Power; Francis E. Putz; Sam Van Holsbeeck

doi:10.20944/preprints202607.0192.v1

Submitted:

02 July 2026

Posted:

03 July 2026

You are already at the latest version

Abstract

Climate change, altered ecosystems and expanding development in fire-prone landscapes are increasing fire risk in the wildland–urban interface (WUI). This study uses Noosa, southeast Queensland, Australia, as a proof-of-concept case study to test irrigated Green Firebreaks (iGFBs). Using the AMICUS Vesta Mk2 fire-behaviour model, fire spread and fireline intensity were compared across dry eucalypt control scenarios, non-irrigated green firebreak scenarios, and irrigated green firebreak scenarios, receiving 1 and 2 mm m⁻2 day-1 of water. In line with future climate predictions, these scenarios were all compared under progressively worsening fire-weather conditions. The drought-affected dry eucalypt control produced the highest predicted fire spread and fireline intensity, while non-irrigated green firebreak scenarios reduced fire behaviour, but they may still exceed typical suppression thresholds under catastrophic conditions. In contrast, iGFB scenarios consistently reduced both fire spread and fireline intensity across all fire-weather classes. Sensitivity analysis indicated that the model outputs were most responsive to drought- and moisture-related assumptions, supporting the importance of fuel moisture in the performance of the iGFB concept. Although iGFBs are not a stand-alone solution suitable for all settings, the findings provide a region-specific proof of concept for iGFBs and support the need for further applied research.

Keywords:

irrigated green fire-break

;

wildland urban interface

;

green firebreak (GFB)

;

fire behaviour modelling

;

AMICUS

;

Vesta Mk2

Subject:

Environmental and Earth Sciences - Environmental Science

1. Introduction

Wildfire extent and intensity are predicted to increase under future climate scenarios, placing growing pressure on existing fire management approaches [1]. These challenges are compounded in wildland urban interface (WUI) environments, where expanding human development intersects with fire-prone vegetation [2]. Decades of reports, inquiries, and research into Australian fire disasters, including the post-Black Summer, 2020 NSW Bushfire Inquiry, have identified the need for enhanced preparedness and mitigation to strengthen disaster risk reduction [3,4,5]. The Australian Productivity Commission notes the considerable cost-benefit of proactive investments, but ongoing reactive emphasis [6]. As the potential for fire disasters in the WUI increases, complementary options to proactively mitigate fire are needed to enhance preparedness.

Green firebreaks (GFBs) are a proactive option that uses strategically placed low-flammability vegetation to reduce fire spread and fireline intensity; however, under extreme heat and drought conditions, effectiveness may be compromised [7]. Under climatic heating and drying trends, any vegetation near homes is becoming more flammable. A review of GFBs identified irrigation as a means of strengthening and maintaining fuel moisture and enhancing fire resistance [8]. Research on GFBs and proactive water management, such as irrigation for wildfire mitigation, is limited in fire management literature.

Fire behaviour modelling is widely used to support fire management by informing risk assessment, suppression planning, zoning, firebreak placement and prescribed burning [9]. Methods for predicting fire behaviour have been evolving, with increasingly detailed software programs [8], and more vegetation options have supported the predictive accuracy [10]. Contemporary models estimate fire behaviour metrics such as rate of spread and fireline intensity from environmental and fuel inputs, including vegetation and weather [11]. Field-based testing of landscape-scale mitigation strategies is often costly, risky, or impractical, and modelling provides a way to assess how changes in vegetation structure and moisture influence fire behaviour under defined conditions.

Fire behaviour models are designed to support fire management decision-making, and as they allow key design variables to be manipulated, they can be used to test new approaches. Scenario-based modelling can compare alternative vegetation and moisture configurations under increasingly severe fire-weather conditions, and has been used to explore GFB as a fire management strategy[12,13]. CSIRO AMICUS software is suited to this type of analysis because it allows vegetation and moisture inputs to be varied while generating outputs for fire spread and fireline intensity [14,15]. This makes it a useful platform for testing proof-of-concept fire mitigation scenarios in WUI landscapes.

