Assimilating SAR-derived water level data into a hydraulic model: a case study

Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data

The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter

The Sequential Importance Sampling with Resampling (SISR) particle filter and the SISR with parameter resampling particle filter (SISR-PR) are evaluated for their performance in soil moisture assimilation and the consequent effect on baseflow generation. With respect to the resulting soil moisture time series, both filters perform appropriately. However, the SISR filter has a negative effect on the baseflow due to inconsistency between the parameter values and the states after the assimilation. In order to overcome this inconsistency, parameter resampling is applied along with the SISR filter, to obtain consistent parameter values with the analyzed soil moisture state. Extreme parameter replication, which could lead to a particle collapse, is avoided by the perturbation of the parameters with white noise. Both the modeled soil moisture and baseflow are improved if the complementary parameter resampling is applied. The SISR filter with parameter resampling offers an efficient way to deal with biased observations. The robustness of the methodology is evaluated for 3 model parameter sets and 3 assimilation frequencies. Overall, the results in this paper indicate that the particle filter is a promising tool for hydrologic modeling purposes, but that an additional parameter resampling may be necessary to consistently update all state variables and fluxes within the model

Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium resolution SAR data and identifiability techniques

Single satellite synthetic aperture radar (SAR) data are now regularly used to estimate hydraulic model parameters such as channel roughness, depth and water slope. However, despite channel geometry being critical to the application of hydraulic models and poorly known a priori, it is not frequently the object of calibration. This paper presents a unique method to simultaneously calibrate the bankfull channel depth and channel roughness parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of moderate-resolution (150 m) ENVISAT satellite SAR-derived flood extent maps and a binary performance measure for a 30 × 50 km domain covering the confluence of the rivers Severn and Avon in the UK. The unknown channel parameters are located by a novel technique utilising the information content and dynamic identifiability analysis (DYNIA) (Wagener et al., 2003) of single and combinations of SAR flood extent maps to find the optimum satellite images for model calibration. Highest information content is found in those SAR flood maps acquired near the peak of the flood hydrograph, and improves when more images are combined. We found that model sensitivity to variation in channel depth is greater than for channel roughness and a successful calibration for depth could only be obtained when channel roughness values were confined to a plausible range. The calibrated reach-average channel depth was within 0.9 m (16 % error) of the equivalent value determined from river cross-section survey data, demonstrating that a series of moderate-resolution SAR data can be used to successfully calibrate the depth parameters of a 2-D hydraulic model

Flood modeling and prediction using Earth Observation data

The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limita- tions are discussed throughput, and the article concludes by looking at new developments

Application of hydrogen peroxide as an innovative method of treatment for Legionella control in a hospitalwater network

Objectives: To evaluate the effectiveness of hydrogen peroxide (HP) use as a disinfectant in the hospital water network for the control of Legionella spp. colonization. Methods: Following the detection of high levels of Legionella contamination in a 136-bed general hospital water network, an HP treatment of the hot water supply (25 mg/L) was adopted. During a period of 34 months, the effectiveness of HP on Legionella colonization was assessed. Legionella was isolated in accordance with ISO-11731 and identification was carried out by sequencing of the mip gene. Results: Before HP treatment, L. pneumophila sg 2-15 was isolated in all sites with a mean count of 9950±8279 cfu/L. After one-month of HP treatment, we observed the disappearance of L. pneumophila 2-15, however other Legionella species previously not seen were found; Legionella pneumophila 1 was isolated in one out of four sampling sites (2000 cfu/L) and other non-pneumophila species were present in all sites (mean load 3000±2887 cfu/L). Starting from September 2013, HP treatment was modified by adding food-grade polyphosphates, and in the following months, we observed a progressive reduction of the mean load of all species (p < 0.05), resulting in substantial disappearance of Legionella colonization. Conclusion: Hydrogen peroxide demonstrated good efficacy in controlling Legionella. Although in the initial phases of treatment it appeared unable to eliminate all Legionella species, by maintaining HP levels at 25 mg/L and adding food-grade polyphosphates, a progressive and complete control of colonization was obtained

Analyzing the impact of agro-environmental conditions on the dynamics of hazelnut yield in Chile

Analyzing the impact of agro-environmental conditions on the dynamics of hazelnut yield in Chile. 9. International Congress on Hazelnu

Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data

This paper explores a method to combine the time and space continuity of a large-scale inundation model with discontinuous satellite microwave observations, for high-resolution flood hazard mapping. The assumption behind this approach is that hydraulic variables computed from continuous spatially-distributed hydrodynamic modeling and observed as discrete satellite-derived flood extents are correlated in time, so that probabilities can be transferred from the model series to the observations. A prerequisite is, therefore, the existence of a significant correlation between a modeled variable (i.e., flood extent or volume) and the synchronously-observed flood extent. If this is the case, the availability of model simulations over a long time period allows for a robust estimate of non-exceedance probabilities that can be attributed to corresponding synchronously-available satellite observations. The generated flood hazard map has a spatial resolution equal to that of the satellite images, which is higher than that of currently available large scale inundation models. The method was applied on the Severn River (UK), using the outputs of a global inundation model provided by the European Centre for Medium-range Weather Forecasts and a large collection of ENVISAT ASAR imagery. A comparison between the hazard map obtained with the proposed method and with a more traditional numerical modeling approach supports the hypothesis that combining model results and satellite observations could provide advantages for high-resolution flood hazard mapping, provided that a sufficient number of remote sensing images is available and that a time correlation is present between variables derived from a global model and obtained from satellite observations