Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China

Climate determines the spatiotemporal distribution pattern of forest fires by affecting vegetation and the extent of drought. Thus, analyzing the dynamic change of the forest fire season and its response to climate change will play an important role in targeted adjustments of forest fire management policies and practices. In this study, we studied the spatiotemporal variations in forest fire occurrence in Fujian Province, China using the Mann–Kendall trend test and correlation analysis to analyze Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 and meteorological data. The results show that forest fire occurrence rose first and then declined over the years, but the proportion of forest fires during the fire prevention period decreased. The forest fires increased significantly in spring and summer, exceeding the forest fires occurring in the fire prevention period in 2010. The spatial distribution of forest fires decreased from northwest to southeast coastal areas, among which the number of forest fires in the northwest mountainous areas was large in autumn and winter. The fire risk weather index was strongly and positively correlated with forest fire occurrence across various sites in the province. The findings accentuate the need for properly adjusting the fire prevention period and resource allocation, strengthening the monitoring and early warning of high fire risk weather, and publicizing wildfire safety in spring and summer. As the forest fire occurrence frequency is high in the western and northwest mountainous areas, more observation towers and forest fire monitoring facilities should be installed

Investigating Drought Events and Their Consequences in Wildfires: An Application in China

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China's fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models

Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.Other UBCNon UBCReviewedFacult

Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed

Future climate change is expected to impact the natural systems. This study used future climate data of general circulation models (GCMs) to investigate the impacts of climate change during the future period (2062–2095) relative to the historical period (1981–2014) on the hydrological system of the Minjiang river watershed, China. A previously calibrated soil and water assessment tool (SWAT) was employed to simulate the future hydrology under the impacts of changes in temperature, precipitation, and atmospheric CO2 concentration for four shared socioeconomic pathways (SSP 1, 2, 3, and 5) of the CMIP6. The study revealed that the impacts of increase in future temperature, i.e., increase in ET, and decrease in surface runoff, water, and sediment yield will be countered by increased atmospheric [CO2], and changes in the hydrological parameters in the future will be mostly associated to changes in precipitation. Data of the GCMs for all the SSPs predicts increase in precipitation of the watershed, which will cause increase in surface runoff, water yield, and sediment yield. Surface runoff will increase more in SSP 5 (47%), while sediment and water yield will increase more in SSP 1, by 33% and 23%, respectively. At the seasonal scale, water yield and surface runoff will increase more in autumn and winter in SSP 1, while in other scenarios, these parameters will increase more in the spring and summer seasons. Sediment yield will increase more in autumn in all scenarios. Similarly, the future climate change is predicted to impact the important parameters related to the flow regime of the Minjiang river, i.e., the frequency and peak of large floods (flows > 14,000 m3/s) will increase along the gradient of scenarios, i.e., more in SSP 5 followed by 3, 2, and 1, while duration will increase in SSP 5 and decrease in the other SSPs. The frequency and duration of extreme low flows will increase in SSP 5 while decrease in SSP 1. Moreover, peak of extreme low flows will decrease in all scenarios except SSP 1, in which it will increase. The study will improve the general understanding about the possible impacts of future climate change in the region and provide support for improving the management and protection of the watershed’s water and soil resources

Investigating Drought Events and Their Consequences in Wildfires : An Application in China

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China’s fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models.Forestry, Faculty ofNon UBCForest Resources Management, Department ofReviewedFacultyResearche

The Influence of Landcover and Climate Change on the Hydrology of the Minjiang River Watershed