The application of modelling to test nature-based fire mitigation interventions such as GFBs and irrigated vegetation systems remains limited. To address this gap, a conceptual iGFB design was developed for Noosa, Queensland, Australia (Smith et al., 2026, in press). The present study builds on this framework by evaluating whether irrigation can influence fire behaviour under increasingly severe fire weather conditions. Specifically, it tests the hypothesis that iGFBs reduce fire spread and fireline intensity relative to non-irrigated vegetation systems.

The CSIRO AMICUS Vesta Mk2 modelling platform was used to compare fire behaviour across contrasting dry and wet eucalypt vegetation and moisture scenarios under defined fire-weather conditions. The models were based on more extreme fire weather and drought conditions as worst-case scenarios, which are becoming more likely with climate change. Simulation scenarios were developed to compare (i) dry eucalypt vegetation under severe drought conditions as the control, (ii) non-irrigated green firebreaks, under drought conditions, and (iii) irrigated green firebreaks receiving 1- and 2-mm m⁻² day⁻¹ of water. These scenarios were evaluated across a gradient of fire weather conditions to assess differences in fire spread and fireline intensity. The study provides region-specific proof of concept for iGFB and assesses whether further applied research is warranted.

2. Methodology

2.1. Modelling Approach

2.1.1. Study Area

The study area is located in Southeast Queensland, a region projected to experience rising temperatures, drying trends, and more extreme fire-weather days [16]. The analysis focuses on the Noosa Local Government Area (26.3⁰S, 152.9⁰E), which covers 872 km². Noosa is characterised by a mix of coastal and hinterland landscapes, a sub-tropical climate, and approximately 1500 mm of mean annual rainfall falling over 107 days [17].

With a population of 59,274, the population density is 68 persons km^-2 [18]. Around 40% of the land area comprises protected areas, interspersed with urban and peri-urban development [19]. This landscape configuration creates extensive WUI zones in which residential areas are closely integrated with fire-prone vegetation (Smith et al 2026 in Press).

Noosa was selected as the case study because it is experiencing increasing fire risk and vulnerability, and its WUI has been identified as particularly complex to manage [19]. In addition, local policy settings and planning frameworks that emphasise environmental protection and climate response provide a relevant context for exploring proactive, vegetation-based mitigation approaches such as irrigated green firebreaks [12,20].

2.1.2. Scenario Design

This study is built on the GFB concept, which proposes that strategically placed vegetation and ecosystems can reduce fire spread and fireline intensity [8]. Landscape fuel moisture characteristics influence fire behaviour and risk [21]. The effectiveness of GFBs may decline under extreme drought and severe fire-weather conditions, when vegetation moisture decreases, and flammability increases [7,22]. This limitation provided the rationale for testing whether irrigation could strengthen vegetation-based fire mitigation by maintaining fuel moisture.

The design focused on manipulating vegetation type and fuel moisture as key variables influencing fire behaviour. Three landscape configurations were defined: (i) a control scenario representing dry eucalypt vegetation under drought conditions, (ii) a non-irrigated GFB scenario, and (iii) irrigated GFB (iGFB) design scenarios. To evaluate this iGFB concept, a simplified design was implemented within the fire behaviour modelling framework (Table 1).

The iGFB design was represented using a wet eucalypt forest as a proxy for a higher-moisture vegetation system, combined with irrigation inputs based on a Design Irrigation Rate for native vegetation in Queensland of 10 mm m^⁻2 week⁻¹ [26]. Irrigation scenarios were varied to simulate different levels of moisture availability, allowing comparison of fire behaviour under progressively wetter conditions. By systematically varying vegetation and moisture inputs, the modelling framework enables assessment of how iGFB configurations influence fire spread and fireline intensity under increasingly extreme fire weather conditions.

