The Catastrophe of the Niedów Dam—The Causes of the Dam’s Breach, Its Development, and Consequences

Due to extreme rainfall in 2010 in the Lusatian Neisse River catchment area (in Poland), a flood event with a return period of over 100 years occurred, leading to the failure of the Niedów dam. The earth-type dam constructed for cooling the Turów power plant was washed away, resulting in the rapid release of nearly 8.5 million m3 of water and the flooding of the downstream area with substantial material losses. Here we analyze the conditions and causes of the dam’s failure, with special attention given to the mechanism and dynamics of the compound breaching process, in which the dam’s upstream slope reinforcement played a specific and remarkable role. The paper also describes a numerical approach for simulating a combined flood event downstream from the dam with the use of a two-dimensional hydrodynamic model (MIKE21). Considering the specific local conditions, i.e., wide floodplain, meandering character of the main channel, embankment overtopping, and available data set, an iterative solution of the unsteady state problem is proposed. This approach enables realistic flood propagation estimates to be delivered, the dam breach outflow to be reconstructed, and several important answers concerning the consequences of the dam’s failure to be provided. Finally, the paper presents the reconstruction of the dam that is more resilient to extreme hydrological conditions under changing climate.

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The catastrophe of the Niedów dam – the causes of the dam's breach, its development and consequences

10.5194/nhess-2021-199 ◽

2021 ◽

Author(s):

Stanisław Kostecki ◽

Robert Banasiak

Keyword(s):

Extreme Rainfall ◽

Iterative Solution ◽

Numerical Approach ◽

Flood Event ◽

Dam Breach ◽

Data Set ◽

Local Conditions ◽

The Earth ◽

Rapid Release ◽

Downstream Area

Abstract. Due to extreme rainfall in 2010 in the Lusatian Neisse River catchment area, a flood event with a return period of over 100 years occurred, leading to the failure of the Niedów dam. The earth-type dam was washed away, resulting in the rapid release of nearly 8.5 million m3 of water and the flooding of the downstream area with substantial material losses. The paper analyses the conditions and causes of the dam’s failure, with special attention given to the mechanism and dynamics of the compound breaching process, in which the dam’s upstreeam slope reinforcement played a remarkable role. The paper also describes a numerical approach for simulating a combined flood event along the Lusatian Neisse River with the use of a two-dimensional hydrodynamic model (MIKE21). The flood event occurred downstream from the dam. Considering the specific local conditions and available data set, an iterative solution of the unsteady state problem is proposed. This approach enables realistic flood propagation estimates to be delivered, the dam breach outflow to be reconstructed, and several important answers concerning the consequences of the dam’s failure to be provided.

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The catastrophe of the Niedów dam – the dam break causes, development and consequences

10.5194/nhess-2020-372 ◽

2020 ◽

Author(s):

Stanisław Kostecki ◽

Robert Banasiak

Keyword(s):

Numerical Approach ◽

Dam Failure ◽

Water Volume ◽

Empirical Formulas ◽

Dam Breach ◽

Data Set ◽

Local Conditions ◽

The Earth ◽

Rapid Release ◽

Downstream Area

Abstract. Due to extreme rainfalls in 2010 in the Lusatian Neisse river catchment, a ﬂood event with a return period of over 100 years took place leading to the failure of the Niedów dam. The earth type dam was washed away with a result of a rapid release of nearly 8.5 million m3 of water volume and ﬂooding of the downstream area with substantial material losses. The paper analyses the conditions and causes of the dam’s failure, while special attention is given to the mechanism and dynamics of the compound breaching process. Several empirical formulas for the dam breach prediction are tested with regards to their usefulness in assessing breach dimensions and outﬂow peak discharge. The paper also describes a numerical approach to simulate the ﬂood propagation downstream the dam with the use of a two-dimensional hydrodynamic model. Considering the speciﬁc local conditions and available data set, an iterative, reversed solution of the unsteady state problem is made. This approach enabled to deliver realistic ﬂood propagation estimates, veriﬁcation of the dam breach outﬂow, and several important answers on the dam failure consequences.

