Report on Methodological Challenges in Assessing Climate-Driven Wildfire Risk and Implications for Sustainable Development Goals

Abstract

Accurate assessment of wildfire danger is fundamental to achieving several Sustainable Development Goals (SDGs), notably SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 11 (Sustainable Cities and Communities). The Canadian Fire Weather Index (FWI), a key metric for this purpose, requires noon-specific meteorological data, which is often unavailable in climate models. This report evaluates the use of daily aggregated data as a proxy by comparing it against the standard noon-based method for the period 1980–2023. Findings indicate that while the standard method shows a significant global increase of approximately 65% in extreme fire-weather days, daily approximations tend to overestimate these trends by 5–10%. This discrepancy, particularly pronounced in up to 15 million km² of vulnerable regions, introduces significant uncertainty into climate impact projections, potentially undermining effective policy and action toward climate resilience and ecosystem protection. This report recommends prioritizing sub-daily data in climate models and adopting daily mean approximations as the least-biased alternative to ensure that strategies for achieving the SDGs are based on the most accurate scientific evidence.

1. Introduction: Wildfire Risk in the Context of Global Sustainability

1.1 The Challenge of Accurate Climate Projections

The Canadian Fire Weather Index (FWI) is a globally utilized system for assessing how meteorological conditions affect wildfire danger. Its calculation is standardized to use data recorded at local noon, which best correlates with peak fire activity in the afternoon. This precision is vital for developing strategies to protect ecosystems and communities, directly supporting the objectives of SDG 15 (Life on Land) and SDG 11 (Sustainable Cities and Communities).

1.2 Data Gaps and Their Impact on Sustainable Development

A significant challenge arises from the fact that global climate models, which are essential for projecting future risks under SDG 13 (Climate Action), typically provide only daily aggregated values (e.g., daily mean or maximum temperature) rather than noon-specific data. This forces researchers to use approximations, creating systematic biases and inconsistencies that can compromise the reliability of future fire danger projections. Inaccurate risk assessments can lead to flawed adaptation strategies, misallocation of resources, and a failure to adequately protect vulnerable populations and ecosystems from the escalating impacts of climate change.

1.3 Study Objectives

This report aims to quantify the discrepancy between FWI trends calculated using standard noon-specific data versus various daily approximations. By identifying the magnitude and spatial patterns of these differences, the analysis provides critical insights into the reliability of current climate change impact studies on wildfire risk. The findings are intended to inform the scientific community and policymakers, promoting more robust methodologies that align with the urgent need for evidence-based action to achieve global sustainability targets.

2. Analysis of Findings: Discrepancies in Wildfire Danger Trends

2.1 Global Trend Analysis (1980-2023)

The analysis of global fire weather trends reveals critical differences between the standard calculation method and common approximations:

• Baseline Trend: Using the standard noon-specific data, the annual count of extreme fire-weather days (FWI95d) increased globally by approximately 65% over the 44-year period. This starkly illustrates the intensifying threat of climate change, reinforcing the urgency of SDG 13 (Climate Action).
• Overestimation by Proxies: All four tested combinations of daily aggregated data overestimated this increasing trend. The overestimations ranged from 5% to 10%, suggesting that many current climate impact studies may be inflating projections of future wildfire risk.
• Worst-Performing Proxy: The most significant overestimation was produced by the combination of daily maximum temperature and minimum relative humidity. This is concerning as this combination is a widely adopted proxy in the absence of sub-daily data.

2.2 Regional Discrepancies and SDG Implications

The overestimation of fire danger is not uniform, with specific regions showing significant divergence from the baseline trend. These areas, including the western United States, southern Africa, parts of Asia, and the Iberian Peninsula, cover up to 15 million km².

• Impact on SDG 15 (Life on Land): Many of these regions are critical biodiversity hotspots. Inaccurate risk assessments can compromise conservation efforts and sustainable forest management by misdirecting resources and promoting inappropriate land management strategies.
• Impact on SDG 11 (Sustainable Cities and Communities): These areas also contain expanding wildland-urban interfaces. Overstating risk could lead to inefficient allocation of funds for resilience, while the underlying real trend still poses a grave danger to lives, property, and infrastructure.

2.3 The Critical Role of Relative Humidity

The primary driver of these discrepancies is the approximation of relative humidity (RH). The divergence is most acute in arid and semi-arid regions where air temperature is rising while absolute humidity is declining. This decoupling amplifies the sensitivity of the FWI to RH changes, making the choice of proxy particularly impactful. This finding highlights the complex, interconnected processes that must be understood to effectively address SDG 13 and SDG 15.

3. Discussion: Implications for Achieving the Sustainable Development Goals

3.1 Undermining SDG 13 (Climate Action)

The systematic overestimation of wildfire danger trends by daily data proxies can distort our understanding of climate change impacts. To take effective action on climate change and its impacts, policymakers require the most accurate risk assessments possible. Flawed projections can lead to maladaptation, where resources are invested in addressing an inflated version of the problem, potentially diverting attention from more appropriate solutions.

3.2 Threatening SDG 15 (Life on Land) and SDG 3 (Good Health and Well-being)

Protecting terrestrial ecosystems and halting biodiversity loss requires a precise understanding of fire regimes. Exaggerated fire danger trends could lead to misguided land management policies. Furthermore, accurate predictions are essential for public health systems to prepare for the severe air quality impacts of wildfire smoke, a direct threat to human health under SDG 3.

