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The need for robust rainfall estimation has increased with more frequent and intense floods due to human-induced land use and climate change, especially in urban areas. Besides the existing rainfall measurement systems, citizen science can offer unconventional methods to provide complementary rainfall data for enhancing spatial and temporal data coverage. This demand for accurate rainfall data is particularly crucial in the context of smart city innovations, where real-time weather information is essential for effective urban planning, flood management, and environmental sustainability. Therefore, this study provides proof-of-concept for a novel method of estimating rainfall intensity using its recorded audio in an urban area, which can be incorporated into a smart city as part of its real-time weather forecasting system. This study proposes a convolutional neural network (CNN) inversion model for acoustic rainfall intensity estimation. The developed CNN rainfall sensing model showed a significant improvement in performance over the traditional approach, which relies on the loudness feature as an input, especially for simulating rainfall intensities above 60 mm/h. Also, a CNN-based denoising framework was developed to attenuate unwanted noises in rainfall recordings, which achieved up to 98% accuracy on the validation and testing datasets. This study and its promising results are a step towards developing an acoustic rainfall sensing tool for citizen-science applications in smart cities. However, further investigation is necessary to upgrade this proof-of-concept for practical applications.

Modelling the water transport along the soil–plant–atmosphere continuum is fundamental to estimating and predicting transpiration fluxes. A Finite-difference Ecosystem-scale Tree Crown Hydrodynamics model (FETCH3) for the water fluxes across the soil–plant–atmosphere continuum is presented here. The model combines the water transport pathways into one vertical dimension, and assumes that the water flow through the soil, roots, and above-ground xylem can be approximated as flow in porous media. This results in a system of three partial differential equations, resembling the Richardson–Richards equation, describing the transport of water through the plant system and with additional terms representing sinks and sources for the transfer of water from the soil to the roots and from the leaves to the atmosphere. The numerical scheme, developed in Python 3, was tested against exact analytical solutions for steady state and transient conditions using simplified but realistic model parameterizations. The model was also used to simulate a previously published case study, where observed transpiration rates were available, to evaluate model performance. With the same model setup as the published case study, FETCH3 results were in agreement with observations. Through a rigorous coupling of soil, root xylem, and stem xylem, FETCH3 can account for variable water capacitance, while conserving mass and the continuity of the water potential between these three layers. FETCH3 provides a ready-to-use open access numerical model for the simulation of water fluxes across the soil–plant–atmosphere continuum.

The biophysical processes occurring in the unsaturated zone have a direct impact on the water table dynamics. Representing these processes through the application of unsaturated zone models of different complexity has an impact on the estimates of the volumes of water flowing between the unsaturated zone and the aquifer. These fluxes, known as net recharge, are often used as the shared variable that couples unsaturated to groundwater models. However, as recharge estimates are always affected by a degree of uncertainty, model–data fusion methods, such as data assimilation, can be used to inform these coupled models and reduce uncertainty. This study assesses the effect of unsaturated zone models complexity (conceptual versus physically based) to update groundwater model outputs, through the assimilation of actual evapotranspiration rates, for a water-limited site in South Australia. Actual evapotranspiration rates are assimilated because they have been shown to be related to the water table dynamics and thus form the link between remote sensing data and the deeper parts of the soil profile. Results have been quantified using standard metrics, such as the root mean square error and Pearson correlation coefficient, and reinforced by calculating the continuous ranked probability score, which is specifically designed to determine a more representative error in stochastic models. It has been found that, once properly calibrated to reproduce the actual evapotranspiration–water table dynamics, a simple conceptual model may be sufficient for this purpose; thus using one configuration over the other should be motivated by the specific purpose of the simulation and the information available.

