Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics
arXiv:2510.02605v2 Announce Type: replace Abstract: While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded in enhanced physical-conceptual understanding. Here, we report on a CONUS-wide large-sample study (spanning diverse hydro-geo-climatic conditions) using ML-augmented physically-interpretable catchment-scale models of varying complexity based in the Mass-Conserving Perceptron (MCP). Results were evaluated using attribute masks such as snow regime, forest cover, and cl
The increasing sophistication of AI and ML techniques is enabling their application to complex environmental modeling, pushing towards more interpretable and actionable insights.
This development represents a significant step towards more accurate and understandable hydrological models, critical for managing water resources in the face of climate change and increasing demand.
The integration of ML with physically-interpretable models fundamentally changes how we can predict and understand water movement across diverse regions, moving beyond purely black-box AI approaches.
- · Hydrology researchers
- · Water resource managers
- · Agricultural sector
- · Climate scientists
- · Regions unprepared for water variability
- · Outdated hydrological modeling techniques
Improved local and regional water availability forecasts will enable better resource allocation and drought/flood mitigation strategies.
Enhanced predictability of water resources could influence land use planning, agricultural practices, and infrastructure investments, particularly in water-stressed areas.
The application of this modeling to other environmental systems could accelerate climate adaptation strategies and resource management across various sectors.
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