Cover
Preface
1 The Need for Mapping, Modeling, and Predicting Flood Hazard and Risk at the Global Scale
1. 1.1. INTRODUCTION
2. 1.2. BRIEF OVERVIEW OF RECENT ADVANCES IN GLOBAL FLOOD HAZARD AND RISK MODELING
3. 1.3. GLOBAL FLOOD RISK INFORMATION IN HIGH‐LEVEL DISASTER RISK MANAGEMENT ADVOCACY
4. 1.4. APPLICATIONS FOR INTERNATIONAL DEVELOPMENT ORGANIZATIONS
5. 1.5. APPLICATIONS FOR THE REINSURANCE INDUSTRY
6. 1.6. APPLICATIONS FOR GLOBAL FLOOD FORECASTING AND EARLY WARNING
7. 1.7. COMMUNICATING GLOBAL FLOOD RISK: THE AQUEDUCT GLOBAL FLOOD ANALYZER
8. 1.8. THE WAY FORWARD
9. REFERENCES
Part I: Flood Hazard Mapping and Modeling from Remote Sensing
1. 2 Rainfall Information for Global Flood Modeling
  1. 2.1. INTRODUCTION
  2. 2.2. ROLE OF RAINFALL IN LARGE‐SCALE FLOOD MODELING
  3. 2.3. GENERAL CONSIDERATIONS AND REQUIREMENTS
  4. 2.4. PRECIPITATION INFORMATION SOURCES
  5. 2.5. FUTURE DIRECTIONS
  6. 2.6. CONCLUSIONS
  7. ACKNOWLEDGMENTS
  8. REFERENCES
2. 3 Flood Risk Mapping From Orbital Remote Sensing
  1. 3.1. INTRODUCTION
  2. 3.2. MICROWAVE RADIOMETRY FOR MEASURING RIVER DISCHARGE
  3. 3.3. PRODUCTION OF SIGNAL/DISCHARGE RATING CURVES
  4. 3.4. ASSESSING RIVER WATCH ACCURACY
  5. 3.5. SATELLITE GAUGING SITE SELECTION
  6. 3.6. FLOOD MAPPING FROM OPTICAL SATELLITES
  7. 3.7. REMOTE SENSING‐BASED FLOOD HAZARD QUANTIFICATION
  8. 3.8. CONCLUSION
  9. REFERENCES
3. 4 Flood Mapping Using Synthetic Aperture Radar Sensors From Local to Global Scales
  1. 4.1. INTRODUCTION
  2. 4.2. PRINCIPLES OF SAR: IMPLICATIONS FOR FLOOD MAPPING
  3. 4.3. COMMON SAR‐BASED FLOOD MAPPING METHODS
  4. 4.4. IMAGE INTERPRETATION: CHALLENGES AND SOLUTIONS
  5. 4.5. REPRESENTATION OF UNCERTAINTIES
  6. 4.6. CASE STUDIES
  7. 4.7. SUMMARY AND PERSPECTIVES
  8. ACKNOWLEDGMENTS
  9. REFERENCES
4. 5 Flood Hazard Mapping in Data‐Scarce Areas
  1. 5.1. INTRODUCTION
  2. 5.2. STUDY SITE
  3. 5.3. METHODOLOGY
  4. 5.4. RESULTS
  5. 5.5. DISCUSSION
  6. 5.6. CONCLUSIONS
  7. ACKNOWLEDGMENTS
  8. REFERENCES
5. 6 Global Flood Monitoring Using Satellite Precipitation and Hydrological Modeling
  1. 6.1. INTRODUCTION
  2. 6.2. GLOBAL FLOOD MONITORING SYSTEM (GFMS)
  3. 6.3. EVALUATION OF THE DRIVE MODEL AND THE GFMS
  4. 6.4. A TYPICAL EXAMPLE OF REAL‐TIME FLOOD DETECTION AND INUNDATION MAPPING BY THE GFM
  5. 6.5. ONGOING AND FUTURE WORK
  6. 6.6. SUMMARY AND CONCLUSIONS
  7. REFERENCES
6. 7 Flood Hazard Mapping for the Humanitarian Sector
  1. 7.1. INTRODUCTION
  2. 7.2. BACKGROUND INTERFERENCE ISSUES
  3. 7.3. THE PROCESS
  4. 7.4. CONCLUSION
  5. REFERENCES
Part II: Flood Hazard Modeling and Forecasting
1. 8 Modeling and Mapping of Global Flood Hazard Layers
  1. 8.1. INTRODUCTION
  2. 8.2. FLOOD MODELING DEVELOPMENTS AT THE GLOBAL SCALE
  3. 8.3. CURRENT GLOBAL MODELS
  4. 8.4. APPLICATIONS
  5. 8.5. OUTLOOK
  6. 8.6. CONCLUSIONS
  7. REFERENCES
2. 9 Estimating Change in Flooding for the 21st Century Under a Conservative RCP Forcing
  1. 9.1. INTRODUCTION
  2. 9.2. METHOD
  3. 9.3. RESULTS
  4. 9.4. DISCUSSION
  5. 9.5. CONCLUSIONS
  6. ACKNOWLEDGMENTS
  7. REFERENCES
3. 10 From Precipitation to Damage
  1. 10.1. INTRODUCTION
  2. 10.2. MODEL CHAIN
