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.

scholarly journals Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

2017 ◽  
Vol 21 (10) ◽  
pp. 5385-5399 ◽  
Author(s):  
Edouard Goudenhoofdt ◽  
Laurent Delobbe ◽  
Patrick Willems
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Regional Frequency Analysis ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Temporal Sampling ◽  
Rainfall Extremes ◽  
Regional Frequency

Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965–2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

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.

Estimation of Extreme Rainfall in South Africa and Development of Methods to Account for Non-stationary Climate Data

2020 ◽  
Author(s):  
Katelyn Johnson ◽  
Jeff Smithers
Keyword(s):  
South Africa ◽  
Frequency Analysis ◽  
Return Period ◽  
Extreme Rainfall ◽  
Climate Data ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
New Approach ◽  
Design Rainfall ◽  
High Return

<p>The estimation of design rainfalls and design floods are required by engineers and hydrologists to design and quantify the risk of failure of hydraulic structures. Extreme design rainfall quantities such as high-return period rainfalls and the probable maximum precipitation (PMP) are needed to design high-hazard hydraulic structures. In South Africa, previous design rainfall estimates have been produced up to the 200 year return period. PMP estimates were last determined nearly 50 years ago based on only 30 years of data. Most studies on extreme rainfall reported are based on frequency analysis assuming stationary conditions. Previous studies in South Africa have assumed a stationary climate. However, the assumption of a stationary climate in rainfall and flood frequency analysis has been challenged owing to evidence of climate change. Recent literature indicates that the magnitude and frequency of extreme rainfall events has been changing and this is likely to continue in future. Hence, methods to account for trends in extreme rainfall events in a changing environment need to be developed. In addition, the concept of PMP, particularly as used for the design and safety evaluation of large dams in South Africa, is being challenged with the recommendation that high-return period design rainfalls be used in these assessments. The aims of this study are: (i) to estimate extreme design rainfall values, with a focus on return periods greater than 200 years, (ii) to update PMP estimates using updated data and modernised methods, and (iii) to account for non-stationary climate data in the estimation of these extreme rainfall events in South Africa. Frequency analysis using LH-moments, which more accurately fit the upper tail of distributions, have been used to estimate high-return period design rainfalls. Regular L-moments are shown to overestimate the extreme rainfall quantities when compared to LH-moments by giving undue favour to outliers. PMP estimates have been determined using a storm maximisation and transposition approach. Radial Basis Functions (RFBs) have been used to transpose PMP estimates to ungauged locations, producing PMPs for the entire country. Approximately 80 % of the new PMPs are greater than the previous estimates. This is probably due to the many limitations of the old approach and differences used in the new approach, indicating that the new approach undertaken in this study may provide improved estimates. The PMP represents the upper limit of extreme rainfall, however, comparisons of high-return period rainfalls to the PMP show that the PMP is sometimes exceeded by the high-return period rainfalls. To develop methods to estimate extreme design rainfall events in a non-stationary climate, this study explores the impacts of climate drivers, such as the Southern Oscillation Index (SOI), and changes in atmospheric variables, such as dew point temperature, on high-return period rainfalls and the PMP.</p>

scholarly journals At-site and regional frequency analysis of extreme precipitation from radar-based estimates

2017 ◽  
Author(s):  
Edouard Goudenhoofdt ◽  
Laurent Delobbe ◽  
Patrick Willems
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Precipitation ◽  
Rain Gauge ◽  
Regional Frequency Analysis ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Rain Gauges ◽  
Rainfall Extremes ◽  
Regional Frequency

Abstract. In Belgium, only rain gauge time-series have been used so far to study extreme precipitation at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, independent sliding 1 h and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The extremes are fitted to the exponential distribution using regression in QQ-plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift of convective cells and strong radar signal attenuation. Differences between radar and gauge values are caused by spatial and temporal sampling, gauge rainfall underestimations and radar errors due to the relation between reflectivity and rain rate. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis is performed on radar data within 20 km of the locations of 4 rain gauges with records from 1965 to 2008. Assuming that the extremes are correlated within the region, the fit to the two closest rain gauge data is within the confidence interval of the radar fit, which is small due to the sample size. In Brussels, the extremes on the period 1965–2008 from a rain gauge are significantly lower than the extremes from an automatic gauge and the radar on the period 2005–2016. For 1 h duration, the location parameter varies slightly with topography and the scale parameter exhibits some variations from region to region. The radar-based extreme value analysis can be extended to other durations.

scholarly journals Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

2019 ◽  
Vol 23 (5) ◽  
pp. 2225-2243 ◽  
Author(s):  
Guo Yu ◽  
Daniel B. Wright ◽  
Zhihua Zhu ◽  
Cassia Smith ◽  
Kathleen D. Holman
Keyword(s):  
Soil Moisture ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Extreme Rainfall ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Agricultural Watershed ◽  
Flood Frequency Analysis ◽  
Antecedent Conditions ◽  
Hydrologic Change

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

Reevaluation of Design Waves Off the Western Indian Coast Considering Climate Change

2016 ◽  
Vol 50 (1) ◽  
pp. 88-98 ◽  
Author(s):  
Pentapati Satyavathi ◽  
Makarand C. Deo ◽  
Jyoti Kerkar ◽  
Ponnumony Vethamony
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
General Circulation ◽  
Generalized Pareto Distribution ◽  
Circulation Model ◽  
Offshore Structures ◽  
Future Climate Change ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wave Data

AbstractKnowledge of design waves with long return periods forms an essential input to many engineering applications, including structural design and analysis. Such extreme or long-term waves are conventionally evaluated using observed or hindcast historical wave data. Globally, waves are expected to undergo future changes in magnitude and behavior as a result of climate change induced by global warming. Considering future climate change, this study attempts to reevaluate significant wave height (Hs) as well as average spectral wave period (Tz) with a return period of 100 years for a series of locations along the western Indian coastline. Historical waves are simulated using a numerical wave model forced by wind data extracted from the archives of the National Center for Environmental Prediction and the National Center for Atmospheric Research, while future wave data are generated by a state-of-the-art Canadian general circulation model. A statistical extreme value analysis of past and projected wave data carried out with the help of the generalized Pareto distribution showed an increase in 100-year Hs and Tz along the Indian coastline, pointing out the necessity to reconsider the safety of offshore structures in the light of global warming.

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 FLOOD FREQUENCY ANALYSIS AND IMPACT ASSESSMENT FOR CLIMATE CHANGE IN THE NAGARA RIVER BASIN

2020 ◽  
Vol 8 (1) ◽  
pp. 79-86
Author(s):  
Morihiro HARADA ◽  
Yasuyuki MARUYA ◽  
Toshiharu KOJIMA ◽  
Daisuke MATSUOKA ◽  
Yujin NAKAGAWA ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
River Basin ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis
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