Practices for Integrated Flood Prediction and Response Systems (2021) / Chapter Skim
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65 Introduction The information in the following sections was derived from detailed interviews with individuals from seven states selected to serve as case examples (Idaho, Iowa, New York, North Carolina, South Carolina, Texas, and Washington)
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66 Practices for Integrated Flood Prediction and Response Systems membership has allowed for lengthy institutional knowledge that has benefited the committee's programs and contributed to its success. This committee also spans several different sections to make up a multidisciplinary team that helps disseminate information.
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Case Examples of Integrated Flood Prediction and Response Systems 67 (Appendix F) are written each time an alert is generated by USGS gages (streamflow)
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68 Practices for Integrated Flood Prediction and Response Systems Figure 49. Supplemental BridgeWatch report for Bridge 19260.
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Case Examples of Integrated Flood Prediction and Response Systems 69 Figure 50. Supplemental BridgeWatch report for Bridge 31595.
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70 Practices for Integrated Flood Prediction and Response Systems As a result, alerts are first evaluated by the Bridge Asset Management section. If necessary, inspectors or local maintenance personnel are sent out to take photographs and make observations that will be used along with plans and historical information to assess the current safety of the bridge.
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Case Examples of Integrated Flood Prediction and Response Systems 71 consists of gently rolling plains of rich, deep topsoil. Roughly two-thirds of the state falls within the Mississippi River watershed.
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72 Practices for Integrated Flood Prediction and Response Systems The Iowa Flood Center uses IFIS (Appendix E) to provide real-time flood monitoring and forecasting, as well as inundation maps to assess impacts to communities from flooding due to NEXRAD rainfall, USGS gages, and Iowa Flood Center sensors.
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Case Examples of Integrated Flood Prediction and Response Systems 73 communicates internally through an email list, emergency management center, and conference calls. When working with other state agencies, it was noted that collaboration between districts, design, and emergency operations is a practice providing a common line of communication and decision making regarding flood events.
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Figure 52. Screenshot of Iowa Draft Flood Hazard Products map.
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Case Examples of Integrated Flood Prediction and Response Systems 75 implemented BridgeWatch. The program has eliminated the human element for determining when flooding is severe enough to warrant inspection of infrastructure by monitoring weatherrelated assets and comparing those inputs with rainfall and stage thresholds for highway overtopping and scour critical bridges.
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76 Practices for Integrated Flood Prediction and Response Systems A bridge will be closed if it is deemed unsafe by meeting critical thresholds during a flood watch. In the event a bridge is closed, the team will conduct a post-flood inspection of the bridge before it is reopened by a licensed professional engineer.
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Case Examples of Integrated Flood Prediction and Response Systems 77 Figure 53. NYSDOT response.
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78 Practices for Integrated Flood Prediction and Response Systems Other challenges include predicting flood conditions in coastal backwater zones (e.g., Long Island)
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Case Examples of Integrated Flood Prediction and Response Systems 79 • Numerical computational fluid dynamics (Delft3D) hydraulic modeling to assess bridge scour • Evaluation of two-dimensional hydraulics models to improve scour predictions and countermeasures, including fiberoptic scour monitoring technology use • Predictions of roadway washout locations during extreme rainfall events based on forecasted rainfall, hydrologic/hydraulic modeling, and machine learning algorithms to predict washout locations and develop a network of safe routes within a watershed.
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80 Practices for Integrated Flood Prediction and Response Systems Flood Warning NCDOT recently launched the BridgeWatch application, which it expects to use internally for flood warning, among other things. Initially, NCDOT will need to determine the appropriate optimal thresholds from both stream gage data and precipitation forecasts to trigger alerts to provide effective flood warning for bridges in the state.
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Case Examples of Integrated Flood Prediction and Response Systems 81 growth of the FIMAN-T system for the whole state of North Carolina difficult, so NCDOT plans to use virtual gages in the future. Virtual gages would involve the future modeling of rain on grid for multiple rain events or having the capability to run models in cloud computing applications before the event.
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82 Practices for Integrated Flood Prediction and Response Systems efforts. NCDOT hopes to optimize its gages and to increase gage coverage throughout North Carolina to fill in gaps across the state.
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Case Examples of Integrated Flood Prediction and Response Systems 83 SCDOT is centralized with all preconstruction activities conducted within its headquarters. For flood response, SCDOT works at the state level with the South Carolina Emergency Management Division, other state agencies, and the National Guard.
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84 Practices for Integrated Flood Prediction and Response Systems in the form of a graph, which can be used in reports. Figures 57 and 58 present example graphs on the flood monitoring spreadsheets.
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Case Examples of Integrated Flood Prediction and Response Systems 85 actual events and applies the Unit Hydrograph Method of the Natural Resources Conservation Service. SCDOT is in discussions with SCDNR on using the Unit Hydrograph Method, which allows for weighted peak rate factors and rainfall durations of less than 24 hours.
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Figure 59.
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Figure 60.
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88 Practices for Integrated Flood Prediction and Response Systems SCDOT to set thresholds for predicted rainfall, actual rainfall, predicted surge, and flows at structures. This capability allows SCDOT to monitor the system and flag locations that could have issues.
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Case Examples of Integrated Flood Prediction and Response Systems 89 events. Coordination meetings are held with all units involved in preplanning, monitoring, evacuation support and traffic counterflow, and response.
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90 Practices for Integrated Flood Prediction and Response Systems data and working together during an event. SCDOT collaborates with USGS, SCDNR, FHWA, and other agencies but believes that there is always room to improve communication efforts.
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Case Examples of Integrated Flood Prediction and Response Systems 91 TxDOT posts flood monitoring guidelines in an online platform. This webpage (Appendix E)
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92 Practices for Integrated Flood Prediction and Response Systems A presentation of "Streamflow Measurement at TxDOT Bridges" was prepared for TxDOT by the Center for Water and Environment at the University of Texas at Austin (Appendix F)
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Figure 62. UT-TxDOT radar gage sites.
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Figure 63. Channel bed measurement data.
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Case Examples of Integrated Flood Prediction and Response Systems 95 as was published in NOAA's Atlas 14, and to recent extreme rainfall events. The updated rainfall depths published in Atlas 14 showed that a substantial increase in depths occurs in a band starting in Beaumont and crossing through central Texas (Austin and San Antonio)
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96 Practices for Integrated Flood Prediction and Response Systems area maintenance crews also conduct drive-by assessments of locations to see whether erosion is present after big storms. In an emergency, maintenance personnel can take actions to respond.
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Case Examples of Integrated Flood Prediction and Response Systems 97 maintenance crew within the region in which the site is located to ensure that the region is effectively monitoring it. The DOT works with the maintenance division within each area to track bridges, pavement, and all other highway aspects.
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From page 98... ...
98 Practices for Integrated Flood Prediction and Response Systems • A coordinated and updated asset inventory document that is accessible to the key state DOT staffs is a method applied to keep the state DOT informed. • The advancements in technology -- especially with GIS, data models, and sensors -- has enabled both easier data collection and communication between stakeholders, and better coordinated model prediction efforts.
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