Object Based Modelling for the Irrigation Suitability using Hydrogeochemical Parameters and Land use Dynamics in the Lower Ganga

The north Indian Ganga basin is one of the densely populated basins of the world. Most agricultural and industrial contaminants drained throughout the river length are likely to be accumulated in the lower part of the Ganga basin. We used ten derived irrigation suitability parameters, which are obtained from 495 sampling points locations, besides using long term climate data (GLDAS_NOAH025_M) using “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) model to get the irrigation suitability map. Multi-Criteria Decision Making (MCDM) using TOPSIS helps make the best choices from the available nite number of alternatives based on their ranking. The obtained entropy weight for irrigation suitability parameters such as Electrical Conductivity (Ec), Sodium Adsorption Ratio (SAR), Magnesium Hardness (MH), Sodium Percent (Na%), Total Hardness (TH), Kelly’s ratio (KR), Permeability Index (PI), Chloride concentration (Cl − ), Groundwater Level Fluctuation (GWLF), and lang factor (Df) are found to be 0.08, 0.14, 0.02, 0.02, 0.04, 0.08, 0.01, 0.32, 0.29 and 0.01 respectively. We nd that SAR, Cl − , and GWLF controls the water quality for irrigation in the Lower Ganga basin since these parameters have relatively higher entropy weights (more than 0.10). The results obtained from the computed performance index or the closeness coecient show that the area percent having very good, good, and very poor groundwater quality in the Lower Ganga basin is 34.67%,42.36%, and 22.97%, respectively. The LULC change pattern indicates that the percentage change of water and agricultural land was -11.96 and -0.86%, whereas an increase in the settlement area of 131.42% for the period between 2000 and 2015. of the transition matrix the persistent land-use class having constant area. The transition matrix for the land-use class having a persistent area of agriculture, forest, shrubland, bare area, and found to be km2, km2,


Introduction
Water is an essential natural resource vital for the sustainability of all life forms on the planet earth (Zia et al., 2013). Groundwater plays an important role for developing countries like India, as 70% of rural populations are dependent on agricultural productivity to sustain their livelihood. Due to rapid increase in population, groundwater demand also increases (Rodell et al.,2009;Bhanja et al.,2017). Groundwater shortage becomes one of the major challenges in India due to variability in rainfall and depletion of surface water. India's total population is approximately 1.24 billion, as per the 2011 census contributing to 17.80% of the global population (FAO,2013). The groundwater contributes about 53% of the total irrigation potential with about 50% of irrigated land depends upon the groundwater (CWC,2006; Nag and Das,2017) and helps in cultivating in the four seasons (Pre-monsoon, Monsoon, Post-monsoon, and Post-monsoon) of India. The estimated utilizable surface water, total replenishable groundwater resources, and groundwater resources available for irrigation are found to be 690 BCM, 433 BCM, and 369.6 BCM, respectively (CWC,2007). In India, the agriculture sector utilizes approx. 89% of the total ground-water consumption (Mukherjee et al., 2015).
Highly contaminated groundwater can cause changes in soil penetrability, soil pH, soil structure, and supplement take-up by plants (Ayers and Westcot, 1985;Suarez et al.,2006;Iqbal et al., 2020). Study of the hydrogeochemistry of groundwater is essential because it can indicate about the groundwater quality for irrigation purpose, and proper planning and management for groundwater sustainable agricultural development which would enhance the agricultural productivity in a region. The groundwater quality deteriorates due to the use of chemicals, untreated factory discharge, and agricultural fertilizers used to enhance agricultural production. The point source contamination releases from the urban and industry in the form of wastewater, spills, leaks, runoff, and leachate are the factors responsible for the groundwater contamination. There are three main sources of groundwater contamination related with agricultural activity i.e. fertilizers (nitrate and phosphate), crop protections (pesticides and herbicides), and veterinary medicines (antibiotics) which reach into the groundwater through percolation (Burri et al., 2019). The quality of irrigation water directly affects soil and crops (Rahman et al., 2012).
A number of research paper have been published globally to identify the fate of groundwater contamination using arti cial neural network (Wagh et al., 2016), multivariate statistics (Gharbi et al., 2019), fuzzy-TOPSIS (Kim et al.,2013), entropy method for agricultural land use (Hao et al.,2017), analytic hierarchy process (Okada et al.,2008), TOPSIS based on informative weight and ranking . Peiyue et al., (2011) studied the drivers of hydrogeochemical evolution assessment and mechanisms of groundwater quality using the TOPSIS method for Pengyang county NW China and observed that the groundwater is t for human consumption.
