scholarly journals Calibration and Validation of the Integrated Biosphere Simulator (IBIS) for a Brazilian Semiarid Region

2013 ◽  
Vol 52 (12) ◽  
pp. 2753-2770 ◽  
Author(s):  
Ana Paula M. A. Cunha ◽  
Regina C. S. Alvalá ◽  
Gilvan Sampaio ◽  
Marília Harumi Shimizu ◽  
Marcos Heil Costa
Keyword(s):  
Latent Heat ◽  
Land Surface ◽  
Natural Vegetation ◽  
Semiarid Region ◽  
Surface Model ◽  
Model Parameters ◽  
Extensive Literature ◽  
Surface Atmosphere ◽  
Integrated Biosphere Simulator ◽  
Ibis Model

AbstractThe reliability of predictions from climate and weather models is linked to an adequate representation of the land surface processes. To evaluate performance and to improve predictions, land surface models are calibrated against observed data. Despite an extensive literature describing methods of land surface model calibration, few studies have applied a calibration method for semiarid natural vegetation, especially for the semiarid northeast of Brazil, which presents caatinga as its natural vegetation. Caatinga is a highly dynamic ecosystem with the physics at the land surface–atmosphere interface still poorly understood. Therefore, in this study a multiobjective hierarchical method, which provides means to estimate optimal values of the model parameters through calibration, is evaluated. This method is applied to caatinga by using the Integrated Biosphere Simulator (IBIS). Results demonstrated that the calibrated set of vegetation parameters produced a considerably different energy balance from the default parameters. In general, the model was able to simulate the partition of the available energy into sensible and latent heat fluxes when the calibrated parameters were used. The IBIS model was not able to capture short-term, intense changes in latent heat flux from a dry condition to a wetter condition, however, even when the new set of calibrated parameters was used. Therefore, the parameter optimization may not be sufficient if processes are missing or misrepresented. This study is one of the first to understand the physics at the land surface–atmosphere interface in the caatinga ecosystem and to evaluate the ability of the IBIS model to represent the biophysical interactions in this important ecosystem.

scholarly journals The interactions between soil–biosphere–atmosphere (ISBA) land surface model multi-energy balance (MEB) option in SURFEXv8 – Part 2: Introduction of a litter formulation and model evaluation for local-scale forest sites

2017 ◽  
Vol 10 (4) ◽  
pp. 1621-1644 ◽  
Author(s):  
Adrien Napoly ◽  
Aaron Boone ◽  
Patrick Samuelsson ◽  
Stefan Gollvik ◽  
Eric Martin ◽  
...  
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Soil Temperature ◽  
Latent Heat ◽  
Land Surface ◽  
Forest Floor ◽  
Land Surface Model ◽  
Local Scale ◽  
Surface Model ◽  
Model Parameters

Abstract. Land surface models (LSMs) need to balance a complicated trade-off between computational cost and complexity in order to adequately represent the exchanges of energy, water and matter with the atmosphere and the ocean. Some current generation LSMs use a simplified or composite canopy approach that generates recurrent errors in simulated soil temperature and turbulent fluxes. In response to these issues, a new version of the interactions between soil–biosphere–atmosphere (ISBA) land surface model has recently been developed that explicitly solves the transfer of energy and water from the upper canopy and the forest floor, which is characterized as a litter layer. The multi-energy balance (MEB) version of ISBA is first evaluated for three well-instrumented contrasting local-scale sites, and sensitivity tests are performed to explore the behavior of new model parameters. Second, ISBA-MEB is benchmarked against observations from 42 forested sites from the global micro-meteorological network (FLUXNET) for multiple annual cycles.It is shown that ISBA-MEB outperforms the composite version of ISBA in improving the representation of soil temperature, ground, sensible and, to a lesser extent, latent heat fluxes. Both versions of ISBA give comparable results in terms of simulated latent heat flux because of the similar formulations of the water uptake and the stomatal resistance. However, MEB produces a better agreement with the observations of sensible heat flux than the previous version of ISBA for 87.5 % of the simulated years across the 42 forested FLUXNET sites. Most of this improvement arises owing to the improved simulation of the ground conduction flux, which is greatly improved using MEB, especially owing to the forest litter parameterization. It is also shown that certain processes are also modeled more realistically (such as the partitioning of evapotranspiration into transpiration and ground evaporation), even if certain statistical performances are neutral. The analyses demonstrate that the shading effect of the vegetation, the explicit treatment of turbulent transfer for the canopy and ground, and the insulating thermal and hydrological effects of the forest floor litter turn out to be essential for simulating the exchange of energy, water and matter across a large range of forest types and climates.

