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Applied Water Science

, 8:112 | Cite as

Long-term trend analysis of water-level response to rainfall of Gulbarga watershed, Karnataka, India, in basaltic terrain: hydrogeological environmental appraisal in arid region

  • L. Bharathkumar
  • M. A. Mohammed-AslamEmail author
Open Access
Original Article
  • 1.1k Downloads

Abstract

Hydrogeological environment of an area includes surface and subsurface water resources. Groundwater is continuously getting recharged by surface water sources and rainfall. Recharge and discharge of an area can be reflected by water-table rise and drawdown. Water table of an area represents the groundwater strategy in the formation and majorly depends on the amount of precipitation. An attempt is carried out to analyze the correlation between rainfall and water table in arid climatic condition within the Gulbarga watershed, Karnataka, India. Rainfall and water-table data were collected from Department of Mines and Geology, Government of Karnataka, which consists of information from 2001 to 2011. Month-wise data are processed into distinctive four seasonal data, namely winter, summer, monsoon and post-monsoon. The rainfall data are analyzed, and accordingly they have been classified as good year and bad year depending upon the amount of precipitation. The study focuses on the need of rainwater harvesting and artificial recharge structures to improve groundwater resource sustainability for future generation.

Keywords

Rainfall analysis Good/bad year Water-table analysis Correlation 

Introduction

Watershed is an area of land enclosed within ridges from which water drains to a particular point along a stream. Each watershed can be classified into three regions, (1) runoff, (2) recharge and (3) storage, depends on rainfall infiltration. Usually, the watershed with large runoff region has low groundwater levels, where a watershed with large storage region will have good groundwater levels (Bharathkumar and Mohammed-Aslam 2015). Karnataka State in India ranks second, next to Rajasthan in drought condition (Jayasree and Venkatesh 2015). Rainwater gained its importance with the emerging need and to meet the demands of water resources. Overexploitation of groundwater resource collectively results in declining water levels in most part of the country (Dhakate et al. 2013). Aquifer characterization will be the starting point for groundwater management and decision-making (Ahmed 2009; Mohammed-Aslam and Balasubramanian 2010; Mohammed-Aslam et al. 2010). Groundwater is affected by climate change through various hydrological processes. The trends in climate variables were seen reflected in groundwater trends (Okkonen and Kløve 2010). Groundwater resource of a region is one of the building blocks for balanced economic development of the area (Raghavendra 2013). The availability of groundwater in the terrain is based on the presence and pervasiveness of secondary permeability in the forms of fractures and/or weathered zones and the degree of connectivity of these structures (Yidana et al. 2014). The water table represents the groundwater reservoir, and changes in its level represent the changes in groundwater storage (Raghavendra 2013). The rise in the water table indicates the condition when the recharge exceeds discharge, whereas fall in water table indicates the situation when the discharge exceeds recharge (Seeyan et al. 2014). Water resources are extremely limited but renewable, exhibiting diversity in their quality and quantity (Moon et al. 2004). One of the major solutions to meet ever-increasing water demands would be storing the available rainwater through rainwater harvesting techniques (Jothiprakash and Sathe 2009). The studies of seasonal groundwater table fluctuations are important in understanding the development and management of existing sub-watershed system. Variations of rainfall and groundwater table depth are closely related as rainfall is the primary source of recharge to aquifers. The timing and quantity of recharge reaching the water table has significant consequences for water resources and for the movement of pollutants into groundwater (Lee et al. 2006). Direct surface recharge is very important in recent decades to manage water resource effectively (Rahimi et al. 2014). However, the correlation may sometimes be imperfect because of the differences in rainfall intensity and distribution that produce different amounts of recharge for the same amount of rainfall (Jasmin et al. 2010). The aim of the research is to analyze groundwater level data of past 11 years to correlate with rainfall distribution. An attempt is carried here to analyze the long-term fluctuation in water-level response to rainfall, for understanding the hydrogeological setup of an arid environment.

