RMWSPy (v 1.1): A Python code for spatial simulation and inversion for environmental applications. (April 2021)

• Record Type: Journal Article
• Authors: Hörning, Sebastian; Haese, Barbara
• Is Part Of: Environmental modelling & software. Volume 138(2021)
• Publication Date: 2021-04
• Subjects: Geostatistical simulation -- Stochastic inversion -- Environmental modelling -- Random mixing -- Python; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.104970
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Spatial simulation and inversion problems are omnipresent in earth and environmental sciences. An open-source Python package (RMWSPy) for conditional spatial random field simulation and inversion based on a generalized implementation of the Random Mixing Whittaker-Shannon (RMWS) algorithm is presented in this paper. The RMWS algorithm has successfully been applied to a variety of environmental modelling problems, ranging from inverse groundwater flow and transport modelling to precipitation simulation incorporating incomplete observations. RMWSPy provides great flexibility due to its variety of linear and non-linear conditioning constraints. The generalized implementation isolates the core algorithm from the user-defined problem statement. In this paper, RMWSPy is introduced using a synthetic inversion example for spatial precipitation estimation which combines rain gauge data and integral rain rates obtained from Commercial Microwave Link data. The required Python scripts are described and the results of one precipitation event are presented and discussed. Highlights: Combined geostatistical simulation and inversion algorithm/code for environmental modelling problems. Generalized implementation for plug-and-play like use. Flexible with a wide variety of use cases. Simple Python-based code + example applications.