Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies

Maruyama, Atsushi; Kuwagata, Tsuneo

doi:10.1016/j.agrformet.2010.02.011

Cited by 68 publications

(57 citation statements)

References 42 publications

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“…Values for soil parameters for clay loam are shown in (Table 7). These RMSE values are comparable to those reported in earlier studies (Kimura and Kondo, 1998;Maruyama and Kuwagata, 2010). One of the major reasons for the errors of LHF and SHF between simulations and observations is thought to be a problem in flux observations.…”

Section: Validationsupporting

confidence: 79%

“…Betts (2005) pointed out that integration of crop growth models (CGMs) into climate models is needed for accurate climate simulations by climate models. To consider the influence of agricultural land on climate in climate simulations, several land surface models (LSMs) or dynamic vegetation models (DVMs) incorporated with a CGM have been developed (Tsvetsinskaya et al, 2001;Kucharik, 2003;Gervois et al, 2004;Bondeau et al, 2007;Osborne et al, 2007;Lokupitiya et al, 2009;Maruyama and Kuwagata, 2010;Levis et al, 2012;Osborne et al, 2015). Masutomi et al (2016) have developed a new LSM-CGM combined model, called MATCRO-Rice, by incorporating a CGM into an LSM, MATSIRO (Takata et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation

et al. 2016

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Abstract. We conducted two types of validation for the simulations by MATCRO-Rice developed by Masutomi et al. (2016). In the first validation, we compared simulations with observations for latent heat flux (LHF), sensible heat flux (SHF), net carbon uptake by crop, and paddy rice yield from 2003 to 2006 at the site where model parameters are parameterized. In the second validation, we compared the observed and simulated paddy rice yields over Japan from 1991 to 2010 between observations and simulations. The 4-year average root mean square errors (RMSEs) of the first validation for LHF and SHF were 18.20 and 15.47 W m −2 , respectively. These values for errors are comparable to those reported in earlier studies. The comparison of biomass growth during growing periods from 2003 to 2006 at the parameterization site shows that the simulations were in agreement with the observations, indicating that the model can reproduce the net carbon uptake by crops well. The 4-year average RMSE of the first validation for crop yield in the same period was 410.6 kg ha −1 , which accounted for 8.1 % of the mean observed yields. The error of the second validation for crop yield was 16.7 % and the correlation of crop yields between observations and simulations from 1991 to 2010 was significant at 0.663 (P < 0.01). These results indicate that MATCRO-Rice has high ability to accurately and consistently simulate LHF, SHF, net carbon uptake by crop, and crop yield.

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Section: Validationsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation

et al. 2016

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“…To overcome this limitation, several recent studies achieved dynamical coupling of LSMs, climate or hydrological models with crop growth models (Boegh et al, 2004;Mo et al, 2005;Pauwels et al, 2007;Maruyama and Kuwagata, 2010;Lei et al, 2010;Li et al, 2013). Efforts have also been made to include dynamic vegetation representation in LSMs (Cox et al, 2000;Dai, 2003;Van den Hoof et al, 2011) and hydrologic models (Calvet et al, 1998;Calvet and Soussana, 2001;Li et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Tsarouchi

Buytaert

Mijic

2014

Hydrol. Earth Syst. Sci.

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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.

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“…For example, applying a regional climate model combined with a crop growth model (CGM) to the United States, Tsvetsinskaya et al (2001) showed that crop growth can change the surface temperature by 2-4 • C. Maruyama and Kuwagata (2010) showed that crop growing season can affect the amount of evapotranspiration by using a land surface model (LSM) combined with a CGM. Levis et al (2012) incorporated a CGM into an earth system model, and showed that the timing of crop sowing can change the amount of precipitation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies

Cited by 68 publications

References 42 publications

A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation

A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation

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

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

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