Study area

Detailed study has been conducted in Gulbarga watershed that lies in Gulbarga Taluk of Karnataka as shown in Fig. 1. The study area covers an area of about 225.54 sq kms and covers major part of Gulbarga City and southern part of adjoining villages up to Saradagi Dam. The major lithology comprises Deccan trap basaltic formation. Surface water and groundwater conditions are in safe zone, and the area needs proper management to sustainably utilize the resource. The climatic condition is arid zone, and the major source of groundwater is from rainfall recharge. The southwestern monsoon occurs from June to September and constitutes about 70% of the rainfall. The normal rainfall is about 777 mm. The streams originate from north of the study area and reaches Saradagi Dam. The water table varies from 4.7 to 11.54 meters below ground level. The study area is drained by the fifth-order stream, which is connected to Saradagi Dam.
Fig. 1

Study area map

Methodology

Groundwater recharge is an important and required necessary activity in managing and developing water resource of a watershed. Rainfall is the major source of groundwater recharge. The infiltration of water is mainly governed by lithology, land use practice and elevation of the terrain. Spatial maps were prepared using ArcGIS 10.1 and Erdas Imagine 9.3 to analyze the factors controlling infiltration rate and surface runoff phenomenon.

Month-wise data of water table and rainfall were collected from Department of Mines and Geology, Government of Karnataka. Excel spreadsheets were used for the data analysis. The seasonal water-level fluctuations in Gulbarga watershed have been analyzed. Rainfall data are one of the important dataset in spatial domain, controlling the water resources budget of the region. Rainfall data of last 11 years were collected from Department of Mines and Geology. Good year and bad year analysis was conducted to analyze the effect of groundwater recharge. Good year and bad year were calculated based on the above and below values of average rainfall (Peiris 1989). The methodology for discriminating the good year and bad year is illustrated in Eq. 1.

The formula for good year and bad year analysis is as follows:
$$ A \, = \, \left( {{\text{Maximum}}\;{\text{rainfall }}{-}{\text{ Minimum}}\;{\text{rainfall}}} \right) \, / \, 2. $$
(1)
  • If yearly average rainfall > A, the year is good year

  • If yearly average rainfall < A, the year is bad year

Water-level data were collected from Department of Mines and Geology. The data consist of groundwater levels of observatory wells from Gulbarga watershed for 11 years from 2001 to 2011. The observatory well characteristics are mentioned in Table 1. The data are processed to seasonal fluctuation by considering winter (January to February), summer (March, April and May), monsoon (June, July, August and September) and post-monsoon (October, November and December) seasons.
Table 1

Observatory well characteristics

Serial number

Well number

Latitude

Longitude

Well type

Geology

Well use

1

OW1

17°26′45″

76°53′45″

Dug well

Basalt

Public supply

2

OW2

17°10′45″

76°47′45″

Dug well

Limestone

Public supply

3

OW3

17°07′24″

76°38′00″

Dug well

Basalt

Public supply

4

OW4

17°24′09″

76°43′30″

Bore well

Basalt

Monitoring

5

OW5

17°21′00″

76°44′00″

Bore well

Basalt

Monitoring

6

OW6

17°18′10″

76°54′48″

Bore well

Basalt

Public supply

In order to find the controlling factors with respect to the water-table behavior and pattern, correlation has been attempted with the seasonal changes of rainfall. Correlation is the relation between dependence of one variable with respect to another. The correlation value ranges from + 1 to − 1, where + 1 is indicative of high positive correlation and − 1 is indicative of the low negative correlation. Zero values are indicative of the non-dependency of the parameters. Rainfall and water-table correlation were analyzed to show the dependency between two parameters.

