The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IFC to update forecasts every 15 min for over 1,000 Iowa communities. The input to the system comes from a radar-rainfall algorithm, developed in-house, that maps rainfall every 5 min with high spatial resolution. The algorithm uses Level II radar reflectivity and other polarimetric data from the Weather Surveillance Radar-1988 Dual-Polarimetric (WSR-88DP) radar network. A large library of flood inundation maps and real-time river stage data from over 200 IFC “stream-stage sensors” complement the IFC information system. The system communicates all this information to the general public through a comprehensive browser-based and interactive platform. Streamflow forecasts and observations from Iowa can provide support for a similar system being developed at the National Water Center through model intercomparisons, diagnostic analyses, and product evaluations.

Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m 3 m 23 were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.

We investigate the propagation of radio waves underwater and between water and air to facilitate setting up hybrid wireless sensor networks with both surface and subsurface nodes. Our investigation includes signal attenuation, antenna radiation patterns, multipath due to reflections from the surface and substrate, noise, and reflection losses transmitting from one medium to another.

We have developed a robust sensor for mounting on bridges over rivers and streams. These bridge-mounted river stage sensors (BMRSS) make periodic measurements of the distance from the sensor to the water level below. Properly interpreted, these measurements provide river-stage information, data of great importance to society and crucial to effective flood forecasting. The traditional approach to river stage measurement is the installation of pipes in rivers, digging stilling wells, and the construction of attendant brick-and-mortar infrastructure. The cost of this approach limits the deployment to larger rivers. In most instances, river-stage data from smaller tributaries are few, even though such data can greatly enhance the quality of flood-forecasting models' outputs. In contrast, BMRSS units are an order of magnitude less expensive and allow for widespread deployment. BMRSS units incorporate an ultrasonic distancemeasuring module, a solar panel/battery/charge controller, and a GPS receiver. In recent years, the Internet access through commercial cellular networks has become ubiquitous, even in most rural areas. BMRSS units incorporate cell modems and transmit data through the Internet to servers at the Iowa Flood Center. Here, the data are ingested into relational databases and made available to flood forecasting models and information systems. We have deployed and operated more than 220 BMRSS units across Iowa, many for several years continuously.INDEX TERMS Sensor systems and applications, cellular networks, hazards, instrumentation and measurement.

This article presents the data collected and analyzed using the University of Iowa’s X-band polarimetric (XPOL) radars that were part of the spring 2013 hydrology-oriented Iowa Flood Studies (IFloodS) field campaign, sponsored by NASA’s Global Precipitation Measurement (GPM) Ground Validation (GV) program. The four mobile radars have full scanning capabilities that provide quantitative estimation of the rainfall at high temporal and spatial resolutions over experimental watersheds. IFloodS was the first extensive test of the XPOL radars, and the XPOL radars demonstrated their field worthiness during this campaign with 46 days of nearly uninterrupted, remotely monitored, and controlled operations. This paper presents detailed postcampaign analyses of the high-resolution, research-quality data that the XPOL radars collected. The XPOL dual-polarimetric products and rainfall are compared with data from other instruments for selected diverse meteorological events at high spatiotemporal resolutions from unprecedentedly unique and vast data generated during IFloodS operations. The XPOL data exhibit a detailed, complex structure of precipitation viewed at multiple range resolutions (75 and 30 m). The inter-XPOL comparisons within an overlapping scanned domain demonstrate consistency across different XPOL units. The XPOLs employed a series of heterogeneous scans and obtained estimates of the meteorological echoes up to a range oversampling of 7.5 m. A finer-resolution (30 m) algorithm is described to correct the polarimetric estimates for attenuation at the X band and obtain agreement of attenuation-corrected products with disdrometers and NASA S-band polarimetric (NPOL) radar. The paper includes hardware characterization of Iowa XPOL radars conducted prior to the deployment in IFloodS following the GPM calibration protocol.