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STUDY OF SPATIO-TEMPORAL ANALYSIS OF ANNUAL RAINFALL VARIABILITY IN UTTAR PRADESH

Authors:
  • Motherhood University
  • Sanskriti University Mathura

Abstract and Figures

Uttar Pradesh is Humid subtropical and semi arid climatic region situated between 23° 52' N and 31° 28' N latitudes and 77° 3' and 84° 39'E longitudes. The state is divided into 18 divisions and 71 districts. The statistical analysis of annual rainfall data of past to present 100 years (1915-2014) ranged from 532.7mm in year in 1991 to 1313.1 mm in year 2013 with an average annual rainfall of the area is 929.6 mm. The average rainfall with 2013 showing the highest positive rainfall anomaly (2.26) while the other years show rainfall below normal with 1991 Showing the lowest negative rainfall deviation (-2.34). The calculated value of standard deviation reveals that deviation of rainfall is of 169.7 mm. in a century. The trend analysis in XLSTAT 2014.6.02 ver. observed trend of rainfall, the R 2 value 0.018 means that only 1.8 percent variation is observed in hundred years. The coefficient of skewness has been computed as-0.06 for annual rainfall indicates a negative trend or going to decline pattern. The maximum standard deviation value and CV(%) is observed 210 & 23% in year 1935-44 and minimum standard deviation and CV(%) is observed 80.7 & 10% in year 1995-04. The overall decadal dataset observed decadal maximum rainfall 1328.9 in year 1955-64 whereas minimum rainfall 493.9mm in year 2005-14 observed. In future, expected annual rainfall may be less in year 2025 observed 881.9mm in the state. In the year 2021; expected rainfall may be 893mm. The geostatistical analysis is the ARCGIS 10.3.1 extension used for interpolation and kriging. The prediction map of dataset year 1995-2004 was highest rainfall in east side of some place of Uttar Pradesh. The western part of Uttar Pradesh covered less rainfall the other side cover area. The central part of state decadal map covered maximum area in year 1966-74. The objective of this study is to analyze the recent and future trend of annual rainfall pattern.
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*Corresponding Author
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Journal of Plant Development Sciences Vol. 8 (3) : 97-110. 2016
STUDY OF SPATIO-TEMPORAL ANALYSIS OF ANNUAL RAINFALL
VARIABILITY IN UTTAR PRADESH
Avadhesh Kumar Koshal* and Prafull Kumar
ICAR- P.D.F.S.R. College of Agriculture Modipuram, Meerut (U.P.) S.V.P.U.A. &T.,
Meerut (U.P.)
Email: akkoshal@hotmail.com
Received-10.03.2016, Revised-21.03.2016
Abstract: Uttar Pradesh is Humid subtropical and semi arid climatic region situated between 23° 52' N and 31° 28' N
latitudes and 77° 3' and 84° 39'E longitudes. The state is divided into 18 divisions and 71 districts. The statistical analysis of
annual rainfall data of past to present 100 years (1915-2014) ranged from 532.7mm in year in 1991 to 1313.1 mm in year
2013 with an average annual rainfall of the area is 929.6 mm. The average rainfall with 2013 showing the highest positive
rainfall anomaly (2.26) while the other years show rainfall below normal with 1991 Showing the lowest negative rainfall
deviation (-2.34). The calculated value of standard deviation reveals that deviation of rainfall is of 169.7 mm. in a century.
The trend analysis in XLSTAT 2014.6.02 ver. observed trend of rainfall, the R2 value 0.018 means that only 1.8 percent
variation is observed in hundred years. The coefficient of skewness has been computed as -0.06 for annual rainfall indicates
a negative trend or going to decline pattern. The maximum standard deviation value and CV(%) is observed 210 & 23% in
year 1935-44 and minimum standard deviation and CV(%) is observed 80.7 & 10% in year 1995-04. The overall decadal
dataset observed decadal maximum rainfall 1328.9 in year 1955-64 whereas minimum rainfall 493.9mm in year 2005-14
observed. In future, expected annual rainfall may be less in year 2025 observed 881.9mm in the state. In the year 2021;
expected rainfall may be 893mm. The geostatistical analysis is the ARCGIS 10.3.1 extension used for interpolation and
kriging. The prediction map of dataset year 1995-2004 was highest rainfall in east side of some place of Uttar Pradesh. The
western part of Uttar Pradesh covered less rainfall the other side cover area. The central part of state decadal map covered
maximum area in year 1966-74. The objective of this study is to analyze the recent and future trend of annual rainfall
pattern.
Keywords: Anomaly, GIS, Geostatistical method, Kriging & Monsoon
INTRODUCTION
he rainfall is the meteorological phenomenon is
a major source of water on the earth. It is also
one of the most important climatic variables because
of its two sided effects one is deficient resource,
such as droughts and as a catastrophic agent, such as
floods (Alam, 2011). Agriculture is dependent on
climatic factors: temperature, rainfall, sunshine
hours, relative humidity and air direction. The
changing of rainfall pattern in rabi, kharif & zaid
seasons are change the cropping pattern if changes
are seen for long time. Because water having vital
role to agriculture crops growth and crops are having
short period for complete life cycle. The change of
rainfall pattern is affecting the crops productivity.
