scholarly journals JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

2014 ◽  
Vol 7 (5) ◽  
pp. 6773-6809
Author(s):  
T. Osborne ◽  
J. Gornall ◽  
J. Hooker ◽  
K. Williams ◽  
A. Wiltshire ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Water Security ◽  
Climate Change Impacts ◽  
Heat Fluxes ◽  
Surface Model ◽  
Yield Model ◽  
Area Index ◽  
The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soy bean, maize and rice is presented. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soy bean at the global level, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index and canopy height better than in standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an earth system and crop yield model perspective is encouraging however, more effort is needed to develop the parameterisation of the model for specific applications. Key future model developments identified include the specification of the yield gap to enable better representation of the spatial variability in yield.

scholarly journals JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

2015 ◽  
Vol 8 (4) ◽  
pp. 1139-1155 ◽  
Author(s):  
T. Osborne ◽  
J. Gornall ◽  
J. Hooker ◽  
K. Williams ◽  
A. Wiltshire ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Water Security ◽  
Heat Fluxes ◽  
Surface Model ◽  
Yield Model ◽  
Area Index ◽  
The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soybean, maize and rice. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soybean at the global and country levels, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index, gross primary production and canopy height better than in the standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an Earth system and crop yield model perspective is encouraging. However, more effort is needed to develop the parametrisation of the model for specific applications. Key future model developments identified include the introduction of processes such as irrigation and nitrogen limitation which will enable better representation of the spatial variability in yield.

scholarly journals Multidataset Study of Optimal Parameter and Uncertainty Estimation of a Land Surface Model with Bayesian Stochastic Inversion and Multicriteria Method

2004 ◽  
Vol 43 (10) ◽  
pp. 1477-1497 ◽  
Author(s):  
Youlong Xia ◽  
Mrinal K. Sen ◽  
Charles S. Jackson ◽  
Paul L. Stoffa
Keyword(s):  
Land Surface ◽  
Root Zone ◽  
Model Simulation ◽  
Optimal Parameter ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Optimal Parameters ◽  
Stochastic Inversion ◽  
The Impact

Abstract This study evaluates the ability of Bayesian stochastic inversion (BSI) and multicriteria (MC) methods to search for the optimal parameter sets of the Chameleon Surface Model (CHASM) using prescribed forcing to simulate observed sensible and latent heat fluxes from seven measurement sites representative of six biomes including temperate coniferous forests, tropical forests, temperate and tropical grasslands, temperate crops, and semiarid grasslands. Calibration results with the BSI and MC show that estimated optimal values are very similar for the important parameters that are specific to the CHASM model. The model simulations based on estimated optimal parameter sets perform much better than the default parameter sets. Cross-validations for two tropical forest sites show that the calibrated parameters for one site can be transferred to another site within the same biome. The uncertainties of optimal parameters are obtained through BSI, which estimates a multidimensional posterior probability density function (PPD). Marginal PPD analyses show that nonoptimal choices of stomatal resistance would contribute most to model simulation errors at all sites, followed by ground and vegetation roughness length at six of seven sites. The impact of initial root-zone soil moisture and nonmosaic approach on estimation of optimal parameters and their uncertainties is discussed.

Assimilation of vegetation conditions improves the representation of drought over agricultural areas

2021 ◽  
Author(s):  
David M. Mocko ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Shugong Wang
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Agricultural Drought ◽  
False Alarms ◽  
Surface Model ◽  
Model Errors ◽  
Physical Processes ◽  
Area Index ◽  
Agricultural Areas ◽  
The Impact

AbstractThis study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation towards improved simulation of agricultural drought.

scholarly journals Verification of the new ECMWF ERA-Interim reanalysis over France

2011 ◽  
Vol 15 (2) ◽  
pp. 647-666 ◽  
Author(s):  
C. Szczypta ◽  
J.-C. Calvet ◽  
C. Albergel ◽  
G. Balsamo ◽  
S. Boussetta ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Daily Basis ◽  
Heat Fluxes ◽  
Surface Model ◽  
Carbon Dioxide Fluxes ◽  
Precipitation Estimates ◽  
The Impact ◽  
Grassland Site

Abstract. An evaluation of the global ECMWF atmospheric reanalysis ERA-Interim (with a 0.5° grid) is performed over France, based on the high resolution (8 km) SAFRAN atmospheric reanalysis. The ERA-Interim precipitation, Incoming Solar Radiation (ISR), air temperature, air humidity, and wind speed, are compared with their SAFRAN counterparts. Also, interpolated in situ ISR observations are used in order to consolidate the evaluation of this variable. The daily precipitation estimates produced by ERA-Interim over France correlate very well with SAFRAN. However, the values are underestimated by 27%. A GPCP-corrected version of ERA-Interim is less biased (13%). The ERA-Interim estimates of ISR correlate very well with SAFRAN and with in situ observations on a daily basis. Whereas SAFRAN underestimates the ISR by 6 Wm−2, ERA-Interim overestimates the ISR by 10 Wm−2. In order to assess the impact of the ERA-Interim errors, simulations of the ISBA-A-gs land surface model are performed over the SMOSREX grassland site in southwestern France using ERA-Interim (with and without GPCP rescaling) and SAFRAN. Latent and sensible heat fluxes are simulated, together with carbon dioxide fluxes. The rescaled ERA-Interim performs better than the original ERA-Interim and permits to achieve flux scores similar to those obtained with SAFRAN.

