ADCIRC Model and Static / Dynamic Method Mapping Static and Dynamic Models of Sea Level Rise Enhanced Tidal Inundation Along Florida's Northern and Central Atlantic Coast Brandon S. Dees 1 , Sandra Fox 2 , and Ed Carter 2 . St. John’s River Water Management District (SJRWMD), P.O. Box 1429, Palatka, Florida 32178 SJRWMD contracted with UCF Coastal Hydroscience Analysis, Modeling & Predictive Simulations Laboratory to quantify the extent to which sea level rise (SLR) could reasonably be expected to enhance inundation of coastal areas in extreme events such as hurricanes, as well as through nominal astronomical tides. The increases in sea level that were modeled are based on projected U.S. Army Corps of Engineers (USACE) values using both a static (“bathtub”) method and a dynamic method: 0.13 m, 0.22 m, 0.25 m, 0.51 m, 0.56 m and 1.57 m. (Hagen, Wang, et al. 2014.) Figure 1. Chart taken from the 2014 UCF report showing predicted SLR scenarios by NOAA and USACE, based on a linear extrapolation of tide gauge readings at Mayport Ferry Depot. Table 1. USACE SLR scenarios at Mayport, FL from highest to lowest (Hagen, Wang, et al. 2014.) Scenario Name Case Number Sea Level Rise (m)* 1992 0 0 2050 Low 1 0.13 2050 Intermediate 2 0.22 2050 High 3 0.51 2100 Low 4 0.25 2100 Intermediate 5 0.56 2100 High 6 1.57 Additional parameters are modified to account for effects of the inundation interaction with coastal floodplains, and the simulation is run as previously described. A total of twelve shapefiles were delivered by UCF. These polyline shapefiles represented the inundation extents of both static and dynamic methods for the six inundation scenarios projected by USACE. In several areas of interest within the SJRWMD, the UCF team found that the static method generally produced additional inundation area than were produced with the dynamic simulations. SJRWMD Geospatial Processing and Analysis The twelve product polyline shapefiles were edited by GIS staff at SJRWMD to create areas, facilitating construction of inundation polygon features. These features were edited to remove polygon features representing “dry” areas for each inundation scenario. Finally, the smaller component polygon features were then merged to create a single feature coverage for each inundation scenario. District GIS staff then used the SJRWMD Spatial Data Summary Tool to summarize the area of each Land Use class within the District that would be inundated under each scenario. All features within the Florida Land Use Land Cover Classification System (FLUCCS) series for water (which is assumed as presently inundated) were removed from the 2009 SJRWMD Land Use input layer. Using the inundation polygon for each scenario as the "area of interest" layer, the SJRWMD Spatial Data Summary Tool clips the land use features inside the inundation polygon. This process produced a tabular summary of estimated inundation impact for each land use class and a product geodatabase feature class of inundated land use for each inundation scenario. From multiple iterations of this process, District staff produced a master summary table with the predicted inundated acreage of each land use feature for all twelve inundation scenarios. SJRWMD Spatial Data Summary Tool Conclusion and Discussion Table 2. Table compiled from SJRWMD Spatial Data Summary Tool output. Figure 5. Map of 2009 SJRWMD Inundated Land Use. Product dataset of processing through SJRWMD Spatial Data Summary Tool. Figure 3. Comparison of static vs. dynamic inundation extents for a given SLR scenario. The twelve polyline extent shapefiles were processed to create mask polygon features which served as the AOI in the SJRWMD Spatial Data Summary Tool process. Figure 4. 