Alan D. Snow

A High-Resolution National-Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model†

Abstract Warning systems with the ability to predict floods several days in advance have the potential to benefit tens of millions of people. Accordingly, large‐scale streamflow prediction systems such as the Advanced Hydrologic Prediction Service or the Global Flood Awareness System are limited to coarse resolutions. This article presents a method for routing global runoff ensemble forecasts and global historical runoff generated by the European Centre for Medium‐Range Weather Forecasts model using the Routing Application for Parallel computatIon of Discharge to produce high spatial resolution 15‐day stream forecasts, approximate recurrence intervals, and warning points at locations where streamflow is predicted to exceed the recurrence interval thresholds. The processing method involves distributing the computations using computer clusters to facilitate processing of large watersheds with high‐density stream networks. In addition, the Streamflow Prediction Tool web application was developed for visualizing analyzed results at both the regional level and at the reach level of high‐density stream networks. The application formed part of the base hydrologic forecasting service available to the National Flood Interoperability Experiment and can potentially transform the nations forecast ability by incorporating ensemble predictions at the nearly 2.7 million reaches of the National Hydrography Plus Version 2 Dataset into the national forecasting system.

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood-inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April-June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000-km area in the Midwest and a 109,500-km area in the Mississippi Delta into thirty-nine 1° by 1° tiles, AutoRoute simulated a high-resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F-statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief. (KEY TERMS: flooding; computational methods; rivers/streams; AutoRoute; RAPID; AutoRAPID.) Follum, Michael L., Ahmad A. Tavakoly, Jeffrey D. Niemann, and Alan D. Snow, 2017. AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents. Journal of the American Water Resources Association (JAWRA) 53(2):280-299. DOI: 10.1111/1752-1688.12476

A Comprehensive Python Toolkit for Accessing High-Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large-scale, high-resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high-throughput computing (HTC) and cloud computing provide an ideal resource for large-scale modeling because they are cost-effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI-WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national-scale modeling.