Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model
Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms o...
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MDPI AG
2024-11-01
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| author | Amirhossein Rostami Chi-Hung Chang Hyongki Lee Hung-Hsien Wan Tien Le Thuy Du Kel N. Markert Gustavious P. Williams E. James Nelson Sanmei Li William Straka III Sean Helfrich Angelica L. Gutierrez |
| author_facet | Amirhossein Rostami Chi-Hung Chang Hyongki Lee Hung-Hsien Wan Tien Le Thuy Du Kel N. Markert Gustavious P. Williams E. James Nelson Sanmei Li William Straka III Sean Helfrich Angelica L. Gutierrez |
| author_sort | Amirhossein Rostami |
| collection | DOAJ |
| description | Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70–80% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER’s versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern. |
| format | Article |
| id | doaj-art-16cbc41fefa84166b781a9723c844962 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-16cbc41fefa84166b781a9723c8449622025-08-20T01:55:37ZengMDPI AGRemote Sensing2072-42922024-11-011623435710.3390/rs16234357Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water ModelAmirhossein Rostami0Chi-Hung Chang1Hyongki Lee2Hung-Hsien Wan3Tien Le Thuy Du4Kel N. Markert5Gustavious P. Williams6E. James Nelson7Sanmei Li8William Straka III9Sean Helfrich10Angelica L. Gutierrez11Department of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USADepartment of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USADepartment of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USADepartment of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USADepartment of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USADepartment of Civil and Construction Engineering, Brigham Young University, Engineering Building 430, Provo, UT 84602, USADepartment of Civil and Construction Engineering, Brigham Young University, Engineering Building 430, Provo, UT 84602, USADepartment of Civil and Construction Engineering, Brigham Young University, Engineering Building 430, Provo, UT 84602, USADepartment of Geography and Geoinformation Science, George Mason University, 4400 University Dr., Fairfax, VA 22030, USASpace Science and Engineering Center, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706, USANational Environmental Satellite Data and Information Service, National Oceanic and Atmospheric Administration, 1335 East-West Highway, SSMC1 8th Floor Suite 8300, Silver Spring, MD 20910, USAOffice of Water Prediction, National Weather Service, National Oceanic and Atmospheric Administration, 1325 East-West Highway, Silver Spring, MD 20910, USAFloods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70–80% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER’s versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern.https://www.mdpi.com/2072-4292/16/23/4357VIIRS water fractionsnational water modelflood forecastingEOF analysis |
| spellingShingle | Amirhossein Rostami Chi-Hung Chang Hyongki Lee Hung-Hsien Wan Tien Le Thuy Du Kel N. Markert Gustavious P. Williams E. James Nelson Sanmei Li William Straka III Sean Helfrich Angelica L. Gutierrez Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model Remote Sensing VIIRS water fractions national water model flood forecasting EOF analysis |
| title | Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model |
| title_full | Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model |
| title_fullStr | Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model |
| title_full_unstemmed | Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model |
| title_short | Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model |
| title_sort | forecasting flood inundation in u s flood prone regions through a data driven approach fier using viirs water fractions and the national water model |
| topic | VIIRS water fractions national water model flood forecasting EOF analysis |
| url | https://www.mdpi.com/2072-4292/16/23/4357 |
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