five

SUMMA 3.0.0

收藏
www.hydroshare.org2020-08-20 更新2025-03-26 收录
下载链接:
https://www.hydroshare.org/resource/ce02e7ce903d48019bc73fb6a19cb558
下载链接
链接失效反馈
官方服务:
资源简介:
SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes. SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development. This version was released on July 20, 2020.

SUMMA(Clark 等人,2015a;b;c)是一种水文建模框架,可用于对模型概念化、通量参数化、空间配置和数值求解技术等方面的替代模型进行系统分析。该框架能够配置广泛的水文模型替代方案,我们预期系统性的模型分析将有助于研究人员和从业者理解模型行为中不同模型间差异的原因。当应用于大量流域样本时,SUMMA可能提供关于不同物理过程的主导性和不同建模方法区域适用性变异性的洞见。SUMMA的一个重要应用是选择特定的物理选项以再现现有模型的行为——这些“模型模仿”的应用可以用于在结构化的模型比较实验中定义参考(基准)案例,并有助于诊断不同水文气候制度下单个模型的弱点。SUMMA建立在一套共同的守恒方程和共同的数值求解器之上,这两者共同构成了模型的“结构核心”。不同的建模方法可以在此结构核心中实现,从而允许对替代建模选项进行可控和系统的分析,并为未来的模型开发提供洞见。本版本于2020年7月20日发布。
提供机构:
www.hydroshare.org
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作