Skip to main content

Advertisement

Springer Nature Link
Log in

Identification of Potential Recharge Zones in Drought Prone Area of Bundelkhand Region, India, Using SCS-CN and MIF Technique Under GIS-frame work

  • Original Paper
  • Published:
Water Conservation Science and Engineering Aims and scope Submit manuscript

Abstract

Jaspura watershed a part of Yamuna basin is situated in drought prone area lying in the Banda district of Bundelkhand region, Uttar Pradesh, India. The drastic decline of groundwater level and consistently drying up of the phreatic aquifer has led to the acute shortage of groundwater in the study area. The situation is further aggravated due to base flow in the areas adjoining the major order streams. To mitigate such problem in study area, MIF technique, combined with RS and GIS, has been effectively used to delineate the potential recharge zone using seven thematic layers, viz., LULC, soil, slope, drainage density (Dd), geomorphology, depth to water level map of post-monsoon, and groundwater fluctuation map. Relative rates and weight of each influencing factor have been calculated on the basis of major and minor effect of these thematic layers. Based on their influence on groundwater recharge capacity using seven thematic layers under potential zone, five classes under artificial recharge have been identified, viz., very high (96.4 km2), high (157.4 km2), moderate (146.1 km2), low (72.9 km2), and very low (34.2km2). The runoff in 15 micro-watersheds has been estimated using SCS-CN approach. Integration of runoff and potential recharge zone has yielded the suitable sites and type of groundwater recharge structure. On the basis of its percolation tank (PT), check dam (CD) and sub-surface dam (SD) have been identified as feasible and suitable groundwater recharge structure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data Availability

All the data have been generated by the author and are original in nature.

Code availability

Not applicable.

References

  1. Sharma SK, Vairavamoorthy K (2009) Urban water demand management: prospects and challenges for the developing countries. Water Environ J 23:210–218. https://doi.org/10.1111/j.1747-6593.2008.00134.x

    Article  Google Scholar 

  2. Thakur JK, Thakur RK, Ramanathan AL et al (2011) Arsenic contamination of groundwater in Nepal-an overview. Water (Switzerland) 3:1–20. https://doi.org/10.3390/w3010001

    Article  CAS  Google Scholar 

  3. Taylor RG, Taylor RG (2013). Ground water and climate change. https://doi.org/10.1038/nclimate1744

    Article  Google Scholar 

  4. Agarwal R, Garg PK (2016) Remote sensing and GIS based groundwater potential & recharge zones mapping using multi-criteria decision making technique. Water Resour Manag 30:243–260. https://doi.org/10.1007/s11269-015-1159-8

    Article  Google Scholar 

  5. CGWB (2014) annual report. Govt. of India, Central ground water board Ministry of Water Resources, River Development and Ganga Rejuvenation

  6. Jha, Madan Kumar and SP (2006) Applications of remote sensing and GIS technologies in groundwater hydrology: past, present and future. Bayreuth: BayCEER

  7. Chowdary VM, Ramakrishnan D, Srivastava YK et al (2009) Integrated water resource development plan for sustainable management of mayurakshi watershed, India using remote sensing and GIS. Water Resour Manag 23:1581–1602. https://doi.org/10.1007/s11269-008-9342-9

    Article  Google Scholar 

  8. Singh JP, Singh D, Litoria PK (2009) Selection of suitable sites for water harvesting structures in Soankhad watershed, Punjab using remote sensing and geographical information system (RS&GIS) approach- a case study. J Indian Soc Remote Sens 37:21–35. https://doi.org/10.1007/s12524-009-0009-7

    Article  Google Scholar 

  9. Ibrahim MB (2009) Rainwater harvesting for urban areas: a success story from Gadarif City in Central Sudan. Water Resour Manag 23:2727–2736. https://doi.org/10.1007/s11269-009-9405-6

    Article  Google Scholar 

  10. Kadam AK, Kale SS, Pande NN et al (2012) Identifying potential rainwater harvesting sites of a semi-arid, basaltic region of Western India, using SCS-CN method. Water Resour Manag 26:2537–2554. https://doi.org/10.1007/s11269-012-0031-3

