A web application for hydrogeomorphic flood hazard mapping

Abstract A detailed delineation of flood-prone areas over large regions represents a challenge that cannot be easily solved with today's resources. The main limitations lie in algorithms and hardware, but also costs, scarcity and sparsity of data and our incomplete knowledge of how inundation events occur in different river floodplains. We showcase the implementation of a data-driven web application for regional analyses and detailed (i.e., tens of meters) mapping of floodplains, based on (a) the synthesis of hydrogeomorphic features into a morphological descriptor and (b) its classification to delineate flood-prone areas. We analysed the skill of the descriptor and the performance of the mapping method for European rivers. The web application can be effectively used for delineating flood-prone areas, reproducing the reference flood maps with a classification skill of 88.59% for the 270 major river basins analysed across Europe and 84.23% for the 64 sub-catchments of the Po River.

[1]  Hartwig Deneke,et al.  A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI , 2013 .

[2]  Assefa M. Melesse,et al.  Development of an automated GIS tool for reproducing the HAND terrain model , 2018, Environ. Model. Softw..

[3]  R. Vogel,et al.  Multivariate probabilistic regional envelopes of extreme floods , 2007 .

[4]  P. Arp,et al.  Evaluating digital terrain indices for soil wetness mapping – a Swedish case study , 2014 .

[5]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[6]  José I. Barredo,et al.  Major flood disasters in Europe: 1950–2005 , 2007 .

[7]  P. Bates,et al.  Usefulness and limitations of global flood risk models , 2015 .

[8]  Keith Beven,et al.  Probabilistic flood risk mapping including spatial dependence , 2013 .

[9]  H. Pourghasemi,et al.  Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models , 2017, Water Resources Management.

[10]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[11]  Christos Makropoulos,et al.  A web based DSS for the management of floods and wildfires (FLIRE) in urban and periurban areas , 2016, Environ. Model. Softw..

[12]  M. Brugnach,et al.  Why are decisions in flood disaster management so poorly supported by information from flood models? , 2014, Environ. Model. Softw..

[13]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[14]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[15]  Olgay Sen,et al.  Determination of flood risk: A case study in the rainiest city of Turkey , 2017, Environ. Model. Softw..

[16]  Salvatore Manfreda,et al.  Detection of Flood-Prone Areas Using Digital Elevation Models , 2011 .

[17]  Efi Foufoula-Georgiou,et al.  Floodplain morphometry extraction from a high-resolution digital elevation model: a simple algorithm for regional analysis studies , 2006, IEEE Geoscience and Remote Sensing Letters.

[18]  Salvatore Manfreda,et al.  Investigation on the use of geomorphic approaches for the delineation of flood prone areas , 2014 .

[19]  Nathan R. Swain,et al.  A review of open source software solutions for developing water resources web applications , 2015, Environ. Model. Softw..

[20]  L. Feyen,et al.  Global warming increases the frequency of river floods in Europe , 2015 .

[21]  Roland L. Redmond,et al.  An Automated Technique for Delineating and Characterizing Valley-Bottom Settings , 2000 .

[22]  Ranz,et al.  World Map of the Köppen-Geiger climate classification updated — Source link , 2006 .

[23]  E. Foufoula‐Georgiou,et al.  Fluvial processes and streamflow variability: Interplay in the scale‐frequency continuum and implications for scaling , 2005 .

[24]  D. Tarboton,et al.  River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage , 2018, JAWRA Journal of the American Water Resources Association.

[25]  J. Swets.  The Relative Operating Characteristic in Psychology , 1973, Science.

[26]  E. Vivoni,et al.  Investigating a floodplain scaling relation using a hydrogeomorphic delineation method , 2006 .

[27]  Marcello Sanguineti,et al.  Classifiers for the detection of flood-prone areas using remote sensed elevation data , 2012 .

[28]  Marcos Rodrigo Momo,et al.  HAND contour: a new proxy predictor of inundation extent , 2016 .

[29]  F. Pan,et al.  An algorithm for treating flat areas and depressions in digital elevation models using linear interpolation , 2012 .

[30]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[31]  Albert Y. Zomaya,et al.  Remote sensing big data computing: Challenges and opportunities , 2015, Future Gener. Comput. Syst..

