CI-FLOW Vision
CI-FLOW partners are committed to sustaining a unique inter-disciplinary multi-agency consortium focused on the mitigation of coastal hydrologic hazards in the Tar River Basin of North Carolina by engaging eastern North Carolina citizens into state, federal, and academic research and outreach programs.
The CI-FLOW project evaluates and tests new technologies to produce accurate and timely identification of inland and coastal floods in the Tar-Pamlico and Neuse River basins of coastal North Carolina. The outcome will be a prototype coupled-model system adaptable to any coastal river system to help in fresh water forecasting of floods and flash floods, water management, determination of land use and ecosystem impacts, and coastal storm surge forecasts.
Currently, CI-FLOW partners are focused on improving NWS hydrologic forecast capabilities for coastal watersheds. Researchers will demonstrate the capability to forecast water quantity and quality for multiple locations in the watershed, including the tidal plain, by coupling ensembles of inland river models and coastal ocean/estuary models, each using input from high-resolution weather forecast models and multi-sensor precipitation estimates.
This CI-FLOW vision has captured the interest of other NOAA groups operating programs in the Carolinas including local National Weather Service (NWS) offices, NWS Office of Hydrologic Development (OHD), National Environmental Satellite Data and Information Service (NESDIS), and the Coastal Services Center. The CI-FLOW approach to water information is also establishing a firm foundation for cooperation and collaboration with other federal, state, academic, and tribal agencies and governments and NOAA’s Coastal Estuary River Information System (CERIS).
CI-FLOW Genesis
Hurricanes spur the CI-FLOW flooding research project
In September of 1999, Hurricane/Tropical Storm Dennis and Hurricane Floyd delivered a one-two punch to North Carolina. Catastrophic flooding killed 51 people and left thousands homeless. Long-lasting effects on regional infrastructure included warnings not to drink or bathe in water from taps for fear it may harbor dangerously high levels of fecal coliform bacteria.
In response to these devastating events, NOAA launched the CI-FLOW project. Coastal and Inland FLood Observation and Warning Project (CI-FLOW) is a research and demonstration project focused on improving warnings of floods in coastal North Carolina. CI-FLOW evaluates and tests new technologies and techniques to produce accurate and timely identification of inland and coastal floods and flash floods in the Tar-Pamlico basin.
Flooding is the number one hazardous weather-related killer in the U.S. The CI-FLOW research efforts will reduce the loss of life and property from hydrologic hazards in the Carolinas and across our nation.
In February 2000, the National Severe Storms Laboratory (NSSL), National Sea Grant (NSG) College Program, University of Oklahoma, North Carolina State University (NCSU), and the North and South Carolina Sea Grant programs established a joint project, centered in North Carolina areas affected by Hurricane Floyd. The original collaborators were later joined by the National Weather Service Office of Hydrologic Development and the National Environmental Satellite, Data and Information Service (NESDIS). The primary demonstration area was the Tar-Pamlico River basin. This project, called CI-FLOW, has established a research and demonstration program for the evaluation and testing of new technologies and techniques to produce accurate and timely identification of inland and coastal floods and flash floods.
The previous activities of Project CI-FLOW include 1) implementation in the Tar River Basin of QPE-SUMS (Quantitative Precipitation Estimation and Segregation Using Multiple Sensors; Gourley et al. 2001), a cutting edge multi-sensor precipitation estimation technique, 2) implementation of Vflo™, a physics-based distributed hydrologic model (Vieux 2001; Vieux and Vieux 2002), and coupling of Vflo™ with QPE-SUMS, 3) coupling of the NCSU Estuary-Lower River Flood model (Xie and Pietrafesa 1999) with output from both QPE-SUMS and Vflo™, and 4) replacement of the Vflo™ model with the NWS Hydrology Laboratory's distributed model and linking the model to QPE-SUMS.
In 2003, Vflo™ and QPE-SUMS were producing real-time estimates of precipitation and river stage for key points along the Tar River Basin. The initial products are available to researchers, forecasters, and other potential users through the Internet in real-time. During that time, our team members at the SC Sea Grant Extension office were able to collect information on the critical hot spots for flooding along the Tar-Pamlico River Basin. This information was helpful in determining locations where river stage and flow forecasts will be needed.
In 2004, the NWS/OH Hydrology Lab Research Modeling System (HL-RMS) was introduced and coupled with the FLDWAV channel model to eliminate proprietary software source code and licensing issues (Koren et al., 2004). HL-RMS performed very well in NOAA's NWS-sponsored Distributed Model Intercomparison Project (DMIP) (Smith et al., 2004; Reed et al., 2004). DMIP garnered participation from 12 leading distributed modeling researchers in Canada, Denmark, New Zealand, China, and the US. DMIP was the first extensive comparison of distributed hydrologic models amongst themselves and to traditional lumped models. Results from DMIP proved that HL-RMS performed very well and is a scientifically sound tool for I modeling river basin hydrology (Reed et al., 2004)
Like HL-RMS, FLDWAV represents the 'state-of-the-science' in one-dimensional hydraulic channel routing. FLDWAV is the product of a long and intense development process (Fread, 1992; Fread et al., 1988), and has been proven to accurately reproduce observed river stages and discharges during flood events. Algorithms in FLDWAV solve the complete one-dimensional St. Venant equations for unsteady flow. In addition, FLDWAV has the capability of modeling the effects of a growing list of non-standard hydraulic features including the ability to model dendritic rivers systems and channel networks, to account for the effects of off-channel storage areas connected to the waterway or separated by levees, the ability to account for the effects of hydraulic structures (dams, bridges, levees), and the ability to handle flows in the subcritical and/or supercritical flow regime. FLDWAV is being used operationally to model several coastal rivers including the Columbia, lower Mississippi, and St. Johns rivers. It also serves as the basis for the generation of several types of forecast flood inundation maps (Cajina et aI., 2002).
Under last year's CI-FLOW work plan, HL-RMS and FLDWAV were modified to create a direct linkage between the two. Now, output from HL-RMS can be used as direct input into FLDWAV, allowing the two models to function as 'stand-alone' components that do not need to be tied to any current river forecasting system such as that used by NOAA's NWS. We believe that this combination is one of the first (if not the first) linkage of operational distributed models and advanced channel routing models for river forecasting.
An initial coupling of QPE SUMS with the OH Distributed Modeling System (HL-RMS) occurred by converting QPE SUMS onto the HRAP grid at 1 km resolution. In addition, the HL-RMS was coupled with the OH channel routing model. Furthermore, an effort to connect the channel model and the Estuary model was begun.