Advanced Canopy-Atmosphere-Soil Algorithm Research Articles

Introducing a dynamic photosynthetic model of photoinhibition, heat, and water stress in the next-generation land surface model ACASA

More frequent, intense, and more extended droughts and heatwaves are challenging agricultural productivity and management. This study introduces a new modeling approach to represent the impact of heat and water stress on energy and mass fluxes from orchards. A photosynthesis model that explicitly accounts for dynamic responses of Rubisco and RuBP limitations to stress was linked to the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). ACASA is a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations to calculate plant-atmosphere exchanges of carbon dioxide, water, and heat. Field measurements of leaf area index, stomatal conductance, and photosynthesis were performed to estimate model parameters. Model results were compared to data from an eddy covariance flux tower deployed at an almond orchard. Overall, this new approach, ACASA-DynPM, is in closer agreement with observations of H, λE, and Fc compared to ACASA. On days of high temperatures and water stress, ACASA-DynPM outperforms ACASA. Thus, we argue that accounting for plant physiological stress might be especially relevant when estimating energy and mass fluxes over intensively irrigated agricultural regions. Such biases can have implications for the modeled degree of crop stress and water demands, and eventually, it can impact climate regional estimates when coupled with an atmospheric model.

Footprint-weighted tile approach for a spruce forest and a nearby patchy clearing using the ACASA model

Abstract. The ACASA (Advanced Canopy–Atmosphere–Soil Algorithm) model, with a higher-order closure for tall vegetation, has already been successfully tested and validated for homogeneous spruce forests. The aim of this paper is to test the model using a footprint-weighted tile approach for a clearing with a heterogeneous structure of the underlying surface. The comparison with flux data shows a good agreement with a footprint-aggregated tile approach of the model. However, the results of a comparison with a tile approach on the basis of the mean land use classification of the clearing is not significantly different. It is assumed that the footprint model is not accurate enough to separate small-scale heterogeneities. All measured fluxes are corrected by forcing the energy balance closure of the test data either by maintaining the measured Bowen ratio or by the attribution of the residual depending on the fractions of sensible and latent heat flux to the buoyancy flux. The comparison with the model, in which the energy balance is closed, shows that the buoyancy correction for Bowen ratios >1.5 better fits the measured data. For lower Bowen ratios, the correction probably lies between the two methods, but the amount of available data was too small to make a conclusion. With an assumption of similarity between water and carbon dioxide fluxes, no correction of the net ecosystem exchange is necessary for Bowen ratios >1.5.

The importance of carbon-nitrogen biogeochemistry on water vapor and carbon fluxes as elucidated by a multiple canopy layer higher order closure land surface model

The carbon-nitrogen (CN) biogeochemistry and the prognostic canopy structure scheme used in the Community Land Model version 4.5 (CLM4.5) are integrated into the multiple canopy layer higher order closure Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). The ACASA-CN model inherits the advantages from ACASA and CLM4.5, and it is the first model that represents fully prognostic terrestrial feedbacks with a higher order closure turbulence scheme and vertically resolved canopy structure. The simulations are evaluated in terms of ecological, biogeophysical and biogeochemical aspects at six AmeriFlux eddy covariance sites encompassing a variety of vegetation types and microclimatic conditions across the continental United States. Our model evaluation shows that ACASA-CN reasonably simulates surface layer properties, such as vegetation structure and plant phenology. Our results indicate that the use of a higher order closure turbulence scheme with multiple canopy layer representation is critical to land carbon cycle simulation. Based on our simulations, water vapor exchange between the land surface and the atmosphere is primarily through plant transpiration (78–88%), and canopy evapotranspiration is enhanced with the use of CN biogeochemistry. Our results indicate that the ratio of transpiration to evapotranspiration exhibits a two-stage variation pattern, and the transpiration fraction decreases in dense canopies.

Impact of canopy representations on regional modeling of evapotranspiration using the WRF-ACASA coupled model

In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. The coupled WRF-ACASA model is compared with the widely used intermediate complexity Noah land surface model in WRF (WRF-Noah) for both potential (ETo) and actual evapotranspiration (ETa). Two LAI datasets (USGS and MODIS) are used to study the model responses to surface conditions. Model evaluations over a diverse surface stations from the CIMIS and AmeriFlux networks show that an increase surface representations increase the model accuracy in ETa more so than ETo. Overall, while the high complexity of WRF-ACASA increases the realism of plant physiological processes, the model sensitivity to surface representation in input data such as LAI also increases.

Coupling the high-complexity land surface model ACASA to the mesoscale model WRF

Abstract. In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.

