Predictive groundwater flood hazard mapping in lowland karst

2020 ◽  
Author(s):  
Owen Naughton ◽  
Ted McCormack ◽  
Joan Campanya
Keyword(s):  
Flood Hazard ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Extreme Value Analysis ◽  
Hazard Mapping ◽  
Stochastic Weather Generator ◽  
Value Analysis ◽  
Extreme Flood ◽  
Flood Maps

<p>The management of karst geohazards requires new and novel strategies to address the complexities inherent in karst systems and the challenges posed by a changing climate. The often rapid and widespread interaction between surface and subsurface hydrology can leave karst terrains uniquely susceptible to flooding from groundwater sources. Quantifying the frequency and magnitude of such flooding is a key step in the management of flood risk. Here, we present a novel interdisciplinary approach developed for predictive groundwater flood hazard mapping in the lowland karst plains of Ireland. This approach ties together direct and earth observation-derived hydrograph data, hydrological modelling, stochastic weather generation and extreme value analysis to generate predictive groundwater flood maps for qualifying sites.</p><p>The first step in the approach was the collection of hydrological data for sites susceptible to groundwater flooding. A monitoring network of 50 sites was established in late 2016 to provide baseline data over a 30-month period. Additionally, a methodology for delineating historic flood extents and water elevations from multi-temporal Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery was developed. This allowed hydrograph generation for ungauged sites, whilst also allowing observations of the 2015/2016 extreme flood event at gauged sites which predated monitoring. Next, site-specific hydrological models capable of constructing flood hydrographs from antecedent rainfall and soil moisture conditions were calibrated for 393 sites using a combination of observed and SAR hydrographic data (mean NSE: 0.81). A stochastic weather generator calibrated on 70-year meteorological records was used to generate long-term synthetic rainfall data for each site. These stochastic series, together with long-term average evapotranspiration, were used as input to the site models to produce long-term hydrological series from which annual maxima series were derived. Thereafter, flood frequency analysis was used to estimate predictive flood levels and generate predictive flood maps. This novel applied approach has significantly improved our fundamental scientific understanding of groundwater flooding as a geohazard, whilst also informing regional planning and development to limiting future flood vulnerability.</p>

scholarly journals Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall–snowmelt flood regimes

2014 ◽  
Vol 14 (5) ◽  
pp. 1283-1298 ◽  
Author(s):  
D. Lawrence ◽  
E. Paquet ◽  
J. Gailhard ◽  
A. K. Fleig
Keyword(s):  
Frequency Analysis ◽  
Probabilistic Model ◽  
Extreme Precipitation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Continuous Simulation ◽  
Weather Patterns ◽  
Extreme Flood ◽  
Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom–up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

May long-term historical hydrological data be misleading for flood frequency analysis in current conditions of climate change?

2020 ◽  
Author(s):  
Alexandra Fedorova ◽  
Nataliia Nesterova ◽  
Olga Makarieva ◽  
Andrey Shikhov
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Economic Damage ◽  
Irkutsk Region ◽  
Extreme Flood ◽  
The Town

<p>In June 2019, the extreme flash flood was formed on the rivers of the Irkutsk region originating from the East Sayan mountains. This flood became the most hazardous one in the region in 80 years history of observations.</p><p>The greatest rise in water level was recorded at the Iya River in the town of Tulun (more than 9 m in three days). The recorded water level was more than 5 m above the dangerous mark of 850 cm and more than 2.5 m above the historical maximum water level which was observed in 1984.</p><p>The flood led to the catastrophic inundation of the town of Tulun, 25 people died and 8 went missing. According to preliminary assessment, economic damage from the flood in 2019 amounted up to half a billion Euro.</p><p>Among the reasons for the extreme flood in June 2019 that are discussed are heavy rains as a result of climate change, melting of snow and glaciers in the mountains of the East Sayan, deforestation of river basins due to clearings and fires, etc.</p><p>The aim of the study was to analyze the factors that led to the formation of a catastrophic flood in June 2019, as well as estimate the maximum discharge of at the Iya River. For calculations, the deterministic distributed hydrological model Hydrograph was applied. We used the observed data of meteorological stations and the forecast values ​​of the global weather forecast model ICON. The estimated discharge has exceeded previously observed one by about 50%.</p><p>The results of the study have shown that recent flood damage was caused mainly by unprepared infrastructure. The safety dam which was built in the town of Tulun just ten years ago was 2 meters lower than maximum observed water level in 2019. This case and many other cases in Russia suggest that the flood frequency analysis of even long-term historical data may mislead design engineers to significantly underestimate the probability and magnitude of flash floods. There are the evidences of observed precipitation regime transformations which directly contribute to the formation of dangerous hydrological phenomena. The details of the study for the Irkutsk region will be presented.</p>

scholarly journals Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall-snowmelt flood regimes

