中国地质科学院水文地质环境地质研究所主办
Groundwater Science and Engineering Limited出版
高级检索

,  ,  ,  , 

文章导航 > Journal of Groundwater Science and Engineering > 2022 > 10(1): 1-9

上一篇

下一篇

Gautam Vinay Kumar, Kothari Mahesh, Singh P.K., Bhakar S.R., Yadav K.K.. 2022. Analysis of groundwater level trend in Jakham River Basin of Southern Rajasthan. Journal of Groundwater Science and Engineering, 10(1): 1-9. doi: 10.19637/j.cnki.2305-7068.2022.01.001
Citation: Gautam Vinay Kumar, Kothari Mahesh, Singh P.K., Bhakar S.R., Yadav K.K.. 2022. Analysis of groundwater level trend in Jakham River Basin of Southern Rajasthan. Journal of Groundwater Science and Engineering, 10(1): 1-9. doi: 10.19637/j.cnki.2305-7068.2022.01.001
shu

Analysis of groundwater level trend in Jakham River Basin of Southern Rajasthan

  • 1.

    Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur-313001(Raj.), India

More Information

    摘要

  • Groundwater accounts for about half of the water use for irrigation in India. The fluctuation pattern of the groundwater level is examined by observing rainfall replenishment and monitoring wells. The southern part of Rajasthan has experienced abrupt changes in rainfall and has been highly dependent on groundwater over decades. This study presents the impact of over-dependence on groundwater usage for irrigation and other purposes, spatially and temporally. Hence, the objective of this study is to examine the groundwater level trend by using statistical analysis and geospatial technique. Rainfall factor was also studied in groundwater level fluctuation during 2009-2019. To analyze the influence of each well during recharge or withdrawal of groundwater, thiessien polygonswere generated from them. In the Jakham River basin, 75 wells have been identified for water level trend study using the Mann-Kendall statistical test. The statistics of trend analysis show that 15% wells are experiencing water level decline in pre-monsoon, while very low percentage of wells have such trend during post-monsoon season. The average rate of water level decline is 0.245 m/a in pre-monsoon and 0.05 m/a in post-monsoon. The aquifer recharge potential is also decreasing by year.it is expected that such type of studies will help the policy makers to adopt advanced management practices to ensure sustainable groundwater resource management.

    • HTML全文
  • 加载中

  • Figure 1. 

    下载: 全尺寸图片 幻灯片

    Figure 2. 

    下载: 全尺寸图片 幻灯片

    Figure 4. 

    下载: 全尺寸图片 幻灯片

    Figure 3. 

    下载: 全尺寸图片 幻灯片

    Figure 5. 

    下载: 全尺寸图片 幻灯片

    Figure 6. 

    下载: 全尺寸图片 幻灯片

    Table 1.  Results of Mann-Kendal test statistics for pre- and post monsoon season (2009-2020)

