HydroTerre: Towards an expert system for scaling hydrological data and models from hill-slopes to major-river basins.

摘要

The purpose of this research is to develop HydroTerre, an expert system, as a resource for the larger water research community to improve the hydrological modeling process. The expert system provides modelers with access to hydrological data and model results that scale from hill-slopes to major-river basins anywhere in the continental US. HydroTerre consists of three processes:;• Data workflows that automate the functionality of collecting and processing essential variables for hydrological models anywhere in the US.;• Model workflows that consume the data processes and automatically scale and transform the data into model inputs and generate efficient Penn State Integrated Hydrological Models (PIHM).;• Visual analytic workflows that disseminate the PIHM model process results appropriately per scale and make it feasible for modelers to analyze both the data and model results, finding new features and details otherwise not possible.;The expert system, collectively, captures the critical thinking made by the hydrological modelers via the user interface, using provenance datasets that are shared amongst the modeling community. The expert system has been evaluated at the level HUC-12 catchment scale everywhere in the Continental United States of America (CONUS) with all three workflows. Chapter 2 demonstrates data workflows that provide data bundles of elevation, soil, geology, land cover, and one climate normal (30 years) of forcing data within minutes anywhere in the CONUS using distributed compute resources and High Performance Computing (HPC). The data bundles use federal national data products that would normally take a modeler days to weeks to retrieve using existing national cyber-infrastructure from these agencies.;Data to model workflows transform these data bundles into PIHM inputs and with HPC resources distributes PIHM model workflows dynamically as shown in Chapter 3. These transformations were evaluated millions of times to create database repositories (provenance) for modelers to conduct, share, and reproduce model studies. Additionally, these model results (poor and good) help identify opportunities to improve data and model processes at the level HUC-12 before scaling towards major river basins.;The analysis of model workflows using HPC and web based visual analytic applications is shown in Chapter 4 to explain provenance, reproducibility, and scalability for all three processes at the HUC-12 scale. Using the HydroTerre expert system, it is feasible at the HUC-12 scale to select a catchment via a web application, define model parameters, and retrieve data, model, and visualization results within minutes using distributed computing and HPC environments. The expert user can achieve these modeling steps entirely online without handling data or model arrangements. Hence, HydroTerre increases reproducibility, provenance, and a modeler's ability to create hydrologically correct models.;The focus of Chapter 5 is to identify issues learned from hill-slope modeling using HydroTerre workflows that future research will be required to address, in order to generate hydrologically correct models anywhere in the CONUS at any catchment scale. From the millions of workflows evaluated, missing data, in particular soils, requires new strategies to either replace or find suitable values. The remaining data issue is stream delineation techniques. Both the use of national data products and TauDEM strategies require new visual analytical applications to guide the expert user to correct stream data. To scale both model and visual analytic workflows requires new data structures and domain decomposition strategies that scale both catchment, cyberinfrastructure, and capability of HPC environments.