3,427 research outputs found

    Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

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    To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

    Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2

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    Increasing concentrations of atmospheric carbon dioxide are expected to affect carbon assimilation and evapotranspiration (ET), ultimately driving changes in plant growth, hydrology and the global carbon balance. Direct leaf biochemical effects have been widely investigated, while indirect effects, although documented, elude explicit quantification in experiments. Here, we used a mechanistic model to investigate the relative contributions of direct (through carbon assimilation) and indirect (via soil moisture savings due to stomatal closure, and changes in leaf area index, LAI) effects of elevated CO2 across a variety of ecosystems. We specifically determined which ecosystems and climatic conditions maximise the indirect effects of elevated CO2. The simulations suggest that the indirect effects of elevated CO2 on net primary productivity are large and variable, ranging from less than 10% to more than 100% of the size of direct effects. For ET, indirect effects were on average 65% of the size of direct effects. Indirect effects tended to be considerably larger in water-limited ecosystems. As a consequence, the total CO2 effect had a significant, inverse relationship with the wetness index and was directly related to vapor pressure deficit. These results have major implications for our understanding of the CO2-response of ecosystems and for global projections of CO2 fertilization because, while direct effects are typically understood and easily reproducible in models, simulations of indirect effects are far more challenging and difficult to constrain. Our findings also provide an explanation for the discrepancies between experiments in the total CO2 effect on net primary productivity

    Carbonyl sulfide : comparing a mechanistic representation of the vegetation uptake in a land surface model and the leaf relative uptake approach

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    Land surface modellers need measurable proxies to constrain the quantity of carbon dioxide (CO2) assimilated by continental plants through photosynthesis, known as gross primary production (GPP). Carbonyl sulfide (COS), which is taken up by leaves through their stomates and then hydrolysed by photosynthetic enzymes, is a candidate GPP proxy. A former study with the ORCHIDEE land surface model used a fixed ratio of COS uptake to CO2 uptake normalised to respective ambient concentrations for each vegetation type (leaf relative uptake, LRU) to compute vegetation COS fluxes from GPP. The LRU approach is known to have limited accuracy since the LRU ratio changes with variables such as photosynthetically active radiation (PAR): while CO2 uptake slows under low light, COS uptake is not light limited. However, the LRU approach has been popular for COS-GPP proxy studies because of its ease of application and apparent low contribution to uncertainty for regional-scale applications. In this study we refined the COS-GPP relationship and implemented in ORCHIDEE a mechanistic model that describes COS uptake by continental vegetation. We compared the simulated COS fluxes against measured hourly COS fluxes at two sites and studied the model behaviour and links with environmental drivers. We performed simulations at a global scale, and we estimated the global COS uptake by vegetation to be -756 Gg S yr(-1) , in the middle range of former studies (-490 to -1335 Gg S yr(-1)). Based on monthly mean fluxes simulated by the mechanistic approach in ORCHIDEE, we derived new LRU values for the different vegetation types, ranging between 0.92 and 1.72, close to recently published averages for observed values of 1.21 for C-4 and 1.68 for C-3 plants. We transported the COS using the monthly vegetation COS fluxes derived from both the mechanistic and the LRU approaches, and we evaluated the simulated COS concentrations at NOAA sites. Although the mechanistic approach was more appropriate when comparing to high-temporal-resolution COS flux measurements, both approaches gave similar results when transporting with monthly COS fluxes and evaluating COS concentrations at stations. In our study, uncertainties between these two approaches are of secondary importance compared to the uncertainties in the COS global budget, which are currently a limiting factor to the potential of COS concentrations to constrain GPP simulated by land surface models on the global scale.Peer reviewe

    Integrating remote sensing datasets into ecological modelling: a Bayesian approach

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    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

    Constraints on ecosystem carbon and water flux : estimates in a temperate Australian evergreen forest

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    Land-atmosphere carbon dioxide (CO2) exchange is the least constrained component of the global carbon cycle, yet it is driving most of its inter-annual variability. Seasonal and interannual variations in weather conditions affect biological activity and resulting CO2 exchanges, but the relative effects of phenology and climate on carbon cycling are not well understood. I used four years of eddy covariance data from a eucalypt woodland located near Sydney, South-East Australia, to better constrain carbon and water fluxes from this forest type. At our site, I observed a seasonal pattern of net ecosystem exchange (NEE) that contrasted with other flux tower sites in eucalypt forests. While similar Australian sites acted as a sink of carbon all year, especially in summer, our site behaved as a net sink of carbon in winter and a net source of carbon in summer. This pattern was caused by ecosystem respiration (Reco) driving the seasonal course of NEE, as the seasonal variability in Reco was bigger than that of gross primary production (GPP). GPP was limited by stomatal closure at high vapour pressure deficit in summer, but remained high in winter, while Reco was high in summer, and lower in winter. Leaf area index (LAI) varied seasonally, increasing rapidly mid-summer to reach a maximum in late summer, then decreased until the next year. LAI was a good predictor of canopy photosynthetic capacity (PC). The Community Atmosphere Biosphere Land Exchange (CABLE) land surface model was able to reproduce the seasonal variation in forest NEE but did not entirely capture canopy PC variability. Leaf demography, which is not accounted for in the model, may partly explain the mismatch between observed and simulated PC and should be further investigated. Our estimate of allocation of net primary productivity (NPP) to leaf growth was dynamic seasonally, which contrasts with the CABLE model assumption of a constant allocation factor in the evergreen broadleaf forest biome. Improved representation of dynamic allocation may further improve carbon cycle predictions in evergreen broadleaf forests. A semi-mechanistic model of heterotrophic respiration, the Dual Arrhenius Michaelis Menten model (DAMM), reproduced seasonal variations of Rsoil and Reco as a function of temperature and soil moisture. Daily to seasonal patterns of soil CO2 efflux were similar to those of Reco, but hourly dynamics were different, as Rsoil remained nearly constant overnight while Reco decreased. While decreasing air temperatures overnight may explain decreasing above-ground respiration, advection could also play a role, leading to a systematic data bias. Additional continuous, high frequency measurements of Reco components such as leaf respiration, stem respiration and soil respiration would improve mechanistic understanding of nighttime and daytime Reco. While weather variation was the major control of fluxes, the canopy phenology (leaf area index variations and leaf demography) also played an important role and needs to be incorporated in land surface models

