scholarly journals Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xihua Yang ◽  
Xiaojin Xie ◽  
De Li Liu ◽  
Fei Ji ◽  
Lin Wang
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Climate Impact ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Local Climate ◽  
Erosion Risk ◽  
Daily Rainfall Data ◽  
Interpolation Techniques

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

scholarly journals Evaluation the Best Random Component in Modified Thomas-Fiering Model in Generating Rainfall Data for Akre station

2019 ◽  
Vol 9 (2) ◽  
pp. 186-192
Author(s):  
Fawaz Kh. Aswad ◽  
Ali A. Yousif ◽  
Sayran A. Ibrahim
Keyword(s):  
Mean Squared Error ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rainfall Data ◽  
Random Numbers ◽  
Random Component ◽  
Chi Square ◽  
Daily Rainfall Data ◽  
Chi Square Test ◽  
Kolmogorov Smirnov

In this research, the effect of random component in the modified Thomas-Fiering model to generate daily rainfall data was studied, and Akre station considered a case study. A random component with special distributions: Normal random numbers, Wilson-Hilferty (W-H) transformation, truncated W-H, and Kirby modification to W-H transformation were used. The model applied to the daily rainfall data for Akre station for available years 2000–2006 and the model used to generate the rainfall data for the years 2006 and 2007. The results showed that the correlation coefficients between the observed and generated data were 0.82 for normal random numbers, 0.77 for W-H transformation, 0.89 for truncated –W –H, and 0.87 for KM to W-H transformation. The tests of Chi-square test, Kolmogorov–Smirnov test, root mean squared error (RMSE) test, and mean absolute error (MAE) test were used to compare between observed and generated data. All the results have passed the Chi-square test and Kolmogorov–Smirnov, where the calculated values were less than the tabulated value at 5% significance. For the test RMSE and MAE, the truncated W-H transform was the values of at least two. Therefore, W-H transform is the best for generating the rainfall data at Akre station

scholarly journals Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
M. Oscar Kisaka ◽  
M. Mucheru-Muna ◽  
F. K. Ngetich ◽  
J. N. Mugwe ◽  
D. Mugendi ◽  
...  
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Kriging Interpolation ◽  
Study Region ◽  
Daily Rainfall Data ◽  
Kriging Interpolation Method ◽  
Dry Spell ◽  
Interpolation Techniques ◽  
Mean Square Errors

This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) in Machang’a, Kiritiri, and Kindaruma, respectively. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.

The effect of weather, drainage efficiency and duration of spring cultivations on barley yields in England

1972 ◽  
Vol 7 (2) ◽  
pp. 79-83 ◽  
Author(s):  
L P Smith
Keyword(s):  
Yield Loss ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Work Schedule ◽  
Average Yield ◽  
Daily Rainfall Data ◽  
Arable Farming ◽  
Work Distribution

Daily rainfall data for twenty years in arable farming areas are analysed with respect to four standards of drainage and for three lengths of schedule of spring work. Distribution and frequency in time of available work days are interpreted in terms of lateness of sowing and of barley yield. Formulae are established to calculate average yield loss in terms of drainage standard and work schedule, enabling estimates to be made of the effect of planned improvements.

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 Structural break or long memory: an empirical survey on daily rainfall data sets across Malaysia

2013 ◽  
Vol 17 (4) ◽  
pp. 1311-1318 ◽  
Author(s):  
F. Yusof ◽  
I. L. Kane ◽  
Z. Yusop
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Structural Break ◽  
Daily Rainfall ◽  
Least Square ◽  
Rainfall Data ◽  
Data Sets ◽  
Original Series ◽  
Empirical Survey ◽  
Daily Rainfall Data

Abstract. A short memory process that encounters occasional structural breaks in mean can show a slower rate of decay in the autocorrelation function and other properties of fractional integrated I (d) processes. In this paper we employed a procedure for estimating the fractional differencing parameter in semiparametric contexts proposed by Geweke and Porter-Hudak (1983) to analyse nine daily rainfall data sets across Malaysia. The results indicate that all the data sets exhibit long memory. Furthermore, an empirical fluctuation process using the ordinary least square (OLS)-based cumulative sum (CUSUM) test for the break date was applied. Break dates were detected in all data sets. The data sets were partitioned according to their respective break date, and a further test for long memory was applied for all subseries. Results show that all subseries follows the same pattern as the original series. The estimate of the fractional parameters d1 and d2 on the subseries obtained by splitting the original series at the break date confirms that there is a long memory in the data generating process (DGP). Therefore this evidence shows a true long memory not due to structural break.

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