IMPREX pushes the state of the art in a number of directions. We invest in better seasonal forecasts. We develop new concepts to visualize climate change effects. And we zoom in at many applications used by hydropower companies, ship traffic analysers, water resource managers.
Bart van den Hurk, KNMI

Multi-variable flood damage modelling with limited data using supervised learning approaches

Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Several recent studies have shown that supervised learning techniques applied to a multi-variable data set can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive data set, which is rarely available, and this is currently holding back the widespread application of these techniques. In this paper we enrich a data set of residential building and contents damage from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2-D flood simulations are used to add information on flow velocity, flood duration and the return period to the data set, and cadastre data are used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched data set in combination with the supervised learning techniques delivers a 20% reduction in the mean absolute error, compared to a simple model only based on the water depth, despite several limitations of the enriched data set. We find that with our data set, the tree-based methods perform better than the Bayesian network.

How to cite: Wagenaar, D., de Jong, J., and Bouwer, L. M.: Multi-variable flood damage modelling with limited data using supervised learning approaches, Nat. Hazards Earth Syst. Sci., 17, 1683-1696, https://doi.org/10.5194/nhess-17-1683-2017, 2017.

https://www.nat-hazards-earth-syst-sci.net/17/1683/2017/

Contribution of Potential Evaporation Forecasts to 10-day streamflow forecast skill for the Rhine river, HESS, 2018.

Abstract.

Medium term hydrologic forecast uncertainty is strongly dependent on the forecast quality of meteorological vari- ables. Of these variables, the influence of precipitation has been studied most widely, while temperature, radiative forcing and their derived product potential evapotranspiration (PET) have received little attention from the perspective of hydrologi- cal forecasting. This study aims to fill this gap by assessing the usability of potential evaporation forecasts for 10-day-ahead streamflow forecasting in the Rhine basin, Europe. In addition, the forecasts of the meteorological variables are compared with observations.

Bart van Osnabrugge1,2, Remko Uijlenhoet2, and Albrecht Weerts1,2

1 Deltares, Operational Water Management Department, Delft, The Netherlands
2 Wageningen University, Hydrology and Quantitative Water Management Group, Wageningen, The Netherlands
Correspondence: Bart van Osnabrugge (Bart.vanOsnabrugge@deltares.nl)

Open Access The 2013/14 Thames Basin Floods: Do Improved Meteorological Forecasts Lead to More Skillful Hydrological Forecasts at Seasonal Time Scales?

Jessica NeumannDepartment of Geography and Environmental Science, University of Reading, Reading, United Kingdom - Louise ArnalDepartment of Geography and Environmental Science, University of Reading, and European Centre for Medium Range Weather Forecasts, Reading, United Kingdom - Linus MagnussonEuropean Centre for Medium Range Weather Forecasts, Reading, United Kingdom - Hannah ClokeDepartment of Geography and Environmental Science, and Department of Meteorology, University of Reading, Reading, United Kingdom

https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-17-0182.1

Can seasonal hydrological forecasts inform local decisions and actions? An in-the-moment decision-making activity

 

Jessica L. Neumann et al.

The Gauging and Modeling of Rivers in the Sky

Lavers, D. A., Rodwell, M. J., Richardson, D. S., Ralph, F. M., Doyle, J. D., Reynolds, C. A., et al. (2018). The gauging and modeling of rivers in the sky. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL079019

Future climate risk from compound events, May 2018

Jakob Zscheischler, Seth Westra, Bart J. J. M. van den Hurk, Sonia I. Seneviratne, Philip J. Ward, Andy Pitman, Amir AghaKouchak, David N. Bresch, Michael Leonard, Thomas Wahl & Xuebin Zhang: Future climate risk from compound events, Nature Climate Change (2018), doi:10.1038/s41558-018-0156-3

rdcu.be/OfHk   

Raw and processed hydro-meteorological variables of Jucar river basin for feature selection [Data set], April 2018

Zaniolo, Marta, Giuliani, Matteo, Castelletti, Andrea, & Pulido-Velàzquez, Manuel. (2018). Raw and processed hydro-meteorological variables of Jucar river basin for feature selection [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1185084

10.5281/zenodo.1185083

Automatic design of basin-specific drought indexes for highly regulated water systems, April 2018

Zaniolo, M., Giuliani, M., Castelletti, A. F., and Pulido-Velazquez, M.: Automatic design of basin-specific drought indexes for highly regulated water systems, Hydrol. Earth Syst. Sci., 22, 2409-2424, https://doi.org/10.5194/hess-22-2409-2018, 2018.

Summer drought predictability over Europe: empirical versus dynamical forecasts, Jul 2017

Marco Turco et al 2017 Environ. Res. Lett. 12 084006. https://doi.org/10.1088/1748-9326/aa7859

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast, Jan 2018

Andrej Ceglar, Andrea Toreti, Chloe Prodhomme, Matteo Zampieri, Marco Turco & Francisco J. Doblas-Reyes. Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast. Scientific Reports volume 8, Article number: 1322(2018). doi:10.1038/s41598-018-19586-6