scholarly journals The Interactions between Soil-Biosphere-Atmosphere (ISBA) land surface model Multi-Energy Balance (MEB) option in SURFEX – Part 2: Model evaluation for local scale forest sites

2016 ◽  
Author(s):  
Adrien Napoly ◽  
Aaron Boone ◽  
Patrick Samuelsson ◽  
Stefan Gollvik ◽  
Eric Martin ◽  
...  
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Soil Temperature ◽  
Latent Heat ◽  
Land Surface ◽  
Forest Floor ◽  
Land Surface Model ◽  
Local Scale ◽  
Surface Model ◽  
Model Parameters

Abstract. Land surface models (LSMs) need to balance a complicated trade-off between computational cost and complexity in order to adequately represent the exchanges of energy, water and matter with the atmosphere and the ocean. Some current generation LSMs use a simplified or composite canopy approach that generates recurrent errors in simulated soil temperature and turbulent fluxes. In response to these issues, a new version of the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model has recently been developed which explicitly solves the transfer of energy and water from the upper canopy and the forest floor which is characterized as a litter layer. The so-called Multi Energy Balance (MEB) version of ISBA is first evaluated for three well-instrumented contrasting local scale sites, and sensitivity tests are performed to explore the behavior of new model parameters. Second, ISBA-MEB is benchmarked against observations from 42 forested sites from the global micro-meteorological network (FluxNet) for multiple annual cycles. It is shown that ISBA-MEB outperforms the composite version of ISBA in improving the representation of soil temperature, ground, sensible and to a lesser extent latent heat fluxes. Both versions of ISBA give comparable results in terms of simulated latent heat flux because of the similar formulations of the water uptake and the stomatal resistance. However, MEB produces a better agreement with the observations of sensible heat flux than the previous version of ISBA for 87.5 % of the simulated years across the 42 forested FluxNet sites. Most of this improvement arises owing to the improved simulation of the ground conduction flux, which is greatly improved using MEB, especially owing to the forest litter parameterization. It is also shown that certain processes are also modeled more realistically (such as the partitioning of evapotranspiration into transpiration and ground evaporation), even if certain statistical performances are neutral. The analyses demonstrate that shading effect of the vegetation, the explicit treatment of turbulent transfer for the canopy and ground, and the insulating thermal and hydrological effects of the forest floor litter turn out to be essential for simulating the exchange of energy, water and matter across a large range of forest types and climates.

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals The Impact of Error Accounting in a Bayesian Approach to Calibrating Modeled Turbulent Fluxes in an Open-Canopy Forest

2017 ◽  
Vol 18 (7) ◽  
pp. 2029-2042
Author(s):  
Tony E. Wong ◽  
William Kleiber ◽  
David C. Noone
Keyword(s):  
Markov Chain ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Soil Layer ◽  
Turbulent Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Top Soil ◽  
The Impact ◽  
Calibration Approach

