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Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2022)

Wind disturbance, along with heavy precipitation and strong wind, caused extensive impact on forests, including leaf falling, tree snapping and uprooting. In this dissertation I investigate the impact of wind disturbance, including cyclones and mesoscale convective system, on temperate and tropical forests, from local to landscape scales. I quantified the wind disturbance impact on forests using remote sensing images, and focus on four primary questions: (1) What are the geophysical and biological variables that affect the forest disturbance intensity caused by hurricane Maria? (2) How do these variables vary across different regions that affect disturbance caused by multiple cyclones? (3) How do climate variables affect windthrow disturbance? (4) How does windthrow disturbance respond to climate warming?In the first chapter, I reviewed 8 remote sensing indices and methods that can measure the windstorms’ impact on forest. I summarized the methods to measure the disturbance intensity and compared their advantages and weaknesses. I also explored the environmental factors in literature that drive the spatial variation of the wind-related forest disturbance. In the second chapter, I measured hurricane Maria’s impact on Puerto Rican forests and developed a GEE remote sensing data analysis tool for rapidly quantifying spatial variability of forest disturbance following a cyclone landfall. I also studied the landscape factors which affect the patterns and severity of forest disturbance intensity. Results show that significant disturbance was found in Luquillo Mountains, the mid-west forested area, the southeastern area, and the northeastern coastal area. Forest type, elevation, green vegetation ratio in the preceding year, distance to hurricane landfall, and distance to hurricane track were the most important variables explaining the variance in forest disturbance caused by hurricane Maria. In the third chapter, I quantified the disturbance intensity caused by multiple cyclones in different regions and explored the feature importance in various cyclone studies. Results showed that climate variables, terrain features, and forest properties were significant tin predicting tree damage caused by cyclones, and wind elevation, and the pre-disturbance vegetation condition were strong predictors. Little consistency was found on the variables among studies of different cyclones, and we believed that the complexities of cyclone effects coupled with landscape, soils, states of affected systems, climate change making the existence of an omnipotent cyclone model very difficult. Moving from cyclones over the world to extreme wind events over Amazon region, in the fourth chapter, I explored 38 case studies of windthrow on the land surface and the corresponding mesoscale convective systems (MCS) in the atmosphere. I studied the relationship between windthrow size and features of MCS, including the duration, coldest cloud top temperature, and rainfall. Results showed that there was a positive linear relationship between the duration of MCS lifespan and the size of windthrows. Deeper convective storms resulted in large windthrows. In the fifth chapter, I investigated the mechanism of windthrow over Amazonia. The results demonstrated that CAPE was an appropriate proxy for estimating the density of windthrow events in the Amazon, and a significant increase in windthrow density over this century was projected under a warming climate. Taken together, these results quantified the impact of extreme storms on forests over the tropical and sub-tropical regions and improved the understanding in the mechanism of land-atmosphere interaction, thus contributing to rapid assessment of extreme wind events impacts and the development of mitigation and forest management strategies under climate change.

    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2021)
    Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.
      Cover page: Multi-cyclone analysis and machine learning model implications of cyclone effects on forests
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2020)
      Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. We used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.
        Cover page: Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2022)
        While tropical cyclone regimes are shifting with climate change, the mechanisms underpinning the resistance (ability to withstand disturbance-induced change) and resilience (capacity to return to pre-disturbance reference) of tropical forest litterfall to cyclones remain largely unexplored pantropically. Single-site studies in Australia and Hawaii suggest that litterfall on low-phosphorus (P) soils is more resistant and less resilient to cyclones. We conducted a meta-analysis to investigate the pantropical importance of total soil P in mediating forest litterfall resistance and resilience to 22 tropical cyclones. We evaluated cyclone-induced and post-cyclone litterfall mass (g/m2 /day), and P and nitrogen (N) fluxes (mg/m2 /day) and concentrations (mg/g), all indicators of ecosystem function and essential for nutrient cycling. Across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan, total litterfall mass flux increased from ~2.5 ± 0.3 to 22.5 ± 3 g/m2 /day due to cyclones, with large variation among studies. Litterfall P and N fluxes post-cyclone represented ~5% and 10% of the average annual fluxes, respectively. Post-cyclone leaf litterfall N and P concentrations were 21.6 ± 1.2% and 58.6 ± 2.3% higher than pre-cyclone means. Mixed-effects models determined that soil P negatively moderated the pantropical litterfall resistance to cyclones, with a 100 mg P/kg increase in soil P corresponding to a 32% to 38% decrease in resistance. Based on 33% of the resistance case studies, total litterfall mass flux reached pre-disturbance levels within one-year post-disturbance. A GAMM indicated that soil P, gale wind duration and time post-cyclone jointly moderate the short-term resilience of total litterfall, with the nature of the relationship between resilience and soil P contingent on time and wind duration. Across pantropical forests observed to date, our results indicate that litterfall resistance and resilience in the face of intensifying cyclones will be partially determined by total soil P.
          Cover page: Linking soil phosphorus with forest litterfall resistance and resilience to cyclone disturbance: A pantropical meta‐analysis
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2023)
          This study identifies 38 cases of windthrows in the Amazonia to explore the relationship between windthrows and the characteristics (storm passing time, cloud top temperature, and maximum precipitation) of mesoscale convective systems (MCSs) that produced them. Most of windthrow cases in this study occurred in August and September. The storm passing time is positively correlated with the size of windthrows. MCSs with colder cloud top temperature (with a mean at 206 K)—indicating deeper convection—resulted in large windthrows, while those with warm cloud top (with a mean above 230 K) resulted in relatively small windthrows except for windthrows in the western Amazonia. No significant relationship is found between maximum precipitation intensity and the area of windthrows.
            Cover page: Case Studies of Forest Windthrows and Mesoscale Convective Systems in AmazoniaCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2023)
            Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, the linkage between windthrows at the surface and convective storms in the atmosphere remains unclear. In addition, the current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. Here we find an empirical relationship that maps convective available potential energy, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, our model projects a 51 ± 20% increase in the area favorable to extreme storms, and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon cycle dynamics under climate change.
              Cover page: Amazon windthrow disturbances are likely to increase with storm frequency under global warmingCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2023)
              Windthrows (trees uprooted and broken by winds) are common across the Amazon. They range in size from single trees to large gaps that lead to changes in forest dynamics, composition, structure, and carbon balance. Yet, the current understanding of the spatial variability of windthrows is limited. By integrating remote sensing data and geospatial analysis, we present the first study to examine the occurrence, area, and direction of windthrows and the control that environmental variables exert on them across the whole Amazon. Windthrows are more frequent and larger in the northwestern Amazon (Peru and Colombia), with the central Amazon (Brazil) being another hot spot of windthrows. The predominant direction of windthrows is westward. Rainfall, surface elevation, and soil characteristics explain the variability (20%-50%) of windthrows but their effects vary regionally. A better understanding of the spatial dynamics of windthrows will improve understanding of the functioning of Amazon forests.
                Cover page: Windthrow characteristics and their regional association with rainfall, soil, and surface elevation in the AmazonCreative Commons 'BY' version 4.0 license
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