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Reliability of downscaling rainfall data in the estimation of rainfall trends : A
case study
Article · January 2012
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Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
Reliability of downscaling rainfall data in the estimation of rainfall trends :
A case study
B. BAPUJI RAO, V. P. PRAMOD AND VUM RAO
All India Coordinated Research Project on Agrometeorology, Central Research Institute for Dryland Agriculture
Santoshnagar, HYDERABAD – 500 059
ABSTRACT
Precipitation extremes have become more intense during the twentieth century, and
that this trend is likely to persist in response to the continuing global warming and climate
change. There is substantial climatological evidence to support this statement. However an
understanding on these issues is limited by spatial scaling. Most evidence of past trends
comes from rain gauge data, whereas the future trends are produced by climate models,
which rely on gridded data. In this background, an attempt has been made to study the
trends in rainfall on regional scale, over Andhra Pradesh, for the last 35 years (1971-2005)
with gridded data (of different resolutions) and observed data, and thereby comparing the
results. Results showed that with 0.5o x 0.5o IMD gridded data, 0.25o x 0.25o APHRODITE
gridded data and observed mandal level data an increasing trend in rainfall was examined in
some districts. No decreasing trend in rainfall was observed with the IMD data whereas a
decreasing trend in rainfall was observed in Mahaboobnagar and Adilabad districts with
APHRODITE. Decreasing trend in rainfall was identified with the observed data at several
locations in majority of the districts. The difference in trend results carried out with mandal
level and gridded data reflects the drawbacks in using gridded data in projecting the future
trend in rainfall and shows how it can lead to errors.
Keywords: Rainfall, Mann-Kandall test, Rainfall trend, Climate model.
km falls between 12o 41’ North to 19o 50’ North latitude
and 76o 45’ East to 84o 40’ East longitude situated on
the south-eastern coast of India (Fig. 1). Considering the
geographical position, Andhra Pradesh is divided into
three distinct regions namely Coastal, Telengana and
Rayalaseema. The rainfall of Andhra Pradesh is inclined
by both the South-West and North-East monsoons. The
state receives a mean annual rainfall of 910 mm. Major
portion (67%) of the rainfall is contributed by SouthWest monsoon (June-Sept) followed by North-East
monsoon (Oct-Dec- 24%). The rest (9%) of the rainfall
is received during the winter (Jan-Feb) and summer
(Mar-May) months.
INTRODUCTION
Trends and variations in rainfall have significant
social and political impacts as Indian agriculture is
largely controlled by the Indian summer monsoon
rainfall. The extremes in precipitation have become
more intense during the twentieth century, and that this
trend is likely to continue with continued global
warming and climate change (Karl and Knight, 1998;
Zwiers and Kharin, 1998; Groisman et al., 1999; Kharin
and Zwiers, 2000; Meehl et al., 2000; Frich et al., 2002;
Kiktev et al., 2003; Hegerl et al., 2004 and Goswami et
al., 2006). Future projections produced by global and
regional climate models offer a way to characterize any
trends in extreme behaviour. However, there is an issue
of spatial scaling and ways to compare the output of
regional and climate models with historical data.
Climate model data represent an aggregate over a grid
box whereas historical data are collected from raingauge
at specific point locations. In this context, an attempt is
made here to investigate the rainfall trends for a period
of 35 years (1971-2005) with gridded data and observed
data on regional scale, over Andhra Pradesh, and
thereby comparing the results of both.
The variability in inter-district rainfall is high
and six coastal districts and two Telengana districts
receive more than 1,000 mm per annum, while the
districts of Kurnool, Anantapur and Y.S.R.Kadapa
register rainfall below 700 mm. The coastal district of
East Godaveri receives maximum annual rainfall (1133
mm) while Anantapur (Rayalaseema region) receives
the minimum (548 mm). Because of this wide spatial
variation in annual rainfall an attempt has been made to
study the trends in rainfall over a period of 35 years
with data available from local sources (Revenue
records), closely located weather stations and gridded
data with different spatial resolutions and a comparison
is made.
STUDY AREA
Andhra Pradesh is India’s fifth largest state (in
terms of area) spreading over an area of 2, 76, 754 sq.
162
Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
Fig. 1 Images showing the study area.
rainfall was carried out with Mann-Kendall trend test
using Toolkit software V 1.02 (CRC for Catchment
Hydrology, Australia, 2005). Geographical Information
system (GIS) tool is used in preparing the plots in
response to the trend result.
