scholarly journals Modelling LAI at a regional scale with ISBA-A-gs: comparison with satellite-derived LAI over southwestern France

2009 ◽  
Vol 6 (8) ◽  
pp. 1389-1404 ◽  
Author(s):  
A. Brut ◽  
C. Rüdiger ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
J.-C. Calvet ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
Temperature Response ◽  
Climatic Conditions ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Satellite Signal

Abstract. A CO2-responsive land surface model (the ISBA-A-gs model of Météo-France) is used to simulate photosynthesis and Leaf Area Index (LAI) in southwestern France for a 3-year period (2001–2003). A domain of about 170 000 km2 is covered at a spatial resolution of 8 km. The capability of ISBA-A-gs to reproduce the seasonal and the interannual variability of LAI at a regional scale, is assessed with satellite-derived LAI products. One originates from the CYCLOPES programme using SPOT/VEGETATION data, and two products are based on MODIS data. The comparison reveals discrepancies between the satellite LAI estimates and between satellite and simulated LAI values, both in their intensity and in the timing of the leaf onset. The model simulates higher LAI values for the C3 crops than the satellite observations, which may be due to a saturation effect within the satellite signal or to uncertainties in model parameters. The simulated leaf onset presents a significant delay for C3 crops and mountainous grasslands. In-situ observations at a mid-altitude grassland site show that the generic temperature response of photosynthesis used in the model is not appropriate for plants adapted to the cold climatic conditions of the mountainous areas. This study demonstrates the potential of LAI remote sensing products for identifying and locating models' shortcomings at a regional scale.

scholarly journals Modelling LAI at a regional scale with ISBA-A-gs: comparison with satellite-derived LAI over southwestern France

2009 ◽  
Vol 6 (2) ◽  
pp. 4059-4093
Author(s):  
A. Brut ◽  
C. Rüdiger ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
J.-C. Calvet ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
Temperature Response ◽  
Climatic Conditions ◽  
Surface Model ◽  
Area Index ◽  
Satellite Signal ◽  
Grassland Site

Abstract. A CO2-responsive land surface model (the ISBA-A-gs model of Météo-France) is used to simulate photosynthesis and Leaf Area Index (LAI) in southwestern France for a 3-year period (2001–2003). A domain of about 170 000 km2 is covered at a spatial resolution of 8 km. The capability of ISBA-A-gs to reproduce the seasonal and the inter-annual variability of LAI at a regional scale, is assessed with two satellite-derived LAI products. One originates from the CYCLOPES programme using SPOT/VEGETATION data, and the second is based on MODIS data. The comparison reveals discrepancies between the two satellite LAI estimates and between satellite and simulated LAI values, both in their intensity and in the timing of the leaf onset. The model simulates higher LAI values for the C3 crops and coniferous trees than the satellite observations, which may be due to a saturation effect within the satellite signal. The simulated leaf onset presents a significant delay for mountainous grasslands. In-situ observations at a mid-altitude grassland site show that the generic temperature response of photosynthesis used in the model is not appropriate for plants adapted to the cold climatic conditions of the mountainous areas. This study demonstrates the potential of LAI remote sensing products for identifying and locating models' shortcomings at a regional scale.

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>

Evaluating Soil Water Content in a WRF-Noah Downscaling Experiment

2013 ◽  
Vol 52 (10) ◽  
pp. 2312-2327 ◽  
Author(s):  
Peter Greve ◽  
Kirsten Warrach-Sagi ◽  
Volker Wulfmeyer
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Root Zone ◽  
Regional Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Flux Partitioning

