Is Your Hydrological Model Based on Legacy, Not Accuracy? Why It Matters
"Uncover how historical preferences, not scientific rigor, might be shaping the way we predict water's future—and what we can do about it."
Hydrological models are critical tools for understanding and predicting water-related phenomena, from flood risks to water resource availability. The selection of a specific model is a crucial decision, influencing the outcomes and reliability of any study. Ideally, this choice should hinge on the model's adequacy, reflecting its suitability for the specific research question, landscape, and scale of analysis.
However, a recent study published in the American Geophysical Union unveils a surprising trend: legacy, rather than adequacy, often dictates model selection. This means that practicality, convenience, and established experience often outweigh scientific rigor when hydrologists choose their models. This reliance on legacy can have significant implications for the accuracy and effectiveness of water management strategies worldwide.
To better understand the process of model selection, researchers conducted a bibliometric study, analyzing over 1500 peer-reviewed articles. They explored the use of seven rainfall-runoff models, examining factors such as institutional affiliations, regional preferences, and research topics. The findings challenge conventional assumptions about objectivity in scientific modeling and call for a more critical and adaptable approach.
The Power of the Past: How Legacy Shapes Model Selection
The study's most striking finding is the significant influence of legacy on model selection. Researchers discovered strong regional preferences in model use, with specific models consistently favored by certain institutions. In a remarkable 70% of cases, the selected model could be predicted solely based on the affiliation of the first author. This suggests that familiarity and established practices often trump considerations of model suitability.
- Expertise and Training: Hydrologists invest years in mastering specific models, gaining deep knowledge of their intricacies and nuances. This expertise makes it easier to apply familiar models in new situations.
- Model Ecosystems: Sustained model use fosters the development of supporting tools, datasets, and user interfaces, creating a productive and efficient modeling environment.
- Perceived Adequacy: Modelers often perceive their chosen models as adequate, even if more suitable options exist. This perception can be reinforced by the scientific community's tendency to favor well-established methods.
Moving Towards a More Adaptive Future
The study's findings call for a shift towards more flexible and collaborative modeling approaches. One promising solution is the adoption of modular modeling frameworks (MMFs), which allow users to build models in a pick-and-mix fashion, compare competing hypotheses, and contribute code in easily reusable modules. By promoting adaptability and community involvement, MMFs can help overcome the limitations of legacy-driven model selection and pave the way for more accurate and effective water management strategies. This shift requires grassroots initiatives that make MMFs more user-friendly, provide training opportunities, and encourage collaboration. Ultimately, embracing a more adaptive approach to hydrological modeling will accelerate progress in understanding and predicting the complex dynamics of our water resources.