Flood Time Series Research Articles

ENSEMBLE MODELS FOR FORECASTING FLOODS IN GREAT BRITAIN BASED ON SOLAR ACTIVITY

This study aims to uncover hidden dependencies between streams of solar particles emanating from the Sun and flood events caused by precipitation in the UK. The uniqueness of this work lies in applying solar activity data as input parameters for machine learning and correlating them with real data on precipitation intensity and floods in the UK, which serve as output data. The analysis covered 20 significant floods from October 2001 to December 2019. The analysis was performed on a daily basis, taking into account the time shift between solar emissions and their impact on terrestrial weather conditions, which ranged from 0 to 9 days. The study employs correlation analysis to determine the degree of interrelation between time series of solar activity and floods, laying the foundation for a deeper understanding of possible cause-and-effect relationships. Subsequently, using predictive modeling methods, including decision tree algorithms and ensemble classification models, the potential connection between changes in solar activity and terrestrial floods was explored. An ensemble of models was created using the hard voting method, which takes into account various indicators such as proton density, differential proton flux, and ion temperature, with a time shift of up to 9 days. The analysis showed that these key parameters could effectively predict the occurrence of floods with up to 92% accuracy for the specified period. The results underscore the importance of integrating space weather and solar activity into the models for predicting terrestrial weather conditions, particularly in the context of floods in England. This not only allows for a better understanding of the causes of natural disasters but also enhances the effectiveness of emergency planning and reduces potential damages from them. Thus, this work makes a significant contribution to the field of space weather research and its impact on Earth, particularly in the areas of hydrometeorology and flood risk management.

Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation

<abstract> <p>Flood time series forecasting stands a critical challenge in precise predictive models and reliable error estimation methods. A novel approach utilizing a hybrid deep learning model for both point and interval flood prediction is presented, enhanced by improved kernel density estimation (KDE) for prediction comparison and error simulation. Firstly, an optimized gated recurrent unit-time convolutional network (GRU-TCN) is constructed by tuning the internal structure of the TCN, the activation function, the L2 regularization, and the optimizer. Then, Pearson Correlation is used for feature selection, and the hyperparameters of the improved GRU-TCN are optimized by the subtraction-average-based optimizer (SABO). To further assess the prediction uncertainty, interval predictions are provided via Non-parametric KDE, with an optimized bandwidth setting for accurate error distribution simulation. Experimental comparisons are made on 5-year hydro-meteorological daily data from two stations along the Yangtze River. The proposed model surpasses long short-term memory network (LSTM), TCN, GRU, TCN-LSTM, and GRU-TCN, with a reduction of more than 13% in root mean square error (RMSE) and approximately 15% in mean absolute error (MAE), resulting in better interval estimation and error control. The improved kernel density estimation curves for the errors are closer to the mean value of the confidence intervals, better reflecting the trend of the error distribution. This research enhances the accuracy and reliability of flood predictions and improves the capacity of humans to cope with climate and environmental changes.</p> </abstract>

Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions.

Time-series generative adversarial networks for flood forecasting

Reliable flood forecasting serves as one of the fundamental tasks in flood management. However, the forecast performance of modern data-driven models depends heavily on the quantity and quality of training data. The lack of field data of flood events highly underpins the development of machine learning (ML) flood forecasting techniques. With consideration of the rareness of flood events and the high dimensionality of flood time series, two latest variants of generative adversarial networks (GANs), Time-series Generative Adversarial Network (TimeGAN) and Real-world Time Series Generative Adversarial Network (RTSGAN), were applied to generate synthetic flood timeseries. In the example analysis of the Xijiang River Basin in southern China, results show that time-series GANs can accurately and efficiently mimic the spatiotemporal correlations between flood series from multiple sites, and RTSGAN outperforms TimeGAN especially when the lengths of time series are long. Additionally, gradient boosting regression tree (GBRT), long-short term memory (LSTM) and quantile regression method integrated LSTM networks (QRLSTM) flood forecasting models were trained with real sequences and an extra amount of synthetic sequences. It shows that the expansion of training datasets in this way is promising in reducing prediction errors of classical machine learning models. In the 3-day-ahead flood forecast, the introduction of synthetic training datasets has little positive effect on both GBRT and LSTM. In 24-hour-ahead flood forecast, as the lead period increases, GBRT-RTSGAN outperforms GBRT evidenced by a 6.7% increase in Nash-Sutcliffe efficiency (NSE) metric, a 56.5% reduction in average absolute relative error (AARE) metric and a 33.1% reduction in root-mean square error (RMSE) metric. For interval forecast, the use of synthetic training datasets helps QRLSTM obtain lower prediction interval normalized averaged width (PINAW), however, at the cost of prediction interval coverage probability (PICP).

A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions

Abstract. An accurate and rapid urban flood prediction model is essential to support decision-making for flood management. This study developed a deep-learning-technique-based data-driven model for flood predictions in both temporal and spatial dimensions, based on an integration of long short-term memory (LSTM) network, Bayesian optimization, and transfer learning techniques. A case study in northern China was applied to test the model performance, and the results clearly showed that the model can accurately predict the maximum water depths and flood time series for various hyetograph inputs, with substantial improvements in the computation time. The model predicted flood maps 19 585 times faster than the physically based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case scenario, the difference between the ground truth and model prediction was only 0.76 %, and the spatial distributions of inundated paths and areas were almost identical. With the adoption of transfer learning, the proposed model was well applied to a new case study and showed robust compatibility and generalization ability. Our model was further compared with two baseline prediction algorithms (artificial neural network and convolutional neural network) to validate the model superiority. The proposed model can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real-time control, optimization, and emergency design and planning.

Evidence for an Extreme Cooling Event Prior to the Laschamp Geomagnetic Excursion in Eifel Maar Sediments

We present a timeseries of flood and slumping phases in central Europe for the past 65,000 years from event layers in sediment cores from infilled Eifel maar basins (Germany). Palynological, petrographic and organic carbon (chlorins) records are used to understand the precise timing of these events. Periods of increased flood activity seem to coincide with Heinrich stadials in marine sediment records, which are associated with cold and more arid climate conditions, indicating a vegetation response within the maars’ catchment areas. This multi-proxy correlation reveals prominent slumps at different maar sites during Greenland Stadial (GS) 12. The stratigraphy is based on sediment records from the Auel infilled maar and we thus call this event Auel Cold Event (ACE). Frozen and fractured sediment packages within the slump suggest deep frost or permafrost conditions for the region during the stadial. The results agree well with sediment archives and archeological sites across Europe that report severely cold and arid conditions for the stadial. This supports the assumption that GS12 was indeed one of the coldest periods of the last glacial cycle rather than the Heinrich stadials. Based on our age model, the ACE occurred at 43,500 yr b2k (years before the year 2000), which coincides with the initial weakening of Earth’s magnetic field strength prior to the Laschamp geomagnetic excursion.

Extreme historical droughts and floods in the Hanjiang River Basin, China, since 1426

Abstract. The major droughts and floods in the Hanjiang River Basin, central China, have a significant impact on flood prevention and control in the middle reaches of the Yangtze River and water resources management in the areas of the South–North Water Diversion Middle Line Project. However, there is a lack of understanding of the multi-decadal to centennial-scale patterns of extreme droughts and floods in the area. Applying the yearly drought and flood records from historical documents and precipitation data in the period of instrumental measurements, this study constructs a time series of extreme droughts and floods in the Hanjiang River Basin from 1426–2017 and analyzes the temporal and spatial characteristics of the extreme drought and flood event variations. The results show that there were a total of 45 extreme droughts and 52 extreme floods in the basin over the past 592 years. Extreme droughts and floods were highly variable on a multi-decadal to centennial scale, and the frequencies were higher in the first and last 100 years or so of the study period and lower in between. Spatially, the frequencies of extreme droughts and floods were generally higher in the middle and lower reaches than in the upper reaches. It was also found that there is a good correlation of drought and flood frequencies between the upper Hanjiang River Basin and North China. These results are informative for the study of mechanisms and predictability of multi-decadal to centennial-scale variability of extreme hydroclimatic events in the river basin.

Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas.

Flood forecasting in urban reservoir using hybrid recurrent neural network

Flood forecasting can provide accurate inferences and early warnings for flood control work during the flood season. Due to the variability of local rainfall and the complexity of geographic conditions, existing prediction methods were unable to accurately predict the flooding process in a particular basin. Additionally, the water level sensor generates a significant amount of noise in the inbound flow data during period measurement. To address these issues, this article proposes a real-time flood forecasting model, which is used to accurately predict flood trends and peak times in the flood period. The model uses a convolution kernel function to smooth out local noise and neighborhood values, minimizing the impact of non-stationary series on the training process while retaining the true evolution of the flood in the original data. In our model, we develop a time series attention mechanism that is used to apply various weights to time series input vectors, such as outflow flow and rainfall from upstream reservoirs, this mechanism also improves the accuracy of short-term series prediction. To obtain additional information about the output of the recurrent neural network layer, we also include a multivariate autoregressive integrated moving average module. This method can add a linear component to the output, allowing the prediction result to adapt to the input period's scale shift. This article develops matching models for interval and full basin floods based on the geographical characteristics of China's urban Reservoir and the river basin, thresholds are established based on the outflow from upstream reservoirs, which enables the flood forecasting system to dynamically adjust model parameters in response to the threshold, it also circumvents the scaling problem inherent in flood time series at various scales. We trained and predicted using 25 different types of floods in Ankang Reservoir from 2010 to 2020. Three on-site real-time forecasts of catastrophic flooding at the Ankang Reservoir were conducted in September 2021 to validate the model's accuracy. The algorithm's efficiency in forecasting flood inflows is demonstrated through comparisons to traditional hydrological models and other machine learning networks, and our model consistently forecasts the peak time and total flood volume with the least amount of error in the comparison algorithm.

Frequency Analysis of Snowmelt Flood Based on GAMLSS Model in Manas River Basin, China

With the acceleration of human economic activities and dramatic changes in climate, the validity of the stationarity assumption of flood time series frequency analysis has been questioned. In this study, a framework for flood frequency analysis is developed on the basis of a tool, namely, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We introduced this model to construct a non-stationary model with time and climate factor as covariates for the 50-year snowmelt flood time series in the Kenswat Reservoir control basin of the Manas River. The study shows that there are clear non-stationarities in the flood regime, and the characteristic series of snowmelt flood shows an increasing trend with the passing of time. The parameters of the flood distributions are modelled as functions of climate indices (temperature and rainfall). The physical mechanism was incorporated into the study, and the simulation results are similar to the actual flood conditions, which can better describe the dynamic process of snowmelt flood characteristic series. Compared with the design flood results of Kenswat Reservoir approved by the China Renewable Energy Engineering Institute in December 2008, the design value of the GAMLSS non-stationary model considers that the impact of climate factors create a design risk in dry years by underestimating the risk.

Predicting Outflow Hydrographs of Potential Dike Breaches in a Bifurcating River System Using NARX Neural Networks

Early flood forecasting systems can mitigate flood damage during extreme events. Typically, the effects of flood events in terms of inundation depths and extents are computed using detailed hydraulic models. However, a major drawback of these models is the computational time, which is generally in the order of hours to days for large river basins. Gaining insight in the outflow hydrographs in case of dike breaches is especially important to estimate inundation extents. In this study, NARX neural networks that were capable of predicting outflow hydrographs of multiple dike breaches accurately were developed. The timing of the dike failures and the cumulative outflow volumes were accurately predicted. These findings show that neural networks—specifically, NARX networks that are capable of predicting flood time series—have the potential to be used within a flood early warning system in the future.

Analysis of historical drought and flood characteristics of Hengshui during the period 1649–2018: a typical city in North China

The objective of this study was to reconstruct the long-term drought and flood time series to analyze their changing characteristics in Hengshui City of North China. Disaster records of the city for 550 years (1649–2018) were collected from different sources and sorted to reconstruct the sequences of droughts and floods. Advanced statistical methods for climate data analysis, including the Mann-Kendall test, Sen's slope estimator, Morlet wavelet analysis, mutation point tests, and Rescaled range analysis, were used to analyze the historical changes and predict the direction of possible future changes in droughts and floods. The results showed an increased frequency of droughts and a decreased frequency of floods in Hengshui City, making drought occurrence significantly higher than flood occurrence in the twentieth century. The solar activity cycle of 11-year and its multiple showed the association with 10–15 years and 25 years cycles of droughts and floods, respectively. The mutation points in drought and flood sequences during 1559–1568 and 1909–1918 showed insignificant downward (upward) and upward (downward) trends, respectively, in the drought (flood) subsequence before and after the mutation point. The rescaled range analysis revealed an insignificant decreasing trend in droughts and the continuation of the present decreasing trend in floods in the forthcoming years.

