Quantification Of Rainfall Forecast Uncertainty And Its Impact On Flood Forecasting

Rainfall forecast errors are considered to be the key source of uncertainty in flood forecasting. To quantify the rainfall forecast uncertainty itself and its impact on the total flood forecast uncertainty, a Monte-Carlo based statistical method has been developed. This method takes into account the dependency of the rainfall forecast error with the lead time and the rainfall amount. The forecasted rainfall errors are described by truncated normal distributions, allowing to quantify the full uncertainty distribution of the deterministic rainfall forecast. By means of Monte-Carlo sampling and taking the forecast error autocorrelation into account, the impact of the rainfall forecast uncertainty on a flood forecast was quantified. This was done for the Rivierbeek river in Belgium. In addition, comparison is made between the total flood forecast uncertainty and the uncertainty due to the forecasted rainfall. The total flood forecast uncertainty was quantified by a non-parametric data-based approach. It was concluded that the forecasted rainfall uncertainty contributes for about 30 percent to the total flood forecast uncertainty

On The Added Value Of Radar Data In Hydrological Modelling And Flood Forecasting

Good quality rainfall data are essential in hydrological modelling and flood forecasting. Classically, rain gauge data are being used as rainfall input for hydrological modelling, but they only provide point information. Because rainfall can be highly variable in space, radar images can provide important additional spatial information, but the quantitative rainfall data quality of these images is often limited. Merging techniques between rain gauge and radar data can provide a solution to this problem. In this research, a simple kriging merging technique, making use of two C-band radars, is tested for the Demer catchment in Belgium. Three periods with different types of rainfall were selected: two winter periods with stratiform rainfall and one summer period with convective rainfall. First, it was tested whether the merging technique is able to correct the quantitative radar rainfall information, by comparing the rainfall volumes at rain gauge locations, which were not used during the merging with the observed values. It was found that the merging technique performed well under stratiform conditions, but this was not always the case for the convective conditions. Secondly, the added value of the radar information was tested, by comparing hydrological and hydraulic model outputs, generated by rain gauge and/or radar data, to flow and water level observations. It is found that the added value of the radar data is limited for the winter periods, but that for the summer periods a significant improvement is obtained

NK Cell Phenotype Is Associated With Response and Resistance to Daratumumab in Relapsed/Refractory Multiple Myeloma

The CD38-targeting antibody daratumumab has marked activity in multiple myeloma (MM). Natural killer (NK) cells play an important role during daratumumab therapy by mediating antibody-dependent cellular cytotoxicity via their FcγRIII receptor (CD16), but they are also rapidly decreased following initiation of daratumumab treatment. We characterized the NK cell phenotype at baseline and during daratumumab monotherapy by flow cytometry and cytometry by time of flight to assess its impact on response and development of resistance (DARA-ATRA study; NCT02751255). At baseline, nonresponding patients had a significantly lower proportion of CD16 + and granzyme B + NK cells, and higher frequency of TIM-3 + and HLA-DR + NK cells, consistent with a more activated/exhausted phenotype. These NK cell characteristics were also predictive of inferior progression-free survival and overall survival. Upon initiation of daratumumab treatment, NK cells were rapidly depleted. Persisting NK cells exhibited an activated and exhausted phenotype with reduced expression of CD16 and granzyme B, and increased expression of TIM-3 and HLA-DR. We observed that addition of healthy donor-derived purified NK cells to BM samples from patients with either primary or acquired daratumumab-resistance improved daratumumab-mediated MM cell killing. In conclusion, NK cell dysfunction plays a role in primary and acquired daratumumab resistance. This study supports the clinical evaluation of daratumumab combined with adoptive transfer of NK cells

International Consensus Based Review and Recommendations for Minimum Reporting Standards in Research on Transcutaneous Vagus Nerve Stimulation (Version 2020)

Given its non-invasive nature, there is increasing interest in the use of transcutaneous vagus nerve stimulation (tVNS) across basic, translational and clinical research. Contemporaneously, tVNS can be achieved by stimulating either the auricular branch or the cervical bundle of the vagus nerve, referred to as transcutaneous auricular vagus nerve stimulation(VNS) and transcutaneous cervical VNS, respectively. In order to advance the field in a systematic manner, studies using these technologies need to adequately report sufficient methodological detail to enable comparison of results between studies, replication of studies, as well as enhancing study participant safety. We systematically reviewed the existing tVNS literature to evaluate current reporting practices. Based on this review, and consensus among participating authors, we propose a set of minimal reporting items to guide future tVNS studies. The suggested items address specific technical aspects of the device and stimulation parameters. We also cover general recommendations including inclusion and exclusion criteria for participants, outcome parameters and the detailed reporting of side effects. Furthermore, we review strategies used to identify the optimal stimulation parameters for a given research setting and summarize ongoing developments in animal research with potential implications for the application of tVNS in humans. Finally, we discuss the potential of tVNS in future research as well as the associated challenges across several disciplines in research and clinical practice

Assessment of model improvement actions in river hydrodynamic modelling

The usefulness of hydrodynamic river models much depends on the accuracy of the model. The uncertainty in the results of hydrodynamic river models typically originates from uncertainties in the model parameters (calibration uncertainty), model schematization and input data. Based on a detailed insight in the river system, the modelling process and its shortcomings, most efficient model improvement actions can be designed. In this paper, several model improvement actions are suggested and tested for the hydrodynamic model of the Demer river in Belgium. A detailed model performance evaluation is conducted for that case based on field measurements of river water levels and discharges at different places along the river. The evaluation includes comparison of observed and simulated rainfall-runoff and river peak flows, low flows, cumulative volumes and empirical extreme value distributions.Flanders Hydraulics Research division, Flemish Ministry for Mobility and Public Worksstatus: publishe

