scholarly journals STOCHASTIC GENERATION OF MONTHLY RAINFALL DATA

2010 ◽  
pp. 71-80 ◽  
Author(s):  
R. SRIKANTHAN
Keyword(s):  
Monthly Rainfall ◽  
Rainfall Data ◽  
Stochastic Generation

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

scholarly journals U.K. Rainfall Data: A Long-Term Persistence Approach

2012 ◽  
Vol 51 (10) ◽  
pp. 1904-1913 ◽  
Author(s):  
Luis A. Gil-Alana
Keyword(s):  
Strong Dependence ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Modeling Approaches ◽  
Long Run ◽  
The One

AbstractThis paper looks at the analysis of U.K. monthly rainfall data from a long-term persistence viewpoint. Different modeling approaches are considered, taking into account the strong dependence and the seasonality in the data. The results indicate that the most appropriate model is the one that presents cyclical long-run dependence with the order of integration being positive though small, and the cycles having a periodicity of about a year.

scholarly journals Comparison of Different Rainfall Erosion Estimation Methods for the Island of Crete

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 67 ◽  
Author(s):  
Dimitrios D. Alexakis ◽  
Manolis Grillakis
Keyword(s):  
Temporal Resolution ◽  
Total Duration ◽  
Power Estimation ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Daily Data

Interactions between soil and rainfall plays a vital role in ecological, hydrological and biogeochemical cycles of land. Among those interactions, the phenomenon of rainfall induced soil erosion is crucial to the soil functions, as it affects the soil structure and organic matter content that subsequently affects soil ability to hold moisture and nutrients. The erosive power of a specific rainfall event is regulated by its intensity and total duration. Various methodologies have been developed and tested to estimate the rainfall erosivity in different hydroclimatic regions and using different rainfall measuring timescales. Studies have shown that high temporal resolution measurements provide a more robust erosivity estimation. Nonetheless the sparsity and scarcity of such high temporal resolution data make the accurate estimation of rainfall erosivity difficult. Here, we compare different erosion power estimation methods based on different rainfall timescales for the island of Crete. Sub-daily (30-min) rainfall data based estimation is used as the basis for the assessment of a daily data based estimation methodology and two different methods that use monthly rainfall data. Modified Fournier Index (MFI) is incorporated in the study through different literature approaches and a regression equation is developed between rainfall erosivity power and MFI index for Crete. Results indicate that the use of daily data in the rainfall erosive power estimation is a good approximation of the sub-daily estimation, while formulas based on monthly rainfall data tend to exhibit larger deviations.

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro, Brazil

2017 ◽  
Vol 134 (3-4) ◽  
pp. 955-965 ◽  
Author(s):  
Gustavo Bastos Lyra ◽  
Tamíres Partelli Correia ◽  
José Francisco de Oliveira-Júnior ◽  
Marcelo Zeri
Keyword(s):  
Rio De Janeiro ◽  
Spatial Interpolation ◽  
The State ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Evaluation Of Methods

Modelling Catchment Rainfall Using Sum of Correlated Gamma Variables

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Zakaria, R. ◽  
Howlett, P. G. ◽  
Piantadosi, J. ◽  
Boland, J. W. ◽  
Moslim, N. H.
Keyword(s):  
Spatial Analysis ◽  
Random Variables ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Weighted Sum ◽  
Statistical Parameters ◽  
Dependent Random Variables ◽  
Rainfall Model

One of the major difficulties in simulating rainfall is the need to accurately represent rainfall accumulations. An accurate simulation of monthly rainfall should also provide an accurate simulation of yearly rainfall by summing the monthly totals. A major problem in this regard is that rainfall distributions for successive months may not be independent. Thus the rainfall accumulation problem must be represented as the summation of dependent random variables. This study is aimed to show if the statistical parameters for several stations within a particular catchment is known, then a weighted sum is used to determine a rainfall model for the entire local catchment. A spatial analysis for the sum of rainfall volumes from four selected meteorological stations within the same region using the monthly rainfall data is conducted. The sum of n correlated gamma variables is used to model the sum of monthly rainfall totals from four stations when there is significant correlation between the stations.

scholarly journals Applicability of CHIRPS-based satellite rainfall estimates for South Africa

2021 ◽  
Vol 63 (3) ◽  
pp. 1-12
Author(s):  
J A du Plessis ◽  
J K Kibii
Keyword(s):  
South Africa ◽  
Temporal Distribution ◽  
Pairwise Comparison ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Good Precision ◽  
Alternative Source

Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is compared with observed rainfall data from 46 stations evenly distributed across South Africa. Various metrics, based on a pairwise comparison between the observed and CHIRPS data, were applied to evaluate CHIRPS performance in the estimation of daily and monthly rainfall. The results show that CHIRPS data correlate well with observed monthly rainfall data for all stations used, having an average coefficient of determination of 0.6 and bias of 0.95. This study concludes that monthly CHIRPS data corresponds well, with good precision and relatively little bias when compared to observed monthly rainfall data, and can therefore be considered for use in conjunction with observed rainfall data where no or limited data is available in South Africa for hydrological analysis.

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