scholarly journals Towards assimilation of crowdsourced observations for different levels of citizen engagement: the flood event of 2013 in the Bacchiglione catchment

2017 ◽  
Author(s):  
Maurizio Mazzoleni ◽  
Vivian Juliette Cortes Arevalo ◽  
Uta Wehn ◽  
Leonardo Alfonso ◽  
Daniele Norbiato ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Population Distribution ◽  
Model Performance ◽  
Flood Event ◽  
Target Point ◽  
Citizen Engagement ◽  
Physical Sensors ◽  
Extreme Flood ◽  
Social Sensors

Abstract. Accurate flood predictions are essential to reduce the risk and damages over large urbanized areas. To improve prediction capabilities, hydrological measurements derived by traditional physical sensors are integrated in real-time within mathematic models. Recently, traditional sensors are complemented with low-cost social sensors. However, measurements derived by social sensors (i.e. crowdsourced observations) can be more spatially distributed but less accurate. In this study, we assess the usefulness for model performance of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess potential effects on the model predictions to the extreme flood event occurred in the Bacchiglione catchment on May 2013. Flood predictions are estimated at the target point of Ponte degli Angeli (Vicenza), outlet of the Bacchiglione catchment, by means of a semi-distributed hydrological model. The contribution of the upstream sub-catchment is calculated using a conceptual hydrological model. The flow is propagated along the river reach using a hydraulic model. In both models, a Kalman filter is implemented to assimilate the real-time crowdsourced observations. We synthetically derived crowdsourced observations for either static social or dynamic social sensors because crowdsourced measures were not available. We consider three sets of experiments: (1) only physical sensors are available; (2) probability of receiving crowdsourced observations and (3) realistic scenario of citizen engagement based on population distribution. The results demonstrated the importance of integrating crowdsourced observations. Observations from upstream sub-catchments assimilated into the hydrological model ensures high model performance for high lead time values. Observations next to the outlet of the catchments provide good results for short lead times. Furthermore, citizen engagement level scenarios moved by a feeling of belonging to a community of friends indicated flood prediction improvements when such small communities are located upstream a particular target point. Effective communication and feedback is required between water authorities and citizens to ensure minimum engagement levels and to minimize the intrinsic low-variable accuracy of crowdsourced observations.

scholarly journals Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy)

2018 ◽  
Vol 22 (1) ◽  
pp. 391-416 ◽  
Author(s):  
Maurizio Mazzoleni ◽  
Vivian Juliette Cortes Arevalo ◽  
Uta Wehn ◽  
Leonardo Alfonso ◽  
Daniele Norbiato ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Population Distribution ◽  
Model Performance ◽  
Flood Event ◽  
Citizen Involvement ◽  
Distributed Hydrological Model ◽  
Extreme Flood ◽  
Crowdsourced Data ◽  
Social Sensors ◽  
Over Time

Abstract. To improve hydrological predictions, real-time measurements derived from traditional physical sensors are integrated within mathematic models. Recently, traditional sensors are being complemented with crowdsourced data (social sensors). Although measurements from social sensors can be low cost and more spatially distributed, other factors like spatial variability of citizen involvement, decreasing involvement over time, variable observations accuracy and feasibility for model assimilation play an important role in accurate flood predictions. Only a few studies have investigated the benefit of assimilating uncertain crowdsourced data in hydrological and hydraulic models. In this study, we investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess improvements in the model prediction performance for different spatial–temporal scenarios of citizen involvement levels. To that end, we simulate an extreme flood event that occurred in the Bacchiglione catchment  (Italy) in May 2013 using a semi-distributed hydrological model with the station at Ponte degli Angeli (Vicenza) as the prediction–validation point. A conceptual hydrological model is implemented by the Alto Adriatico Water Authority and it is used to estimate runoff from the different sub-catchments, while a hydraulic model is implemented to propagate the flow along the river reach. In both models, a Kalman filter is implemented to assimilate the crowdsourced observations. Synthetic crowdsourced observations are generated for either static social or dynamic social sensors because these measures were not available at the time of the study. We consider two sets of experiments: (i) assuming random probability of receiving crowdsourced observations and (ii) using theoretical scenarios of citizen motivations, and consequent involvement levels, based on population distribution. The results demonstrate the usefulness of integrating crowdsourced observations. First, the assimilation of crowdsourced observations located at upstream points of the Bacchiglione catchment ensure high model performance for high lead-time values, whereas observations at the outlet of the catchments provide good results for short lead times. Second, biased and inaccurate crowdsourced observations can significantly affect model results. Third, the theoretical scenario of citizens motivated by their feeling of belonging to a community of friends has the best effect in the model performance. However, flood prediction only improved when such small communities are located in the upstream portion of the Bacchiglione catchment. Finally, decreasing involvement over time leads to a reduction in model performance and consequently inaccurate flood forecasts.

scholarly journals Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3242
Author(s):  
András Bárdossy ◽  
Faizan Anwar ◽  
Jochen Seidel
Keyword(s):  
Model Performance ◽  
Flood Event ◽  
Historical Event ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Difficult Situation ◽  
Output Uncertainty ◽  
Time Period ◽  
Extreme Flood ◽  
Historical Observation

We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.

scholarly journals Extreme floods in Europe: going beyond observations using reforecast ensemble pooling

2021 ◽  
Author(s):  
Manuela I. Brunner ◽  
Louise Slater
Keyword(s):  
Sample Size ◽  
Central Europe ◽  
Extreme Events ◽  
Hydrological Model ◽  
Model Performance ◽  
Added Value ◽  
Lead Times ◽  
Flood Events ◽  
New Approach ◽  
Extreme Flood

