Stochastic time series analysis of hydrology data for water resources
01 Nov 2017-Vol. 263, Iss: 4, pp 042140
TL;DR: In this article, the authors proposed to predict the seasonal periods in hydrology using Thomas-Fiering model, which is the most popular method for time series analysis in hydrologic flowseries.
Abstract: The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.
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TL;DR: In this article, the simulation and modeling of climatic parameters such as annual precipitation, temperature and evaporation using stochastic methods (time series analysis) was performed using the ARIMA model.
Abstract: Stochastic models (time series models) have been proposed as one technique to generate scenarios of future climate change Precipitation, temperature and evaporation are among the main indicators in climate study The goal of this study is the simulation and modeling of climatic parameters such as annual precipitation, temperature and evaporation using stochastic methods (time series analysis) The 40-year data of precipitation and 37-year data of temperature and evaporation at Jelogir Majin station (upstream of Karkheh dam reservoir) in western of Iran has been used in this study and based on ARIMA model, The auto-correlation and partial auto-correlation methods, assessment of parameters and types of model, the suitable models to forecast annual precipitation, temperature and evaporation were obtained After model validation and evaluation, the Predicting was made for the ten future years (2006 to 2015) In view of the Predicting made, the precipitation amounts will be decreased than recent years As regards the mean of annual temperature and evaporation, the findings of the Predicting show an increase in temperature and evaporation
13 citations
Cites background from "Stochastic time series analysis of ..."
...Sathish and Khadar Babu (2017) predicted hydrology time series for water resources (such as flood) using stochastic time series analysis (Thomas-Fiering model) from the river basins In India [12]....
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TL;DR: In this paper, a study was carried out to assess the reliability of Thomas-Fiering's method of stream flow prediction, which was extended to 2016 using the model by multiple linear regressions of the gauged and synthetic data of 1956-1973.
Abstract: This study was carried out to assess the reliability of Thomas-Fiering’s method of stream flow prediction. The 19 years gauged data of 1955-1973 was extended to 2016 using the model. Model calibration was done by multiple linear regressions of the gauged and synthetic data of 1956-1973. The linear equations developed for January to December were used for adjustment of the three sets of stream flow data generated for 1974-2016. The reliability assessment was done based on the extent to which the unbiased statistics (mean, standard deviation and correlation coefficients) of the 1955-1973 stream flow data were preserved in the synthetic stream flow for 1955-2016. The comparison was done using linear regression and One-Way ANOVA (95% Confidence level) to check for the reliability of the generated data. The coefficients of determination, P-values, F-values and critical F-values were used to estimate the reliability index. Synthetic data was found to be 95.9% reliable. Keywords : Ofu River, Reliability, Stream flow, Synthetic, Thomas Fiering’s model
5 citations
16 Sep 2019
TL;DR: In this paper, the change in trend of rainfall for the River Ssezibwa Catchment area and stream flow of River Ssibwa was analyzed to find out the trend and detect change point.
Abstract: The study focused on two climatic variables i.e. precipitation and stream flow for analysing change in trend of rainfall for the River Ssezibwa Catchment area and stream flow of River Ssezibwa. This Catchment is found in the districts of Mukono and Buikwe in Uganda. In this area agriculture is majorly dependent on rainfall and irrigation on a small scale. However, rainfall occurrence has become unpredictable over the past few years as result of the changes in patterns of weather. This has caused severe effects on the agricultural cropping system as well as caused negative effects on the natural water resources. Stream flow data of 57 years (1960 – 2017) and rainfall data for 35 years (1982 – 2017) on a daily basis was analysed to find out the trend and detect change point. Trend analysis was done by using the non-parametric analysis while the change point detection was carried out by using the Pettit test (1979).Magnitude of trend for the time series data was carried out using Sen’s Slope estimator and Mann – Kendall test was done to determine the trend. Results from the statistical analysis highlighted that; for stream flow the trend was generally positive and change point detected to be in the year 2000 while for rainfall data analysis indicated that the trend was predominantly negative and change point was in the year 1998.
1 citations
Cites background from "Stochastic time series analysis of ..."
...The recommended that more focus should be put on the use of this model in the field of applied hydrology research (Babu, 2017)....
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Journal Article•
TL;DR: The proposed paper explained in detail, the standard measures like mean, standard deviation, co-efficient of R2 and auto-correlations of the downscaling data in hydrology, the new model is MLE by Gaussian distribution is a method that will find the values of that result are best fit the data sets and Maximum Likelihood Estimation is the best prediction using parameters of water level data sets.