    Well No.LatitudeLongitudePre-monsoonPost-monsoon
    Kendal tau p-value Slope Trend Kendal tau p-value Slope Trend
    1 74.716 24.057 0.564 0.020 0.314 Increasing 0.019 0.150 0.080 No
    2 74.791 24.212 −0.073 0.815 −0.037 No 0.787 0.001 0.603 Increasing
    3 74.645 24.210 0.477 0.050 0.394 Increasing −0.087 0.050 −0.320 Decreasing
    4 74.557 24.383 −0.241 0.347 −0.350 No 0.537 0.028 0.657 Increasing
    5 74.721 24.391 0.225 0.333 0.493 No −0.507 0.028 −0.457 Decreasing
    6 74.622 24.444 −0.261 0.350 −0.227 No 0.294 0.241 0.530 No
    7 74.642 24.398 −0.500 0.400 −0.483 No 0.290 0.180 0.350 No
    8 74.738 24.302 −0.450 0.035 −0.253 Decreasing 0.241 0.347 0.100 No
    9 74.652 24.325 0.611 0.012 0.625 Increasing 0.153 0.034 0.105 Increasing
    10 74.541 24.293 0.400 0.022 0.455 Increasing 0.000 1.000 0.000 No
    11 74.596 24.239 −0.077 0.251 −0.194 No 0.750 0.093 0.180 No
    12 74.713 24.240 −0.093 0.073 −0.754 No 0.436 0.060 0.331 No
    13 74.733 24.131 −0.047 0.213 −0.014 No −0.020 0.036 −0.354 Decreasing
    14 74.644 24.141 0.019 1.000 0.000 No 0.110 0.696 0.062 No
    15 74.603 24.089 0.600 0.013 0.763 Increasing 0.019 1.000 0.000 No
    16 74.635 24.052 0.477 0.051 0.394 Increasing 0.220 0.390 0.250 No
    17 74.687 24.027 −0.485 0.200 −0.353 No −0.093 0.754 −0.036 No
    18 74.651 24.002 −0.019 1.000 0.000 No 0.661 0.006 0.913 Increasing
    19 74.617 24.056 0.167 0.531 0.390 No 0.198 0.390 0.210 No
    20 74.765 24.061 −0.404 0.101 −0.384 No −0.019 1.000 0.000 No
    21 74.711 24.039 −0.073 0.815 −0.037 No 0.304 0.235 0.489 No
    22 74.691 24.065 −0.294 0.241 −0.285 No 0.367 0.138 0.300 No
    23 74.699 24.072 −0.073 0.815 −0.037 No −0.092 0.734 −0.139 No
    24 74.760 24.079 −0.352 0.159 −2.262 No −0.200 0.436 −0.400 No
    25 74.744 24.093 −0.073 0.315 −0.045 No −0.074 0.035 −0.283 Decreasing
    26 74.750 24.103 −0.204 0.433 −0.793 No −0.611 0.012 −0.621 Decreasing
    27 74.696 24.095 −0.073 0.815 −0.037 No −0.035 0.018 −0.210 Decreasing
    28 74.758 24.157 −0.278 0.273 −0.464 No 0.374 0.135 0.383 No
    29 74.726 24.154 −0.035 0.815 −0.037 No 0.035 0.018 0.210 Increasing
    30 74.667 24.101 −0.278 0.273 −0.870 No 0.661 0.006 0.599 Increasing
    31 74.613 24.089 −0.073 0.435 −0.327 No 0.350 0.319 0.452 No
    32 74.668 24.154 0.167 0.531 0.390 No 0.382 0.119 0.800 No
    33 74.649 24.123 −0.073 0.815 −0.037 No 0.210 0.390 0.207 No
    34 74.659 24.207 0.575 0.019 0.900 Increasing 0.491 0.043 0.200 Decreasing
    35 74.643 24.201 −0.015 0.210 −0.150 No 0.374 0.135 0.383 No
    36 74.661 24.250 −0.426 0.085 −0.565 No 0.440 0.072 0.550 No
    37 74.631 24.248 −0.294 0.241 −0.484 No 0.350 0.105 0.303 No
    38 74.687 24.262 −0.352 0.159 −0.490 No 0.220 0.390 0.257 No
    39 74.758 24.200 0.167 0.531 0.390 No 0.641 0.009 0.605 Increasing
    40 74.722 24.176 −0.167 0.231 −0.050 No 0.055 0.876 0.023 No
    41 74.708 24.197 0.025 1.000 0.000 No 0.481 0.033 0.195 Increasing
    42 74.705 24.213 −0.315 0.210 −0.150 No −0.093 0.754 −0.036 No
    43 74.699 24.229 −0.400 0.085 −0.505 No −0.091 0.040 −0.201 Decreasing
    44 74.721 24.261 −0.278 0.273 −0.293 No −0.514 0.035 −0.637 Decreasing
    45 74.780 24.180 0.165 0.525 0.300 No −0.553 0.015 −0.557 Decreasing
    46 74.763 24.281 −0.224 0.387 −0.217 No 0.073 0.815 0.033 No
    47 74.762 24.316 −0.294 0.241 −0.484 No 0.050 0.105 0.065 No
    48 74.748 24.331 0.056 0.876 0.014 No 0.000 1.000 0.000 No
    49 74.769 24.348 0.256 0.376 0.210 No 0.000 1.000 0.000 No
    50 74.679 24.296 −0.093 0.050 −0.080 Decreasing −0.037 0.938 0.002 No
    51 74.622 24.290 0.017 0.376 0.250 No 0.032 0.088 0.335 No
    52 74.611 24.277 0.056 0.879 0.015 No 0.404 0.101 0.450 No
    53 74.664 24.325 0.056 0.376 0.250 No 0.031 0.098 0.305 No
    54 74.676 24.336 0.167 0.528 0.250 No 0.110 0.696 100.000 No
    55 74.593 24.297 0.056 0.376 0.250 No 0.035 0.058 0.210 Decreasing
    56 74.579 24.325 0.130 0.038 0.080 Increasing 0.404 0.101 0.671 No
    57 74.556 24.341 0.035 0.835 0.000 No 0.031 0.098 0.305 No
    58 74.539 24.319 0.426 0.085 0.242 No 0.019 1.000 0.000 No
    59 74.534 24.338 0.062 0.231 0.090 No 0.110 0.696 0.250 No
    60 74.575 24.384 0.241 0.347 0.150 No 0.147 0.585 0.300 No
    61 74.517 24.375 0.019 1.000 0.000 No 0.205 0.325 0.185 No
    62 74.574 24.396 0.241 0.347 0.210 No 0.000 1.000 0.000 No
    63 74.690 24.367 −0.094 0.341 −0.084 No 0.350 0.250 0.152 No
    64 74.644 24.387 −0.367 0.138 −0.387 No 0.455 0.062 0.883 No
    65 74.645 24.387 0.056 0.015 0.230 Increasing −0.205 0.019 −0.250 Decreasing
    66 74.687 24.403 −0.330 0.184 −0.655 No 0.404 0.101 0.391 No
    67 74.656 24.427 0.056 0.256 0.214 No 0.031 0.098 0.305 No
    68 74.608 24.410 0.019 1.000 0.000 No 0.440 0.072 0.320 No
    69 74.762 24.387 0.030 0.046 0.314 Increasing 0.600 0.013 0.550 Increasing
    70 74.734 24.360 −0.120 0.689 −0.035 No 0.095 0.765 0.045 No
    71 74.716 24.385 0.056 0.876 0.014 No −0.091 0.040 −0.225 Decreasing
    72 74.702 24.386 −0.241 0.347 −0.252 No 0.521 0.030 0.609 Increasing
    73 74.758 24.393 0.150 0.252 0.050 No −0.553 0.020 −0.557 Decreasing
    74 74.758 24.391 −0.294 0.241 −0.484 No 0.076 0.820 0.044 No
    75 74.750 24.403 −0.110 0.696 −0.036 No −0.050 0.105 −0.065 No
    *(Here, α= 0.05 and confidence level=95%)
    下载: 导出CSV
    • 参考文献(24)
  • [1]

