Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Close

GRD Journals | Global Research and Development Journal for Engineering | Emerging Research and Innovations in Civil Engineering (ERICE - 2019) | February 2019

e-ISSN: 2455-5703

Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Closest Station Method: Special Reference to Navsari District 1Monalika

Malaviya 2Dr. Vilin Parekh 2 Principal 1 Department of Civil Engineering 1,2 Parul Institute of Engineering and Technology, Vadodara, Gujarat, India Abstract Missing Rainfall data may vary in length from one or two days to several years. Especially in data-sparse areas, estimation of the missing data is necessary in order to utilize partial records. For filling missing rainfall data, various methods are used. To generate one output, some methods need only one input variable like Closest Station Method (CSM) & Artificial Neural Network Method (ANN) and some methods must need more than one input variables like Arithmetic Average Method (AAM), Inverse Distance Method (IDM) & Normal Ratio Method (NRM). Gujarat is divided into eight agroclimatic zones. South Gujarat Agroclimatic zone partly consisting of Bharuch, Navsari and Surat districts is selected for the present study. There are 22 talukas under the study area and 75 rain gauge stations cover selected 3 districts. Daily rainfall data from 1981 to 2015 of respective rain gauge stations are collected from State Water Data Center, Gandhinagar. In order to compute the missing daily rainfall data, the latitudes and longitudes of the different rain gauge stations are converted to x and y co–ordinates using the Franson Coord Trans V 2.3. Cluster analysis is used to group the rain gauge stations into clusters for filling in missing rainfall data. The paper discusses determining missing rainfall data of rain gauge stations of Navsari district by Closest Station Method.

Keyword- Rain Gauge Stations, Rainfall Data, Missing Data, Cluster Analysis, Closest Station Method __________________________________________________________________________________________________

I. INTRODUCTION At least no one rain gauge station is available with full of rainfall data so it is necessary to find missing rainfall data. For that, different methods are available. Some methods need one input variable means rain gauge station to generate output means rainfall data and some methods must need more than one input variables to generate output one. Different researchers used different methods for determining missing rainfall data.

Demirhan and Renwick (2018) focused on the estimation of missing solar irradiance values and for that, they used durations like minutely, hourly, daily, and weekly. An extensive number of imputation methods were used in solar irradiance series. They compared the accuracy of 36 imputation methods.

Records of rainfall were examined by Miro et al. (2017). Multiple Imputation Methods like 6 Linear, 2 Non-linear and 2 Hybrid Methods were used for finding missing rainfall data. Daily rainfall data of 60 years were considered.

Sattari et al. (2016) used Arithmetic Averaging Method (AA), Non-linear Regression Method (NR), Linear Regression Method (LR) and Multiple Linear Regression Method (MLR) methods for finding missing rainfall data. In this study, monthly rainfall data of 29 years from 6 rain gauge stations considered.

Regression method was used by Khalifeloo et al. (2015) for filling missing hydrological records.

Dumedah et al. (2014) introduced artificial neural networks and statistical methods for infilling missing soil moisture records of 13 monitoring stations.

Ghuge and Regulwar (2013) used Artificial Neural Network (ANN) Method for monthly rainfall data of 10 years from 6 rain gauge stations.

Nkuna and Odiyo (2011) examined records of 1 year rainfall data from 5 rain gauge stations and determined the missing rainfall data. For finding missing rainfall data, Artificial Neural Network (ANN) method was used.

Kim and Pachepsky (2010) were used Artificial Neural Network (ANN) method for infilling missing precipitation data for 7 years from 39 weather stations.

Patel et al. (2008) concluded the effectiveness of the artificial neural network method for Mehsana district, Gujarat, India, compared to the arithmetic average method, inverse square distance (ISD) (National Weather Service method), normal ratio method, linear and multiple regression methods.

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Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Closest Station Method: Special Reference to Navsari District (GRDJE / CONFERENCE / ERICE - 2019 / 063)

The aim of the study is to ascertain missing rainfall data using Closest Station Method. The objectives of the study are: to fill the missing climate data, to use Closest Station Method and to utilize Cluster Analysis.

II. STUDY AREA Gujarat is divided into eight agroclimatic zones (Map 1). South Gujarat Agroclimatic zone partly or fully including the districts of Bharuch, Navsari and Surat is selected for the present study (Map 2).

There are 22 talukas and 75 rain gauge stations in and around the study area.

