Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. ...Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography.
Flow velocity measurements using point-velocity meters are normally obtained by sampling one, two or three velocity points per vertical profile. During high floods their use is inhibited due to the ...difficulty of sampling in lower portions of the flow area. Nevertheless, the application of standard methods allows estimation of a parameter, α, which depends on the energy slope and the Manning roughness coefficient. During high floods, monitoring of velocity can be accomplished by sampling the maximum velocity, umax, only, which can be used to estimate the mean flow velocity, um, by applying the linear entropy relationship depending on the parameter, M, estimated on the basis of historical observed pairs (um, umax). In this context, this work attempts to analyze if a correlation between α and M holds, so that the monitoring for high flows can be addressed by exploiting information from standard methods. A methodology is proposed to estimate M from α, by coupling the “historical” information derived by standard methods, and “new” information from the measurement of umax surmised at later times. Results from four gauged river sites of different hydraulic and geometric characteristics have shown the robust estimation of M based on α.
Short‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties ...remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists.
Key Points
Probabilistic flood maps are derived from SAR images
Probabilistic flood maps are assimilated into a flood forecasting model cascade
Water level forecast quality improves substantially in the assimilation time steps, and benefits persist for hours to days
The discharge hydrograph estimation in rivers based on reverse routing modeling and using only water level data at two gauged sections is here extended to the most general case of significant lateral ...flow contribution, without needing to deploy rainfall–runoff procedures. The proposed methodology solves the Saint‐Venant equations in diffusive form also involving the lateral contribution using a “head‐driven” modeling approach where lateral inflow is assumed to be function of the water level at the tributary junction. The procedure allows to assess the discharge hydrograph at ends of a selected river reach with significant lateral inflow, starting from the stage recorded there and without needing rainfall data.
Specifically, the MAST 1D hydraulic model is applied to solve the diffusive wave equation using the observed stage hydrograph at the upstream section as upstream boundary condition. The other required data are (a) the observed stage hydrograph at the downstream section, as benchmark for the parameter calibration, and (b) the bathymetry of the river reach, from the upstream section to a short distance after the downstream gauged section. The method is validated with different flood events observed in two river reaches with a significant intermediate basin, where reliable rating curves were available, selected along the Tiber River, in central Italy, and the Alzette River, in Luxembourg. Very good performance indices are found for the computed discharge hydrographs at both the channel ends and along the tributaries. The mean Nash‐Sutcliffe value (NSq) at the channel ends of two rivers is found equal to 0.99 and 0.86 for the upstream and downstream sites, respectively. The procedure is also validated on a longer stretch of the Tiber River including three tributaries for which appreciable results are obtained in terms of NSq for the computed discharge hydrographs at both the channel ends for three investigated flood events.
•We propose an entropy approach for estimating the flow depth distribution in rivers.•We model the river-cross section geometry by sampling the surface flow velocity.•The procedure is suitable for ...monitoring the discharge during high floods.•A new perspective for flow monitoring also by remote sensing is addressed.
A methodology for determining the bathymetry of river cross-sections during floods by the sampling of surface flow velocity and existing low flow hydraulic data is developed . Similar to Chiu (1988) who proposed an entropy-based velocity distribution, the flow depth distribution in a cross-section of a natural channel is derived by entropy maximization. The depth distribution depends on one parameter, whose estimate is straightforward, and on the maximum flow depth. Applying to a velocity data set of five river gage sites, the method modeled the flow area observed during flow measurements and accurately assessed the corresponding discharge by coupling the flow depth distribution and the entropic relation between mean velocity and maximum velocity. The methodology unfolds a new perspective for flow monitoring by remote sensing, considering that the two main quantities on which the methodology is based, i.e., surface flow velocity and flow depth, might be potentially sensed by new sensors operating aboard an aircraft or satellite.
The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and ...evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In
addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for
soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the
Murray–Darling basin in Australia. The hydro-meteorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are
sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM) . This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average.
A new methodology, based on the synchronous measurement of stage hydrographs in two river sections located some kilometres from each other, was developed to estimate the discharge hydrograph in the ...upstream section. The methodology is based on the one-parameter calibration of a numerical flow routing algorithm, solving the Saint-Venant equations in diffusive or complete form. The methodology was validated using results of laboratory experiments carried out at the Polytechnic of Bari University. A known discharge hydrograph was generated in the upstream tank of a rectangular flume, where two water level sensors were located. Two different bed materials have been used to account for different roughness coefficients. Eight measured discharge hydrographs have been compared with the hydrographs computed using both a diffusive and a fully dynamic model. The diffusive model provides a good estimate of the measured discharge in the experiments with the highest roughness value.