2.2. Scenario Design

Fire behaviour simulations were conducted using the CSIRO AMICUS 0.7.1 beta platform with Vesta Mk2 vegetation models (AMICUS). AMICUS is based on the Australian National Fire Behaviour Database and is used for bushfire prediction, prescribed burn planning, and assessment of fire behaviour under varying environmental conditions [14,15,23,24]. The model allows manipulation of key inputs, including vegetation type, fuel moisture, and fire weather, enabling comparison of alternative landscape configurations. The Vesta Mk2 module incorporates fire behaviour models for Australian vegetation types, including dry and wet eucalypt forests, and has been refined to improve prediction accuracy relative to earlier versions [15,25,26]. Model outputs included rate of fire spread (m h⁻¹) and fireline intensity (kW m⁻¹), which are used as primary indicators of fire behaviour. Fire behaviour was interpreted using three general propagation phases defined within the Vesta Mk2 framework:

Phase 1: reduced fire spread (<120 m h⁻¹) and fireline intensity (10-300 kW m⁻¹);
Phase 2: moderate fire spread (120-150 m h⁻¹), and fireline intensity (300 - 7500 kW m⁻¹); and
Phase 3: increased fire spread (>150 m h⁻¹) influenced by spotting, and higher fireline intensity (>7500 kW m⁻¹) [24].

In addition, a fireline intensity threshold of approximately 4,000 kW m⁻¹ was used as an indicative limit for effective suppression under Queensland conditions [27,28]. These thresholds provide a basis for comparing the relative performance of control, GFB, and iGFB scenarios under different fire weather conditions.

2.2.1. Scenario Parameterisation

Fire behaviour simulations were parameterised to represent conditions beyond those typical of the Noosa region, and in line with climate drought and fire weather predictions, while allowing controlled comparison between vegetation and irrigation scenarios. To reduce variability unrelated to the study objectives, several environmental inputs were standardised across all simulations. These include aspect (0°), slope (6°), elevation (69 m), and cloud cover (10%). These fixed input parameters simplify consideration of simulation outputs, and the inputs for fuel, weather and moisture are expanded to capture the design.

2.2.2. Fuel Inputs

Using the wildfire option in AMICUS, the specified fuel parameters were fuel type, fuel load, elevated fuel hazard, elevated fuel height, bark hazard, and a wind adjustment factor. Fuel parameters were linked to vegetation type, with Vesta Mk2 dry eucalypt used as a control vegetation type at 30 t ha⁻¹, and wet eucalypt used as a proxy for GFB and iGFB vegetation at 15 t ha⁻¹, as a surrogate for a lower-flammability, higher-moisture vegetation system [24].

Control areas were assigned a fuel hazard score of 3.5 on a four-point scale, whereas hazard scores for treated areas were set at 2.0. Fuel heights were assumed to be the same in control and treated areas. Bark hazard scores of 3.5 and 2.0 are assigned to the dry and wet eucalypt forests, respectively. Wind Adjustment Factors were set at 6 for the control scenario and 9 for the wet eucalypt design scenarios.

2.2.3. Weather Inputs

Cloud cover and wind direction were held constant across all scenarios. In contrast, air temperature, relative humidity, and wind speed were manipulated to simulate escalating fire-weather conditions for the control and design scenarios. The AMICUS fire weather parameters used to test the iGFB scenarios (Table 2) were selected to align with Australian Fire Danger Rating System (AFDRS) classes.

2.2.4. Moisture Inputs

In the control and non-irrigated GFB scenarios, drought conditions were set to severe, with rainfall held constant, but fuel moisture-related inputs varied from 7-14 % to capture changing moisture across scenarios (Table 3). AMICUS relies on a predictor model for fuel moisture in native wet eucalypt forest [29,30]. Accordingly, fuel moisture and soil dryness were adjusted, and irrigation rates were represented by modifying the inputs for Last Rainfall (mm) and Time Since Last Rain (days).

In Queensland, the design irrigation rate for native vegetation is approximately 10 mm week⁻¹ [31]. On this basis, the irrigation treatments were set at 1 and 2 mm m⁻2 day⁻¹, equivalent to 7 and 14 mm m^-2 week⁻¹. The purpose of the iGFB design was to maintain more consistent moisture availability, reduce drought effects, and shorten the period during which fuels remain highly combustible [32].