  3. 10.3. APPLICATION OF RFM TO THE ELBE RIVER BASIN AND MULDE CATCHMENT
  4. 10.4. POSSIBILITIES FOR EXTENDING RFM TO THE CONTINENTAL SCALE
  5. 10.5. CONCLUSIONS
  6. ACKNOWLEDGMENTS
  7. REFERENCES
4. 11 Global Flood Risk Modeling and Projections of Climate Change Impacts
  1. 11.1. INTRODUCTION
  2. 11.2. GLOBAL RIVER HYDRODYNAMIC MODEL FOR FLOOD RISK ASSESSMENT
  3. 11.3. GLOBAL FLOOD RISK ASSESSMENT UNDER CLIMATE CHANGE
  4. 11.4. SUMMARY AND DISCUSSIONS
  5. ACKNOWLEDGMENTS
  6. REFERENCES
5. 12 Global Flood Forecasting for Averting Disasters Worldwide
  1. 12.1. INTRODUCTION
  2. 12.2. ORIGINS OF GLOBAL FLOOD FORECASTING
  3. 12.3. CURRENT STATUS OF LARGE‐SCALE FLOOD FORECASTING
  4. 12.4. RESEARCH ON IMPROVING QUALITY AND USABILITY OF HYDROLOGICAL FORECASTS
  5. 12.5. USING GLOBAL FLOOD FORECASTS FOR HUMANITARIAN PREPAREDNESS AND EARLY ACTION
  6. 12.6. FUTURE CHALLENGES AND OPPORTUNITIES OF GLOBAL FLOOD FORECASTING
  7. 12.7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
6. 13 Data Assimilation and River Hydrodynamic Modeling Over Large Scales
  1. 13.1. INTRODUCTION
  2. 13.2. DATA ASSIMILATION
  3. 13.3. EXAMPLES OF ESTIMATING FLOOD VARIABLES
  4. 13.4. CHALLENGES AND OPPORTUNITIES
  5. ACKNOWLEDGMENTS
  6. REFERENCES
7. 14 Global Flood Hazard Mapping, Modeling, and Forecasting
  1. 14.1. CURRENT STATUS
  2. 14.2. CHALLENGES AND PERSPECTIVES
  3. 14.3. SUMMARY AND OUTLOOK
  4. REFERENCES
Index
End User License Agreement

List of Tables

Chapter 02
1. Table 2.1 Comparison of the Main Classes of Rainfall Information
2. Table 2.2 Satellite Multisensor Precipitation Data Sets
3. Table 2.3 Operational Weather Forecast Systems
4. Table 2.4 Atmospheric Reanalysis Data Sets
Chapter 03
1. Table 3.1 Summary of Microwave Discharge Measurement (River Watch) Site Characteristics and Accuracy for Sites Along the Chindwin (108 and 23) and Ayeyarwady (26, 29, 30)
Chapter 04
1. Table 4.1 Summary of Spaceborne SAR Missions and Sensor Characteristics
Chapter 06
1. Table 6.1 Metrics for Model Performance in Streamflow Simulation, at Daily and Monthly Time Intervals for Continuous Years, Against 1121 GRDC River Gauges Across the Globe (‐50°S to 50°N)
2. Table 6.2 Metrics for Model Performance in Streamflow Simulation, at Daily and Monthly Time Intervals for Continuous Years, Against 1121 GRDC River Gauges Across the Globe (‐50°S to 50°N) for Summer Seasons
3. Table 6.3 Metrics for Model Performance in Streamflow Simulation, at Daily and Monthly Time Intervals for Continuous Years, Against 1121 GRDC River Gauges Across the Globe (‐50°S to 50°N) for Winter Seasons
Chapter 09
1. Table 9.1 Percentage Change in Estimated Flood Frequency Per Recurrence Interval Per Continent, Comparing 2000s–2100s
2. Table 9.2 Percentage Change in River Reaches Affected by Flooding Per Flood Magnitude Per Continent, Comparing 2000s–2100s
Chapter 12
1. Table 12.1 Summary of the Information Transferred Through the Global Flood Partnership Triggered by the January 2015 Malawi Floods

List of Illustrations

Chapter 01
1. Figure 1.1 National‐level flood risk profile for Turkey, produced for the World Bank using the GLOFRIS model [World Bank, 2015].