AbstractSoil water content (SWC) depends on and affects the energy flux partitioning at the land–atmosphere interface. Above all, the latent heat flux is limited by the SWC of the root zone on one hand and radiation on the other. Therefore, SWC is a key variable in the climate system. In this study, the performance of the Weather Research and Forecasting model coupled with the Noah land surface model (WRF-Noah) system in a climate hindcast simulation from 1990 to 2008 is evaluated with respect to SWC versus two reanalysis datasets for Europe during 2007 and 2008 with in situ soil moisture observations from southern France. When compared with the in situ observations, WRF-Noah generally reproduces the SWC annual cycle while the reanalysis SWCs do not. The biases in areal mean WRF-Noah SWCs relate to biases in precipitation and evapotranspiration in a cropland environment. The spatial patterns and temporal variability of the seasonal mean SWCs from the WRF-Noah simulations and from the two reanalyses correspond well, while absolute values differ significantly, especially at the regional scale.

scholarly journals Multi-site evaluation of the JULES land surface model using global and local data

2015 ◽  
Vol 8 (2) ◽  
pp. 295-316 ◽  
Author(s):  
D. Slevin ◽  
S. F. B. Tett ◽  
M. Williams
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Local Data ◽  
Area Index ◽  
Phenology Model ◽  
Global Data

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

2018 ◽  
Vol 22 (6) ◽  
pp. 3515-3532 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

scholarly journals Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data

2010 ◽  
Vol 11 (2) ◽  
pp. 509-519 ◽  
Author(s):  
Eleanor Blyth ◽  
John Gash ◽  
Amanda Lloyd ◽  
Matthew Pryor ◽  
Graham P. Weedon ◽  
...  
Keyword(s):  
Land Surface ◽  
Radiant Energy ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Soil Freezing ◽  
Rooting Depth ◽  
Surface Model ◽  
Model Parameters ◽  
Evaporative Demand ◽  
Area Index

Abstract Surface energy flux measurements from a sample of 10 flux network (FLUXNET) sites selected to represent a range of climate conditions and biome types were used to assess the performance of the Hadley Centre land surface model (Joint U.K. Land Environment Simulator; JULES). Because FLUXNET data are prone systematically to undermeasure surface fluxes, the model was evaluated by its ability to partition incoming radiant energy into evaporation and how such partition varies with atmospheric evaporative demand at annual, seasonal, weekly, and diurnal time scales. The model parameters from the GCM configuration were used. The overall performance was good, although weaknesses in model performance were identified that are associated with the specification of the leaf area index and plant rooting depth, and the representation of soil freezing.

scholarly journals Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3924 ◽  
Author(s):  
Toride ◽  
Sawada ◽  
Aida ◽  
Koike
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Low Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Coarse Resolution ◽  
Fine Resolution

The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

scholarly journals Trends in Land Evapotranspiration over Canada for the Period 1960–2000 Based on In Situ Climate Observations and a Land Surface Model

2007 ◽  
Vol 8 (5) ◽  
pp. 1016-1030 ◽  
Author(s):  
Richard Fernandes ◽  
Vladimir Korolevych ◽  
Shusen Wang
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Radiation ◽  
Soil Conditions ◽  
Surface Model ◽  
Eastern Canada ◽  
Area Index ◽  
Increasing Temperature ◽  
Increasing Trends

Abstract An assessment of annual trends in actual evapotranspiration (AET) and associated meteorological inputs is performed at 101 locations across Canada with available long-term hourly surface climate observations to determine if AET in Canada is increasing in relation to observed increases in air temperature. AET was estimated for the dominant land cover class, with representative soil and leaf area index conditions, within a 50 km × 50 km window around each location for the period 1960–2000. The Ecological Assimilation of Land and Climate Observations (EALCO) land surface model, which simulates coupled carbon, energy, and water cycles, was applied to estimate AET on a half-hourly basis at each location using in situ meteorological measurements and ambient atmospheric CO2 concentrations. Increases in annual AET, of up to 0.73% yr−1, were identified at 81 locations, and decreases, of up to 0.25% yr−1, were found at the remaining 20 stations. Statistically significant increasing trends were detected in 35% of the locations with the majority corresponding to Atlantic and Pacific coastal regions. Increasing trends were generally related to increasing temperature and total downwelling surface radiation trends in eastern Canada and increasing temperature, surface radiation, and precipitation trends in western Canada. In sharp contrast to other studies based on simpler AET models, annual AET trends in the prairie climate zone were mixed in terms of increases and decreases with no locations showing statistically significant trends. Future studies focused on scaling AET model estimates to subbasins or basins are required both to account for this spatial variability in soil conditions and to permit water budget closure validation.