AMICUS calculates a drought factor ranging from 1 to 10, with 10 representing extreme drought. This parameter was adjusted so that the control and non-irrigated GFB sites represented more drought-stressed conditions. The design scenarios were scaled from severe drought (0 mm m⁻2 day⁻¹), to drought (1 mm m⁻2 day⁻¹), to moderate drought (2 mm m⁻2 day⁻¹), with the latter exceeding the prescribed regional design irrigation rate for native vegetation [31].

2.2.5. Sensitivity Analysis

Small changes in fire model parameters can influence outputs in non-linear ways [33]. To assess the robustness of the modelled treatment differences, a one-at-a-time sensitivity analysis was conducted on key input parameters expected to influence fire spread and fireline intensity (Table 4).

Parameters tested included vegetation, fuel moisture content, wind and drought factor. Each parameter was varied across a plausible low, baseline, and high setting while all other inputs were held constant. Sensitivity was assessed by comparing the resulting changes in predicted rate of fire spread and fireline intensity for the control, GFB, and iGFB scenarios.

The purpose of this analysis was not to generate new treatment scenarios, but to determine which assumptions most influenced the outputs and whether the relative advantage of the iGFB scenarios remained consistent under plausible variation in model inputs.

3. Results

AMICUS predicted rates of fire spread and fireline intensity results for the control, GFB and two iGFB treatments across five fire-weather scenarios, yielding 20 scenario combinations for comparison. These outputs were used to compare transitions in the fire behaviour phase and the relevance of the indicative suppression threshold of 4,000 kW m⁻¹.

3.1. Modelled Rate of Fire Spread

Under moderate fire weather, fires are predicted to spread nearly four times faster in the dry eucalypt control than in the wet eucalypt GFB without irrigation, and over 80 times faster than in the iGFB receiving 2 mm day⁻¹ of irrigation (Table 5, Figure 1). The dry eucalypt control entered Phase 2 at lower fire-weather levels and transitioned to Phase 3 under high and more severe fire-weather conditions. Under catastrophic conditions, the predicted spread rate exceeded 4 km h⁻¹, indicating a very rapid-moving forest fire.

By contrast, all iGFB scenarios remained within the Phase 1 rate-of-spread range across all fire weather scenarios. Under catastrophic weather conditions, irrigation was associated with substantially lower predicted rates of spread than both the dry eucalypt control and the non-irrigated GFB. Even at only 1 mm m⁻2 day⁻¹, irrigation was predicted to reduce fire spread substantially.

3.2. Modelled Fireline Intensity

Modelled fireline intensity was highly sensitive to drought (Figure 2). Under catastrophic droughts and fire-weather conditions, peak fireline intensities in dry eucalypt control exceed 65,000 kW m⁻¹. The strongest contrast was observed between the dry eucalypt control and the non-irrigated GFB scenario.

Most non-irrigated GFB scenarios and all iGFB scenarios stayed under the 4,000 kW m⁻¹ suppression threshold. However, under catastrophic conditions, the non-irrigated GFB scenario exceeds this threshold. In contrast, the irrigated scenarios remained below the threshold across the tested fire-weather range.

Table 6. Fireline intensity predicted using AMICUS 0.7.1 beta with Vesta Mk2 vegetation.

Modelled fireline intensity (kW m⁻¹) across fire weather	No Rating	Moderate	High	Extreme	Catastrophic
Control (Dry) 0 mm m^-2 day ^-1	6253	13633	24776	54553	65684
GFB (Wet) 0 mm m^-2 day ^-1	253	1739	2709	3487	4048
iGFB (Wet) 1 mm m^-2 day ^-1	88	653	1459	2155	2409
iGFB (Wet) 2 mm m^-2 day ^-1	2	83	84	83	72

3.3. Sensitivity of Model Outputs to Key Input Assumptions

Sensitivity analysis was used to assess how variation in selected input parameters influenced predicted fire spread and fireline intensity in the control and design scenarios. Alterations to the control scenario typically reduced fire spread and fireline intensity, whereas alterations to the iGFB design scenario typically increased both outputs. Despite some variation in magnitude, these changes did not reverse the overall treatment pattern.