DATA AND METHODOLOGY
Datasets from three different sources are used in
this study, the mandal level (1128 mandals) daily
rainfall from Directorate of Economics and Statistics,
Govt. of Andhra Pradesh for the period of 1971-2010
were the first source and the distribution of raingauge
stations is presented in Fig. 2a. India Meteorological
Department (IMD) daily rainfall data on 0.5o x 0.5o
spatial resolution for the period 1951-2007 and Asian
Precipitation – Highly Resolved Observational Data
Integration Towards Evaluation of the Water Resources
(APHRODITE’s water resources) 0.25o x 0.25o gridded
daily rainfall data are the other two sources. Both IMD
and APHRODITE used a well-tested interpolation
method (Shepard, 1968) to interpolate the station data
into regular grids. In order to maintain uniformity, the
length of all datasets is restricted to 35 years from 1971
to 2005 to study the trends in daily rainfall. Mandal and
grid-wise annual rainfall data was arrived by summing
the daily rainfall data. A trend analysis of the annual
RESULTS AND DISCUSSION
The data points of IMD and APHRODITE
representing Andhra Pradesh with 0.5o x 0.5o and 0.25o
x 0.25o are 102 and 382 grid points, respectively (Fig 2b
and 2c). In both gridded dataset there is no grid point to
represent the Hyderabad district. Mandals with rainfall
data less than 10 years are not considered in carrying
out the trend analysis. Mann-Kendall test with mandal
level data shows that out of 1039 mandals, 752 mandals
showed non significant trend, 55 mandals increasing
trend and 232 mandals decreasing trend (Table 1). In
Mahaboobnagar, Medak, Nalgonda, Prakasham and
West Godavari districts none of the mandals showed
any increasing trend in rainfall. About 94% of total
geographical area of Chittor district does not show any
163
Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
showed increasing
APHRODITE data.
significant trend. Medak is the only district in which
45% of the total geographical area showed non
significant trend. In this district 35% of the total
geographical area showed decreasing trend at 99%
significant level. The remaining 14% and 6% of the area
showed declining trend at 95% and 90% significant
level respectively. This shows that decreasing trend in
rainfall is pronounced more in Medak district for the
last 35 years when compared to other distrcits.
Karimnagar falls second in this category after Medak.
About 46% of the total geographical area of Karimnagar
district shows no significant trend, whereas 21% shows
a decreasing trend at 99% significant level, 8% at 95%
significant level and the remaining 7% shows a negative
trend at 90% significant level. Among the 102 grid
points of 0.5o x 0.5o gridded data, 15 points showed
increasing trend over the state which includes
Srikakulam, Khammam, Guntur, Prakasam, Nellore,
Cuddapah, Kurnool and Mahbubnagar districts (Fig.
3b). There is no decreasing trend in rainfall captured
with the IMD dataset. The results are surprising when
trend analysis was carried out with 0.25o x 0.25o
APHRODITE data. If increasing trend was able to find
out with 0.5o x 0.5o IMD data, one would naturally
expect an increasing trend with more fine resolution
datasets in the same districts. But not even a single point
of the 0.25o x 0.25o gridded data depicted any significant
trend in these districts, except Guntur (Fig. 3c). In fact
with mandal level data, seven mandals in Kurnool, one
in Guntur and six in Khammam showed increasing
trend. Mahaboobnagar, Guntur, Rangareddy, Adilabad,
Visakhapatnam and Vizianagaram are the districts in
which increasing/decreasing trend was observed with
the 0.25o x 0.25o gridded data. Mahaboobnagar and
Adilabad are the only two districts in which decreasing
trends were noticed with APHRODITE data. In
comparison with IMD gridded data, APHRODITE
could able to capture decreasing trends in rainfall. Three
grid points in Mahaboobnagar and a grid point in
Adilabad district showed decreasing trend. The
decreasing trend in Mahaboobnagar that was captured
with fine resolution gridded dataset is also reflected
with the mandal level data as well. In the remaining
districts, along with the IMD gridded data, 0.25o x 0.25o
gridded data also could not express any significant
trends in the rainfall. The accuracy in downscaling data
to different scales on a state wide basis is show in Table
2. With mandal level data 5% of the total geographical
area showed increasing trend, 17% showed decreasing
trend and the remaining 71% showed no significant
trend in rainfall. About 15% of the area showed
increasing trend in rainfall with 0.5o x 0.5o IMD gridded
data whereas only 2% of the total geographical area
trend
with
0.25o
x
0.25o
The discrepancy in trend results with 0.5o x 0.5o
IMD and 0.25o x 0.25o APHRODITE data may be
because of the difference in number of raingauge
stations, even though it uses the same interpolation
technique (Shepard, 1968) in converting the station data
into regular grids. The difference in trend results carried
out with mandal level data and gridded dataset reflects
the limitation in using grid data for trend analysis and
shows how it can give negative results. Also depending
upon the trends a rainfall examined with historic data, a
suspicion may arise on the the future projections in
rainfall trends using the gridded data.
CONCLUSIONS
Using the Mann-Kandall trend test, an increasing
trend in rainfall was observed over a period of 35 years
(1971-2005) with 0.5o x 0.5o IMD gridded data in eight
districts districts (Srikakulam, Khammam, Guntur,
Prakasam,
Nellore,
Cuddapah,
Kurnool
and
Mahbubnagar) of Andhra Pradesh. When the same trend
test was carried out with much more fine resolution
0.25o x 0.25o APHRODITE gridded data no significant
trend in rainfall was observed in these districts, except
Guntur. In fact it shows an increasing trend in rainfall
over Rangareddy, Visakhapatnam and Vizianagaram.