    Crop modeling for assessing and mitigating the impacts of extreme climatic events on the US agriculture system

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    The US agriculture system is the world’s largest producer of maize and soybean, and typically supplies more than one-third of their global trading. Nearly 90% of the US maize and soybean production is rainfed, thus is susceptible to climate change stressors such as heat waves and droughts. Process-based crop and cropping system models are important tools for climate change impact assessments and risk management. As data- science is becoming a new frontier for agriculture growth, the incoming decade calls for operational platforms that use hyper-local growth monitoring, high-resolution real-time weather and satellite data assimilation and cropping system modeling to help stakeholders predict crop yields and make decisions at various spatial scales. The fundamental question addressed by this dissertation is: How crop and cropping system models can be “useful” to the agriculture production, given the recent advent of cloud computing and earth observatory power? This dissertation consists of four main chapters. It starts with a study that reviews the algorithms of simulating heat and drought stress on maize in 16 major crop models, and evaluates algorithm performances by incorporating these algorithms into the Agricultural Production Systems sIMulator (APSIM) and running an ensemble of simulations at typical farms from the US Midwest. Results show that current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for using daily mean temperature. Different drought algorithms considerably differed in their patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. In the next chapter of climate change assessment study, I quantify the current and future yield responses of US rainfed maize and soybean to climate extremes with and without considering the effect of elevated atmospheric CO2concentrations, and for the first time characterizes spatial shifts in the relative importance of temperature, heat and drought stresses. Model simulations demonstrate that drought will continue to be the largest threat to rainfed maize and soybean production, yet shifts in the spatial pattern of dominant stressors are characterized by increases in the concurrent stress, indicating future adaptation strategies will have trade-offs between multiple objectives. Following this chapter, I presented a chapter that uses billion-scale simulations to identify the optimal combination of Genotype × Environment × Management for the purpose of minimizing the negative impact of climate extremes on the rainfed maize yield. Finally, I present a prototype of crop model and satellite imagery based within-field scale N sidedress prescription tool for the US rainfed maize system. As an early attempt to integrate advances in multiple areas for precision agriculture, this tool successfully captures the subfield variability of N dynamics and gives reasonable spatially explicit sidedress N recommendations. The prescription enhances zones with high yield potentials, while prevents over-fertilization at zones with low yield potentials

    Iberian peninsula ecosystem carbon fluxes: a model-data integration study

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    Dissertação apresentada para obtenção do Grau de Doutor em Engenharia do Ambiente pela Universidade Nova de Lisboa,Faculdade de Ciências e TecnologiaTerrestrial ecosystems play a key role within the context of the global carbon cycle. Characterizing and understanding ecosystem level responses and feedbacks to climate drivers is essential for diagnostic purposes as well as climate modelling projections. Consequently,numerous modelling and data driven approaches emerge, aiming the appraisal of biosphereatmosphere carbon fluxes. The combination of biogeochemical models with observations of ecosystem carbon fluxes in a model-data integration framework enables the recognition of potential limitations of modelling approaches. In this regard, the steady-state assumption represents a general approach in the initialization routines of biogeochemical models that entails limitations in the ability to simulate net ecosystem fluxes and in model development exercises. The present research addresses the generalized assumption of initial steady-state conditions in ecosystem carbon pools for modelling carbon fluxes of terrestrial ecosystems, from local to regional scales. At local scale, this study aims to evaluate the implications of equilibrium assumptions on modelling performance and on optimized parameters and uncertainty estimates based on a model-data integration approach. These results further aim to support the estimates of regional net ecosystem fluxes, following a bottom-up approach, by focusing on parameters governing net primary production (NPP) and heterotrophic respiration (RH)processes, which determine the simulation of the net ecosystem production fluxes in the CASA model. An underlying goal of the current research is addressed by focusing on Mediterranean ecosystem types, or ecosystems potentially present in Iberia, and evaluate the general ability of terrestrial biogeochemical models in estimating net ecosystem fluxes for the Iberian Peninsula region. At regional scales, and given the limited information available, the main objective is to minimize the implications of the initial conditions in the evaluation of the temporal dynamics of net ecosystem fluxes. Inverse model parameter optimizations at site level are constrained by eddy-covariance measurements of net ecosystem fluxes and driven by local observations of meteorological variables and vegetation biophysical variables from remote sensing products. Optimizations under steady-state conditions show significantly poorer model performance and higher parameter uncertainties when compared to optimizations under relaxed initial conditions. In addition, assuming initial steady-state conditions tend to bias parameter retrievals – reducing NPP sensitivity to water availability and RH responses to temperature – in order to prescribe sink conditions. But nonequilibrium conditions can be experienced in soil and/or vegetation carbon pools under alternative underlying dynamics, which are solely discernible through the integration of additional information sources, circumventing equifinality issues.Portuguese Foundation for Science and Technology (FCT),the European Union under Operational Program “Science and Innovation” (POCI 2010), PhD grant ref. SFRH/BD/6517/2001, co-sponsored by the European Social Fund. Further support,concerning the final months of the PhD, was provided by a Max Planck Society research fellowship

    Integration of remotely sensed data with stand-scale vegetation models

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