Abstract: The water is an important component for human beings, animals, autotrophs and heterotrophs. Actually, human beings continuous ly adopted to their physical environment. Due to the increased population, human needs more water for drinking, agriculture, etc. The proposed paper explained in detail, the standard measures like mean, standard deviation, co-efficient of R2 and auto-correlations of the downscaling data in hydrology. The new model is MLE by Gaussian distribution is a method that we will find the values of that result are best fit the data sets. Simple downscaling approach, in general, can perform well as the parametric method, generate the observed water level using SELGA and SELSGA approaches. Maximum Likelihood Estimation is the best prediction using parameters of water level data sets. The new model is used, the present proposed article is to predict future values using stochastic extended linear group average and stochastic extended linear semi-group average on generated downscaling data sets.
Cites background from "Stochastic time series analysis of ..."
...E-mail: subramanisathish88@gmail.com • SK Khadar Babu, Associate Professor, Department of Mathematics, Vellore Institute of Technology, Vellore-632014, PH-7397091968....
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...Sathish and Khadar Babu, [9] predicted the about stochastic prediction models for food grains time series data....
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...[9] S, Sathish, SK. Khadar Babu, ―Stochastic time series analysis of hydrology data for water resources‖....
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01 Nov 2019
TL;DR: In this article, a Modified Thomas Fiering model for generating and forecasting monthly flow is used to improve the management of operation system for the Roseires reservoir, which is necessary to know the hydrological system of the Blue Nile river.
Abstract: To improve the management of operation system for the Roseires reservoir it is necessary to know the hydrological system of the Blue Nile river, which is the main water source of the reservoir. In this work, a Modified Thomas Fiering model for generating and forecasting monthly flow is used. The methodological procedure is applied on the data obtained at the gauging station of Eldeim in Blue Nile, Sudan. The study uses the monthly flows data for years 1965 to 2009. After estimation the model parameters, the synthetic time series of monthly flows are simulated. The results revealed that the model maintained most of the basic statistical descriptive parameters of historical data. Also, the Modified Thomas Fiering model is applied to predict the values of the next fifty-five years, with excellent results that conserved most basic statistical characteristics of runoff historical series. The Modified Thomas Fiering model is able to realistically reconstruct and predict the annual data and shows promising statistical indices.
References
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TL;DR: In this paper, a simple homogeneity test was applied to a precipitation data set from southwestern Sweden and the significant breaks varied from 5 to 25 per cent for this data set and probably reflect a serious source of uncertainty in studies of climate trends and climatic change all over the world.
Abstract: In climate research it is important to have access to reliable data which are free from artificial trends or changes. One way of checking the reliability of a climate series is to compare it with surrounding stations. This is the idea behind all tests of the relative homogeneity. Here we will present a simple homogeneity test and apply it to a precipitation data set from south-western Sweden. More precisely we will apply it to ratios between station values and some reference values. The reference value is a form of a mean value from surrounding stations. It is found valuable to include short and incomplete series in the reference value. The test can be used as an instrument for quality control as far as the mean level of, for instance, precipitation is concerned. In practice it should be used along with the available station history. Several non-homogeneities are present in these series and probably reflect a serious source of uncertainty in studies of climatic trends and climatic change all over the world. The significant breaks varied from 5 to 25 per cent for this data set. An example illustrates the importance of using relevant climatic normals that refer to the present measurement conditions in constructing maps of anomalies.
1,474 citations
TL;DR: In this paper, a nonparametric Mann-Kendall test is applied to mean anomaly series obtained through averaging the anomalies of some precipitation intensity statistics over five stations: Genoa (1833-1998), Milan (1858−1998), Mantova (1868−1997), Bologna (1879−1998) and Ferrara (18 1979−1996), which provides evidence that the number of rainy days has a stronger and more significant negative trend than the corresponding precipitation amount.
