| 19 Feb 2026
HESS Opinions: Applied hydrologic models in the era of machine learning – retain, revamp, reconcile, or replace?
Abstract. Despite advancements in the performance of machine learning (ML) based hydrologic models, some institutions are hesitant to pursue ML as a replacement for existing conceptual or process-based hydrologic models in many applications. In several of these circumstances, traditional hydrologic models continue to be favored due to their familiarity, reliability, interpretability, established performance benchmarks under varied settings, availability of detailed training modules and a trained workforce, as well as close integration with data, processing, and decision-making pipelines. Recognizing these advantages, this perspective argues for two pragmatic and institutionally compatible paths forward for integration of ML within applied models: (1) reconciling ML as a complementary option in applied hydrologic modeling workflows; and (2) revamping or upskilling hydrologic modeling workflows using ML. To support this perspective, we highlight key opportunities where ML can be used as a tool to enhance results across various stages of the model implementation and operational workflow including data pre-processing, parameter calibration, parameter transferability, data assimilation, solver enhancement, accelerating scenario simulations and post-processing. Each of these two integration strategies can be implemented into current applied model frameworks, thereby combining the strengths of both physical modeling and ML. These strategies can help overcome current bottlenecks and address institutional needs of continuity and compatibility, while also offering the potential to improve model performance with ML.
This manuscript presents a timely and well-articulated opinion on the evolving role of applied hydrologic models in the era of ML. The paper is well structured, written in a clear and accessible style, and supported by illustrative examples spanning forecasting, planning, and decision-support contexts. The proposed roadmap for ML integration is particularly valuable, as it frames adoption not only as a technical evolution but also as an institutional and cultural transition. Overall, the manuscript makes a meaningful contribution to an important and ongoing discussion. However, several issues should be addressed to further strengthen the clarity, rigor, and practical impact of the paper.
Comments
1- The manuscript would benefit from a more explicit decision framework clarifying when users should retain, revamp, reconcile, or replace existing modeling approaches. While the conceptual distinctions are helpful, readers will seek clearer decision criteria or guiding principles. Incorporating a structured comparison (e.g., a decision matrix based on data availability, interpretability needs, regulatory constraints, computational cost, and operational risk) would substantially improve the manuscript’s applicability.
2- The paper surveys a wide range of models, tasks, and ML integration opportunities. Although informative, the breadth risks diluting the central message. The manuscript would be strengthened by prioritizing or highlighting the most impactful and realistic integration pathways (e.g., calibration acceleration, surrogate modeling, bias correction, forcing-data improvement). This would enhance focus and provide clearer guidance for applied users.
3- Sections discussing LLMs)introduce interesting perspectives but would benefit from clearer boundaries regarding current capabilities versus future potential. Framing LLM-related discussions explicitly as emerging prospects would improve precision and avoid overgeneralization. I do recommend to consider papers such as “Can large language models effectively reason about adverse weather conditions?”, which reflects an active and relevant research frontier. Additionally, other emerging computational paradigms could be briefly acknowledged to broaden the forward-looking perspective. For example, quantum computing is increasingly discussed in environmental modeling contexts. The authors may consider citing recent developments such as "HydroQuantum: A new quantum-driven Python package for hydrological simulation" as an example of exploratory directions that, while still nascent, may influence future modeling workflows.