2013 ◽  
Vol 1 (6) ◽  
pp. 6785-6828 ◽  
Author(s):  
D. Lawrence ◽  
E. Paquet ◽  
J. Gailhard ◽  
A. K. Fleig
Keyword(s):  
Frequency Analysis ◽  
Probabilistic Model ◽  
Extreme Precipitation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Continuous Simulation ◽  
Weather Patterns ◽  
Extreme Flood ◽  
Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses are simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a "bottom-up" classification procedure was used for defining a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000 yr (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, with statistical flood frequency analysis based on the annual maximum series, and with the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

scholarly journals Daily maximum runoff frequency analysis under non-stationary conditions due to climate change in the future period: Case study Ghareh Sou basin

2021 ◽  
Author(s):  
Nazanin Sadeghi Loyeh ◽  
Alireza Massah Bavani
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Extreme Value Analysis ◽  
Daily Maximum ◽  
Infiltration Capacity ◽  
Future Period ◽  
Value Analysis ◽  
The Future ◽  
Stationary Conditions

Abstract The frequency analysis of the maximum instantaneous flood is mostly based on the stationary assumption. The purpose of the present study is to compare the results of maximum instantaneous flood analysis under stationary and non-stationary conditions in Ghareh Sou basin, and also answer the question as to whether there is a difference between estimating the return period of maximum instantaneous flood in stationary and non-stationary conditions. First, the values of the temperature, wind speed, and rainfall of the study area under the two scenarios of Representative Concentration Pathway (RCP) 2.6 and 8.5 of the Hadley Centre coupled Model, version3 (HadCM3) model were downscaled. In the following, the Variable Infiltration Capacity (VIC) model was utilized to generate daily runoff. For converting the daily discharge to the maximum instantaneous flood, four methods of Fuller, Sangal, Fill Steiner, and artificial neural network (ANN) were compared. Finally, the maximum instantaneous floods of the future period were introduced to the Non-stationary Extreme Value Analysis (NEVA) software. Based on the results obtained from the research, the lack of considering the non-stationary conditions in the flood frequency analysis can result in underestimating the maximum instantaneous flood, which can also provide more risks for the related hydraulic structures.

scholarly journals Long-Term and Seasonal Trends in Global Wave Height Extremes Derived from ERA-5 Reanalysis Data

2020 ◽  
Vol 8 (12) ◽  
pp. 1015
Author(s):  
Alicia Takbash ◽  
Ian R. Young
Keyword(s):  
Southern Hemisphere ◽  
Wave Height ◽  
Significant Wave Height ◽  
Reanalysis Data ◽  
Extreme Value Analysis ◽  
Winter Storms ◽  
Value Analysis ◽  
Weather Forecasts ◽  
Significant Wave

A non-stationary extreme value analysis of 41 years (1979–2019) of global ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis) significant wave height data is undertaken to investigate trends in the values of 100-year significant wave height, Hs100. The analysis shows that there has been a statistically significant increase in the value of Hs100 over large regions of the Southern Hemisphere. There have also been smaller decreases in Hs100 in the Northern Hemisphere, although the related trends are generally not statistically significant. The increases in the Southern Hemisphere are a result of an increase in either the frequency or intensity of winter storms, particularly in the Southern Ocean.

scholarly journals Regional Flood Frequency Analysis using Dimensionless Index Flood Method

2020 ◽  
Vol 6 (12) ◽  
pp. 2425-2436
Author(s):  
Andy Obinna Ibeje ◽  
Ben N. Ekwueme
Keyword(s):  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Accurate Estimation ◽  
Frequency Curve ◽  
Model Framework ◽  
Index Flood ◽  
Extreme Flood ◽  
Regional Flood Frequency Analysis ◽  
Spatially Distributed ◽  
Regional Flood Frequency

Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed.  Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: , overestimated flood quartiles in an ungauged site of the basin.  It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed. Doi: 10.28991/cej-2020-03091627 Full Text: PDF

scholarly journals Hydro-meteorological characteristics and occurrence probability of extreme flood events in Moroccan High Atlas

2020 ◽  
Vol 11 (S1) ◽  
pp. 310-321 ◽  
Author(s):  
Mohamed El Mehdi Saidi ◽  
Tarik Saouabe ◽  
Abdelhafid El Alaoui El Fels ◽  
El Mahdi El Khalki ◽  
Abdessamad Hadri
Keyword(s):  
Return Period ◽  
Peak Flow ◽  
Extreme Event ◽  
Rare Event ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
High Atlas ◽  
Flood Water ◽  
Extreme Flood ◽  
Water Volumes