2. Figure 1.2 GLOFRIS simulations of the number of people affected per state (expressed as a percentage of the total population per state) for floods of different return periods.
3. Figure 1.3 Screenshot of an early beta version of ThinkHazard!, a tool developed by the World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR). The tool utilizes global flood hazard models and gives the development professional easy access to hazard information as well as practical recommendations for risk reduction.
4. Figure 1.4 Pan‐African flood hazard map for the reference return period of 100 years, as displayed on the GloFAS website (http://globalfloods.jrc.ec.europa.eu/).
5. Figure 1.5 Screenshot of the Aqueduct Global Flood Analyzer, showing a flood risk assessment at the country level for India. The analyzers displays a flood hazard map, as well as estimates of flood risk under current conditions and under future conditions due to climate change and socioeconomic development.
Chapter 02
1. Figure 2.1 Comparison of errors in peak discharge estimation resulting from hydrologic simulations using the Iowa Flood Center (IFC) and the research version of the TRMM TMPA satellite‐based data set (3‐hourly, 0.25°resolution) relative to the NCEP Stage IV reference rainfall data set (hourly, 4‐km resolution) for the 24 May–2 June 2013 flood period for the Iowa River (large watershed) and Turkey River (small watershed). Upper panels show boxplots of peak discharge percent errors, categorized by upstream area. Bottom panels show maps of the peak discharge percent error across the river networks. Simulations are performed using the IFC hydrologic model [Small et al., 2013].
2. Figure 2.2 Precipitation information from three data sources over southeastern Africa for the major flood period of January 2015: (a) GPCC “first guess” rain gauge data; (b) MERRA‐2 atmospheric reanalysis; (c) final version of the IMERG satellite multisensor precipitation product.
3. Figure 2.3 Grid cells containing at least one rain gauge in (a) the 1° daily‐scale GPCC “first guess” and (b) monthly monitoring gridded rain gauge data sets for January 2015. Red indicates that the grid cell contains one rain gauge; blue indicates that the grid cell contains two or more gauges.
4. Figure 2.4 (a) Daily‐scale TMPA rainfall vs. the ensemble median rainfall from the gridded gauge‐based Newman et al. [2015] data set for 100 moderate to heavy rain days in the vicinity of Charlotte, North Carolina, for the 1998–2012 period. (b) Same as (a) but showing the middle 80% of the ensemble spread in addition to the median value. Blue error bars show where the middle 80% of the ensemble spread encompasses the TMPA value; red shows where the TMPA value lies outside the middle 80% of the ensemble spread. Sixty‐five out of 100 TMPA daily values fall within the middle 80% of the ensemble spread.
5. Figure 2.5 GPM LEO constellation satellites that carry PMW radiometers. Note that the TRMM satellite is no longer operational.
6. Figure 2.6 (a)–(c): Comparison of monthly rainfall totals from GPCC 1° First Guess, IMERG, and MERRA‐2 reanalysis for January 2015. (d) and (e): Differences from GPCC.
Chapter 03
1. Figure 3.1 Temporal coverage, 1998 to present, of passive microwave sensors built and operated by NASA and by JAXA (Japanese Space Agency). Each satellite provides daily or near‐daily imaging of the globe.
2. Figure 3.2 Location of Satellite Gauging Site DFO # 30 over Ayeyarwady River and its floodplain in Myanmar. The site is a single pixel selected from the JRC grid; pixel is 10 km in size and produces the daily M value. The 95th percentile of the highest (driest and warmest) values from a 9 x 9 pixel array in the surrounding area produces the background calibration C value.
3. Figure 3.3 Scatter plot comparing WBM‐modeled daily discharge over a 5‐year period (January–December monthly daily maximum, minimum, and mean discharges) to the C/M ratio for River Watch site 30. The relationship is empirical and a better curve could be fit to these data, but a straight line is a useful first‐approximation rating curve.
4. Figure 3.4 Same data as in Figure 3.3, but arranged as time series of (a) maximum, (b) mean, and (c) minimum discharge values. The red line shows the model results and the blue line is the remote sensing as transformed by the rating equation in Figure 3.3.
