Estimation of Temporal R-factor Based on Monthly Precipitation Data

This study assesses temporal variation in rainfall erosivity of Gurushikhar, Rajasthan, (India) on a monthly precipitation basis in the form of the USLE/RUSLE R-factor. The objective of the paper is to theoretically calculate rainfall erosivity when the unavailability of high temporal resolution pluviographic rainfall data such as Indian condition. In the study, the rainfall erosivity has been calculated using the Modified Fourier Index. The results show that the annual rainfall erosivity factor (R) value highest in the year 2017 and lowest in 1974. Conferring to an examination through NASA, earth’s global superficial temperatures in 2017 ranked as second warmest since 1880. Therefore, the rainfall amount was more in 2017 compared to past years, and also rainfall erosivity value suddenly increased in 2017, achieved the highest value. They concluded that the heavy precipitation events in the year are lead to an increase in rainfall erosivity value and risk of soil erosion.

Analysis of Seasonality Precipitation Concentration in Northern of Iraq

This research is about analysis seasonality of precipitation concentration in the north of Iraq, by using multiple methods of precipitation concentration Index .The first is the standard vectors method that determines the date of concentration and the number of the rainy months, the second, precipitation concentration index (PCI) that classify the degree of (PCI) annually, supra-seasonal, seasonal depending on monthly precipitation data from nine metrological stations For 36 years (1979-2014), using Excel, Arc map 10.8 and Oriana software in calculates and representation of precipitation concentration. the result shows that all stations in the study area share the same date (Jan.-Feb.) and the stations differ in the length of the rainy season (7-9) month. and for PCI results, PCI annual shows denote a moderate concentration in the whole study area, PCI supra-seasonal value shows (in the wet season uniform rain distribution, the dry season value shows high concentration, PCI seasonal Shows (autumn) moderate concentration, winter: low concentration in all stations, in the spring: PCI value shows the moderate concentration in Erbil, Kirkuk, Sulaymaniyah, Salaheddin, and the other stations shows uniform rain distribution. Keywords: seasonal rain concentration, mathematical vector, PCI.

Time series analysis on precipitation with missing data using stochastic SARIMA

This paper presents an application of the Box-Jenkins methodology for modeling the precipitation in Iran. Linear stochastic model known as multiplicative seasonal ARIMA was used to model the monthly precipitation data for 44 years. Missing data occurred in between for 34 months for some reason. To fill the gap a SARIMA model was fitted based on the first 180 available observations and the missing observations were substituted by the forecasts for the next 34 months. Then a SARIMA model was fitted for the full data. The result showed that the fitted model represent the full data well.

Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultural and livestock sectors. This study analyzed the fit of 2-parameter distributions gamma (GAM), log-normal (LNORM), Weibull (WEI), generalized Pareto (GP), Gumbel (GUM) and normal (NORM) to monthly precipitation data from 293 rainfall stations across NEB, in the period 1988–2017. The maximum likelihood (ML) method was used to estimate the parameters to fit the models and the selection of the model was based on a modification of the Shapiro-Wilk statistic. The results showed the chosen 2-parameter distributions to be flexible enough to describe the studied monthly precipitation data. The GAM and WEI models showed the overall best fits, but the LNORM and GP models gave the best fits in certain months of the year and regions that differed from the others in terms of their average precipitation.

Investigation of leachate quality from the Trail Road Landfill

Leachate data for the Trail Road sanitary landfill were obtained for stages three (3) and four (4) of the landfill, located in the City of Ottawa, for the period of 10 years from 1996 to 2005. Data included several parameters such as pH, BOD, COD, Ca, Fe, Cl, SO₄, some selected heavy metals such as: Cu, Zn, Pb, and other parameters like Toluene and Vinyl Chloride. Analysis was performed to these data using Microsoft Excel analysis tools. Various graphical and statistical techniques such as Correlation, regression, and contaminant specific analysis were used to characterize leachate from the Trail Road Landfill. The data collected were fitted with trend lines to represent temporal variations. Pearson Product Moment correlation analysis as a multivariate statistical method was later used for identifying linear relationships between the quality of leachate with respect to the water infiltration to the waste, calculated from monthly precipitation data. Results from this research yielded noteworthy temporal variations of many parameters in leachate over the study period. Also, the effect of many factors like the net water infiltrating waste from precipitation and methanogenesis of leachate on the behaviour of leachate parameters was noticeable.