    Akther H, Ahmed MS, Rasheed KBS. 2009. Spatial and temporal analysis of groundwater level fluctuation in Dhaka City, Bangladesh. Asian Journal of Earth Sciences, 3(4): 222-230. doi: 10.3923/ajes.2010.222.230

    [2]

    CGWB. 2012. Groundwater year book-India. Central Ground Water Board Ministry of Water Resources Government of India, Faridabad: 1-63.

    [3]

    CGWB. 2013. Ground water resource estimation methodology. Report of the Ground Water Resource Estimation Committee, Central Ground Water Board (CGWB), Ministry of Water Resources, Government of India, New Delhi, India: 1-75.

    [4]

    Charizopoulos N, Zagana E, Psilovikos A. 2018. Assessment of natural and anthropogenic impacts in groundwater, utilizing multivariate statistical analysis and inverse distance weighted interpolation modeling: The case of a Scopia basin (Central Greece). Environmental Earth Sciences, 77: 1-18. doi: 10.1007/s12665-017-7169-5

    [5]

    Duong Du Bui, Akira Kawamura, Thanh Ngoc Tong, Hideo Amaguchi, Naoko Nakagawa. 2012. Spatio-temporal analysis of recent groundwater-level trends in the Red River Delta, Vietnam. Hydrogeology Journal, 20: 1635-1650. doi: 10.1007/s10040-012-0889-4

    [6]

    Gautam VK, Awasthi MK. 2020. Evaluation of water resources demand and supply for the districts of central Narmada valley zone. International Journal of Current Microbiology and Applied Sciences, 9(2): 3043-3050. doi: 10.20546/ijcmas.2020.902.350

    [7]

    Gautam VK, Awasthi MK, Trivedi A. 2020. Optimum allocation of water and land resource for maximizing farm income of Jabalpur District, Madhya Pradesh. International Journal of Environment and Climate Change, 10(12): 224-233. doi: 10.9734/IJECC/2020/v10i1230299

    [8]

    Gautam VK, Kothari M, Singh PK, et al. 2021. Determination of geomorphological characteristics of Jakham River Basin using GIS technique. Indian Journal of Ecology, 48(6): 1627-1634.

