Characteristics Of Rainfall Events Research Articles

Characteristics of observed rainfall over Odisha: An extreme vulnerable zone in the east coast of India

The present study investigates the spatiotemporal characteristics of rainfall events during the summer monsoon season over Odisha (one of the vulnerable zone for heavy rainfall) with the main aim for heavy-to-extreme rainfall events. India Meteorological Department (IMD) station observations and gridded rainfall analysis datasets for a period of 34 years (1980–2013) are used and four frequency indices (heavy-to-extreme, light-to-moderate, dry days, and wet spells) and four intensity indices (daily maximum rainfall, 5-day maximum rainfall, seasonal rainfall total, and daily intensity index) from both the datasets are evaluated. Furthermore, the above-stated indices are analysed over the four meteorological zones of Odisha, as classified by IMD. The analysis reveals that both the heavy-to-extreme rainfall days and dry days are increasing, while the light-to-moderate rainfall days and wet days are decreasing. It is also found that the rate of increase in rainfall amount and number of wet-day are higher in the southern than northern Odisha. This implies that the climate is becoming drier as one move from south to north and the gradient is also increasing with time. The climatological analysis suggests not only the heavy-to-extreme rainfall days and intensity are more in urban (Khordha) and highly elevated (Eastern Ghat; height ~ 1.6 km) areas but also the trend is increasing over those regions. The Murphy skill score of daily rainfall between two datasets is 0.94; however, the number of the extreme rainfall events is more in station data (297) than the gridded data (150) during the study period.

Impacts of climate change on characteristics of daily‐scale rainfall events based on nine selected GCMs under four CMIP5 RCP scenarios in Qu River basin, east China

AbstractClimate change significantly influences characteristics of rainfall events including rainfall depth, rainfall duration, inter‐event time and temporal patterns that directly affect water resources management, flood defence and hydraulic structure design. In this study, a framework is proposed to analyse daily‐scale rainfall event characteristics based on global climate model (GCM) simulations. This framework includes bias correction of raw GCM‐simulated rainfall series, selection of good‐performing bias‐corrected GCMs based on the mean absolute percentage error (MAPE) and evaluation of selected GCMs' skills in simulating rainfall event characteristics and finally assessment of changes in rainfall event characteristics in the future. In this study, 17 GCMs, four representative concentration pathways (i.e., RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and two future periods (i.e., 2041–2070 and 2071–2100) are considered. After bias correction of the GCMs using the monthly‐scale double gamma distribution, 9 out of 17 GCMs with MAPE values smaller than 20% in the historical period 1971–2000 are selected. In general, these selected GCMs well capture the rainfall characteristics of different rainfall event classes. The multi‐model ensembles suggest that compared to the historical period, the frequency of rainfall events with an extreme depth, short duration and long inter‐event time will increase in the two future periods and the change in 2071–2100 is generally larger than that in 2041–2070, indicating that more extreme climate conditions may occur in Qu River basin in the future. Moreover, the temporal patterns of heavy rainfall events will become more non‐uniform with more concentrated peak rainfall. The frequency of the delayed rainfall type (i.e., peaks occurring at the end of the rainfall event) will increase in the future, which can probably cause more severe floods and is very detrimental to flood defence in this study area.

Seasonal and Regional Differences in Extreme Rainfall Events and Their Contribution to the World’s Precipitation: GPM Observations

In the present study, five-year of precipitation features (PFs) datasets, based on Global Precipitation Measurement (GPM), are used to investigate the global and regional characteristics of extreme rainfall events (EREs). The EREs are defined based on the PFs area, depth (maximum height of radar reflectivity), and the rain rate and called them largest, deepest, and intense EREs, respectively. The EREs are divided into top 10%, 1%, 0.1%, and 0.01% based on their frequency of occurrences. It is observed that occurrences of EREs belonging to less than top 0.01% EREs follow the tropical rainfall climatology over the tropics based on all the parameters. Subtropical oceanic areas consist of a higher frequency of largest EREs, whereas tropical land areas consist of the higher number of deepest EREs. The most intense EREs (top 0.01%) are uniformly distributed over tropical areas and subtropical oceans, and spatial distribution shows that a deepest ERE belongs to intense EREs in the tropical land areas. Large differences between the precipitation contribution from the largest and deepest EREs are seen; for example, the top 1% of largest EREs contribute to ∼80.7% of Earth’s precipitation, whereas the corresponding percentage for deepest EREs is only 53%. On the regional and seasonal scale, South Asia (SAsia) and South America (SA) nearly show common features, as oceanic and land areas consist of largest and deepest EREs, respectively, and contribute to higher precipitation. Subtropical latitudes over South America, including Sierra de Cordoba and La Plata basin, consist of deepest and intense EREs and match with those of the Indo-Gangetic plain over South Asia, which also shows the similar characteristics. EREs based on various parameters are strongly linked over SAsia compared to SA. For example, the largest top 10% EREs have a higher probability to be part of the top 10% deepest and intense EREs over SAsia. The seasonal and regional water budget reveals different characteristics, as in the southern hemisphere, the deeper EREs contribute to the higher fraction of rainfall, but over SAsia, the shallower EREs could also contribute to significant rainfall.