For the control scenario, model outputs were most sensitive to drought-related inputs, particularly the shift from extreme to no drought. Fuel moisture and fuel load showed moderate sensitivity. The wind adjustment factor was less sensitive; however, the model already included moderate wind. The vegetation change to wet eucalypt was not sensitive and was flagged in the software as lacking input boundaries.

Table 7. Control Sensitivity Results – Rate of Spread.

Control - Rate of Spread (m h^-1) across fire weather scenarios	No Rating	Moderate	High	Extreme	Catastrophic
Control (Dry) 0 mm m¹ day¹	403	880	1598	3520	4238
Drought Factor (2)	24	50	66	74	89
Fuel Moisture (20%)	89	190	251	280	337
Fuel Load (10t)	47	501	1265	2350	4219
Wind Adjustment Factor (10)	265	589	1315	2377	4222
Vegetation (Wet not Dry)	403	880	1598	3520	4238

Table 8. Control Sensitivity Results – Fireline Intensity.

Control - Fireline Intensity (kW h^-1) across fire weather scenarios	No Rating	Moderate	High	Extreme	Catastrophic
Control (Dry) 0 mm m¹ day¹	6253	13633	24776	54553	65684
Drought Factor (2)	364	777	1026	1145	1377
Fuel Moisture (20%)	1280	2728	3602	4022	4836
Fuel Load (10t)	241	2586	6537	17832	21798
Wind Adjustment Factor (10)	4102	9137	20381	53646	65435
Vegetation (Wet)	6253	13633	24766	54553	65684

Figure 3. Control Sensitivity Chart highlighting sensitivity to change, especially wind and vegetation.

For the iGFB 2mm design scenario, model outputs were more variable than in the control. The rate of spread was more sensitive to drought and vegetation assumptions, whereas fireline intensity was more sensitive to fuel load and fuel moisture. Wind adjustment factor also had a strong influence on the design scenario outputs; however, the change was significant, from the very high to the lowest rate. Overall, the sensitivity analysis indicated that absolute model outputs varied with key assumptions, but the relative advantage of the iGFB scenario was generally maintained across the tested parameter range.

Table 9. iGFB2 Design Sensitivity Results – Rate of Spread.

iGFB2 - Rate of Spread (m h^-1) across fire weather scenarios	No Rating	Moderate	High	Extreme	Catastrophic
iGFB 2 mm m^-2 day^-1	0	11	11	10	9
Fuel Load (30t)	2	3	4	5	6
Fuel Moisture (7%)	1	12	14	15	17
Vegetation (Dry)	1	16	20	23	35
Drought Factor (10)	1	13	15	19	97
Wind Adjustment (2.5)	63	251	332	370	445

Table 10. iGFB2 Design Sensitivity Results – Fireline Intensity.

iGFB2 - Fireline Intensity (kW h^-1) across fire weather scenarios	No Rating	Moderate	High		Extreme	Catastrophic
iGFB mm m^⁻2 day^⁻1	2	83		84	83	72
Vegetation (Dry)	24	50		66	74	89
Drought Factor (10)	24	51		67	75	90
Fuel Moisture (7%)	5	95		106	112	135
Fuel Load (30t)	6	102		118	129	170
Wind Adjustment (2.5)	489	1948		2571	2871	3452

Figure 4. Design Sensitivity Chart highlighting sensitivity to change, especially the response to wind adjustment changes.

4. Discussion

This study used control, GFB, and iGFB scenarios to test whether irrigation could reduce modelled fire spread and fireline intensity under increasingly severe fire-weather conditions. Across the tested scenarios, the dry eucalypt control consistently produced the highest predicted rate of spread and fireline intensity, while the irrigated scenarios produced the lowest values. These results support the hypothesis that irrigation may strengthen green firebreak performance by maintaining lower fire behaviour relative to non-irrigated vegetation systems.