    Article  Google Scholar 

  11. Agarwal R, Garg PK, Garg RD (2013) Remote sensing and GIS based approach for identification of artificial recharge sites. Water Resour Manag 27:2671–2689. https://doi.org/10.1007/s11269-013-0310-7

    Article  Google Scholar 

  12. Wada Y, Van Beek LPH, Van Kempen CM et al (2010) Global depletion of groundwater resources. Geophys Res Lett 37:1–5. https://doi.org/10.1029/2010GL044571

    Article  Google Scholar 

  13. Eyquem J (2007) Using fluvial geomorphology to inform integrated river basin management. Water Environ J 21:54–60. https://doi.org/10.1111/j.1747-6593.2006.00046.x

    Article  Google Scholar 

  14. Fuentes I, Vervoort RW (2020) Site suitability and water availability for a managed aquifer recharge project in the Namoi basin. Australia J Hydrol Reg Stud 27:100657. https://doi.org/10.1016/j.ejrh.2019.100657

    Article  Google Scholar 

  15. Bouwer H (2002) Artificial recharge of groundwater: hydrogeology and engineering. Hydrogeol J 10:121–142. https://doi.org/10.1007/s10040-001-0182-4

    Article  CAS  Google Scholar 

  16. Ibrahim-Bathis K, Ahmed SA (2016) Geospatial technology for delineating groundwater potential zones in Doddahalla watershed of Chitradurga district, India. Egypt J Remote Sens Sp Sci 19:223–234. https://doi.org/10.1016/j.ejrs.2016.06.002

    Article  Google Scholar 

  17. Bhattacharya AK (2010) Artificial ground water recharge with a special reference to India. Int J Res Rev Appl Sci - IJRRAS 4:214–221

    CAS  Google Scholar 

  18. Shaban A, Khawlie M, Abdallah C (2006) Use of remote sensing and GIS to determine recharge potential zones: The case of Occidental Lebanon. Hydrogeol J 14:433–443. https://doi.org/10.1007/s10040-005-0437-6

    Article  Google Scholar 

  19. Yeh HF, Lee CH, Hsu KC, Chang PH (2009) GIS for the assessment of the groundwater recharge potential zone. Environ Geol 58:185–195. https://doi.org/10.1007/s00254-008-1504-9

    Article  Google Scholar 

  20. Singh LK, Jha MK, Chowdary VM (2017) Multi-criteria analysis and GIS modeling for identifying prospective water harvesting and artificial recharge sites for sustainable water supply. J Clean Prod 142:1436–1456. https://doi.org/10.1016/j.jclepro.2016.11.163

    Article  Google Scholar 

  21. Science C, Ahmed A (2020) Artificial recharge structures for groundwater augmentation in Mysuru Taluk of Karnataka State, India using geospatial technology. J Environ Sci Comput Sci Eng Technol. https://doi.org/10.24214/jecet.a.9.4.65274

  22. Kim B, Anderson K, Lee SH, Kim H (2014) A real option perspective to value the multi-stage construction of rainwater harvesting systems reusing septic tank. Water Resour Manag 28:2279–2291. https://doi.org/10.1007/s11269-014-0613-3

    Article  Google Scholar 

  23. Jung K, Lee T, Choi BG, Hong S (2015) Rainwater harvesting system for contiunous water supply to the regions with high seasonal rainfall variations. Water Resour Manag 29:961–972. https://doi.org/10.1007/s11269-014-0854-1

    Article  Google Scholar 

  24. Jha MK, Chowdary VM, Chowdhury A (2010) Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeol J 18:1713–1728. https://doi.org/10.1007/s10040-010-0631-z

    Article  Google Scholar 

  25. Access O, Sarda VK (2017) Open Access Suitable sites identification for artificial groundwater recharge structures at sub-watershed level using Remote Sensing and GIS – a case study in Indian Punjab Virender Kumar Sarda American Journal of Engineering Research ( AJER ). 49–58