Classification of Localized Heavy Rainfall Events in South Korea

Characteristics of localized heavy rainfall events (LHREs) in South Korea are analyzed using high-resolution precipitation reanalysis data from the Korea Meteorological Administration (KMA). The LHREs, defined as summertime heavy rainfall events with 30 mm/h or more, are detected using in-situ measurements, and their spatio-temporal structures are classified into the three groups using precipitation reanalysis data. The first group is mainly composed of band-type precipitation around the central regions of South Korea (CHREs). The second group predominantly consists of LHREs in the southern regions (SHREs). The third group contains diverse and isolated rainfall events (IHREs). Each group exhibits a different rainfall persistency, diurnal cycle, and occurrence frequency. In general, CHREs peak mostly in July and have shorter time scales compared to SHREs. SHREs are more nocturnal in nature with maximum frequencies in both July and August. IHREs, which consist of 47% of total LHREs, are composed of both nocturnal rainfall events in the southern coastal regions and daytime rainfall events in the inland regions. These results suggest that LHREs in South Korea have multiple characteristics and cannot be simply explained by one or two representative mechanisms.

Assessment of heavy precipitation events associated with floods due to strong moisture transport during summer monsoon over India

Integrated water vapor transport (IVT) detection algorithm is implemented over the Arabian Sea basin for the identification of strong moisture transport events (SMTs) over Indian subcontinent during 1979–2013. Attempt has been made through the spatiotemporal characteristics whether SMTs are responsible for the occurrence of heavy precipitation events (HPEs) and floods. The results demonstrated that the persistent SMTs are related to HPEs towards flooding over parts of Indian subcontinent during summer monsoon. In the study a total of 364 SMTs have been identified. It is seen that the SMT axes is reaching from eastward direction crosses over land mass of west coast of India and moves in north-westerly direction through Assam, Bihar and Uttarakhand. The results reveal a better correlation of occurrence of HPEs with the presence of SMTs and a large portion of SMTs lead to HPEs, as well as large fraction of HPEs occurs after SMTs. The characteristics of specific flood and rainfall events associated with the occurrence of SMTs show that existence of strong relation between presence of SMTs and extreme precipitation events for the western region where SMTs make landfalls (over Mumbai in 2005), for the north western region where SMTs penetrate inland (over Uttarakhand in 2013), whereas the relations is weakened in the north eastern region (e.g., over Bihar in 2008). The results emphasize that the existence of large amount of atmospheric moisture over the study regions 2–3 days before extreme precipitation events is due to SMTs. Given the narrowness of SMTs, the location of SMTs over each region is related to the occurrence or lack of extreme precipitation events. In addition, rainfall intensity enhanced with the duration of persistent SMTs of the majority of identified events. A significant negative (positive) dependence was found between SMT totals and the Southern Oscillation Index (SOI) (Indian Ocean Dipole (IOD)), with a greater frequency of SMTs associated with lower SOI (higher IOD) values. This study is helpful in inspiring more detailed analysis of the relationship between SMTs and extreme precipitation events over India.

Dynamics of extreme rainfall events in summer in southern Uruguay

We investigate the characteristics of extreme rainfall events in southern Uruguay during the summer season. The focus of this work is to understand the dynamics of these events. Therefore, we define the southern area with a clustering analysis of the meteorological stations. Then, we divide the events into three classes, considering the principal components of the 2m temperature. For each class, we calculate composites of atmospheric variables. We find three basic dynamics that can be associated to extreme events. The first group is characterized by atmospheric instability and a cold front that triggers the event. The second group is associated with deep convection that was initiated in central Argentina. The third group is represented by an intense surface low pressure in Uruguay that is favoured by the 200 hPa circulation. The results are in good agreement with the previous studies.

