Chapter-4 Analysis on Rainfall Variability and Distribution

Transcription

1 4.1 Introduction Chapter-4 Analysis on ainfall Variability and Distribution The distribution pattern of rainfall in the state of Gujarat is most uneven and varies considerably from year to year and region to region. The state of Gujarat has been divided into four different regions having variable rainfalls from 12mm to 2mm. As the variability of rainfall is high, for planning of water resources, it is necessary to study the rainfall variability at spatial and temporal scales. The study of rainfall variability will also help in drought and flood risk assessment, relief and rehabilitation during extreme events, and finally local level contingency planning. Furthermore, the rainfall variability in Sabarmati basin is still vague and uncertain. In fact the sparse rainfall data collection stations and limited extent of temporal data has been bottleneck for many hydrologic and water resources applications. A large number of papers have analyzed inter annual variability of the summer (June-September) mean monsoon rainfall average over India. The relationship between intra-seasonal and inter-annual variability during the Asian summer monsoon has been studied by Sperber et al 1998, Munot et al In their work on intra-seasonal, inter-annual and decadal scale variability in summer monsoon rainfall over India, both papers found marked variations in the intraseasonal variability of daily rainfall between years. The analysis by Munot et al (1999) exhibits, that all-india rainfall is positively related to the rainfall of NW-India. The correlation coefficient between All-India and NW-India has been found to be highly significant i.e.822, and on average monsoon found to be active for 7 days (23 June- 8 Sept) for NW-region. Krishnamurty and Shukla (2) studied the intra- 32

2 seasonal and seasonally persisting patterns of Indian summer monsoon (ISM) rainfall. The analysis shows that the rainfall over India during the monsoon season consists of intra seasonal oscillations on different time scales fluctuating about seasonally persisting components. The other researchers such as Kawemura et al (2) focused on the recent changes of Indian summer monsoon (ISM) with ENSO relationship and found a remarkable change in the relationship between ENSO and ISM. They also found that the recent weakening of relationship between ISM and ENSO based on IM actually represents the change in dominance of spatial correlation pattern from northwest to northeast after late 197s. Although significant progress has been made on rainfall variability with dependents climatic parameters, but the spatial extent and use of analysis for decision making has been limited. The rainfall variability analysis can be used to gain a better understanding of the available data and information before complex hydrological assimilation schemes for rainfall are developed. Therefore, this research chapter aims to analyze the variability of rainfall at spatial and temporal scales for Sabarmati basin based on station rainfall data analysis. The additional parameters such as rainfall departure, rainfall distribution, and frequency of rainfall, effect of terrain on rainfall were also analyzed. For analysis on extreme events like drought and flood, not only a reliable spatial data are required, but also sufficient length rainfall data time series are required. 4.2 Methodology The spatial and temporal variability of monsoon are analyzed based on daily rainfall records for monsoon season for a period from 1 st May to 3 th October each year,although the IMD considers monsoon season from 1 st June to 3 th September. However in this research study, an extended monsoon season from 1 st May to 3 th October has been proposed and considered. This extended period will take into account early monsoon onsets, late monsoon departures and climate change signals if any. The station rainfall data at daily time scale for 26 stations for the year 1961 to 27 (47 years) has been collected from Government of Gujarat, State Water Data 33

