Nathan Swain

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.

From Global to Local: Providing Actionable Flood Forecast Information in a Cloud-Based Computing Environment†

Global and continental scale flood forecast provide coarse resolution flood forecast, but from the perspective of emergency management, flood warnings should be detailed and specific to local conditions. The desired refinement can be provided by the use of downscaling global scale models and through the use of distributed hydrologic models to produce a high-resolution flood forecast. Three major challenges associated with transforming global flood forecasting to a local scale are addressed in this work. The first is using open-source software tools to provide access to multiple data sources and lowering the barriers for users in management agencies at local level. This can be done through the Tethys Platform that enables web water resources modeling applications. The second is finding a practical solution for the computational requirements associated with running complex models and performing multiple simulations. This is done using Tethys Cluster that manages distributed and cloud computing resources as a companion to the Tethys Platform for web app development. The third challenge is discovering ways to downscale the forecasts from the global extent to the local context. Three modeling strategies have been tested to address this, including downscaling of coarse resolution global runoff models to high-resolution stream networks and routing with Routing Application for Parallel computatIon of Discharge (RAPID), the use of hierarchical Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) distributed models, and pre-computed distributed GSSHA models.

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.

Correlations between Total Solids, Total Suspended Solids, Total Volatile Suspended Solids, and Phosphate at Deer Creek Reservoir

Deer Creek Reservoir, located in Utah, supplies municipal and agricultural water for Utah and Salt Lake counties. During the past four decades the high levels of total phosphorus and dissolved oxygen in the water have introduced both taste and odor problems from algae growth, which have necessitated additional treatment to clean the water. In an attempt to discover why late summer algae blooms continue to persist at Deer Creek, the Brigham Young University Deer Creek Research Group collected data using several water quality laboratory tests on samples from 11 different sampling sites within the reservoir: total solids (TS), total suspended solids (TSS), total volatile suspended solids (TVSS), and phosphate. These tests were performed on samples collected during the summers of 2010 (May through October) and 2011 (April through November). Samples from Secchi depth were used for this analysis because of excessive variability introduced if samples from above and below the thermocline and at bottom layers of the reservoir were included. The purpose of this study is to determine if any correlations exists between these three measurements: solid, phosphate, and Secchi depths. We used total suspended solids as an indicator for algal mass. We suspect that phosphate is being trapped in solid material, specifically sediment, and being released into the reservoir slowly over time. Our analysis shows that solids at Deer Creek do not exhibit significant correlations with phosphate or Secchi depths. We suggest that a different approach to the phosphate problem be used that we should analyze and correlate Deer Creek phosphate with sediment oxygen demand (SOD) measurements taken using SOD chambers to correlate algae with potential phosphate release