Knowledge of the full rainfall drop size distribution (DSD) is critical for characterising liquid water precipitation for applications such as rainfall retrievals using electromagnetic signals and atmospheric model parameterisation. Southern Hemisphere temperate latitudes have a lack of DSD observations and their integrated variables. Laser-based disdrometers rely on the attenuation of a beam by falling particles and are currently the most commonly used type of instrument to observe the DSD. However, there remain questions on the accuracy and variability in the DSDs measured by co-located instruments, whether identical models, different models or from different manufacturers. In this study, raw and processed DSD observations obtained from two of the most commonly deployed laser disdrometers, namely the Parsivel1 from OTT and the Laser Precipitation Monitor (LPM) from Thies Clima, are analysed and compared. Four co-located instruments of each type were deployed over 3 years from 2014 to 2017 in the proximity of Melbourne, a region prone to coastal rainfall in south-eastern Australia. This dataset includes a total of approximately 1.5 million recorded minutes, including over 40 000 min of quality rainfall data common to all instruments, equivalent to a cumulative amount of rainfall ranging from 1093 to 1244 mm (depending on the instrument records) for a total of 318 rainfall events. Most of the events lasted between 20 and 40 min for rainfall amounts of 0.12 to 26.0 mm. The co-located LPM sensors show very similar observations, while the co-located Parsivel1 systems show significantly different results. The LPM recorded 1 to 2 orders of magnitude more smaller droplets for drop diameters below 0.6 mm compared to the Parsivel1, with differences increasing at higher rainfall rates. The LPM integrated variables showed systematically lower values compared to the Parsivel1. Radar reflectivity–rainfall rate (ZH–R) relationships and resulting potential errors are also presented. Specific ZH–R relations for drizzle and convective rainfall are also derived based on DSD collected for each instrument type. Variability of the DSD as observed by co-located instruments of the same manufacturer had little impact on the estimated ZH–R relationships for stratiform rainfall, but differs when considering convective rainfall relations or ZH–R relations fitted to all available data. Conversely, disdrometer-derived ZH–R relations as compared to the Marshall–Palmer relation ZH=200R1.6 led to a bias in rainfall rates for reflectivities of 50 dBZ of up to 21.6 mm h−1. This study provides an open-source high-resolution dataset of co-located DSD to further explore sampling effects at the micro scale, along with rainfall microstructure.

Abstract The monitoring of wildfire smoke is important to help mitigate impacts on people such as by sending early warnings to affected areas. Received signal levels (RSLs) from radio links have been used as an opportunistic way to accurately measure rainfall and humidity. Radio links provide integrated measurements along their paths and are an exceptional untapped resource to complement air quality stations in areas affected by smoke events, or in developing countries without air quality monitoring capability. This study analyzed radio link signal fluctuations during smoke events associated with the 2019–2020 Australian wildfires. Concurrently, the atmospheric boundary layer was characterized using atmospheric soundings and surface observations, as well as air quality proxies such as particulate matter concentrations less than 2.5 μm (10 μm), or PM2.5 (PM10). Observations showed that dry air containing large amounts of smoke within a surface layer above the ground acted as a lid, reducing dispersion, trapping and maintaining high ground‐level concentrations of smoke. These conditions also created anomalous propagation conditions for radio links and operational weather radars. Power‐law relations between signal fluctuations and PM10 and PM2.5 were derived based on the link data collected and the closest air quality station observations. While there was variability in retrieval performance across smoke events, the best performance determination coefficients exceeded 0.5, with an RMSE on the order of less than 50 μg m−3 for concentrations of more than 400 μg m−3. Mid‐range link lengths (5–20 km) provided the best results.

Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.

The diversity in hydrologic models has historically led to great controversy on the correct approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

It is generally accepted that the ground heat flux accounts for a significant fraction of the surface energy balance. In land surface models, the ground heat flux is typically estimated through a numerical solution of the heat conduction equation. Recent research has shown that this approach introduces errors in the estimation of the energy balance. In this paper, we calibrate a land surface model using a numerical solution of the heat conduction equation with four different vertical spatial resolutions. It is found that the thermal conductivity is the most sensitive parameter to the spatial resolution. More importantly, the thermal conductivity values are directly related to the spatial resolution, thus rendering any physical interpretation of this value irrelevant. The numerical solution is then replaced by an analytical solution. The results of the numerical and analytical solutions are identical when fine spatial and temporal resolutions are used. However, when using resolutions that are typical of land surface models, significant differences are found. When using the analytical solution, the ground heat flux is directly calculated without calculating the soil temperature profile. The calculation of the temperature at each node in the soil profile is thus no longer required, unless the model contains parameters that depend on the soil temperature, which in this study is not the case. The calibration is repeated, and thermal conductivity values independent of the vertical spatial resolution are obtained. The main conclusion of this study is that care must be taken when interpreting land surface model results that have been obtained using numerical ground heat flux estimates. The use of exact analytical solutions, when available, is recommended.

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In terms of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2008–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness, and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored under different catchment hydrological systems in future studies.