The Kharif crop is the summer crop or monsoon
crop in India. Kharif crops are usually sown with the
beginning of the first rains in July, during the south-
west monsoon season. The South west monsoon is
very important for Uttar Pradesh agriculture. The
major socio-economic infrastructures are dependent
on rainfall because greater percentages of the
peoples are dependent agriculture related work
(www.gktoday.in). Rainfall in pre-monsoon and
winter season had a decreasing trend whereas it had
an increasing trend during monsoon and post
monsoon seasons (Rimi et al.). The IMD (Indian
meteorology Department, New Delhi) divided Indian
season in four categories: pre-monsoon (April-June),
monsoon (July-September), post-monsoon (October-
November) and winter season (December-March).
IMD defines a four month period from June to
September as Indian summer monsoon (ISM) period
(Attri and Tyagi, 2010). The Statistical techniques
are essential tools for analyzing large datasets. It also
helps us to identify which of the many pieces of
information derived from observations of the climate
system are worthy of synthesis and interpretation. It
is also helpful for testing hypotheses, estimations of
parameters and in predictions of the data set
(Cobanovic, 2002). The climatic research is
complex, large level and long time period’s process.
The natural or human-induced factors are cause of
climates change. Agricultural statistics are needed to
provide information used to monitor trends and
estimate future prospects. Geostatistics assumes that
at least some of the spatial variations of natural
phenomena can be modelled by random processes
with spatial autocorrelation. Many methods are
associated with geostatistics, but they are all in the
Kriging family. Ordinary, Simple, Universal,
probability, Indicator, and Disjunctive kriging, along
with their counterparts in cokriging, are available in
the geostatistical analysis. The changing climatic
condition has been attributable to rainfall (Adger et
al. 2003 & Obot et al. 2010), studies have also
shown that the climate is changing based on the
changing pattern of rainfall (Goswami et al. 2006 &
Adger et al. 2003). Prediction of the spatial and
T
RESEARCH ARTICLE
98 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
temporal variability in rainfall is a major problem in
an agricultural country like India and particularly in
the state U.P. Measurement and prediction of change
using statistical methods is a very important tool for
decision making.
Objective
The objective of this study is to analyze the recent
and future trend of annual rainfall pattern.
Study area
Uttar Pradesh is Humid subtropical (warm summer)
and semi arid climatic region situated between 23°
52' N and 31° 28' N latitudes and 77° 3' and 84° 39'E
longitudes, this is the fifth largest state in the
country after Rajasthan, Maharashtra, Madhya
Pradesh and Andhra Pradesh in area (Fig.1). Total
geographical area of the state is 24,170 thousand
hectare which is 7.33% of total area of India out of
which 16,573 thousand hectare is under cultivation
(FSI, 2015). It is divided into three distinct
hypsographical regions: The Himalayan region in the
North, The Gangetic plain in the centre & The
Vindya hills and plateau in the south. It is lies
largely in the plains formed by the Ganges and
Yamuna rivers. State climate is subtropical and
congenial for agriculture. Uttar Pradesh is largest
producer of wheat, potato, sugarcane and milk
whereas third largest producer of rice. For
administrative purposes, the state is divided into 18
divisions and 71 districts. The state divided into nine
agro-climatic zones, namely, Bhabhar & Tarai,
Western Plain, Central-Western Plain, South-
Western Plain, Central Plain, Bundelkhand, North-
Eastern Plain, Eastern Plain, and Vindhyan region. It
is also divided into four economic regions, viz.,
Western, Eastern, Central and Bundelkhand (Guha &
Basu, 1996). The western region comprises of 27
districts and the eastern region 27 districts. Ten
districts constitute the central region whereas the
Bundelkhand region has only 7 districts. Rising of
urbanization, populations and de-forestation are
causing adverse impacts on the state’s biosphere.
Fig.1 Study area
MATERIAL AND METHOD
The monthly rainfall data were collected from IMD,
New Delhi & India water portal for the periods
1915-2014 (India water Portal, IMD New Delhi &
NASA/POWER Agrometeorology website). The
district wise collected monthly data were converted
to annual time scale before statistical and
interpolation analysis is done. The rainfall data
processed on Excel sheets according to the
requirements to obtain critical area maps using
ArcGIS 10.2 software. ESRI’s Geo-statistical analyst
extension has been used for these analyses. The
rainfall surfaces were predicted using ordinary
kriging method.
