Evaluation and Projection of Global Burned Area Based on Global Climate Models and Satellite Fire Product
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. GCM Datasets on BA
2.2.2. Satellite-Based BA Monitor Dataset
2.3. Methods
2.3.1. Statistical Indicators
2.3.2. Bayesian Model Averaging (BMA)
2.3.3. Mann–Kendall (MK) Trend Test
2.3.4. Uncertainty Analysis
3. Results
3.1. Evaluation of Historical Fire BA Simulation
3.1.1. Annual Assessment
3.1.2. Monthly Assessment
3.1.3. Seasonal Assessment
3.1.4. Comparisons of Global BAF Classes
3.2. Multi-Model Ensemble for Historical Fire BA Simulations
3.2.1. Model Ranking and Screening
3.2.2. The Weights of Optimal Models and BMA Simulation
3.2.3. Spatial Comparison of Monthly BA Between BMA Model and GFED4s
3.3. Future Fire BA Prediction Based on the BMA Model
3.3.1. Validation of the Projections for the 2015–2040 Period
3.3.2. Future Temporal Changes in the Annual BA Under Different Scenarios
3.3.3. Future Spatial Changes in BA Under Different Scenarios
4. Discussion
4.1. Uncertainty Analysis of BA Under Different Scenarios
4.2. Uncertainty in Historical Simulation Results
4.3. Effects of Different Land Cover Types
4.4. Selection of Different Assessment Methods
4.5. Future Implications of BA Increases
4.6. Potential Limitations of GCMs and Satellite Data for BA Projections
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Source | Country | Organization | Horizontal Grid (Lat × lon) |
---|---|---|---|---|
GFED4s (a) | MODIS and TRMM | USA | NASA | - |
CESM2 (b) | CMIP6 | USA | NCAR | 288 × 192 |
CESM2-FV2 (c) | CMIP6 | USA | NCAR | 288 × 192 |
CESM2-WACCM (d) | CMIP6 | USA | NCAR | 288 × 192 |
CESM2-WACCM-FV2 (e) | CMIP6 | USA | NCAR | 288 × 192 |
CMCC-ESM2 (f) | CMIP6 | Italy | CMCC | 288 × 192 |
CMCC-CM2-SR5 (g) | CMIP6 | Italy | CMCC | 288 × 192 |
NorESM2-LM (h) | CMIP6 | Norway | NCC | 144 × 96 |
NorESM2-MM (i) | CMIP6 | Norway | NCC | 288 × 192 |
GFDL-ESM4 (j) | ISIMIP | USA | NOAA-GFDL | 288 × 180 |
UKESM1-0-LL (k) | ISIMIP | UK | MOHC | 192 × 144 |
Scenario | Radiative Forcing | Radiative Forcing in 2100/(W·m−2) | SSP |
---|---|---|---|
SSP1-2.6 (SSP126) | low | 2.6 | Sustainability |
SSP3-7.0 (SSP370) | high | 7.0 | Regional rivalry |
SSP5-8.5 (SSP585) | high | 8.5 | Fossil-fueled development |
Model | (b–a) | (c–a) | (d–a) | (e–a) | (f–a) | (g–a) | (h–a) | (i–a) | (j–a) | (k–a) | |
---|---|---|---|---|---|---|---|---|---|---|---|
Region | |||||||||||