5. Figure 3.5 Daily (4‐day forward running mean) discharge values for satellite gauging site 30 on the Ayeyarwady River. The low flow threshold (green line) is the 20% percentile discharge for each day; the flood thresholds use recurrence intervals computed using the Log Pearson III distribution and the annual maximum daily values. Major flooding in 2015 approached the calculated 5‐year recurrence interval; the flood of record, in 2004, was produced by a very damaging tropical storm [Brakenridge et al., 2016b]; flooding here exceeded the 25‐year threshold.
6. Figure 3.6 Model‐ and ground station‐based rating curves for Trinity River, Texas, River Watch site 446 in the Dartmouth Flood Observatory/University of Colorado array. The WBM model was used to produce the black line rating curve, which is fit to widely scattered data. A colocated USGS gauging station was used to produce the red line rating curve, with much better correlation to the remote sensing. Comparison of the two curves indicates a WBM model positive discharge bias increasing with higher discharges. Also, at this location, the WBM model may perform relatively poorly because it does not incorporate upstream river control structures.
7. Figure 3.7 MODIS remote‐sensing‐produced map of major 2015 flooding in Myanmar (light red), and also the typical annual high water (2014, light blue). Recurrence intervals at the River Watch measurement sites for the 2014 “flood” are approximately 1.5 years. Dark blue is a permanent water mapped by the Shuttle Water Boundary Data (https://lta.cr.usgs.gov/srtm_water_body_dataset) from February 2000 and represents typical winter surface water extent.
8. Figure 3.8 Recurrence interval and peak discharge estimates for River Watch site 30 along the lower Ayeyarwady River. The microwave measurement site is defined by the white 10 km square. Inundation for (a) a normal winter flow of 6253 m³/sec on 11–22 February 2000; (b) observed via MODIS at 250 m spatial resolution, flooded area for a typical summer monsoonal “flood,” r = 1.5 years (27,138 m³/sec, observed 2013); and (c) observed via MODIS again, flooded area for major flooding in 2004, r = 24 years (50,579 m³/sec. Note that during this flood, a major distributary to the east (the area is at the head of the Ayeyarwady delta) is also flooded.
9. Figure 3.9 Recurrence interval and peak discharge estimates for River Watch site 26 along the upper Ayeyarwady River. Inundation for (a) a normal winter flow of ~200 m³/sec in February 2000; (b) a typical monsoon season flood, recurrence interval of 1.1 years, ~9000 m³/sec, in 2002; and (c) inundation during a rare flood, recurrence interval of 21 years, 18,200 m³/sec, in 2013.
Chapter 04
1. Figure 4.1 Distribution of hazard types for Charter activations between 2000 and 2010. From EM‐DAT: The OFDA/CRED International Disaster Database (www.emdat.be – Université catholique de Louvain, Brussels, Belgium).
2. Figure 4.2 Summary of satellite‐based SAR missions that are applicable for flood studies, with corresponding wavelength bands and frequencies illustrated.
3. Figure 4.3 Different scattering mechanisms displayed by radar interactions with water and land surfaces. Based on Martinis et al. [2015].
4. Figure 4.4 The image shows example subsets of problem areas in SAR‐based flood mapping taken from a TerraSAR‐X (HH, 3‐m Stripmap) scene acquired on 25 July 2007 covering the Severn River flood event. The urban area shown here lies to the west of Tewkesbury, UK.
5. Figure 4.5 Swaths of the CSK (different colors) and S1 (turquoise) images of the area hit by the November 2014 Po River basin event. Two predictions of the FEWS‐Po model available through the DEWETRA platform are also shown. The horizontal red line corresponds to the critical reference flood, while yellow and orange lines indicate intermediate levels of warning. Western part of the Po River basin: run of 11 November 2014, h: 12:30 UTC. Eastern part of the Po River basin: run of 17 November 2014, h: 00:30 UTC. From Boni et al. [2016].
6. Figure 4.6 Performance assessment of the NRT classification algorithm for (a) CSK‐derived flood map and (b) the location of zones A (dark blue) and B (light blue) in the reference map. (c) The CSK intensity image (dB) is displayed. In (d), a false color composite of Landsat 8 for the same area is included. From Boni et al. [2016].
7. Figure 4.7 HSBA‐Flood block diagram. RG: Region growing. CD: Change detection. After Chini et al. [2017].
8. Figure 4.8 Contingency maps between (a) Envisat‐WS or (b) TerrasSAR‐X flood map and validation data set. After Chini et al. [2017].