Abstract Flood frequency analysis could be a tool to help decision-makers to size hydraulic structures. To this end, this article aims to compare two analysis methods to see how rare an extreme hydrometeorological event is, and what could be its return period. This event caused many deadly floods in southwestern Morocco. It was the result of unusual atmospheric conditions, characterized by a very low atmospheric pressure off the Moroccan coast and the passage of the jet stream further south. Assessment of frequency and return period of this extreme event is performed in a High Atlas watershed (the Ghdat Wadi) using historical floods. We took into account, on the one hand, flood peak flows and, on the other hand, flood water volumes. Statistically, both parameters are better adjusted respectively to Gamma and Log Normal distributions. However, the peak flow approach underestimates the return period of long-duration hydrographs that do not have a high peak flow, like the 2014 event. The latter is indeed better evaluated, as a rare event, by taking into account the flood water volumes. Therefore, this parameter should not be omitted in the calculation of flood probabilities for watershed management and the sizing of flood protection infrastructure.

scholarly journals UAV-DEMs for Small-Scale Flood Hazard Mapping

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1717 ◽  
Author(s):  
Antonio Annis ◽  
Fernando Nardi ◽  
Andrea Petroselli ◽  
Ciro Apollonio ◽  
Ettore Arcangeletti ◽  
...  
Keyword(s):  
High Resolution ◽  
Hydrologic Modeling ◽  
Flood Hazard ◽  
Hydraulic Modeling ◽  
Flood Frequency ◽  
Hazard Mapping ◽  
Small Scale ◽  
Ungauged Basins ◽  
Modeling Framework ◽  
Flood Simulations

Devastating floods are observed every year globally from upstream mountainous to coastal regions. Increasing flood frequency and impacts affect both major rivers and their tributaries. Nonetheless, at the small-scale, the lack of distributed topographic and hydrologic data determines tributaries to be often missing in inundation modeling and mapping studies. Advances in Unmanned Aerial Vehicle (UAV) technologies and Digital Elevation Models (DEM)-based hydrologic modeling can address this crucial knowledge gap. UAVs provide very high resolution and accurate DEMs with low surveying cost and time, as compared to DEMs obtained by Light Detection and Ranging (LiDAR), satellite, or GPS field campaigns. In this work, we selected a LiDAR DEM as a benchmark for comparing the performances of a UAV and a nation-scale high-resolution DEM (TINITALY) in representing floodplain topography for flood simulations. The different DEMs were processed to provide inputs to a hydrologic-hydraulic modeling chain, including the DEM-based EBA4SUB (Event-Based Approach for Small and Ungauged Basins) hydrologic modeling framework for design hydrograph estimation in ungauged basins; the 2D hydraulic model FLO-2D for flood wave routing and hazard mapping. The results of this research provided quantitative analyses, demonstrating the consistent performances of the UAV-derived DEM in supporting affordable distributed flood extension and depth simulations.

Comparison of the Long-Term Trends of the Largest Waves in the Ice-Free Arctic Waters From Different Reanalysis Products

2018 ◽  
Author(s):  
Takuji Waseda ◽  
Takehiko Nose ◽  
Adrean Webb
Keyword(s):  
Arctic Ocean ◽  
The Arctic ◽  
Extreme Value Analysis ◽  
Positive Trend ◽  
Value Analysis ◽  
The Arctic Ocean ◽  
Ship Route ◽  
Long Term Trends ◽  
Horizontal Grid

The long-term trends of the expected largest waves in the ice-free Arctic waters from Laptev to Beaufort Seas was studied analyzing the ERA-interim reanalysis from 1979 to 2016. The analysis showed that the positive trend is largest in October and increased almost 70 cm in 38 years. For ships navigating the Northern Ship Route, it is important to know what the possible largest waves to expect during its cruise. In view of conducting the extreme value analysis, the uncertainty of the largest wave needs to be validated. However, the observation in the Arctic Ocean is limited. We, therefore, rely on the reanalysis wave products in the Arctic Ocean, whose uncertainty is yet to be determined. ERA-Interim and ERA-5 are compared in the Laptev, the East Siberian, Chukchi and Beaufort Seas. The comparison is relevant as the two products differ in its horizontal grid resolution and availability of the satellite altimeter significant wave height data assimilation. During 2010–2016 when the ERA5 is available, only a small difference from ERA-Interim was detected in the mean. However, the expected largest waves in the domain tended to be large for the ERA-5, 8% normalized bias. The tendency was quite similar with a high correlation of 0.98.

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