BONA | −0.120 | −0.156 | −0.123 | −0.155 | 0.293 | 0.675 | −0.150 | −0.063 | −0.031 | 0.004 | |
TENA | 0.490 | 0.539 | 0.679 | 0.376 | 2.261 | 2.968 | 0.230 | 0.517 | 2.674 | 2.512 | |
CEAM | 2.230 | 1.543 | 2.493 | 2.260 | 3.356 | 3.585 | 1.049 | 1.946 | 2.018 | 1.885 | |
NHSA | −0.125 | −0.432 | −0.275 | −0.238 | 1.927 | 2.385 | −0.516 | −0.729 | 10.176 | 9.676 | |
SHSA | 4.663 | 4.220 | 4.921 | 2.805 | 3.170 | 3.923 | 2.057 | 4.441 | 8.402 | 7.130 | |
EURO | 0.156 | 0.514 | 0.459 | 0.485 | 1.178 | 1.324 | 0.783 | 0.546 | 0.142 | 0.167 | |
MIDE | 0.443 | 0.458 | 0.471 | 0.427 | 0.306 | 0.286 | 0.502 | 0.466 | −0.062 | −0.057 | |
NHAF | −4.377 | −5.378 | −4.315 | −6.011 | −5.466 | −5.602 | −4.119 | −3.694 | 1.481 | 1.045 | |
SHAF | −4.604 | −2.815 | −4.384 | −5.819 | −8.980 | −9.232 | −6.773 | −3.850 | 8.077 | 7.032 | |
BOAS | −0.257 | −0.388 | −0.271 | −0.410 | 0.536 | 0.750 | −0.380 | −0.012 | −0.302 | −0.321 | |
CEAS | −0.120 | −0.156 | −0.123 | −0.155 | 0.293 | 0.675 | −0.150 | −0.063 | −0.031 | 0.004 | |
SEAS | 0.296 | −0.510 | 0.216 | −0.578 | 0.234 | −0.066 | −0.046 | 0.400 | 0.026 | 0.074 | |
EQAS | −0.136 | 0.075 | −0.295 | 0.042 | 0.520 | 0.075 | 0.111 | −0.130 | −0.049 | −0.236 | |
AUST | −2.044 | −2.331 | −2.351 | −2.148 | −2.206 | −2.903 | −2.858 | −2.858 | −2.231 | −2.597 |
Model | GFED4s | (b) | (c) | (d) | (e) | (f) | (g) | (h) | (i) | (j) | (k) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Region | ||||||||||||
BONA | 6–8 | 7–9 | 7–10 | 7–9 | 7–10 | 7–9 | 7–9 | 6–10 | 6–9 | 6–10 | 6–9 | |
TENA | 3–4, 7–9 | 7–9 | 7–10 | 7–10 | 7–10 | 7–9 | 7–9 | 7–9 | 6–10 | 6–10 | 6–10 | |
CEAM | 3–5 | 4–7 | 4–8 | 3–7 | 4–8 | 4–8 | 4–8 | 4–8 | 3–7 | 2–6 | 2–6 | |
NHSA | 12–3 | 1–5 | 2–5 | 2–5 | 2–5 | 1–5 | 1–5 | 2–5 | 2–5 | 1–4 | 1–4 | |
SHSA | 7–10 | 8–11 | 8–11 | 8–11 | 8–11 | 8–10 | 8–10 | 8–11 | 8–11 | 7–11 | 7–11 | |
EURO | 7–10 | 7–9 | 7–9 | 7–9 | 7–9 | 7–9 | 7–9 | 7–9 | 7–9 | 6–9 | 6–9 | |
MIDE | 6–10 | 7–10 | 7–10 | 7–10 | 7–10 | 6–9 | 6–9 | 6–10 | 6–10 | 7–10 | 6–10 | |
NHAF | 11–2 | 12–4 | 12–4 | 12–4 | 1–4 | 12–3, 5 | 1–5 | 1–4 | 12–4 | 12–4 | 12–4 | |
SHAF | 6–10 | 9–11 | 9–12 | 9–11 | 9–11 | 8–11 | 8–11 | 8–11 | 8–11 | 6–10 | 6–19 | |
BOAS | 4–5, 7–8 | 4, 7–9 | 4–5, 8–9 | 4–5, 7–9 | 4–5, 8 | 7–8 | 7–9 | 4–5, 7–9 | 7–9 | 6–9 | 6–8 | |
CEAS | 4–9 | 6–9 | 6–9 | 6–9 | 6–9 | 6–9 | 7–9 | 7–10 | 4–9 | 5–9 | 5–9 | |
SEAS | 1–4 | 2–6 | 1–5 | 2–5 | 1–5 | 2–5 | 2–5 | 1–6 | 2–6 | 1–5 | 1–5 | |
EQAS | 8–10 | 9–10, 12 | 8–11 | 8–10 | 8–11 | 8–11 | 8–10 | 3–4, 8–11 | 8–11 | 8–11 | 8–11 | |
