Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins. (December 2017)

• Record Type: Journal Article
• Authors: Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew
• Is Part Of: Computers & geosciences. Volume 109(2017)
• Page Start: 295
• Page End: 304
• Publication Date: 2017-12
• Subjects: Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.08.016
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: 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. Highlights: Robust meteorological spatial forcing data development for physically based models. The Python framework can be used for research or operational applications. Parallel processing and multi-threading allow for large modeling domains at high resolution. Real time and historical applications for water supply recourses in two large basins.