Understanding runoff processes in a basin is of paramount importance for the effective planning and management of water resources, in particular in data-scarce regions such as the Upper Blue Nile. Hydrological models representing the underlying hydrological processes can predict river discharges from ungauged catchments and allow for an understanding of the rainfall–runoff processes in those catchments. In this paper, such a conceptual process-based hydrological model is developed and applied to the upper Gumara and Gilgel Abay catchments (both located within the Upper Blue Nile Basin, the Lake Tana sub-basin) to study the runoff mechanisms and rainfall–runoff processes in the basin. Topography is considered as a proxy for the variability of most of the catchment characteristics. We divided the catchments into different runoff production areas using topographic criteria. Impermeable surfaces (rock outcrops and hard soil pans, common in the Upper Blue Nile Basin) were considered separately in the conceptual model. Based on model results, it can be inferred that about 65% of the runoff appears in the form of interflow in the Gumara study catchment, and baseflow constitutes the larger proportion of runoff (44–48%) in the Gilgel Abay catchment. Direct runoff represents a smaller fraction of the runoff in both catchments (18–19% for the Gumara, and 20% for the Gilgel Abay) and most of this direct runoff is generated through infiltration excess runoff mechanism from the impermeable rocks or hard soil pans. The study reveals that the hillslopes are recharge areas (sources of interflow and deep percolation) and direct runoff as saturated excess flow prevails from the flat slope areas. Overall, the model study suggests that identifying the catchments into different runoff production areas based on topography and including the impermeable rocky areas separately in the modeling process mimics the rainfall–runoff process in the Upper Blue Nile Basin well and yields a useful result for operational management of water resources in this data-scarce region.

Evapotranspiration (ET) plays a key role in hydrological impact studies and operational flood forecasting models as ET represents a loss of water from a catchment. Although ET is a major component of the catchment water balance, the evapotranspiration input for rainfall–runoff models is often simplified in contrast to the detailed estimates of catchment averaged precipitation. In this study, an existing conceptual rainfall–runoff model calibrated for and operational in the Bellebeek catchment in Belgium firstly has been validated and its sensitivity to different available potential ET input has been studied. It has been shown that when applying a calibrated rainfall–runoff model, the model input should be consistent with the input used for the calibration process, not only on the volume of ET, but also on the seasonal pattern. Secondly, estimates of the actual evapotranspiration based on measurements of a large aperture scintillometer (LAS) have been used as model forcing in the rainfall–runoff model. From this analysis, it has been shown that the actual evapotranspiration is a crucial factor in simulating the catchment water balance and the resulting stream flow. Regarding the actual evapotranspiration estimates from the LAS, it has been concluded that they can be considered realistic in summer months. In the months where stable conditions prevail (autumn, winter and (early) spring), an underestimation of the actual evapotranspiration is made, which has an important impact on the catchment's water balance.

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

The catchment averaged actual evapotranspiration rate is a hydrologic model variable that is difficult to quantify. Evapotranspiration rates – up till present – cannot be continuously observed at the catchment scale. The objective of this paper is to estimate the evapotranspiration rates (or its energy equivalent, the latent heat fluxes LE) for a heterogeneous catchment of 102.3 km2 in Belgium using three fundamentally different algorithms. One possible manner to observe this variable could be the continuous measurement of sensible heat fluxes (H) across large distances (in the order of kilometers) using a large aperture scintillometer (LAS), and converting these observations into evapotranspiration rates. Latent heat fluxes are obtained through the energy balance equation using a series of sensible heat fluxes measured with a LAS over a distance of 9.5 km in the catchment, and point measurements of net radiation (Rn) and ground heat flux (G) upscaled to catchment average through the use of TOPLATS, a physically based land surface model. The resulting LE-values are then compared to results from the remote sensing based surface energy balance algorithm ETLook and the land surface model. Firstly, the performance of ETLook for the energy balance terms has been assessed at the point scale and at the catchment scale. Secondly, consistency between daily evapotranspiration rates from ETLook, TOPLATS and LAS is shown.

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.

In a hydrological modelling scenario, often the modeller is confronted with external data, such as remotely-sensed soil moisture observations, that become available to update the model output. However, the scale triplet (spacing, extent and support) of these data is often inconsistent with that of the model. Furthermore, the external data can be cursed with epistemic uncertainty. Hence, a method is needed that not only integrates the external data into the model, but that also takes into account the difference in scale and the uncertainty of the observations. In this paper, a synthetic hydrological modelling scenario is set up in which a high-resolution distributed hydrological model is run over an agricultural field. At regular time steps, coarse-scale field-averaged soil moisture data, described by means of possibility distributions (epistemic uncertainty), are retrieved by synthetic aperture radar and assimilated into the model. A method is presented that allows to integrate the coarse-scale possibility distribution of soil moisture content data with the fine-scale model-based soil moisture data. The method is subdivided in two steps. The first step, the disaggregation step, employs a scaling relationship between field-averaged soil moisture content data and its corresponding standard deviation. In the second step, the soil moisture content values are updated using two alternative methods.

