scholarly journals Simple scaling of extreme precipitation in North America

2017 ◽  
Vol 21 (11) ◽  
pp. 5823-5846 ◽  
Author(s):  
Silvia Innocenti ◽  
Alain Mailhot ◽  
Anne Frigon
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Probability Distributions ◽  
Spatial Scales ◽  
Extreme Rainfall ◽  
Parameter Estimates ◽  
Infrastructure Design ◽  
Simple Scaling ◽  
Intensity Duration Frequency ◽  
Extreme Probability

Abstract. Extreme precipitation is highly variable in space and time. It is therefore important to characterize precipitation intensity distributions on several temporal and spatial scales. This is a key issue in infrastructure design and risk analysis, for which intensity–duration–frequency (IDF) curves are the standard tools used for describing the relationships among extreme rainfall intensities, their frequencies, and their durations. Simple scaling (SS) models, characterizing the relationships among extreme probability distributions at several durations, represent a powerful means for improving IDF estimates. This study tested SS models for approximately 2700 stations in North America. Annual maximum series (AMS) over various duration intervals from 15 min to 7 days were considered. The range of validity, magnitude, and spatial variability of the estimated scaling exponents were investigated. Results provide additional guidance for the influence of both local geographical characteristics, such as topography, and regional climatic features on precipitation scaling. Generalized extreme-value (GEV) distributions based on SS models were also examined. Results demonstrate an improvement of GEV parameter estimates, especially for the shape parameter, when data from different durations were pooled under the SS hypothesis.

scholarly journals Simple Scaling of extreme precipitation in North America

2016 ◽  
Author(s):  
Silvia Innocenti ◽  
Alain Mailhot ◽  
Anne Frigon
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Probability Distributions ◽  
Spatial Scales ◽  
Extreme Rainfall ◽  
Parameter Estimates ◽  
Infrastructure Design ◽  
Simple Scaling ◽  
Intensity Duration Frequency ◽  
Extreme Probability

Abstract. Extreme precipitation is highly variable in space and time. It is therefore important to characterize precipitation intensity distributions at several temporal and spatial scales. This is a key issue in infrastructure design and risk analysis, for which Intensity-Duration-Frequency (IDF) curves are the standard tools used for describing the relationships among extreme rainfall intensities, their frequencies, and their durations. Simple Scaling (SS) models, characterizing the relationships among extreme probability distributions at several durations, represent a powerful means for improving IDF estimates. This study tested SS models for approximately 2700 stations in North America. Annual Maxima Series (AMS) over various duration intervals from 15 h to 7 days were considered. The range of validity, magnitude, and spatial variability of the estimated scaling exponents were investigated. Results provide additional guidance for the influence of both local geographical characteristics, such as topography, and regional climatic features on precipitation scaling. Generalized Extreme Value (GEV) distributions based on SS models were also examined. Results demonstrate an improvement of GEV parameter estimates, especially for the shape parameter, when data from different durations were pooled under the SS hypothesis.

scholarly journals Characterising the space–time structure of rainfall in the Sahel with a view to estimating IDAF curves

2014 ◽  
Vol 18 (12) ◽  
pp. 5093-5107 ◽  
Author(s):  
G. Panthou ◽  
T. Vischel ◽  
T. Lebel ◽  
G. Quantin ◽  
G. Molinié
Keyword(s):  
Extreme Rainfall ◽  
Rain Gauge ◽  
Space Time ◽  
Reduction Factor ◽  
Dynamic Scaling ◽  
Catchment Scale ◽  
Dynamical Scaling ◽  
Space And Time ◽  
Simple Scaling ◽  
Intensity Duration Frequency

Abstract. Intensity–duration–area–frequency (IDAF) curves are increasingly demanded for characterising the severity of storms and for designing hydraulic structures. Their computation requires inferring areal rainfall distributions over the range of space scales and timescales that are the most relevant for hydrological studies at catchment scale. In this study, IDAF curves are computed for the first time in West Africa, based on the data provided by the AMMA-CATCH Niger network, composed of 30 recording rain gauges having operated since 1990 over a 16 000 km2 area in south-western Niger. The IDAF curves are obtained by separately considering the time (intensity–duration–frequency, IDF) and space (areal reduction factor, ARF) components of the extreme rainfall distribution. Annual maximum intensities are extracted for resolutions between 1 and 24 h in time and from point (rain gauge) to 2500 km2 in space. The IDF model used is based on the concept of scale invariance (simple scaling) which allows the normalisation of the different temporal resolutions of maxima series to which a global generalised extreme value (GEV) is fitted. This parsimonious framework allows one to use the concept of dynamic scaling to describe the ARF. The results show that coupling a simple scaling in space and time with a dynamical scaling that relates to space and time allows one to satisfactorily model the effect of space–time aggregation on the distribution of extreme rainfall.