  26. Jasrotia AS, Kumar A, Singh R (2016) Integrated remote sensing and GIS approach for delineation of groundwater potential zones using aquifer parameters in Devak and Rui watershed of Jammu and Kashmir, India. Arab J Geosci 9:. https://doi.org/10.1007/s12517-016-2326-9

  27. Matomela N, Tianxin L, Morahanye L et al (2019) Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management J Appl Adv Res 4:16. https://doi.org/10.21839/jaar.2019.v4i1.263

    Article  Google Scholar 

  28. Tailor D, Shrimali NJ (2016) Surface runoff estimation by SCS curve number method using gis for Rupen-Khan watershed, Mehsana district, Gujarat soil. J Indian Water Resour Soc 36:2–6

    Google Scholar 

  29. Psomiadis E, Soulis KX, Efthimiou N (2020) Using SCS-CN and earth observation for the comparative assessment of the hydrological effect of gradual and abrupt spatiotemporal land cover changes. Water (Switzerland) 12:. https://doi.org/10.3390/W12051386

  30. Ibrahim GRF, Rasul A, Hamid AA, et al (2019) Suitable site selection for rainwater harvesting and storage case study using Dohuk governorate. Water (Switzerland) 11:. https://doi.org/10.3390/w11040864

  31. Hamad S (2020) Surface runoff estimation of Wadi Ba Al-Arid watershed NE Libya using SCS-CN, GIS and RS data. Iran J Earth Sci 12:168–175

    Google Scholar 

  32. Satheeshkumar S, Venkateswaran S, Kannan R (2017) Rainfall–runoff estimation using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India. Model Earth Syst Environ 3:1–8. https://doi.org/10.1007/s40808-017-0301-4

    Article  Google Scholar 

  33. Mishra SK, Singh VP (2004) Validity and extension of the SCS-CN method for computing infiltration and rainfall-excess rates. Hydrol Process 18:3323–3345. https://doi.org/10.1002/hyp.1223

    Article  Google Scholar 

  34. Tyagi JV, Mishra SK, Singh R, Singh VP (2008) SCS-CN based time-distributed sediment yield model. J Hydrol 352:388–403. https://doi.org/10.1016/j.jhydrol.2008.01.025

    Article  Google Scholar 

  35. Amutha R, Porchelvan P (2009) Estimation of surface runoff in malattar sub-watershed using SCS-cn method. J Indian Soc Remote Sens 37:291–304. https://doi.org/10.1007/s12524-009-0017-7

    Article  Google Scholar 

  36. Li J, Liu C, Wang Z, Liang K (2015) Two universal runoff yield models: SCS vs. LCM J Geogr Sci 25:311–318. https://doi.org/10.1007/s11442-015-1170-2

    Article  Google Scholar 

  37. Chanu SN, Thomas A (2015) Estimation of curve number and runoff of a micro-watershed using soil conservation service method Estimation of Curve Number and runoff of a micro- watershed using Soil Conservation Service Curve Number method. J Soil Water Conserv 4:

  38. Ramakrishnan D, Bandyopadhyay A, Kusuma KN (2009) SCS-CN and GIS-based approach for identifying potential water harvesting sites in the Kali Watershed, Mahi River Basin, India. J Earth Syst Sci 118:355–368. https://doi.org/10.1007/s12040-009-0034-5

    Article  Google Scholar 

  39. Vojtek M, Vojteková J (2019) Land use change and its impact on surface runoff from small basins: a case of Radiša basin. Folia Geogr 61:104–125

    Google Scholar 

  40. Lu X, Zhang J, Li T, Zhang Y (2017) Hyperspectral image classification based on semi-supervised rotation forest. Remote Sens 9:. https://doi.org/10.3390/rs9090924

  41. Ling L, Yusop Z, Yap WS, et al (2020) A calibrated, watershed-specific SCS-CN method: application to Wangjiaqiao watershed in the three Gorges Area, China. Water (Switzerland) 12:. https://doi.org/10.3390/w12010060