3 Centre (SWDC) The rainfall data shows that some of these stations do not have continuous daily data series. Therefore, for analysis of rainfall, 2 stations for a period of 1976 to 27 (32 years) in Sabarmati basin have been selected. These stations are located in the districts of Ahmedabad, Gandhinagar, Sabarkantha, Kheda, Anand and Mehasana of Gujarat state. The monthly and yearly rainfall data records for rainfall amounts and number of rainy days were obtained from the daily rainfall data series. The basin average and the standard deviation has been calculated using yearly rainfall data. The other parameters such as rainfall departure, rainfall distribution, frequency and probability of rainfall and effect of terrain on rainfall amount were also analyzed for yearly data series. The yearly rainfall analysis has been carried out using rainfall anomaly for station level rainfall from a gauged network. The seasonal rainfall of Sabarmati basin exhibits considerable inter-annual variability with excessive or deficient yeas. The excessive/deficient years have effects on the economy of the region, so it is necessary to identify those years. The excessive and deficient rainfall years have been identified using following formulae. Let fi be the rainfall of i th year, then if i i, the year is called an excess rainfall year and if,, the year is called the deficient year. Let us assumed that if f (u,t ) is the rainfall at station u for time t, and ( u, t..., ( u, t) f ( t) f 1 f I at point u 1, u 2,., u I, therefore the spatial mean and standard deviation for time t can be represented as 1 f ( t) I I i1 f ( ui, t) (4.1) 1 I 2 2 ( t) [ f ( ui, t) f ( t)] (4.2) I i1 Anomaly = n( u, t) i f ( u, t) i ( t) f ( t) (4.3) 34

4 The rainfall distribution can be geographically meaningful by analyzing the station cross correlationship. The cross correlationship between two data series can be calculated using Pearson s formulae. Correl( f 1, f 2 ( f 1 f 1)( f 2 f 2 ) ). (4.4) ( f 1 f 1 2 ) ( f 2 f 2 ) 2 where f 1 and f 2 are the sample mean for the series f1and f2,and 1 and 2 are standard deviation for series f1and f2. The cross correlation ship is applicable only if both standard deviations are finite and non-zero. In our study, we used the pair wise cross correlation procedure to establish the relationship between the average yearly rainfalls for different stations. The scatter plots have been drawn for determining the correlation among the different stations within a region. Also, the mean basin cross correlation ship coefficient has been calculated. Information on rainfall frequency is an important input in planning domestic or industrial water supply, agricultural planning, hydropower and other water use sectors. The frequency analysis has been carried out for determining the probability of average rainfall in any year. Let,..., f 1 f 2 fn f 1, f 2,... f are the yearly rainfall. Let are the same yearly rainfall arranged in descending order of magnitude. The suffix m is called the rank of observations. The highest value among the observations gets a rank 1 and the least gets a rank n. Let is the probability with which the value is equaled or exceeded. f m f m eturn period r f f m n 1 1 m n f m m and (4.) n f m.. (4.6) The probability plots for all the rain gauge stations are prepared to determine the probability of average annual rainfall. Skew ness is used to determine the measure of the asymmetry of the probability distribution of a real-valued random variable. The skew ness value can be positive or negative, or even undefined. If skew ness is positive, the data are positively skewed or skewed right, meaning that the 3

5 right tail of the distribution is longer than the left. If skew ness is negative, the data are negatively skewed or skewed left, meaning that the left tail is longer. If skew ness =, the data are perfectly symmetrical. But a skew ness of exactly zero is quite unlikely for real-world data, The skew ness is calculated using following equation: skewness n x ( ( n 1)( n 2) i x ) s 3 where, x i = rainfall at year i, x = average rainfall, n = number of years of rainfall data, s = standard deviation A thumb rule is used to interpret the skew ness number (Bulmer, M. G., Principles of Statistics (Dover, 1979) If skew ness is less than 1 or greater than +1, the distribution is highly skewed. If skew ness is between 1 to. or between. to +1, the distribution is moderately skewed. If skew ness is between. and +. the distribution is approximately symmetric. To analyze the spatial distribution of rainfall amount and rainy days, Kriging interpolation has been used. Kriging is a method of interpolation. Kriging is based on the assumption that the parameter being interpolated can be treated as a regionalized variable. A regionalized variable is intermediate between a truly random variable and a completely deterministic variable in that it varies in a continuous manner from one location to the next and therefore points that are near each other have a certain degree of spatial correlation, but points that are widely separated are statistically independent (Davis, 1986). Ordinary kriging interpolation has been carried out in ArcGIS for rainfall amounts and rainy days in Sabarmati basin using data of 2 rain gauge station for a period of 32 years (1976 to 27). 4.3 Analysis and results: The seasonal mean, intra-seasonal variation of rainfall and number of rainy days in Sabarmati basin for 2-stations using 32 years ( ) data is analyzed as 36

