Analysis of Trends and the Persistence Structure in the Daily Rainfall Occurrences in Indiana

2013 ◽  
pp. 153-162 ◽  
Author(s):  
M. L. Kavvas ◽  
J. W. Delleur
Keyword(s):  
Daily Rainfall ◽  
Rainfall Occurrences

scholarly journals Copula based multisite model for daily precipitation simulation

2009 ◽  
Vol 13 (12) ◽  
pp. 2299-2314 ◽  
Author(s):  
A. Bárdossy ◽  
G. G. S. Pegram
Keyword(s):  
Daily Precipitation ◽  
Daily Rainfall ◽  
Separation Distance ◽  
Point Of View ◽  
Simulated Rainfall ◽  
Precipitation Simulation ◽  
New Ideas ◽  
Equilateral Triangles ◽  
Mutual Separation ◽  
Rainfall Occurrences

Abstract. From the point of view of multisite stochastic daily rainfall modelling, there are two new ideas introduced in this paper. The first is the use of asymmetrical copulas to model the spatial interdependence structure of the rainfall amounts together with the rainfall occurrences in one relationship. The second is in the evaluation of the (necessary but often ignored) congregating behaviour of the higher values of simulated rainfall; this evaluation is performed by calculating the entropy of the observations at all the near equilateral triangles that can be formed from the sequences at the gauge sites, as a function of their mutual separation distance. It turns out that the model captures the qualities desired and offers a fresh approach to a relatively mature problem in hydrometeorology.

scholarly journals Copula based multisite model for daily precipitation simulation

2009 ◽  
Vol 6 (3) ◽  
pp. 4485-4534 ◽  
Author(s):  
A. Bárdossy ◽  
G. Pegram
Keyword(s):  
Daily Precipitation ◽  
Daily Rainfall ◽  
Separation Distance ◽  
Point Of View ◽  
Simulated Rainfall ◽  
Precipitation Simulation ◽  
New Ideas ◽  
Mutual Separation ◽  
Spatial Interdependence ◽  
Rainfall Occurrences

Abstract. From the point of view of multisite stochastic daily rainfall modelling, there are two new ideas introduced in this paper. The first is the use of asymmetrical copulas to model the spatial interdependence structure of the rainfall amounts together with the rainfall occurrences in one relationship. The second is in the evaluation of the (necessary but often ignored) clustering behaviour of the simulated rainfall; this evaluation is performed by calculating the entropy of the observations at all the acute angled triangles that can be formed from the sequences at the gauge sites, as a function of their mutual separation distance. It turns out that the model captures the qualities desired and offers a fresh approach to a relatively mature problem in hydrometeorology.

scholarly journals An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 63
Author(s):  
Sirikanya Cheevaprasert ◽  
Rajeshwar Mehrotra ◽  
Sansarith Thianpopirug ◽  
Nutchanart Sriwongsitanon
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Low Frequency ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Research Area ◽  
Single Site ◽  
Future Research ◽  
Rainfall Occurrences

This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&90, ATM-WI30&365, ATM-WI90&365 and ATM-WI30&90&365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&90 and ATM-WI30&365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.

scholarly journals A Markov chain model for daily rainfall occurrences  at east Thanjavur district

MAUSAM ◽  
2022 ◽  
Vol 46 (4) ◽  
pp. 383-388
Author(s):  
M. THIYAGARAJAN ◽  
RAMA DOSS ◽  
RAMA RAJ
Keyword(s):  
Markov Chain ◽  
Poisson Process ◽  
State Estimation ◽  
Statistical Inference ◽  
Markov Chain Model ◽  
Daily Rainfall ◽  
Chain Model ◽  
New Approach ◽  
State 1 ◽  
Rainfall Occurrences

 The occurrences and non-occurrences of the rainfall can be described by a two-state Markov chain. A dry date is denoted by state 0 and wet date is denoted by state 1. We have taken the sample which follows a Poisson process with known parameter. Using this Poisson sample we have given a new approach to affect statistical inference for the law of the Markov chain and state estimation concerning un-observed past values or not yet observed future values. The paper aims at comparing the earlier fit of the data with the new approach.      

