Reanalysis in Earth System Science: Towards Terrestrial Ecosystem Reanalysis

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics

Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model

Previous experiments demonstrated the relationships between the radar backscattering coefficient, sigma /sub o/ and crop parameters such as fresh biomass, plant height and Leaf Area Index (LAI). Topsoil water content also influences the backscattered signal and is as such a required input parameter in the physical and semi-empirical models that extract vegetation parameters from sigma /sub o/. In an operational environment, it is not possible to measure soil moisture over an entire agricultural region. As the vegetation cover hampers the radar remote sensing of soil moisture, near surface soil moisture can be simulated using a hydrological model. In this paper, it is investigated whether soil moisture values obtained through the hydrological model TOPLATS can be used in a crop parameter retrieval algorithm. The data set used for this investigation was collected from March to September 2003 in the Loamy Region, Belgium. During this period, 18 agricultural fields were sampled for vegetation parameters and soil moisture. In addition, 11 ERS-2 images of that period were acquired of which 6 coincided with the field measurement dates. Because the necessary catchment data were not available, TOPLATS was calibrated on a point scale for every field with in situ soil moisture. The calibrated TOPLATS model was applied to simulate soil moisture values at the ERS-2 acquisition dates for which no soil moisture field measurements were available. In parallel, the Water Cloud model was calibrated using the biophysical parameters measured on the field in order to retrieve LAI estimates from ERS SAR time series. In a second step, the simulated soil moisture values corresponding to the SAR acquisition dates were used as input in the Cloud model as substitutes of field measurements, and the propagation of the soil moisture estimate error in the LAI retrieval algorithm was studied. Finally the experimental results were discussed in the perspective of a regional crop monitoring system and the operational feasibility is assessed.Anglai

Estimation of the spatially distributed surface energy budget for AgriSAR 2006, part I: Remote sensing model intercomparison

A number of energy balance models of variable complexity that use remotely sensed boundary conditions for producing spatially distributed maps of surface fluxes have been proposed. Validation typically involves comparing model output to flux tower observations at a handful of sites, and hence there is no way of evaluating the reliability of model output for the remaining pixels comprising a scene. To assess the uncertainty in flux estimation over a remote sensing scene requires one to conduct pixel-by-pixel comparisons of the output. The objective of this paper is to assess whether the simplifications made in a simple model lead to erroneous predictions or deviations from a more complex model and under which circumstances these deviations most likely occur. Two models, the S-SEBI and TSEB algorithms, which have potential for operationally monitoring ET with satellite data are described and a spatial inter-comparison is made. Comparisons of the spatially distributed flux maps from the two models are made using remotely sensed imagery collected over an agricultural test site in Northern Germany. With respect to model output for radiative and conductive fluxes no major differences are noted. Results for turbulent flux exchange demonstrate that under relatively dry conditions and over tall crops model output differs significantly. The overall conclusion is that under unstressed conditions and over homogeneous landcover a simple index model is adequate for determining the spatially distributed energy budget

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m3/m3 to 0.046 m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale