scholarly journals Regionalization of Rainfall Intensity-Duration-Frequency (IDF) Curves With L-Moments Method Using Neural Gas Networks

2021 ◽  
Author(s):  
Mohammadreza Mahmoudi ◽  
Saeid Eslamian ◽  
Saeid Soltani
Keyword(s):  
Flood Control ◽  
Rain Gauge ◽  
Single Site ◽  
Regional Frequency Analysis ◽  
Measuring Device ◽  
Gas Networks ◽  
Neural Gas ◽  
L Moments ◽  
Idf Curves ◽  
Intensity Duration Frequency

Abstract Floods are one of the most frequent and destructive natural events which lead to lots of human and financial losses with damage to the houses, farms, roads, and other buildings. Intensity-duration-frequency (IDF) curves are the main and practical tools that have been used for flood control studies including the design of the water structures. In many cases, there is not any measuring device at the desired place or their information are not useful if there is any available. In this case, it is not possible to extract these curves through the conventional methods. Regionalizing the IDF curves is a method that has solved the issues mentioned in the common methods. In this research, the regionalized IDF curves are extracted in Khozestan province, Iran using 21 rain gauge stations through L-moments and neural gas networks. Clustering is one of the most effective steps and a prerequisite for regional frequency analysis (RFA) that divides the region and existing stations into hydrologically homogenous regions. In this study, clustering is done using two new models named neural gas (NG) and growing neural gas (GNG) network. Comparing the regional IDF curves with single site curves, it was found that neural gas network models had a more accurate performance and higher efficiency so that they had the lowest estimate error amount among other models. Also, due to the acceptable difference between regional and single site curves, the efficiency of L-Moments in RFA was evaluated as appropriate.

Interpolation of Regionalized Intensity Duration Frequency (IDF) Estimates based on the observed precipitation data of Baden Wurttemberg (BW), Germany

2021 ◽  
Author(s):  
Bushra Amin ◽  
András Bárdossy
Keyword(s):  
Mean Square Error ◽  
Hydrological Cycle ◽  
Information Criteria ◽  
Regional Frequency Analysis ◽  
Mean Square ◽  
Time Duration ◽  
Ungauged Sites ◽  
Marquardt Algorithm ◽  
Idf Curves ◽  
Intensity Duration Frequency

<p>This study is intended to carry out the spatial mapping with ordinary Kriging (OK) of regional point Intensity Duration Frequency (IDF) estimates for the sake of approximation and visualization at ungauged location. Precipitation IDF estimates that offer us valuable information about the frequency of occurrence of extreme events corresponding to different durations and intensities are derived through the application of robust and efficient regional frequency analysis (RFA) based on L-moment algorithm. IDF curves for Baden Wrttemberg (BW) are obtained from the long historical record of daily and hourly annual maximum precipitation series (AMS) provided by German Weather Service from 1960-2020 and 1949-2020 respectively under the assumption of stationarity. One of the widely used Gumbel (type 1)  distribution is applied for IDF analysis because of its suitability for modeling maxima. The uncertainty in IDF curves is determined by the bootstrap method and are revealed in the form of the prediction and confidence interval for each specific time duration on graph. Five metrics such as root mean square error (RMSE), coefficient of determination (R²), mean square error (MSE), Akaike information criteria (AIC) and Bayesian information criteria (BIC) are used to assess the performance of the employed IDF equation. The coefficients of 3-parameteric non-linear IDF equation is determined for various recurrence interval by means of Levenberg–Marquardt algorithm (LMA), also referred to as damped least square (DLS) method. The estimated coefficients vary from location to location but are insensitive to duration. After successfully determining the IDF parameters for the same return period, parametric contour or isopluvial maps can be generated using OK as an interpolation tool with the intention to provide estimates at ungauged locations. These estimated regional coefficients of IDF curve are then fed to the empirical intensity frequency equation that may serve to estimate rainfall intensity for design purposes for all ungauged sites. The outcomes of this research contribute to the construction of IDF-based design criteria for water projects in ungauged sites located anywhere in the state of BW.</p><p>In conclusion, we conducted IDF analysis for the entire state of BW as it is considered to be more demanding due to the increased impact of climate change on the intensification of hydrological cycle as well as the expansion of urban areas rendering watershed less penetrable to rainfall and run-off, the better understanding of spatial heterogeneity of intense rainfall patterns for the proposed domain.</p>

scholarly journals Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

2017 ◽  
Vol 21 (5) ◽  
pp. 2389-2404 ◽  
Author(s):  
Francesco Marra ◽  
Efrat Morin ◽  
Nadav Peleg ◽  
Yiwen Mei ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Remote Sensing ◽  
Return Period ◽  
Eastern Mediterranean ◽  
Rain Gauge ◽  
Catchment Scale ◽  
Weather Radars ◽  
The Earth ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Frequency Curves