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An efficient technique for CT scan images classification of COVID-19

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201985 ◽

2020 ◽

pp. 1-14

Author(s):

Esraa Hassan ◽

Noha A. Hikal ◽

Samir Elmuogy

Keyword(s):

Neural Network ◽

Diagnostic Tool ◽

Deep Neural Network ◽

Data Augmentation ◽

Performance Metrics ◽

Classification Model ◽

Data Set ◽

Training Models ◽

The Earth ◽

Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

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Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Geophysics ◽

10.1190/geo2018-0082.1 ◽

2019 ◽

Vol 84 (1) ◽

pp. R1-R10 ◽

Cited By ~ 11

Author(s):

Zhendong Zhang ◽

Tariq Alkhalifah ◽

Zedong Wu ◽

Yike Liu ◽

Bin He ◽

...

Keyword(s):

Field Data ◽

Extreme Values ◽

Time Lag ◽

Waveform Inversion ◽

Optimal Time ◽

Full Waveform Inversion ◽

Data Set ◽

Velocity Models ◽

Full Waveform ◽

The Earth

Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

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The particle and magnetic environments surrounding close-in exoplanets

Proceedings of the International Astronomical Union ◽

10.1017/s1743921315010698 ◽

2015 ◽

Vol 11 (S320) ◽

pp. 397-402

Author(s):

A. A. Vidotto ◽

R. Fares ◽

M. Jardine ◽

C. Moutou ◽

J.-F. Donati

Keyword(s):

Magnetic Fields ◽

Radio Emission ◽

Magnetic Field Intensity ◽

Three Dimensional ◽

Stellar Winds ◽

Local Conditions ◽

Hot Jupiters ◽

The Earth ◽

Weather Events ◽

Host Stars

AbstractThe proper characterisation of stellar winds is essential for the study of propagation of eruptive events (flares, coronal mass ejections) and the study of space weather events on exoplanets. Here, we quantitatively investigate the nature of the stellar winds surrounding the hot Jupiters HD46375b, HD73256b, HD102195b, HD130322b, HD179949b. We simulate the three-dimensional winds of their host stars, in which we directly incorporate their observed surface magnetic fields. With that, we derive the wind properties at the position of the hot-Jupiters’ orbits (temperature, velocity, magnetic field intensity and pressure). We show that the exoplanets studied here are immersed in a local stellar wind that is much denser than the local conditions encountered around the solar system planets (e.g., 5 orders of magnitude denser than the conditions experienced by the Earth). The environment surrounding these exoplanets also differs in terms of dynamics (slower stellar winds, but higher Keplerian velocities) and ambient magnetic fields (2 to 3 orders of magnitude larger than the interplanetary medium surrounding the Earth). The characterisation of the host star's wind is also crucial for the study of how the wind interacts with exoplanets. For example, we compute the exoplanetary radio emission that is released in the wind-exoplanet interaction. For the hot-Jupiters studied here, we find radio fluxes ranging from 0.02 to 0.13 mJy. These fluxes could become orders of magnitude higher when stellar eruptions impact exoplanets, increasing the potential of detecting exoplanetary radio emission.

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Monitoring the Ocean Heat Content and the Earth Energy imbalance from space altimetry and space gravimetry: the MOHeaCAN product

10.5194/egusphere-egu21-1225 ◽

2021 ◽

Author(s):

Marti Florence ◽

Ablain Michaël ◽

Fraudeau Robin ◽

Jugier Rémi ◽

Meyssignac Benoît ◽

...