Results and discussion

The lithology of this area comprises basalt and limestone formations as shown in Fig. 2. Basalt usually has low-to-medium permeability that depends on the presence of primary and secondary porosity. Fractured basalt serves as good groundwater storage in Deccan trap formations. Limestone is another variety of rock present in the study area, which is sedimentary formation with high groundwater yielding property. Since limestone is having solution cavities, significant changes in permeability for short distances are expected in this terrain.
Fig. 2

Lithology map of Gulbarga watershed

Remote sensing and GIS applications provide an excellent platform to analyze watershed management and development through prioritization studies (Bharathkumar and Mohammed-Aslam 2016). Land use–land cover map was prepared by supervised classification in ERDAS Imagine 9.3 as shown in Fig. 3. Landsat 8, acquired on December 6, 2015, was used to prepare land use–land cover map.
Fig. 3

Land use–land cover map

Land use–land cover diversity directly influences the recharge condition and surface runoff processes. The map and statistics of the LULC classes are shown in Fig. 3 and Table 2, respectively. Land cover allows greater infiltration to groundwater; conversely, land use restricts the infiltration of groundwater to subsurface. The greater part is covered by 12,929.29 hectares of fallow land, and least part is covered by 69.66 hectares of surface waterbody.
Table 2

Area of LULC classes

Class

Area in hectares

Area in percentage

Settlements

2455.329

10.85

Water

69.66

0.30

Vegetation

6916.84

30.59

Stony waste

238.95

1.05

Fallow land

12,929.29

57.18

Cartosat DEM data are used for terrain elevation analysis as shown in Fig. 4. The elevation of the watershed ranges from 300 meters to 470 meters above mean sea level. Water level follows the elevation of the terrain. Lower elevation is toward southern part of watershed which acts as catchment zone, and higher elevation is toward northern part of watershed which acts as runoff zone.
Fig. 4

Digital elevation model map

The rainfall data are analyzed to get seasonal rainfall fluctuations. Based on the precipitated amount of rainfall, good year or bad year classification was carried out to analyze the hydrological environment of successive years (Khadeeja 2015; Peiris 1989). Hydrogeologically, the greater intensity of infiltration due to higher amounts of rainfall is expected in good year, whereas such conditions do not prevail during the bad year causing the poor infiltration. Therefore, the occurrence of water table bears a direct relation with respect to the depth from ground level. During the good year, since the rainfall amount is high, water table is confined to shallow depth. Bad year is a year with poor rainfall and deeper groundwater level. The water-level monitoring data were found to match clearly with the data of season-wise average rainfall of good year and bad year as shown in Table 3 and Fig. 5.
Table 3

Season-wise average rainfall and good year/bad year categories

Year

Winter rainfall (mm)

Summer rainfall (mm)

Monsoon rainfall (mm)

Post-monsoon rainfall (mm)

Yearly average (mm)

Good/bad year

2001

0.344

0.324

201

24.33

56.49

Good year

2002

0.305

0.433

113.275

2.329

29.08

Bad year

2003

0.068

1.382

119.625

13

33.51

Bad year

2004

0.48

41.33

117.025

34.833

48.41

Good year

2005

0.52

14.666

141.2

30.333

46.67

Good year

2006

0.3

52.333

112.625

14.233

44.87

Good year

2007

0.28

45.215

149.4

27.5333

55.60

Good year

2008

0.25

31.3

115.975

29.166

44.17

Good year

2009

0.65

16.466

132

32.966

45.52

Good year

2010

0.5

6.3

128.122

42.458

44.34

Good year

2011

0.45

10.45

112.547

29.365

38.20

Bad year

Fig. 5

Column chart representing average rainfall from 2001 to 2011

The lowest average rainfall recorded in the year 2002 was 29.08 mm. The highest average of rainfall recorded in the year 2001 was 56.49 mm. The yearly average rainfall is found to be 42.78 mm/year. The rainfall values above average were considered as good year, and those of below average were considered as bad year. The analysis clearly showed that the 2002, 2003 and 2011 years having less rainfall were considered as bad years. There was an acute shortage of water resource and faced severe drought during those years. The remaining years were good years and have a rainfall greater than the mean value during the assessment period in this study.