Abstract. Intensity–duration–frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2–5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( ∼  50,  ∼  100, and  ∼  150 % for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.

scholarly journals Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

2012 ◽  
Vol 9 (12) ◽  
pp. 14205-14230
Author(s):  
A. Chebbi ◽  
Z. K. Bargaoui ◽  
M. da Conceição Cunha
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Optimization Approach ◽  
Rain Gauges ◽  
Hydrological Variability ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Rain Gauge Network

Abstract. Based on rainfall intensity-duration-frequency (IDF) curves, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimisation can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short and a long term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. So, it is proposed to virtually augment it by 25, 50, 100 and 160% which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

scholarly journals Regional frequency analysis of daily maximum rainfall in Haryana

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 835-846
Author(s):  
MOHIT NAIN ◽  
B. K. HOODA
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Quantile Estimation ◽  
Regional Frequency Analysis ◽  
Daily Maximum ◽  
Maximum Rainfall ◽  
L Moments ◽  
Regional Frequency

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure ( DIST Z ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions;Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

scholarly journals Comparing Intensity–Duration–Frequency curves derived from CMORPH and radar rainfall estimates over the Eastern Mediterranean

2016 ◽  
Author(s):  
Francesco Marra ◽  
Efrat Morin ◽  
Nadav Peleg ◽  
Yiwen Mei ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Return Period ◽  
Eastern Mediterranean ◽  
Rain Gauge ◽  
Catchment Scale ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Rainfall Extremes ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Tail Distributions

Abstract. Intensity–Duration–Frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment scale information is required. Remotely sensed rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high resolution information on rainfall extremes at regional or even global scales: their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the Eastern Mediterranean (covering Mediterranean, semiarid and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker tail distributions than satellite, in particular for short durations, and that the shape parameters depends on the spatial and temporal aggregation scales. The spatial correlation between radar-IDFs and satellite-IDFs is as high as 0.7 for 2–5 years return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period (~ 50 %, ~ 100 % and ~ 150 % for Mediterranean, semiarid and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.

Regional frequency analysis of daily maximum rainfall in Haryana

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 835-846
Author(s):  
MOHIT NAIN ◽  
B. K. HOODA
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Quantile Estimation ◽  
Regional Frequency Analysis ◽  
Daily Maximum ◽  
Maximum Rainfall ◽  
L Moments ◽  
Regional Frequency

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure (  ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions; Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

scholarly journals Introduction of a simple formula for estimating approximate intensity-duration-frequency curves from daily rain gauge data

2021 ◽  
Author(s):  
Rasmus Benestad ◽  
Julia Lutz ◽  
Anita Verpe Dyrrdal ◽  
Jan Erik Haugen ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Simple Formula ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Complete Representation ◽  
Orographic Rainfall ◽  
Return Levels ◽  
Gauge Data ◽  
Idf Curves ◽  
Intensity Duration Frequency

<p>A simple formula for estimating approximate values of return levels for sub-daily rainfall is presented. It was derived from a combination of simple mathematical principles, approximations and fitted to 10-year return levels taken from intensity-duration-frequency (IDF) curves representing 14 sites in Oslo. The formula has subsequently been evaluated against IDF curves from independent sites elsewhere in Norway. Since it only needs 24 h rain gauge data as input, it can provide approximate estimates for the IDF curves used to describe sub-daily rainfall return levels. In this respect, it can be considered as a means of downscaling regarding the timescale, given an approximate power-law dependency between temporal scales. One clear benefit of this framework is that observational data is far more abundant for 24 hr rain gauge records than for sub-daily measurements. Furthermore, it does not assume stationarity and is well-suited for projecting IDF curves for a future climate. This method also provides a framework that strengthens the connection between climatology and meteorology to hydrology, and can be applied to risk management in terms of flash flooding. The proposed formula can also serve as a 'yardstick' to study how different meteorological phenomena with different timescales influence the local precipitation, such as convection, weather fronts, cyclones, atmospheric rivers, or orographic rainfall. An interesting question is whether the slopes of the IDF curves change as a consequence of climate change and if it is possible to predict how they change. One way to address this question is to apply the framework to simulations by convective-permitting regional climate models that offer a complete representation of both sub-daily and daily precipitation over time and space. </p>

scholarly journals Evaluating the performance of a max-stable process for estimating intensity-duration-frequency curves

