The knowledge of micrometeorological conditions in flooded rice fields is crucial for better modelling the behaviour of the crop in mid and high latitudes, where the thermal mitigation provided by water layer is significant against the climatic risk of low temperatures in spring and early summer. Two micrometeorological models (a mechanistic and an empirical one) for the simulation of thermal profile related to water and near water temperatures were calibrated and validated with data gauged in some rice fields located near Milano, Italy. The mechanistic model is based on the solution of the equation of the surface energy balance; the empirical one is funded on Gaussian filters. Both the models need as input data only daily values of maximum and minimum temperatures and therefore are suitable for agroecological operational purposes. The results show that the models could improve the performances of crop simulation models physically based, currently used for crop growth, development and production analysis. In fact, the comparison between measured and simulated temperature values indicates that both the presented models are able to reproduce the effect of water on temperature (average RRMSE = 14% for the mechanistic model and 7% for the empirical one; modelling efficiency always positive). (c) 2004 Elsevier B.V. All rights reserved.
Analysis and modelling of water and near water temperatures in flooded rice (Oryza sativa L.)
Mariani L;
2005-01-01
Abstract
The knowledge of micrometeorological conditions in flooded rice fields is crucial for better modelling the behaviour of the crop in mid and high latitudes, where the thermal mitigation provided by water layer is significant against the climatic risk of low temperatures in spring and early summer. Two micrometeorological models (a mechanistic and an empirical one) for the simulation of thermal profile related to water and near water temperatures were calibrated and validated with data gauged in some rice fields located near Milano, Italy. The mechanistic model is based on the solution of the equation of the surface energy balance; the empirical one is funded on Gaussian filters. Both the models need as input data only daily values of maximum and minimum temperatures and therefore are suitable for agroecological operational purposes. The results show that the models could improve the performances of crop simulation models physically based, currently used for crop growth, development and production analysis. In fact, the comparison between measured and simulated temperature values indicates that both the presented models are able to reproduce the effect of water on temperature (average RRMSE = 14% for the mechanistic model and 7% for the empirical one; modelling efficiency always positive). (c) 2004 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
