scholarly journals Optimal Inverse Estimation of Ecosystem Parameters from Observations of Carbon and Energy Fluxes

2018 ◽  
Author(s):  
Debsunder Dutta ◽  
David S. Schimel ◽  
Ying Sun ◽  
Christiaan van der Tol ◽  
Christian Frankenberg
Keyword(s):  
Seasonal Variability ◽  
Additional Data ◽  
Energy Fluxes ◽  
Linear Inversion ◽  
Area Index ◽  
Flux Tower ◽  
Community Land Model ◽  
Physiological Processes ◽  
Inverse Estimation ◽  
Spectrally Resolved

Abstract. Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (Vcmax), slope of the Ball-Berry stomatal conductance model (BBslope) and leaf area index (LAI) are crucial for modeling the plant physiological processes and canopy radiative transfer. These parameters are large sources of uncertainty in predictions of carbon and water fluxes. In this study, we develop an optimal inversion framework to use the Soil Canopy Observation Photochemistry and Energy fluxes (SCOPE) model for estimating Vcmax, BBslope and LAI by constraining observations of coupled carbon and energy fluxes from eddy covariance towers. We adapted SCOPE to follow the biochemical implementation of the Community Land Model and applied a moving window Bayesian non-linear inversion framework using SCOPE to invert the ecosystem parameters Vcmax, BBslope and LAI that best match flux-tower observations of Gross Primary Productivity (GPP) and Latent Energy (LE) fluxes. We applied this inversion framework to plant species having both the C3 and C4 photosynthetic pathways across three different ecosystems. Our results demonstrate the applicability of the approach in terms of capturing the seasonal variability and posterior error reduction (40–90 %) of key ecosystem parameters. The optimized parameters capture the diurnal and seasonal variability in the GPP and LE fluxes well when compared to flux tower observations (0.95 > R2 > 0.79). This study thus demonstrates the feasibility of parameter inversions using SCOPE, which can be easily adapted to incorporate additional data sources such as spectrally resolved reflectance and solar induced chlorophyll fluorescence.

scholarly journals Optimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxes

2019 ◽  
Vol 16 (1) ◽  
pp. 77-103 ◽  
Author(s):  
Debsunder Dutta ◽  
David S. Schimel ◽  
Ying Sun ◽  
Christiaan van der Tol ◽  
Christian Frankenberg
Keyword(s):  
Seasonal Variability ◽  
Additional Data ◽  
Water Fluxes ◽  
Energy Fluxes ◽  
Area Index ◽  
Thermal Emissions ◽  
Community Land Model ◽  
Physiological Processes ◽  
Inverse Estimation ◽  
Spectrally Resolved

Abstract. Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (Vcmax), slope of the Ball–Berry stomatal conductance model (BBslope) and leaf area index (LAI) are crucial for modeling plant physiological processes and canopy radiative transfer. These parameters are large sources of uncertainty in predictions of carbon and water fluxes. In this study, we develop an optimal moving window nonlinear Bayesian inversion framework to use the Soil Canopy Observation Photochemistry and Energy fluxes (SCOPE) model for constraining Vcmax, BBslope and LAI with observations of coupled carbon and energy fluxes and spectral reflectance from satellites. We adapted SCOPE to follow the biochemical implementation of the Community Land Model and applied the inversion framework for parameter retrievals of plant species that have both the C3 and C4 photosynthetic pathways across three ecosystems. We present comparative analysis of parameter retrievals using observations of (i) gross primary productivity (GPP) and latent energy (LE) fluxes and (ii) improvement in results when using flux observations along with reflectance. Our results demonstrate the applicability of the approach in terms of capturing the seasonal variability and posterior error reduction (40 %–90 %) of key ecosystem parameters. The optimized parameters capture the diurnal and seasonal variability in the GPP and LE fluxes well when compared to flux tower observations (0.95>R2>0.79). This study thus demonstrates the feasibility of parameter inversions using SCOPE, which can be easily adapted to incorporate additional data sources such as spectrally resolved reflectance and fluorescence and thermal emissions.

scholarly journals Vineyard Energy Partitioning Between Canopy and Soil Surface: Dynamics and Biophysical Controls

2017 ◽  
Vol 18 (7) ◽  
pp. 1809-1829 ◽  
Author(s):  
Peng Zhao ◽  
Xiaotao Zhang ◽  
Sien Li ◽  
Shaozhong Kang
Keyword(s):  
Latent Heat ◽  
Seasonal Variability ◽  
Field Experiments ◽  
Soil Surface ◽  
Energy Partitioning ◽  
Field Energy ◽  
Sensible Heat ◽  
Energy Fluxes ◽  
Area Index ◽  
Available Energy