[32]  V. T. Chow.  Handbook of applied hydrology , 2017 .

[33]  Witold F. Krajewski,et al.  Towards an integrated Flood Information System: Centralized data access, analysis, and visualization , 2013, Environ. Model. Softw..

[34]  Praveen Kumar,et al.  Hydrogeomorphological differentiation between floodplains and terraces , 2018 .

[35]  Mustafa Neamah Jebur,et al.  Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .

[36]  Florian Pappenberger,et al.  The Monetary Benefit of Early Flood Warnings in Europe , 2015 .

[37]  D. Stephenson.  Use of the “Odds Ratio” for Diagnosing Forecast Skill , 2000 .

[38]  J. Aerts,et al.  Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data. , 2015, The Science of the total environment.

[39]  D. Montgomery,et al.  Source areas, drainage density, and channel initiation , 1989 .

[40]  P. Bates,et al.  A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. , 2010 .

[41]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[42]  Agostino Manzato,et al.  A Note On the Maximum Peirce Skill Score , 2007 .

[43]  M. Fiorentino,et al.  DEM-Based Approaches for the Delineation of Flood-Prone Areas in an Ungauged Basin in Africa , 2016 .

[44]  Venkatesh Merwade,et al.  A DEM-based approach for large-scale floodplain mapping in ungauged watersheds , 2017 .

[45]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[46]  J. V. Soares,et al.  HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia , 2008 .

[47]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[48]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[49]  Mustafa Neamah Jebur,et al.  Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS , 2013 .

[50]  Stefan Hochrainer-Stigler,et al.  Increasing stress on disaster-risk finance due to large floods , 2014 .

[51]  Jutta Thielen,et al.  The european flood alert system EFAS - Part 2: statistical skill assessment of probabilistic and deterministic operational forecasts. , 2008 .

[52]  L. Feyen,et al.  Development and evaluation of a framework for global flood hazard mapping , 2016 .

[53]  R. M. Wallace,et al.  Terrain Analysis Using Digital Elevation Models , 2001 .

[54]  Salvatore Manfreda,et al.  Geomorphic classifiers for flood-prone areas delineation for data-scarce environments , 2017 .

[55]  Hilde Vernieuwe,et al.  ROC‐based calibration of flood inundation models , 2014 .

[56]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[57]  Salvatore Manfreda,et al.  Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models , 2015, Natural Hazards.

[58]  Paul D. Bates,et al.  How much physical complexity is needed to model flood inundation? , 2012 .

[59]  L. Alfieri,et al.  GloFAS – global ensemble streamflow forecasting and flood early warning , 2012 .

[60]  N. Bhowmik.  Hydraulic geometry of floodplains , 1984 .

[61]  Roberto Rudari,et al.  A procedure for drainage network identification from geomorphology and its application to the prediction of the hydrologic response , 2005 .

[62]  L. B. Leopold,et al.  The hydraulic geometry of stream channels and some physiographic implications , 1953 .

[63]  B. Kramer,et al.  Peirce, Youden, and Receiver Operating Characteristic Curves , 2007 .

[64]  Paul D. Bates,et al.  Downscaling coarse grid hydrodynamic model simulations over large domains , 2014 .

[65]  J. M. Van Der Knijff,et al.  LISFLOOD : a GIS-based distributed model for river basin scale water balance and flood simulation , 2008 .

[66]  H. Savenije,et al.  Hydrology and Earth System Sciences Hydrological Landscape Classification: Investigating the Performance of Hand Based Landscape Classifications in a Central European Meso-scale Catchment , 2022 .

[67]  J. Vogt,et al.  Development and demonstration of a structured hydrological feature coding system for Europe , 2010 .

[68]  Steven J. Burian,et al.  A new open source platform for lowering the barrier for environmental web app development , 2016, Environ. Model. Softw..

[69]  P. Bates,et al.  Advances in pan‐European flood hazard mapping , 2014 .

[70]  Guy Schumann,et al.  A simple global river bankfull width and depth database , 2013 .

[71]  Lan Zhao,et al.  SWATShare - A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models , 2016, Environ. Model. Softw..

[72]  P. Bates,et al.  A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas , 2012 .

[73]  H. Winsemius,et al.  Automated global water mapping based on wide-swath orbital synthetic-aperture radar , 2012 .