METHODOLOGY
Time series analysis of the monthly and annual
rainfall values were used to illustrate the trend in the
behaviour of rainfall and in estimating seasonal
variation. Linear regression analysis was also
employed using Microsoft Excel statistical tool as it
has proved effective in investigating trends in many
climatic time series (Hutchinson, 1985 & Ayoade,
1973). One of the important indices standardized
anomalies was evaluated.
Several statistics are applied to monthly rainfall
series such as mean, variance, standard deviation,
coefficient of variation, and skewness. For
JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 8 (3) 99
identifying the trend in the rainfall data, the
statistical analysis of linear regression was used. All
these different analyses constitute the continuity of
the study of Uttar Pradesh rainfall, which started
about a century ago. The descriptive statistical
analyses are:
(i) Mean is the arithmetic average of a set of
values or distribution and represents the
average of the data set.
() =
Where x is the rainfall data & N= Number of
years
(ii) The Standard deviation (STD) is measure of
the dispersion of a set of data from its mean.
 =( )²
(iii) The median is the middle value when the data
is arranged in order of size.
(iv) The coefficient of variation is a normalized
measure of dispersion of a probability
distribution which is defined as the ratio of
the standard deviation σ to the mean .
 =
100
(v) Deviation score =   
(vi) Standardized anomalies, also referred to as
normalized anomalies, are calculated by
dividing anomalies by the climatological
standard deviation.
=( )

Where x is the annual rainfall totals, is the
mean of the entire series and STD is the
standard deviation from the mean of the
series.
(vii) Skewness is a measure of the asymmetry of
the probability distribution. The skewness
value can be positive or negative, or even
undefined. It is a dimensionless quantity.
Skewness = Mean Mode
Standard deviation
Recently, geographic information systems GIS
interpolation technique has emerged as a method to
map the distribution of evapotranspiration,
temperature and precipitation (Haberlandt 2007 &
Cheng et al. 2007) and it gives the layout and
drawing tools necessary to present the results
visually. GIS technique assist researchers and
practitioners to understand the natural environment
(Jang et al. 2007). That method was successfully
used to study spatial distributions of precipitation by
Dingman et. al. The Geostatistical analysis provides
many tools to help determine which parameters to
use, and also provides reliable defaults that can be
used to make a surface quickly. The geostatistical
analysis is the ARCGIS extension used for
interpolation and kriging. There is numerous
interpolation methods are used for rainfall data
analysis. After detail study of kriging method is
observed, two interpolation methods are explained
distribution pattern of rainfall for the study area after
different decadal dataset study.
RESULT AND DISCUSSION
The annual rainfall data series during the period
1915 to 2014 are analysis using time series analysis.
The result shows that over Uttar Pradesh state.
South-west rainfall or monsoon season covered
almost districts over the state in June to September
months. It is a most dominant session of the cyclic
rainfall. The Kharif crops production is dependent on
this rainfall.
Standardized anomalies of Annual Rainfall
Table 1 depicts the computed annual mean rainfall
and standardized anomalies within the year under
consideration (1915 to 2014) over Uttar Pradesh
State. Fig. 2 shows the standardized rainfall
deviations viz; 1915 to 1919, 1922, 1924-25, 1927,
1931, 1933, 1936, 1938,1942-43, 1946 to 1951,
1955-56, 1958, 1960 to 1964, 1967, 1969 to 1971,
1973, 1975, 1977-78, 1980 to 1982, 1985, 1990,
1994, 1996, 2003, 2007 -08 and 2012-13 are years
with above average rainfall with 2013 Showing the
highest positive rainfall anomaly (2.26) while the
other years show rainfall below normal with 1991
Showing the lowest negative rainfall deviation
(-2.34).