Abstract For sparse planting crops, soil surface plays an important role in energy balance processes within the soil–canopy–atmosphere continuum; thus, it is necessary to partition field energy fluxes into soil surface and canopy to provide useful information to reduce agricultural water use and to develop evapotranspiration models. Field experiments were conducted in vineyards during four growing seasons to examine the energy partitioning among soil surface, canopy, and field separately. Vineyard energy fluxes including latent heat (LE) were measured by eddy covariance system and canopy latent heat LEc was obtained from sap flow. Then, soil surface latent heat LEs was calculated as the difference between LE and LEc. The Bowen ratio and the ratio of latent heat to available energy were used to examine energy partitioning. Results indicate daily and hourly LEs obtained from LE and LEc overestimated microlysimeter-derived values by 13.0% and 10.8%, respectively. Seasonal-average latent heat accounted for 59.0%–64.3%, 65.8%–77.8%, and 56.6%–62.5% of corresponding available energy for vineyard, canopy, and soil surface, respectively. Soil water content and canopy were the main controlling factors on energy partitioning. Surface soil moisture explained 32%, 11%, and 52% of the seasonal variability in energy partitioning at field, canopy, and soil surface, respectively. Leaf area index explained 41% and 26% of the seasonal variability in energy partitioning at field and soil surface. Air temperature was related to canopy and field energy partitioning. During wet periods, soil can absorb sensible heat from the canopy and LEs may exceed soil surface available energy, while during dry periods, the canopy may absorb sensible heat from the soil and LEc may exceed canopy available energy.

scholarly journals Inverse estimation of Vcmax, leaf area index, and the Ball-Berry parameter from carbon and energy fluxes

2006 ◽  
Vol 111 (D8) ◽  
Author(s):  
Adam Wolf ◽  
Kanat Akshalov ◽  
Nicanor Saliendra ◽  
Douglas A. Johnson ◽  
Emilio A. Laca
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Energy Fluxes ◽  
Area Index ◽  
Inverse Estimation

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

scholarly journals Effects of Dynamic Crop Growth on the Simulated Precipitation Response to Irrigation*

2015 ◽  
Vol 19 (14) ◽  
pp. 1-31 ◽  
Author(s):  
Keith J. Harding ◽  
Tracy E. Twine ◽  
Yaqiong Lu
Keyword(s):  
Environmental Variability ◽  
Wrf Model ◽  
Crop Growth ◽  
Climate Impacts ◽  
Rapid Expansion ◽  
Area Index ◽  
Community Land Model ◽  
Precipitation Response ◽  
Simulated Precipitation ◽  
Dynamic Crop Growth

Abstract The rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain over the region. Greater simulated rainfall increases (12.42 mm) occur with dynamic crops compared to static crops (9.08 mm), with the greatest differences during drought years (+20.1 vs +5.9 mm). Water use for irrigation significantly impacts precipitation with dynamic crops (R2 = 0.29), but no relationship exists with static crops. Dynamic crop growth has the largest effect on the simulated impact of irrigation on precipitation during drought years, with little impact during nondrought years, highlighting the need to simulate the dynamic response of crops to environmental variability within Earth system models to improve prediction of the agroecosystem response to variations in climate.

scholarly journals MODIS vegetation products as proxies of photosynthetic potential along a gradient of meteorologically and biologically driven ecosystem productivity

2016 ◽  
Vol 13 (19) ◽  
pp. 5587-5608 ◽  
Author(s):  
Natalia Restrepo-Coupe ◽  
Alfredo Huete ◽  
Kevin Davies ◽  
James Cleverly ◽  
Jason Beringer ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Photosynthetic Active Radiation ◽  
Ecosystem Structure ◽  
Ecosystem Productivity ◽  
Area Index ◽  
Active Radiation ◽  
Flux Tower ◽  
Photosynthetic Potential ◽  
Assimilation Capacity