Investigation of leachate quality from the Trail Road Landfill

Leachate data for the Trail Road sanitary landfill were obtained for stages three (3) and four (4) of the landfill, located in the City of Ottawa, for the period of 10 years from 1996 to 2005. Data included several parameters such as pH, BOD, COD, Ca, Fe, Cl, SO₄, some selected heavy metals such as: Cu, Zn, Pb, and other parameters like Toluene and Vinyl Chloride. Analysis was performed to these data using Microsoft Excel analysis tools. Various graphical and statistical techniques such as Correlation, regression, and contaminant specific analysis were used to characterize leachate from the Trail Road Landfill. The data collected were fitted with trend lines to represent temporal variations. Pearson Product Moment correlation analysis as a multivariate statistical method was later used for identifying linear relationships between the quality of leachate with respect to the water infiltration to the waste, calculated from monthly precipitation data. Results from this research yielded noteworthy temporal variations of many parameters in leachate over the study period. Also, the effect of many factors like the net water infiltrating waste from precipitation and methanogenesis of leachate on the behaviour of leachate parameters was noticeable.

Assessment of Meteorological Drought with Application of Standardized Precipitation Evapotranspiration Index (SPEI) for Tripura, Northeast India

Drought is one of the major water-related natural hazards. Understanding the spatial and temporal variation of rainfall is of great importance in water resources planning and management as it is related with food security and management of scarce water resource, which becomes critical in case of drought events. The advent of GIS to produce spatially interpolated drought map helps the water managers to undertake appropriate measures in drought relief and prioritization of drought mitigation works. Limitation of literature on Tripura suggests that study of drought over Tripura could help in strengthening of mitigation planes and rationalization of disaster management policies. Hence, the present study is focused to investigate the drought persistence and severity in the Tripura state of India during the period 1980-2013, using Standardized Precipitation Evapotranspiration Index (SPEI). Three time scale i.e., 3, 6 and 12 month time scales were opted for the study. Gridded monthly precipitation data distributed over the four districts of Tripura was used for drought analysis. Significant drought events were detected over the study area during the selected period. Annual analysis of SPI time series showed that the study area received the intense drought during the year 1985. Geospatial technique was used to generate the SPEI drought map for the year 1985.

Improved Teleconnective Predictability of Monthly Precipitation Amounts Using Canonical Correlation Analysis

This study aimed to evaluate the application of the canonical correlation analysis (CCA) to predict monthly precipitation amounts (predictands) by benefitting from 17 large-scale climate indices (predictors) in Iran. Monthly precipitation data, covering the period of 1987–2017, were collected from 100 weather stations across the country. Monthly precipitations were predicted using the multiple linear regression (MLR) models, based on the 1- to 6-month lead times of the original and canonical predictors. The cross-validation was conducted to compare the prediction skills of the two sets of MLR models constructed on the basis of the original predictors (MLOrigPr) and the canonical predictors (MLCCAPr). The analyses revealed the dominant teleconnections and that there are the interannual variations in responses of precipitation to them suggesting that a signal only is not sufficient to achieve a robust understanding of the associations. At the 1-month lead time, the MLR models based on the canonical predictors outperformed those based on the original predictors. However, the skill of both models was reduced by increasing the lead times up to 6 months. Averaging on all stations, around 61.4% and 26.3% of the observed values falls into the 95% prediction intervals of the MLCCAPr and MLOrigPr models, respectively. Furthermore, the MLCCAPr models were found to be more spatially universal than the MLOrigPr ones. These findings corroborated the advantage of using the CCA in improving the teleconnective predictability of precipitation in Iran.

Pluviometric patterns in the São Francisco River basin in Minas Gerais, Brazil

ABSTRACT This work aimed to evaluate the spatial-temporal variability of precipitation in the Minas Gerais section of the São Francisco River basin, an area of substantial socio-environmental relevance for the country and which has presented recent events of water scarcity. Multivariate and non-parametric statistical analyses were applied to the monthly precipitation data from 131 pluviometric stations, covering a period from 1989 to 2018. The results indicated distinct homogeneous pluviometric regions with greater spatial variability in rainfall patterns in the southern regions of the basin. Results from the temporal analysis indicated seasonality in the rainfall patterns for all seasons, with the rainy period predominantly occurring between October and March for the entire Minas Gerais section of the São Francisco River basin. No rainfall trend was identified in 78% of the stations, with the other stations (22%) showing a trend toward a reduction in rainfall volume.