Recurrence analysis of extreme event-like data

Abstract. The identification of recurrences at various timescales in extreme event-like time series is challenging because of the rare occurrence of events which are separated by large temporal gaps. Most of the existing time series analysis techniques cannot be used to analyze an extreme event-like time series in its unaltered form. The study of the system dynamics by reconstruction of the phase space using the standard delay embedding method is not directly applicable to event-like time series as it assumes a Euclidean notion of distance between states in the phase space. The edit distance method is a novel approach that uses the point-process nature of events. We propose a modification of edit distance to analyze the dynamics of extreme event-like time series by incorporating a nonlinear function which takes into account the sparse distribution of extreme events and utilizes the physical significance of their temporal pattern. We apply the modified edit distance method to event-like data generated from point process as well as flood event series constructed from discharge data of the Mississippi River in the USA and compute their recurrence plots. From the recurrence analysis, we are able to quantify the deterministic properties of extreme event-like data. We also show that there is a significant serial dependency in the flood time series by using the random shuffle surrogate method.

Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.

Biases in national and continental flood risk assessments by ignoring spatial dependence

Recently, flood risk assessments have been extended to national and continental scales. Most of these assessments assume homogeneous scenarios, i.e. the regional risk estimate is obtained by summing up the local estimates, whereas each local damage value has the same probability of exceedance. This homogeneity assumption ignores the spatial variability in the flood generation processes. Here, we develop a multi-site, extreme value statistical model for 379 catchments across Europe, generate synthetic flood time series which consider the spatial correlation between flood peaks in all catchments, and compute corresponding economic damages. We find that the homogeneity assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139%, 188% and 246% for the United Kingdom (UK), Germany and Europe, respectively. Our study demonstrates the importance of considering the spatial dependence patterns, particularly of extremes, in large-scale risk assessments.

Lake Topography and Active Storage From Satellite Observations of Flood Frequency

AbstractTopography is critical information for water resources management in lakes, and remote sensing provides a unique opportunity to estimate topography in ungauged regions. We introduce here a new method that estimates nearshore topography of water bodies based on a flood frequency map and time series of water levels by assuming the equivalence between flood frequency and water level exceedance probability at a given area. Test cases are performed for two lakes and 12 hydropower reservoirs using the proposed Flood2Topo app. This new application generates the bottom level pixel by pixel and level‐area‐active storage relationship directly from the topography map, without the need to fit functions. Flood extent estimates from the Landsat based Global Surface Water (GSW) data set, the current state‐of‐the‐art, were used to run Flood2Topo, together with water levels from satellite altimetry and in situ gauges. Results show bottom level root mean square deviation (RMSD) values of 18.5 and 146 cm for Lake Poopó (Bolivia) and Lake Curuai (Amazon basin), respectively. For reservoir active storage, RMSD normalized values ranged from 2% to 11% for 11 reservoirs (average NRMSD of 6.4%). The method can be applied to any seasonally flooded area using any data set. Considering the surface water occurrence map from the GSW data set, the method is applicable to 35.8% (86%) of the global seasonally flooded areas delimited by flood frequencies between 0 and 95% (99%) over 35 years. The flood frequency‐based method is a promising tool for obtaining data for hydrodynamic simulations and monitoring of ungauged water bodies.

Flood-type trend analysis for alpine catchments

ABSTRACT In many places, magnitudes and frequencies of floods are expected to increase due to climate change. To understand these changes better, trend analyses of historical data are helpful. However, traditional trend analyses do not address issues related to shifts in the relative contributions of rainfall versus snowmelt floods, or in the frequency of a particular flood type. We present a novel approach for quantifying such trends in time series of floods using a fuzzy decision tree for event classification and applied it to maximal annual and seasonal floods in 27 alpine catchments for the period 1980–2014. Trends in flood types were studied with Sen’s slope and double mass curves. Our results reveal a decreasing number of rain-on-snow and an increasing number of short rainfall events in all catchments, with flash floods increasing in smaller catchments. Overall, the results demonstrate the value of incorporating a fuzzy flood-type classification into flood trend analyses.

