Identification of Potential Infiltration Zones through Overlay Analysis in GIS Environment Using Reservoir Frequencies, Spreads and Other Parameters

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1 Paper Reference Number: PN-139 Identification of Potential Infiltration Zones through Overlay Analysis in GIS Environment Using Reservoir Frequencies, Spreads and Other Parameters Thiyam Tamphasana Devi Research Scholar, Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati , Assam, India Y. B. Katpatal Professor, Department of Civil Engineering, Visvesvaraya National Institute of Technology, Nagpur , Maharastra, India Keywords: Reservoir spread & frequencies, SCS-CN, correlation, GIS ABSTRACT An overlay analysis was performed within the GIS environment for the identification of potential infiltration zones in semi-arid region (Bhandara district, Maharastra, India). Six parameters (slope, average groundwater fluctuation, infiltration depth calculated by SCS-CN method, percentage change in frequency & spread of surface reservoir, lithology) were selected for this overlay analysis according to their degree of association with infiltration. Spatial and temporal changes of surface reservoir frequencies & spreads are also playing a very important role in the infiltration phenomenon is examined in this study. Weights are assigned to the parameters on the scale of 1 to 6 according to their contribution to the central theme. The final result comes from overlay analysis was compared with each parameter for confirmation of the analysis and considerably acceptable result was obtained. And, a simple correlation was also carried out between average groundwater fluctuations, percentage change in frequency & spread of surface reservoirs and estimated infiltration depth; and with other parameters. Non-linear correlation was observed in this analysis. The correlation (R 2 =0.006 & 0.012) between infiltration depth and reservoir frequencies & spreads indicates that the existence of many lakes in this region is because of low infiltration. Due to the presence of impermeable soil

2 in this region, very low quantities of infiltration are predicted. Study indicates that direct methods of groundwater recharge must be adopted in the district to enhance the groundwater scenarios in the zones identified through this analysis as the potential infiltration zones. 1. INTRODUCTION In the coming decades, the accelerating population growth, surface water pollution, and climate change together may produce a drastic decline in fresh water supply. Groundwater is considered to be the purest form of water available on earth. The selected study area normally receives less rainfall as compare with other parts of the country, but exist large number lakes (around 3500). The surface water accumulated in these lakes is almost polluted because of human development and construction of industries to nearby districts. Bhandara is an agricultural centre for the farmers around its region majorly growing rice. They grow both kharif and ravi crops but predominantly kharif crops which requires adequate water supply (900 to 2500 mm) (Jai Vir Sing 2010). India with 2.3% of the world s land supports more than 16% of the world s population with only 4% of world s freshwater resource. People in this area suffer from drinking water supply as surface water is almost polluted. For the supply of potable water it depends upon wells. Groundwater is the only means for potable water supply in this area. Hence, not only the quantification and conservation of surface water resources for the need of the hour for ensuring sustainable livelihood but also identification of infiltration potential zones is essential for the assessment of groundwater yield potential of the watershed, recharging the ground water zones and sustainable management of groundwater in future. Scientific Integrated Ground water Management requires scientific information about all the contributing parameters which is directly or indirectly related with the hydrological phenomenon of groundwater. The basic influence factors of infiltration are physical characteristics of topography and physical, chemical & biological characteristics of soil etc. (Patra, 2003). For this study, the infiltration depth estimated by Soil Conservation Services Curve Number (SCS-CN) is also considered as this method is easy and widely acceptable by many hydrologists and scientists. Infiltration depth directly influences the potentiality of infiltration. Apart from this other parameters like slope, average groundwater fluctuation, geological units and changes of frequency & spreads of reservoirs are also affecting the potentiality of infiltration which is considered in this study for overlay analysis. The integrated approach of Geographic Information System (GIS) tool, overlay analysis is a common, widely used method of analyzing and evaluating geospatial data. Overlay analysis utilizes map layers in a GIS to discover relationships across the layers. Overlay analysis is used to investigate geographic patterns and to determine locations that meet specific or multi criteria. 2. LOCATION OF STUDY AREA Bhandara district lies in the northeastern part of the state of Maharashtra. The geographical area of the district is about 4090 Sq. Km. It lies between North latitude to and East longitude to It has an average elevation of 244 metres (800 feet). The weather is