Integration of hydrogeochemical parameter of groundwater and Geographical Information System (GIS) plays an important role in determining the spatial distribution of various elements that affect the water quality and gives adequate information to the decision-makers for proper management and sustainable development of groundwater resources (Lasserre et al., 1999;Ni et

Study Area
The lower Ganga basin is comprised of the north-east part of India having angular extension lies between 21°32'16"N and 27°13'14"N, and 86°13' 43"E, and 89° 5' 43"E ( Fig. 1). The altitudes of the study region vary from -2 m and 3605 m (MSL), having at an average altitude of 80.72 m above mean sea level rise. The agribusiness is the primary nancial endeavor of the Lower Ganga basin. The total area of the Lower Ganga basin consists of three administrative state boundaries, namely West Bengal, Bihar, and Jharkhand, with a total catchment area of ~ 71613.18 km 2 .The estimated population as per the United Nation's World Population Prospects (UN-WPP) for the year 2020 obtained as 87462511 (CIESIN,2018). The soil texture of this region found the dominance of sandy clay loam, along with silty clay, clay responsible for the low in ltration into the subsurface. Four speci c seasons are used for cropping namely early Kharif (black gram/urad), Kharif (rice, red gram), summer (pulses, green gram) and Rabi (Masur,gram).

Data And Methodology
A total 495 samples of hydrogeochemical parameters acquired from the Central Groundwater Board (CGWB), Government of India reports.
These parameters include potential of hydrogen (pH), electrical conductivity (Ec), total dissolved solids (TDS), uoride (F-), chloride (Cl-), bicarbonate (HCO3-), sulfate (SO42-), nitrate (NO3-), total hardness (TH), calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+). The pre-monsoon data for the months of April-May 2016, samples have been collected from the bore well, dug well, and tube well from the 31 districts covering the study area. The methodology and collection of the hydrogeochemical data is consistent with standard conventions as per BIS 2012 standards. The derived irrigation parameter like Ec, SAR, MH, %Na, TH, KR, PI, Cl − , GWLF, and Df are used for the suitability mapping of the irrigation water quality. The complete methodology in this study are described in the following steps as (Fig. 2): (1) delineation of study area and sub-basin, (2) generation of raster data from point data, (3) selection of irrigation suitability criteria, (4) entropy weight calculation using normalization matrix, (5) prioritization of sub-basin using TOPSIS model, (6) land-use change dynamics.
The rst and foremost part of the analysis is the delineation of the Lower Ganga basin using a published map by Water Resource Information System for the Ganga River (India-WRIS,2012). The georecti cation of the map was done into the GIS platform having the geographic coordinate system of WGS 1984 for further analysis. The SRTM DEM having a spatial resolution of 1-arc second (~30m) was used for the drainage analysis for the sub-basin classi cation. Based on the drainage pattern characteristics, the Lower Ganga basin is classi ed into ve sub-basins (Hasan & Rai, 2020). The point data of the hydrogeochemical parameters are converted to raster surface using Inverse Distance Weightage Method (IDW) (Fig. 3). Further, the zonal statistics of each sub-basin for the prioritization of irrigation suitability using the TOPSIS method was done. For the study of irrigation suitability of groundwater, these parameters (SAR, MH, Na%, KR, PI, Df) are computed using the following equations (Wilcox,1955;Todd,1959;Kelly,1963;Doneen,1964;Paiwal,1972;Brušková ,2007) (1-6) given below: . Lang Factor (Df)= P/T (6) Analysis of climate parameters using Lang factor (Df) helps to evaluate climatic regions based on groundwater uses (Brušková ,2007;Alwan et al., 2019). The Df is de ned as the ratio of the long term average annual precipitation (P) in mm to the air temperature (T) in o C. The meteorological drought is the long term phenomenon caused due to the large lack of precipitation and higher evapotranspiration rate (Gregor,2013).
The long term precipitation and temperature data from 1948 to 2019 were obtained from the Global Land Data Assimilation System (GLDAS), having a spatial resolution of 0.25*0.25 degree (Rodell et al., 2004). Both the precipitation and air temperature data are derived from the GLDAS_NOAH land surface model for the calculation of the lang factor. These data acts as the time average forcing eld, which results from the remote sensing satellite data.
The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been used since 1981 (Hwang and Yoon, 1981) in solving several types of multi-parametric decision-making problems. Hwang and Yoon give the details of the TOPSIS approach, helps to choose the best alternatives using the shortest Euclidean distance from the positive ideal solution, whereas the negative ideal solution having the farthest Euclidean distance showing the worst alternatives. For the application of TOPSIS, it must have decreasing or increasing nature, units incommensurable, and feature class must be numeric (Behzadian et al., 2012).
The following steps are involved in determining the TOPSIS model's entropy for the analysis of the irrigation quality of groundwater.
To study the spatial distribution of irrigation water quality for proper planning and management, the TOPSIS model has been applied using entropy weight de ned in equation 3 to 8. The rst step for the TOPSIS is the non-linear ranking analysis, where the formation of the decision matrix "X=(xij)m*n" where m=1, 2, 3, …. i, describes the alternatives showing sub-basin characteristics, whereas n=1,2, 3,.,.,j, represents the quality parameter for the irrigation suitability. To remove the dataset's inconsistency in the system, the normalization of the matrix was done, which helps in forming the standardized decision matrix having a unit vector representing the magnitude and direction. The calculation of the relative weights of the parameter is based on Shannon's entropy information theory, which acts as the fuzziness of the parameter using the principle of probability and removes the biasness in the data (Shannon and Weaver, 1947;Zeleny, 1996;Wang and Lee, 2009). The formation of the ideal positive solution matrix based on the optimal value of the attribute, whereas the negative ideal solution obtained from the worst estimated entropy weight matrix. Important steps are as follows: Step 1: Calculation of normalized decision matrix,r i j , and entropy, e j are de ned as Step 2: Calculation of relative weights (w j ) using Shannon's entropy information theory: Step Step 4: Calculation of the Euclidean distance of separation from each alternative to the positive S + i and negative S − i ideal solution Step 5: Calculation of performance score or relative coe cient of closeness, Pi One critical step in the TOPSIS technique is to compute the Euclidean distance of each parameter from the positive ideal best and negative ideal worst solution using equation 10. Finally, the performance index is derived using equation 11, which shows the relative closeness of each alternative based on the attribute parameter. The coe cient of relative closeness ranges from 0 to 1. The performance score approaches to 1 represent the evaluation of criteria that are ideally close to the ideal positive solution, having better irrigation water quality (Gorgij et al., 2019). The ranking of the sub-basin is obtained based on the performance score as described above in descending order. The annual rate of land-use change (r) over the period from T1 to T2 having the land use class area of A1 and A2 is expressed based on the compound interest law as given below (Puyravaud, 2003):

Results And Discussion
Irrigation Suitability Parameter The evaluation of groundwater quality for irrigation suitability is important for enhancing agricultural productivity in the region. The richness of the soil depends upon the concentration of the dissolved ions in groundwater, which also affects the rate of in ltration, soil quality, and plant fertility, both physically and chemically (Ebong et al., 2017).
Excessive groundwater use in the coastal regions results in intrusion of seawater, leading to the deterioration of groundwater quality, thus increasing the salinity of the freshwater aquifer systems in the coastal areas (Pillay et al, 1994). Based on Ec, the groundwater is classi ed as excellent (<250 µS/cm), good (250-750 µS/cm), permissible (750-2000 µS/cm), doubtful (2000-3000 µS/cm), and unsuitable (>3000 µS/cm) for irrigation purpose (Richards,1954). We nd that the electrical conductivity varies between 66.14 us/cm and 5278.59 µS/cm in the study area with a mean of 812.48 µS/cm (Fig. 3a). The mean value of Ec for Sub-basin 1 (647.25 µS/cm) and 2 (576.92 µS/cm) shows the good quality of groundwater. In contrast, sub-basin 3 (864.23 µS/cm), sub-basin 4 (779.51 µS/cm), and sub-basin 5 (1182.92 µS/cm) shows the permissible limit (Richards,1954). The higher concentration of Ec along the shoreline is due to the intermixing of freshwater and saline water by seawater intrusion. The detailed variation of Ec for each of the sub-basins is given in Fig. 4a. The high value of Ec shows, higher concentration of the salinity level in groundwater. The presence of salt into the groundwater helps in plant growth. Still, the excess concentration will lead to deteriorating the soil fertility and decreases permeability and aeration, which can affect crop germination and agricultural productivity. The major causes of increased salinity could be the weathering and leaching of rocks, seawater intrusion, excess evapotranspiration, discharge of untreated industrial waste, and excessive use of fertilizers in agricultural activities.
Sodium Adsorption Ratio (SAR) SAR indicates the in uence of sodium ion concentration concerning the calcium and magnesium ions present in the groundwater and its interactions with the soil. SAR plays a crucial role in understanding the quality of the water used for agricultural purposes. It determines the impact of a high concentration of sodium ion in groundwater on agricultural productivity (Bhunia et al., 2018). High salinity decreases the osmotic activity and affects soil productivity because an increase in sodium will decrease the concentration of calcium and magnesium ions in the soil (Subramani et al., 2005;Prasanth et al., 2012). A higher concentration of sodium ions causes the alkalinity in the soil (Singh et al., 2011). For irrigation purposes, the groundwater can be categorized into four classes based on SAR i.e. excellent (<10 meq/l), good (10-18 meq/l), doubtful (18-26 meq/l), and unsuitable (>26 meq/l) (Richards,1954;Todd,1980). The spatial variation of SAR in the study region is found to be between 0.08 meq/l and 17.61 meq/l with a mean value of 2.28 meq/l (1σ = ±1.78). The computed mean value of SAR (Fig. 3b) of individual sub-basin found to be below 10 meq/l, which shows excellent condition for irrigation suitability. SAR results show that this type of groundwater for all types of soil taking care of excessive concentration of Na+ ions. The descriptive statistics and guidelines for SAR are given in Table 6.
Magnesium Hardness (MH) or Magnesium Adsorption Ratio (MAR) MH was developed by Paliwal in 1972 using Ca 2+ and Mg 2+ for studying irrigation water quality and helps in achieving groundwater equilibrium mostly (Hem, 1985). It is found that Ca 2+ and Mg 2+ help in maintaining the equilibrium in water (Paliwal, 1972;Giggenbach, 1988). Paliwal (1972), classi ed groundwater into two groups based on MH i.e. suitable (MH <50) and unsuitable (MH >50). We nd that the MH values range between 0.06 meq/l and 90.84 meq/l in the study areas, with a mean value of 45.81 meq/l (1σ = 13.34) (Fig. 3c). Fig. 4a clearly shows that the mean value of all the sub-basin lies within 50meq/l except sub-basin 2 (54.32meq/l). The lower value of MAR (<50meq/l) is favourable for crop yield. Thus, the sub-basin 2 have a high concentration of MH (>50 meq/l), which will affect the agricultural productivity of the crop and increases the alkalinity of the soil in the study area.
Sodium Percent (Na%) or Sodium Hazard or Soluble Sodium Percentage (SSP) Na% is an essential parameter for the irrigation water classi cation. The soil water interaction of sodium ion reduces the permeability of soil (Nag and Das, 2017). The sodium percent is calculated the relative ratio of cations Na + and K + with respect to Ca 2+ , Mg 2+ , Na + , and K + present in the groundwater sample (Wilcox, 1955;Raghunath, 1987). Depending upon the ionic concentration (Na%), the groundwater is classi ed into ve categories: excellent (<20), good , permissible (40-60), doubtful (60-80), and unsuitable (>80) for irrigation suitability (Wilcox,1955).
Higher the value of Na% means water quality is not appropriate for irrigation, which causes soil permeability to be de occulated and impaired. Total Hardness (TH) TH of the samples in the study area lies between 1.19 mg/l and 1134.31 mg/l having a mean value of 223.15 mg/l (1σ = 80.26), greater than 300 mg/l, which shows the quality of groundwater is unsuitable for irrigation (Fig. 3e). As per the classi cation is given by Sawyer and McCartly in 1967, irrigation groundwater classi es into four classes as soft (<75 mg/l), moderate (75-150 mg/l), hard (150-300 mg/l), and very hard (>300 mg/l). The mean value of the spatial distribution of TH in the study area falls between 150-300 mg/l for all the ve sub-basin, which suggests a hard quality of groundwater for the Lower Ganga basin (Fig. 4a).

Kelly's Ratio (KR)
KR is also an important parameter for the analysis of groundwater quality (Kelly, 1963), and is de ned as the concentration of sodium ion over Ca 2+ and Mg 2+ . The higher value of KR reduces soil fertility because it increases the concentration of Na + ion concentration into the soil. Based on KR, the groundwater for irrigation are classi ed into two categories as suitable (KR<1), and unsuitable (KR>1) (Kelly,1963). The overall concentration of KR in the study area varies from 0.05meq/l to 11.84 meq/l, with a mean value of 0.79 meq/l (1σ = 0.66) in the study region ( Fig. 3f). Results show that all the sub-basins are under suitable conditions for irrigation, except sub-basin-5 which has KR value of 1.12 meq/l, which is higher than the recommended value. This indicates unsuitable groundwater for irrigation water quality (Fig. 4b).