2020 ◽  
Author(s):  
Oscar E. Jurado ◽  
Jana Ulrich ◽  
Henning W. Rust
Keyword(s):  
Reference Model ◽  
Stable Process ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Dependence Structure ◽  
Stable Model ◽  
Asymptotic Dependence ◽  
Skill Scores ◽  
Idf Curves ◽  
Intensity Duration Frequency

<p>A recent development in the modeling of intensity-duration-frequency (IDF) curves involves the use of a spatial max-stable process to explicitly account for asymptotic dependence between durations. To accomplish this, we use a duration-space instead of a geographic-space, following Tyralis and Langousis (2018). The resulting IDF curves can then be used to estimate extreme rainfall for any arbitrary rainfall duration. We aim to determine whether the use of a model that explicitly accounts for the dependence between durations could improve the estimates of extreme rainfall. The performance of the max-stable process is compared to the duration dependent GEV (d-GEV) approach for IDF-curve estimation proposed by Koutsoyiannis et al. (1998). The max-stable approach explicitly models the dependence via a parametric model, while the d-GEV approach assumes that the durations are independent. The performance of both approaches is assessed for two scenarios, in a controlled simulation experiment, and for observations from a rain gauge. A Brown-Resnick max-stable process and a duration-dependent GEV was fitted to the data in both scenarios. The performance is measured using the Quantile Skill Score (QSS) with the d-GEV as the reference model. The resulting skill scores show that correctly specifying the dependence structure leads to the max-stable model perfomring similarly to the d-GEV. This pattern was observed also for low and high levels of dependence.</p>

scholarly journals Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

2013 ◽  
Vol 17 (10) ◽  
pp. 4259-4268 ◽  
Author(s):  
A. Chebbi ◽  
Z. K. Bargaoui ◽  
M. da Conceição Cunha
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Optimization Approach ◽  
Rain Gauges ◽  
Hydrological Variability ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Rain Gauge Network

Abstract. Based on rainfall intensity-duration-frequency (IDF) curves, fitted in several locations of a given area, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimization can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables, and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short- and a long-term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. Therefore, it is proposed to augment it by 25, 50, 100 and 160% virtually, which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

scholarly journals Regional Frequency Analysis of Maximum Monthly Rainfall in Haryana State of India Using L-Moments

2021 ◽  
Author(s):  
Mohit Nain ◽  
B. K. Hooda
Keyword(s):  
Return Period ◽  
Goodness Of Fit ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Regional Frequency Analysis ◽  
Goodness Of Fit Test ◽  
Pearson Type ◽  
L Moments ◽  
Regional Frequency

The paper aims to select the appropriate regional frequency distribution for the maximum monthly rainfall and estimation of quantiles using L-moments for the 27 rain gauge stations in Haryana. These 27 rain gauge stations were grouped into three homogeneous regions (Region-1, Region-2, and Region-3) using Ward’s method of cluster analysis. To confirm the homogeneity of each region, L-moments based measure of heterogeneity was used. For each homogeneous region, a regional distribution was selected with the help of the L-moments ratio diagram and goodness-of-fit test. Results of the goodness-of-fit test and L-moments ratio diagram indicated that Generalized Logistic and Generalized Extreme Value distributions were best- fitted regional frequency distributions for the Region-1 and Region-2 respectively while for Region-3, Pearson Type-3) was best-fitted distribution. The quantiles for each region were calculated and the regional growth curves were developed. The accuracy measurements were determined using Monte Carlo simulations for the regional quantiles. Results of simulations showed that uncertainty in regional quantiles measured by Root Mean Square Error value and 90 percent error limits were small when the return period was low but uncertainty in quantiles increases as the return period increases.

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