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.

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 A review of methods to account for impacts of non-stationary climate data on extreme rainfalls for design rainfall estimation in South Africa

2021 ◽  
Vol 63 (3) ◽  
pp. 1-7
Author(s):  
K A Johnson ◽  
J C Smithers ◽  
R E Schulze
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Frequency Analysis ◽  
Extreme Rainfall ◽  
Flood Frequency Analysis ◽  
Extreme Value Analysis ◽  
Data Series ◽  
Value Analysis ◽  
Global Circulation Models ◽  
Design Rainfall

Frequency analysis of extreme rainfall and flood events are used to determine design rainfalls and design floods which are needed to design hydraulic structures such as dams, spillways and culverts. Standard methods for frequency analysis of extreme events are based on the assumption of a stationary climate. However, this assumption in rainfall and flood frequency analysis is being challenged with growing evidence of climate change. As a consequence of a changing climate, the frequency and magnitude of extreme rainfall events are reported to have increased in parts of South Africa, and these and other changes in extreme rainfall occurrences are expected to continue into the future. The possible non-stationarity in climate resulting in changes in rainfall may impact on the accuracy of the estimation of extreme rainfall quantities and design rainfall estimations. This may have significant consequences for the design of new hydraulic infrastructure, as well as for the rehabilitation of existing infrastructure. Hence, methods that account for non-stationary data, such as caused by climate change, need to be developed. This may be achieved by using data from downscaled global circulation models in order to identify non-stationary climate variables which affect rainfall, and which can then be incorporated into extreme value analysis of a non-stationary data series.

Flood frequency analysis of simulated flows

1992 ◽  
Vol 28 (9) ◽  
pp. 2375-2385 ◽  
Author(s):  
A. Allen Bradley ◽  
Kenneth W. Potter
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis
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