Research and application of intelligent extraction and derivation technology for distributed hydrological models

Distributed hydrological models are effective tools for studying complex hydrological processes and supporting the development of smart water management. However， the current model construction process suffers from issues such as heavy workloads in fundamental data collection and organization， high repetitiveness in input data processing， and poor portability of model parameters， which limit modeling and interoperability efficiency. To address these challenges， this paper proposes a one-click extraction and derivation technology for distributed hydrological models based on a cloud platform. This technology integrates GIS spatial analysis tools with hydrological topology coding rules to establish two derivation modes based on polygon and point features. It enables rapid extraction of basic information and computational unit data from a distributed hydrological model. By leveraging the mapping relationships between computational units of the old and new models， it inherits the structure， data， and parameters of the original model， thereby generating an independent and complete new model. This study， using the delineated boundaries of Jiangxi Province and the Poyang Lake Basin as the study area， conducted research including data collection， model construction， parameter calibration， extraction and derivation， and application validation. The results show that this technology can substantially reduce the effort in data collection and preprocessing， significantly improve model construction efficiency， and ensure that the newly derived model maintains the simulation accuracy of the original model. This research provides a new technical method and practical support for the construction of smart water management and digital twin river basins.