The control scenarios were intentionally configured to represent severe drought conditions and therefore indicate the types of behaviour that may occur where low fuel moisture combines with worsening fire weather. Notably, even in the absence of elevated fire-weather conditions, the control scenarios produced fireline intensities above the indicative suppression threshold of 4,000 kW m⁻¹. This result reinforces concern about the influence of drought on fire behaviour, even before additional fire-weather escalation is considered. In this respect, the results are consistent with research from the Black Summer fires, which identified drought and fire weather as stronger drivers of large fires than fuel alone [34].

The non-irrigated GFB scenario performed better than the dry eucalypt control across the tested fire-weather classes, indicating that vegetation change alone may reduce predicted fire behaviour relative to drought-affected dry eucalypt conditions. However, the non-irrigated GFB scenario still exceeded the suppression threshold under catastrophic conditions. These findings suggest that the advantages of GFBs without irrigation are most evident under mild-to-moderate fire-weather conditions and far superior to the dry eucalypt control, while also reinforcing concerns that such systems may lose effectiveness as vegetation dries under increasingly extreme conditions [7,8], especially when winds are not buffered.

By contrast, irrigated scenarios have fire spread and fireline-intensity benefits across all fire-weather scenarios, with greater benefits under more irrigation. Irrigating at 1 mm m^⁻2 day⁻¹ provides the greatest benefits when fire weather is absent; however, all these scenarios remained below Phase 3, and within suppression thresholds, reaching a maximum of 2409 kW m⁻¹ as fire weather became catastrophic. The 2 mm m^⁻2 day⁻¹ scenarios transitioned to Phase 2 only under Extreme and Catastrophic fire weather, but the peak fireline intensity of 561 kW m⁻¹ remains well within the suppression threshold. These findings suggest that irrigation may strengthen green firebreak function by maintaining lower fire behaviour under severe drought and fire-weather conditions. In practical terms, lower spread rates and lower fireline intensities may increase the time available for warning, preparation, evacuation, or suppression.

Several caveats are important. This was a simplified, model-based analysis of the effects of vegetation and irrigation on fire behaviour in a single case-study WUI landscape in south-east Queensland. The results should therefore be interpreted as conceptual rather than prescriptive. The AMICUS Vesta Mk2 fire spread model performs better than earlier models [23], but has a noted mean absolute error that may overpredict spread rates [10]. While conservative prediction is generally preferable to underestimation in fire management contexts, this uncertainty still affects the interpretation of the absolute values generated by the model.

While the dry eucalypt control scenarios were classified by AMICUS as having “good” model quality, the wet eucalypt design scenarios were classified only as “fair”. This does not invalidate the comparison, but it does indicate lower confidence in the underlying validation of the design-side simulations. An anomaly in the rate of spread results was noted; at 2 mm m^⁻2 day⁻¹ irrigation, it stabilized at moderate and higher fire weather (11 m h⁻¹ ), and then declined as the fire weather became extreme (10 m h⁻¹ ) and catastrophic (9 m h⁻¹ ). Given the small magnitude of this change, it did not alter the overall treatment pattern, but it may reflect reduced model performance at low spread rates or behaviour near the limits of the model’s valid operating range [14,26].

The sensitivity analysis reinforced consideration of drought and fire weather as the AMICUS model is driven more by environmental factors such as moisture, wind, and vegetation than by fuel load. Although vegetation is a less dynamic variable, the model shows that changes in moisture and wind can dramatically alter fire behaviour. At the same time, the sensitivity analysis suggested that although absolute values varied with parameter changes, the overall advantage of the iGFB scenarios was generally maintained across the tested range. This strengthens the interpretation that the effect of irrigation in the model was not solely an artefact of a single parameter setting.

More broadly, the present findings highlight both the usefulness and the limits of existing fire behaviour models when applied to novel, water-based mitigation concepts. The iGFB design intentionally manipulates vegetation and moisture, but these variables are not parameterised for this purpose within AMICUS, and the design scenarios rely on less-validated wet eucalypt proxy inputs. Further work is therefore needed to test whether the same patterns emerge under alternative modelling frameworks, broader parameter sets, and, ultimately, field-based investigation.