Keyword(s):

Time Scales ◽

Radiant Energy ◽

Heat Content ◽

Ocean Heat Content ◽

Global Ocean ◽

Argo Data ◽

Data Set ◽

Energy Imbalance ◽

Temporal Sampling ◽

The Earth

The Earth Energy Imbalance (EEI) is a key indicator to understand climate change. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of <0.3 W.m-2 at decadal time scales is necessary to evaluate the long term mean EEI associated with anthropogenic forcing. Ideally an accuracy of <0.1 W.m-2 at decadal time scales is desirable if we want to monitor future changes in EEI.In the frame of the MOHeaCAN project supported by ESA, the EEI indicator is deduced from the global change in Ocean Heat Content (OHC) which is a very good proxy of the EEI since the ocean stores 93% of the excess of heat&#160; gained by the Earth in response to EEI. The OHC is estimated from space altimetry and gravimetry missions (GRACE). This &#8220;Altimetry-Gravimetry'' approach is promising because it provides consistent spatial and temporal sampling of the ocean, it samples nearly the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean&#8217;s entire depth. Consequently, it complements the OHC estimation from the ARGO network.&#160;The MOHeaCAN product contains monthly time series (between August 2002 and June 2017) of several variables, the main ones being the regional OHC (3&#176;x3&#176; spatial resolution grids), the global OHC and the EEI indicator. Uncertainties are provided for variables at global scale, by propagating errors from sea level measurements (altimetry) and ocean mass content (gravimetry). In order to calculate OHC at regional and global scales, a new estimate of the expansion efficiency of heat at global and regional scales have been performed based on the global ARGO network.&#160;A scientific validation of the MOHeaCAN product has also been carried out performing thorough comparisons against independent estimates based on ARGO data and on the Clouds and the Earth&#8217;s Radiant energy System (CERES) measurements at the top of the atmosphere. The mean EEI derived from MOHeaCAN product is 0.84 W.m-2 over the whole period within an uncertainty of &#177;0.12 W.m-2 (68% confidence level - 0.20 W.m-2 at the 90% CL). This figure is in agreement (within error bars at the 90% CL) with other EEI indicators based on ARGO data (e.g. OHC-OMI from CMEMS) although the best estimate is slightly higher. Differences from annual to inter-annual scales have also been observed with ARGO and CERES data. Investigations have been conducted to improve our understanding of the benefits and limitations of each data set to measure EEI at different time scales.The MOHeaCAN product from &#8220;altimetry-gravimetry&#8221; is now available and can be downloaded at https://doi.org/10.24400/527896/a01-2020.003. Feedback from interested users on this product are welcome.

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Risk Analysis of Earth-Rock Dam Breach Based on Dynamic Bayesian Network

Water ◽

10.3390/w11112305 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2305 ◽

Cited By ~ 8

Author(s):

Li ◽

Wang ◽

Ge ◽

Wei ◽

Keyword(s):

Risk Analysis ◽

Bayesian Network ◽

Dynamic Bayesian Network ◽

Time Factors ◽

Extended Model ◽

Bayesian Network Model ◽

Dam Breach ◽

The Earth ◽

Operation Period ◽

Static Risk

Despite the fact that the Bayesian network has great advantages in logical reasoning and calculation compared with the other traditional risk analysis methods, there are still obvious shortcomings in the study of dynamic risk. The risk factors of the earth-rock dam breach are complex, which vary with time during the operation period. Static risk analysis, limited to a specific period of time, cannot meet the needs of comprehensive assessment and early warning. By introducing time factors, a dynamic Bayesian network model was established to study the dynamic characteristics of dam-breach probability. Combined with the calculation of the conditional probability of nodes based on the Leaky Noisy-Or gate extended model, the reasoning results of Bayesian networks were modified by updating the data of different time nodes. Taking an earth-rock dam as an example, the results show that it has less possibility to breach and keep stable along the time axis. Moreover, the factors with vulnerability and instability were found effective, which could provide guidance for dam risk management.