  42. Sultan D, Tsunekawa A, Haregeweyn N et al (2017) Analyzing the runoff response to soil and water conservation measures in a tropical humid Ethiopian highland. Phys Geogr 38:423–447. https://doi.org/10.1080/02723646.2017.1302869

    Article  Google Scholar 

  43. Fontes JC, Pereira LS, Smith RE (2004) Runoff and erosion in volcanic soils of Azores: simulation with OPUS. CATENA 56:199–212. https://doi.org/10.1016/j.catena.2003.10.011

    Article  Google Scholar 

  44. de Winnaar G, Jewitt GPW, Horan M (2007) A GIS-based approach for identifying potential runoff harvesting sites in the Thukela River basin, South Africa. Phys Chem Earth 32:1058–1067. https://doi.org/10.1016/j.pce.2007.07.009

    Article  Google Scholar 

  45. Elhakeem M, Papanicolaou AN (2009) Estimation of the runoff curve number via direct rainfall simulator measurements in the state of Iowa, USA. Water Resour Manag 23:2455–2473. https://doi.org/10.1007/s11269-008-9390-1

    Article  Google Scholar 

  46. Soulis KX, Valiantzas JD, Dercas N, Londra PA (2009) Investigation of the direct runoff generation mechanism for the analysis of the SCS-CN method applicability to a partial area experimental watershed. Hydrol Earth Syst Sci 13:605–615. https://doi.org/10.5194/hess-13-605-2009

    Article  Google Scholar 

  47. Xiao B, Wang QH, Fan J et al (2011) Application of the SCS-CN model to runoff estimation in a small watershed with high spatial heterogeneity. Pedosphere 21:738–749. https://doi.org/10.1016/S1002-0160(11)60177-X

    Article  Google Scholar 

  48. Fan F, Deng Y, Hu X, Weng Q (2013) Estimating composite curve number using an improved SCS-CN method with remotely sensed variables in guangzhou, China. Remote Sens 5:1425–1438. https://doi.org/10.3390/rs5031425

    Article  Google Scholar 

  49. Al-Hasan AAS, Mattar YES (2014) Mean runoff coefficient estimation for ungauged streams in the Kingdom of Saudi Arabia. Arab J Geosci 7:2019–2029. https://doi.org/10.1007/s12517-013-0892-7

    Article  CAS  Google Scholar 

  50. Van Mullem JA (1989) Applications of the Green-Ampt infiltration model to watersheds in Montana and Wyoming. Mont State Univ Bozeman, Mont

    Google Scholar 

  51. Abraham S, Huynh C, Vu H (2020) Classification of soils into hydrologic groups using machine learning. Data 5:. https://doi.org/10.3390/data5010002

  52. McKeever V, Owen W, Rallison R (1972) Soil conservation service. Hydrology. National Engineering Handbook. USA 1972:1–127

    Google Scholar 

  53. Li X, Chen W, Cheng X, Wang L (2016) A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sens 8:. https://doi.org/10.3390/rs8060514

  54. Millard K, Richardson M (2015) On the importance of training data sample selection in random forest image classification: a case study in peatland ecosystem mapping. Remote Sens 7:8489–8515. https://doi.org/10.3390/rs70708489

    Article  Google Scholar 

  55. Amani M, Mahdavi S, Afshar M et al (2019) Canadian wetland inventory using Google Earth Engine: the first map and preliminary results. Remote Sens 11:1–20. https://doi.org/10.3390/RS11070842

    Article  Google Scholar 

  56. Mahdianpari M, Salehi B, Mohammadimanesh F, Motagh M (2017) Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS J Photogramm Remote Sens 130:13–31. https://doi.org/10.1016/j.isprsjprs.2017.05.010

    Article  Google Scholar 

  57. Xia J, Falco N, Benediktsson JA et al (2017) Hyperspectral image classification with rotation random forest Via KPCA. IEEE J Sel Top Appl Earth Obs Remote Sens 10:1601–1609. https://doi.org/10.1109/JSTARS.2016.2636877

    Article  Google Scholar 

  58. Rodriguez-Galiano VF, Chica-Rivas M (2014) Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models. Int J Digit Earth 7:492–509. https://doi.org/10.1080/17538947.2012.748848