Map 1: Gujarat Agro Climate Zones Map

Navsari is an administrative district in the state of Gujarat in India. It is located at 20.9467 0 N and 72.95200 E and covers an area of 2,211 km2.

Map 2: South Gujarat Agro Climatic Zone

The paper discusses determining missing rainfall data of 25 rain gauge stations, which are located in Navsari district.

III. DATA COLLECTION There are great differences between temperatures of days and nights. Summers starts from March and ends in June, Monsoons from June to October and winters from November to February.

Daily rainfall data from 1981 to 2015 of respective rain gauge stations of Navsari district are collected from State Water Data Center, Gandhinagar (Table 1).

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Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Closest Station Method: Special Reference to Navsari District (GRDJE / CONFERENCE / ERICE - 2019 / 063)

SR.No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Station Name

District

Tahsil/ Taluk Jalalpor Navsari Gandevi

Latitude

Longitude

From

To

Years

Aat Navsari 20 53’45” 72 52’35” Abrama Navsari 20051’15” 72054’20” Amalsad Navsari 20049’00” 72057’00” Ashtagam Navsari Navsari 20054’34” 73001’42” (Supa) Auranga @ Bhervi Navsari Chikhali 20035’42” 73004’55” Bhinar Navsari Vansda 20048’39” 73021’25” Chikhali Navsari Chikhali 20046’00” 73004’08” Choravni Navsari Vansda 20037’42” 73023’58” Dholikuva Navsari Chikhali 20050’00” 73012’37” Gandeva Navsari Gandevi 20051’25” 73003’30” [Kharel] Gandevi Navsari Navsari 20048’49” 73000’16” Ghodmal Navsari Vansda 20041’22” 73018’45” Godthal-Agasi Navsari Chikhali 20040’08” 73013’59” Kaveri @ Harangam Navsari Chikhali 20047’10” 73008’55” Kharera @ Kavdej Navsari Vansda 20042’30” 73018’33” Mankunia Navsari Vansda 20041’48” 73023’55” Maroli (Bazar) Navsari Jalalpor 21000’45” 72053’22” Kaveri @ Mindhabari Navsari Vansda 20043’40” 73019’50” Purna @ Navsari Navsari Navsari 20058’02” 72057’53” Onjal Navsari Jalalpor 20050’04” 72050’34” Sara Navsari Vansda 20048’43” 73024’42” Satem Navsari Navsari 20055’20” 73005’10” Ugat Navsari Navsari 20057’16” 73002’37” Vansda Navsari Vansda 20044’54” 73023’38” Vesma Navsari Jalalpor 21002’35” 72058’45” Table 1: Details of Raingauge Stations of Navsari district

1981 1981 1981

1998 2001 2007

18 21 27

1981

2015

35

1981 1981 1981 1981

2015 2010 2015 2015

35 30 35 35

1981

2007

27

1981 1981 1981

2015 2015 1998

35 35 18

1999 1981 1981

2015 2015 2010

17 35 30

1981 1995 1983 1982 1981 1981 1981

2015 2015 1998 2001 2001 2010 2010

35 21 16 20 21 30 30

0

0

The five talukas (Jalalpore, Navsari, Gandevi, Chikhali and Bansda) of Navsari district are highlighted (Map 3).

Map 3: Navsari district with its highlighted five talukas

IV. METHODOLOGY In order to compute the missing daily rainfall data the latitudes and longitudes of the different rain gauge stations were converted to x and y co–ordinates using the Franson Coord Trans V 2.3. The rain gauge stations in and around study area were utilized for forming the groups of stations to compute the missing data using cluster analysis.

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Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Closest Station Method: Special Reference to Navsari District (GRDJE / CONFERENCE / ERICE - 2019 / 063)

Cluster analysis is one of the statistical techniques often used in meteorology and climatology to identify homogeneous climate groups and for climate classification. The aim of the cluster analysis is to group the rain gauge stations into clusters for filling in missing data. It is performed by clustering algorithms. All the clustering algorithms follow the basic four steps routine to identify homogeneous groups.

1) Calculation of the specified distance between all the rain gauge stations.

2) Formation of a new cluster merging from the two closest entries, based on a defined criterion.

3) Recalculation of the distance between all the entries, and 4) Repetition of steps 5) until all entries merge into one cluster Data clustering algorithms can be of different types such as joining (tree clustering), two way joining (block clustering) and k–means clustering.