Abstract. A direct relationship between gross ecosystem productivity (GEP) estimated by the eddy covariance (EC) method and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) has been observed in many temperate and tropical ecosystems. However, in Australian evergreen forests, and particularly sclerophyll and temperate woodlands, MODIS VIs do not capture seasonality of GEP. In this study, we re-evaluate the connection between satellite and flux tower data at four contrasting Australian ecosystems, through comparisons of GEP and four measures of photosynthetic potential, derived via parameterization of the light response curve: ecosystem light use efficiency (LUE), photosynthetic capacity (Pc), GEP at saturation (GEPsat), and quantum yield (α), with MODIS vegetation satellite products, including VIs, gross primary productivity (GPPMOD), leaf area index (LAIMOD), and fraction of photosynthetic active radiation (fPARMOD). We found that satellite-derived biophysical products constitute a measurement of ecosystem structure (e.g. leaf area index – quantity of leaves) and function (e.g. leaf level photosynthetic assimilation capacity – quality of leaves), rather than GEP. Our results show that in primarily meteorological-driven (e.g. photosynthetic active radiation, air temperature, and/or precipitation) and relatively aseasonal ecosystems (e.g. evergreen wet sclerophyll forests), there were no statistically significant relationships between GEP and satellite-derived measures of greenness. In contrast, for phenology-driven ecosystems (e.g. tropical savannas), changes in the vegetation status drove GEP, and tower-based measurements of photosynthetic activity were best represented by VIs. We observed the highest correlations between MODIS products and GEP in locations where key meteorological variables and vegetation phenology were synchronous (e.g. semi-arid Acacia woodlands) and low correlation at locations where they were asynchronous (e.g. Mediterranean ecosystems). However, we found a statistical significant relationship between the seasonal measures of photosynthetic potential (Pc and LUE) and VIs, where each ecosystem aligns along a continuum; we emphasize here that knowledge of the conditions in which flux tower measurements and VIs or other remote sensing products converge greatly advances our understanding of the mechanisms driving the carbon cycle (phenology and climate drivers) and provides an ecological basis for interpretation of satellite-derived measures of greenness.

scholarly journals Evaluation of MEGAN-CLM parameter sensitivity to predictions of isoprene emissions from an Amazonian rainforest

2014 ◽  
Vol 14 (17) ◽  
pp. 23995-24041 ◽  
Author(s):  
J. A. Holm ◽  
K. Jardine ◽  
A. B. Guenther ◽  
J. Q. Chambers ◽  
E. Tribuzy
Keyword(s):  
Land Surface ◽  
Field Measurements ◽  
Leaf Temperature ◽  
Surface Model ◽  
Amazon Forest ◽  
Biophysical Parameters ◽  
Area Index ◽  
Central Amazon ◽  
Community Land Model ◽  
Environmental Controls

Abstract. Tropical trees are known to be large emitters of biogenic volatile organic compounds (BVOC), accounting for up to 75% of the global isoprene budget. Once in the atmosphere, these compounds influence multiple processes associated with air quality and climate. However, uncertainty in biogenic emissions is two-fold, (1) the environmental controls over isoprene emissions from tropical forests remain highly uncertain; and (2) our ability to accurately represent these environmental controls within models is lacking. This study evaluated the biophysical parameters that drive the global Model of Emissions of Gases and Aerosols from Nature (MEGAN) embedded in a biogeochemistry land surface model, the Community Land Model (CLM), with a focus on isoprene emissions from an Amazonian forest. Upon evaluating the sensitivity of 19 parameters in CLM that currently influence isoprene emissions by using a Monte Carlo analysis, up to 61% of the uncertainty in mean isoprene emissions was caused by the uncertainty in the parameters related to leaf temperature. The eight parameters associated with photosynthetic active radiation (PAR) contributed in total to only 15% of the uncertainty in mean isoprene emissions. Leaf temperature was strongly correlated with isoprene emission activity (R2 = 0.89). However, when compared to field measurements in the Central Amazon, CLM failed to capture the upper 10–14 °C of leaf temperatures throughout the year (i.e., failed to represent ~32 to 46 °C), and the spread observed in field measurements was not representative in CLM. This is an important parameter to accurately simulate due to the non-linear response of emissions to temperature. MEGAN-CLM 4.0 overestimated isoprene emissions by 60% for a Central Amazon forest (5.7 mg m−2 h−1 vs. 3.6 mg m−2 h−1), but due to reductions in leaf area index (LAI) by 28% in MEGAN-CLM 4.5 isoprene emissions were within 7% of observed data (3.8 mg m−2 h−1). When a slight adjustment to leaf temperature was made to match observations, isoprene emissions increased 24%, up to 4.8 mg m−2 h−1. Air temperatures are very likely to increase in tropical regions as a result of human induced climate change. Reducing the uncertainty of leaf temperature in BVOC algorithms, as well as improving the accuracy of replicating leaf temperature output in land surface models is warranted in order to improve estimations of tropical BVOC emissions.

scholarly journals Initial growth of coffee (Coffea arabica L. var) castillo in the coffee zone of Nariño

2019 ◽  
Vol 36 (E) ◽  
pp. 124-137
Author(s):  
Hernando Criollo E. ◽  
Johanna Muñoz B. ◽  
Jorge Checa B. ◽  
Wilmer Noguera R.
Keyword(s):  
Plant Height ◽  
Statistical Design ◽  
Climatic Conditions ◽  
Initial Growth ◽  
Area Index ◽  
Physiological Variables ◽  
Physiological Processes ◽  
Number Of Leaves ◽  
La Florida ◽  
Coffee Cultivation