    Article  Google Scholar 

  59. Abdel-Rahman EM, Mutanga O, Adam E, Ismail R (2014) Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS J Photogramm Remote Sens 88:48–59. https://doi.org/10.1016/j.isprsjprs.2013.11.013

    Article  Google Scholar 

  60. Van Beijma S, Comber A, Lamb A (2014) Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens Environ 149:118–129. https://doi.org/10.1016/j.rse.2014.04.010

    Article  Google Scholar 

  61. Tamiminia H, Salehi B, Mahdianpari M et al (2020) Google Earth engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J Photogramm Remote Sens 164:152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001

    Article  Google Scholar 

  62. Ghimire B, Rogan J, Galiano V et al (2012) An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience Remote Sens 49:623–643. https://doi.org/10.2747/1548-1603.49.5.623

    Article  Google Scholar 

  63. Cánovas-García F, Alonso-Sarría F, Gomariz-Castillo F, Oñate-Valdivieso F (2017) Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery. Comput Geosci 103:1–11. https://doi.org/10.1016/j.cageo.2017.02.012

    Article  Google Scholar 

  64. Foody GM (2013) Thematic Map Comparison. Photogramm Eng Remote Sens 70:627–633. https://doi.org/10.14358/pers.70.5.627

  65. Jasrotia AS, Majhi A, Singh S (2009) Water balance approach for rainwater harvesting using remote sensing and GIS techniques, Jammu Himalaya, India. Water Resour Manag 23:3035–3055. https://doi.org/10.1007/s11269-009-9422-5

    Article  Google Scholar 

  66. Bhalla RS, Pelkey NW, Prasad KVD (2011) Application of GIS for evaluation and design of watershed guidelines. Water Resour Manag 25:113–140. https://doi.org/10.1007/s11269-010-9690-0

    Article  Google Scholar 

  67. Krois J, Schulte A (2014) GIS-based multi-criteria evaluation to identify potential sites for soil and water conservation techniques in the Ronquillo watershed, northern Peru. Appl Geogr 51:131–142. https://doi.org/10.1016/j.apgeog.2014.04.006

    Article  Google Scholar 

  68. Rais S, Javed A (2014) Identification of artificial recharge sites in Manchi Basin, Eastern Rajasthan (India) using remote sensing and GIS techniques. J Geogr Inf Syst 06:162–175. https://doi.org/10.4236/jgis.2014.62017

    Article  Google Scholar 

  69. Kumar T, Jhariya DC (2017) Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques. Geocarto Int 32:1367–1388. https://doi.org/10.1080/10106049.2016.1213772

    Article  Google Scholar 

  70. Jha BM (2007) Manual on artificial recharge of groundwater. Cent Gr Water Board, Minist Water Resour 198

  71. Waikar ML, Nilawar AP (2014) Identification of groundwater potential zone using remote sensing and GIS technique. 3:12163–12174

  72. Singh A, Panda SN, Kumar KS, Sharma CS (2013) Artificial groundwater recharge zones mapping using remote sensing and gis: a case study in Indian Punjab. Environ Manage 52:61–71. https://doi.org/10.1007/s00267-013-0101-1

    Article  Google Scholar 

  73. Selvam S, Magesh NS, Chidambaram S et al (2015) A GIS based identification of groundwater recharge potential zones using RS and IF technique: a case study in Ottapidaram taluk, Tuticorin district, Tamil Nadu. Environ Earth Sci 73:3785–3799. https://doi.org/10.1007/s12665-014-3664-0

    Article  Google Scholar 

  74. Gogate NG, Rawal PM (2015) Identification of potential stormwater recharge zones in dense urban context: a case study from Pune city Int J. Environ Res 9(1259):1268. https://doi.org/10.22059/ijer.2015.1017

    Article  Google Scholar 

  75. Senanayake IP, Dissanayake DMDOK, Mayadunna BB, Weerasekera WL (2016) An approach to delineate groundwater recharge potential sites in Ambalantota, Sri Lanka using GIS techniques. Geosci Front 7:115–124. https://doi.org/10.1016/j.gsf.2015.03.002