Table 1. Average Annual rainfall & Standardized rainfall anomaly of Uttar Pradesh (1915-2014)
Year
Average
Rainfall (mm)
Standardized
rainfall anomaly
Year
Standardized
rainfall anomaly
1915
971.5
0.25
1965
-1.64
1916
1099.9
1.00
1966
-1.14
1917
1188.1
1.52
1967
1.08
1918
611.5
-1.87
1968
-1.70
1919
954.7
0.15
1969
0.15
1920
801.3
-0.76
1970
1.23
1921
825.1
-0.62
1971
1.57
1922
1139.5
1.24
1972
-0.74
100 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
1923
927.0
-0.02
1973
0.86
1924
1033.4
0.61
1974
-1.57
1925
1242.4
1.84
1975
0.44
1926
889.2
-0.24
1976
-0.46
1927
976.7
0.28
1977
0.28
1928
653.7
-1.63
1978
0.26
1929
914.6
-0.09
1979
-1.82
1930
907.5
-0.13
1980
1.99
1931
969.1
0.23
1981
0.67
1932
763.6
-0.98
1982
0.94
1933
1065.2
0.80
1983
-0.05
1934
864.0
-0.39
1984
-0.76
1935
925.6
-0.02
1985
0.66
1936
1309.2
2.24
1986
-0.28
1937
837.8
-0.54
1987
-2.13
1938
972.4
0.25
1988
-0.09
1939
873.9
-0.33
1989
-0.92
1940
804.7
-0.74
1990
0.92
1941
626.8
-1.78
1991
-2.34
1942
1052.1
0.72
1992
-1.77
1943
983.5
0.32
1993
-0.77
1944
914.1
-0.09
1994
0.40
1945
880.7
-0.29
1995
-0.47
1946
952.0
0.13
1996
0.33
1947
1025.8
0.57
1997
-1.03
1948
1179.3
1.47
1998
-0.33
1949
1122.6
1.14
1999
-0.62
1950
971.9
0.25
2000
-1.45
1951
934.6
0.03
2001
-1.41
1952
884.5
-0.27
2002
-1.44
1953
924.8
-0.03
2003
0.64
1954
885.7
-0.26
2004
-0.70
1955
1123.2
1.14
2005
-0.13
1956
1145.3
1.27
2006
-0.25
1957
907.2
-0.13
2007
0.30
1958
991.4
0.36
2008
2.03
1959
892.8
-0.22
2009
-0.54
1960
1057.5
0.75
2010
-0.52
1961
1115.4
1.09
2011
-0.22
1962
990.2
0.36
2012
0.25
1963
1115.9
1.10
2013
2.26
1964
1035.0
0.62
2014
-0.28
Average Rainfall (mm) 929.6
JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 8 (3) 101
Fig. 2. Standardized rainfall anomaly of Uttar Pradesh from 1915-2014
Rainfall departure and cumulative departure of
rainfall
The departure and cumulative departure from
average rainfall for the study area has been depicted
in Table.2. The trend of annual departure from the
computed value of average annual rainfall reveals
that;
(a) Years showing annual positive departure with
respect to average annual rainfall were 1915-17,
1919, 1922, 1924-25, 1927, 1931, 1933, 1936,
1938, 1942-43, 1946-51, 1955-56, 1958, 1960-
64, 1967, 1969-71, 1973, 1975, 1977-78, 1980-
82, 1985, 1990, 1994, 1996, 2003, 2007-08 &
2012-13. The positive trend of rainfall shows the
favourable conditions for recharge.
(b) Years showing annual negative departure with
respect to average annual rainfall were 1918,
1920-21, 1923, 1926, 1928-30, 1932,1934-
35,1937,1939-1941,1944-45,1952-54,1957,
1959, 1965-66,1968,1972, 1974, 1976,
1979,1983-84,1986-89,1991-93, 1995, 1997-02,
2004-06, 2009-11 & 2014. The negative trend of
rainfall shows the unfavourable conditions for
recharge.
(c) Years showing negative annual cumulative
departure from average rainfall were observed in
a centum data 1915, 1920-21, 1941 and 2000 to
2014.
Table 2. The annual rainfall data and its departure and cumulative departure from average rainfall in Uttar
Pradesh (1915-2014)
Year
Annual
rainfall
(mm)
Departure
from
average
rainfall
Cumulative
departure
from
average
rainfall
Year
Annual
rainfall
(mm)
Departure
from
average
rainfall
Cumulative
departure
from
average
rainfall
1915
971.5
41.9
-42
1965
650.6
-279.0
1389
1916
1099.9
170.3
128
1966
736.5
-193.1
1196
1917
1188.1
258.5
387
1967
1112.4
182.8
1379
1918
611.5
-318.2
69
1968
641.6
-288.0
1091
1919
954.7
25.1
94
1969
955.1
25.4
1116
1920
801.3
-128.3
-35
1970
1137.6
208.0
1324
1921
825.1
-104.6
-139
1971
1196.0
266.4
1590
1922
1139.5
209.8
71
1972
804.1
-125.6
1465
1923
927.0
-2.6
68
1973
1075.8
146.2
1611
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
Anomaly
Anomaly
102 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
1924
1033.4
103.8
172
1974
663.1
-266.6
1344
1925
1242.4
312.8
485
1975
1004.4
74.8
1419
1926
889.2
-40.5
444
1976
850.9
-78.8
1341
1927
976.7
47.1
491
1977
976.7
47.1
1388
1928
653.7
-276.0
215
1978
974.3
44.7
1432
1929
914.6
-15.1
200
1979
620.5
-309.1
1123
1930
907.5
-22.1
178
1980
1267.8
338.2
1461
1931
969.1
39.4
218
1981
1043.8
114.2
1576
1932
763.6
-166.0
52
1982
1088.9
159.3
1735
1933
