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Development and Validation of Alluvial Risk Identification Methodologies Through the Integrated Use of Remote Sensing from Satel

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Development and Validation of Alluvial Risk Identification Methodologies Through the Integrated Use of Remote Sensing from Satel DEVELOPMENT AND VALIDATION OF ALLUVIAL RISK IDENTIFICATION METHODOLOGIES THROUGH THE INTEGRATED USE OF REMOTE SENSING FROM SATELLITE AND HYDRAULIC MODELING WITH PARTICULAR REFERENCE TO POST- EVENT ANALYSIS AND NOWCASTING PHASES Ph.D. in Environmental and Hydraulic engineering – XXXII cycle Ph.D. candidate Supervisor Eng. Vincenzo Scotti Prof. Francesco Cioffi Academic year: 2018/2019 1 Summary 1 PREFACE ..................................................................................................................................................... 7 2 INTRODUCTION ....................................................................................................................................... 12 2.1 FLOODING RISK ASSESSMENT FROM SATELLITE ............................................................................. 13 2.1.1 REMOTE SENSING FROM SATELLITE FOR THE RETRIEVAL OF SOIL MOISTURE ....................... 17 2.1.2 REMOTE SENSING FROM SATELLITE FOR THE SUBSIDENCE PHENOMENA ASSESSMENT ...... 20 2.1.3 REMOTE SENSING FROM SATELLITE FOR THE DETECTION OF FLOODING EXTENSION .......... 23 2.1.4 REMOTE SENSING FROM SATELLITE FOR THE PRECIPITATION ESTIMATION .......................... 26 2.2 OTHER TECHNIQUES IN FLOODING RISK ASSESSMENT ................................................................... 27 2.2.1 HYDRAULIC MODELLING IN FLOOD RISK ASSESSMENT ........................................................... 28 2.2.2 FLOODING RISK ASSESSMENT USING SOCIAL MEDIA MARKER ............................................... 29 2.2.3 ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE NOWCASTING PHASE OF FLOOD RISK ASSESSMENT ............................................................................................................................................ 31 2.3 AIM OF THE THESIS .......................................................................................................................... 35 3 APPLICATION TO CASE STUDIES OF THE REMOTE SENSING FOR FLOODING RISK ANALYSIS ................. 37 3.1 ANALYSIS OF THE SUBSIDENCE IN THE COASTAL AREA AND HYDRAULIC RISK IN MAZZOCCHIO AREA (LT) ..................................................................................................................................................... 38 3.2 INTEGRATION OF SATELLITE INFORMATION (SAR) WITH HYDRAULIC MODELING: CASE STUDY OF THE STRYMON BASIN .................................................................................................................................. 52 3.3 RECONSTRUCTION OF FLOODING MAP IN INSTRUMENTED AREA (CASE STUDY: HARVEY HURRICANE, HOUSTON) .............................................................................................................................. 59 3.4 RECONSTRUCTION OF FLOODING MAP IN A NON-INSTRUMENTED AREA, CASE STUDY OF THE DIAMREY TYPHOON IN QUANG NGAI (VIETNAM) ....................................................................................... 73 3.5 ANALYSIS OF PRECIPITATION DATASET ALWAYS AVAILABLE AS INPUT OF HYDRAULIC MODELING IN A NOT INSTRUMENTED AREA ................................................................................................................. 77 4 THE TWO-DIMENSIONAL MODEL FHM 2D .............................................................................................. 89 4.1 THE NECESSITY OF A MORE ACCURATE HYDRAULIC MODEL .......................................................... 89 4.2 GOVERNING EQUATIONS ................................................................................................................ 94 4.3 NUMERICAL MODEL ........................................................................................................................ 95 4.4 TIME INTEGRATION ......................................................................................................................... 96 4.5 DISCRETIZATION OF NUMERICAL FLUXES ....................................................................................... 99 4.6 SOURCE TERM DISCRETIZATION .................................................................................................... 105 4.7 NUMERICAL TESTS ......................................................................................................................... 110 4.8 APPLICATION FHM-2D TO A REAL CASE STUDY ............................................................................. 124 5 FLOOD RISK ASSESSMENT IN NOWCASTING PHASE.............................................................................. 133 5.1 THE NOWCASTING PHASE ............................................................................................................. 133 2 5.2 A REAL TIME FLOOD SURROGATE MODEL .................................................................................... 133 5.3 PRELIMINARY RESULTS .................................................................................................................. 139 5.4 CONCLUSIONS AND ONGOING DEVELOPMENTS .......................................................................... 142 6 SUMMARY AND PERSPECTIVES ............................................................................................................. 144 6.1 SUMMARY ..................................................................................................................................... 144 7 REFERENCES ........................................................................................................................................... 150 3 Figure 1 - Typology of sensors system onboard the satellite .......................................................................... 13 Figure 2 - Satellite Radar Systems available now and into the future (from Tapete, D., & Cigna, F. (2019)). 15 Figure 3 - Example of soil moisture detection with ERS SAR satellite ............................................................. 19 Figure 5 - flooding detection from SAR satellite .............................................................................................. 24 Figure 6 - Social marker posted during hurricane Harvey in Houston that allows to localize the area affected to flood ............................................................................................................................................................ 30 Figure 7 - Feed forward artificial neural network structure using a sigmoid activation function ................... 34 Figure 8 - On the left in red the area of interest of Mazzocchio is highlighted, while on the right the Mazzocchio's basin ground elevation. ............................................................................................................. 39 Figure 9- Examples of applications of satellite interferometry. ...................................................................... 40 Figure 10 - PS-InSAR technique. ...................................................................................................................... 41 Figure 11 - Results of interferometric measurements (PS-InSAR) obtained from Sentinel-1 represented on Google Earth. In red it was delineated the boundary of Mazzocchio basin (AoI). .......................................... 42 Figure 12 - Example of a historical series of movement of a PS. ..................................................................... 43 Figure 13 - IDW interpolation of interferometric data - a) Vertical displacement velocity [mm / year]; b) Subsidence from January 2018 compared to October 2014. ........................................................................... 44 Figure 14 - Identification of 16 targets in map of subsidence of January 2018. ............................................. 45 Figure 15 - Subsidence trend for the 16 selected targets (2014-2018 period). ............................................... 46 Figure 16 - Examples of photographs for the 10 and 12 targets that allowed to view the presence or absence of subsidence. .................................................................................................................................................. 47 Figure 17 - Location ant typologies of wells presents in AoI ........................................................................... 48 Figure 18 - Results of the correlation between PCA and Target (individual data of each target in the image on the left. Correlation map in the image on the right). ................................................................................. 49 Figure 19- Precipitation and subsidence trends inherent to the 16 selected targets. ..................................... 50 Figure 20 - SPI index values and subsidence trend inherent to the 16 selected targets. ................................ 50 Figure 21 - Subsidence - Drought Correlation (SPI elaborated by the pluviometric data of the Pontinia station)............................................................................................................................................................. 51 Figure 22 - Location map. In yellow was represented the SAR subset processed image; in black the Serres basin, and at least the meteo-stations (polygons) 1: Lithotopos, 2: Koimissi Serron, 3: Achladochori, water level telemetry stations (polygons) 4: Trimeristis, 5: Nigrita, 6: Aggitis. ......................................................... 53 Figure 23 - Water/non-water. Binary images water/non-water from October 2014 to March 2015. ........... 57 Figure 24 - Flooding maps obtained from Hydraulic modelling .....................................................................
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