Current Status of Time Series Analysis in Hydrological Sciences

International Journal of Engineering Research and, 2017

The main sources of water are natural sources like rainwater, oceans, rivers, lakes, streams, ponds, springs etc., and man-made sources like dams, wells, tube wells, hand-pumps, canals, etc. Agriculture and plantation depends heavily on water and its source is rainfall. Mathematical models are required to predict future water demand and climate changes. Time series are valuable sources of information that can be consulted for the characterization of variables in several areas of knowledge. It is basically a measurement of data taken in chronological order within a certain time. In hydrology, such series are employed in water systems management as tools for the hydrological cycle understanding. The purpose of most water quality and stream flow studies is to point out the information and necessary knowledge to manage water resources as well as their use, control and development. This paper uses a statistical approach to analyze Cauvery water inflow into Karnataka Reservoir. Data used in the study is taken on a monthly basis during the period from 1974 to 2014.

International Journal of Modelling and Simulation, 1984

Two case studies are presented in this paper showing clearly the practical importance of the use of time series methods in the hyd ro lo gic design and the operation of water resources systems . The first one deals with the ro utin g and the estimat ion of flood hyd rographs at the out let of a stream-waters hed system and the second one with the forecasting of monthly rainfall sums.

Environmental Modelling & Software, 2012

The program Menyanthes combines a variety of functions for managing, editing, visualizing, analyzing and modeling hydrogeologic time series. Menyanthes was initially developed within the scope of the PhD research of the first author, whose primary aim was the integration of data and physically-based methods for modeling time series of groundwater heads. As such, time series analysis forms the heart of Menyanthes. Within Menyanthes, time series can be modeled using both the ARMA and PIRFICT methods. The PIRFICT method is a new method of time series analysis that has practical advantages and facilitates physical interpretation and implementation of knowledge on physical behavior. Analytic solutions to specific hydrogeologic problems may be used as response function, along with their physically-based parameters. A more general approach is possible using Skew-Gaussian distribution functions, which prove to fit the behavior of hydrogeologic (and other) systems well. Use of such functions within the PIRFICT method substantially simplifies the model identification procedure, as compared to the traditional Box-Jenkins procedure. PIRFICT models may be fitted to a large number of time series in batch. Spatial patterns that emerge in the results provide useful, additional, and independent information, which adds another dimension to time series analysis. Their interpretation is supported by the spatial visualization and analysis tools of Menyanthes. The PIRFICT method also facilitates the integration of time series and spatially-distributed models via, e.g., moment-generating differential equations. The PIRFICT method may prove to be of use for other types of time series as well, both within and outside the realm of environmental sciences.

Stochastic Hydrology and Hydraulics, 1995

Current methods of estimation of the univariate spectral density are reviewed and some improvements are suggested. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements rather than competes with the popular ARIMA model building approach. A new diagnostic check for ARMA model adequacy based on the nonparametric spectral density is introduced. Two new algorithms for fast computation of the autoregressive spectral density function are presented. A new style of plotting the spectral density function is suggested. Exploratory spectral analysis of a number of hydrological time series is performed and some interesting periodicities are suggested for further investigation. The application of spectral analysis to determine the possible existence of long memory in riverflow time series is discussed with long riverflow, treering and mud varve series. A comparison of the estimated spectral densities suggests the ARMA models fitted previously to these datasets adequately describe the low frequency component. The software and data used in this paper are available by anonymous ftp from fisher.stats.uwo.ca in the directory pub\mhts.

AIP Conference Proceedings, 2022

The rainfall data analysis and its timely prediction is very important for agricultural practices planning. In case of heavy/scanty rainfall prediction, early forecasting helps to plan for disaster management in areas of high risk. In this article, time series analysis of rainfall for the state of Odisha is carried out. Odisha is a state which is constantly being hit by waves of droughts, floods and cyclones. Thus, it becomes extremely important to analyze and forecast for rainfall in this region so that proper measures to avert disasters can be undertaken. For time series modeling, rainfall data for last 50 years has been analyzed using Auto-Regressive Integrated Moving Average (ARIMA) method. Several suitable ARIMA models are identified based on significant parameters. The variability in prediction and actual provides for the consistency of model. The months of highest and lowest precipitation have been identified. Later on the efforts to identify regions in Odisha for scanty and heavy rainfall has been made and predictions are made for 10 years into the future time period. It can be seen that accurate predictions help for crop planning and adopting better agronomic practices for crops, proper disaster management in risk zones and providing timely relief to those in affected areas.