scholarly journals Distinguishing between early and late covering crops in the land surface model Noah-MP: Impact on simulated surface energy fluxes and temperature

2019 ◽  
Author(s):  
Kristina Bohm ◽  
Joachim Ingwersen ◽  
Josipa Milovac ◽  
Thilo Streck
Keyword(s):  
Land Use ◽  
Surface Energy ◽  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Energy Fluxes ◽  
Area Index ◽  
Surface Energy Fluxes ◽  
Vegetation Fraction

Abstract. Land surface models are essential parts of climate and weather models. The widely used Noah-MP land surface model requires information on the leaf area index (LAI) and green vegetation fraction (GVF) as key inputs of its evapotranspiration scheme. The model aggregates all agricultural areas into a land use class termed Cropland and Pasture. In a previous study we showed that, on a regional scale, GVF has a bimodal distribution formed by two crop groups differing in phenology and growth dynamics: early covering crops (ECC, ex.: winter wheat, winter rapeseed, winter barley) and late covering crops (LCC, ex.: corn, silage maize, sugar beet). That result can be generalized for Central Europe. The present study quantifies the effect of splitting the land use class Cropland and Pasture of Noah-MP into ECC and LCC on surface energy fluxes and temperature. We further studied the influence of increasing the LCC share, which in the study area (the Kraichgau region, southwest Germany) is mainly the result of heavily subsidized biomass production, on energy partitioning at the land surface. We used the GVF dynamics derived from high-resolution (5 m × 5 m) RapidEye satellite data and measured LAI data for the simulations. Our results confirm that GVF and LAI strongly influence the partitioning of surface energy fluxes, resulting in pronounced differences between ECC and LCC simulations. Splitting up the generic crop into ECC and LCC had the strongest effect on land surface exchange processes in July–August. During this period, ECC are at the senescence growth stage or already harvested, while LCC have a well-developed, ground-covering canopy. The generic crop resulted in humid bias, i.e. an increase of evapotranspiration by +0.5 mm d−1 (LE: 1.3 MJ m−2 d−1), decrease of H by 1.2 MJ m−2 d−1 and decrease of surface temperature by −1 °C. The bias increased as the shares of ECC and LCC became similar. The observed differences will impact the simulations of processes in the planetary boundary layer. Increasing the LCC share from 28 to 38 % in the Kraichgau region led to a decrease of LE and a heating up of the land surface in the early growing season. Over the second part of the season, LE increased and the land surface cooled down by up to 1 °C.

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better?

2018 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium Range Weather Forecast (ECMWF) recently released a first 7-year segment of its latest atmospheric reanalysis: ERA-5 over 2010–2016. ERA-5 important changes relative to ERA-Interim former atmospheric reanalysis include a higher spatial and temporal resolution as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a Land-Surface-Model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. ERA-5 impact on the ISBA LSM with respect to ERA-Interim is assessed over a data-rich area: North America. A comprehensive evaluation of ERA-5 impact is conducted using remote sensing and in-situ observations covering a substantial part of the land surface storage and fluxes. The remote sensing observations include: (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary productivity, (iii) satellite derived estimates of surface soil moisture as well as (iv) satellite derived estimates of Leaf Area Index. The in-situ observations cover (i) soil moisture, (ii) turbulent heat fluxes and Net Ecosystem Exchange (NEE), (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified with the use of these 8 independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERAInterim.. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extend, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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