Statistical modelling of 5-day average rainfall probability of occurrence in Australia during 1950–2013

The increasing concern about climate change has increased the necessity for precise figures about deviations in statistical characteristics of rainfall events in both temporal and spatial scales. Many areas across Australia have high variability patterns of rainfall and inadequate rainfall records which has various effects on the availability of food and water. Modelling spatial and temporal rainfall is essential for prediction and simulation purposes in numerous aspects of planning, agriculture, forestry, meteorology and hydrology. Two different models are generally used to describe the two main features of rainfall: the occurrence and the amount. This paper describes the occurrence probability of 5-day average rainfall (successive 5-day rainfall mean) in Australia. Daily accumulated rainfall observations of 105 meteorological stations spanning from 1950 to 2013 were collected from Australian Bureau of Meteorology for the study. Logistic regression is applied to predict rainfall probability of occurrence in all the meteorological stations using nine observational rainfall stations as case studies. Weather conditions such as the 5-day periods which is defined as the consecutive 5-day average rainfall in a year and the annual rainfall which is defined as the annual daily rainfall mean factors were used as the predictors. The fitted logistic regression models predicted the occurrence and non-occurrence 5-day average rainfall events quite well with good accuracies. The predictors significantly affected the fitted models in all stations. Analysis of the levels of each of the predictors showed that the parameters of the 5-day period factors were more influential in all the models, particularly during the rainy season in most stations relative to that of the annual factors. The fitted models could be used to simulate rainfall probabilities for the stations with insufficient rainfall histories and could assist agronomist and various stakeholders in the planning of their various operations.

Characteristics of various rainfall events over South Peninsular India during northeast monsoon using high-resolution gridded dataset (1901–2016)

Northeast monsoon (October–December, NEM) is of a huge societal and economic importance over South Peninsular India (SPI) as the region does not experience noteworthy rainfall during the southwest monsoon (June–September; ISM) over India. The aim of the present study is to unveil the behavior of various NEM rainfall events over SPI in a long-term period to unravel the effects of global warming on regional rainfall. This study involves the analysis of long-term climatology, variability, trends, and global teleconnections of various categorical rainfall events as defined by India Meteorological Department (IMD) and its associated rainfall over SPI and its six subdivisions during NEM using a high-resolution (0.25° × 0.25°) data from IMD for the period 1901–2016. The results suggest that the variability of NEM rainfall has increased in the recent period (1959–2016). The seasonal rainfall has increased over Tamil Nadu, Rayalaseema, as well as SPI because of an increase in the number of high-intensity rainfall events in the recent period with respect to the earlier period (1901–1958), while it has decreased over the other subdivisions. The percentage contribution of moderate rainfall events to the seasonal rainfall is more compared to the other events. It is also found that the promising relationship of various NEM rainfall events and its associated rainfall with ENSO, IOD, and ISMR have weakened in the recent decades after 1988. This study is very useful in determining the effects on various sectors due to the variability of heavy rainfall events over SPI during this season and assists the risk management sectors in adapting advanced technologies for a sustainable future in the present global warming era.

巴丹吉林沙漠西南缘不同强度降水的基本气候特征

巴丹吉林沙漠西南缘不同强度降水的基本气候特征

Analysis of the Temporal Distribution of Rainfall Using the Heavy Storm Distribution Method Reflecting Concentrated Duration Characteristics in Busan

In this study, we proposed and analyzed a rainfall temporal distribution model for determining the flood discharge of a hydraulic structure. The heavy storm distribution (HSD) method proposed in this study can generate the temporal distribution of rainfall more suitably than existing techniques by reflecting the concentrated duration characteristics of extreme rainfall events. Here, the concentration duration means a time period in which rainfall comprises 70% to 80% of the total duration of an extreme rainfall event, and the concentrated duration of 24 h heavy rainfall is about 3 h. For the application of the HSD method, extreme rainfall events were selected using actual rainfall data from Busan, and the characteristics of the concentrated duration were analyzed. Through this, the type of HSD in Busan was determined and the temporal distribution of rainfall was produced. In addition, we compared this method with the Huff method, Yen and Chow method, Mononobe method, and SCS method. As a result, the HSD method proposed in this study better reflected the intensive duration characteristics of extreme rainfall events compared to existing methods. Keywords: Rainfall Time Distribution, HSD, Huff, Concentrated Duration

Detection of future changes in trends and scaling exponents in extreme short-term rainfall at selected stations in Slovakia