Permeability Index (PI) Another important parameter to understand the water quality for irrigation purposes is PI. Doneen (1964) classify irrigation water quality based on PI into three categories: good (PI <80), moderate (80< PI< 100), and poor (PI >100) based on the concentration of Na + , HCO 3 − , Ca 2+ , and Mg 2+ into the soil. Results show that the PI values of the groundwater samples in the study areas range from 26.41meq/l to 170.21 meq/l with a mean value of 67.92 meq/l (1σ = 13.11) (Fig. 3g). The mean value of PI in the study area is below 80 which indicates that the quality of soil is not much affected by long term irrigation in the area. PI values of all sub-basins fall lies below 80meq/l, which indicates the good quality of groundwater for irrigation suitability shown in Fig. 4b.
Chloride Concentration (meq/l) The presence of Chloride in the groundwater acts as a fundamental micronutrient as a cofactor in the oxidation of water in photosynthesis and also acts as an enzyme activator in the crops (Kafka , 2011). The anion concentration of chloride is stable and helps maintain the balance among cation ions of Na + , K + , Ca 2+ , and Mg 2+ in the soil. Mass (1990)  may cause infertility to crops due to prolonged presence of Cl − into the groundwater (Fig. 4b). The mean value of sub-basin 5 obtained to be 7.19 meq/l, which higher than all the other four sub-basin due to the presence of high concentration salinity in this region. The interaction of freshwater and seawater takes by seawater intrusion to have a large number of stream con uences in the lower part of the study area.

Groundwater Level Fluctuation (GWLF)
The ground-based well data of groundwater levels collected by CGWB across India in four seasons (Rabi, Pre-Monsoon, Monsoon, Post-Monsoon, Kharif) from the Water Resource Information System (WRIS) archives of the year 2016 having pre-monsoon and post-monsoon data for the understanding the groundwater level uctuation in the study area. The data were converted into raster format using geostatistical interpolation by applying inverse distance weighted (IDW) method. The water level depends upon the amount of precipitation, topography, land use type, hydrogeology, and hydrological soil group of the region. In the Lower Ganga basin, groundwater is used for irrigation. The premonsoon groundwater level varies from 0.41m to 27.79m, having the mean value to 6.85m. Most of the aquifer in this region is recharge due to the monsoon rainfall, which subsequently decreases the groundwater level. The observed post-monsoon minimum, maximum, and mean groundwater level from the well locations heads in this region found at 0.30m, 22.03m, and 4.96m, respectively. The decline in groundwater level is basically due to the excessive pumping of groundwater for irrigation purposes used for the crop. The groundwater level uctuation varies from -14.12m and 24.18m with a mean value of 1.90m in the Lower Ganga basin (Fig. 3i). The decreasing order of mean groundwater uctuation found in sub-basin 1 (4.19m), sub-basin 3 (2.86m), sub-basin 4 (1.80m), sub-basin 2 (1.76m) and sub-basin 5 (0.84m) (Fig. 4b). The higher uctuation represents the unstable groundwater, whereas less uctuation shows the static groundwater condition, which further stays in the dry period in the study area.

Lang factor (Df)
Analysis of the climate parameters using Lang factor (Df) helps in evaluating climatic regions based on groundwater uses (Brušková ,2007; Alwan et al., 2019). The long term climatic regions are classi ed on the basis of Lang factor are described as: dry region (Df<60), relatively dry (60<Df<70), transient (70<Df<80), wet (80<Df<100), and very wet (Df>100) determined by using hydroclimatic parameters such as precipitation and air temperature (Brušková ,2007;Gregor,2012). The Df value in the Lower Ganga basin varies from 52.40 to 94.08, with a mean value over the temporal scale of 71 years found to be 63.15 (Fig. 3j). The results show that the average region of the Lower Ganga basin is under relatively dry conditions. Sub-basin 3 (53.02) and sub-basin 4 (58.47) have a signi cant dry condition and proper irrigation scheduling required over this region. Sub-basin 2 (67.70) and sub-basin 5 (65.77) have dry conditions, whereas sub-basin 1 having mean values of Df is 72.02 represents the area having transition phase (Fig. 4b). The lower value of Df responsible for a decline in groundwater recharge, whereas higher Df values help in getting a good amount of available groundwater resources for irrigation purposes (Alwan et al., 2019).