AMICUS provides a snapshot of the initial fire behaviour rather than the full temporal complexity of a long-duration wildfire. The persistence and cascading nature of major wildfire events mean that the longer-term performance of irrigated green firebreaks cannot be inferred from the present modelling exercise alone [35] [36]. Further research is therefore warranted on the feasibility, implementation, and trade-offs of irrigated green firebreaks, including issues such as vegetation selection, irrigation design, water sourcing, and longer-term system performance under real-world WUI conditions.

5. Conclusions

This study used a simplified modelling framework to examine how vegetation type and irrigation influenced predicted fire spread and fireline intensity in a wildland-urban interface (WUI) case study in Noosa, south-east Queensland, Australia. Across the tested fire-weather scenarios, the dry eucalypt control consistently produced the highest predicted rates of fire spread and fireline intensity, while the non-irrigated green firebreak (GFB) scenarios generally reduced fire behaviour but showed reduced effectiveness under the most severe fire-weather conditions. In contrast, the irrigated green firebreak (iGFB) scenarios produced the lowest predicted rates of spread and fireline intensity across the tested fire-weather range, with the strongest reductions observed under the higher irrigation treatment.

These findings provide an initial proof of concept that irrigation may strengthen green firebreak function by helping maintain lower fire behaviour under increasingly severe fire-weather conditions. The sensitivity analysis further indicated that the treatment effect was most responsive to drought-, moisture-, and wind-related assumptions, but that the relative advantage of the iGFB scenarios was generally maintained across the tested range. In this way, iGFBs may have potential to complement fire management in the WUI by slowing fire spread and reducing fireline intensity relative to non-irrigated vegetation systems. However, these findings are based on a simplified modelling framework and should be interpreted cautiously. iGFBs should not be considered a stand-alone solution, suitable for all settings, or capable of stopping all fires. Rather, they are more appropriately viewed as a site-specific component of a broader integrated fire management strategy.

The principal contribution of this paper is the testing of irrigated green firebreaks as a proactive fire-mitigation concept within a region-specific modelling framework. On that basis, this study supports the case for further applied research to assess the feasibility, performance, and limitations of iGFBs under real-world conditions.

Author Contributions

Conceptualisation, methodology, validation, investigation, resources, data curation, and original draft preparation, JDS.; writing—review and editing, visualisation, supervision, and project administration, J.D.S., A.P., F.E.P., and S.V.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The lead author’s PhD studies were supported by scholarships from the University of the Sunshine Coast and Natural Hazards Research Australia.

Acknowledgments

We acknowledge the past, present, and emerging traditional owners’ management of Country, as well as the important traditional approaches and scientific research that we seek to build upon. We also extend our gratitude to our research colleagues and peers for their ongoing support.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. Modelled rate of fire spread across fire-weather scenarios for the dry eucalypt control, non-irrigated green firebreak (GFB), and irrigated green firebreak (iGFB) scenarios. Values were predicted using AMICUS Vesta Mk2 under no rating, moderate, high, extreme, and catastrophic fire-weather conditions. The dry eucalypt control showed the highest predicted spread rates across all fire-weather classes, while the iGFB scenarios remained substantially lower, with the 2 mm day⁻¹ irrigation treatment producing the lowest predicted spread rates.

Figure 2. Modelled fireline intensity across fire-weather scenarios for the dry eucalypt control, non-irrigated green firebreak (GFB), and irrigated green firebreak (iGFB) scenarios. Values were predicted using AMICUS Vesta Mk2 under no rating, moderate, high, extreme, and catastrophic fire-weather conditions. The dry eucalypt control showed the highest predicted spread rates across all fire-weather classes, while the iGFB scenarios remained substantially lower, with the 2 mm day⁻¹ irrigation treatment producing the lowest predicted fireline intensity.