Abstract This paper analyses projected changes in short-term rainfall events during the warm season (April – October) in an ensemble of 30 regional climate model (RCM) simulations. The analysis of trend changes and changes in scaling exponents was done for the Hurbanovo, Bratislava, Oravská Lesná, and Myjava stations in Slovakia. The characteristics of maximum rainfall events were analysed for two scenario periods, one past and one future (1960–2000 and 2070–2100) and compared to the characteristics of the actual observed events. The main findings from the analysis show that 60-min short-term events for most of the RCM simulations will either increase or remain constant. On the other hand, the depths and intensities of daily events are projected to increase significantly; in some cases they were found to be ten times larger. Trends in future events at the Hurbanovo station were found to be insignificant. In the other stations positive trends in future rainfall events prevail, except for daily rainfall at the Myjava station, which shows a negative trend. Using results from the selected simulations, the scaling exponents estimated are on average lower than the exponents of the data observed. On the other hand, due to the higher daily precipitation amounts in the future seen to all the scenarios, the downscaled values of short-term rainfall at all the stations analysed might be considerably higher in the future horizons, which could subsequently affect future design rainfall values for engineering designs.

Runoff generation in mountain catchments: long-term hydrological monitoring in the Rio Vauz Catchment, Italy

Trying to obtain a more detailed understanding of the hydrological functioning of mountain catchments represents an important challenge in the effort of counteracting possible consequences of climate and land use change on water resources availability. Long-term (> 10 years) hydro-meteorological monitoring in small (typically < 10 km2) experimental catchments constitutes a valuable tool to achieve these goal. One of these sites is the Rio Vauz Catchment (1.9 km2), in the Italian Dolomites, that represents an excellent example of long-term snowmelt-dominated catchment in Dolomitic regions. The strong elevation gradient of the Rio Vauz Catchment and the different physiographic properties of its nested subcatchments make this a unique site for investigating fundamental runoff generation mechanisms in mountain headwaters. In this work, we provide a review of physical processes that have been inferred from 12 years of hydrological monitoring in this catchment. We present the available dataset and summarize the main hydrological mechanisms that explain the internal functioning of the Rio Vauz Catchment, primarily focusing on three characterizing hydrological behaviours, namely thresholds, hysteresis and connectivity. The main control on surface and subsurface runoff threshold response is constituted by a combination of soil moisture antecedent conditions, rainfall amount and topography. Changes in hysteresis patterns (clockwise and anti-clockwise loops) between streamflow and soil moisture, water table depth and electrical conductivity were governed by distinct runoff generation processes and rainfall event characteristics. Hillslope-riparian-stream subsurface connectivity was controlled by antecedent wetness conditions and rainfall amount. The composition in environmental tracers (stable isotopes of water and electrical conductivity) in different water sources and the application of tracer-based mixing models helped to distinguish the geographical sources to runoff and to quantify the role of rainfall and snowmelt in streamflow. Finally, we define a perceptual model of runoff generation processes for dry and wet conditions that can be considered representative for many mountain headwater catchments in the world.

Evaluation of regional climate models performance in simulating rainfall climatology of Jemma sub-basin, Upper Blue Nile Basin, Ethiopia

This study examines the performance of 10 Regional Climate Model (RCM) outputs which are dynamically downscaled from the fifth phase of Coupled Model Inter-comparison Project (CMIP5) GCMs using different RCMs parameterization approaches. The RCMs are evaluated based on their ability to reproduce the magnitude and pattern of monthly and annual rainfall, characteristics of rainfall events and variability related to Sea Surface Temperature (SST) for the period 1981–2005. The outputs of all RCMs showed wet bias, particularly in the higher elevation areas of the sub-basin. Wet bias of annual rainfall ranges from 9.60% in CCLM4 (HadGEM2-ES) model to 110.9% in RCA4 (EC-EARTH) model. JJAS (June-September) rainfall is also characterized by wet bias ranges from 0.76% in REMO (MPI-ESM-LR) model to 100.7% in RCA4 (HadGEM2-ES) model. GCMs that were dynamically downscaled through REMO (Max Planck Institute) and CCLM4 (Climate Limited-Area Modeling) performed better in capturing the rainfall climatology and distribution of rainfall events. However, GCMs dynamically downscaled using RCA4 (SMHI Rossby Center Regional Atmospheric Model) were characterized by overestimation and there are more extreme rainfall events in the cumulative distribution. Most of the RCMs’ rainfall over the sub-basin showed a teleconnection with Sea Surface Temperature (SST) of CMIP5 GCMs in the Pacific and Indian Oceans, but weak. The ensemble mean of all 10 RCMs simulations was superior in capturing the seasonal pattern of the rainfall and had better correlation with observed annual (Correl = 0.6) and JJAS season rainfall (Correl = 0.5) than any single model (S-RCM). We recommend using GCMs downscaled using REMO and CCLM4 RCMs and stations based statistical bias correction to manage elevation based biases of RCMs in the Upper Blue Nile Basin, specifically in the Jemma sub-basin.