For the assessment of irrigation water quality, the following criteria are included such as Ec, SAR, MH, Na%, TH, KR, PI, Cl − , GWLF, and Df are used in this study area. The ve alternatives (sub-basins) and ten parameters used for the normalization matrix using equation 7, help in standardization of the datasets. The entropy of each parameter was computed to determine randomness for irrigation suitability.  KR (0.08), PI (0.01), and Df (0.01) was found to be less than 0.10 which shows these parameters are of secondary importance in assessing irrigation suitability of groundwater quality.
Determination of a positive ideal solution helps in maximizing the bene ts and minimizing the cost (Table 3). In contrast, a negative ideal solution maximizes cost and minimizes the bene t in selecting alternatives (Table 4). Higher the value of the performance index or coe cient of closeness means closer the separation distance from the ideal positive best solution, and farther from the negative ideal worst solution using Euclidean distances (Mohapatra & Patnaik, 2013), as shown in Tables 8 and 9. Therefore, it is important to keep in mind for hydrophysical quality analysis, the fate of the concentration should be taken into consideration. The classi cation of TOPSIS based on the coe cient of performance index (Pi) as very good (0.75<Pi<1.00), good (0.50<Pi<0.75), moderate (0.25<Pi<0.50), poor (0.125<Pi<0.50), and very poor (0<Pi<0.125) (Bagherzadeh and Gholizadeh, 2016).
The Euclidean distance of the separation helps in the selection of the best and worst criteria alternatives depending upon the degree of relative closeness. Therefore, the selection of the alternatives depends upon the highest relative closeness from the ideal positive solution.
As per the TOPSIS model applied in the Lower Ganga basin for irrigation suitability mapping, the obtained performance index or the coe cient of the closeness of sub-basin 1 (0.91) and sub-basin 2 (0.97) showing higher values means very good quality of groundwater available for the irrigation purpose. On the other hand, compared to sub-basin 3 (0.60) and sub-basin 4 (0.70), it has good groundwater quality for irrigation. The results also show that the sub-basin 5 (0.07) showing the performance index minimum of all sub-basin, indicating very poor quality of groundwater for irrigation purposes ( Table 5).
The irrigation water quality of the upper northern region has a very good groundwater quality for irrigation due to the accumulation of soil nutrients coming from the river stream. Therefore, these regions having agricultural productivity very high. Land-use change pattern over the period from 2000,2005,2010, and 2015 was studied to determine the of land-use conversion from one class to another. Land use/land cover in the study region is shown in Fig. 6 and also in Table 6 (Fig. 7).
The land use transformation matrix helps in evaluating the trajectory of land-use patterns over the spatiotemporal scale. As the determination of land-use change dynamics suggests in determining the environmental changes and sustainable development in the study area. The land-use transition matrix helps understand the shift in land use class "from-to" another class over a period of time using remote sensing data (Jensen,2005 Interestingly, there is a substantial increase in the settlement area from 570.51 km 2 to 1320. 3  As the land use, land cover change explains the impact of anthropogenic activities with respect to the natural system. Currently, there are not many places on earth where the expansion of arable land is feasible, and it almost always comes with a high environmental cost at the same time.

Conclusion
In this paper, we applied TOPSIS model approach to understand the suitability of groundwater for irrigation purpose in the Lower Ganga, and also focused on the understanding the land-use dynamics in this region. Analysis of ten water quality parameters from 495 sampling points, reveals that three parameters, namely SAR (0.14), Cl − (0.32), and GWLF ( It is a matter of concern that there is a decrease in the agricultural land area, which is obtained as 923.01 m 2 /person in the year 2000, with available agriculture land of 744.86 m 2 /person in the year 2015. Therefore, results are highly important for the policymakers and local authorities who are to take preventive measure against the groundwater quality deterioration and food security. Results are extremely useful to achieve the United Nations Sustainable Development Goal (UN-SDG) to accomplish the agenda in 2030.

Declarations
Con ict of interest: The authors declare, they have no con ict of interest. Availability of data and material: The data used in this manuscript are available on their respective website and free available.
Code Availability: No code applicable.
Ethics Approval: We con rm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.
Consent to participate and publication: The author's willingly to participate in the publication of the above manuscript.        Figure 1 Physiographic description of study area with sample point location