ALBERT

Description: 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.

Description: 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.

Description: 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 (20 000) of model runs using randomly generated parameter sets were performed and goodness of fit measures for all fluxes for each of these runs 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 were detected and improvements suggested.

Description: 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.

Description: In this study, the Weather Research and Forecasting Model (WRF) 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 are simple and lack the capability to simulate carbon dioxide, for example, the popular NOAH LSM. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF–ACASA and WRF–NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF–ACASA model not only maintains model accuracy, it 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 impacts of different land surface and model components on atmospheric and surface conditions.