Evapotranspiration rates at the catchment scale are very difficult to quantify. One possible manner to continuously observe this variable could be the estimation of sensible heat fluxes (H) across large distances (in the order of kilometers) using a large aperture scintillometer (LAS), and inverting these observations into evapotranspiration rates, under the assumption that the LAS observations are representative for the entire catchment. The objective of this paper is to assess whether measured sensible heat fluxes from a LAS over a long distance (9.5 km) can be assumed to be valid for a 102.3 km2 heterogeneous catchment. Therefore, a fully process-based water and energy balance model with a spatial resolution of 50 m has been thoroughly calibrated and validated for the Bellebeek catchmentin Belgium. A footprint analysis has been performed. In general, the sensible heat fluxes from the LAS compared well with the modeled sensible heat fluxes within the footprint. Moreover, as the modeled Hwithin the footprint has been found to be almost equal to the modeled catchment averaged H, it can be concluded that the scintillometer measurements over a distance of 9.5 km and an effective heightof 68 m are representative for the entire catchment.

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.

With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multi-mission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filter-based data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services.

The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

Background & Aims: The efficacy of a low fermentable oligo-, di-, monosaccharides and polyols (FODMAP) diet in irritable bowel syndrome (IBS) is well established. After the elimination period, a reintroduction phase aims to identify triggers. We studied the impact of a blinded reintroduction using FODMAP powders to objectively identify triggers and evaluated the effect on symptoms, quality of life, and psychosocial comorbidities., Methods: Responders to a 6-week low FODMAP diet, defined by a drop in IBS symptom severity score (IBS-SSS) compared with baseline, entered a 9-week blinded randomized reintroduction phase with 6 FODMAP powders (fructans, fructose, galacto-oligosaccharides, lactose, mannitol, sorbitol) or control (glucose). A rise in IBS-SSS (≥50 points) defined a FODMAP trigger. Patients completed daily symptom diaries and questionnaires for quality of life and psychosocial comorbidities., Results: In 117 recruited patients with IBS, IBS-SSS improved significantly after the elimination period compared with baseline (150 ± 116 vs 301 ± 97, P < .0001, 80% responders). Symptom recurrence was triggered in 85% of the FODMAP powders, by an average of 2.5 ± 2 FODMAPs/patient. The most prevalent triggers were fructans (56%) and mannitol (54%), followed by galacto-oligosaccharides, lactose, fructose, sorbitol, and glucose (respectively 35%, 28%, 27%, 23%, and 26%) with a significant increase in abdominal pain at day 1 for sorbitol/mannitol, day 2 for fructans/galacto-oligosaccharides, and day 3 for lactose., Conclusion: We confirmed the significant benefit of the low FODMAP diet in tertiary-care IBS. A blinded reintroduction revealed a personalized pattern of symptom recurrence, with fructans and mannitol as the most prevalent, and allows the most objective identification of individual FODMAP triggers. Ethical commission University hospital of Leuven reference number: s63629; Clinicaltrials.gov number: NCT04373304., (Copyright © 2024 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background: Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer with one of the highest cancer-related mortality rates worldwide. Early diagnosis is crucial for improving the therapeutic options and reducing the disease-related mortality., Aim: To investigate serum N-glycomics as diagnostic markers for HCC., Methods: We performed a comprehensive search in PubMed, EMBASE, Web of Science and Scopus through August 17, 2023. Eligible studies assessed the potential use of serum N-glycomics as diagnostic biomarkers for HCC. Study selection, data extraction and quality assessment were performed by two independent reviewers., Results: Of the 48 articles included, 11 evaluated the utility of N-glycomics for the diagnosis of HCC in whole serum while the remaining articles focused on specific protein glycoforms or protein levels. Of these specific proteins, haptoglobin, alpha-fetoprotein (AFP), kininogen (Kin), α-1-antitrypsin and Golgi protein 73 (GP73) were the most frequently studied. Increased levels of fucosylation and branching presented as the most prevalent post-translational modifications of glycoproteins in patients with HCC compared to controls. Notably, glycomics-based biomarkers may provide a clinical benefit for the diagnosis of early HCC, as several algorithms achieved AUCs between 0.92-0.97. However, these were based on single studies with limited sample sizes and should therefore be validated., Conclusions: Alterations in serum N-glycomics, characterised by increased levels of fucosylation and branching, have potential as diagnostic biomarkers for HCC. Optimisation of study design, patient selection and analysing techniques are needed before clinical implementation will be possible., (© 2023 John Wiley & Sons Ltd.)