6 exhibited in Figure 4.1 below. The seasonal rainfall analysis for stations during monsoon season shows higher inter-annual variability. It may be noted that temporal variations of the time series is higher then the spatial variation among stations. The mean seasonal rainfall amount for different stations is found to be between 671mm to 96mm, having standard deviation of 31mm to 416mm. The mean seasonal basin rainfall has been calculated using station average method and Theissen polygon method. The station level mean seasonal rainfall has been found between 77mm to 774mm with standard deviation of 339mm.The rainfall amount shows large variability over the basin mean and have been found below average for the years 1979, 198,1986, 1987, 199, 1999, and 2. Some years such as 1976, 1981, 1983, 199, 1994, 1997, 1998 and 26 reported more then seasonal mean rainfall. The excessive and deficient rainfall years have been shown in Figure 4.1. The analysis on spatial mapping on location of rainfall stations has been carried out as exhibited in Figure 4.2. The analysis depicts that the stations located in alluvium plain along Sabarmati river receive less rainfall as compared to stations in higher altitudes. The stations located in alluvium plain receive around 1% less rainfall as compared to stations in higher altitudes. 2 AHD BDL BJ BYD BHL HMT CDL ID CPW LML DH MHD MNS TTI PW VSI SP VJA SJT VP BASIN AVG.F One std.dev.(+) One std.dev.(-) ainfall in mm Year Figure 4.1 Time series of mean seasonal rainfall with ± one at 2 stations during 1976 to 27 37

7 Figure 4.2 mean rainfall per season for 2 stations during 1976 to 27 The monthly station rainfall has been estimated for all the stations, with the aim to understand the temporal variability between and within the station. Figure 4.3 exhibits the monthly rainfall analysis for 2-stations during the months of May, June, July, August, September, and October. A large time window has been considered to account for early monsoon arrival and late monsoon departures, as a result of climate change. The analysis shows that July and August receive maximum rainfall of the season while May and October minimum. The total rainfall during various months such as July and August (77%), June and Sept (22%) and remaining during May and Oct (1%) from 77mm of the mean seasonal basin rainfall. The inter-monthly variability between months of May and October shows very high variability as compared to months of July and August. 38

8 4 Monthly ainfall variability May June July Aug sep oct 3 ainfall in mm 2 1 S-W EGION N-W EGION N-E EGION AHD BJ VSI SJT CDL PW MHD BDL DH LML MNS PS ain gauge station VJA VP BYD BHL CP HMT ID TTI Figure 4.3 Monthly ainfall variability for 2 stations during 1976 to 27 On the basis of geography, the basin has been divided into three parts- region 1-S-W (stations located in South and South-West), egion 2- N-W (stations located in North-west and North) and region 3 N-E (stations located in North-East). The cross correlation coefficient is important to understand the geographical distribution of rainfall to each region and to understand the relationship between rainfall series of all the regions, the correlation coefficients for stations within the region and for all the regions has been computed and presented in Table 4.1(A) and Table 4.1(B) The analysis shows that the rainfall for various stations in region 1 has been found highly variable. In the region 2 and region 3, correlation coefficient is highly significant. The analysis of correlation among mean seasonal rainfall of the regions of Sabarmati basin shows that region 3 receives more rainfall compared to rest of regions and mean season basin rainfall. The number of rainy days per monsoon season for various stations shows an altered trend. The stations in alluvium plain have reported less number of rainy days while the stations in higher altitude more. The plot on number of rainy days has been shown in Figure 4.4. The analysis of monthly rainy days for May, June, July, August, September and October exhibits that there have been less number of rainy days during monsoon arrival and departure. The peak number of rainy days has been recorded in the month of July and August. The number of rainy days again becomes low at the end of monsoon season. It is also analysed that rainfall intensity found to 39