scholarly journals Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

2022 ◽  
Vol 3 ◽  
Author(s):  
Azbina Rahman ◽  
Xinxuan Zhang ◽  
Paul Houser ◽  
Timothy Sauer ◽  
Viviana Maggioni
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Random Errors ◽  
Area Index ◽  
Leaf Mass ◽  
The Impact

As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.

scholarly journals Impact of the use of a CO<sub>2</sub> responsive land surface model in simulating the effect of climate change on the hydrology of French Mediterranean basins

2011 ◽  
Vol 11 (10) ◽  
pp. 2803-2816 ◽  
Author(s):  
S. Queguiner ◽  
E. Martin ◽  
S. Lafont ◽  
J.-C. Calvet ◽  
S. Faroux ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Regional Climate ◽  
Regional Scale ◽  
Land Surface Model ◽  
Climate Scenario ◽  
Climate Impact ◽  
Surface Model ◽  
Present Climate ◽  
The Impact

Abstract. In order to evaluate the uncertainty associated with the impact model in climate change studies, a CO2 responsive version of the land surface model ISBA (ISBA-A-gs) is compared with its standard version in a climate impact assessment study. The study is performed over the French Mediterranean basin using the Safran-Isba-Modcou chain. A downscaled A2 regional climate scenario is used to force both versions of ISBA, and the results of the two land surface models are compared for the present climate and for that at the end of the century. Reasonable agreement is found between models and with discharge observations. However, ISBA-A-gs has a lower mean evapotranspiration and a higher discharge than ISBA-Standard. Results for the impact of climate change are coherent on a yearly basis for evapotranspiration, total runoff, and discharge. However, the two versions of ISBA present contrasting seasonal variations. ISBA-A-gs develops a different vegetation cycle. The growth of the vegetation begins earlier and reaches a slightly lower maximum than in the present climate. This maximum is followed by a rapid decrease in summertime. In consequence, the springtime evapotranspiration is significantly increased when compared to ISBA-Standard, while the autumn evapotranspiration is lower. On average, discharge changes are more significant at the regional scale with ISBA-A-gs.

scholarly journals Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

2019 ◽  
Vol 11 (23) ◽  
pp. 2842 ◽  
Author(s):  
Daniel Shamambo ◽  
Bertrand Bonan ◽  
Jean-Christophe Calvet ◽  
Clément Albergel ◽  
Sebastian Hahn
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Extreme Event ◽  
Cloud Model ◽  
Large Fraction ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
The Difference ◽  
The Impact

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model.

scholarly journals Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

2012 ◽  
Vol 16 (8) ◽  
pp. 2567-2583 ◽  
Author(s):  
N. Ghilain ◽  
A. Arboleda ◽  
G. Sepulcre-Cantò ◽  
O. Batelaan ◽  
J. Ardö ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Continental Scale ◽  
Western Africa ◽  
Large Gradient ◽  
The Impact

Abstract. Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations.

scholarly journals Impact of precipitation and land biophysical variables on the simulated discharge of European and Mediterranean rivers

2012 ◽  
Vol 16 (9) ◽  
pp. 3351-3370 ◽  
Author(s):  
C. Szczypta ◽  
B. Decharme ◽  
D. Carrer ◽  
J.-C. Calvet ◽  
S. Lafont ◽  
...  
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Area Index ◽  
Total Runoff ◽  
Global Precipitation Climatology Centre ◽  
Hydrological System ◽  
The Impact

Abstract. This study investigates the impact on river discharge simulations of errors in the precipitation forcing, together with changes in the representation of vegetation variables and of plant transpiration. The most recent European Centre for Medium-Range Weather Forecasts reanalysis (ERA-Interim) is used to drive the Interactions between Soil, Biosphere, and Atmosphere–Total Runoff Integrating Pathways (ISBA-TRIP) continental hydrological system over Europe and the Mediterranean basin over the 1991–2008 period. As ERA-Interim tends to underestimate precipitation, a number of precipitation corrections are proposed. In particular, the monthly Global Precipitation Climatology Centre (GPCC) precipitation product is used to bias-correct the 3-hourly ERA-Interim estimates. This correction markedly improves the match between the ISBA-TRIP simulations and the river discharge observations from the Global Runoff Data Centre (GRDC), at 150 gauging stations. The impact on TRIP river discharge simulations of various representations of the evapotranspiration in the ISBA land surface model is investigated as well: ISBA is used together with its upgraded carbon flux version (ISBA-A-gs). The latter is either driven by the satellite-derived climatology of the Leaf Area Index (LAI) used by ISBA, or performs prognostic LAI simulations. The ISBA-A-gs model, with or without dynamically simulated LAI, allows a better representation of river discharge at low water levels. On the other hand, ISBA-A-gs does not perform as well as the original ISBA model at springtime.

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