Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest

Zobitz, John M.; Moore, David J. P.; Quaife, Tristan; Braswell, B. H.; Bergeson, Andrew; Anthony, Jeremy Alan; Monson, Russell K.

doi:10.1016/j.agrformet.2014.04.011

Cited by 27 publications

(18 citation statements)

References 115 publications

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“…Simultaneous data assimilations, i.e., simultaneous assimilations of observations using dynamical method (case 3) and the Kalman-Takens (case 6) lead to the largest RMSE reductions of 41.27% with 39.07%, respectively. This is in agreement with the founding of previous literature (see, e.g., Montzka et al, 2012;Renzullo et al, 2014;Zobitz et al, 2014;Tian et al, 2017), which suggested that better results can be achieved by assimilating multi-satellite products when properly accounting for the measurement errors. Larger impacts on results are found for assimilating GRACE compared to satellite soil moisture observations.…”

Section: Discussionsupporting

confidence: 93%

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

Khaki

Hoteit

Kühn

et al. 2019

Science of The Total Environment

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With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.

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Section: Discussionsupporting

confidence: 93%

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

Khaki

Hoteit

Kühn

et al. 2019

Science of The Total Environment

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“…Increasingly the focus in carbon cycle data assimilation is moving towards using multiple different data streams as independent constraints, with the aim of bringing more information at different spatial and temporal scales and constraining several processes at once in order to reduce the likelihood of model equifinality (where multiple sets of parameters achieve the same reduction in modeldata misfit). Recent examples include the combination of in situ eddy covariance flux observations and ground-based information on vegetation structure and C stocks (Richardson et al, 2010;Ricciuto et al, 2011;Keenan et al, 2012Keenan et al, , 2013Thum et al, 2016), or in situ flux data and satellite FAPAR (Kato et al, 2013;Zobitz et al, 2014;Bacour et al, 2015) or atmospheric CO 2 and biomass data using a simple biosphere model (Saito et al, 2014). This is a non-trivial task however, especially when optimizing a complex LSM (see MacBean et al, 2016), which has many parameters acting from local to global scales.…”

Section: Introductionmentioning

confidence: 99%

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

et al. 2016

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Abstract. Large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr−1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.

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“…Data-model fusion and data-assimilation techniques have been developed to include observational knowledge in numerical models (e.g., Luo et al 2011). For example, Zobitz et al (2014) used satellite-derived reflectance and CO 2 flux measurement data at a subalpine forest in the United States to optimize the parameters of a process-based model. This approach efficiently uses large amounts of data related to different ecosystem properties.…”

Section: Advanced Use Of Observational Datamentioning

confidence: 99%

Synergies between observational and modeling studies at the Takayama site: toward a better understanding of processes in terrestrial ecosystems

Ito

Saitoh

Sasai

2014

Ecological Research

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This paper reviews past and ongoing ecological studies at the Takayama forest site, one of Japan's long‐term research sites with intensive studies; the emphasis here is on the synergies between observational and modeling studies. This synergism has been encouraged because global environmental change is a complicated and interdisciplinary issue that requires this type of collaboration. The increasing amount and quality of observational data available from this site helps researchers to better constrain their model simulations because substantial uncertainties remain in the behavior of models in their current form. In addition, modeling studies encourage observational research by integrating observational data within a comprehensive framework. They do this by demanding more long‐term and high‐quality data related to specific ecological processes, and by identifying high‐priority processes and parameters that should be studied. We describe the types of modeling studies that have been conducted and how observational data from the Takayama site have improved the accuracy of these models. Many terrestrial ecosystem models have been applied to data from the site, both to validate the present forms of the models and to refine the structure and parameterization of the models. The development of new or improved terrestrial ecosystem models will be further facilitated by the requirements to simulate atmosphere–ecosystem exchanges and internal biogeochemical processes, as well as simulating their responses to a changing environment. We conclude by discussing the remaining research gaps and opportunities for deepening our understanding of terrestrial ecosystems through future collaborative studies.

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Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest

Cited by 27 publications

References 115 publications

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

Synergies between observational and modeling studies at the Takayama site: toward a better understanding of processes in terrestrial ecosystems

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