Chapter 05
1. Figure 5.1 The Blue Nile basin and study area.
2. Figure 5.2 Flood experience of 1‐in‐20‐year event in Africa.
3. Figure 5.3 Comparison of simulated flood extent using the 1‐in‐20‐year flood derived from in situ data and using (a) REC or (b) PMC.
4. Figure 5.4 The diagram shows the estimation of the 1‐in‐20‐year design flood estimation depending on the number of observations (ranging from 3 to 26). The black solid line is the value estimated using 26 annual maximum discharges (uncertainty due to flood data and other sources are not shown here); the grey area is the uncertain estimation bounded by tenth and ninetieth percentiles; grey dash line is the median; black dash line is the design flood estimation using PMC, and black dotted line is the design flood estimated using REC.
Chapter 06
1. Figure 6.1 Outline of the GFMS providing flood intensity estimates and forecasts, time histories, and high‐resolution (1 km) inundation maps. The data flow is indicated by the arrows from top to the bottom. An example of 3‐hourly time step precipitation is given in (a), while the core of the GFMS system is the DRIVE model shown in (b). Examples of output of the GFMS are shown in (c)–(e) including the flood detection and intensity (the routed runoff in depth above threshold derived based on 15‐year retrospective simulation), experimental global flood inundation mapping every 3 hours at 1 km resolution, and time series of model outputs (streamflow and flood detection).
2. Figure 6.2 DRIVE model is coupled VIC model (left) with DRTR (right) routing model. The DRTR routing model concept on river basin drainage system at (a) grid and (b)–(d) subgrid scales using a real river basin (Mbemkuru River basin, southeast of Tanzania) as example. The light blue lines in (a) are the baseline high resolution (1 km) river network from HydroSHEDS and the red lines are the DRT‐derived coarse‐resolution rivers (1/8th degree in this case). More details for VIC model can be found in Liang et al. [1994].
3. Figure 6.3 Example of the DRIVE model major outputs from the real‐time GFMS with screenshots from http://flood.umd.edu. The examples show the model global outputs of (a) routed runoff, (b) streamflow, and (c) flood detection and intensity (water depth [mm] above flood threshold) at a 3‐hour time interval (15Z01Jul2013). (d) An example of global TMPA 3B42V7 real‐time rainfall input data at the same time interval is shown. (c1‐6) The example shows the spatial‐temporal evolution (at daily interval) of the flood event that happened in North India during 15 June 2013 to 20 June 2013.
4. Figure 6.4 Snapshots from the real‐time GFMS (online: http://flood.umd.edu) for two major flood waves, covering April to early June 2013, in subbasin rivers upstream of the Mississippi River, including (a and b) the flood detection and intensity (water depth above flood threshold), (c and d) previous 7‐day accumulated precipitation according to TMPA V7RT, and (e and f) streamflow. All data are at 1/8th (~12 km) resolution.
5. Figure 6.5 (a) The DRIVE‐RT simulated streamflow against observed data from 29 USGS gauges on the rivers of the upper Mississippi River basin for a 2‐year retrospective period (12 June 2011 to 12 June 2013). All USGS gauges are shown in filled circles, while their colors are turned into green when the model estimated positive daily NSCs at the corresponding locations. (b)–(e) show the observed and simulated daily hydrographs for four of the gauges, with locations indicated in (a), during the spring and early summer flooding period (1 April to 9 June 2013).
6. Figure 6.6 Definition of spatial window for matching between simulated and reported flood events. The blue lines are rivers defined by DRT according to DEM information, while the area in yellow indicates the spatial coverage for a flood event derived by the flood definition algorthm.
7. Figure 6.7 Example of well‐reported areas (shaded yellow) and their corresponding FAR metrics (according to DRIVE‐RT for all floods with duration greater than 1 day) in the parts of Asia that tend to have more floods.
8. Figure 6.8 The flood detection metrics (a) POD, (b) FAR, and (c) CSI, across 38 well‐reported areas for DRIVE‐RP and DRIVE‐RT results for all floods with duration greater than 3 days, against DFO flood inventory data during 2001 to 2011. The numbers of dams upstream of each well‐reported area are listed along the x‐axis.
9. Figure 6.9 DRIVE‐RP model performance (monthly NSC) in reproducing monthly streamflow during 2001–2011, when driven by TMPA RP research precipitation data, at 1121 GRDC streamflow gauges across the globe. All GRDC gauges are shown as filled circles, while at each gauge, if the model performance is of a positive value for monthly NSC, the gauge color turns into green or purple in accordance to the value of NSC.
