Particle Filters for Flood Forecasting  (PFFF)

Particle Filters for Flood Forecasting  (PFFF)

A collaboration between: Dr Renaud Hostache, Luxembourg Institute of Science and Technology (LIST); Professors Nancy K Nichols and Peter Jan vanLeeuwen, University of Reading; Ms Concetta di Mauro, Luxembourg Institute of Science and Technology (LIST)

The objective  of this DARE pilot project is to investigate the application of advanced filters to assimilate high-resolution flood extent information derived from SAR images (75m spatial resolution) for the purposes of improving near real-time flood forecasts.  The forecasting system is composed of a hydrological model loosely coupled to a hydraulic model with uncertain rainfall forcing (from ERA interim).  The ensemble of model outputs is compared to satellite-derived flood probability maps taking into account satellite image classification uncertainty.  Standard ensemble Kalman filter (EnKF) methods that assume a normal distribution of the observation errors cannot be applied and therefore new filters need to be developed for the assimilation.  From experiments already carried out at LIST, three challenges arise:  (i) to prevent ensemble members/particles being given a weight of zero solely due to local mismatch at a few pixels;  (ii) to reduce biases due to over-prediction of flood extent (false positive) being penalized more strongly than under-prediction; and  (iii) to reduce the risk of particle degeneration, where weights for all but a few particles go to zero. The aim of the project will be to assess how these challenges can be met using new advanced filters that are being developed at the University of Reading, such as equal-weight particle filters and variational mapping particle filters.

Flood forecasting chains have been set up to enable the evaluation of the proposed data assimilation filters in controlled environments using synthetic (twin) experiments.  Two studies have been carried out using these systems.

  1. We first use a variant of a Particle Filter (PF), namely a PF with Sequential Importance Sampling (PF-SIS), to assimilate flood extent in near real-time into a hydrological hydraulic-model cascade. To reduce the risk of particle degeneration, a “tempering” power factor is applied to the conditional probability of the observation given the model prediction (also called likelihood in a PF). This allows inflation of the model posterior distribution variance. Various values of the “tempering coefficient”, leading to different Effective Ensemble Size (EES) are evaluated. The experiment shows that the assimilation framework yields correct results in the sense that the assimilation updates the particle weights so that the updated predictions move towards the synthetic truth. It also shows that the proposed tempering factor helps in reducing degeneracy while inflating posterior distribution variance. Fig. 1 shows the synthetic truth together with the ensemble expectations (ensemble weighted means) for the open loop (no assimilation) and the assimilation (using various tempering factor values) runs.  As shown in this figure, the experiment also demonstrates that the reduction of degeneracy is at the cost of a slight degradation of the overall performance as the higher the EES, the lower the performance of the assimilation run. This is shown by the black and blue lines moving closer to the synthetic truth (compared to orange and light blue line Figure 1).
  2. We also investigated how innovative satellite earth observations of soil moisture and flood extents can help in reducing errors and uncertainties in conceptual hydro-meteorological modelling, especially in ungauged areas where potentially no, or limited, runoff records are available. A spatially distributed conceptual hydrological model was developed to allow for the prediction of soil moisture and flood extents. Using rainfall and potential evapotranspiration time series derived from the globally and freely available ERA5 database as forcing of this model, long-term simulations of soil moisture, discharge and flood extents were carried out. Time series of soil moisture and flood extent observations derived from freely available satellite image databases were then jointly assimilated into the hydrological model in order to retrieve optimal parameter sets. The performance of the calibrated model was evaluated using the tempered PF in twin experiments. This synthetic experiment shows that the assimilation of long time series (~10 years) of observations of flood extents and soil moisture maps acquired every three days enable a satisfactory calibration of the hydrological model. The Nash Sutcliffe Efficiency, computed based on the comparison of simulated and synthetic discharge time series, reach high values (above 0.95) both during the calibration period and a 10-year validation period.

Figure 1: Water surface elevation time series at Saxons Lode: synthetic truth (red), open-loop (green) and assimilation experiments using the standard PF-SIS (black), and using various tempering factor values (blue, light blue and orange) enabling various effective ensemble sizes to be reached (indicated between parentheses as percentage of the ensemble size). The vertical dashed lines indicate the assimilation time steps. PF-SIS=Particle Filter with Sequential Importance Sampling. EES=Effective Ensemble Size.