3 very extreme in all seasons with temperatures in summers as high as 45 degrees Celsius and in winters as cool as 8 degrees Celsius. Nagpur district is located on its western side and Gadchiroli district on its southern side. To the north and east of it, lies Madhya Pradesh. As per census data of 2001, the total population of the district is about 11,35,835. There are around 3500 lakes in this district. The maximum part of the district falls in the survey if India toposheets 55 O/12, 55 P/9, 55 O/11, 55 O/15, 55 O/16, 55 P/13, 65 C/4. The location of the study area has been presented in fig MATERIALS AND METHODS Fig. 1. Location map of study area (Bhandara district) 3.1 Materials The data and materials used in this study are given as follows: i. Drainage map, geology, contour map were digitized from the SOI (Survey of India). Topo sheets 1: 50,000 scale.

4 ii. District map, watershed map and soil map of the area were acquired from the Journal of Land Resource atlas Bhandara District, published by National Bureau of Soil Survey, publ.22(december 1994) and digitized in ArcGIS 9.2. iii. Indian Remote Sensing satellite (IRS-LISS III) data (23.5m spatial resolution) for the months of April and October 2009 were used for the digitization of surface water bodies (lakes). iv. Annual Average Rainfall data (2009) collected from Groundwater Survey and Development Agency Maharashtra, India, which is used for the estimation of infiltration depth by SCS-CN method. v. Average groundwater fluctuation level (2003) for 73 number of tube wells were collected from Groundwater Survey and Development Agency Maharashtra, India. 3.2 Methods Maps collected from concerned departments were geometrically corrected and digitized to extract various spatial informations. The basic method adopted in this study is overlay analysis of selected parameters to identify the potential infiltration zones using GIS software (ArcGis 9.2) It is a method which allows geospatial data to be analyzed and evaluated. Vector overlay analysis was performed. Later a simple correlation was carried out between the selected parameters Overlay Analysis By using weight average method overlay analysis was done based on selected parameters. The parameters selected for the overlay analysis of groundwater potential are: i. Slope ii. Geology iii. Average groundwater fluctuation level (2003) iv. Infiltration depth (SCS-CN) v. Percentage change in water spread vi. Percentage change in frequencies Each parameter is assigned weights from 1 to 6 scales as per the degree of contribution to the central theme. The logic of assigning weights to each polygon of each theme is given in table 1.

5 Table 1. Logic of assigning weights Sr. No. Value Logic 1 1 Least contribution to central theme. 2 2 Least to moderate contribution to central theme 3 3 Moderate contribution to central theme. 4 4 Moderate to high contribution to central theme. 5 5 High contribution to central theme. 6 6 Very high contribution to central theme Slope classes Slope of the area stands among major parameters, which govern the occurrence and movement of groundwater. Keeping this in view the slope map of the area has been prepared from the combined information extracted from remote sensing data, drainage network, and contours. The information extracted from these sources was utilized for preparation of slope map of the area. Southern part is flatter than Northern part. The slope map prepared has been presented in fig. 2. The area coming under different slope categories was calculated.

6 Fig. 2. Slope classes map of study area This parameter is inversely proportional with infiltration. As the slope increases infiltration decreases. Six classes of slope observed in the study area and accordingly their associations with infiltration, weights are assigned Geology Geological units were digitized from the toposheets collected from Soil Survey of India (SOI). Mostly, the study area is covered by mica schist, granitic gneisses and granitic gneiss with magmatic granite which are having moderate infiltration capacity, alluvium soil is along the

7 periphery of the river and very small part of laterite also exist. Hence, according to the nature of the existence of lithology, we expect moderate infiltration in this region. Geological units are shown in fig. 3. Fig. 3. Geology map of study area Average Groundwater Fluctuation Level

8 Average groundwater fluctuation (AGFL, 2003) is based on water table levels of wells exist in this study area. The samples of around 73 tube wells were used. The contour map of AGFL is presented in figure 4. Central part of the study area shows less groundwater fluctuation as compare with outer part of the study area. High fluctuation (13.5m) found in the granitic gneiss region of which is having medium infiltration capacity. And very low (2.5m) in the granitic gneiss with magmatic & quartzite and gritty quartzite found which are having low infiltration capacity. Contour map of AGWF of the study area is presented in fig. 4. Fig. 4. Contour map of AGWF of study area Infiltration Depth Calculated by SCS-CN Method Watershed wise infiltration depth was calculated by Soil Conservation Services Curve number (SCS-CN) method. SCS-CN method is associated with curve number and curve number depends on landuse/landcover, hydrological soil group (HSG) and Antecedent Moisture Content (AMC). HSG are four types as A, B, C, D from highest to lowest infiltration rate respectively and AMC as I,II & III condition from driest to wettest. Average condition (AMC II) is considered in this