Predictions of hydrological models are highly uncertain due to both the nature of the modelled system and the meteorological forcings. Soil moisture information derived from satellite data can help ...to reduce this uncertainty. Indeed, data assimilation techniques offer the possibility to dynamically correct the model evolution in order to improve the model output. However, several questions concerning the use of these techniques are still without answer. The aim of this work is trying to better understand what conditions allow a successful assimilation of satellite soil moisture products. To this end, three different products, along with several options in terms of filter design, were tested and their impact on data assimilation performances was evaluated.
Sea Surface Salinity (SSS) is an Essential Ocean and Climate Variable, which is increasingly used as part of climate studies. SSS measurements are available from three satellite missions, SMOS, ...Aquarius and SMAP, each with very different instrument features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce SSS Climate Data Record (CDR) to include satellite measurements, based on well-established user needs. To generate a homogeneous CDR, instrumental differences are carefully controlled by analysing SSS discrepancies, then adjusted based on in-depth analysis of the measurements themselves together with independent reference data. However, no spatial smoothing or temporal relaxation to reference data is applied in order to maintain the variability contained in the original data set. SSS CCI fields are well suited for monitoring weekly to interannual variability from the ocean basin scale to the large mesoscale. Thus, they depict that over the 2010-2019 decade, seasonal have varied greatly from year to year, sometimes by more than +/-0.4 over large regions. When monthly SSS CCI are compared with in situ Argo salinities, the robust standard deviation of their difference, at global scale, is 0.15, while r2 is 0.97. This high level of performance highlights the benefit of the SSS CCI merging approach compared to individual satellite SSS fields alone. The correlation with independent ship SSS (r2>0.9) further highlights the excellent performance of the data set. SSS CCI data are freely available and will be updated and extended in the future as more satellite data become available.
•Crude extracts from seaweed were screened against fruit postharvest pathogens.•Grey mould/strawberries and brown rot/peaches were mostly suppressed.•A direct antifungal activity play a relevant role ...in seaweed-based control.•Fatty acids could be involved in suppression rather than phenolic compounds.•Inducers of resistance elicited by polysaccharides could be implicated in suppression.
Fungal infections are the main cause of decay on fresh fruit during postharvest phase determining severe losses. Postharvest control is performed by fungicides, but their intense use have aroused issue relating to environmental protection and human health prompting to search alternative control means. The use of biofuel-used seaweed extracts by a supercritical carbon dioxide technique could be a valid alternative during postharvest handling of fresh fruit. The aim of this work was to assess the in vitro and in vivo activity of extracts from two brown seaweeds (Laminaria digitata and Undaria pinnatifida) and three red seaweeds (Porphyra umbilicalis, Eucheuma denticulatum and Gelidium pusillum) against three postharvest pathogens (Botrytis cinerea, Monilinia laxa and Penicillium digitatum) using three concentrations of extract (10, 20 and 30gL−1). The total content of fatty acids of the extracts was determined by CG-MS, those of polysaccharides by HIC, and phenolic compounds (phlorotannins) by HPLC-DAD. Twenty fatty acids were quantified in the extracts, while three polysaccharides categories and three phlorotannins classes were identified only in brown seaweed extracts. L. digitata, U. pinnatifida and P. umbilicalis showed the highest antifungal efficacy on in vitro cultures of the pathogens. L. digitata and U. pinnatifida completely inhibited mycelia growing and conidial germination of B. cinerea and M. laxa at the highest dose tested and strongly reduced those of P. digitatum. P. umbilicalis extract strongly inhibited mycelia and conidia growth on all the fungi. E. denticulatum and G. pusillum showed a lower but still significant reduction of mycelia growing and conidia germination on all the pathogens. In trials performed in vivo on wounded fruit, L. digitata, U. pinnatifida and P. umbilicalis extracts strongly suppressed grey mould on strawberries, brown rot on peaches, and green mould on lemons at 30gL−1 dose both in preventive and curative treatments; E. denticulatum and G. pusillum poorly reduced disease development. In all cases, a dose-effect of the treatments was observed with an increase of fruit decay inhibition and reduction of disease severity as the dose of extract applied over the wound increased. Moreover, an increased peroxidase activity in the strawberries/B. cinerea and peaches/M. laxa systems by preventive treatment with 30gL−1 extract was observed. The antifungal activity could be mainly ascribed to a direct toxicity of fatty acids found at the highest concentrations in L. digitata, U. pinnatifida and P. umbilicalis rather than to those of phenolic compounds and phlorotannins; but it could be related to possible peroxidase-mediated systemic resistance mechanisms elicited by the polysaccharides.
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