2009 SJRWMD Land Use coverage. A similar coverage with water features omitted was used as the input layer for SJRWMD Spatial Data Summary Tool processing. Figures 6-9. Graphics created from Table 2 depicting relative effects of inundation on wetlands vs. non-wetland features and detailed effects on non-wetland features in the Case 5 inundation scenario. Wetlands generally appear to be most vulnerable to inundation due to predicted SLR in all scenarios. However, a significant amount of non- wetland acreage will also be affected, even in the most conservative estimates. EST INUNDATION IMPACT Inundation Scenarios Case 1 (+0.13m SLR) Case 2 (+0.22m SLR) Case 3 (+0.51m SLR) Case 4 (+0.25m SLR) Case 5 (+0.56m SLR) Case 6 (+1.57m SLR) Static Dynamic Static Dynamic Static Dynamic Static Dynamic Static Dynamic Static Dynamic 2009 Detailed SJRWMD Land Use Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Residential 3213 2882 3348 2997 4532 3858 3397 3036 4961 4028 30984 23212 Commercial 124 101 127 104 154 139 129 106 169 143 2468 1802 Industrial 29 28 32 29 53 41 34 29 56 46 448 354 Extractive 63 42 90 59 137 115 91 59 139 116 177 173 Institutional 59 55 63 66 125 94 71 71 162 100 5208 2111 Recreation 1809 1677 1904 1744 2401 2136 1935 1783 2532 2226 6446 5312 Open land 44 21 44 23 52 49 44 39 78 51 558 463 Agriculture 224 138 251 193 433 338 263 227 586 401 12982 11684 Upland nonforested 961 588 1088 820 1842 1393 1133 920 2062 1554 23888 15004 Coniferous forest 435 377 515 408 1187 700 545 418 1353 798 6044 5119 Hardwood forest 2388 1983 2595 2176 3720 3058 2667 2265 4100 3255 21817 15776 Tree plantation 131 40 182 81 584 174 203 86 753 206 6650 4815 Hardwood forested wetland 24190 11994 40295 18493 77494 66702 46038 22005 81862 72169 129223 124370 Coniferous forested wetland 758 230 1555 388 6657 4876 2852 537 7111 5676 14280 13954 Mixed forested wetland 1383 598 1633 733 4027 2879 1759 804 4678 3351 19976 17210 Nonforested wetland 80937 69721 87048 77200 109986 99732 88709 79599 113400 105352 163011 159013 Nonvegetated wetland 2136 1887 2233 2113 2473 2480 2268 2151 2494 2508 3178 3148 Barren land 191 139 250 187 410 373 271 200 439 402 1053 982 Transportation, Communication, Utilities 231 199 253 208 351 288 263 211 372 304 3042 1765 Figure 2. Slide from UCF report with ADCIRC mesh detail of the Lower St. John’s River Basin (Hagen, Wang, et al. 2014) The amount of affected Residential area begins to show a moderate increase beginning in the high 2050 / intermediate 2100 predicted SLR of 0.5 – 0.56m. In addition, breaching and significant inundation begin to occur on the barrier islands in the intermediate and high 2100 prediction scenarios. • Although Sea Level Rise will enhance tidal inundation and significantly impact natural features, anthropocentric features will be affected as well. • Using a similar methodology, SJRWMD can use future Land Use datasets as they become available to perform analyses and predict trends of the potential impacts of Sea Level Rise to core District functions. Credits Sandra Fox, M.S., GISP Ed Carter, Hydrologist III Brandon S. Dees, MSGIS The ADvanced CIRCulation Model (ADCIRC) is a hydrodynamic circulation model used by federal agencies and academia to model tidal, wind, and wave-driven circulation in coastal waters. For the purposes of this study, the static method entails taking each node of the maximum water surface elevation previously calculated by ADCIRC simulation for the current sea level (i.e., adjusted with a geoid offset for elevation obtained from a NOAA tide gauge and based from the 1992 tidal epoch that NOAA utilizes), and adding the specified SLR magnitude across the study area. If a node adjacent to a "wet" node was designated "dry" (i.e., node elevation value > MSL) in the current sea level mesh, it is checked to see if its value is less than the new computed maximum water surface elevation. If it is, it will be designated as "wet“ in the new model output. This process is reiterated until the process results in zero node status changes. The dynamic method is meant to more closely depict the influence of astronomic tide-generated flow. However, the SLR magnitude for the scenario is included within the geoid offset parameter of the model.
