Development and evaluation of flood forecasting models for forecast-based financing using a novel model suitability matrix

Abstract Forecast-based financing is a financial mechanism that facilitates humanitarian actions prior to anticipated floods by triggering release of pre-allocated funds based on exceedance of flood forecast thresholds. This paper presents a novel model suitability matrix that embeds application-specific needs and contingencies at local level on a pilot project of forecast-based financing. The added value of this flexible framework is demonstrated on a set of hydrological and machine learning models. The model suitability matrix facilitates transparency and traceability of subjectivity in model evaluation. This paper advocates a stronger interface between model developers and end users for upscaling of forecast-based financing.

[1]  Herman Guillermo Dolder,et al.  A Method for Using Pre-Computed Scenarios of Physically-Based Spatially-Distributed Hydrologic Models in Flood Forecasting Systems , 2015 .

[2]  Jaap Schellekens,et al.  Recent developments in operational flood forecasting in England, Wales and Scotland , 2009 .

[3]  David G. Tarboton,et al.  An overview of current applications, challenges, and future trends in distributed process-based models in hydrology , 2016 .

[4]  Bernhard Lehner,et al.  Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems , 2013 .

[5]  N. Arnell,et al.  The impacts of climate change on river flood risk at the global scale , 2016, Climatic Change.

[6]  Stefan Brönnimann,et al.  Weather Extremes in an Ensemble of Historical Reanalyses , 2017 .

[7]  Peter Salamon,et al.  A software framework for construction of process-based stochastic spatio-temporal models and data assimilation , 2010, Environ. Model. Softw..

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

[9]  R. Muir-Wood,et al.  Flood risk and climate change: global and regional perspectives , 2014 .

[10]  Robert F. Adler,et al.  Seasonal Evolution and Variability Associated with the West African Monsoon System , 2004 .

[11]  Daran R. Rudnick,et al.  Spatial and Temporal Variation in Precipitation in Togo , 2017 .

[12]  F. Pappenberger,et al.  The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System , 2010 .

[13]  András Bárdossy,et al.  Radar‐based flood forecasting in small catchments, exemplified by the Goldersbach catchment, Germany , 2008 .

[14]  Kwok-wing Chau,et al.  Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.

[15]  Tim R. McVicar,et al.  Design and development of the Australian Water Resources Assessment system , 2012 .

[16]  Florian Pappenberger,et al.  Ensemble flood forecasting: a review. , 2009 .

[17]  Stefan Uhlenbrook,et al.  Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia–Sudan) , 2017 .

[18]  J. Minx,et al.  Climate Change 2014 : Synthesis Report , 2014 .

[19]  Ernest Amoussou,et al.  Analyse hydrométéorologique des crues dans le bassin-versant du Mono en Afrique de l'Ouest avec un modèle conceptuel pluie- débit , 2014 .

[20]  H. Elsenbeer,et al.  Distributed modeling of storm flow generation in an Amazonian rain forest catchment: Effects of model parameterization , 1999 .

[21]  Jaap Schellekens,et al.  Real-Time Geospatial Data Handling and Forecasting: Examples From Delft-FEWS Forecasting Platform/System , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Jean-Pascal van Ypersele de Strihou Climate Change 2014 - Synthesis Report , 2015 .

[23]  C. Michel,et al.  Un modèle pluie-débit journalier à trois paramètres , 1989 .

[24]  A. H. Weerts,et al.  Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River , 2019, Water Resources Research.

[25]  S. J. Connor,et al.  Validation of high‐resolution satellite rainfall products over complex terrain , 2008 .

[26]  Yuqiong Liu,et al.  The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations , 2010, Environ. Model. Softw..

[27]  Ernest Amoussou,et al.  Variabilité pluviométrique et dynamique hydro-sédimentaire du bassin versant du complexe fluvio-lagunaire Mono-Ahémé-Couffo (Afrique de l'ouest) , 2010 .

[28]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .

[29]  Isabel F. Trigo,et al.  A Thermodynamically Based Model for Actual Evapotranspiration of an Extensive Grass Field Close to FAO Reference, Suitable for Remote Sensing Application , 2016 .

[30]  Eric F. Wood,et al.  Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide , 2013 .

[31]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[32]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[33]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[34]  P. Coulibaly,et al.  Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .

[35]  Jaap Schellekens,et al.  The Delft-FEWS flow forecasting system , 2013, Environ. Model. Softw..

[36]  R. Muñoz‐Carpena,et al.  Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments , 2013 .

[37]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[38]  Bhogendra Mishra,et al.  Satellite rainfall for food security on the African continent: performance and accuracy of seven rainfall products between 2001 and 2016 , 2018 .

[39]  Yukiko Hirabayashi,et al.  Global-scale river flood vulnerability in the last 50 years , 2016, Scientific Reports.

[40]  Victor Ongoma,et al.  Rainfall Characteristics over Togo and their related Atmospheric circulation Anomalies , 2015 .

[41]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[42]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[43]  Damien Sulla-Menashe,et al.  A Global Land Cover Climatology Using MODIS Data , 2014 .

[44]  Jean-François Guégan,et al.  Climate Drives the Meningitis Epidemics Onset in West Africa , 2005, PLoS medicine.

[45]  Song Han,et al.  Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.

[46]  Wouter Dorigo,et al.  Flood risk under future climate in data sparse regions: Linking extreme value models and flood generating processes , 2014 .

[47]  James D. Brown,et al.  The Science of NOAA's Operational Hydrologic Ensemble Forecast Service , 2014 .

[48]  Avi Ostfeld,et al.  Data-driven modelling: some past experiences and new approaches , 2008 .

[49]  Martijn Gough Climate change , 2009, Canadian Medical Association Journal.

[50]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  B. Hurk,et al.  Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts , 2014 .

[52]  Göran Lindström,et al.  Development and test of the distributed HBV-96 hydrological model , 1997 .

[53]  Jaap Schellekens,et al.  MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data , 2016 .

[54]  Mark Mulligan,et al.  Practical use of SRTM data in the tropics : Comparisons with digital elevation models generated cartographic data , 2004 .

[55]  Jaap Schellekens,et al.  Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations , 2015 .

[56]  G. Mahé,et al.  Dynamique et modélisation des crues dans le bassin du Mono à Nangbéto (Togo/Bénin) , 2014 .