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 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 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.

Assessing the impact of permafrost-vegetation interaction on treeline dynamics in Siberia with the individual-based, spatially explicit treeline model LAVESI coupled to the permafrost land-surface model CryoGrid

2021 ◽  
Author(s):  
Stefan Kruse ◽  
Simone M. Stünzi ◽  
Moritz Langer ◽  
Julia Boike ◽  
Ulrike Herzschuh
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Radiation Budget ◽  
Spatially Explicit ◽  
Surface Model ◽  
Data Set ◽  
Larch Species ◽  
The Individual ◽  
The Impact

<p>Tundra-taiga ecotone dynamics play a relevant role in the global carbon cycle. However, it is rather uncertain whether these ecosystems could develop into a carbon source rather than continuing atmospheric carbon sequestration under global warming. This knowledge gap stems from the complex permafrost-vegetation interactions, not yet fully understood. Consequently, shedding light on the role of current and future active layer dynamics is crucial for an accurate prediction of treeline dynamics, and thus for aboveground forest biomass and carbon stock developments.</p><p>We make use of a coupled model version combining CryoGrid, a one-dimensional permafrost land-surface model, with LAVESI, an individual-based and spatially explicit forest model for larch species (<em>Larix </em>Mill.) in Siberia. Following a parametrization against an extensive field data set of 100+ forest inventories conducted along the Siberian treeline (97-169° E), we run simulations for the upcoming centuries forced by climatic change scenarios.</p><p>The coupled model setup benefits from the detailed process implementation gained while developing the individual models. Therefore, we can reproduce the energy transfer and thermal regime in permafrost ground as well as the radiation budget, nitrogen and photosynthetic profiles, canopy turbulence, and leaf fluxes, while at the same time, predicting the expected establishment, die-off, and treeline movements of larch forests. In our analyses, we focus on vegetation and permafrost dynamics and reveal the magnitudes of different feedback processes between permafrost, vegetation, and current and future climate in Northern Siberia.</p>

scholarly journals Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction

2013 ◽  
Vol 14 (5) ◽  
pp. 1373-1400 ◽  
Author(s):  
Joseph A. Santanello ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Ken Harrison ◽  
Shujia Zhou
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Critical Role ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface States ◽  
Surface Model ◽  
Near Surface ◽  
Heat And Moisture ◽  
The Impact

Abstract Land–atmosphere (LA) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. In this study, the authors examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled Weather Research and Forecasting Model (WRF) forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystems in NASA's Land Information System (LIS-OPT/LIS-UE). The impact of the calibration on the 1) spinup of the land surface used as initial conditions and 2) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. In addition, the sensitivity of this approach to the period of calibration (dry, wet, or average) is investigated. Results show that the offline calibration is successful in providing improved initial conditions and land surface physics for the coupled simulations and in turn leads to systematic improvements in land–PBL fluxes and near-surface temperature and humidity forecasts. Impacts are larger during dry regimes, but calibration during either primarily wet or dry periods leads to improvements in coupled simulations due to the reduction in land surface model bias. Overall, these results provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.

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