Abstract Land surface models are notorious for containing many parameters that control the exchange of heat and moisture between land and atmosphere. Properly modeling the partitioning of total evapotranspiration (ET) between transpiration and evaporation is critical for accurate hydrological modeling, but depends heavily on the treatment of turbulence within and above canopies. Previous work has constrained estimates of evapotranspiration and its partitioning using statistical approaches that calibrate land surface model parameters by assimilating in situ measurements. These studies, however, are silent on the impacts of the accounting of uncertainty within the statistical calibration framework. The present study calibrates the aerodynamic, leaf boundary layer, and stomatal resistance parameters, which partially control canopy turbulent exchange and thus the evapotranspiration flux partitioning. Using an adaptive Metropolis–Hastings algorithm to construct a Markov chain of draws from the joint posterior distribution of these resistance parameters, an ensemble of model realizations is generated, in which latent and sensible heat fluxes and top soil layer temperature are optimized. A set of five calibration experiments demonstrate that model performance is sensitive to the accounting of various sources of uncertainty in the field observations and model output and that it is critical to account for model structural uncertainty. After calibration, the modeled fluxes and top soil layer temperature are largely free from bias, and this calibration approach successfully informs and characterizes uncertainty in these parameters, which is essential for model improvement and development. The key points of this paper are 1) a Markov chain Monte Carlo calibration approach successfully improves modeled turbulent fluxes; 2) ET partitioning estimates hinge on the representation of uncertainties in the model and data; and 3) despite these inherent uncertainties, constrained posterior estimates of ET partitioning emerge.

The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

2021 ◽  
Author(s):  
Eduardo Emilio Sanchez-Leon ◽  
Natascha Brandhorst ◽  
Bastian Waldowski ◽  
Ching Pui Hung ◽  
Insa Neuweiler ◽  
...  
Keyword(s):  
Plant Physiology ◽  
Data Assimilation ◽  
Land Surface ◽  
Reference Model ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Sources Of Uncertainty

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

Sensitivity of Latent Heat Fluxes to Initial Values and Parameters of a Land-Surface Model

2010 ◽  
Vol 9 (4) ◽  
pp. 984-1001 ◽  
Author(s):  
Jörg Schwinger ◽  
Stefan J. Kollet ◽  
Charlotte M. Hoppe ◽  
Hendrik Elbern
Keyword(s):  
Latent Heat ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Initial Values

Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR)

2020 ◽  
Author(s):  
Stephan Thober ◽  
Matthias Kelbling ◽  
Florian Pappenberger ◽  
Christel Prudhomme ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
High Resolution ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Surface Model ◽  
Model Parameters ◽  
Environmental Models ◽  
Energy Cycle

<p>The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.</p><p>MPR translates local geophysical properties into model parameters. It consists of two steps: 1) local high-resolution geophysical data sets (e.g. soil maps) are translated into model parameters using a transfer function. 2) the high-resolution model parameters are scaled to the model resolution using suitable upscaling operators (e.g., harmonic mean). The MPR technique was introduced into the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013) and it is key factor for its success on transferring parameters across scales and locations.  </p><p>In this study, we apply MPR to vegetation and soil parameters in the land surface model HTESSEL. This model is the land-surface component of the European Centre for Medium-Range Weather Forecasting seasonal forecasting system. About 100 hard-coded parameters have been extracted to allow for a comprehensive sensitivity analysis and parameter estimation.</p><p>We analyze simulated evaporation and runoff fluxes by HTESSEL using parameters estimated by MPR in comparison to a default HTESSEL setup over Europe. The magnitude of simulated long-term fluxes deviates the most (up to 10% and 20% for evapotranspiration and runoff, respectively) in regions with a large subgrid variability in geophysical attributes (e.g., soil texture). The choice of transfer functions and upscaling operators influences the magnitude of these differences and governs model performance assessed after calibration against observations (e.g. streamflow).</p><p><strong>References:</strong></p><p>Samaniego L., et al.  <strong>https://doi.org/10.1029/2008WR007327</strong></p><p>Kumar, R., et al.  <strong>https://doi.org/10.1029/2012WR012195</strong></p>

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