Table 1. Conceptual overview of the three configurations used in the AMICUS Vesta Mk2 modelling framework: (1) dry eucalypt under drought conditions as the control, (2) non-irrigated green firebreak (GFB) represented by wet eucalypt vegetation, and (3) irrigated green firebreak (iGFB) represented by wet eucalypt vegetation receiving 1 or 2 mm day⁻¹ of irrigation. All scenarios were evaluated across five fire-weather classes, from no rating to catastrophic, and compared using predicted rate of fire spread and fireline intensity.

Scenarios	Control-Dry Forest in drought	GFB - Wet Forest in drought		Design 1 - iGFB Wet Forest	Design 2 - iGFB Wet Forest
Vegetation (Vesta Mk 2)	Dry eucalypt	Wet eucalypt
Irrigation estimates	0 mm m⁻² day⁻¹			1 mm m⁻² day⁻¹	2 mm m⁻² day⁻¹

Purpose	Predicted conditions under drought	Represents green firebreak proxy without irrigation		Green firebreaks with different rates of irrigation
Assumption	Reduced fuel moisture increases fire risk	Higher moisture decreases fire risk relative to control		Maintaining higher fuel moisture, with irrigation, decreases fire spread and intensity
Fire Weather Parameters (AFDRS aligned)	No Rating	Moderate	High	Extreme	Severe	Catastrophic
Model Outputs - AMICUS Vesta Mk2	Fire Spread (m h¹ )
	Fireline Intensity (kW h¹ )
	Design scenarios (Control, GFB, iGFB1, & iGFB2) are compared across increasing fire weather to assess relative fire behaviour and the effectiveness of irrigation.

Table 2. AMICUS parameters used to represent increasing fire weather potential.

Fire Weather Potential	No Rating	Moderate	High	Extreme	Catastrophic
Air Temperature (C0)	20	25	30	35	40
Wind Speed (km h-1)	10	20	30	40	60
Relative Humidity (%)	60	50	40	20	10

Table 3. Moisture parameters for the control (dry eucalypt forest), non-irrigated GFBs, and iGFBs scenarios.

Moisture Parameters	Control 0 mm	GFB 0 mm	iGFB 1 mm	iGFB 2 mm
Fuel Moisture Content (%)	7	10	12	14
Last Rainfall (mm)	20	20	1	1
Time Since Last Rain (days)	60	60	1	1
Soil Dryness (mm)	200	175	100	50
Calculated Drought Factor (1-10)	10	10	6.9	4.4

Table 4. Sensitivity parameters with baseline, altered setting and rationale.

Sensitivity Parameters	Baseline	Alteration	Rationale
Fuel type (vegetation)	Control - Dry eucalypt	Wet eucalypt	To consider the impact of vegetation
Fuel type (vegetation)	iGFB 2 - Wet eucalypt	Dry eucalypt	To consider the impact of vegetation
Fuel load (t ha^-1)	Control - 30	10	To consider the impact of fuel load
Fuel load (t ha^-1)	iGFB 2 - 15	30	To consider the impact of fuel load
Fuel moisture content (%)	Control - 7%	20%	To consider the impact of fuel moisture
Fuel moisture content (%)	iGFB 2 - 14%	7%	To consider the impact of fuel moisture
Drought factor (1 to 10)	Control - 10	2	To consider the impact of drought sensitivity
Drought factor (1 to 10)	iGFB 2 - 2	10	To consider the impact of drought sensitivity
Wind adjustment factor (2-10)	Control - 6	10	To consider the impact of wind
Wind adjustment factor (2-10)	iGFB 2 - 9	2	To consider the impact of wind

Table 5. Rate of fire spread predicted using AMICUS 0.7.1 beta with Vesta Mk2 vegetation.

Rate of Spread (m h⁻¹) across fire weather	No Rating	Moderate	High	Extreme	Catastrophic
Control (Dry) 0 mm m^-2 day ^-1	403	880	1598	3520	4238
GFB (Wet) 0 mm m^-2 day ^-1	33	224	350	450	522
iGFB (Wet) 1 mm m^-2 day ^-1	4	26	44	84	106
iGFB (Wet) 2 mm m^-2 day ^-1	0	10	11	11	11

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