For the present study the joining algorithm was of concern as forming the groups of rain gauge stations were required based on the distances.

Hierarchical tree is a way to investigate grouping in data, simultaneously over a variety of scales, by creating a cluster tree called a dendrogram. The tree is not a single set of clusters, but rather a multi–level hierarchy, where clusters at one level are joined as clusters at the next higher level.

Hierarchical tree can be agglomerative (“bottom–up”) or divisive (“top–down”). Agglomerative algorithms begin with each element as a separate cluster and merge them into successively larger clusters. Divisive algorithms begin with the whole set and proceed to divide it into successively smaller clusters.

The joining or tree clustering method uses the dissimilarities/similarities or distances between objects when forming the clusters. Similarities are a set of rules that serve as criteria for grouping or separating items.

The distances (similarities) can be based on a single dimension or multiple dimensions, with each dimension representing a rule or condition for grouping objects. The most straightforward way of computing distances between objects in a multi– dimensional space is to compute Euclidean distances.

Fig. 1: Part of Various Rain gauge Stations in South Gujarat Agroclimatic Zone

Though there are number of rain gauge stations in the vicinity of the rain gauge station where missing data are to be calculated, it happens rarely that there is more than one station where complete data are available, for filling the missing data.

An attempt is made to fill the missing data using only one rain gauge station and to identify the same, amongst the different methods available, the closest station will be used, as other methods require more than one rain gauge station.

V. RESULTS AND ANALYSIS The latitudes and longitudes of the different rain gauge stations are converted to x and y co–ordinates using the Franson Coord Trans V 2.3 and the distances between rain gauge stations in and around Bharuch, Navsari and Surat District were calculated.

For example, Aat rain gauge station of Navsari district is surrounding by five rain gauge stations (Figure 2).

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Determining Missing Rainfall Data of Rain Gauge Stations in South Gujarat Agroclimatic Zone by Closest Station Method: Special Reference to Navsari District (GRDJE / CONFERENCE / ERICE - 2019 / 063)

Fig. 2: Distances between Aat rain gauge station and its surrounding rain gauge stations (All the distances are in kms)

Thus, distances between each and every 25 rain gauge stations and their surrounding rain gauge stations are found out by Franson Coord Trans V 2.3.

Then, considering proximity of each station with reference to a particular station the dates on which the rainfall data is missing of particular rain gauge station were established. Missing rainfall dates of 25 rain gauge stations are found out.

Missing rainfall values of 25 rain gauge stations of Navsari district are found out by Closest Station Method.

REFERENCES

[1] Gift D, Jeffrey PW, Li C (2014) Assessing artificial neural networks and statistical methods for infilling missing soil moisture records. Journal of Hydrology. S0022-1694(14)00353-9 [2] Harshannand KG, Regulwar DG (2013) Artificial Neural Network Method for Estimation of Missing Data. International Journal of Advanced Technology in Civil Engineering. ISSN: 2231 –5721(13), Volume-2, Issue-1 [3] Haydar D, Zoe R (2018) Missing value imputation for short to mid-term horizontal solar irradiance data. Applied Energy. 225 (18) 998–1012 [4] Khalifeloo MH, Munira M, Mohammad H (2015) Multiple Imputation for Hydrological Missing Data by using a Regression Method (Klang River Basin). IJRET: International Journal of Research in Engineering and Technology.

[5] Kim JW, Yakov AP (2010) Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology. 394 (10) 305–314 [6] Miró JJ, Vicente C, María JE (2017) Multiple Imputation of Rainfall Missing Data in the Iberian Mediterranean Context.

Atmospheric Research. S0169-8095(17), 30125-4 [7] Nkuna TR, Odiyo JO (2011) Filling of missing rainfall data in Luvuvhu River Catchment using artificial neural networks.

Physics and Chemistry of the Earth. 36(11), 830-835 [8] Patel NR, Suryanarayana TMV, Shete DT (2008) Comparison of ANN and conventional methods for predicting missing climate data. Proc. of International conference on “Operations Research for a Growing Nation in conjunction with 41st Annual convention of Operation Research Society of India, Tirupati.

[9] Sattari MT, Ali RJ, Andrew K (2016) Assessment of different methods for estimation of missing data in precipitation studies.

Hydrology Research 1-13 [10] Shete DT, Patel NR (2012) Missing Climate Data. Journal of Applied Hydrology, Aandhra University 1-22

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