9 study paper. The relation of runoff with rainfall is given as follows by (Ponce and Hawkins, 1996). Q=(P-0.2S) 2 /(P+0.8S) and; I=P-Q Where, I is infiltration (cm), Q is runoff (cm); P is rainfall (cm); S is the potential maximum soil moisture retention after runoff begins (cm); Ia is the initial abstraction (cm), Ia = 0.2S is taken in this study. The runoff curve number, CN, is then related as, S= (1000/CN)-10 Generally, CN has a range from 0 to 100; lower numbers indicate low runoff potential while larger numbers are for increasing runoff potential. If the area consists of patches of landuse/landcover then a weighted curve number (CN w ) for the watershed can be obtained by weighing them in proportion of the area and given as follows: CN w = ( CN i *A i )/A Where, CN i is the curve number from 1 to any number N; A i is the area for CN i ; and A is the total area of watershed. There are eight landuse/landcover classes in this region. Its percentage area cover is as crop land (13.43%), fallow (23.51%), Habitation (0.2%), land with scrub (28.23%), land without scrub (5.9%), open forest (21.53%), dense forest (3.46%) and outcrop (1.14%). The soil covers are laterite, medium black, older alluvial, red loamy and red sandy. The percentage of HSG cover is B (60%), C (30%) and A (10%). Watershed wise CN is derived for different landuse/landcover and for different HGS. Calculated infiltration map is presented in fig. 5.

10 Fig. 5. Infiltration depth map by SCS-CN method of study area Percentage change in Reservoir spread and frequencies: Surface water area of lakes of two seasons April and October from LISS III (2009) were digitized using ArcGIS 9.2. Total number of lakes digitized in April season is 503 and in October, it is Total surface area found in April and October is 25.1 km 2 and km 2. Its percentage change in spread and frequencies were calculated in watershed wise. This has been done to understand the interaction between the weathered domain and the recharge. Percentage change in spread & frequencies of surface reservoir in presented in fig. 6 (a) & (b) respectively.

11 (a) Fig. 6. (a) Percentage change in spread (b) (b) Percentage change in frequency 3.3 Assigned Weights Specific range of value is selected for each parameter for assigning weights as maximum value minus minimum value for that particular parameter then divided by range of scale. Mathematically it is given as; Range of value to be assigned= (Maximum value- Minimum value)/ Range of scale The assigned weights of each parameter are given in table 2.

12 Sl. No % Change in Spread % Change in Freq. Table 2. Assigned weights of the six parameters Infiltration Depth (mm) Overlay Analysis (Scale=1-6) AGWL (m) (2003) Slope (m/km) Lithology phyllite, tuffaceous carbonaceous phyllite quartzite and gritty quartzite granitic gneiss migmatic quarzite and muscovite, meta basalt granitic gneisses, 4 mica schist sandstone, laterite >10 alluvium 6 weights 4. RESULT AND DISCUSSION Overlay analysis observed good response for identifying the effective regions of infiltration. The final sum of weight is classified into five categories according to the effectiveness of the infiltration capacity and is given in table 3. Table 3. Final weights with description Final weights description Very low Low Medium High Very high As per above weight assigned, the result of overlay analysis for effective regions of infiltration of the study area is shown in fig.7. From the overlay analysis, less than 10 slopes falls on the high 1 2 3

13 category of effectiveness of infiltration, slopes falls on medium category and slope falls on low category. It is supportive to the result. The alluvium part is observing very high infiltration region and is acceptable result. Granitic gneiss is observing medium and remaining parts are also acceptable results. But in the sandstone area, the result gives medium infiltration region and it is not acceptable. In case of final result with average groundwater fluctuation level, not so good result was observed as compare with slopes but as in the lithology pattern, the sandstone area which is having high fluctuation, indicating medium infiltration region. Most part of the study area is observing medium potential infiltration zones from the overlay analysis. Very high zones are observing very less part. The second dominating zone is low potential infiltration zones. Some parts indicates of high potential infiltration zones and a few parts covered by very low zones.