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
ADCIRC Model and Static / Dynamic Method
Mapping Static and Dynamic Models of Sea Level Rise Enhanced Tidal Inundation Along Florida's Northern and Central Atlantic CoastBrandon S. Dees1, Sandra Fox2, and Ed Carter2. St. John’s River Water Management District (SJRWMD), P.O. Box 1429, Palatka, Florida 32178
SJRWMD contracted with UCF Coastal Hydroscience Analysis, Modeling & Predictive Simulations Laboratory to quantify the extent to which sea level rise (SLR) could reasonably be expected to enhance inundation of coastal areas in extreme events such as hurricanes, as well as through nominal astronomical tides. The increases in sea level that were modeled are based on projected U.S. Army Corps of Engineers (USACE) values using both a static (“bathtub”) method and a dynamic method: 0.13 m, 0.22 m, 0.25 m, 0.51 m, 0.56 m and
1.57 m. (Hagen, Wang, et al. 2014.)
Figure 1. Chart taken from the 2014 UCF report showing predicted SLR scenarios by NOAA and USACE, based on a linear extrapolation of tide gauge readings at Mayport Ferry Depot.
Table 1. USACE SLR scenarios at Mayport, FL from highest to lowest(Hagen, Wang, et al. 2014.)
Scenario Name Case NumberSea Level Rise
(m)*
1992 0 0
2050 Low 1 0.13
2050 Intermediate 2 0.22
2050 High 3 0.51
2100 Low 4 0.25
2100 Intermediate 5 0.56
2100 High 6 1.57
Additional parameters are modified to account for effects of the inundation interaction with coastal floodplains, and the simulation is run as previously described.
A total of twelve shapefiles were delivered by UCF. These polyline shapefiles represented the inundation extents of both static and dynamic methods for the six inundation scenarios projected by USACE. In several areas of interest within the SJRWMD, the UCF team found that the static method generally produced additional inundation area than were produced with the dynamic simulations.
SJRWMD Geospatial Processing and Analysis
The twelve product polyline shapefiles were edited by GIS staff at SJRWMD to create areas, facilitating construction of inundation polygon features. These features were edited to remove polygon features representing “dry” areas for each inundation scenario. Finally, the smaller component polygon features were then merged to create a single feature coverage for each inundation scenario.District GIS staff then used the SJRWMD Spatial Data Summary Tool to summarize the area of each Land Use class within the District that would be inundated under each scenario.
All features within the Florida Land Use Land Cover Classification System (FLUCCS) series for water (which is assumed as presently inundated) were removed from the 2009 SJRWMD Land Use input layer.Using the inundation polygon for each scenario as the "area of interest" layer, the SJRWMD Spatial Data Summary Tool clips the land use features inside the inundation polygon. This process produced a tabular summary of estimated inundation impact for each land use class and a product geodatabase feature class of inundated land use for each inundation scenario. From multiple iterations of this process, District staff produced a master summary table with the predicted inundated acreage of each land use feature for all twelve inundation scenarios.
SJRWMD Spatial
Data Summary Tool
Conclusion and Discussion
Table 2. Table compiled from SJRWMD Spatial Data Summary Tool output.
Figure 5. Map of 2009 SJRWMD Inundated Land Use. Product dataset of processing through SJRWMD Spatial Data Summary Tool.
Figure 3. Comparison of static vs. dynamic inundation extents for a given SLR scenario. The twelve polylineextent shapefiles were processed to create mask polygon features which served as the AOI in the SJRWMD Spatial Data Summary Tool process.
Figure 4. 2009 SJRWMD Land Use coverage. A similar coverage with water features omitted was used as the input layer for SJRWMD Spatial Data Summary Tool processing. Figures 6-9. Graphics created from Table 2 depicting relative effects of inundation on wetlands vs. non-wetland features and
detailed effects on non-wetland features in the Case 5 inundation scenario.
Wetlands generally appear to be most vulnerable to inundation due to predicted SLR in all scenarios. However, a significant amount of non-wetland acreage will also be affected, even in the most conservative estimates.
