Work

Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments

Year: 2025

Type: article

Abstract: . Process-based hydrological modeling is a long-standing strategy for simulating and predicting complex water processes over large, hydro-climatically diverse domains, yet model parameter estimation (calibration) remains a persistent challenge for large-scale applications. New techniques and concepts arising in the artificial intelligence (AI) context for hydrology point to new opportunities to tackle this problem in process-based models. This study presents a machine learning (ML) based calibration strategy for large-domain modeling, implemented using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) land/hydrology model coupled with the mizuRoute channel routing model. We explore various ML methods to develop and evaluate a model emulation and parameter estimation scheme, applied here to optimizing SUMMA parameters for streamflow simulation. Leveraging a large-sample catchment dataset, the large-sample emulator (LSE) approach integrates static catchment attributes, model parameters, and performance metrics, providing a basis for large-domain regionalization to unseen watersheds. The LSE approach is compared with a single-site emulator (SSE), demonstrating improved calibration outcomes across temporal and spatial cross-validation experiments. The joint training of the LSE framework yields comparable performance to traditional individual basin calibration while enabling potential for parameter regionalization to out-of-sample, unseen catchments. Motivated by the need to optimize complex hydrology models over continental-scale domains to support national water security applications, this work introduces a scalable strategy for the calibration of large-domain process-based hydrological models.

Source: Hydrology and earth system sciences

DOI: 10.5194/hess-29-4515-2025

Authors: Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, Naoki Mizukami

Institutions: NSF National Center for Atmospheric Research, NSF NCAR Climate and Global Dynamics Laboratory, Colorado School of Mines, Wuhan University, NSF NCAR Research Applications Laboratory

Language: English

Topic: Hydrology and Watershed Management Studies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,725