A writing workflow for communicating mechanistic models

Abstract

Mechanistic models are widely used in hydrodynamics, water quality, biogeochemistry, and ecological modeling because they represent systems as interacting processes rather than statistical relationships. Their scientific value depends on both model performance and clear communication of model structure, modeling decisions, and causal pathways. In practice, clear communication is difficult to achieve. Mechanistic models are developed through nonlinear, iterative workflows, while manuscripts impose a linear narrative structure. As a result, key modeling choices and mechanistic logic are often presented out of causal order or scattered across sections, making papers difficult to follow.

This paper identifies common structural reasons why mechanistic modeling papers are hard to write and read. These challenges include mixing model description with interpretation, managing layered process hierarchies, and deciding where calibration and results belong in a linear manuscript. In response, the paper introduces a practical writing workflow that helps authors organize mechanistic modeling manuscripts around causal logic rather than development chronology.

The workflow emphasizes early articulation of scientific purpose, explicit identification of dominant mechanisms, and figure ordering that reflects explanatory priority. An illustrative hydrodynamic modeling example shows how applying the workflow clarifies scope, improves narrative coherence, and strengthens mechanistic explanation. The workflow complements existing guidance on model documentation and reproducibility by addressing manuscript-level organization. It provides a repeatable, transferable approach for clearer and more interpretable mechanistic modeling papers.