Abstract: Recent studies on changes in precipitation intensity encompassing North America have found evidence for an increase in the relative amount of precipitation contributed by heavy and extreme rainfall events in the last 80 years. Within this context, the purpose of this paper is to verify whether such a signal can also be detected in northern Italy, where daily precipitation data are available from the beginning of the 19th century. The analysis is performed by applying the non-parametric Mann–Kendall test to mean anomaly series obtained through averaging the anomalies of some precipitation intensity statistics over five stations: Genoa (1833–1998), Milan (1858–1998), Mantova (1868–1997), Bologna (1879–1998) and Ferrara (1879–1996). It provides evidence that in northern Italy, the number of rainy days has a stronger and more significant negative trend than the corresponding precipitation amount, both on a yearly basis and in all of the seasons. As a consequence, precipitation intensity has a positive trend. The increase in precipitation intensity causes a significant positive trend in the proportion of total precipitation contributed by heavy precipitation events (i.e. daily precipitation >25 mm and daily precipitation >50 mm). The trend is mainly caused by the last 60–80 years, and is particularly evident in the periods of 1930–1945 and 1975–1995. The increase in precipitation intensity is connected to a modification of the distribution of daily precipitation values in a year, with trends that grow from the lower to the upper percentiles, and up to 4 mm/100 years for the 95th percentile. Copyright © 2000 Royal Meteorological Society
192 citations
TL;DR: Methods for the detection and estimation of trends which are suitable for the type of data sets available from water quality and atmospheric deposition monitoring programmes are considered and a broad overview of the topic of trend analysis is given.
Abstract: Methods for the detection and estimation of trends which are suitable for the type of data sets available from water quality and atmospheric deposition monitoring programmes are considered. Parametric and non-parametric methods which are based on the assumption of monotonic trend and which account for seasonality through blocking on season are described. The topics included are heterogeneity of trend, missing data, covariates, censored data, serial dependence and multivariate extensions. The basis for the non-parametric methods being the method of choice for current large data sets of short to moderate length is reviewed. A more general definition of trend as the component of gradual change over time is consistent with another group of methods and some examples are given. Spatial temporal data sets and longer temporal records are also briefly considered. A broad overview of the topic of trend analysis is given, with technicalities left to the references cited. The necessity of defining what is meant by trend in the context of the design and objectives of the programme is emphasized, as is the need to model the variability in the data more generally.
144 citations
TL;DR: In this paper, trends are estimated for different durations of annual extreme rainfall using the regional average Mann-Kendall S trend test, and a bootstrap methodology is used to account for the observed spatial correlation.
Abstract: Information on intensity-duration-frequency of rainfall is commonly required for a variety of hydrologic applications. In this study, trends are estimated for different durations of annual extreme rainfall using the regional average Mann-Kendall S trend test. The method of L-moments was employed to delineate homogeneous regions. The trend test was modified to account for observed autocorrelation, and a bootstrap methodology was used to account for the observed spatial correlation. Numerical analysis was performed on 44 rainfall stations from the province of Ontario, Canada, for a 20 year time frame. This was done using data from homogeneous regions established using the L-moments procedure for the annual maximum observations for the following durations: 5, 10, 15 and 30 min, and 1, 2, 6 and 12 h. Depending on different rainfall durations, four or five homogeneous regions were delineated. Based on a 5% significance level, approximately 23% of the regions tested had a significant trend, predominantly for short-duration storms. Serial dependency was observed in 2.3% of data sets and spatial correlation was found in 18% of the regions. The presence of serial and spatial correlation had a significant impact on trend determination.
124 citations
TL;DR: In this article, urbanization occurring during and/or after the gauging period is quantified using spatially and temporally distributed land use data and relationships between measures of urbaniza- tion and the presence or absence of significant trends in the discharge series are presented.
Abstract: A typical flood frequency analysis is based on gauged annual maximum discharges. One assumption behind the analysis is that the measured discharge signal is stationary. The validity of this assumption can be difficult to establish, particularly where urbaniza- tion has occurred within the gauged watershed, altering the response of the affected watershed to precipitation. This alteration can produce a nonstationary streamflow signal that can be significant, depending on the percentage of the watershed altered. As urbanization increases, peak discharges are shown to increase, producing a positive trend in the annual maximum series. Urbanization occurring during and/or after the gauging period is quantified using spatially and temporally distributed land use data. Three statistical tests ~a parametric t-test on the slope of the linear relationship between the flood series and time and two nonparametric tests: the Kendall's Tau and the Spearman Rank Correlation! are performed on both the annual maximum discharge and annual maximum discharge-precipitation ratios series to test for trends or nonstationary signals corresponding to periods of urbanization. A case study suggests that the ratios are more effective than the discharges alone for identifying nonstationarity resulting from urbanization. In addition, relationships between measures of urbaniza- tion and the presence or absence of significant trends in the discharge series are presented.
98 citations