7Accurate inundation forecasting provides vital information about the behaviour of fluvial flood water. 8 Using data assimilation with an Ensemble Transform Kalman Filter we combine forecasts from a numerical 9 hydrodynamic model with synthetic observations of water levels. We show that reinitialising the model 10 with corrected water levels can cause an initialization shock and demonstrate a simple novel solution. In 11 agreement with others, we find that although assimilation can accurately correct water levels at observation 12 times, the corrected forecast quickly relaxes to the open loop forecast. Our new work shows that the time 13 taken for the forecast to relax to the open loop case depends on domain length; observation impact is longer-14lived in a longer domain. We demonstrate that jointly correcting the channel friction parameter as well as 15 water levels greatly improves the forecast. We also show that updating the value of the channel friction 16 parameter can compensate for bias in inflow. 17 Keywords Data assimilation, inundation forecasting, fluvial flooding, observation impact, joint state-parameter 18 estimation, ensemble Kalman filter.Highlights 20 • Data assimilation is applied to simulated flood forecasts and SAR-like observations 21 • Reinitialisation shock due to water level correction is removed using a novel method 22 • Observation impact is linked to domain length when updating only water levels 23 • Updating the channel friction parameter leads to marked improvement in forecast skill 24 • Updating the channel friction parameter can compensate for biased inflow 25 Software Availability 26The inundation simulations in this work were generated using Clawpack 5.2.2, a collection of FORTRAN and 27 python code available from http://www.clawpack.org/. Details of the amended Clawpack source code as used 28 in this work are freely available on request from the corresponding author, as is the python code used to perform 29 data assimilation on the inundation simulation output. Please contact e.s.cooper@pgr.reading.ac.uk for details. 30 1 1 Introduction 31 Data assimilation can improve the accuracy of predictions from flood inundation models by combining forecasts 32 from the model with observations of the system, taking into account uncertainty in both the model predictions 33 and the observations. In this study we use a sequential data assimilation method comprising a forecast-update 34 dynamic feedback loop. During each forecast step, the numerical model runs an inundation simulation. When 35 an observation (or set of observations) is available the simulation is interrupted and the update step is performed; 36 updating combines observational data and model predictions to give a better estimate of the state. The next 37 forecast step then starts, with the adjusted water levels as the initial condition. An update is carried out each 38 time a new observation or set of observations is available. 39There are a number of numerical inundation models that can predict the behaviour o...

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link ABSTRACTThe PEA technique is used to measure the distribution of space charge in an epoxy resin after polarisation for one week at an applied field of 7.14kV/mm over a range of temperatures. The decay of the space charge is followed for times up to 114 hours after removal of the voltage and analysed in terms of a number of alternative decay mechanisms. It is shown that the rate-determining stage of the decay mechanism is that of a thermally activated process that has been associated with charge de-trapping. At times greater than 10 2 s the de-trapping process behaves as though the space charge field does not exist and the retention time of the space charge depends only upon the depth of the deepest occupied traps and the temperature.

Abstract. Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of floodwater during a river flood event. This observational information typically covers a large spatial area but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR-derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way, a suitable observation operator is required. In this paper we show that different types of observation operators can produce very different corrections to predicted water levels; this impacts the quality of the forecast produced. We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms with which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain.

Abstract. The COSMOS-UK observation network has been providing field-scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high-temporal resolution data in near-real time. Each site utilizes a cosmic-ray neutron sensor, which counts epithermal neutrons at the land surface. These measurements are used to derive field-scale near-surface soil water content, which can provide unique insight for science, industry, and agriculture by filling a scale gap between localized point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real-time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological forecasting, and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185 (Stanley et al., 2021).

We present a new water level dataset extracted from images taken by four Farson Digital Ltd river cameras for a Tewkesbury, UK flood event (21st November – 5th December 2012). This data article presents the new water level data together with a description of metadata, data acquisition, and extraction methods. The water level information was extracted from the images using measured points in the field-of-view of each camera using Leica GNSS and Total Station instruments with high spatial accuracy of order of 1 cm. We use river gauge data to verify the new dataset. The new dataset has a short duration but includes the rising limb, peak discharge and falling limb of the flood event. It has potential for verifying future automatic water level extraction methods and for development of automatic flood alert methods and can provide valuable information in data assimilation systems used for improving inundation forecasts.

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Abstract. Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of flood water during a river flood event. This observational information typically covers a large spatial area, but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR-derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way a suitable observation operator is required. In this paper we show that different types of observation operator can produce very different corrections to predicted water levels; this impacts on the quality of the forecast produced. We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms by which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain.

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear, because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure we find improved estimates of soil moisture for the JULES land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK). The spatial resolution of these COSMOS probes is much more representative of the 1 km model grid than traditional point based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain we find an average 22 % reduction in root-mean squared error, a 16 % reduction in unbiased root-mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.