Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors

One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.

[1]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[2]  J. Thielen,et al.  The European Flood Alert System – Part 1: Concept and development , 2008 .

[3]  Marco Sandri,et al.  A Bias Correction Algorithm for the Gini Variable Importance Measure in Classification Trees , 2008 .

[4]  Yu Zhang,et al.  Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Cubango River Basin, Africa , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  G. Tutz,et al.  An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.

[6]  Robert M. Hirsch,et al.  U.S. stream flow measurement and data dissemination improve , 2004 .

[7]  F. Branger,et al.  Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: A Bayesian approach , 2014 .

[8]  Kellie J. Archer,et al.  Empirical characterization of random forest variable importance measures , 2008, Comput. Stat. Data Anal..

[9]  Mekonnen Gebremichael,et al.  Upstream satellite remote sensing for river discharge forecasting: Application to major rivers in South Asia , 2013 .

[10]  Alexander A. Chukhlantsev,et al.  Microwave radiometry of vegetation canopies , 2006 .

[11]  Carolin Strobl,et al.  The behaviour of random forest permutation-based variable importance measures under predictor correlation , 2010, BMC Bioinformatics.

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

[13]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[14]  Kerrie M. Tomkins,et al.  Uncertainty in streamflow rating curves: methods, controls and consequences , 2014 .

[15]  S. Yitzhaki,et al.  The Gini Methodology , 2013 .

[16]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[17]  Jonathan Cheung-Wai Chan,et al.  Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery , 2008 .

[18]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

[19]  Patrice M. Pelletier,et al.  Uncertainties in the single determination of river discharge: a literature review , 1988 .

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

[21]  Son V. Nghiem,et al.  Calibration of satellite measurements of river discharge using a global hydrology model , 2012 .

[22]  G. Brakenridge,et al.  Orbital microwave measurement of river discharge and ice status , 2007 .

[23]  C. J. van Westen,et al.  Remote sensing and GIS for natural hazards assessment and disaster risk management , 2012 .

[24]  Son V. Nghiem,et al.  Space‐based measurement of river runoff , 2005 .

[25]  Dai Yamazaki,et al.  Development of the Global Width Database for Large Rivers , 2014 .

[26]  R. Rosso A linear approach to the influence of discharge measurement error on flood estimates , 1985 .

[27]  Tom De Groeve,et al.  Flood monitoring and mapping using passive microwave remote sensing in Namibia , 2010 .

[28]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[29]  James D. Malley,et al.  Predictor correlation impacts machine learning algorithms: implications for genomic studies , 2009, Bioinform..

[30]  U. Grömping Dependence of Variable Importance in Random Forests on the Shape of the Regressor Space , 2009 .

[31]  Z. Kundzewicz Changes in Flood Risk in Europe , 2012 .

[32]  Faisal Hossain,et al.  Validation of a TRMM-based global Flood Detection System in Bangladesh , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[34]  De Groeve Tom,et al.  The Global Flood Detection System , 2007 .

[35]  Thomas Lengauer,et al.  Classification with correlated features: unreliability of feature ranking and solutions , 2011, Bioinform..

[36]  Daniel Neagu,et al.  Interpreting random forest models using a feature contribution method , 2013, 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI).

[37]  De Groeve Tom,et al.  Near Real-time Flood Alerting for the Global Disaster Alert and Coordination System , 2007 .

[38]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[39]  De Groeve Tom,et al.  Global real-time detection of major floods using passive microwave remote sensing , 2009 .

[40]  Kristin K. Nicodemus,et al.  Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures , 2011, Briefings Bioinform..

[41]  C. Aldrich,et al.  Empirical comparison of tree ensemble variable importance measures , 2011 .

[42]  F. Pappenberger,et al.  A pan-African Flood Forecasting System , 2014 .

[43]  P. Döll,et al.  High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management , 2011 .

[44]  Keith Beven,et al.  Influence of uncertain boundary conditions and model structure on flood inundation predictions. , 2006 .

[45]  Yang Hong,et al.  Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins , 2012, IEEE Geoscience and Remote Sensing Letters.

[46]  E. S. Melnikov,et al.  Circum-Arctic map of permafrost and ground-ice conditions , 1997 .

[47]  P. Bates,et al.  Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models , 2009 .

[48]  L. G. Abril,et al.  The Similarity between the Square of the Coefficient of Variation and the Gini Index of a General Random Variable // Similitud entre el cuadrado del coeficiente de variación y el índice de Gini en una variable aleatoria general , 2010 .

[49]  Alessandro Anav,et al.  Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 , 2013, Remote. Sens..

[50]  F. Pappenberger,et al.  Deriving global flood hazard maps of fluvial floods through a physical model cascade , 2012 .