1065.2
135.5
187
1983
921.6
-8.0
1727
1934
864.0
-65.6
121
1984
800.7
-128.9
1598
1935
925.6
-4.1
117
1985
1041.2
111.6
1709
1936
1309.2
379.6
497
1986
882.9
-46.7
1663
1937
837.8
-91.8
405
1987
567.7
-361.9
1301
1938
972.4
42.7
448
1988
913.5
-16.1
1285
1939
873.9
-55.7
392
1989.0
772.7
-156.9
1128
1940
804.7
-125.0
267
1990.0
1085.6
156.0
1284
1941
626.8
-302.8
-36
1991
532.7
-396.9
887
1942
1052.1
122.5
87
1992
628.4
-301.3
586
1943
983.5
53.8
141
1993
799.1
-130.5
455
1944
914.1
-15.5
125
1994
997.0
67.4
522
1945
880.7
-48.9
76
1995
849.4
-80.2
442
1946
952.0
22.4
99
1996
985.0
55.4
498
1947
1025.8
96.1
195
1997
755.2
-174.4
323
1948
1179.3
249.7
444
1998
874.2
-55.4
268
1949
1122.6
193.0
637
1999
825.0
-104.7
163
1950
971.9
42.3
680
2000
683.8
-245.9
-83
1951
934.6
4.9
685
2001
690.9
-238.8
-322
1952
884.5
-45.1
639
2002
685.2
-244.4
-566
1953
924.8
-4.8
635
2003
1038.6
108.9
-457
1954
885.7
-43.9
591
2004
810.8
-118.8
-576
1955
1123.2
193.6
784
2005
907.5
-22.1
-598
1956
1145.3
215.6
1000
2006
887.4
-42.3
-640
1957
907.2
-22.4
977
2007
981.1
51.5
-589
1958
991.4
61.8
1039
2008
1274.4
344.8
-244
1959
892.8
-36.8
1002
2009
838.3
-91.4
-335
1960
1057.5
127.8
1130
2010
841.7
-87.9
-423
1961
1115.4
185.8
1316
2011
892.1
-37.5
-461
1962
990.2
60.5
1377
2012
971.4
41.7
-419
1963
1115.9
186.2
1563
2013
1313.1
383.5
-36
1964
1035.0
105.3
1668
2014
881.4
-48.2
-84
Annual average rainfall (mm) = 929.6
Statistical parameters of Annual rainfall
The statistical analysis of annual rainfall data of past
to present 100 years (1915-2014) ranged from
532.7mm in year in 1991 to 1313.1 mm in year
2013 with an average annual rainfall of the area is
929.6 mm (Table -3).
JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 8 (3) 103
Table 3. Mann-Kendall trend tests of rainfall data (1915-2014)
XLSTAT 2014.6.02 - Mann-Kendall trend tests - on 14-11-2015 at 16:20:43
Time series: Workbook = up100.xlsx / Sheet = Sheet1 / Range = Sheet1!$B$1:$B$101 / 100 rows and 1 column
Date data: Workbook = up100.xlsx / Sheet = Sheet1 / Range = Sheet1!$A$1:$A$101 / 100 rows and 1 column
Significance level (%): 5
Summary statistics:
Variable
Observations
Obs. with
missing
data
Obs.
without
missing
data
Minimum
Maximum
Mean
Std. deviation
UP
100
0
100
532.739
1313.112
929.637
169.736
Mann-Kendall trend test / Lower-tailed test (UP):
Kendall's
tau
-0.096
S
-474.00
Var(S)
112750.00
p-value
(one-
tailed)
0.079
alpha
0.05
The exact p-value could not be computed. An approximation has been used to compute the p-value.
Test interpretation:
H0: There is no trend in the series
Ha: There is a negative trend in the series
As the computed p-value is greater than the significance level alpha=0.05, one cannot reject the null hypothesis H0.
The risk to reject the null hypothesis H0 while it is true is 7.95%.
The continuity correction has been applied.
Sen's slope:
-0.84
Confidence interval:
] -33.383 ,
32.960 [
The computed value of the median 925.2mm
indicates ideal rainfall of the area. The coefficient of
variation (%) was found to be 18.26. The calculated
value of standard deviation reveals that deviation of
rainfall is of 169.7 mm. in a century. The coefficient
of skewness has been computed as -0.06 for annual
rainfall indicates a negative trend. The trend analysis
was done in XLSTAT 2014.6.02 ver. Fig.3 &4
shows that the trend of rainfall, the R2 value 0.018
means that only 1.8 percent variation is observed in
hundred years.
Fig. 3 Trend analysis of Average rainfall of Uttar Pradesh
104 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
Fig.4. Box-Cox analysis of Average rainfall of Uttar Pradesh
Table 4. Computation of statistical parameters of Uttar Pradesh
Statistical parameters
Computed value
Annual Rainfall (mm)
Mean
929.6 mm
Min
532.7 mm
Max
1313.1 mm
Median
925.2 mm
Std. Dev.
169.7
CV%
18.26
Coefficient of skewness
-0.06
Trend analysis of annual rainfall
A trend analysis of the average south west rainfall of
Uttar Pradesh state for 100 year period from 1915 to
2014 was statistical test MS Excel in Table 5. Trend
analysis was also performed on seasonal scale to
examine if there are trends in the data at this scale.