3.3 A Call for SDG 17 (Partnerships for the Goals)

This report’s findings underscore the need for enhanced collaboration within the global climate science community, a core principle of SDG 17. Improving the data infrastructure for sustainable development is a shared responsibility. The recommendations below are a call to action for climate modeling centers, data repositories, and researchers to work together to improve the accuracy and reliability of the data that underpins global climate policy.

4. Recommendations for More Reliable Wildfire Risk Assessment

To ensure that efforts to mitigate wildfire risk and adapt to climate change are effective and aligned with the Sustainable Development Goals, the following actions are recommended:

1. Enhance Climate Data Infrastructure: Climate modeling centers participating in future initiatives like the Coupled Model Intercomparison Project Phase 7 (CMIP7) should prioritize making key meteorological variables (temperature, humidity, wind speed) available at a sub-daily temporal resolution. This would significantly improve the accuracy of FWI calculations and strengthen the evidence base for policies related to SDG 13 and SDG 15.
2. Adopt Least-Biased Methodologies: When sub-daily data is unavailable, researchers and policymakers should use approximations based on daily mean values for temperature and relative humidity. The common practice of using daily maximum temperature and minimum relative humidity should be reassessed, as this study demonstrates it introduces the largest bias and overestimation of risk.
3. Focus on Regional-Scale Dynamics: Further research is needed to understand the regional-scale climate dynamics that drive wildfire danger, particularly in data-scarce yet highly vulnerable regions. This will support the development of tailored adaptation strategies that protect unique ecosystems (SDG 15) and vulnerable communities (SDG 11).

1. Which SDGs are addressed or connected to the issues highlighted in the article?

SDG 13: Climate Action

• The article directly addresses the consequences of climate change, stating that “high FWI conditions have become increasingly frequent, prolonged, and severe under ongoing greenhouse gas emissions.” The entire study is focused on improving the assessment of climate-driven wildfire danger, which is a critical aspect of taking action to combat climate change and its impacts.

SDG 15: Life on Land

• The research is centered on wildfire danger, a major threat to terrestrial ecosystems, particularly forests. The article discusses “landscape flammability” and the increasing fire risk in forested regions across the globe, including the “western United States, southern Africa, and parts of Asia.” This connects directly to the goal of protecting, restoring, and promoting the sustainable use of terrestrial ecosystems and sustainably managing forests.

SDG 17: Partnerships for the Goals

• The article emphasizes the need for global scientific collaboration and improved data sharing. It references international initiatives like the “Coupled Model Intercomparison Project (CMIP)” and recommends that future projects “prioritizing the inclusion of sub-daily meteorological data” to enhance accuracy. This highlights the importance of partnerships to mobilize and share knowledge and technology for sustainable development.

2. What specific targets under those SDGs can be identified based on the article’s content?

SDG 13: Climate Action

1. Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries. The article’s analysis of the Fire Weather Index (FWI) and the “annual count of extreme fire-weather days” aims to improve the tools used to understand and adapt to wildfires, which are a significant climate-related natural disaster.
2. Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warning. The research contributes to this target by refining the scientific basis for FWI calculations, which are essential for early warning systems and enhancing the institutional capacity of fire management agencies to make accurate projections.

SDG 15: Life on Land

1. Target 15.2: By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globally. Accurate FWI assessments are crucial for sustainable forest management, as they help predict and mitigate the risk of wildfires, a major driver of forest degradation and loss.
2. Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world. The article identifies increasing fire weather trends in semi-arid regions like “southern Africa,” where wildfires can exacerbate land degradation and desertification.

SDG 17: Partnerships for the Goals

1. Target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation. The study relies on global reanalysis datasets (ERA5) and contributes to a global body of scientific knowledge. Its recommendation for improving data availability in projects like CMIP7 is a direct call to enhance international cooperation and access to scientific data.
2. Target 17.18: By 2020, enhance capacity-building support to developing countries… to increase significantly the availability of high-quality, timely and reliable data. The article’s central argument is that the lack of “sub-daily” or “noon-specific” data in climate models leads to unreliable projections. It advocates for more timely and reliable data to improve FWI accuracy globally.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

For Targets 13.1 and 15.3

• Indicator: Annual count of extreme fire-weather days (FWI95d). The article uses this as its primary metric to assess the trend in climate-related hazards. It quantifies this indicator, stating that “noon-based FWI95d show a global increase of ~65% (11.66 days over 44 years).”
• Indicator: Trends in meteorological variables. The study analyzes trends in “air temperature,” “relative humidity,” and “dew point temperature” as underlying drivers of fire risk, which can be monitored to track changing hazard levels.

For Target 15.2

• Indicator: Area with significant overestimation of fire risk trends. The article quantifies this as “up to 15 million km²,” identifying regions where models may be providing flawed information for forest management. Reducing this area of uncertainty would indicate progress.

For Targets 17.6 and 17.18

• Indicator: Availability of sub-daily vs. daily aggregated climate data in global models. The article highlights the problem that “climate models often provide only daily aggregated values.” An increase in the availability of sub-daily data in global repositories like the Earth System Grid Federation (ESGF) would be a direct measure of progress.
• Indicator: Bias in FWI trend calculations from proxy data. The article quantifies this bias, noting that “daily approximations tend to overestimate these trends by 5–10%.” Reducing this bias through improved data and methods would indicate an improvement in the reliability of shared scientific tools.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article. In this table, list the Sustainable Development Goals (SDGs), their corresponding targets, and the specific indicators identified in the article.

Source: nature.com