Geostatistical Analysis of Rainfall Temperature and Evaporation Data of Owerri for Ten Years

Selemo, A. O. I.; Ananaba, S. E.; Nwagbara, Johnbosco O.; Egejuru, V. E.; Nwugha, V.

doi:10.4236/acs.2012.22020

Cited by 3 publications

(3 citation statements)

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“…On evapotranspiration, the study further found a fairly constant variation in evaporation from 2002 indicating deforestation ( [8], ibid), which in our opinion could result in less amount of rainfall.…”

Section: Introductionmentioning

confidence: 57%

“…According to the Interna- for Owerri metropolis over the period 1986 to 2016, and concluded that LST has increased in Owerri for both the lowest and highest temperatures [7], connoting increases in night time and day time temperatures respectively. Alexander Selemo and co-authors in their study of Owerri for the ten year period from 1998 to 2007, observed that the average annual temperature showed an increasing trend from 2001 [8]. All these point to warming climate change situation in Owerri.…”

Section: Introductionmentioning

confidence: 95%

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Climate Study of Owerri and Comparative Analysis of Different Methods of Base Factor Computation

Echebima¹,

Ndukwu²,

Obafemi³

2019

ACS

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This study analyzed three decades of the temperature and rainfall data of Owerri, South Eastern Nigeria and in the process carried out a comparative analysis of using three different methods namely the mean, median and jackknife to derive the base data upon which the anomalies are computed with a view to ascertaining if any significant differences exist in the obtained results. And for rainfall which is seasonal, a further z-score method was employed to remove the effect of seasonality by dividing the anomalies with its standard deviation. Linear regression analysis was used to interpret the anomaly plots and the trend in climate from 1986 to 2015 in Owerri extracted. For the temperature analysis, the mean and median methods gave similar result which makes the mean method an acceptable method due to its simplicity, taking cognizance of the heavy computation in the other methods. Interestingly, for the rainfall analysis, the mean and jackknife methods gave the same result which also makes the mean method acceptable to avoid the heavy computations of the other two. ANOVA on the anomalies so derived showed no significant difference in their mean, thus underpinning the choice of the mean method. Using the outcome of the mean method therefore, the analyzed mean minimum temperature and mean maximum temperature indicate that the night time temperature has increased in Owerri within the period under consideration by 0.0186˚C each year and the daytime temperature increased by 0.01˚C per year respectively, thus indicating that a warming climate change has taken place in Owerri. Within the same period, the rate for rainfall on the other hand showed a decrease. In summary, the simple mean method of base computation suffices over the median and jackknife methods in climate studies for Owerri, a trend of warming climate has taken place in Owerri with increases in both daytime and night temperatures but with a reverse trend in rainfall which will require further inquiry.

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Section: Introductionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 95%

Climate Study of Owerri and Comparative Analysis of Different Methods of Base Factor Computation

Echebima¹,

Ndukwu²,

Obafemi³

2019

ACS

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“…wet and dry) which exists within the region (Okoro et al, 2014;Okorie, 2015). The study area experiences high rainfall amounts of about 1600-2900 mm (Selemo et al, 2012;Okorafor et al, 2018). The relative humidity ranges from 75-90% (Udokporo et al, 2015) with mean minimum temperature of 23.5°C and mean maximum temperature of 32.9°C (Okorie, 2015).…”

Section: Description Of Study Areamentioning

confidence: 99%

Assessment of Rainfall Erosivity Factor (R) for Selected Locations in Imo State, Southeastern Nigeria

O.O¹,

C.C²,

C.O³

et al. 2019

IJEAST

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Soil erosion in southeastern Nigeria is a major environmental issue of which rainfall is a major contributor to its elevated level(s). This study gives a critical assessment on the rainfall erosivity factor (R) for selected sites in Imo State representing different soil groups geologically by making use of mean monthly rainfall data and GIS techniques. By applying empirical methods the rainfall intensity for all the locations were obtained and was further determined at three different intervals of 30-minutes, 45minutes and 60-minutes respectively. Through the use of the revised universal soil loss equation (RUSLE) the rainfall erosivity factor (R) was obtained for each of the locations and they are as follows; 6054.93 MJmm/ha/hr, 5211.48 MJmm/ha/hr, 5977.29 MJmm/ha/hr, 6171.57 MJmm/ha/hr, 6478.06 MJmm/ha/hr, 5567.54 MJmm/ha/hr, 5350.84 MJmm/ha/hr and 5470.29 MJmm/ha/hr for Ehime Mbano,

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Assessment and Mapping of Rainfall Erosivity Index (R) for Majha Region, Punjab Is a State in Northern, India

Kumar¹

2021

ijsrtm

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Purpose: This study gives a critical assessment of the rainfall erosivity factor (R) for selected sites in the Majha region, representing different locations use of mean monthly rainfall data. Methodology: By applying empirical methods, the rainfall intensity for all the locations were obtained and was further determined at three different intervals of 30-minutes, 45-minutes and 60-minutes, respectively. The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE). Main Findings: Using RUSLE, the rainfall erosivity factor (R) for each of the locations was measured as follows; EI = 3878.49 (MJmmha-1hr-1), EI = 4013.71 (MJmmha-1hr-1), EI = 4302.24 (MJmmha-1hr-1) for Majha region of Amritsar, Tarntaran and Pathankot respectively. A close observation of the data obtained revealed that as rainfall intensity increased with the duration, the rainfall erosivity index reduced or decreased. Implications of study: Nevertheless, it is expected that if proper cover crop and management practices are applied despite the region, the study area falls within, rainfall erosivity can be cushioned, thus reducing further erosion tendencies and enhancing food production chances from productive lands within the area. The novelty of study: The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE).

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Geostatistical Analysis of Rainfall Temperature and Evaporation Data of Owerri for Ten Years

Cited by 3 publications

References 1 publication

Climate Study of Owerri and Comparative Analysis of Different Methods of Base Factor Computation

Climate Study of Owerri and Comparative Analysis of Different Methods of Base Factor Computation

Assessment of Rainfall Erosivity Factor (R) for Selected Locations in Imo State, Southeastern Nigeria

Assessment and Mapping of Rainfall Erosivity Index (R) for Majha Region, Punjab Is a State in Northern, India

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