The trend analysis helps to measure the deviation
from the trend and also provides information
pertaining to the nature of trend. The analysis can be
used as a tool to forecast the future behaviour of the
trend. The method of least square fit for straight line
has been used for trend analysis of the behaviour of
annual rainfall. After trend analysis of data observed
rainfall trend is going to decline pattern.
Table 5. Time series analysis of South-west rainfall (mm) data of Uttar Pradesh
Year
X
Y
X2
XY
Trend
value
Year
X
Y
X2
XY
Trend
value
1915
-49
971.5
2401
-47605.2
968.7
1965
1
650.6
1
650.6
929.2
1916
-48
1099.9
2304
-52795.4
967.9
1966
2
736.5
4
1473.0
928.5
1917
-47
1188.1
2209
-55841.7
967.1
1967
3
1112.4
9
3337.3
927.7
1918
-46
611.5
2116
-28127.5
966.3
1968
4
641.6
16
2566.4
926.9
1919
-45
954.7
2025
-42962.9
965.5
1969
5
955.1
25
4775.3
926.1
1920
-44
801.3
1936
-35256.7
964.7
1970
6
1137.6
36
6825.7
925.3
JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 8 (3) 105
1921
-43
825.1
1849
-35478.1
963.9
1971
7
1196.0
49
8372.2
924.5
1922
-42
1139.5
1764
-47857.4
963.2
1972
8
804.1
64
6432.5
923.7
1923
-41
927.0
1681
-38006.9
962.4
1973
9
1075.8
81
9682.2
922.9
1924
-40
1033.4
1600
-41337.2
961.6
1974
10
663.1
100
6630.5
922.1
1925
-39
1242.4
1521
-48453.2
960.8
1975
11
1004.4
121
11048.8
921.4
1926
-38
889.2
1444
-33788.7
960.0
1976
12
850.9
144
10210.4
920.6
1927
-37
976.7
1369
-36139.1
959.2
1977
13
976.7
169
12697.2
919.8
1928
-36
653.7
1296
-23531.8
958.4
1978
14
974.3
196
13640.8
919.0
1929
-35
914.6
1225
-32009.7
957.6
1979
15
620.5
225
9307.9
918.2
1930
-34
907.5
1156
-30856.6
956.8
1980
16
1267.8
256
20285.0
917.4
1931
-33
969.1
1089
-31979.3
956.1
1981
17
1043.8
289
17744.9
916.6
1932
-32
763.6
1024
-24436.6
955.3
1982
18
1088.9
324
19600.0
915.8
1933
-31
1065.2
961
-33020.6
954.5
1983
19
921.6
361
17510.7
915.0
1934
-30
864.0
900
-25920.7
953.7
1984
20
800.7
400
16014.3
914.3
1935
-29
925.6
841
-26841.0
952.9
1985
21
1041.2
441
21865.9
913.5
1936
-28
1309.2
784
-36658.0
952.1
1986
22
882.9
484
19424.4
912.7
1937
-27
837.8
729
-22620.7
951.3
1987
23
567.7
529
13058.2
911.9
1938
-26
972.4
676
-25281.2
950.5
1988
24
913.5
576
21924.8
911.1
1939
-25
873.9
625
-21848.4
949.7
1989.0
25
772.7
625
19318.5
910.3
1940
-24
804.7
576
-19312.1
949.0
1990.0
26
1085.6
676
28226.2
909.5
1941
-23
626.8
529
-14416.4
948.2
1991
27
532.7
729
14384.0
908.7
1942
-22
1052.1
484
-23146.4
947.4
1992
28
628.4
784
17594.1
908.0
1943
-21
983.5
441
-20652.5
946.6
1993
29
799.1
841
23173.7
907.2
1944
-20
914.1
400
-18282.1
945.8
1994
30
997.0
900
29910.5
906.4
1945
-19
880.7
361
-16733.7
945.0
1995
31
849.4
961
26332.5
905.6
1946
-18
952.0
324
-17136.7
944.2
1996
32
985.0
1024
31521.1
904.8
1947
-17
1025.8
289
-17438.1
943.4
1997
33
755.2
1089
24922.0
904.0
1948
-16
1179.3
256
-18869.5
942.6
1998
34
874.2
1156
29723.4
903.2
1949
-15
1122.6
225
-16838.9
941.9
1999
35
825.0
1225
28873.6
902.4
1950
-14
971.9
196
-13606.5
941.1
2000
36
683.8
1296
24615.5
901.6
1951
-13
934.6
169
-12149.3
940.3
2001
37
690.9
1369
25561.7
900.9
106 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
1952
-12
884.5
144
-10614.0
939.5
2002
38
685.2
1444
26037.9
900.1
1953
-11
924.8
121
-10173.1
938.7
2003
39
1038.6
1521
40503.6
899.3
1954
-10
885.7
100
-8857.3
937.9
2004
40
810.8
1600
32431.9
898.5
1955
-9
1123.2
81
-10108.9
937.1
2005
41
907.5
1681
37209.1
897.7
1956
-8
1145.3
64
-9162.3
936.3
2006
42
887.4
1764
37269.9
896.9
1957
-7
907.2
49
-6350.4
935.6
2007
43
981.1
1849
42188.2
896.1
1958
-6
991.4
36
-5948.4
934.8
2008
44
1274.4
1936