Seasonal rainfall and water-table analysis

The obtained monthly rainfall and water-table data were scaled down to seasonal datasets and analyzed with correlation coefficients to determine the dependency between the parameters. The analyzed seasonal statistics of rainfall and water table are represented in Tables 4, 5, 6 and 7 and Figs. 6, 7, 8 and 9.
Table 4

Winter rainfall and water-table records

Year

Rainfall

Average water levels (mbgl) from observatory wells (maintained by DMG)

OW1

OW2

OW3

OW4

OW5

OW6

2001

0.344

3.2

5.6

6.65

1.93

9.05

3.28

2002

0.305

2.78

5.88

4.96

1.63

9.14

2.83

2003

0.068

3.6

10.28

9.39

3.2

10.63

5.18

2004

0.48

4.85

9.95

13.38

3.58

11.88

5.1

2005

0.52

4.9

13

12.33

2.88

11.58

6.15

2006

0.3

3.03

6.55

7.5

2.73

11.2

5.8

2007

0.28

3.38

13.05

6.23

1.83

11.35

6.78

2008

0.25

3.23

7

6.95

3.7

8.98

5.45

2009

0.65

3.7

6.23

6.25

1.9

7.2

6.8

2010

0.5

3.58

6.88

6.63

2.23

6.51

7.2

2011

0.45

3.45

8.05

4.13

4.4

9.63

9.2

Table 5

Summer rainfall and water-table records

Year

Rainfall

Water levels (mbgl) of observatory wells (maintained by DMG)

OW1

OW2

OW3

OW4

OW5

OW6

2001

0.324

4.15

9.3

8.1

3.2

10.63

3.63

2002

0.433

4.03

10.63

9.72

3.45

11.37

4.58

2003

1.382

5.35

12.35

10.95

3.83

11.05

6.32

2004

41.33

6.22

13.43

14.52

4.27

13.57

6.35

2005

14.666

5.25

14.32

13.35

4.53

12.93

6.78

2006

52.333

3.92

10.12

7.82

5.83

12.57

7.2

2007

45.215

4.18

13.85

6.83

2.4

12.2

5.9

2008

31.3

3.22

7.32

7.05

1.4

10.85

7.98

2009

16.466

4.15

7

7.07

2.37

7.82

8.4

2010

6.3

4.28

7.35

7.22

2.489

6.9

7.6

2011

10.45

4.15

11.93

5.88

4.73

9.58

8.6

Table 6

Monsoon rainfall and water-table records

Year

Rainfall

Water levels (mbgl) of observatory wells (maintained by DMG)

OW1

OW2

OW3

OW4

OW5

OW6

2001

201

4.58

11.83

8.74

2.46

10.53

3.39

2002

113.275

4.62

10.23

11.76

2.41

10.31

5.1

2003

119.625

5.41

10.45

13.25

3.88

10.71

6.54

2004

117.025

6.45

14.82

14.98

4.5

14.3

6.75

2005

141.2

4.3

10

11.73

4.73

13.7

5.4

2006

112.625

3.26

12.45

6.7

2.75

10.4

6.4

2007

149.4

3.06

6.79

6.54

2.08

10.3

6.48

2008

115.975

4.45

7.18

6.95

2

11.05

5.897

2009

132

4.34

7.61

7.79

2.69

7.8

7.54

2010

128.122

3.6

6.54

6.43

3.05

6.49

5.98

2011

112.547

4.3

12.4

7.4

4.7

10.54

6.45

Table 7

Post-monsoon rainfall and water-table records

Year

Rainfall

Water levels (mbgl) of observatory wells (maintained by DMG)