The importance of coffee cultivation in Nariño is reflected in the fact that 64% of its municipalities grow coffee. The ruggedness of its Andean topography provides great diversity in terms of climatic conditions, which, in one way or another, affect the behavior of coffee in all its physiological processes. Therefore, this study sought to identify the variation in the growth processes and production processes in the different coffee areas of this department, including the coffee-growing municipalities Sandoná, Consacá, La Florida and La Unión in the Department of Nariño, using experimental lots located at different altitude ranges (B <1600msnm; M between 1600 and 1800msnm and A >1800msnm). The statistical design used for each municipality was Random Complete Blocks with three treatments and sixteen repetitions. The recorded climatic variables included photosynthetically active radiation, ambient temperature, precipitation and relative humidity, and the evaluated physiological variables were plant height, number of leaves, basal stem diameter, number of primary branches, number of secondary branches, length of primary branches, number of knots per branch and leaf area index. The variable plant height was statistically higher in the upper zone (A) in the municipalities La Florida (79.95 cm) and Consacá (64.31cm); in La Florida, the number of branches and the LAI were higher in the upper zone plants, while the diameter of the stems was higher in the middle zone. In the other municipalities, these variables were not affected by the altitude.

scholarly journals Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0)

2017 ◽  
Author(s):  
Gordon B. Bonan ◽  
Edward G. Patton ◽  
Ian N. Harman ◽  
Keith W. Oleson ◽  
John J. Finnigan ◽  
...  
Keyword(s):  
Heat Flux ◽  
Friction Velocity ◽  
Sensible Heat Flux ◽  
Roughness Sublayer ◽  
Sensible Heat ◽  
Area Index ◽  
Community Land Model ◽  
Diurnal Range ◽  
Radiative Temperature ◽  
Canopy Model

Abstract. Land surface models used in climate models neglect the roughness sublayer and parameterize within-canopy turbulence in an ad hoc manner. We implemented a roughness sublayer turbulence parameterization in a multi-layer canopy model (CLM-ml v0) test if this theory provides a tractable parameterization extending from the ground through the canopy and the roughness sublayer. We compared the canopy model with the Community Land Model (CLM4.5) at 7 forest, 2 grassland, and 3 cropland AmeriFlux sites over a range of canopy height, leaf area index, and climate. The CLM4.5 has pronounced biases during summer months at forest sites in mid-day latent heat flux, sensible heat flux, and gross primary production, nighttime friction velocity, and the radiative temperature diurnal range. The new canopy model reduces these biases by introducing new physics. The signature of the roughness sublayer is most evident in sensible heat flux, friction velocity, and the diurnal cycle of radiative temperature. Within-canopy temperature profiles are markedly different compared with profiles obtained using Monin–Obukhov similarity theory, and the roughness sublayer produces cooler daytime and warmer nighttime temperatures. The herbaceous sites also show model improvements, but the improvements are related less systematically to the roughness sublayer parameterization in these short canopies. The multi-layer canopy with the roughness sublayer turbulence improves simulations compared with the CLM4.5 while also advancing the theoretical basis for surface flux parameterizations.

scholarly journals Projected effects of vegetation feedback on drought characteristics of West Africa using a coupled regional land–vegetation–climate model

2019 ◽  
Author(s):  
Muhammad Shafqat Mehboob ◽  
Yeonjoo Kim ◽  
Jaehyeong Lee ◽  
Myoung-Jin Um ◽  
Amir Erfanian ◽  
...  
Keyword(s):  
West Africa ◽  
Vegetation Dynamics ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Global Climate Models ◽  
Congo Basin ◽  
Area Index ◽  
Vegetation Feedback ◽  
Community Land Model

Abstract. This study investigates the projected effect of vegetation feedback on drought conditions in West Africa using a regional climate model coupled to the National Center for Atmospheric Research Community Land Model, the carbon-nitrogen (CN) module, and the dynamic vegetation (DV) module (RegCM-CLM-CN-DV). The role of vegetation feedback is examined based on simulations with and without the DV module. Simulations from four different global climate models are used as lateral boundary conditions (LBCs) for historical and future periods (i.e., historical: 1981–2000; future: 2081–2100). With utilizing the Standardized Precipitation Evapotranspiration Index (SPEI), we quantify the duration, frequency, and severity of droughts over the focal regions of the Sahel, the Gulf of Guinea, and the Congo Basin. With the vegetation dynamics being considered, future droughts become more prolonged and enhanced over the Sahel, whereas for the Guinea Gulf and Congo Basin, the trend is opposite. Additionally, we show that simulated annual leaf greenness (i.e., the Leaf Area Index) well-correlates with annual minimum SPEI, particularly over the Sahel, which is a transition zone, where the feedback between land-atmosphere is relatively strong. Furthermore, we note that our findings based on the ensemble mean are varying, but consistent among three different LBCs except for one LBC. Our results signify the importance of vegetation dynamics in predicting future droughts in West Africa, where the biosphere and atmosphere interactions play an important role in the regional climate setup.

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