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 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 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 Observation-based implementation of ecophysiological processes for a rubber plant functional type in the community land model (CLM4.5-rubber_v1)

2018 ◽  
Author(s):  
Ashehad A. Ali ◽  
Yuanchao Fan ◽  
Marife D. Corre ◽  
Martyna M. Kotowska ◽  
Evelyn Hassler ◽  
...  
Keyword(s):  
Land Use ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Productivity ◽  
Measured Data ◽  
Biogeochemical Processes ◽  
Water Fluxes ◽  
Area Index ◽  
Community Land Model ◽  
Leaf Litterfall

Abstract. Land-use change has a strong impact on carbon, energy and water fluxes and its effect is particularly pronounced in tropical regions. Uncertainties exist in the prediction of future land-use change impacts on these fluxes by land surface models due to scarcity of suitable measured data for parametrization and poor representation of key biogeochemical processes associated with tropical vegetation types. Rubber plantations (Havea brasilliensis) are a crucial land-use type across tropical landscapes that has greatly expanded in recent decades. Here, we first synthesize the relevant data for describing the biogeochemical processes of rubber from our past measurement campaigns in Jambi province, Indonesia. We then use these data-sets to develop a rubber plant functional type (PFT) for the Community Land Model (CLM4.5). Field measured data from small-holder plantations on leaf litterfall, soil respiration, latex harvest, leaf area index, transpiration, net primary productivity, and above-ground and fine root biomass were used to develop and calibrate a new PFT-based model (CLM4.5-rubber). CLM-rubber predictions adequately captured the annual net primary productivity and above-ground biomass as well as the seasonal dynamics of leaf litterfall, soil respiration, soil moisture and leaf area index. All of the predicted water fluxes of CLM-rubber were very similar to a site-specific calibrated soil water model. Including temporal variations in leaf life span enabled CLM-rubber to better capture the seasonality of leaf litterfall. Increased sensitivity of stomata to soil water stress and the enhancement of growth and maintenance respiration of fine roots in response to soil nutrient limitation enabled CLM-rubber to capture the magnitude of transpiration and leaf area index. Since CLM-rubber predicted reasonably well the carbon and water use, we think that the current model can be used for larger-scale simulations within Jambi province because more than 99 % of the rubber plantations are smallholder owned in Jambi province and have low soil fertility.

How forests change transit times of transpiration, evaporation and streamflow – a modeling approach

2021 ◽  
Author(s):  
Ingo Heidbüchel ◽  
Jie Yang ◽  
Jan H. Fleckenstein
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Cycle ◽  
Rooting Depth ◽  
Water Fluxes ◽  
Area Index ◽  
Transit Times ◽  
Physically Based ◽  
Forested Areas ◽  
The Individual

<p>In a recent paper we investigated how different catchment and climate properties influence transit time distributions. This was done by employing a physically-based spatially explicit 3D model in a virtual catchment running many different scenarios with different combinations of catchment and climate properties. We found that the velocity distribution of water fluxes through a catchment is more sensitive to certain properties while other factors appear less relevant. Now we expanded the approach by adding vegetation to the model and thus introducing new hydrologic processes (transpiration and evaporation) to the simulated water cycle. On the one hand we wanted to know how these new processes would influence transit times of the water fluxes to the stream, on the other hand we were interested in how exactly differences in the vegetation itself (e.g. rooting depth and leaf area index) would alter the various flux velocities (including transit times of transpiration and evaporation). It was very interesting to observe that streamflow in forested areas appeared to become older on average. We also found that transpiration was generally younger if the vegetation had shallower roots and/or a larger leaf area index. The biggest difference in the age of evaporation was detected for different amounts of subsequent precipitation (evaporation was generally younger in a wetter climate). In conclusion, we found that forests influence the age of the different water fluxes within a catchment. According to our results the overall hydrologic cycle is decelerated when adding vegetation to a model that otherwise only simulates evaporation.</p><p>Still, in order to make meaningful predictions on the age of hydrologic fluxes, it is not constructive to single out specific catchment and climate properties. The multitude of influences from different parameters makes it very challenging to find rules and underlying principles in the integrated catchment response. Therefore it is necessary to look at the individual parameters and their potential interactions and interdependencies in a bottom-up approach.</p>

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 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 Identification and Modeling Carbon and Energy Fluxes from Eddy Covariance Time Series Measurements in Rice and Rainfed Crops

2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Víctor Cicuéndez ◽  
Javier Litago ◽  
Víctor Sánchez-Girón ◽  
Laura Recuero ◽  
César Sáenz ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Balance ◽  
Gross Primary Production ◽  
Meteorological Variables ◽  
Water Fluxes ◽  
Energy Fluxes ◽  
Global Carbon

Gross primary production (GPP) represents the carbon (C) uptake of ecosystems through photosynthesis and it is the largest flux of the global carbon balance. Our overall objective in this research is to identify and model GPP dynamics and its relationship with meteorological variables and energy fluxes based on time series analysis of eddy covariance (EC) data in two different agroecosystems, a Mediterranean rice crop in Spain and a rainfed cropland in Germany. Crops exerted an important influence on the energy and water fluxes dynamics existing a clear feedback between GPP, meteorological variables and energy fluxes in both type of crops.

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