Estimating IDF-Relations consistently using a duration-dependent GEV with spatial covariates

2020 ◽  
Author(s):  
Jana Ulrich ◽  
Madlen Peter ◽  
Oscar E. Jurado ◽  
Henning W. Rust
Keyword(s):  
Extreme Precipitation ◽  
Probability Distributions ◽  
Skill Score ◽  
Exceedance Probability ◽  
Ungauged Sites ◽  
Extreme Precipitation Events ◽  
One Step ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
A Site

<p>Intensity-Duration-Frequency (IDF) Curves are a popular tool in Hydrology for estimating the properties of extreme precipitation events. They describe the relationship between rainfall intensity and duration for a given non-exceedance probability (or frequency). For a site where precipitation measurements are available, these curves can be estimated consistently over durations using a duration-dependent GEV (d-GEV, after Koutsoyiannis et al. 1998). In this approach, the probability distributions are modeled simultaneously for all durations.</p><p>Additionally, we integrate covariates to describe the spatial variability of the d-GEV parameters so that we can model the distribution of extreme precipitation for a range of durations and locations in one step. Thus IDF Curves can be estimated even at ungauged sites. Further advantages are parameter reduction and more efficient use of the available data. We use the Quantile Skill Score to investigate under which conditions this method leads to an improved estimate compared to the single-site approach and to evaluate the performance at ungauged sites.</p>

scholarly journals Extreme rainfall and IDF equations for Alagoas State, Brazil

2020 ◽  
Vol 15 (6) ◽  
pp. 1
Author(s):  
Álvaro José Back ◽  
Sabrina Baesso Cadorin ◽  
Sérgio Luciano Galatto
Keyword(s):  
Hydrological Modeling ◽  
Probability Distributions ◽  
Gumbel Distribution ◽  
Extreme Rainfall ◽  
Maximum Rainfall ◽  
Gev Distribution ◽  
Significance Level ◽  
Intensity Duration Frequency ◽  
Conservation Projects ◽  
Anderson Darling

Intensity-duration-frequency (IDF) equations have important applications in several engineering areas such as urban drainage designs, hydrological modeling, and soil conservation projects. This study analyzes the annual maximum series and fits IDF equations for 44 rainfall stations in Alagoas State, Brazil. We adjusted parameters of the Gumbel distribution (GD) and the Generalized Extreme Value (GEV) distribution. The fitting of the observed data to the probability distributions, as well as the selection of the best distribution, were based on the Kolmogorov-Smirnov and Anderson-Darling tests at a 5% significance level. The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations. The estimated IDF equations showed a good fit, with determination coefficients above 0.991. The maximum rainfall intensities have spatial variation following the climatic zones of the state. The fitted equations allow estimating rainfall intensities from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of less than 6.83 mm h-1.

scholarly journals Intensity–duration–frequency (IDF) rainfall curves in Senegal

2018 ◽  
Vol 18 (7) ◽  
pp. 1849-1866 ◽  
Author(s):  
Youssouph Sane ◽  
Geremy Panthou ◽  
Ansoumana Bodian ◽  
Theo Vischel ◽  
Thierry Lebel ◽  
...  
Keyword(s):  
Spatial Vision ◽  
Extreme Rainfall ◽  
Sub Saharan Africa ◽  
Parameter Estimates ◽  
Infrastructure Development ◽  
Demographic Pressure ◽  
Tropical Regions ◽  
Scaling Parameter ◽  
Idf Curves ◽  
Intensity Duration Frequency

Abstract. Urbanization resulting from sharply increasing demographic pressure and infrastructure development has made the populations of many tropical areas more vulnerable to extreme rainfall hazards. Characterizing extreme rainfall distribution in a coherent way in space and time is thus becoming an overarching need that requires using appropriate models of intensity–duration–frequency (IDF) curves. Using a 14 series of 5 min rainfall records collected in Senegal, a comparison of two generalized extreme value (GEV) and scaling models is carried out, resulting in the selection of the more parsimonious one (four parameters), as the recommended model for use. A bootstrap approach is proposed to compute the uncertainty associated with the estimation of these four parameters and of the related rainfall return levels for durations ranging from 1 to 24 h. This study confirms previous works showing that simple scaling holds for characterizing the temporal scaling of extreme rainfall in tropical regions such as sub-Saharan Africa. It further provides confidence intervals for the parameter estimates and shows that the uncertainty linked to the estimation of the GEV parameters is 3 to 4 times larger than the uncertainty linked to the inference of the scaling parameter. From this model, maps of IDF parameters over Senegal are produced, providing a spatial vision of their organization over the country, with a north to south gradient for the location and scale parameters of the GEV. An influence of the distance from the ocean was found for the scaling parameter. It is acknowledged in conclusion that climate change renders the inference of IDF curves sensitive to increasing non-stationarity effects, which requires warning end-users that such tools should be used with care and discernment.