14 Fig. 7. Potential infiltration zones map of study area 4.1 Correlation A simple correlation was performed for different conditions and different parameters to bring our study significant and understandable. Let us see them one by one as follows: (i) Correation of Infiltration depth with % change in spread

15 Fig. 8. (a) Infiltration depth and % change in spread (b) Infiltration depth and % change in frequency In the above two cases, the correlation of linearity is very weak. We get the value of R 2 as and for the correlation of infltration with % change in spread & % change in frequency respectively. (ii) Correlation of AGWF with % change in spread & frequency Fig. 9. (a) AGWF and % change in spread (b) AGWF and % change in frequency Non-linearity is observed in this case also so, it indicates that there is no chance of predictable value of AGWF from % change in spread & frequency. 5. CONCLUSION From the overlay analysis; due to the presence of impermeable soil in this region, very low quantities of infiltration are predicted. Study indicates that direct methods of groundwater

16 recharge must be adopted in the district to enhance the groundwater scenarios in the zones identified through this analysis as the potential infiltration zones. From correlation result, the present study indicates some link between the change in the parameters of the lakes with time during post monsoon and premonsoon period. It was anticipated that change in the dimension and number of the lakes is linked to their presence in a particular type of lithology and the change is directly linked to infiltration changes caused due to hydrological change in the lithologies. It is concluded that existence of many lakes in this region is because of the most of the region is covered by Feldspar minerals which is converted to clay, sandy clay and silty clay minerals after its weathering. This prevents the percolation of water to the underground beneath. However, more detail study of the region is required for more confirmation of the conclusion. Ackowledgement: I would like to thank Dr.Bimlesh Kumar, Assisstant Professor, Department of Civil Engineering, Indian Institute of Technology,Guwahati for his valuable encouragement while writting this paper. REFERENCES Anil Kumar and Devendra Kumar (2008). Predicting Direct Runoff from Hilly Watershed Using Geomorphology and Stream-Order-Law Ratios: Case Study, Journal of hydrologic engineering, ASCE. Chakraborty, Debashis et al. Spatial Modeling for Hydrological Response Behavior of an arid watershed, India- RS & GIS Approach, Journal of Spatial Hydrology Spring, Vol. 5, No. 1. Conkle Chris et al. (2006). Hydrology Manual, Los Angels Country Department of Public Works. Gupta K. K. et al. (1997). Estimation of water harvesting potential for a semi arid area using GIS and remote sensing, RS and GIS for Design and Operation of Water Resources Systems (Proceedings of Rabat Symposium S3), IAHS Publ. No Gupta P. K. and Panigrahy S. (2008). Predicting the spatio-temporal variation of run-off generation in India using remotely sensed input and Soil Conservation Service curve number model, Current Science, Vol. 95, No. 1. Koren V. I. et al. (2000). Use of soil property data in the derivation of conceptual rainfallrunoff model parameters, Presented at 15th Conference on Hydrology, AMS, January 9-14, 2000, Long Beach, CA. Patil J. P. et al. (2007). Development of a GIS Interface for Estimation of Runoff from Watersheds, Springer Science. Patra K.C. (2008). Hydrology and water Resources Engineering, second edition, Narosa publications.

17 Author d Information: Author (s) Affiliation: 1. Thiyam Tamphasana Devi Research Scholar, Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati d.thiyam@iitg.ernet.in Telephone Number: Fax: Mobile Number: Mailing Address: Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati , Assam, India Author Photograph: 2.Y. B. Katpatal Professor, Department of Civil Engineering, Visvesvaraya National Institute of Technology, Nagpur Thiyam Tamphasana Devi Brief Biography: Thiyam Tamphasana Devi: Currently pursuing Ph.D in the Department of Civil Engineering, Indian Institute of Technology, Guwahati, India. Research going on currently is the study of the application of Arrowhead impeller, its submergence depth influence on power consumption in stirred tanks. M-tech passed from Visvesvaraya National Institute of Technology, Nagpur, India in water resource engineering with highest grade (winner of Academic Excellence Prize) in June M-tech project was on the area of the application of RS & GIS in watershed management.