OW1

OW2

OW3

OW4

OW5

OW6

2001

24.33

2.43

4.5

4.69

1.02

8.93

1.39

2002

2.329

3.1

8.37

9.03

0.93

9.47

4.28

2003

13

4.13

9.03

11.62

1.97

10.03

4.67

2004

34.833

4.72

12.38

11.7

1.73

10.33

5.65

2005

30.333

2.42

5.7

11.04

1.18

6.6

5.64

2006

14.233

2.86

9.8

5.52

1.3

10.08

6.45

2007

27.5333

2.4

6.13

6.3

1.6

7.72

7.65

2008

29.166

4.13

5.32

5.8

1.48

10.5

7.58

2009

32.966

3.15

6.3

5.98

1.68

5.23

8.45

2010

42.458

3.2

5.6

4.32

2.07

8.02

9.45

2011

29.365

4.5

11.3

6.9

2.57

11.25

8.45

Fig. 6

Line chart representing winter rainfall and water-table trends

Fig. 7

Line chart representing summer rainfall and water-table trends

Fig. 8

Line chart representing monsoon rainfall and water-table trends

Fig. 9

Line chart representing post-monsoon rainfall and water-table trends

Correlation analysis

The analysis clearly illustrated that both negative and positive correlations exist between rainfall and water table as shown in Table 8. The highest positive correlation was recorded up to 0.552, and the highest negative correlation was recorded up to − 0.677. Lower value of water level represents shallow water-table condition. Hence, negative correlation indicates shallow water level with respect to higher amount of rainfall and positive correlation indicates deeper water level with respect to lower amount of rainfall. Negative correlation indicates higher dependency and positive relationships between the parameters.
Table 8

Correlation between rainfall and water-table records

Seasonal rainfall (mm)

Water levels (mbgl) of observatory wells (maintained by DMG)

OW1

OW2

OW3

OW4

OW5

OW6

Winter

− 0.096

0.090

− 0.090

− 0.347

0.387

0.468

Summer

0.205

0.015

0.167

0.510

0.252

− 0.005

Monsoon

− 0.065

− 0.134

− 0.254

− 0.034

− 0.677

− 0.131

Post-monsoon

− 0.205

− 0.254

0.466

− 0.323

0.552

0.125

Hence, locations which are having higher negative correlations are good in respect of water resources management. Higher correlation is recorded during the monsoon season as the rainfall plays an important role in groundwater recharge during the season. Post-monsoon season is the next dependency season followed by monsoon. The results clearly illustrate the dependency of water table on rainfall recharge. It states that proper rainwater harvesting is the only solution to manage water resource in an effective manner.

Conclusion

The economy of an area depends mainly on the available water resources. Recently, it has been found that much emphasis has been given on conserving water resources. Water table of an area is an important available identity to study aquifer and groundwater changes in the formation. Consequently, deeper water table results in reducing soil moisture and hence reduces the crop yield which affects the economy of the nation. The drawdown of water table is indicative of the discharge of water from the aquifer, whereas raise in water table is indicative of the recharge of water to the aquifer. The depth of occurrence of water table mainly depends on precipitation, whereas precipitation in turn to some extent is controlled by the vegetation (Raghavendra and Mohammed Aslam 2016). The correlation analysis showed that the maximum probability of positive correlation was found between rainfall and water table. This analysis is helpful in focusing the research for better groundwater resource management by proper way of recharging methods in hard rock terrain. The hydrological environment should be preserved safely by recharge awareness and recharge structures. Proper utilization of water resource is an important step in conserving the water resource.