Evaluated Crop Evapotranspiration over a Region of Irrigated Orchards with the Improved ACASA–WRF Model

Abstract Among the uncertain consequences of climate change on agriculture are changes in timing and quantity of precipitation together with predicted higher temperatures and changes in length of growing season. The understanding of how these uncertainties will affect water use in semiarid irrigated agricultural regions depends on accurate simulations of the terrestrial water cycle and, especially, evapotranspiration. The authors test the hypothesis that the vertical canopy structure, coupled with horizontal variation in this vertical structure, which is associated with ecosystem type, has a strong impact on landscape evapotranspiration. The practical result of this hypothesis, if true, is validation that coupling the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) and the Weather Research and Forecasting (WRF) models provides a method for increased accuracy of regional evapotranspiration estimates. ACASA–WRF was used to simulate regional evapotranspiration from irrigated almond orchards over an entire growing season. The ACASA model handles all surface and vegetation interactions within WRF. ACASA is a multilayer soil–vegetation–atmosphere transfer model that calculates energy fluxes, including evapotranspiration, within the atmospheric surface layer. The model output was evaluated against independent evapotranspiration estimates based on eddy covariance. Results indicate the model accurately predicts evapotranspiration at the tower site while producing consistent regional maps of evapotranspiration (900–1100 mm) over a large area (1600 km2) at high spatial resolution (Δx = 0.5 km). Modeled results were within observational uncertainties for hourly, daily, and seasonal estimates. These results further show the robustness of ACASA’s ability to simulate surface exchange processes accurately in a complex numerical atmospheric forecast model such as WRF.

Urban metabolism and climate change: A planning support system

Patterns of urban development influence flows of material and energy within urban settlements and exchanges with its surrounding. In recent years the quantitative estimation of the components of the so-called urban metabolism has increasingly attracted the attention of researchers from different fields. To contribute to this effort we developed a modelling framework for estimating the carbon exchanges together with sensible and latent heat fluxes and air temperature in relation to alternative land-use scenarios. The framework bundles three components: (i) a Cellular Automata model for the simulation of the urban land-use dynamics; (ii) a transportation model for estimating the variation of the transportation network load and (iii) the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) model tightly coupled with the mesoscale weather forecasting model WRF. We present and discuss the results of an example application on the City of Florence.

Evaluation of the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) model performance over Mediterranean maquis ecosystem

The Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) model is used to predict energy, water and carbon fluxes over a Mediterranean maquis site located in North-Western Sardinia (Italy) and the model performance is evaluated. Flux simulations are compared with Eddy Covariance field measurements collected from 2004 to 2007. The site experiences a drought season during the summer months in which the vegetation becomes water stressed. Results from the months of January, April, and July are analyzed to demonstrate the model behavior in different environmental conditions. In general, simulated and observed fluxes matched when both the thermal and moisture regime are optimal. During the July water stress period the model underestimated latent heat and carbon fluxes due to a strong stress response linked to soil properties and plant physiological characteristics. The selection of values for key parameters, e.g. maximum ideal photosynthetic capacity (RUBISCO), wilting point, soil water content, and root and leaf area ratio, is crucial to obtain close agreement between simulated and observed fluxes. The model was designed so that the most sensitive parameters are measurable quantities. Using the ACASA model to predict energy and mass fluxes between the vegetation and atmosphere appears promising in this context, and it could significantly improve our ability to estimate fluxes for use in future studies.

Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site

Abstract. The sensitivity and predictive uncertainty of the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) was assessed by employing the Generalized Likelihood Uncertainty Estimation (GLUE) method. ACASA is a stand-scale, multi-layer soil-vegetation-atmosphere transfer model that incorporates a third order closure method to simulate the turbulent exchange of energy and matter within and above the canopy. Fluxes simulated by the model were compared to sensible and latent heat fluxes as well as the net ecosystem exchange measured by an eddy-covariance system above the spruce canopy at the FLUXNET-station Waldstein-Weidenbrunnen in the Fichtelgebirge Mountains in Germany. From each of the intensive observation periods carried out within the EGER project (ExchanGE processes in mountainous Regions) in autumn 2007 and summer 2008, five days of flux measurements were selected. A large number (20000) of model runs using randomly generated parameter sets were performed and goodness of fit measures for all fluxes for each of these runs were calculated. The 10% best model runs for each flux were used for further investigation of the sensitivity of the fluxes to parameter values and to calculate uncertainty bounds. A strong sensitivity of the individual fluxes to a few parameters was observed, such as the leaf area index. However, the sensitivity analysis also revealed the equifinality of many parameters in the ACASA model for the investigated periods. The analysis of two time periods, each representing different meteorological conditions, provided an insight into the seasonal variation of parameter sensitivity. The calculated uncertainty bounds demonstrated that all fluxes were well reproduced by the ACASA model. In general, uncertainty bounds encompass measured values better when these are conditioned on the respective individual flux only and not on all three fluxes concurrently. Structural weaknesses of the ACASA model concerning the soil respiration calculations and the simulation of the latent heat flux during dry conditions were detected, with improvements suggested for each.