 

Merging SAR-derived flood footprints with flood hazard maps for improved urban flood mapping

Merging SAR-derived flood footprints with flood hazard maps for improved urban flood mapping

Contributors: David Mason (University of Reading), John Bevington (JBA), Sarah Dance (University of Reading), Beatriz Revilla-Romero (JBA), Richard Smith (JBA), Sanita Vetra-Carvalho (University of Reading), Hannah Cloke (University of Reading).

This DARE pilot project is investigating a method of improving the accuracy of rapid post-event flood mapping in urban areas by merging pre-computed flood return period (FRP) maps with satellite synthetic aperture radar (SAR)-derived flood inundation maps. SAR sensors have the potential to detect flooding through cloud during both day- and night-time. The inputs are JBA’s Flood Foresight dynamic flood inundation extent and depth maps (updated every 3 hours), and a high resolution SAR image sequence. The SAR returns are used only in rural areas, including those adjacent to the urban areas, so that there is no need to take radar shadow and layover caused by buildings in urban areas into account. Also, rural SAR water level observations should be able to correct errors in model water elevations, because the JBA model thinks that all flooding is fluvial. On the other hand, it is an advantage to use the model’s FRP maps in urban areas, because these know where urban areas that are low are protected from flooding.

The project developed a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived FRP maps. The SAR flood detection is based on the fact that water generally appears dark in a SAR image. Urban areas that are protected (e.g. by embankments) have high return periods in the FRP maps, and their effective heights are correspondingly increased. The SAR water levels found in the rural areas are interpolated over the urban areas to take into account the fall-off of levels down the reach. The model waterline heights are similarly interpolated. The interpolated height maps from SAR and model are combined to a single map, which is used to estimate whether an urban pixel is flooded. The method was tested on urban flooding in West London in February 2014 (see image 3) and Tewkesbury in July 2007. It was compared to a previously-developed method that used SAR returns in both the rural and urban areas. The present method using SAR returns solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas, and was more accurate than the previous method. A journal paper is in preparation.

 

Images: Urban flooding in West London in February 2014

Operational meteorology for the public good, or for profit?

Operational meteorology for the public good, or for profit?

Most countries have a national weather service, funded by the government. A key role is to provide a public weather service, publishing forecasts to help make everyday business and personal decisions, as well as providing weather warnings for hazardous events. Large sums of money are invested in research and development of forecasting systems; supercomputing resources and in observing networks. For example in 2018-19 the UK Met Office invested £55 million in satellite programmes. International cooperation between weather services means that weather data obtained by one country are usually distributed to others through the World Meteorological Organisation (WMO) Global Transmission Service (GTS) in near real time, and for the common good. Is this all about to change?

In the future there is likely to be an increasing need for smart, local forecasts for the safety of autonomous vehicles (e.g. to allow the vehicle to respond to rain, snow, ice etc). Such vehicles also provide an observing platform able to take local measurements of weather that could be used to improve forecasts.  But who owns the data (the driver, the car owner, the car manufacturer…) and can it be distributed for the common good? Can the data be trusted? What about privacy concerns?

IBM weather infographic

Across the observation sector, access to space is getting less expensive. For example, depending on the specifications, a nanosatellite can be built and placed in orbit for 0.5 million euros.  Furthermore, industry is beginning to run its own numerical weather prediction models (e.g., IBM weather).  This means that there are a growing number of companies investing in earth observation and numerical weather prediction,  and wanting financial returns on their investments.

Do we need a new paradigm for weather prediction?

Flood Inundation Mapping with Data Assimilation or Summary of Zhiqi Hu MSc thesis

Flood Inundation Mapping with Data Assimilation or Summary of Zhiqi Hu MSc thesis

Due to climate change flooding is predicted to increase in frequency and intensity across the globe and it is imperative we can produce accurate and timely flood forecasts for decision-makers before and during floods.

Zhiqi Hu, an MSc in Atmospheric Ocean & Climate student at University of Reading, worked with us and JBA Consulting during her masters project investigating if a probabilistic ensemble weighting method can improve  Flood Foresight ensemble flood map forecasts using satellite observations during the flood event in India, Brahmaputra river basin in August 2017. Her work is summarised in this poster.