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首页> 外文期刊>European Journal of Soil Science >The behaviour of soil process models of ammonia volatilization at contrasting spatial scales
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The behaviour of soil process models of ammonia volatilization at contrasting spatial scales

机译：空间尺度上氨挥发的土壤过程模型行为

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摘要

Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic.
机译：过程模型通常在土壤科学中用于获得空间尺度的预测，该空间尺度与模型开发的尺度或可获得模型输入信息的尺度不同。发生这种情况时，模型及其输入需要汇总或分解到应用程序规模，这是一个复杂的问题。此外，聚合模型预测的有效性取决于模型是否描述了在目标规模上确定过程结果的关键过程。因此，在不同的空间尺度上可能需要不同的模型。在本文中，我们开发了一种诊断框架，该框架使我们能够判断模型是否适合在一个或多个空间尺度上使用，无论是在这些尺度上的变化预测方面，还是在输入分解方面。我们表明，对预测的协方差与测量的过程结果进行空间嵌套分析是一种有效的方法。这适用于在施用尿素后导致氨从土壤挥发的过程模型。我们确定了嵌套方案在不同尺度下的组件相关性，以此作为评估模型行为的诊断工具。这些相关性表明，该模型在采样方案的规模上模拟目标过程的空间变化分量的程度如何。汇总相关性被认为是评估特定规模的预测模型最相关的方法，因为它们可以衡量某个规模的聚合预测与测量结果的聚合值的相关程度。在两种情况下，使用模型进行预测。在第一种情况下，仅使用模型进行预测，而最有用的诊断是一致性聚合相关性。在第二种情况下，将模型预测与观察结果同化，观察结果应纠正预测中的偏差以及方差中的误差;聚合相关性将是最合适的诊断方法。

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