Assessing the performance of near real-time rainfall products to represent spatiotemporal characteristics of extreme events: case study of a subtropical catchment in south-eastern Brazil

ABSTRACTThis study evaluates the performance of four Near Real-Time (NRT) satellite rainfall products in estimating the spatiotemporal characteristics of different extreme rainfall events in a subtropical catchment in south-eastern Brazil. The Climate Prediction Centre Morphing algorithm (CMORPH), Tropical Rainfall Measuring Mission, Multisatellite Precipitation Analysis in real time (TMPA-RT), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Global Cloud Classification System (PERSIANN-GCCS), and the Hydro-Estimator are evaluated for monsoon seasons, based on their capability to represent four types of rainfall events distinguished for: (1) local and short duration, (2) long-lasting event, (3) short and spatial extent, and (4) spatial extent and long lasting. Since the events are defined relative to a percentile, the relative performance variation at different threshold levels (75th, 90th, and 95th) is also evaluated. The data from the 13 Automatic Weather Stations (AWSs) for the period from 2007 to 2014 are used as the reference. The results show that the product performance highly depends on the spatiotemporal characteristics of rainfall events. All four products tend to overestimate intense rainfall in the study area, especially in high altitude zones. CMORPH had the best overall performance to estimate different types of extreme spatiotemporal events. The results allow for developing a better understanding of the accuracy of the NRT products for the estimation of different types of rainfall events.

A hybrid support vector regression–firefly model for monthly rainfall forecasting

Long-term prediction of rainfalls is one of the most challenging tasks in stochastic hydrology owing to the highly random characteristics of rainfall events. In this paper, a novel approach is adopted to develop a hybrid regression model for 1-month-ahead rainfall forecasting at two rain gauge locations (namely: Tabriz and Urmia stations), in northwest Iran. The approach is based on the integration of support vector regression (SVR) and firefly algorithm (FFA) that results in truthful rainfall forecasts. The proposed hybrid model was trained and validated using weak stationary state of monthly rainfall data obtained from the gauges. The efficiency results of the model were also cross-validated with those of stand-alone SVR- and genetic programming-based forecasting models developed as the benchmarks in this study. For both rain gauge locations, the results showed that the hybrid model significantly outperforms the benchmarks. With respect to the average efficiency results at the gauge locations, the FFA-induced improvement in the SVR forecasts was matched by an approximately 30% decrease in root-mean-square error and around 100% increase in Nash–Sutcliffe efficiency. Such a promising accuracy in the proposed model may recommend its application at monthly rainfall forecasting in the present semiarid region.

도암호 유역에서 비점오염물질의 유출부하 특성

In order to investigate the runoff characteristics of nonpoint source pollutants in the Lake Doam watershed, water quality and flow rate were monitored for 38-rainfall events from 2009 to 2016. The EMC values of SS, COD, TN and TP were in the range of 33~2,169, 3.5~56.9, 0.09~7.65 and 0.06~2.21 mg L-¹, respectively. As a result of analyzing the effect of rainfall factor on the nonpoint source pollutant load, EMCs of SS, COD and TP showed a statistically significant correlation with rainfall (RA) (p<0.01) and SS showed highly significant correlation with maximum rainfall intensity (MRI, R=0.48). The load ranges of SS, COD, TN and TP were 10.4~11,984.6, 1.1~724.4, 0.6~51.6 and 0.03~22.85 ton event-¹, respectively, showing large variation depending on the characteristics of rainfall events. The effect of rainfall on the load was analyzed. SS, COD and TP showed a positive correlation, but TN did not show any significant correlation. The annual load of SS was the highest with 88,645 tons year-¹ in 2011 when rainfall was the highest with 1,669 mm. The result of impact analysis of nonpoint source pollution reduction project and land-use change on runoff load showed that pollutant load significantly reduced from 2009 to 2014 but SS and TP loads were increased from 2014 to 2016 due to increase in construction area. Therefore, we suggested that nonpoint source pollution abatement plan should be continued to reduce the soil loss and pollutants during rainfall, and countermeasures to reduce nonpoint source pollution due to construction need to be established.