Hydrological Sciences Journal, 1997

This paper presents an investigation of trend in historic hydrological time series of areal annual precipitation on and mean annual runoff from the basin of the Evinos River in western Greece. Using different tests for randomness, a statistically significant long lasting decreasing trend was found in the precipitation record, whereas an also significant fluctuating "local" trend was detected in the runoff record. Further, it is shown that the effects of precipitation change on mean annual runoff can be quantified through a magnification factor. Détection de tendances dans les séries hydrologiques du bassin versant de la rivière Evinos Résumé Ce travail présente une analyse des tendances de séries historiques annuelles de précipitations et de débits recueillies dans de bassin versant de la rivière Evinos, située en Grèce occidentale. Après avoir utilisé différentes méthodes statistiques permettant de dignostiquer si ces séries sont ou non de type purement aléatoire, une tendance significative à la décroissance, de longue durée, a été mise en évidence dans le cas des précipitations, alors que des tendances fluctuantes, de courtes durées, ont été détectées dans le cas des débits. Il a été de plus montré que les effets sur le débit annuel des modifications concernant les précipitations pouvaient être quantifiés grâce à un facteur d'amplification.

IOP Conference Series: Materials Science and Engineering

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.

Ramesh Rainfall studies are of utmost utility for understanding nature & hence the behaviour of climate changes. Time series is a set of observations taken at specified times usually at equal interval. The inherent variability displayed by many hydrological time series usually mask trends and periodic patterns. This situation has often led to " something " the original time series so that the effects of random variations are reduced and trends or cyclical patterns enhanced. Thus a set of data depending on time is called a Time series. Here, Rainfall series represent the time series. The time series analysis is helpful to compare the actual performance and analyse the cause of variations. By comparing different time series we can draw important conclusion. Graphical method implies in increasing trend for pre-monsoon, southwest monsoon, northeast monsoon and annually.Geo-informatics module consists of GIS mapping for Location map, Geomorphology map and Season wise Rainfall maps are generated. Autocorrelation indicates the periodicity observed as 37,16 & 6 years (PM), 12, 37 & 16 years (SWM), 8, 18 & 6 years (NEM) and 16, 22 & 8 years (Annual) respectively. Power spectral depicts the cyclicity of 37, 4 & 3 years (PM), 2, 4& 2 years (SWM), 3, 7 & 2 years (NEM) and 2, 4 & 2 years (Annual) respectively. Moving average displays prominent positive correlation coefficients at lags of 18 to 42 years in PM & SWM and 12 to 24 years in NEM & Annual. The southwest and southeast parts of the study area experience the heavy rainfall whereas the least rainfall areas are the northern parts of the study area.The short term and long term cyclicity observed in Autocorrelation, power spectrum and Moving Average. Spatial variation of rainfall for the three seasons and annual has been studied Copy Right, IJAR, 2014,. All rights reserved

Earth Sciences Research Journal, 2018

Time series models are often used in hydrology and meteorology studies to model streamflows series in order to make forecasting and generate synthetic series which are inputs for the analysis of complex water resources systems. In thispaper we introduce a new modeling approach for hydrologic and meteorological time series assuming a continuous distribution for the data, where both the conditional mean and conditional varianceparameters are modeled. Bayesian methods using standard MCMC (Markov Chain Monte Carlo Methods) are used to simulate samples for the joint posterior distribution of interest. Two applications to real data sets illustrate the proposedmethodology, assuming that the observations come from a normal, a gamma or a beta distribution. A first example is given by a time series of monthly averages of natural streamflows, measured in the year period ranging from1931 to 2010 in Furnas hydroelectric dam, Brazil. A second example is given with a time series of 313 air humidity d...

In evaluating the long-term variations in meteorological and hydrological records, the idea of "regime shifts" or change point is becoming very popular in recent years. This concept received a formidable motivation after a step-like change in the global climate system during the late 1970's although a realization of the significance of that event came more gradually. In this study, the Singular Spectrum Analysis (SSA), a powerful non-parametric technique in time series analysis, has been applied to rainfall data sets of some locations in peninsular Malaysia. The method follows a sequential application to reconstruct the time series by embedding to produce a trajectory matrix which later decomposed using singular value decomposition (SVD). The approach is able to detect and extract patterns such as trends, oscillations or noise from the observed rainfall data sets. This result may give more precise insight into the climate period background of peninsular Malaysia.