Abstract. We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

Process-based spatio-temporal models simulate changes over time using equations that represent real world processes. They are widely applied in geography and earth science. Software implementation of the model itself and integrating model results with observations through data assimilation are two important steps in the model development cycle. Unlike most software frameworks that provide tools for either implementation of the model or data assimilation, this paper describes a software framework that integrates both steps. The software framework includes generic operations on 2D map and 3D block data that can be combined in a Python script using a framework for time iterations and Monte Carlo simulation. In addition, the framework contains components for data assimilation with the Ensemble Kalman Filter and the Particle filter. Two case studies of distributed hydrological models show how the framework integrates model construction and data assimilation.

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Teaching numerical modelling in the environmental sciences not only needs good software and course material but also an understanding of how to program the models in the computer. Conventional environmental modelling procedures require computer science and programming skills, which may detract from the important understanding of the environmental processes involved. An alternative strategy is to build a generic toolkit or modelling language that operates with concepts and operations that are familiar to the environmental scientist. PCRaster is such a spatio-temporal environmental modelling language developed at Utrecht University, the Netherlands. It is used for teaching modelling in classrooms and over the Web (distance learning) at three levels: (1) explaining environmental processes and models, where models with a fixed structure of model equations are evaluated by changing model parameters, (2) teaching model construction, where students learn to program spatial and temporal models with the language, and (3) teaching all phases of scientific modelling related to field research. So far, we have received positive responses to these courses, largely because the software provides a set of easily learned functions matching the conceptual thought processes of a geoscientist that can be used at all levels of teaching.