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- Author or Editor: Rafael L. Bras x
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Abstract
The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables.
Abstract
The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables.
Abstract
Persistent and prolonged periods of dry or moist conditions are often evident in the interannual variability of continental-type climates This variability appears as fluctuations around several distinct and preferred moisture states. These fluctuations and transitions between the preferred states are commonly attributed to large-scale changes in atmospheric circulation patterns possibly caused by oceanic influence.
This paper argues that a major contributing factor to the persistent dry or moist behavior could be due to feedback and nonlinear interaction between the components of the hydrologic cycle in both the land and the atmosphere. A model that couples the water balance of continental landmasses and the overlying atmosphere is presented. The large-scale variabilities in atmospheric circulation are introduced by way of simple randomness in key forcing parameters. The result is a multiplicative-noise stochastic differential equation for the water balance dynamics of continental-type climates that includes land surface-atmosphere interaction.
The solution to this differential equation exhibits a bimodal probability distribution function for soil moisture and precipitation. Extended periods of anomalous dry conditions (drought) or alternatively wet conditions (pluvial), with abrupt transitions between them, are present in the model. The statistics of persistent anomalous conditions are analyzed for two climatic classifications. The probability distribution function for transitions out of droughts are developed for the modeled climates.
Abstract
Persistent and prolonged periods of dry or moist conditions are often evident in the interannual variability of continental-type climates This variability appears as fluctuations around several distinct and preferred moisture states. These fluctuations and transitions between the preferred states are commonly attributed to large-scale changes in atmospheric circulation patterns possibly caused by oceanic influence.
This paper argues that a major contributing factor to the persistent dry or moist behavior could be due to feedback and nonlinear interaction between the components of the hydrologic cycle in both the land and the atmosphere. A model that couples the water balance of continental landmasses and the overlying atmosphere is presented. The large-scale variabilities in atmospheric circulation are introduced by way of simple randomness in key forcing parameters. The result is a multiplicative-noise stochastic differential equation for the water balance dynamics of continental-type climates that includes land surface-atmosphere interaction.
The solution to this differential equation exhibits a bimodal probability distribution function for soil moisture and precipitation. Extended periods of anomalous dry conditions (drought) or alternatively wet conditions (pluvial), with abrupt transitions between them, are present in the model. The statistics of persistent anomalous conditions are analyzed for two climatic classifications. The probability distribution function for transitions out of droughts are developed for the modeled climates.