Directional wind shear within an old-growth temperate rainforest: observations and model results

Observed and modeled estimates of wind speed and direction within an old growth temperate rainforest are analyzed and compared. The UCD Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) predicts strong directional shear of the horizontal wind velocity within and above the canopy. The modeled shear is less during the day than at night, due to diurnal variations in buoyancy-controlled turbulent coupling. Diurnal patterns of observed wind direction shear depart from the predicted shear in the lowest 15% of the canopy ( z/ h c<0.15), likely related to local mountain/valley breezes. The modeled and observed directional wind shear has significant implications regarding the interpretation of many footprint analyses and models. Model estimates of shear are sensitive to the vertical distribution, and the total amount, of leaf area index (LAI). Tall and/or dense vegetation, especially if concentrated high in the canopy, is associated with greater integrated ageostrophic (antitriptic) flow and directional shear than for shorter and/or less dense canopies. These results have implications for the role of vegetation type and distribution on simulated convergence patterns at the regional and synoptic scales within the surface-layer. Sensitivity analyses confirm prior findings that the pressure gradient force is largely responsible for the presence of a simulated subcanopy wind speed maximum, with the direction approaching that of the antitriptic wind in the understory given a strong pressure gradient force. Results also show that second-order diabatic processes influence the shear by affecting the amount of turbulent mixing in thermodynamically stable and unstable conditions.

Coupling between the University of California, Davis, Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) and MM5: Preliminary Results for July 1998 for Western North America

Abstract The University of California, Davis, Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) is coupled to the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) as a land surface scheme. Simulations for July 1998 over western North America show that this coupling, the first between a mesoscale model and a land surface model of this complexity, is successful. Comparisons among model output, National Centers for Environmental Prediction–NCAR reanalysis fields, and station data show that MM5–ACASA generally reproduces near-surface temperature in a realistic fashion, but with a stronger diurnal cycle than observations suggest. A control run using the existing Louis/European Centre for Medium-Range Weather Forecasts land surface formulation produces unrealistically low temperatures associated with high latent heating and precipitation amounts over much of the model domain. Simulations of heat and moisture fluxes using the Biosphere–Atmospher...

The UCD Advanced Canopy‐Atmosphere‐Soil Algorithm: Comparisons with observations from different climate and vegetation regimes

AbstractThe University of California, Davis (UCD), Advanced Canopy‐Atmosphere‐Soil Algorithm (ACASA) is presented and its output is compared with a comprehensive set of observations at six diverse sites. ACASA is a multi‐layer canopy‐surface‐layer model that solves the steady‐slate Reynolds‐averaged fluid flow equations to the third‐order. These equations include an explicit representation of the steady‐state, horizontally homogeneous, diabatic set of vector and scalar fluxes and flux transports. ACASA includes a fourth‐order, near‐exact technique to calculate leaf, stem, and soil surface temperatures and surface energy fluxes at various levels within the canopy. Plan! physiological response to micro‐environmental conditions is also included using Ball‐Berry/von Caemmerer‐Farquhar formulations. Observed energy fluxes and microenvironmental conditions from a grass field in the Netherlands, deciduous and coniferous forests in Canada, tropical pasture and forest in Brazil, and an ancient temperate rainforest in the USA are compared with simulated values.Results indicate that simulated and observed estimates of monthly to annual means of all surface fluxes agree within 95% confidence thresholds for all six sites. Observed and simulated hourly estimates of net radiation are also in excellent agreement for all sites considered. Observed and simulated hourly sensible‐ and latent‐heat flux estimates are in very good statistical agreement in most cases. Differences that exist between ACASA and observed sensible‐and latent‐heat flux estimates are of the same magnitudes as observational uncertainties. Estimates of observed and simulated hourly values of canopy and ground heal storage are within 95% statistical confidence limits of agreement with observations in most cases. Simulated and measured values of daytime intra‐canopy mean wind speed, temperature, and specific humidity agree with 95% confidence within both a tropical and temperate rainforest at all levels. Results also indicate that, in general, ACASA produces flux estimates closer to observations with significantly less scatter than does the Biosphere‐Atmosphere Transfer Scheme. Sensitivity tests show that reducing the vertical resolution, linearizing surface temperature calculations, and/or simplifying the treatment of surface‐layer turbulence each altered mean sensible‐ and latent‐heat flux estimates by amounts that are statistically significant in many cases. Results show that simplifying the model alters flux predictions in manners not simply related to vegetation character, and that using ACASA at its full complexity for all vegetation regimes is warranted. Increasing the vertical resolution beyond 20 layers improved flux predictions at tropical locations but had little impact elsewhere.