35,072 research outputs found
Integrating remote sensing datasets into ecological modelling: a Bayesian approach
Process-based models have been used to simulate 3-dimensional complexities of
forest ecosystems and their temporal changes, but their extensive data
requirement and complex parameterisation have often limited their use for
practical management applications. Increasingly, information retrieved using
remote sensing techniques can help in model parameterisation and data
collection by providing spatially and temporally resolved forest information. In
this paper, we illustrate the potential of Bayesian calibration for integrating such
data sources to simulate forest production. As an example, we use the 3-PG
model combined with hyperspectral, LiDAR, SAR and field-based data to
simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and
SAR data are used to estimate LAI dynamics, tree height and above ground
biomass, respectively, while the Bayesian calibration provides estimates of
uncertainties to model parameters and outputs. The Bayesian calibration
contrasts with goodness-of-fit approaches, which do not provide uncertainties
to parameters and model outputs. Parameters and the data used in the
calibration process are presented in the form of probability distributions,
reflecting our degree of certainty about them. After the calibration, the
distributions are updated. To approximate posterior distributions (of outputs
and parameters), a Markov Chain Monte Carlo sampling approach is used (25
000 steps). A sensitivity analysis is also conducted between parameters and
outputs. Overall, the results illustrate the potential of a Bayesian framework for
truly integrative work, both in the consideration of field-based and remotely
sensed datasets available and in estimating parameter and model output uncertainties
Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates
Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes
Upscaling fluxes from towers to regions, continents and global scales using datadriven approaches
Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes
Global patterns, trends, and drivers of water use efficiency from 2000 to 2013
Water use efficiency (WUE; gross primary production [GPP]/evapotranspiration [ET]) estimates the tradeoff between carbon gain and water loss during photosynthesis and is an important link of the carbon and water cycles. Understanding the spatiotemporal patterns and drivers of WUE is helpful for projecting the responses of ecosystems to climate change. Here we examine the spatiotemporal patterns, trends, and drivers of WUE at the global scale from 2000 to 2013 using the gridded GPP and ET data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results show that the global WUE has an average value of 1.70 g C/kg H2O with large spatial variability during the 14-year period. WUE exhibits large variability with latitude. WUE also varies much with elevation: it first remains relatively constant as the elevation varies from 0 to 1000 m and then decreases dramatically. WUE generally increases as precipitation and specific humidity increase; whereas it decreases after reaching maxima as temperature and solar radiation increases. In most land areas, the temporal trend of WUE is positively correlated with precipitation and specific humidity over the 14-year period; while it has a negative relationship with temperature and solar radiation related to global warming and dimming. On average, WUE shows an increasing trend of 0.0025 g C·kgâ1 H2O·yrâ1 globally. Our global-scale assessment of WUE has implications for improving our understanding of the linkages between the water and carbon cycles and for better projecting the responses of ecosystems to climate change
Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model
We developed a water-centric monthly scale simulation model (WaSSI-C) by integrating empirical water and carbon flux measurements from the FLUXNET network and an existing water supply and demand accounting model (WaSSI). The WaSSI-C model was evaluated with basin-scale evapotranspiration (ET), gross ecosystem productivity (GEP), and net ecosystem exchange (NEE) estimates by multiple independent methods across 2103 eight-digit Hydrologic Unit Code watersheds in the conterminous United States from 2001 to 2006. Our results indicate that WaSSI-C captured the spatial and temporal variability and the effects of large droughts on key ecosystem fluxes. Our modeled mean (±standard deviation in space) ET (556 ± 228 mm yrâ1) compared well to Moderate Resolution Imaging Spectroradiometer (MODIS) based (527 ± 251 mm yrâ1) and watershed water balance based ET (571 ± 242 mm yrâ1). Our mean annual GEP estimates (1362 ± 688 g C mâ2 yrâ1) compared well (R2 = 0.83) to estimates (1194 ± 649 g C mâ2 yrâ1) by eddy flux-based EC-MOD model, but both methods led significantly higher (25â30%) values than the standard MODIS product (904 ± 467 g C mâ2 yrâ1). Among the 18 water resource regions, the southeast ranked the highest in terms of its water yield and carbon sequestration capacity. When all ecosystems were considered, the mean NEE (â353 ± 298 g C mâ2 yrâ1) predicted by this study was 60% higher than EC-MOD\u27s estimate (â220 ± 225 g C mâ2 yrâ1) in absolute magnitude, suggesting overall high uncertainty in quantifying NEE at a large scale. Our water-centric model offers a new tool for examining the trade-offs between regional water and carbon resources under a changing environment
First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
The importance of semi-arid ecosystems in the global carbon cycle as sinks
for CO2 emissions has recently been highlighted. Africa is a carbon sink and
nearly half its area comprises arid and semi-arid ecosystems. However, there
are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa,
particularly savannas and dry tropical woodlands. In order to improve on
existing remote-sensing based methods for estimating carbon uptake across
semi-arid Africa we applied and tested the recently developed plant phenology
index (PPI). We developed a PPI-based model estimating gross primary
productivity (GPP) that accounts for canopy water stress, and compared it
against three other Earth observation-based GPP models: the temperature and
greenness model, the greenness and radiation model and a light use efficiency
model. The models were evaluated against in situ data from four semi-arid sites
in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results
from the four GPP models showed reasonable agreement with in situ GPP measured
from eddy covariance flux towers (EC GPP) based on coefficient of variation,
root-mean-square error, and Bayesian information criterion. The PPI-based GPP
model was able to capture the magnitude of EC GPP better than the other tested
models. The results of this study show that a PPI-based GPP model is a
promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.Comment: Accepted manuscript; 12 pages, 4 tables, 9 figure
Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks
The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth\u27s climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO2 uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO2 uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycleâclimate models
Alternative Insurance Indexes for Drought Risk in Developing Countries
The paper compares the risk coping potential of insurances that are based on indices derived from weather (rainfall and temperature) data as well as from crop model and remote sensing analyses. Corresponding indices were computed for the case of wheat production in the Aleppo region of northern Syria, representative for agricultural production systems in many developing countries. The results demonstrate that weather derivatives such as the rainfall sum index (RSI) and the rainfall deficit index (RDI) have a very good potential for coping with risk in semiarid areas. Crop simulation model index (CSI) on the other hand could serve as an alternative to RSI and RDI when historical farm yield data is not available or not reliable. In such cases we simulated historical yields using the CropSyst cropping system simulation model. Remote sensing data could be used to establish index insurances where weather stations are sparsely located and (daily time step) weather data thus not available. The study analyzes two indexes estimated from the Normalized Differential Vegetation Index (NDVI): (1.) the farm level NDVI (FNDVI) and (2.) the area level NDVI (ANDVI). FNDVI may have a very high potential for securing farm revenues, but may be prone to moral hazard since farm management changes and subsequent gains or losses in crop production are directly revealed by the NDVI when high resolution images are used. Therefore, we recommend ANDVI for developing countries since the index is estimated for the whole agricultural zone similar to traditional area-yield insurances.risk management, index insurance, alternative index, CropSyst, NDVI, Risk and Uncertainty,
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