9 be more in North-East part of the basin towards Aravali hill range as compared to rest of the basin. Table 4.2 depicts the mean rainfall and number of rainy days for all the 2 stations during Table 4.1 (A) Correlation among rainfall of regions of Sabarmati basin EGION S-W N-W N-E S-W N-W N-E 1. 4

10 Table 4.1 (B) Correlation among rainfall of stations of Sabarmati basin EGION S-W STATION AHD BJ CDL MHD PW SJT VSI AHD BJ CDL MHD PW SJT VSI 1. EGION N-W STATION BDL DH LML MNS SP VJA VP BDL DH LML MNS SP VJA VP 1. EGION N-E STATION BYD BHL CPW HMT ID TTI - BYD BHL CPW HMT ID TTI

11 Table 4.2 Mean rainfall and number of rainy days for 2 stations Name of station Mean ainfall (mm) Mean ainy days egion AHD PW CDL VSI BJ MHD SJT egion Mean 7 32 LML MNS SP VJA 8 32 BDL VP DH egion Mean BHL CPW 96 4 HMT ID BYD TTI egion Mean Basin Mean S-W N-W N-E 42

12 Figure 4.4 Average number of rainy days for 2 stations during 1976 to 27 2 June July Aug sep oct May 16 Average no. of rainy days 12 8 S-W EGION N-W EGION N-E EGION 4 AHD BJ VSI SJT CDL PW MHD BDL DH LML MNS PS VJA ain gauge station Figure 4. Monthly average rainy days over Sabarmati basin for 2 stations during 1976 to 27 VP BYD BHL CP HMT ID TTI 43

13 ain gauge station Virpur ansipur Badoli Sojitra Chandola Vasai aipur weir Mansa Mahemdabad Limla dam Dharoi Titoi Vijapur Himatnagar Ahmedabad Bareja Bayad Idar Chhapara weir Bhiloda Anomaly Figure 4.6 ainfall anomalies at basin scale for 2 rain gauge stations during 1976 to 27 The basin rainfall anomaly has been calculated using equations ( ) with the purpose to understand rainfall variability from normal. The rainfall anomaly on mean seasonal rainfall has been computed for 2-stations in the basin as given in Figure 4.6. The analysis reveals that 11 stations from 2 have negative anomaly while rest have positive anomaly. It is found that stations in western region show -ve anomaly while North-East region shows positive anomaly. As discussed earlier that N-E region is a part of Aravali hill range. The stations located in N-E region such as Chhapra weir, Bhiloda and Idar have rainfall anomalies +.4, +.31 and +.31 respectively. However, the stations located in Western region such as aipur weir, Virpur and ansipur shows negative anomalies -.18, -.28 and -.29 respectively. Some rainfall stations in South-West regions have exceptional rainfall anomalies like Ahmedabad (+.3) and Bareja (+.12). It appears that rainfall stations may have effect of urbanization as referred by Kishtawal et al (21). The station level rainfall anomaly and cyclic trend of rainfall can be seen from Figure 4.7 below. It has been found that in the year 1976, 1977, 1999, 1994, 1997, 23 and 26 majority of rainfall stations have positive anomaly and in the year 1979, 1982, 198,1989, 1992, 1999, 2 and 21, majority of stations have negative anomaly. Then the pattern changed with almost 6-7 % of stations having negative anomalies for about 3 years and positive 44

14 anomalies for one year and then negative anomalies for next year. This cycle continue up to 199. After that, the trend changed with negative anomalies for three years and positive for one year. 4 Ahmedabad Badoli Bareja Bayad Bhiloda Chandola Chhapara weir Dharoi Himatnagar Idar Limla dam Mahemdabad Mansa aipur weir ansipur Sojitra Titoi Vasai Vijapur Virpur ainfall Anomaly Year Figure 4.7 ainfall anomalies of 2 stations per year during 1976 to 27 The frequency analysis (Figure 4.8) has been carried out to determine the occurrence of average rainfall for a station and the occurrence of basin mean rainfall. It has been found that during 32 years, around 3% years received mean rainfall in S- W egion. ainfall distribution in N-W egion shows less occurrence of mean rainfall compared to other regions. Majority of stations in N-E egion received more then mean rainfall for about 2/3rd of period. The frequency of receiving more then mean seasonal rainfall is more in N-E region compared to other regions AHD 1 8 Bareja CLA S S