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Heights of SuperDARN F region echoes estimated from the analysis of HF radio wave propagation

Annales Geophysicae ◽

10.5194/angeo-25-1987-2007 ◽

2007 ◽

Vol 25 (9) ◽

pp. 1987-1994 ◽

Cited By ~ 11

Author(s):

A. V. Koustov ◽

D. André ◽

E. Turunen ◽

T. Raito ◽

S. E. Milan

Keyword(s):

Electron Density ◽

Hf Radar ◽

Field Of View ◽

Data Set ◽

Density Distributions ◽

F Region ◽

The Earth ◽

Earth Magnetic Field ◽

Field Lines ◽

Radar Wave

Abstract. Tomographic estimates of the electron density altitudinal and latitudinal distribution within the Hankasalmi HF radar field of view are used to predict the expected heights of F region coherent echoes by ray tracing and finding ranges of radar wave orthogonality with the Earth magnetic field lines. The predicted ranges of echoes are compared with radar observations concurrent with the tomographic measurements. Only those events are considered for which the electron density distributions were smooth, the band of F region HF echoes existed at ranges 700–1500 km, and there was a reasonable match between the expected and measured slant ranges of echoes. For a data set comprising of 82 events, the typical height of echoes was found to be 275 km.

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MODEL INDEPENDENT CONSTRAINTS ON NON-ELECTRONIC FLAVORS IN THE SOLAR BORON NEUTRINO FLUX

Modern Physics Letters A ◽

10.1142/s021773230602024x ◽

2006 ◽

Vol 21 (22) ◽

pp. 1761-1768 ◽

Cited By ~ 2

Author(s):

S. DEV ◽

SANJEEV KUMAR ◽

SURENDER VERMA

Keyword(s):

General Model ◽

Sterile Neutrino ◽

Neutrino Flux ◽

Flux Measurement ◽

Data Set ◽

The Earth ◽

Independent Analysis ◽

Antineutrino Flux ◽

Model Independent ◽

Salt Phase

We perform the most general model-independent analysis of the latest 391-Day Salt Phase SNO Data Set incorporating the super-Kamiokande ES flux measurement and obtain bounds on the antineutrino and sterile neutrino flux in the solar 8 B neutrino flux reaching the detectors on the earth. The muon/tauon antineutrino flux is found to be disallowed at 1.4σ C.L. The sterile flux is found to be nonzero at about 1.26 standard deviations.

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The Magnetics Information Consortium (MagIC) Data Repository: Successes and Continuing Challenges

10.5194/egusphere-egu2020-12088 ◽

2020 ◽

Author(s):

Nicholas Jarboe ◽

Rupert Minnett ◽

Catherine Constable ◽

Anthony Koppers ◽

Lisa Tauxe

Keyword(s):

Earth Science ◽

Journal Article ◽

Research Capacity ◽

Digital Data ◽

Data Repository ◽

Data Set ◽

Data Repositories ◽

The Earth ◽

Ocean Sciences ◽

Space Sciences

MagIC (earthref.org/MagIC) is an organization dedicated to improving research capacity in the Earth and Ocean sciences by maintaining an open community digital data archive for rock and paleomagnetic data with portals that allow users access to archive, search, visualize, download, and combine these versioned datasets. We are a signatory of the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS)'s Enabling FAIR Data Commitment Statement and an approved repository for the Nature set of journals. We have been in collaboration with EarthCube's GeoCodes data search portal, adding schema.org/JSON-LD headers to our data set landing pages and suggesting extensions to schema.org when needed. Collaboration with the European Plate Observing System (EPOS)'s Thematic Core Service Multi-scale laboratories (TCS MSL) is ongoing with MagIC sending its contributions' metadata to TCS MSL via DataCite records.Improving and updating our data repository to meet the demands of the quickly changing landscape of data archival, retrieval, and interoperability is a challenging proposition. Most journals now require data to be archived in a "FAIR" repository, but the exact specifications of FAIR are still solidifying. Some journals vet and have their own list of accepted repositories while others rely on other organizations to investigate and certify repositories. As part of the COPDESS group at Earth Science Information Partners (ESIP), we have been and will continue to be part of the discussion on the needed and desired features for acceptable data repositories.We are actively developing our software and systems to meet the needs of our scientific community. Some current issues we are confronting are: developing workflows with journals on how to publish the journal article and data in MagIC simultaneously, sustainability of data repository funding especially in light of the greater demands on them due to data policy changes at journals, and how to best share and expose metadata about our data holdings to organizations such as EPOS, EarthCube, and Google.

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