On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes

Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote-sensing-based products provide an alternative way to verify or constrain land models given their global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global-scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using the recently developed land model by the Climate Modeling Alliance, CliMA Land. Our model results suggested that (1) when using the same model inputs, model-predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences between canopy complexity levels increased compared to the scenario of a uniform canopy; and (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs and (2) using a complex canopy model with angular distribution and a hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

Seasonal variation of dry and wet islands in Beijing considering urban artificial water dissipation

Urbanization has resulted in dry/wet island effects in built-up areas. Compared to the limited number of observational datasets, simulations can provide data with richer spatial distribution, thereby proving to be more helpful for revealing the spatial distribution of dry/wet islands. This study simulated dry/wet island effects during typical summer and winter conditions in Beijing by coupling the Artificial Water Dissipation Urban Canopy Model with the Weather Research and Forecasting model. Observations of relative humidity, absolute humidity, and temperature from weather stations in Beijing were used to verify the model. The results showed that in 2020, Beijing was prone to be a dry island during summer, with the relative humidity approximately 5–10% lower than the surrounding suburbs. The dry island effect was not obvious in winter, and Beijing tended to be a wet island. The influence of artificial water dissipation on dry/wet islands is higher in winter than in summer. By considering the water vapor from artificial water dissipation, humidity in urban areas can be simulated more accurately.

On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes

Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote sensing based products provide an alternative way to verify or constrain land models given its global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered big-leaf canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using a recently developed Land model by the Climate Modeling Alliance. Our model results suggested that (1) when using the same model inputs, model predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences among canopy complexity levels increased compared to the scenario of a uniform canopy; (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs, and (2) using complex canopy model with angular distribution and hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

Creating a Field-Wide Forage Canopy Model Using UAVs and Photogrammetry Processing

Alfalfa canopy structure reveals useful information for managing this forage crop, but manual measurements are impractical at field-scale. Photogrammetry processing with images from Unmanned Aerial Vehicles (UAVs) can create a field-wide three-dimensional model of the crop canopy. The goal of this study was to determine the appropriate flight parameters for the UAV that would enable reliable generation of canopy models at all stages of alfalfa growth. Flights were conducted over two separate fields on four different dates using three different flight parameters. This provided a total of 24 flights. The flight parameters considered were the following: 30 m altitude with 90° camera gimbal angle, 50 m altitude with 90° camera gimbal angle, and 50 m altitude with 75° camera gimbal angle. A total of 32 three-dimensional canopy models were created using photogrammetry. Images from each of the 24 flights were used to create 24 separate models and images from multiple flights were combined to create an additional eight models. The models were analyzed based on Model Ground Sampling Distance (GSD), Model Root Mean Square Error (RMSE), and camera calibration difference. Of the 32 attempted models, 30 or 94% were judged acceptable. The models were then used to estimate alfalfa yield and the best yield estimates occurred with flights at a 50 m altitude with a 75° camera gimbal angle; therefore, these flight parameters are suggested for the most consistent results.

Explore the Accuracy of the Pedestrian Level Temperature Estimated by the Combination of LCZ with WRF Urban Canopy Model through the Microclimate Measurement Network

Due to the urban heat island effect becoming more evident in the cities in Taiwan, the urban climate has become an essential factor in urban development. Taiwan is located on the border of tropical and subtropical climate zones, the climate condition is hot and humid, and the city shows high-density development. The dense urban development has increased the heat storage capacity of the ground and buildings. However, if only the climate stations set by the Central Meteorological Bureau to observe the climate data are applied, the predicted results differ from the actual urban climate conditions due to the small number of these stations and the too far distance between them. Therefore, this study employs the local climate zone (LCZ), which can classify the land features by considering both land use and land cover, and can be freely generated from satellite images. The LCZ classification method can view the type of the city through the height and density of obstacles. This study also combines the urban canopy model (UCM) of the mesoscale climate prediction model and weather research and forecasts (WRF). This approach can calculate vertical and horizontal planes of the city, such as building volume, road width, the influence of streets and roofs, roof heat capacity, building wall heat capacity, etc., to predict the climatic conditions in different lands in the study area. Simultaneously, to understand the actual distribution of urban climate more accurately, this study used the microclimate measurement network built in the research area to produce pedestrian-level temperature distribution and compared the estimated results with the actual measured values for urban climate assessment. This study can understand the cause of urban heat islands and assist urban planners more appropriately formulate heat island mitigation strategies in different regions.