    Article  Google Scholar 

  76. Allafta H, Opp C, Patra S (2021) Identification of groundwater potential zones using remote sensing and GIS techniques: a case study of the shatt Al-Arab Basin. Remote Sens 13:1–28. https://doi.org/10.3390/rs13010112

    Article  Google Scholar 

  77. Etikala B, Golla V, Li P, Renati S (2019) Deciphering groundwater potential zones using MIF technique and GIS: a study from Tirupati area, Chittoor District, Andhra Pradesh, India. HydroResearch 1:1–7. https://doi.org/10.1016/j.hydres.2019.04.001

    Article  Google Scholar 

  78. Kirubakaran M, Johnny JC, Ashokraj C, Arivazhagan S (2016) A geostatistical approach for delineating the potential groundwater recharge zones in the hard rock terrain of Tirunelveli taluk, Tamil Nadu, India. Arab J Geosci 9:. https://doi.org/10.1007/s12517-016-2419-5

  79. Abbasi NA, Xu X, Lucas-Borja ME et al (2019) The use of check dams in watershed management projects: examples from around the world. Sci Total Environ 676:683–691. https://doi.org/10.1016/j.scitotenv.2019.04.249

    Article  CAS  Google Scholar 

  80. Ravi Shankar MN, Mohan G (2005) A GIS based hydrogeomorphic approach for identification of site-specific artificial-recharge techniques in the Deccan Volcanic Province. J Earth Syst Sci 114:505–514. https://doi.org/10.1007/BF02702026

    Article  Google Scholar 

  81. Polyakov VO, Nichols MH, McClaran MP, Nearing MA (2014) Effect of check dams on runoff, sediment yield, and retention on small semiarid watersheds. J Soil Water Conserv 69:414–421. https://doi.org/10.2489/jswc.69.5.414

    Article  Google Scholar 

  82. Piton G, Carladous S, Recking A et al (2017) Why do we build check dams in Alpine streams? An historical perspective from the French experience. Earth Surf Process Landforms 42:91–108. https://doi.org/10.1002/esp.3967

    Article  Google Scholar 

  83. Dashora Y, Dillon P, Maheshwari B et al (2018) A simple method using farmers’ measurements applied to estimate check dam recharge in Rajasthan, India. Sustain Water Resour Manag 4:301–316. https://doi.org/10.1007/s40899-017-0185-5

    Article  Google Scholar 

  84. Onder H, Yilmaz M (2005) A tool of sustainable development and management of groundwater resource. J Jpn Soc Civ Eng 62:35–45. https://doi.org/10.1007/978-3-642-41714-6_210225

    Article  Google Scholar 

Download references

Acknowledgements

We express sincere thanks to the Editor in Chief and volunteer reviewers. I would also like to express my sincere gratitude to my supervisor Dr. S. K. Tiwari for the continuous support of my Ph.D study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this paper.

Author information

Authors and Affiliations

  1. Department of Geology, CAS, Institute of Science, BHU, Varanasi, UP, 221005, India

    Prashant Pandey & Abhishek Kumar Chaurasia

  2. National Resource Centre, Earth Science, UGC-HRDC, BHU, Varanasi, UP, 221005, India

    S. K. Tiwari

  3. Department of Civil Engineering, MNNIT, Allahabad, UP, 211004, India

    H. K. Pandey

  4. Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, 247667, India

    Sachchidanand Singh

Authors
  1. Prashant Pandey
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. S. K. Tiwari
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. H. K. Pandey
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Abhishek Kumar Chaurasia
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Sachchidanand Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Prashant Pandey.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, P., Tiwari, S.K., Pandey, H.K. et al. Identification of Potential Recharge Zones in Drought Prone Area of Bundelkhand Region, India, Using SCS-CN and MIF Technique Under GIS-frame work. Water Conserv Sci Eng 6, 105–125 (2021). https://doi.org/10.1007/s41101-021-00105-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41101-021-00105-0

Keywords