scholarly journals Assessing of IDF curves for hydrological design by simple scaling of 1-day precipitation totals

2010 ◽  
Vol 18 (3) ◽  
pp. 1-6 ◽  
Author(s):  
M. Bara ◽  
S. Kohnová ◽  
J. Szolgay ◽  
L. Gaál ◽  
K. Hlavčová
Keyword(s):  
Extreme Rainfall ◽  
Meteorological Station ◽  
Short Term ◽  
Scaling Properties ◽  
Daily Data ◽  
Design Values ◽  
Simple Scaling ◽  
Recurrence Intervals ◽  
Idf Curves ◽  
Intensity Duration Frequency

Assessing of IDF curves for hydrological design by simple scaling of 1-day precipitation totalsIn this paper the scaling properties of short term extreme rainfall in Slovakia were investigated. The simple scaling theory was applied to the intensity-duration-frequency (IDF) characteristics of a short duration rainfall. This method allows for the estimation of the design values of rainfall of selected recurrence intervals and durations shorter than a day by using only the daily data. The scaling behavior of rainfall intensities was examined, and the possibility of using simple scaling in Slovakia was verified. The methodology for the simple scaling of rainfall is demonstrated using an example of the meteorological station in Ilava.

Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales

2021 ◽  
Author(s):  
Qiaohong Sun ◽  
Francis Zwiers ◽  
Xuebin Zhang ◽  
Jun Yan
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Spatial Scales ◽  
External Forcing ◽  
Annual Maximum ◽  
Time Pattern ◽  
Detection And Attribution ◽  
Attribution Analysis ◽  
Station Data

<p>Previous detection and attribution analyses suggest that human-induced increases in greenhouse gases have contributed to observed changes in extreme precipitation. However, all previous detection and attribution studies of observed changes in extreme precipitation i) use station data that have been heavily processed via gridding, transformation, and spatial and temporal averaging or other dimension reduction approaches, as well as using climate models to estimate the responses to external forcing, ii) also use models to estimate the unforced natural variability of extreme precipitation. Both aspects reduce user confidence in detection and attribution results.</p><p>We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing and use climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. We use records of the annual maximum one day (Rx1day) or five consecutive days (Rx5day) precipitation accumulations from 5,081 land-based stations spanning the period 1950-2014 with at least 45 years of coverage, including at least 3 years in the period 2010-2014. Expected responses to external forcings are estimated from ALL and NAT forcings simulations from large ensemble simulations performed with CanESM2 and from a multi-model ensemble that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Changes at these stations are evaluated by fitting non-stationary Generalized Extreme Value (GEV) distributions at individual stations to the logarithms of observed Rx1day and Rx5day values. Non-stationarity in the GEV distribution is permitted by using location parameters that are allowed to be linearly dependent on climate model-simulated responses in ln(Rx1day) and ln(Rx5day) to different forcings. We perform the detection and attribution analysis across different spatial scales, including the global, continental, and regional scales.</p><p>The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia, and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistency between our study and previous studies substantially increases confidence in detection and attribution findings concerning extreme precipitation. The attributed effects of anthropogenic forcing on extreme precipitation include substantially decreased waiting times between extreme annual maximum events in regions where anthropogenic influence has been detected and intensification of extreme precipitation that, at a global scale is consistent with the Clausius-Clapeyron rate of about 7% per 1°C of warming.  </p>

scholarly journals Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales

2021 ◽  
pp. 1-51
Author(s):  
Qiaohong Sun ◽  
Francis Zwiers ◽  
Xuebin Zhang ◽  
Jun Yan
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Spatial Scales ◽  
Coupled Model ◽  
Anthropogenic Influence ◽  
Land Area ◽  
Temporal Dimension ◽  
Time Pattern ◽  
Global Land

AbstractThis study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction or transformation to unitless indices and uses climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.

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