10. Figure 6.10 The percentage of gauges in each latitude band (defined in section 6.4.4.2) for which the DRIVE model showed positive daily NSCs using TMPA‐RP and TMPA‐RT precipitation input. The x‐axis values are the central latitude for each band.
11. Figure 6.11 The daily NSC (a) and (b), and MARE (c) and (d) metrics for the region of South America from (a, c) DRIVE‐RP and (b, d) DRIVE‐RT model results.
12. Figure 6.12 Malaysia flooding in December 2014.
13. Figure 6.13 High resolution (1 km) inundation estimates over affected areas in Malaysia.
14. Figure 6.14 The typical distribution map of the GFMS users for a 3‐day time window (22–24 March 2016 for this snapshot).
Chapter 07
1. Figure 7.1 Three and‐a‐half decades of global flood effects, CRED‐EMDAT; Data sources include UN agencies, USAID Office of Foreign Disaster Assistance (OFDA), governments, International Federation of Red Cross and Red Crescent Societies, nongovernmental organizations, insurance companies, research institutes, and press agencies.
2. Figure 7.2 Sample map from the preparedness phase for Super Typhoon Hagupit (Philippines, December 2014) using the GFS precipitation forecast and the ECMWF strike probability ensemble.
3. Figure 7.3 Risk zones identified by the ECMWF ensemble forecast for Tropical Storm Mahasen (Myanmar, May 2013).
4. Figure 7.4 Forecasts from ECMWF, NCEP GFS, and COLA were used on a preparedness map for tropical cyclone Giovanna (Madagascar, February 2012). Dark purple highlights on the map indicate flood inundation limits from the Dartmouth Flood Observatory (DFO) historical archive.
5. Figure 7.5 Pakistan floods from September 2011 with imagery overlay.
6. Figure 7.6 January 2013 floods in Mozambique; combination of passive (MODIS) and actively tasked analysis.
7. Figure 7.7 NASA MODIS analysis (purple) for flood‐affected areas in Nigeria July–November 2012.
8. Figure 7.8 Historical flood inundation limits (in pink) from DFO archives 1985–present. While not exactly a flood‐hazard map, the highlighted areas provided a rough preliminary risk picture, which helped direct some postdisaster damage assessment resources during Typhoon Parma (Philippines, October 2009).
9. Figure 7.9 Historical flood inundation limits overlaid on affected areas map of December 2014 floods in Sri Lanka.
10. Figure 7.10 Circles indicate heavily affected areas during the May 2014 Balkans floods. Many of these sites fell outside of the EU JRC modeled areas for flood hazard (red highlight).The JRC model output is a 1:100‐year return map based on uncertain boundary conditions and low accuracy topography [Alfieri et al., 2013]. Although the technical limitations are very clear to the scientists, the humanitarian community tends to think of a flood hazard model as a defacto indicator of all areas at risk. Ground‐level expectations from the high level science should be commensurate with its operational usefulness.
Chapter 08
1. Figure 8.1 Number of disasters, estimated people impacted and estimated damage of disasters that occurred in 2014 (statistics from Guha‐Sapir et al. [2015]). Figures are distributed by disaster type.
2. Figure 8.2 Comparison of (a) raw SRTM DEM with LiDAR and (b) corrected SRTM with LiDAR for (c) western Belize. Cross sections A, B, and C transect the Belize River valley and compare the LiDAR elevation profile (black) with the uncorrected SRTM profile (green) and corrected SRTM profile (purple). From Sampson et al. [2016].
3. Figure 8.3 A description of observed data used in a global flood frequency analysis, shows the Koppen‐Geiger main climate classifications used to partition stations, along with the locations of each of the GRDC stations. (a) The proportion of discharge stations in each climate zone; (b) a histogram of upstream catchment areas. From Smith et al. [2015].
4. Figure 8.4 (a) Aggregated flood results for six models for a 1‐in‐100‐year return period fluvial flood hazard for the African Continent. Color scale indicates how many models predict flooding. (b) Detail for the lower Nile. (c) Detail for the lower Niger, showing areas of strong agreement (narrow confined floodplains at the confluence of Benue and Niger rivers) and areas of disagreement in the Niger coastal delta. From Trigg et al. [2016].
5. Figure 8.5 (a) Flooded area as percentage of continental area for all models and return periods. (b) Percentage of catchment area flooded mapped for all models for a 1‐in‐100‐year return period hazard showing significant spatial differences. From Trigg et al. [2016].
