Process-based Adaptive Watershed Simulator (PAWS)

Brief Information

Free Software

About Process-based Adaptive Watershed Simulator (PAWS)

The Process-based Adaptive Watershed Simulator (PAWS) is a comprehensive, computationally-efficient parallel hydrologic model designed for large-scale simulation. The model is now coupled to the Community Land Model (CLM), and therefore is able to simulate Carbon/Nitrogen cycling, ecosystem dynamics and their interactions with the water cycle. Due to its comprehensiveness, efficiency, and flexibility, this tool provides a useful platform for the integration of biogeochemistry, fluid mechanics, and human dimensions into a uniform modeling framework, to investigate their mutual interactions, to test hypothesis about causal relationships and to assess future changes.

Today's water resources problem is one convoluted by rapid global change and complex interplays. Predictions must be made at various spatio-temporal scales, beyond previous observed range of variability [Wagener 2010, WRR], under new sets of norms and in the context of strong feedbacks. Understanding and predicting hydrologic responses and their feedback on climate and other subsystems are of both scientific value and socio-economic urgency.

As an effort to help tackle water resources problems in the new era, PAWS was initially developed as Dr. Shen's Ph.D. dissertation topic at Computational Hydrology and Reactive Transport lab, Michigan State University, with help from labmate Jie Niu. PAWS was developed with three key features in mind:

1. Comprehensive, physically-based governing equations: these equations (e.g. Richards Equation) are derived deductively from established physical laws and able to reproduce lab/field observations. Channels are explicitly represented.
2. Computational efficiency: efficient algorithms and sub-cell mechanisms allow the study of large-scale, long-term impacts and make optimization and uncertainty analysis tractable.
3. Flexibility: versatile program structure facilitates fast integration of disciplines and novel processes and permits multi-scale modeling. Multiple mechanisms account for the effect of model resolution.