This difference in trend results with the two gridded
dataset (IMD and APHRODITE) may be because of the
difference in number of rain gauge stations, even though
it uses the same interpolation technique in converting
the station data into regular grids. With the mandal level
data, in addition to the increasing trend, a declining
trend in rainfall is also captured. No increasing trend in
rainfall was observed in Mahaboobanagr, Medak,
Nalgonda, Prakasham and West Godavari districts. IMD
0.5o x 0.5o grid data could not sense any decreasing
trend. The difference in trend results carried out with
mandal level data and gridded dataset reflects the
limitation in using grid data for trend analysis and
shows how it can lead to errors in climate change
projections. Since rainfall is a crucial agroclimatological factor and its analysis is an important
factor for agricultural planning in India, it is worthwhile
to use the data collected from local sources, wherever
available.
ACKNOWLEDGEMENTS
This research is supported by National Initiative
for
Climate
Resilient
Agriculture
(NICRA),
Government of India and authors are thankful to Mrs.
Latha and Ms. Pallavi for their help in analysis and
preparing the GIS maps.
164
Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
Fig. 2 Location maps of 1128 raingauge stations in Andhra Pradesh (a), IMD grid points on 0.5o x 0.5o (b) and
APHRODITE’s 0.25o x 0.25o scale in (c) in Andhra Pradesh.
165
Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
Fig. 3 Annual rainfall trends using mandal level data (a), IMD 0.5o x 0.5o gridded data (b) and APHRODITE’s
0.25o x 0.25o gridded data
166
Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
Table 1: Area under increasing/decreasing trend in annual rainfall in different districts of Andhra Pradesh
based on block level data
District
Total
Increasing Increasing Increasing Decreasing Decreasing Decreasing
Geographi
Trend
Trend
Trend
Trend
Trend
Trend
No
Not
Significant
cal Area of
(99%
(95%
(90%
(99%
(95%
(90%
Available
District Significant Significant Significant Significant Significant Significant
Trend
(Acres)
Level)
Level)
Level)
Level)
Level)
Level)
Adilabad
3447417
8%
3%
1%
Nil
5%
4%
67%
11%
Anantapur
4024417
Nil
2%
3%
2%
2%
4%
87%
Nil
Chittoor
3116714
Nil
4%
Nil
Nil
1%
1%
94%
Nil
Y.S.R.Kadapa
3171945
Nil
5%
Nil
2%
Nil
1%
87%
4%
East Godavari
2280711
Nil
9%
Nil
9%
10%
2%
67%
3%
Guntur
2250910
6%
2%
7%
16%
1%
1%
56%
11%
Hyderabad
36378
Nil
Nil
Nil
Nil
Nil
Nil
Nil
Nil
Karimnagar
2427396
2%
Nil
Nil
21%
8%
7%
46%
16%
Khammam
3352217
3%
6%
2%
Nil
4%
7%
69%
8%
Krishna
1852116
Nil
Nil
2%
7%
18%
17%
49%
6%
Kurnool
3684690
Nil
7%
5%
3%
1%
1%
81%
2%
Mahaboobnagar
3882951
Nil
Nil
Nil
5%
7%
7%
81%
Nil
Medak
1988944
Nil
Nil
Nil
35%
14%
6%
45%
Nil
Nalgonda
3034113
Nil
Nil
Nil
11%
3%
8%
68%
10%
S.P.S.R.Nellore
2772851
Nil
1%
2%
2%
9%
Nil
83%
2%
Nizamabad
1657969
5%
Nil
2%
5%
7%
13%
69%
Nil
Prakasham
3655788
Nil
Nil
Nil
9%
15%
1%
50%
24%
Ranga Reddy
1584109
Nil
1%
2%
Nil
Nil
6%
88%
3%
Srikakulam
1257898
Nil
Nil
3%
5%
7%
8%
78%
Nil
Visakhapatnam
2396860
1%
1%
2%
Nil
1%
5%
87%
4%
Vizianagaram
1288026
2%
Nil
6%
14%
4%
Nil
69%
5%
Warangal
2679450
Nil
2%
1%
12%
5%
10%
68%
3%
West Godavari
1606901
Nil
Nil
Nil
9%
19%
3%
68%
Nil
167
Journal of Agrometeorology [ISSN: 0972-1665]4, Volume-14, Special Issue 2012
B. Bapuji Rao, V. P. Pramod and Vum Rao [Page 162-168]
Table 2: Accuracy in downscaling data to different scales on a state wide-basis
Data Source
% area in the total geographical area showing
Increasing Trend
Decreasing Trend
No Trend
Mandal level data
5
17
71
0.5o x 0.5o
15
-
85
0.25o x 0.25o
2
1
97
Karl TR and Knight RW (1998) Secular trends of
precipitation amount, frequency and intensity in
the United States. Bull Am Meteorol Soc., 79:
231-241.
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