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 nation's 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.

The interactive nature of web applications or "web apps" makes them a well-suited medium for conveying complex scientific concepts to lay audiences and creating decision support tools that harness cutting edge modeling techniques and promote the work of environmental scientists and engineers. Despite this potential, the technical expertise required to develop web apps represents a formidable barrier-even for scientists and engineers who are skilled programmers. This paper describes four hurdles that contribute to this barrier and introduces an approach to overcoming these hurdles. We present an open source implementation of this approach, a development and hosting environment for environmental web apps called Tethys Platform. Several case studies are provided that demonstrates how the approach, as implemented within Tethys Platform, successfully lowers the barrier to web app development in the environmental domain.

Water resources web applications or "web apps" are growing in popularity as a means to overcome many of the challenges associated with hydrologic simulations in decision-making. Water resources web apps fall outside of the capabilities of standard web development software, because of their spatial data components. These spatial data needs can be addressed using a combination of existing free and open source software (FOSS) for geographic information systems (FOSS4G) and FOSS for web development. However, the abundance of FOSS projects that are available can be overwhelming to new developers. In an effort to understand the web of FOSS features and capabilities, we reviewed many of the state-of-theart FOSS software projects in the context of those that have been used to develop water resources web apps published in the peer-reviewed literature in the last decade (2004e2014).

Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time period to develop a model for a reservoir which relates reflectance in various bands to measured algal (or chlorophyll-a) concentrations and use that model and associated imagery to determine spatial algal concentrations in the reservoir. In this work, we extend these techniques to use historical Landsat data over long time periods to develop seasonal models that will more accurately describe the conditions throughout the growing season. Previous work at Deer Creek included the development of a chlorophyll-a model using data from the months of August to September. This model did not account for seasonal variation and algal succession, which affects the relationship between measured reflectance and algal concentration. Early summer algal blooms are dominated by diatoms (yellow-brown), while the algae vary from chlorophyta (green) in the mid-summer to cyanobacteria (blue-green) in late summer months. This study presents and explores the development and use of seasonal algorithms based on reflective characteristics of various algal communities to create a more accurate model for the reservoir. This study uses water quality data collected over a 20-year period during non-ice conditions along with associated Landsat data. As the field measurements were not taken to support remote sensing measurements, this study evaluates the use of historical data to support remote sensing analysis. It is assumed that reservoir conditions do not change rapidly, the field data can be used to develop correlations with satellite imagery taken within a day of the field measurements, and the seasonal algal communities have different reflective properties (or colors). We present statistical analysis that shows the seasonal algorithms better fit the data than the non-seasonal model and the traditional model calibrated with late-season data. We recommend the use of sub-seasonal algorithms to more accurately model and predict water quality throughout the growing season.

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