Enhancing High-resolution Terrain Data Model for Improving the Delineation of Multi-scale Hydrological Connectivity

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The project is to create a new geospatial and hydrological modeling approach that improves the identification of rivers and streams using high-resolution digital elevation models (HRDEMs). Accurate identification of these river and stream networks is necessary for monitoring environmental process such as the transport of nutrients and aquatic species. The results will create an intelligent hydrological model by combining methods in geographic information sciences, artificial intelligence and cyberinfrastructure that will enable multi-scale analysis of river and stream networks. The model, algorithms, and datasets generated from this project will be freely available to the public and benefit a broad scope of natural resources management activities, such as watershed monitoring, wetland conservation, and aquatic species protection. The results of this work will be incorporated into educational curriculums and disseminated to the broader educational community.There is a critical need for datasets and modeling approaches that can be used to generate hydrologically corrected HRDEMs and delineate hydrologic features like wetlands, road culverts and bridges at a fine scale. To improve the modeling accuracy, this project will develop a geospatial artificial intelligence-hydrological modeling framework that is compatible with HRDEMs. A deep learning model and an image dataset of drainage crossing locations will be developed to identify the locations of hydraulic drainage structures as virtual flow barriers. The effects of hydraulic drainage structures, HRDEM resolutions, and flow direction algorithms on the delineated hydrologic features and their connectivity will be assessed via controlled experiments for an optimal combination. The model will be implemented on high-performance computing clusters to speed up the geocomputation. This project will substantially advance the state-of-the-art of terrain-based hydrologic modeling integrating methods in geographic information science, artificial intelligence, and cyberinfrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.