Changes in the climate and landcover are the two most important factors that influence terrestrial hydrological systems. Today, watershed-scale hydrological models are widely used to estimate the individual impacts of changes in the climate and landcover on watershed hydrology. The Minjiang river watershed is an ecologically and economically important, humid, subtropical watershed, located in south-eastern China. Several studies are available on the impacts of recent climate change on the watershed; however, no efforts have been made to separate the individual contributions of climate and landcover changes. This study is an attempt to separate the individual impacts of recent (1989–2018) climate and landcover changes on some of the important hydrological components of the watershed, and highlight the most influential changes in climate parameters and landcover classes. A calibrated soil and water assessment tool (SWAT) was employed for the study. The outcomes revealed that, during the study period, water yield decreased by 6.76%, while evapotranspiration, surface runoff and sediment yield increased by 1.08%, 24.11% and 33.85% respectively. The relative contribution of climate change to landcover change for the decrease in the water yield was 95%, while its contribution to the increases in evapotranspiration, surface runoff and sediment yield was 56%, 77% and 51%, respectively. The changes in climate parameters that were most likely responsible for changes in ET were increasing solar radiation and temperature and decreasing wind speed, those for changes in the water yield were decreasing autumn precipitation and increasing solar radiation and temperature, those for the increase in surface runoff were increasing summer and one-day maximum precipitation, while those for the increasing sediment yield were increasing winter and one-day maximum precipitation. Similarly, an increase in the croplands at the expense of needle-leaved forests was the landcover change that was most likely responsible for a decrease in the water yield and an increase in ET and sediment yield, while an increase in the amount of urban land at the expense of broadleaved forests and wetlands was the landcover change that was most likely responsible for increasing surface runoff. The findings of the study can provide support for improving management and protection of the watershed in the context of landcover and climate change

Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed

Future climate change is expected to impact the natural systems. This study used future climate data of general circulation models (GCMs) to investigate the impacts of climate change during the future period (2062–2095) relative to the historical period (1981–2014) on the hydrological system of the Minjiang river watershed, China. A previously calibrated soil and water assessment tool (SWAT) was employed to simulate the future hydrology under the impacts of changes in temperature, precipitation, and atmospheric CO2 concentration for four shared socioeconomic pathways (SSP 1, 2, 3, and 5) of the CMIP6. The study revealed that the impacts of increase in future temperature, i.e., increase in ET, and decrease in surface runoff, water, and sediment yield will be countered by increased atmospheric [CO2], and changes in the hydrological parameters in the future will be mostly associated to changes in precipitation. Data of the GCMs for all the SSPs predicts increase in precipitation of the watershed, which will cause increase in surface runoff, water yield, and sediment yield. Surface runoff will increase more in SSP 5 (47%), while sediment and water yield will increase more in SSP 1, by 33% and 23%, respectively. At the seasonal scale, water yield and surface runoff will increase more in autumn and winter in SSP 1, while in other scenarios, these parameters will increase more in the spring and summer seasons. Sediment yield will increase more in autumn in all scenarios. Similarly, the future climate change is predicted to impact the important parameters related to the flow regime of the Minjiang river, i.e., the frequency and peak of large floods (flows > 14,000 m3/s) will increase along the gradient of scenarios, i.e., more in SSP 5 followed by 3, 2, and 1, while duration will increase in SSP 5 and decrease in the other SSPs. The frequency and duration of extreme low flows will increase in SSP 5 while decrease in SSP 1. Moreover, peak of extreme low flows will decrease in all scenarios except SSP 1, in which it will increase. The study will improve the general understanding about the possible impacts of future climate change in the region and provide support for improving the management and protection of the watershed’s water and soil resources

Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China

Climate determines the spatiotemporal distribution pattern of forest fires by affecting vegetation and the extent of drought. Thus, analyzing the dynamic change of the forest fire season and its response to climate change will play an important role in targeted adjustments of forest fire management policies and practices. In this study, we studied the spatiotemporal variations in forest fire occurrence in Fujian Province, China using the Mann–Kendall trend test and correlation analysis to analyze Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 and meteorological data. The results show that forest fire occurrence rose first and then declined over the years, but the proportion of forest fires during the fire prevention period decreased. The forest fires increased significantly in spring and summer, exceeding the forest fires occurring in the fire prevention period in 2010. The spatial distribution of forest fires decreased from northwest to southeast coastal areas, among which the number of forest fires in the northwest mountainous areas was large in autumn and winter. The fire risk weather index was strongly and positively correlated with forest fire occurrence across various sites in the province. The findings accentuate the need for properly adjusting the fire prevention period and resource allocation, strengthening the monitoring and early warning of high fire risk weather, and publicizing wildfire safety in spring and summer. As the forest fire occurrence frequency is high in the western and northwest mountainous areas, more observation towers and forest fire monitoring facilities should be installed.Forestry, Faculty ofNon UBCReviewedFacultyResearche