TROPICAL DAILY RAINFALL AMOUNT MODELLING USING MARKOV CHAIN-MIXED EXPONENTIAL (MCME)

2015 ◽  
Vol 74 (11) ◽  
Author(s):  
Fadhilah Yusof ◽  
Lee Mee Yung ◽  
Zulkifli Yusop
Keyword(s):  
Markov Chain ◽  
Daily Rainfall ◽  
Predictive Ability ◽  
First Order ◽  
Rainfall Occurrence ◽  
Rainfall Process ◽  
Proposed Model ◽  
Rainfall Model ◽  
Rainfall Occurrences ◽  
Mixed Exponential Distribution

This study is concerned with the development of a stochastic rainfall model that can generate many sequences of synthetic daily rainfall series with the similar properties as those of the observed. The proposed model is Markov chain-mixed exponential (MCME). This model is based on a combination of rainfall occurrence (represented by the first-order two-state Markov chain) and the distribution of rainfall amounts on wet days (described by the mixed exponential distribution). The feasibility of the MCME model is assessed using daily rainfall data from four rainfall stations (station S02, S05, S07 and S11) in Johor, Malaysia. For all the rainfall stations, it was found that the proposed MCME model was able to describe adequately rainfall occurrences and amounts. Various statistical and physical properties of the daily rainfall processes also considered. However, the validation results show that the models’ predictive ability was not as accurate as their descriptive ability. The model was found to have fairly well ability in predicting the daily rainfall process at station S02, S05 and S07. Nonetheless, it was able to predict the daily rainfall process at station S11 accurately. 

Penentuan Metode Intensitas Hujan Berdasarkan Karakteristik Hujan dari Stasiun Pengamat Hujan Disekitar Kecamatan Karawang Timur

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Melisa Permatasari ◽  
M. Candra Nugraha ◽  
Etih Hartati
Keyword(s):  
Drainage System ◽  
Daily Rainfall ◽  
Intensity Analysis ◽  
Rain Intensity ◽  
Rainy Period ◽  
Discharge Plan ◽  
Calculation Results ◽  
Value Determination ◽  
Maximum Daily Rainfall ◽  
Method Approach

<p>The rain intensity is the high rainfall in unit of time. The length of rain will be reversed by the amount rain intensity. The shorter time the rain lasts, the greater of the intensity and re-period of its rain. The value of rain intensity is required to calculate the flood discharge plan on the drainage system planning area in East Karawang district. Determining the value rain intensity is required the maximum daily rainfall data obtained from the main observer stations in the Plawad station planning area. The method of determination rain intensity analysis can be done with three methods: Van Breen, Bell Tanimoto and Hasper der Weduwen. Selected method is based on the smallest deviation value. Determination deviation value is determined by comparing rain intensity value of Van Breen method, Bell Tanimoto, Hasper der Weduwen. By comparing rain intensity value of the Van Breen method, Bell Tanimoto, Hasper der Weduwen with the results of calculating three methods through the method approach Talbot, Sherman and Ishiguro. Calculation results show that the method of rain has smallest deviation standard is method Van Breen with Talbot approach for rainy period (PUH) 2, 5, 10, 25, 50 and 100 years.</p>

The Influence of Geographical Factors on Extreme Rainfalls in Lampung Province

2019 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
M Welly
Keyword(s):  
Geographical Location ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Design Flood ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Ungauged Basin ◽  
Flood Discharge ◽  
Design Rainfall ◽  
Geographical Factors

Many people in Indonesia calculate design rainfall before calculating the design flooddischarge. The design rainfall with a certain return period will eventually be convertedinto a design flood discharge by combining it with the characteristics of the watershed.However, the lack of a network of rainfall recording stations makes many areas that arenot hydrologically measured (ungauged basin), so it is quite difficult to know thecharacteristics of rain in the area concerned. This study aims to analyze thecharacteristics of design rainfall in Lampung Province. The focus of the analysis is toinvestigate whether geographical factors influence the design rainfall that occurs in theparticular area. The data used in this study is daily rainfall data from 15 rainfallrecording stations spread in Lampung Province. The method of frequency analysis usedin this study is the Gumbel method. The research shows that the geographical location ofan area does not have significant effect on extreme rainfall events. The effect of risingearth temperatures due to natural exploitation by humans tends to be stronger as a causeof extreme events such as extreme rainfall.Keywords: Influence, geographical, factors, extreme, rainfall.

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