6. Figure 8.6 Examples of defended and undefended hazard layers produced for the entire upper Mississippi basin, using a global model approach. The data have been used to explore the impacts of levees on floodplain connectivity.
Chapter 09
1. Figure 9.1 Yukon River near its delta in Arctic Alaska. (a) Recurrence intervals with associated simulated peak water discharge for the time period 1975 to 2004, normalized by observations of the same time period using a Log‐Pearson Type III distribution for the Yukon River at Pilot Station (61°56'04″, 162°52'50″); (b) the frequency of each recurrence interval for the 2000s (black) and 2100s (grey) at that same location; (c) near true color LandSat imagery with approximately bankfull conditions represented as black for the Yukon River during June 2003; and (d) near true color LandSat imagery flooding (black) with a recurrence interval between 25 and 50 years at that same location during May 2009. White toward blue colors in (c) and (d) indicate cloud cover.
2. Figure 9.2 The 2‐year recurrence interval as established from a Log‐Pearson III distribution based on 30‐year daily‐simulated global water discharge defines the “bankfull discharge.” Bankfull discharge less than 30 m3/sec (grey areas) are not taken into consideration.
3. Figure 9.3 Flooded river reaches for the 30‐year period anchored in the 2000s and 2100s, respectively, wherein flooding defined as a minimum frequency to classify as flooding is one time exceedance of the 10‐year recurrence discharge. Light orange indicates areas that are as such flooded both during the 2000s and 2100s. Blue represents as such flooded areas only during the 2000s, and red indicates areas only flooded as such during the 2100s.
4. Figure 9.4 Spatial representation of percentage change flood frequency with the 2000s as reference, between the 2000s and 2100s for all floods that have (a) a larger than the bankfull (2‐year) recurrence interval, and (b) larger than 100‐year recurrence interval.
Chapter 10
1. Figure 10.1 Structure of RFM, Regional Flood Model.
2. Figure 10.2 (a) The Elbe River Basin (only German territory) and (b) the Mulde catchment.
3. Figure 10.3 Comparison of inundated areas for the 2002 flood. Red: derived from remote sensing; blue: derived from RFM simulation; green: agreement of remote sensing derived and RFM derived areas. From Falter et al. [2016].
4. Figure 10.4 (a) Derived flood risk approach based on continuous simulation consisting of a cascade of models from precipitation to flood damage. Flood risk is estimated by analyzing the simulated flood damage. The approach is an extension of (b) the derived flood frequency approach based on continuous simulation.
5. Figure 10.5 Distribution of damage for one of the Mulde subcatchments. During the 10,000‐year simulation, 774 damage events occur in this subcatchment.
6. Figure 10.6 Two events with the same flood peak for subbasin 995 but different (a) hydrographs and (b) inundation patterns. The colors of the inundation areas show the difference in water depth between the two events.
7. Figure 10.7 Distribution of (a) return periods and (b) losses for three floods in the Mulde catchment. Each flood results in the same overall damage of approximately 68 million Euro.
Chapter 11
1. Figure 11.1 Procedures to extract subgrid topography parameters for CaMa‐Flood. (a) Baseline elevation data: SRTM3 DEM elevation (m); (b) baseline hydrography data: HydroSHEDS drainage area (log10 km²); (c) unit‐catchment discretization. For the unit‐catchment marked with the orange boundary, the blue vector represents the channel length, the ground elevation is given as the elevation of the point with a red arrow. (d) Subgrid topography parameters: L, channel length; W, channel width; B, channel depth; Z, ground elevation; Ac, catchment area; and Df(Af), floodplain elevation profile. Sr and Sf represent river and floodplain water storage, respectively; Af represents flooded area, Dr and Df represent river and floodplain water depth, respectively, and Qr and Qf represent river and floodplain discharge to down stream, respectively. (e) Floodplain elevation profile function. The black line represents the original cumulative distribution function (CDF) curve of the elevations within a unit‐catchment; the green line represents the approximated floodplain elevation profile using linear interpolation. (f) River network map indicating the downstream unit‐catchment of each unit‐catchment.
2. Figure 11.2 Diagnostic downscaling of water depth. (a) Simulated water depth at 0.25‐degree resolution. (b) Downscaled water depth at 500‐m resolution.
3. Figure 11.3 Simulated river hydrodynamics in the Amazon main stem. (a) Daily river discharge at the Obidos gauging station (55.5W, 1.9S). (b) Water surface elevation along the main stem of the Amazon River, averaged for May–June 1996. The blue, red, and green lines represent the Full, Kinematic, and No Floodplain experiments, respectively. The gray line in (a) represents Global Runoff Data Center (GRDC) in situ gauged discharge. The black line in (b) represents the river bed elevation.