56073.9
895.3
1959
-5
892.8
25
-4464.0
934.0
2009
45
838.3
2025
37722.3
894.5
1960
-4
1057.5
16
-4229.9
933.2
2010
46
841.7
2116
38718.3
893.8
1961
-3
1115.4
9
-3346.2
932.4
2011
47
892.1
2209
41929.7
893.0
1962
-2
990.2
4
-1980.4
931.6
2012
48
971.4
2304
46626.4
892.2
1963
-1
1115.9
1
-1115.9
930.8
2013
49
1313.1
2401
64342.5
891.4
1964
0
1035.0
0
0.0
930.0
2014
881.4
890.6
Σ=0
Σy=
92963.7
Σx2=
80850
Σx2= -
63298.2
Forecasting of annual rainfall
On the basis, the future forecast of rainfall for a
period of ten years from 2016 to 2025 has been made
(Table 6), which shows a negative trend for the
coming years. In future, expected annual rainfall
may be less in year 2025 observed 881.9mm in the
state. In the year 2021; expected rainfall may be
893mm. The trend analysis gives the scenario of
current to expected future situation. So in view of
future rainfall is going tobe decline. It will also
affect the production of rabi and Kharif season crops.
Table 6. Expected future Annual rainfall (mm) trend
Expected future rainfall trend (mm)
Year
Annual Rainfall (mm)
2016
889.0
2017
888.2
2018
887.4
2019
886.7
2020
885.9
2021
893.0
2022
884.3
2023
883.5
2024
882.7
2025
881.9
The statistical data of hundred years (1915 to 2014)
rainfall dataset of Uttar Pradesh was divided in ten
decadal datasets viz. 1915-24, 1925-34, 1935-44,
1945-54, 1955-64, 1965-74, 1975-84, 1985-94,
1995-04 & 2005-14 were analyzed (Table 7) and
observed 5th decadal dataset (1955-64) having
JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 8 (3) 107
maximum rainfall 1037.4mm whereas in 8th dataset
observed minimum rainfall 822.1mm. The
maximum standard deviation value and CV(%) is
observed 210 & 23% in year 1935-44 and
minimum standard deviation and CV(%) is
observed 80.7 & 10% in year 1995-04. The overall
decadal dataset observed decadal maximum rainfall
1328.9 in year 1955-64 whereas minimum rainfall
493.9mm in year 2005-14 observed. The highest
coefficient of skewness observed negative value -
0.49 in year 1945-54. The highest median value of
decadal dataset is observed 1060.4.
Table 7. Decadal Computation of statistical parameters of Uttar Pradesh
Parameters
Mean Decadal Uttar Pradesh
1915-
24
1925-
34
1935-
44
1945-
54
1955-
64
1965-
74
1975-
84
1985-
94
1995-
04
2005-
14
Mean
955.2
924.6
930.0
976.2
1037.4
897.3
955.0
822.1
819.9
977.3
SD
191.4
179.5
210.0
178.0
142.7
135.0
143.9
94.1
80.7
174.3
CV
0.20
0.19
0.23
0.18
0.14
0.15
0.15
0.11
0.10
0.18
CV%
20.0
19.4
22.6
18.2
13.8
15.0
15.1
11.4
9.8
17.8
MIN
541.5
543.1
502.0
569.1
684.8
601.9
713.4
626.3
660.2
493.9
MAX
1291.6
1256.7
1263.5
1311.5
1328.9
1107.1
1266.9
1049.8
973.9
1283.1
MEDIAN
1015.5
977.2
989.7
1006.4
1060.4
937.8
995.9
819.3
815.2
980.8
COFF OF
SKEWNESS
-0.48
-0.37
-0.44
-0.49
-0.41
-0.44
-0.12
0.29
-0.09
-0.33
GIS anlysis of time series data
Spatial interpolation s of decadal study
Geographical Information System (GIS) plays a vital
role in interpolating and displaying various attributes
of rainfall. It is effectively used in this attempt to
compute and produce maps. ArcGIS Geostatistical
Analyst is an interactive tools are use for generate
optimal surfaces from sample data and evaluate
predictions for better decision making
(http://www.esri.com).
Geostatistical methods (krigings) are widely used in
spatial interpolation from point measurement to
continuous surfaces. Spatial interpolation with the
geostatistical and Inverse Distance Weighting (IDW)
algorithms outperformed considerably interpolation
with the Thiessen polygon that is commonly used in
various hydrological models.