AUST | 8–11 | 10–2 | 10–2 | 10–2 | 10–1 | 10–2 | 10–2 | 10–2 | 10–2 | 8–1 | 9–1 |
Region | Better Models |
---|---|
BONA | (b)(c)(d)(e)(h)(i)(j)(k) |
TENA | (b)(c)(e)(h)(i) |
CEAM | (h)(c)(e) |
NHSA | (h)(c)(e) |
SHSA | (h)(c)(e)(f) |
EURO | (j)(k) |
MIDE | (j)(k) |
NHAF | (h)(j)(k) |
SHAF | (f)(g) |
BOAS | (b)(c)(d)(e)(h)(j)(k) |
CEAS | (j)(k) |
SEAS | (c)(e)(f)(g)(h) |
EQAS | (h)(i) |
AUST | (b)(c)(d)(e)(f)(h)(i)(g) |
Model | <1% | <2% | 2~5% | 5~10% | 10–50% | >50% |
---|---|---|---|---|---|---|
GFED4s (a) | 93.828% | 95.559% | 1.840% | 0.931% | 1.455% | 0.215% |
CESM2 (b) | 92.810% | 94.507% | 2.085% | 1.407% | 1.971% | 0.029% |
CESM2-FV2 (c) | 92.948% | 94.558% | 2.195% | 1.352% | 1.873% | 0.021% |
CESM2-WACCM (d) | 92.803% | 94.494% | 2.010% | 1.458% | 2.020% | 0.018% |
CESM2-WACCM-FV2 (e) | 93.181% | 94.799% | 2.218% | 1.395% | 1.586% | 0.003% |
CMCC-ESM2 (f) | 90.540% | 92.435% | 3.007% | 2.469% | 2.086% | 0.004% |
CMCC-CM2-SR5 (g) | 90.394% | 92.262% | 2.917% | 2.480% | 2.338% | 0.003% |
NorESM2-LM (h) | 91.371% | 93.772% | 2.801% | 1.858% | 1.567% | 0.002% |
NorESM2-MM (i) | 91.647% | 93.728% | 2.537% | 1.727% | 1.979% | 0.029% |
GFDL-ESM4 (j) | 93.437% | 94.608% | 1.637% | 1.164% | 2.025% | 0.566% |
UKESM1-0-LL (k) | 93.580% | 94.824% | 1.645% | 1.133% | 1.877% | 0.522% |
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Wang, X.; Di, Z.; Zhang, W.; Zhang, S.; Sun, H.; Tian, X.; Meng, H.; Wang, X. Evaluation and Projection of Global Burned Area Based on Global Climate Models and Satellite Fire Product. Remote Sens. 2024, 16, 4751. https://doi.org/10.3390/rs16244751
Wang X, Di Z, Zhang W, Zhang S, Sun H, Tian X, Meng H, Wang X. Evaluation and Projection of Global Burned Area Based on Global Climate Models and Satellite Fire Product. Remote Sensing. 2024; 16(24):4751. https://doi.org/10.3390/rs16244751
Chicago/Turabian StyleWang, Xueyan, Zhenhua Di, Wenjuan Zhang, Shenglei Zhang, Huiying Sun, Xinling Tian, Hao Meng, and Xurui Wang. 2024. "Evaluation and Projection of Global Burned Area Based on Global Climate Models and Satellite Fire Product" Remote Sensing 16, no. 24: 4751. https://doi.org/10.3390/rs16244751
APA StyleWang, X., Di, Z., Zhang, W., Zhang, S., Sun, H., Tian, X., Meng, H., & Wang, X. (2024). Evaluation and Projection of Global Burned Area Based on Global Climate Models and Satellite Fire Product. Remote Sensing, 16(24), 4751. https://doi.org/10.3390/rs16244751