4. Figure 11.4 Simulated and observed flooded area in the Amazon River basin. Top: high‐water season (May–June 1996). Bottom: low‐water season (Sepember–October 1995). (a) Water depth from the Full experiment; (b) water depth from the Kinematic experiment; (c) L‐band radar observations [Hess et al., 2003]. Blue: open water; green: inundated floodplains; background grayscale colors represent elevations.
5. Figure 11.5 Simulated global flood projection. (a) Return period (year) of the 1/100 flood in 20C between 2071 and 2100, that is, the relative frequency of the historical 1/100 flood in the future period. (b) Number of global climate models (GCMs) showing the same direction (decrease or increase) in flood frequency change. RCP8.5 is shown. Sea surface and dry regions (average annual river discharge for 1979–2010 calculated by a retrospective off‐line simulation [Kim et al., 2011] forced by observation‐based climate data smaller than 0.01 mm/day) are excluded.
6. Figure 11.6 Procedures to calculate annual maximum inundation area for 1971–2100.
7. Figure 11.7 Global flood exposure (in millions) for a 1/100 flood in 20C or greater. (a) Ensemble means of the historical simulations (thick black line) and the future simulations for each climate scenario (colored thick lines). The shading denotes the standard deviation among Atmosphere‐Ocean General Circulation Models (AOGCMs). (b) The maximum and minimum range (whiskers), mean (horizontal thick lines within each bar), standard deviation (height of box), and individual values among AOGCMs (colored markers within each bar) averaged over 2071–2100. (c) Relationship between global flood exposure and global surface temperature over land. The thick black line and colored lines indicate the ensemble mean of all GCMs and the results of each GCM, respectively.
Chapter 12
1. Figure 12.1 GloFAS forecast of 5 January 2015 showing the probability of exceeding the 20‐year return period (shading) and details of a hydrograph forecast for a location in southern Malawi (the yellow, red, and purple colors refer to the 2‐, 5‐, and 20‐year return periods, respectively).
2. Figure 12.2 GloFAS forecast over Myanmar on 1 September 2015 showing (a) the accumulated precipitation forecast over the next 10 days (ensemble median) and (b) probability of the streamflow forecast exceeding the 20‐year (purple) and 5‐year (red) return periods. The numbers indicate the corresponding probabilities of threshold exceedance.
3. Figure 12.3 Comparison of (a) observed precipitation for an intense rainfall event in northern Italy on 13 September 2015 against (b) the median ECMWF NWP ensemble prediction for the same period forecast 24 hours beforehand.
4. Figure 12.4 Performance of the Global Flood Awareness System in the main world rivers for the period 2009–2010. Shades of blue indicate the maximum lead time (days) of skillful ensemble streamflow predictions in early flood detection (i.e., ROC area > 0.7).
5. Figure 12.5 Mean daily (1979–2010) precipitation in (a) GPCP and differences with (b) CRU, (c) GPCC, and (d) ERA.
6. Figure 12.6 Return periods computed from the simulations using the (a) CRU, (b) GPCC, and (c) ERA precipitation forcing based on the streamflow magnitude corresponding to a 20‐year return level of the GPCP simulations.
7. Figure 12.7 Ensemble Streamflow Prediction (ESP) and seasonal forecasts setup.
8. Figure 12.8 Example of the reverse‐ESP and ESP behavior with time. The black vertical line represents lead time 0, the starting time of the forecasts. The colored circles on this black line are the respective initial discharges for both systems.
Chapter 13
1. Figure 13.1 Spatially averaged error decrease in simulated water surface elevation after data assimilation, for Ohio River case study.
2. Figure 13.2 Spatially averaged error decrease in simulated river discharge after data assimilation, for Ohio River case study.
3. Figure 13.3 (a) Time series of inundated area for a specific flood event for the truth and open‐loop simulations. (b) Forecast error reduction time series for different observation times (forecast initialization): 11, 5, 1, and 0 days prior to the flood peak.
4. Figure 13.4 Averaged error decrease in water surface elevation for each river reach of assimilated observations.
Chapter 14
1. Figure 14.1 World map showing selected supersites and their level of completion (in order to be labeled a “supersite”) in terms of Earth Observation (EO) flood data collection at the Dartmouth Flood Observatory (DFO), University of Colorado Boulder. Only major world rivers are shown. Note that other supersites can be added as needed or requested by the community.

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Geophysical Monograph 233

Global Flood Hazard

Applications in Modeling, Mapping, and Forecasting