The potential of mathematical models is widely acknowledged for examining components and interactions of natural systems, estimating the changes and uncertainties on outcomes, and fostering communication between scientists with different backgrounds and between scientists, managers and the community. For favourable reception of models, a systematic accrual of a good knowledge base is crucial for both science and decision-making. As the roles of models grow in importance, there is an increase in the need for appropriate methods with which to test their quality and performance. For biophysical models, the heterogeneity of data and the range of factors influencing usefulness of their outputs often make it difficult for full analysis and assessment. As a result, modelling studies in the domain of natural sciences often lack elements of good modelling practice related to model validation, that is correspondence of models to its intended purpose. Here we review validation issues and methods currently available for assessing the quality of biophysical models. The review covers issues of validation purpose, the robustness of model results, data quality, model prediction and model complexity. The importance of assessing input data quality and interpretation of phenomena is also addressed. Details are then provided on the range of measures commonly used for validation. Requirements for a methodology for assessment during the entire model-cycle are synthesised. Examples are used from a variety of modelling studies which mainly include agronomic modelling, e.g. crop growth and development, climatic modelling, e.g. climate scenarios, and hydrological modelling, e.g. soil hydrology, but the principles are essentially applicable to any area. It is shown that conducting detailed validation requires multi-faceted knowledge, and poses substantial scientific and technical challenges. Special emphasis is placed on using combined multiple statistics to expand our horizons in validation whilst also tailoring the validation requirements to the specific objectives of the application.

中文翻译：

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验证生物物理模型：问题和方法。回顾

数学模型的潜力已得到广泛认可，可用于检查自然系统的组成部分和相互作用，估计结果的变化和不确定性以及促进具有不同背景的科学家之间以及科学家，管理人员与社区之间的交流。为了良好地接受模型，系统地积累良好的知识库对于科学和决策至关重要。随着模型角色的重要性日益提高，对测试其质量和性能的适当方法的需求也在增加。对于生物物理模型，数据的异质性和影响其输出有用性的因素范围常常使难以进行全面分析和评估。结果是，自然科学领域的建模研究通常缺乏与模型验证相关的良好建模实践要素，即模型与其预期目的相对应。在这里，我们回顾了当前可用于评估生物物理模型质量的验证问题和方法。审查涵盖了验证目的，模型结果的鲁棒性，数据质量，模型预测和模型复杂性等问题。还讨论了评估输入数据质量和现象解释的重要性。然后提供有关通常用于验证的度量范围的详细信息。综合了整个模型周期内评估方法的要求。从各种建模研究中使用示例，这些建模研究主要包括农艺建模，例如作物生长和发育，气候建模，例如农业。G。气候情景和水文模拟，例如土壤水文，但这些原则基本上适用于任何地区。结果表明，进行详细的验证需要多方面的知识，并构成重大的科学和技术挑战。特别强调使用合并的多个统计信息来扩大我们的验证范围，同时还要根据应用程序的特定目标定制验证要求。