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Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

机译：资源空间建模框架（SMRF）：用于为山区雪域建模开发空间强迫数据的模块化框架

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摘要

In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km(2). The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.
机译：在美国西部和世界许多山区，关键的水资源和气候条件很难监测，因为观测网络通常非常稀疏。来自高山积雪的关键资源是流入河流和水库的水，这些水将用于灌溉，防洪，发电和生态系统服务。由于气候条件不稳定，在迅速变化的气候中进行供水预测变得越来越困难。作为响应，运营供水管理者已开始从统计技术转向使用基于物理的模型。当我们开始将基于物理的模型从研究过渡到运营使用时，我们必须解决模型启动的最困难和最耗时的方面：需要可靠的方法来开发和分发输入强迫数据。在本文中，我们提出了一个新的开源框架，即资源空间模型框架（SMRF），该框架可以自动化并简化常见的强制数据分发方法。它的计算效率很高，并且可以用于研究和运营应用。我们提供一个示例，说明SMRF如何在1000-7000 km（2）的区域上生成分辨率为50-100 m的基于物理运行的降雪模型所需的所有强迫数据。该方法已成功地在美国爱达荷州的博伊西河盆地和美国加利福尼亚的Tuolumne流域实时和历史应用。这些应用程序使用气象站测量值和数字天气预报模型输出作为输入。 SMRF极大地简化了建模工作流程，将模型建立时间从数周缩短至数天，并使基于物理的降雪模型的近实时应用成为可能。

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  • 来源
    《Computers & geosciences》 |2017年第12期|295-304|共10页
  • 作者

    Havens Scott; Marks Danny; Kormos Patrick; Hedrick Andrew;

  • 作者单位

    USDA ARS, 800 Pk Blvd Plaza 4,Suite 105, Boise, ID 83712 USA;

    USDA ARS, 800 Pk Blvd Plaza 4,Suite 105, Boise, ID 83712 USA;

    USDA ARS, 800 Pk Blvd Plaza 4,Suite 105, Boise, ID 83712 USA;

    USDA ARS, 800 Pk Blvd Plaza 4,Suite 105, Boise, ID 83712 USA;

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