Abstract
Abstract. In the last decade, agent-based modelling (ABM) became a popular modelling technique in social sciences, medicine, biology, and ecology. ABM was designed to simulate systems that are highly dynamic and sensitive to small variations in their composition and their state. As hydrological systems, and natural systems in general, often show dynamic and non-linear behaviour, ABM can be an appropriate way to model these systems. Nevertheless, only a few studies have utilized the ABM method for process-based modelling in hydrology. The percolation of water through the unsaturated soil is highly responsive to the current state of the soil system; small variations in composition lead to major changes in the transport system. Hence, we present a new approach for modelling the movement of water through a soil column: autonomous water agents that transport water through the soil while interacting with their environment as well as with other agents under physical laws.
Highlights
Agent-based modelling (ABM) is a relatively new modelling approach or dogma that was born in social sciences to simulate the interactions of autonomous, encapsulated software agents in a predefined environment that form an emergent system through their interactions and their coupled decisionmaking processes (Macal and North, 2010; Jennings, 2000; North, 2014)
The modelling of physical hydrological systems by ABM is sparse in literature, some approaches were made (Servat, 2000; Folino et al, 2006; Parsons and Fonstad, 2007; Reaney, 2008; Rakotoarisoa et al, 2014; Shao et al, 2015), but they either relied on a less-dynamic predecessor of ABM, the so-called cellular automata, or were restricted to surface flow models
With a simple synthetic model set-up we showed that an Integrated Platform for Agent-based modelling framework (IPA) model compares well with other spatially distributed models, in this case with a model created in the hydrological framework cmf (Kraft et al, 2011)
Summary
Agent-based modelling (ABM) is a relatively new modelling approach or dogma that was born in social sciences to simulate the interactions of autonomous, encapsulated software agents in a predefined environment that form an emergent system through their interactions and their coupled decisionmaking processes (Macal and North, 2010; Jennings, 2000; North, 2014). IPA builds upon the agent-based development environment GAMA (Taillandier et al, 2012, 2014) and is published open source on GitHub. Following the requirements of an ABM framework, we introduce the class of the dynamic agents, the class of static agents, and the global agent, which controls the modelling experiment as an embracing supervisor (Macal and North, 2010; North, 2014; Fig. 2). We run GAMA in headless mode to save computational time with 2–4 cores for parallel computing of agent states
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