Understanding the mechanism of photosynthetic seasonality in Amazonian evergreen forests is critical for its formulation in global climate and carbon cycle models. However, the control of the unexpected photosynthetic seasonality is highly uncertain. Here we use eddy-covariance data across a network of Amazonian research sites and a novel evapotranspiration (E) and two-leaf-photosynthesis-coupled model to investigate links between photosynthetic seasonality and climate factors on monthly scales. It reproduces the GPP seasonality (R-2=0.45-0.69) with a root-mean-square error (RMSE) of 0.67-1.25gCm(-2)d(-1) and a Bias of -0.03-1.04gCm(-2)d(-1) for four evergreen forest sites. We find that the proportion of diffuse and direct sunlight governs the photosynthetic seasonality via their interaction with sunlit and shaded leaves, supported by a proof that canopy light use efficiency (LUE) has a strong linear relationship with the fraction of diffuse sunlight for Amazonian evergreen forests. In the transition from dry season to rainy season, incident total radiation (Q) decreased while LUE and diffuse fraction increased, which produced the large seasonal increase (similar to 34%) in GPP of evergreen forests. We conclude that diffuse radiation is an important environmental driver of the photosynthetic seasonality in tropical Amazon forests yet depending on light utilization by sunlit and shaded leaves. Besides, the GPP model simulates the precipitation-dominated GPP seasonality (R-2=0.40-0.69) at pasture and savanna sites. These findings present an improved physiological method to relate light components with GPP in tropical Amazon. Plain Language Summary Understanding the mechanism of photosynthetic seasonality in Amazonian evergreen forests is critical for its formulation in global climate and carbon cycle models. However, the control of the unexpected photosynthetic seasonality is highly uncertain. Here we use eddy-covariance data across a network of Amazonian research sites and a novel evapotranspiration (E) and two-leaf-photosynthesis-coupled model to investigate links between photosynthetic seasonality and climate factors on monthly scales. It reproduces the GPP seasonality (R2= 0.45-0.69) for four evergreen forest sites. We find that the proportion of diffuse and direct sunlight governs the photosynthetic seasonality via their interaction with sunlit and shaded leaves, supported by a proof that canopy light-use efficiency (LUE) has a strong linear relationship with the fraction of diffuse sunlight for Amazonian evergreen forests. We conclude that diffuse radiation is an important environmental driver of the photosynthetic seasonality in tropical Amazon forests yet depending on light utilization by sunlit and shaded leaves. Besides, the GPP model simulates the precipitation-dominated GPP seasonality (R2= 0.40 similar to 0.69) at pasture and savanna sites. These findings present an improved physiological method to relate light components with GPP in Amazon.

Modelling solar radiation data at a high spatiotemporal resolution for an urban environment can inform many different applications related to climate action, such as urban agriculture, forest, building, and renewable energy studies. However, the complexity of urban form, vastness of city-wide coverage, and general dearth of climatological information pose unique challenges doing so. To address some climate action goals related to reducing building emissions in the City of Victoria, British Columbia, Canada, applied geomatics and climatology were used to model solar radiation data suitable for informing renewable energy feasibility studies, including photovoltaic system sizing, costing, carbon offsets, and financial payback. The research presents a comprehensive review of solar radiation attenuates, as well as methods of accounting for them, specifically in urban environments. A novel methodology is derived from the review and integrates existing models, data, and tools – those typically available to a local government. Using Light Detection and Ranging (LiDAR), a solar climatology, Esri’s ArcGIS Solar Analyst tool, and Python scripting, daily insolation (kWh/m2) maps are produced for the city of Victoria. Particular attention is paid to the derivation of daily diffuse fraction from atmospheric clearness indices, as well as LiDAR classification and generation of a Digital Surface Model (DSM). Novel and significant improvements in computation time are realized through parallel processing. Model results exhibit strong correlation with empirical data and support the use of Solar Analyst for urban solar assessments when great care is taken to accurately and consistently represent model inputs and outputs integrated in a methodological approach. / Graduate