Time Series Analysis of Nebraska Daily Rainfall Data to Simulate Atrazine Runoff

2007 ◽  
Vol 43 (3) ◽  
pp. 651-663
Author(s):  
D.D. Adelman ◽  
J.S. Stansbury
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Series Analysis ◽  
Daily Rainfall Data

scholarly journals Bayesian Time Series Modelling of the Italian Daily Rainfall Data using Mixed Distribution

2019 ◽  
Vol 8 (4) ◽  
pp. 2279-2288
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Daily Rainfall Data ◽  
Mixed Distribution ◽  
Time Series Modelling ◽  
Bayesian Time Series ◽  
The Ideal

A combination of continuous and discrete elements is referred to as a mixed distribution. For example, daily rainfall data consist of zero and positive values. We aim to develop a Bayesian time series model that captures the evolution of the daily rainfall data in Italy, focussing on directly linking the amount and occurrence of rainfall. Two gamma (G1 and G2) distributions with different parameterisations and lognormal distribution were investigated to identify the ideal distribution representing the amount process. Truncated Fourier series was used to incorporate the seasonal effects which captures the variability in daily rainfall amounts throughout the year. A first-order Markov chain was used to model rainfall occurrence conditional on the presence or absence of rainfall on the previous day. We also built a hierarchical prior structure to represent our subjective beliefs and capture the initial uncertainties of the unknown model parameters for both amount and occurrence processes. The daily rainfall data from Urbino rain gauge station in Italy were then used to demonstrate the applicability of our proposed methods. Residual analysis and posterior predictive checking method were utilised to assess the adequacy of model fit. In conclusion, we clearly found that our proposed method satisfactorily and accurately fits the Italian daily rainfall data. The gamma distribution was found to be the ideal probability density function to represent the amount of daily rainfall.

scholarly journals Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

2018 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Sanjaya Natadiredja ◽  
I Ketut Sukarasa ◽  
Gusti Ngurah Sutapa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Satellite Data ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Observation Data ◽  
Similar Data ◽  
Strong Positive Correlation ◽  
Cause Analysis ◽  
Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

scholarly journals ANALISIS DERET WAKTU CURAH HUJAN UNTUK MENGKAJI PERUBAHAN IKLIM DI DAERAH TANGKAPAN AIR PROPINSI LAMPUNG TIME SERIES ANALYSIS OF RAINFALL TO STUDY CLIMATE CHANGE IN WATERSHED AREA, LAMPUNG PROVINCE

Agromet ◽  
2009 ◽  
Vol 23 (1) ◽  
pp. 61
Author(s):  
Tumiar Katarina Manik
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Time Series Analysis ◽  
Seasonal Pattern ◽  
Daily Rainfall ◽  
Climate System ◽  
Rainfall Pattern ◽  
Agricultural Activities ◽  
Series Analysis ◽  
Local Rainfall

One important climate factor for tropical area is rainfall. Changes in rainfall pattern will cause numerous problems especially in agricultural activities. Rainfall pattern could also lead to either flood or drought; problems which will not only affect agricultural activities but also socio-economic situation of broad community. Therefore, study of local climate variability focusing on rainfall related to the global warming is important. Time series analysis ( correlogram and periodogram) of daily rainfall was chosen to investigate the phenomena of global warming in local scale. Data (1974-2004) was collected from Sumberjaya, Air Hitam and Fajar Bulan; three stations located inside one of the important watershed in Lampung Province. From the collelogram, in general daily rainfall in this upland and forest area shows independency up to the year of 1990. No seasonal pattern could be an indicator that rains in this area are controlled more by local topography and land cover condition then by larger scale of climate system such as monsoon. After 1990 there were some weak sign of seasonal pattern. This could be interpreted as a sign that larger climate system started influence the local rainfall and as the global warming increases, it could be predicted that local rainfall pattern will be controlled more by the larger climate system. The periodogram shows that rainfall in this area has weak annual periodic. Data from Sumberjaya on 1990-1994 and 1999-2006 showed that annual periodic were getting stronger; a sign that larger climate system started dominating the area.

Accuracy Validation of Flood Forecasting Method Based on Time Series Analysis Using Observed Water Level and Rainfall Data

2020 ◽  
Author(s):  
Naoki Koyama ◽  
Tadashi Yamada
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Water Level ◽  
First Grade ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Flood Prediction ◽  
Series Analysis ◽  
Level Data ◽  
Water Level Data

<p>The aim of this paper is to verify the accuracy of the real-time flood prediction model, using the time-series analysis. Forecast information of water level is important information that encourages residents to evacuate. Generally, flood forecasting is conducted by using runoff analysis. However, in developing countries, there are not enough hydrological data in a basin. Therefore, this study assumes where poor hydrologic data basin and evaluates it through reproducibility and prediction by using time series analysis which statistical model with the water level data and rainfall data. The model is applied to the one catchment of the upper Tone River basin, one of the first grade river in Japan. This method is possible to reproduce hydrograph, if the observation stations exist several points in the basin. And using the estimated parameters from past flood events, we can apply this method to predict the water level until the flood concentration time which the reference point and observation station. And until this time, the peak water level can be predicted with the accuracy of several 10cm. Prediction can be performed using only water level data, but by adding rainfall data, prediction can be performed for a longer time.</p>

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