Objectives: The objective of this study was to develop and validate the Knowledge of Suicide Scale (KSS), elaborated to assess adherence to myths about suicide., Methods: The KSS is a self-questionnaire including 22 statements relating to myths about suicide for which the respondent is asked to rate his degree of adherence on a scale ranging from 0 ("strongly disagree") to 10 ("completely agree"). Using the script concordance test scoring method, the respondents' scores were compared with those of experts to obtain, for each item, a score between 0 (maximum deviation with the experts) and 1 (minimum deviation with the experts). One thousand and thirty-five individuals (222 psychiatric interns, 332 medical interns in the first semester excluding psychiatry and 481 journalism students) were included., Results: According to the exploratory factor analysis, the KSS is a two-dimensional scale: the first subscale includes 15 items and the second seven items. The tool showed excellent face validity, correct convergent and divergent validities (multi-method multi-feature analyzes), and good internal consistency (Cronbach's alpha coefficient between 0.66 and 0.83 for scales and subscales). The KSS is moderately and negatively correlated with the Stigma of Suicide Scale (r=-0.3). It significantly discriminates groups with different expected levels of knowledge regarding suicide (P<0.001)., Conclusions: The KSS demonstrated good psychometric properties to measure adherence to myths about suicide. This tool could be useful in assessing the effectiveness of suicide prevention literacy improvement programs., (Copyright © 2023 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.)

Aims: Cardiovascular diseases (CVD) are the leading cause of death worldwide. The coronavirus disease 2019 (COVID-19) pandemic has disrupted healthcare systems, causing delays in essential medical services, and potentially impacting CVD treatment. This study aims to estimate the impact of the pandemic on delayed CVD care in Europe by providing a systematic overview of the available evidence., Methods and Results: PubMed, Embase, and Web of Science were searched until mid-September 2022 for studies focused on the impact of delayed CVD care due to the pandemic in Europe among adult patients. Outcomes were changes in hospital admissions, mortality rates, delays in seeking medical help after symptom onset, delays in treatment initiation, and change in the number of treatment procedures. We included 132 studies, of which all were observational retrospective. Results were presented in five disease groups: ischaemic heart diseases (IHD), cerebrovascular accidents (CVA), cardiac arrests (CA), heart failures (HF), and others, including broader CVD groups. There were significant decreases in hospital admissions for IHD, CVA, HF and urgent and elective cardiac procedures, and significant increases for CA. Mortality rates were higher for IHD and CVA., Conclusion: The pandemic led to reduced acute CVD hospital admissions and increased mortality rates. Delays in seeking medical help were observed, while urgent and elective cardiac procedures decreased. Adequate resource allocation, clear guidelines on how to handle care during health crises, reduced delays, and healthy lifestyle promotion should be implemented. The long-term impact of pandemics on delayed CVD care, and the health-economic impact of COVID-19 should be further evaluated., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Purpose of Review: Intestinal transplantation (ITx), whether isolated or combined with other organs, is now a valid treatment option in some patients with chronic intestinal failure or extensive venous mesenteric thrombosis. The aim in these patients is not only to restore nutritional autonomy, but also to minimize the risk of complications, both short and long term. Despite parenteral nutrition playing a central part in the management of intestinal failure patients, there are little data about the perioperative and postoperative nutritional management of ITx patients, due to small patient populations per centre. In this review, we collected the scientific data available to date., Recent Findings: In this review, we will bundle the limited scientific information about diet after intestinal and multivisceral transplantation combined with recommendations from our own clinical practice in 28 ITx patients in University Hospitals Leuven, Belgium. We will discuss the immediate preoperative period, surgical complications necessitating dietary interventions and the late postoperative phase in a stable outpatient transplant recipient., Summary: Although no specific research has been done in the field of ITx, we can extrapolate some findings from other solid organ transplants. Prehabilitation might prove to be of importance; Preserving kidney and liver function in the pretransplant period should be pursued. Transition from parenteral to enteral and oral nutrition can be complex due to inherent surgical procedures and possible complications. Ultimately, the goal is to give patients nutritional autonomy, while also minimizing the risk of foodborne infections by teaching patients well tolerated food practices., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)