Near-real-time flood forecasting for an urban coastal catchment: An approach in combination of numerical weather and 3-way coupled hydrodynamic flood modelling

<p>The rapid increase in heavy precipitation flooding events highlights the need for efficient flood forecasting techniques to facilitate flood hydrological research and effective flood management by civic bodies. The current study aims to develop a near-real-time flood forecasting framework by integrating a 3-way coupled hydrodynamic flood model framework with numerical weather modelling based rainfall forecasts. The proposed framework has been demonstrated over Mumbai city in India, which is subjected to flooding every year during the monsoon months. A fine-resolution atmospheric simulation with the Weather Research and Forecasting (WRF) model has been performed for rainfall forecasts, which serve as an input to the flood model. To access the impact of urbanization on rainfall extremes, three scenarios are considered in the WRF simulations, i.e., WRF model: (1) without Urban canopy model (WRF-NoUCM), (2) coupled with a single-layer Urban canopy model (WRF-SUCM), and (3) coupled with a multi-layer Urban canopy model (WRF-MUCM). Further, a three-way coupled flood model has been developed where the MIKE 11 model (streamflow) with the drainage network (stormwater drains) and the MIKE 21 model (overland flow) have been considered for flood inundation and subsequently hazard mapping. In addition, the tidal elevation is provided along the coastline in the model setup. The flood maps developed by three WRF forecasted rainfall scenarios have been compared with that of the maps developed with observed rainfall. The extent to which the scenarios have been able to imitate the pattern and extent of flooding generated by observed rainfall has been investigated to decide the best scenario to be adapted in the comprehensive flood forecasting network. This state-of-art flood forecasting approach may be implemented in other flood-prone coastal regions as a major non-structural flood management strategy to reduce flood risk and vulnerabilities for the people dwelling in those regions.</p>

Understanding in and above canopy-atmosphere interactions by combining large-eddy simulations with a comprehensive observational set

<p>The vegetated canopy plays a key role in regulating the surface fluxes and, therefore, the global energy, water and carbon cycles. In particular, vulnerable ecosystems like the Amazonia basin can be very sensitive to changes in vegetation that exert subsequent shifts in the partition of the energy, water and carbon in and above the canopy. Despite this relevance, most 3D atmospheric models represent the vegetated canopy as a flat 2D layer with, at most, a rough imitation of its effect in the atmospheric boundary layer through a modified roughness length. Thus, the representations often describe quite crudely the surface fluxes. In this work, particular emphasis is placed in the biophysical processes that take place within the canopy and its impact above. Our approach is to represent the coupling of the flow between the canopy and the atmosphere including the following processes: radiative transfer, photosynthesis, soil evaporation and CO2 respiration, combined with the mostly explicit atmospheric turbulence within and above the canopy. To this end, we implemented in LES a detailed multi-layer canopy model that solves the leaf energy balance for sunlit and shaded leaves independently, regulating the exchange of heat, moisture and carbon between the leaves and the air around. This allows us to connect the mechanistically represented processes occurring at the leaf level and strongly regulated by the transfer of diffuse and direct radiation within the canopy to the turbulent mixing explicitly resolved at the meter scale.</p><p>We test and validate this combined photosynthesis-turbulence-canopy model by simulating a representative clear day transitioning to shallow cumulus. We based our evaluation on observations by the GoAmazon2014/5 campaign in Brazil in 2014. More specifically, we systematically validate the in-canopy radiation profiles; sources, sinks and turbulent fluxes of moisture, heat and CO2, and main state variables within the canopy, and also study the effects of these in the air above. Preliminary results show an encouraging satisfactory match to the observed evolution of the profiles. As a first exploration and demonstration of the capabilities of the model, we test the effects of a coarser in-canopy resolution, a different radiation scheme and the use of a more simple 2D canopy representation.</p>