EST INUNDATION IMPACT
Inundation Scenarios
Case 1 (+0.13m SLR) Case 2 (+0.22m SLR) Case 3 (+0.51m SLR) Case 4 (+0.25m SLR) Case 5 (+0.56m SLR) Case 6 (+1.57m SLR)
Static Dynamic Static Dynamic Static Dynamic Static Dynamic Static Dynamic Static Dynamic
2009 Detailed SJRWMD Land Use Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage Acreage
Residential 3213 2882 3348 2997 4532 3858 3397 3036 4961 4028 30984 23212
Commercial 124 101 127 104 154 139 129 106 169 143 2468 1802
Industrial 29 28 32 29 53 41 34 29 56 46 448 354
Extractive 63 42 90 59 137 115 91 59 139 116 177 173
Institutional 59 55 63 66 125 94 71 71 162 100 5208 2111
Recreation 1809 1677 1904 1744 2401 2136 1935 1783 2532 2226 6446 5312
Open land 44 21 44 23 52 49 44 39 78 51 558 463
Agriculture 224 138 251 193 433 338 263 227 586 401 12982 11684
Upland nonforested 961 588 1088 820 1842 1393 1133 920 2062 1554 23888 15004
Coniferous forest 435 377 515 408 1187 700 545 418 1353 798 6044 5119
Hardwood forest 2388 1983 2595 2176 3720 3058 2667 2265 4100 3255 21817 15776
Tree plantation 131 40 182 81 584 174 203 86 753 206 6650 4815
Hardwood forested wetland 24190 11994 40295 18493 77494 66702 46038 22005 81862 72169 129223 124370
Coniferous forested wetland 758 230 1555 388 6657 4876 2852 537 7111 5676 14280 13954
Mixed forested wetland 1383 598 1633 733 4027 2879 1759 804 4678 3351 19976 17210
Nonforested wetland 80937 69721 87048 77200 109986 99732 88709 79599 113400 105352 163011 159013
Nonvegetated wetland 2136 1887 2233 2113 2473 2480 2268 2151 2494 2508 3178 3148
Barren land 191 139 250 187 410 373 271 200 439 402 1053 982
Transportation, Communication, Utilities 231 199 253 208 351 288 263 211 372 304 3042 1765
Figure 2. Slide from UCF report with ADCIRC mesh detail of the Lower St. John’s River Basin (Hagen, Wang, et al. 2014) The amount of affected Residential area begins to show a moderate increase beginning in the high 2050 / intermediate 2100 predicted SLR of 0.5 – 0.56m. In addition, breaching and significant inundation begin to occur on the barrier islands in the intermediate and high 2100 prediction scenarios.
• Although Sea Level Rise will enhance tidal inundation and significantly impact natural features, anthropocentric features will be affected as well.
• Using a similar methodology, SJRWMD can use future Land Use datasets as they become available to perform analyses and predict trends of the potential impacts of Sea Level Rise to core District functions.
Credits
Sandra Fox, M.S., GISP Ed Carter, Hydrologist III
Brandon S. Dees, MSGIS
The ADvanced CIRCulation Model (ADCIRC) is a hydrodynamic circulation model used by federal agencies and academia to model tidal, wind, and wave-driven circulation in coastal waters.
For the purposes of this study, the static method entails taking each node of the maximum water surface elevation previously calculated by ADCIRC simulation for the current sea level (i.e., adjusted with a geoidoffset for elevation obtained from a NOAA tide gauge and based from the 1992 tidal epoch that NOAA utilizes), and adding the specified SLR magnitude across the study area.
If a node adjacent to a "wet" node was designated "dry" (i.e., node elevation value > MSL) in the current sea level mesh, it is checked to see if its value is less than the new computed maximum water surface elevation. If it is, it will be designated as "wet“ in the new model output.
This process is reiterated until the process results in zero node status changes.
The dynamic method is meant to more closely depict the influence of astronomic tide-generated flow. However, the SLR magnitude for the scenario is included within the geoidoffset parameter of the model.
Related Documents
