scholarly journals Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

2014 ◽  
Vol 18 (10) ◽  
pp. 4223-4238 ◽  
Author(s):  
G. M. Tsarouchi ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Coupled Model ◽  
Crop Growth ◽  
Original Model ◽  
Surface Model ◽  
Strong Impact ◽  
Crop Growth Model ◽  
Mean Error ◽  
The Impact

Abstract. Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4–11.6 mm month−1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 2.2–3.4 mm month−1. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions.

scholarly journals Coupling a land surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

2014 ◽  
Vol 11 (6) ◽  
pp. 6843-6880
Author(s):  
G. M. Tsarouchi ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Crop Growth ◽  
Original Model ◽  
Surface Model ◽  
Crop Growth Model ◽  
Mean Error ◽  
The Impact

Abstract. Land surface models are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the land surface model JULES with the crop growth model InfoCrop. JULES in its current version does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parameterised it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges river basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm m−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 7–14 mm m−1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm m−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 1–2 mm m−1. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions.

scholarly journals A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 1: Model description

2016 ◽  
Vol 9 (11) ◽  
pp. 4133-4154 ◽  
Author(s):  
Yuji Masutomi ◽  
Keisuke Ono ◽  
Masayoshi Mano ◽  
Atsushi Maruyama ◽  
Akira Miyata
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Agricultural Land ◽  
Land Surface Model ◽  
Crop Growth ◽  
Paddy Rice ◽  
Model Description ◽  
Surface Model ◽  
Crop Growth Model ◽  
Combined Model

Abstract. Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called an LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this paper is to present the full description of MATCRO-Rice. The most important feature of MATCRO-Rice is that it can consistently simulate latent and sensible heat fluxes, net carbon uptake by crop, and crop yield by exchanging variables between the LSM and CGM. This feature enables us to apply the model to a wide range of integrated issues.

scholarly journals Validation of IVINE crop growth model using MACSUR2 project measurements in a few European vineyards.

2020 ◽  
Author(s):  
Claudio Cassardo ◽  
Valentina Andreoli ◽  
Federico Spanna
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Input Data ◽  
Grid Point ◽  
Land Surface Model ◽  
Crop Growth ◽  
Meteorological Conditions ◽  
Surface Model ◽  
Crop Growth Model ◽  
Environmental Forcing

<p>The numerical crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was originally developed at the dept. of Physics, Univ. of Torino, as a research model with the aim to simulate grapevine phenological and physiological processes. Since vines are generally strongly sensitive to meteorological conditions, the model should be able to evaluate the environmental forcing effects on vine growth and, eventually, on its production. IVINE model requires a set of hourly meteorological and soil data as boundary conditions; the more relevant input for the model to correctly simulate the plant growth are: air temperature and soil moisture. Among the principal IVINE outputs, we mention: the main philological stages (dormancy exit, bud-break, fruit set, veraison, and harvest), the leaf development, the yield, the berry sugar concentration, and the predawn leaf water potential. The IVINE requires to set some experimental parameters depending on the cultivar; at present, IVINE is optimized for Nebbiolo and other common varieties (such as, for example, cvs. Barbera, Vermentino, Cannonau, etc for Italy), but validation experiments have been performed only for Nebbiolo variety, due to the difficulty to gather all required measurements useful to drive the model and to compare its outputs for several consecutive years in the same vineyard. In the frame of the second part of the EU JPI-FACCE project named MACSUR (Modelling European Agriculture with Climate Change for Food Security), some data relative to vineyards displaced in several European countries were made available, thus we tried to execute simulations with IVINE in those vineyards. Since input data required by IVINE were not all present, we decided to extract input data from the international GLDAS database in the nearest grid point to the experimental vineyard, and to run the trusted land surface model UTOPIA on those points in order to evaluate soil variables required by IVINE. The main results obtained by those simulations, as well as the few possible validations with experimental observations, will be shown and commented. As a summary, we can say that the simulation carried out with IVINE seems able to well account for the interannual variability of the meteorological conditions, and the used settings seems able to allow a sufficiently valid simulation of the pheno-physiological conditions of the vineyards, but the approximation in the input data causes departures larger than if local measurements would be used.</p>

scholarly journals A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice Ver. 1) – Part I: Model description

2016 ◽  
Author(s):  
Yuji Masutomi ◽  
Keisuke Ono ◽  
Masayoshi Mano ◽  
Atsushi Maruyama ◽  
Akira Miyata
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Agricultural Land ◽  
Land Surface Model ◽  
Crop Growth ◽  
Paddy Rice ◽  
Model Description ◽  
Surface Model ◽  
Crop Growth Model ◽  
Combined Model

Abstract. Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, accurate simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this paper is to present the full description of MATCRO-Rice. The most important feature of MATCRO-Rice is that it can consistently simulate latent and sensible heat fluxes, net carbon flux, and crop yield by exchanging variables between the LSM and CGM. This feature enables us to apply the model to a wide range of integrated issues.

scholarly journals On the impact of recent developments of the LMDz atmospheric general circulation model on the simulation of CO<sub>2</sub> transport

2018 ◽  
Vol 11 (11) ◽  
pp. 4489-4513 ◽  
Author(s):  
Marine Remaud ◽  
Frédéric Chevallier ◽  
Anne Cozic ◽  
Xin Lin ◽  
Philippe Bousquet
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Circulation Model ◽  
Surface Model ◽  
Strong Impact ◽  
Vertical Resolution ◽  
Atmospheric General Circulation ◽  
Inverse Systems ◽  
The Impact ◽  
Physical Parameterizations

Abstract. The quality of the representation of greenhouse gas (GHG) transport in atmospheric general circulation models (GCMs) drives the potential of inverse systems to retrieve GHG surface fluxes to a large extent. In this work, the transport of CO2 is evaluated in the latest version of the Laboratoire de Météorologie Dynamique (LMDz) GCM, developed for the Climate Model Intercomparison Project 6 (CMIP6) relative to the LMDz version developed for CMIP5. Several key changes have been implemented between the two versions, which include a more elaborate radiative scheme, new subgrid-scale parameterizations of convective and boundary layer processes and a refined vertical resolution. We performed a set of simulations of LMDz with different physical parameterizations, two different horizontal resolutions and different land surface schemes, in order to test the impact of those different configurations on the overall transport simulation. By modulating the intensity of vertical mixing, the physical parameterizations control the interhemispheric gradient and the amplitude of the seasonal cycle in the Northern Hemisphere, as emphasized by the comparison with observations at surface sites. However, the effect of the new parameterizations depends on the region considered, with a strong impact over South America (Brazil, Amazonian forest) but a smaller impact over Europe, East Asia and North America. A finer horizontal resolution reduces the representation errors at observation sites near emission hotspots or along the coastlines. In comparison, the sensitivities to the land surface model and to the increased vertical resolution are marginal.

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