Notes

References

  1. Ahmed AA (2009) Using lithologic modeling techniques for aquifer characterization and groundwater flow modeling of the Sohag area, Egypt. Hydrogeol J 17(5):1189–1201CrossRefGoogle Scholar
  2. Bharathkumar L, Mohammed-Aslam MA (2015) Influence of lineaments on drainage morphometry using geoinformatics: a case study of Nethravathi watershed. Int J Res 2(5):1103–1116Google Scholar
  3. Bharathkumar L, Mohammed-Aslam MA (2016) Prioritizing groundwater potential zones using morphometric analysis: a case study of Gulbarga watershed. IOSR J Appl Geol Geophys 4(3):78–84Google Scholar
  4. Dhakate R, Rao VVSG, Raju BA, Mahesh J, Rao STM, Sankaran S (2013) Integrated approach for identifying suitable sites for rainwater harvesting structures for groundwater augmentation in basaltic terrain. Water Resour Manag 27(5):1279–1299CrossRefGoogle Scholar
  5. Jasmin I, Murali T, Malllikarjuna P (2010) Statistical analysis of groundwater table depths in upper Swarnamukhi river basin. J Water Resour Prot 02(June):577–584CrossRefGoogle Scholar
  6. Jayasree V, Venkatesh B (2015) Analysis of rainfall in assessing the drought in semi-arid region of Karnataka State, India. Water Resour Manag 29(15):5613–5630CrossRefGoogle Scholar
  7. Jothiprakash V, Sathe MV (2009) Evaluation of rainwater harvesting methods and structures using analytical hierarchy process for a large scale industrial area. J Water Resour Prot 1:427–438CrossRefGoogle Scholar
  8. Khadeeja P (2015) Spatial and temporal variability of rainfall in Anand district of Gujrat state. Aquat Procedia 4:713–720CrossRefGoogle Scholar
  9. Lee LJE, Lawrence DSL, Price M (2006) Analysis of water-level response to rainfall and implications for recharge pathways in the Chalk aquifer, SE England. J Hydrol 330(3–4):604–620CrossRefGoogle Scholar
  10. Mohammed-Aslam MA, Balasubramanian A (2010) History of channel modifications—a review. J Ecol Nat Environ 2(10):207–212Google Scholar
  11. Mohammed-Aslam MA, Kondoh A, Mohamed Rafeekh P, Manoharan AN (2010) Evaluating groundwater potential of a hard-rock aquifer using remote sensing and geophysics. Spat Hydrol 10(1):76–88Google Scholar
  12. Moon SK, Woo NC, Lee KS (2004) Statistical analysis of hydrographs and water-table fluctuation to estimate groundwater recharge. J Hydrol 292(1–4):198–209CrossRefGoogle Scholar
  13. Okkonen J, Kløve B (2010) A conceptual and statistical approach for the analysis of climate impact on ground water table fluctuation patterns in cold conditions. J Hydrol 388(1–2):1–12CrossRefGoogle Scholar
  14. Peiris TSG (1989) An alternative approach for analysis of data from a long term experiment. COCOS 7:14–20CrossRefGoogle Scholar
  15. Raghavendra G (2013) Groundwater fluctuation and the flow pattern in the Kamarwadi sub-basin of Bhima River. Res Rev J Eng Technol 9873:101–108Google Scholar
  16. Raghavendra BR, Mohammed Aslam MA (2016) Monitoring vegetation condition using spectral vegetation indices: a case study of rice crops in Raichur district, Karnataka, India. IOSR J Appl Geol Geophys 4(2):64–68Google Scholar
  17. Rahimi S, Shadman Roodposhti M, Ali Abbaspour R (2014) Using combined AHP-genetic algorithm in artificial groundwater recharge site selection of Gareh Bygone Plain, Iran. Environ Earth Sci 72(6):1979–1992CrossRefGoogle Scholar
  18. Seeyan S, Merkel B, Abo R (2014) Investigation of the relationship between groundwater level fluctuation and vegetation cover by using NDVI for Shaqlawa Basin, Kurdistan Region—Iraq. J Geogr Geol 6(3):187–202Google Scholar
  19. Yidana SM, Fynn OF, Chegbeleh LP, Loh Y, Obeng MA (2014) Analysis of recharge and groundwater flow in parts of a weathered aquifer system in Northern Ghana. J Appl Water Eng Res 2(2):91–104CrossRefGoogle Scholar

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© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Central University of KarnatakaGulbargaIndia

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