15 1 Chandola 1 Mahemdabad Sojitra 1 aipur weir Vasai Limla dam 1 Mansa

16 1 ansipur 1 Badoli Dharoi 1 Vijapur Virpur Bayad 1 Bhiloda Chhapara weir 1 Idar

17 1 Himatnagar 1 Titoi Figure 4.8 distribution for three region To measure the asymmetry of the probability distribution, skew ness has been calculated. The stations located in S-W region are symmetric except Mahemdavad (MHD) and Sojitra (SJT) which are moderately skewed. In the N-W region rain gauge stations Dharoi (DH) and ansipur (SP) are highly skewed while other stations are moderately skewed. In the N-E region, % stations are moderately skewed while remaining is symmetric. Table 4.3 skew ness for rain gauge stations for year 1976 to 27 Station AHD BJ CDL MHD PW SJT VSI emarks Skew ness egion S-W Station BDL DH LML MNS SP VJP VP Skew ness egion N-W Station BYD BHL CPW HMT ID TTI Skew ness egion N-E 4.4 Discussion In this study, rainfall distribution and rainfall variability has been analyzed using station recorded daily rainfall. Spatial and temporal variability of rainfall over Sabarmati basin has been analyzed. egional classification of Basin has been done to understand the rainfall variability. On the basis of daily rainfall of 2 stations in Sabarmati basin for the period of 32 years ( ), monthly rainfall amount and seasonal rainfall amounts are computed and analyzed for identifying rainfall variability at spatial and temporal scale. It is seen that the temporal variability is 48

18 more compared to the spatial variability. The analysis also shows that there is no specific trend. The basin has been divided into three regions- S-W region, N-W region and N-E region for understanding the distribution and variability of rainfall between the stations. It is seen that on average, S-W region receives 7 mm of seasonal rainfall, N-W region 728 mm, N-E region receives 842 mm of rainfall and the mean seasonal basin rainfall is 77 mm. egion N-E receives more rainfall compared to rest of the regions. It shows that rainfall variability is less between the regions. The variability of rainfall at individual station has been found more compared to the variability of rainfall at region level. The deviation of rainfall from normal rainfall found to be less at spatial level as compared to temporal level. In the S-W region, the deviation is closed to zero, in N-W region it has been found to negative(except Vijapur) and in N-E region positive at all stations. The mean number of rainy days for the basin found to be 36 and 32, 36 and 38 for S-W, N-W and N-E regions. 4. Conclusion The analysis shows that the movement of rain front is from lower region of basin to upper region along South-West to North-East. The frequency of mean seasonal station rainfall and mean seasonal basin rainfall has been depicted in Figure 4.8 Spatial and temporal variability of rainfall over Sabarmati basin has been analyzed and found very high. egional classification of Basin has been done to understand the rainfall variability. Information about the arrival of rainfall, rainy days during the monsoon season, pattern, anomalies (positive and negative) and frequency of mean seasonal rainfall at station level has been obtained. Although being not optimum for flood or drought forecasting and contingency planning, it produces valuable information about large scale atmospheric induced variations. The review of the state of the art research led to the impression that concepts on use of rainfall data for extreme weather analysis are still 49

19 vague. The identification of rainfall deficit years for drought contingency planning can be successfully used if rainfall is co-related with large scale predictor such as geopotential height, or air humidity, or sea surface temperature. However, these results need to be considered carefully, nevertheless these observations are encouraging and may provide valuable information for climate change studies, water resources planning and drought or flood contingency services.