Abstract. Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System (EFAS) to increase the sample size available to estimate the frequency of extreme local and regional flood events. We assess the added value of such pooling, determine where in Central Europe one might expect the most extreme events, and evaluate how event extremeness is related to physiographic and meteorological catchment characteristics. We work with a set of 234 catchments from the Global Runoff Data Center for which performance of simulated floods is satisfactory when compared to observed streamflow. We pool EFAS-simulated flood events for 10 perturbed ensemble members and lead times from 22 to 46 days, where flood events are only weakly dependent (< 0.25 average correlation across lead times). The resulting large ensemble (130 time series instead of one) enables analyses of very extreme events, which occur less than twice a century. We demonstrate that such ensemble pooling produces more robust estimates with considerably reduced uncertainty bounds (by ~80 % on average) than observation-based estimates but may equally introduce biases arising from the simulated meteorology and hydrological model. Our results show that specific flood return levels are highest in steep and wet regions and are comparably low in regions with strong flow regulation through dams. Furthermore, our pooled flood estimates indicate that the probability of regional flooding is higher in Central Europe and Great Britain than in Scandinavia. We conclude that reforecast ensemble pooling is an efficient approach to increase sample size and to derive robust local and regional flood estimates in regions with sufficient hydrological model performance.

Investigating hydrological model versatility to simulate extreme flood events 

2021 ◽  
Author(s):  
Daniela Peredo Ramirez ◽  
Maria-Helena Ramos ◽  
Vazken Andréassian ◽  
Ludovic Oudin
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Original Model ◽  
Model Structure ◽  
Model Adaptation ◽  
Flood Events ◽  
Runoff Generation ◽  
Local Climate ◽  
Extreme Flood ◽  
The Impact

&lt;p&gt;&lt;span&gt;High-impact flood events in the Mediterranean region are often the result of a combination of local climate and topographic characteristics of the region. Therefore, the way runoff generation processes are represented in hydrological models is a key factor to simulate and forecast floods. In this study, we adapt an existing model in order to increase its versatility to simulate flood events occurring under different conditions: during or after wet periods and after long and dry summer periods. The model adaptation introduces a dependency on rainfall intensity in the production function. The impact of this adaptation is analysed considering model performance over selected flood events and also over a continuous 10-year period of flows. The event-based assessment showed that the adapted model structure performs better than or equal to the original model structure in terms of differences in the timing of peak discharges, regardless of the season of the year when the flood occurs. The most important improvement was observed in the simulation of the magnitude of the flood peaks. A visualisation of model versatility is proposed, which allows detecting the time steps when the new model structure tends to behave more similarly or differently from the original model structure in terms of runoff production. Overall, the results show the potential of the model adaptation proposed to simulate floods originated by different hydrological processes and the value of increasing hydrological model versatility to simulate extreme events.&lt;/span&gt;&lt;/p&gt;

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

Assessing invertebrate response to an extreme flood event at a regional scale utilizing past survey data

Limnology ◽  
2021 ◽  
Vol 22 (2) ◽  
pp. 169-177
Author(s):  
Yo Miyake ◽  
Hiroto Makino ◽  
Kenta Fukusaki
Keyword(s):  
Survey Data ◽  
Regional Scale ◽  
Flood Event ◽  
Extreme Flood

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

scholarly journals SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 872
Author(s):  
Vesna Đukić ◽  
Ranka Erić
Keyword(s):  
Soil Moisture ◽  
Czech Republic ◽  
Hydrological Model ◽  
Flash Flood ◽  
Model Performance ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
River Catchment ◽  
Hydrological Variable ◽  
Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

scholarly journals Assessing the population dynamics of Escherichia coli in a metropolitan river after an extreme flood event

2016 ◽  
Vol 15 (2) ◽  
pp. 196-208 ◽  
Author(s):  
Nicole M. Masters ◽  
Aaron Wiegand ◽  
Jasmin M. Thompson ◽  
Tara L. Vollmerhausen ◽  
Eva Hatje ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Population Dynamics ◽  
Antibiotic Resistance ◽  
Virulence Genes ◽  
Wastewater Treatment Plants ◽  
Vero Cells ◽  
Flood Event ◽  
Typing Method ◽  
E Coli ◽  
Extreme Flood

We investigated Escherichia coli populations in a metropolitan river after an extreme flood event. Between nine and 15 of the 23 selected sites along the river were sampled fortnightly over three rounds. In all, 307 E. coli were typed using the PhP typing method and were grouped into common (C) or single (S) biochemical phenotypes (BPTs). A representative from each of the 31 identified C-BPTs was tested for 58 virulence genes (VGs) associated with intestinal and extra-intestinal E. coli, resistance to 22 antibiotics, production of biofilm and cytotoxicity to Vero cells. The number of E. coli in the first sampling round was significantly (P &lt; 0.01) higher than subsequent rounds, whereas the number of VGs was significantly (P &lt; 0.05) higher in isolates from the last sampling round when compared to previous rounds. Comparison of the C-BPTs with an existing database from wastewater treatment plants (WWTPs) in the same catchment showed that 40.6% of the river isolates were identical to the WWTP isolates. The relatively high number of VGs and antibiotic resistance among the C-BPTs suggests possessing and retaining these genes may provide niche advantages for those naturalised and/or persistent E. coli populations which may pose a health risk to the community.

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