Regional flood frequency analysis for flood index estimation in hydrologic regions with limited flood data

The objective of this study is to establish a regional relationship between mean annual peak flood and the catchment area based on the annual peak flood frequency analysis. Annual peak flood time series for various gauging sites of six basins in north part of Iraq are used in analysis. The procedure of analysis is approached by scaling a regional flood frequency distribution (index-flood of catchment) which is defined here as the mean annual maximum flood discharge and estimated by linear regression using physiographic and climatic catchment properties. The index-flood method was found to have slightly better prediction accuracies over the direct-regression method. The coefficient of determination R2 between reference and estimated index-floods is relatively high in most cases for all the gauging stations. All regions give very high R 2 correlation and the best model is the model (2), which is also associated with minimum value of RMSE.

Statistical parameters of time series of spring runoff flood of rivers of the Yesil water-economic basin

Есіл су шаруашылығы алабы өзендерінің көктемгі су тасу ағындысының негізгі сипаттамалары қарастырылды (көктемгі су тасудың сипаттық даталары: су тасудың басталу датасы, су тасу шыңының датасы, су тасудың аяқталу датасы және су тасу ұзақтығы). Есіл алабы өзендерінің байқалған су режимі өзгерісі, көктемгі ағынды сипаттамаларының көпжылдық қатарының тұрақсыздығы климаттық факторлармен айқындалады. Көктемгі су тасу ағындысы және сипаттық даталар бойынша жиналған бастапқы ақпаратты талдау жекелеген ағынды сипаттамаларының уақыттық қатарларында 1970 жылдан 1990 жылдарға дейін бағыттық өзгерістердің бар екенін, оның уақыттық қатарлардың әртектілігін тудыруға себеп болғанын көрсетті. Ағынды қатарларының статистикалық параметрлерін және қамтамасыздығы әртүрлі ағынды мәндерін есептеу кезінде Крицкий-Менкель үлестірімі және Пирсонның III типті биномдық үлестірім пайдаланылды (біртекті қатар үшін) және ағынды қатары әртекті болған жағдайда қамтамасыздығы әртүрлі ағынды мәндерін есептеу құрамдас үлестірім қисықтары бойынша жүргізілді. Көктемгі су тасудың ағынды сипаттамларының бағыттық өзгерістері талданып, Есіл алабы өзендерінің ең жоғары су өтімдері мен ағынды қабаттарының қамтамасыздық қисықтарының параметрлері есептелді (су шаруашылығы алабының 42 гидрологиялық бекеті бойынша көпжылдық деректер пайдаланылды). Алынған нәтижелер, Есіл алабы өзендерінің ең мол суының статистикалық параметрлерінің өзгерісі антропогендік факторлардың ықпалында екенін, ал олардың орташаланған түсу даталарының ығысуы тек қана климаттық факторлардың әсерінен болғанын растайды.

At Site Flood Frequency Analysis of Baitarani River at Champua Watershed, Odisha

For any development of hydraulic structures and dam modelling, flood frequency analysis is an effective tool to determine the appropriate measures and strategy. Flood frequency analysis has been conventionally used in hydraulic engineering and floodplain management. The present study is an attempt to estimate the expected flood using two probability distributions: Gumbel distribution and Log Pearson III distribution at Champua watershed, Upper Baitarani River Basin, Odisha. The analysis is based on annual maximum flood time series from 1991 to 2018 (28 years) obtained from Water Resources Information System at the Champua gauging station. Three Goodness of fit methods namely Kolmogorov Smirnov, Anderson Darling and Chi Squared tests are used to choose the better model. From the analysis, expected flood for return period 2, 10, 25, 50, 100 and 1000 years are calculated. Gumbel give an expected flood 521.72 cumecs while Log Pearson III give an expected flood of 493.17 cumecs for 2 years return period. It is observed that Gumbel estimated a higher values for all the said return period except for 1000 years where Log Pearson III predicted a much higher values. Goodness of test show inconsistent results. While Chi-squared test indicate Gumbel Method as the better model, the other two tests show that Log Pearson III is the better fitting model for the given dataset. Therefore, Log Pearson III is chosen as the best model. However, the results from both the distributions can be referred for storm management.