ESRI’s Geo-statistical analyst extension has been
used for these analyses. The rainfall surfaces were
predicted using ordinary kriging method. The co-
kriging analysis has been done to improve the
accuracy of prediction, by including the elevation as
a covariate. (Mesnard, 2013). It is applied to study
Spatio-temporal distributions of the annual rainfall in
Uttar Pradesh.
In this research, the spatial distribution of rainfall for
different decadal pattern in a century, and the
prediction of rainfall have been made using
geostatistical analysis in geographic information
system (GIS) software. ESRI’s ArcGIS
Geostatistical Analyst generate optimal surfaces
from sample data and evaluate predictions for better
decision making & used for decadal datasets
analyses. The spatial temporal decadal maps are
generated and observed trends and pattern of rainfall.
The prediction map of dataset year 1995-2004 was
highest rainfall in east side of some place of Uttar
Pradesh. The western part of Uttar Pradesh covered
less rainfall the other side cover area. The central
part of state decadal map covered maximum area in
year 1966-74 (Fig.5).
108 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
Fig.5. Geostatistical analysis of decadal rainfall (mm) pattern in Uttar Pradesh
Fig.6. Geostatistical analysis of a centum (average), annual and normal rainfall (mm) pattern in Uttar Pradesh
The prediction map shown in Fig. 6 observed the
pattern of rainfall of a century based average data
study the lowest rainfall observed in western part of
the Uttar Pradesh but maximum rainfall (1100-1200
mm) covered part in red tone observed in small areas
of eastern Uttar Pradesh. The central part of the
Uttar Pradesh covered rainfall range 900-1000 mm
in violet color. The annual rainfall map study the
maximum rainfall 1000 to 1100mm covered in
eastern part of U.P. The normal rainfall pattern is
like to annual rainfall.
CONCLUSION
Normal rainfall of region during 1915-2014 is
929.6.mm. The maximum rainfall recoded
1313.1mm in year 2013 and lowest rainfall recoded
532.7mm in year 1991. The south west monsoon
plays a vital role in rainfall for water cycle. It
contributes the highest percentage of rainfall and
kharif season crops are most of dependent on
rainfall. The average rainfall with 2013 showing
the highest positive rainfall anomaly (2.26) while
the other years show rainfall below normal with
1991 Showing the lowest negative rainfall deviation
(-2.34). The calculated value of standard deviation
reveals that deviation of rainfall is of 169.7 mm. in a
century. The trend analysis in XLSTAT 2014.6.02
ver. observed trend of rainfall, the R2 value 0.018
means that only 1.8 percent variation is observed in
hundred years. The coefficient of skewness has been
computed as -0.06 for annual rainfall indicates a
JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 8 (3) 109
negative trend or going to decline pattern. In future,
expected annual rainfall may be less in year 2025
observed 881.9mm in the state. In the year 2021;
expected rainfall may be 893mm. The trend analysis
gives the scenario of current to expected future
situation. Geographical Information System (GIS)
plays a vital role in interpolating and displaying
various attributes of rainfall. The spatial temporal
decadal maps are generated and observed trends and
pattern of rainfall. The prediction map of dataset
year 1995-2004 was highest rainfall in east side of
some place of Uttar Pradesh. The western part of
Uttar Pradesh covered less rainfall the other side
cover area. The central part of state decadal map
covered maximum area in year 1966-74.
The statistical data of hundred years (1915 to 2014)
rainfall dataset of Uttar Pradesh was divided in ten
decadal datasets; and observed 5th decadal dataset
(1955-64) having maximum rainfall 1037.4mm
whereas in 8th dataset observed minimum rainfall
822.1mm. Water is a vital component for
agricultural crops and in abnormal period crops are
irrigated by available source viz. tube well,
submersible, canal, irrigation channels and other
sources. Today rainfall is not regular fashion so
farmers are not more dependent much more on
rainfall. The source of irrigation, mechanization and
knowledge of current situation of weather and
climate change related pattern and adaptation of
technology is maintend to crops yield trend. The
precise technologies are use for fast and reliable
information for proper future management. The
spatial temporal decadal maps are generated and
observed trends and prediction of rainfall patterns.
The geostatistical methods are use for pattern of
rainfall study. The statistical and geostatistical
methods for temporal data studies are helpful for
planning and efficient use of agriculture water
resources. Techniques of geostatistics are used to
perform traditional statistical analysis and spatial
analysis with ArcGIS, geostatistical software and
statistical software XLSAT in order to obtain the
knowledge of characteristics of distribution and
spatial variability of rainfall in different parts of
Uttar Prdesh.
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110 AVADHESH KUMAR KOSHAL AND PRAFULL KUMAR
Rainfall data: IMD New Delhi, India water Portal
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... The start of change in the rainfall trend for the above mentioned districts is observed with distinct difference from 2009 onwards. Koshal and Kumar [5] observed trend analysis in XLSTAT 2014.6.02 ver. ...
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