Multivariate Analysis of Erosivity Indices and Rainfall Physical Characteristics Associated with Rainfall Patterns in Rio de Janeiro

ABSTRACT The identification of areas with greater erosive potential is important for planning soil and water conservation. The objective of this study was to evaluate the physical characteristics of rainfall events in the state of Rio de Janeiro, Brazil, and their interactions with rainfall patterns through multivariate statistical analysis. Rainfall depth, kinetic energy, 30-min intensity (I30), duration of rainfall events, and the erosivity indices KE >10, KE >25, and EI30 in 36 locations (stations) were subjected to principal component analysis [...]

Plant cover and hydrological response in a seasonally dry tropical forest (SDTF)

The scarcity of information on the processes of rainfall-flow limits understanding of the hydrology of dry regions of the world. In order to minimise the problem, this study was developed to investigate the influence of the characteristics of rainfall events and plant cover on the effective precipitation (Pe) in a seasonally dry tropical forest (SDTF) in the Northeast of Brazil. The study was carried out in two paired watersheds, one with SDTF under regeneration for 35 years (CR35) and the other under thinned SDTF for 5 years (TC). A historical series of five years (2009-2013) was analysed, with a total of 203 rainfall events, where only those rainfall events that generated a Pe > 1.0 mm were considered. CR35 had a greater number of Pe events (47) than TC (35). Rainfall depth and intensity were the factors that best explained the effective precipitation under both types of vegetation cover. The influence of herbaceous vegetation on the reduction of surface runoff was demonstrated by the smaller runoff depth and the greater potential for soil water storage in the watershed under thinned Caatinga. This fact leads to the conclusion that the technique of thinning is suitable management for Caatinga vegetation, and is capable of promoting the retention of soil water.

Statistical analyses of extreme rainfall events in Thessaloniki, Greece

Concern about climate change has amplified the need for accurate information about variations in statistical characteristics of extreme rainfall events in both smaller temporal and spatial scale. For this reason, in this paper the hourly precipitation measurements obtained at the Department of Meteorology and Climatology of the Aristotle University of Thessaloniki meteorological station, during the period 1947–2003, are used. A new threshold determining method is proposed, in order to define the extreme rainfall event in Thessaloniki, based upon the polynomial trends of the cumulative distribution functions, retrieved from hourly precipitation measurements. Through this approach, three statistical goodness-of-fit tests were carried out, in order to come up with the best fitting probability distributions, for annual and seasonal extreme precipitation conditions. Based upon this fitting distribution procedure, return periods for different precipitation extreme values were calculated. Results indicate that the value of 6.5mm/h is the hourly threshold of extreme precipitation in Thessaloniki over the studied period. Furthermore, the Generalized Pareto, and the Johnson SB, the log-Gamma and the Lognormal are the best fitted probability models for the description of annual and seasonal extreme rainfall events, respectively. Last but not least, the contribution of hourly data in the accuracy of the results is discussed.

El Niño Southern Oscillation cycle indicator for modeling extreme rainfall intensity over India

Since the global climate change altering the characteristics of extreme rainfall events, modeling the behavior of extreme rainfall is becoming indispensable. Among various physical processes, the El Niño Southern Oscillation (ENSO) cycle is a significant process that can trigger occurrences of extreme hydro-climatological events, such as floods, droughts and cyclones. Consequently, modeling the relationship between ENSO cycle and regional climate anomaly is gaining more attention in recent years. Studies in literature used various ENSO indices to quantify ENSO cycle in the statistical models which aim to model/forecast extreme rainfall and fail to report the best ENSO index for modeling extreme rainfall over India. Hence, this study is carried out to answer two research questions: (1) What is the best ENSO index for modeling extreme rainfall intensity over India? (2) What is the significance of identifying the best ENSO indicator for modeling extreme rainfall intensity over India? Towards answering these questions, the change in the linear relationship between ENSO cycle and extreme rainfall over India due to the choice of ENSO cycle indicator is examined. Results of this study indicate that the ENSO index Southern Oscillation Index (SOI) is the best ENSO index for modeling extreme rainfall over India. The results also show a significant change in ENSO-Extreme rainfall relationship due to the choice of ENSO index. In particular, 42% is the average increase in correlation coefficient between monsoon season extreme rainfall and ENSO cycle when the best ENSO index is chosen instead of the second-best ENSO index and it is around 37% for the non-monsoon season.