Quantification of rainfall forecast uncertainty and its impact on flood forecasting

Rainfall forecast errors are considered to be the key source of uncertainty in flood forecasting. To quantify the rainfall forecast uncertainty itself and its impact on the total flood forecast uncertainty, a Monte-Carlo based statistical method has been developed. This method takes into account the dependency of the rainfall forecast error with the lead time and the rainfall amount. The forecasted rainfall errors are described by truncated normal distributions, allowing to quantify the full uncertainty distribution of the deterministic rainfall forecast. By means of Monte-Carlo sampling and taking the forecast error autocorrelation into account, the impact of the rainfall forecast uncertainty on a flood forecast was quantified. This was done for the Rivierbeek river in Belgium. In addition, comparison was made between the total flood forecast uncertainty and the uncertainty due to the forecasted rainfall. The total flood forecast uncertainty was quantified by a non-parametric data-based approach. It was concluded that the forecasted rainfall uncertainty contributes for about 30 percent to the total flood forecast uncertainty.status: publishe

Increasing river flood preparedness by real-time warning based on wetness state conditions

High soil saturation levels can be considered as a primary indicator for potential river flooding. Therefore it is advisable to visualize real-time soil moisture information in flood forecasting or warning systems. Monitoring of soil moisture, however, is not an easy task due to its variable nature in time, space and depth. This paper presents and compares methods to assess the severity of the soil moisture state of hydrological catchments considered in a typical operational flood forecasting system. The severity of the relative soil moisture state is obtained and mapped by comparing simulation results of a lumped conceptual hydrological model, directly, by making use of the soil moisture component of the model or indirectly considering the baseflow component. The values are compared in real time after with the results of a long term simulation. Another approach uses rainfall, evapotranspiration and the river flow observations. By applying a baseflow filter to the river flow observations and an advanced method for empirical catchment water balance computation, two additional soil moisture indicators can be definied, namely the filtered baseflow and the water balance based relative soil moisture content. It is shown that each of the methods allow to obtain useful estimates of the soil moisture state of a catchment in real time. Secondly, a method has been set up to calculate the exceedance probability of a predefined discharge threshold, e.g. flood threshold, at the outlet or a given location in the catchment. The exceedance probability is calculated by a logit relation with the soil moisture indicator. The different soil moisture indicators are compared in their predicting capabilities by calculating and comparing the probability of detection, the false alarm rate and the critical success index. Interestingly, the application of such logit relation or the use of a simple water balance computation for the catchment, based on real-time rainfall, evapotranspiration and river flow observations, leads to more reliable exceedance probability estimates than the common direct use of total runoff results from a state-of-the art rainfall-runoff model. Mapping the exceedance probability for the different hydrological catchments together with the width of the confidence interval on this probability is proposed as a useful tool to increase the preparedness for potential floods, since this kind of maps provide a quick overview of the catchments in which flood problems can be expected, hence on which flood crisis management bodies have to focus their attention.status: publishe

Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a ‘three dimensional error’ matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the probability of flooding of a certain area, based on the uncertainty assessment of the flood forecasts. By using this type of maps, water managers can focus their attention on the areas with the highest flood probability. Also the larger public can consult these maps for information on the probability of flooding for their specific location, such that they can take pro-active measures to reduce the personal damage. The method of quantifying the uncertainty was implemented in the operational flood forecasting system for the navigable rivers in the Flanders region of Belgium. The method has shown clear benefits during the floods of the last two years.status: publishe

On the added value of radar data in hydrological modelling and flood forecasting

Good quality rainfall data are essential in hydrological modelling and flood forecasting. Classically, rain gauge data are being used as rainfall input for hydrological modelling, but they only provide point information. Because rainfall can be highly variable in space, radar images can provide important additional spatial information, but the quantitative rainfall data quality of these images is often limited. Merging techniques between rain gauge and radar data can provide a solution to this problem. In this research, a simple kriging merging technique, making use of two C-band radars, is tested for the Demer catchment in Belgium. Three periods with different types of rainfall were selected: two winter periods with stratiform rainfall and one summer period with convective rainfall. First, it was tested whether the merging technique is able to correct the quantitative radar rainfall information, by comparing the rainfall volumes at rain gauge locations, which were not used during the merging with the observed values. It was found that the merging technique performed well under stratiform conditions, but this was not always the case for the convective conditions. Secondly, the added value of the radar information was tested, by comparing hydrological and hydraulic model outputs, generated by rain gauge and/or radar data, to flow and water level observations. It is found that the added value of the radar data is limited for the winter periods, but that for the summer periods a significant improvement is obtained.status: publishe

Method for testing the accuracy of rainfall-runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context

Catchment hydrological models are widely used in climate change impact studies. The considered climate scenarios often involve increases in rainfall intensities. These increases might go beyond the range of historical events used in model calibration and validation. The hydrological impact results of climate change consequently are under these circumstances highly uncertain. This paper presents a method to test the validity of hydrological models for that type of impact analysis. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. The method is demonstrated, based upon the results of three conceptual rainfall–runoff models (NAM, PDM, VHM) for three catchments in Belgium. It is shown how the performance of the model in predicting impacts of rainfall increases can be integrated in the calibration process and how this method can increase the reliability of climate change impact results on peak flows. The paper addresses the importance of the relation between the soil moisture content and the overland flow coefficient, as it controls the generation of peak flows in the considered rainfall–runoff models.status: publishe