    [9]

    Gautam VK, Kothari, M, Singh PK, et al. 2022. Decadal groundwater level changes in Pratapgarh District of Southern Rajasthan, India. Ecology Environment & Conservation, 28(1): 283-289.

    [10]

    Halder S, Roy MB, Roy PK. 2020. Analysis of groundwater level trend and groundwater drought using Standard Groundwater Level Index: A case study of an eastern river basin of West Bengal, India. SN applied science: 507-531.

    [11]

    Helsel DR, Hirsch RM. 2002. Statistical methods in water resources. In: Techniques of Water Resources Investigations, Book 4, Chap. 3. US Geological Survey, Reston, VA.

    [12]

    Kendall, MG. 1975. Rank correlation methods. London: Griffin.

    [13]

    Ketata M, Gueddari M, Bouhlila, R. 2012. Use of geographical information system and water quality index to assess groundwater quality in El khairat deep aquifer (enfidha, central east tunisia). Arabian Journal Geosciences, 5: 1379-1390. doi: 10.1007/s12517-011-0292-9

    [14]

    Kumar P, SK Chandniha, Lohani AK, et al. 2018. Trend analysis of groundwater level using non-parametric tests in alluvial aquifers of Uttar Pradesh, India. Current world environment, 13(1): 44-54. doi: 10.12944/CWE.13.1.05

    [15]

    Mann HB. 1945. Nonparametric tests against trend. Econometrica, 13(3): 245-259. doi: 10.2307/1907187

    [16]

    Maréchal JC, Dewandel B, Ahmed S, et al. 2006. Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge. Journal of Hydrology, 329(1-2): 281-293. doi: 10.1016/j.jhydrol.2006.02.022

    [17]

    Nema S, Awasthi MK, Nema RK. 2016. Trend analysis of annual and seasonal rainfall in Tawa command area. International Journal of Environment, Agriculture and Biotechnology, (4): 952-957.

    [18]

    Panda DK, Mishra A, Kumar A. 2012. Trend quantification in groundwater levels of Gujarat in western India. Hydrological Sciences Journal, 57(7): 1325-1336. doi: 10.1080/02626667.2012.705845

    [19]

    Pathak AA, Dodamani BM. 2018. Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha River Basin, India. Natural Resources Research.

    [20]

    Patle GT, Singh, DK, Sarangi, A, et al. 2015. Time series analysis of groundwater levels and projection of future trend. Journal Geological Society of India, 85: 232-242. doi: 10.1007/s12594-015-0209-4

    [21]

    Pavelic P, Patankar U, Acharya S, et al. 2012. Role of groundwater in buffering irrigation production against climate variability at the basin scale in South-West India. Agriculture Water Manage, 103: 78-87. doi: 10.1016/j.agwat.2011.10.019

    [22]

    Selvam S, Venkatramanan S, Chung SY, et al. 2016. Identification of groundwater contamination sources in Dindugal district of Tamil Nadu, India using GIS and multivariate statistical analyses. Arabian Journal of Geosciences, 9(5): 407. doi: 10.1007/s12517-016-2417-7

    [23]

    Sen PK. 1968. Estimates of regression coefficient based on Kendall’s tau. Journal of the American Statistical Assocication, 63: 1379-1389. doi: 10.1080/01621459.1968.10480934

    [24]

    Thakur GS, Thomas T. 2011. Analysis of groundwater levels for detection of trend in Sagar district, Madhya Pradesh. Journal of the Geological Society of India, 77: 303-308. doi: 10.1007/s12594-011-0038-z

    • 相关文章
  • 施引文献
  • 资源附件(0)
  • 加载中

图(6)

表(1)

计量
  • 文章访问数:  1702
  • PDF下载数:  13
  • 施引文献:  0
出版历程
收稿日期:  2021-05-07
录用日期:  2021-12-12
刊出日期:  2022-03-15

目录

    版权所有：中国地质调查局发展研究中心 copyright @2020
    返回