Introduction

Waterlogging (WL), which occurs when the soil is saturated with water and further water input can no longer be absorbed (Hillel 2003) or when the surface water cannot be adequately removed by poor drainage systems (Qureshi et al. 2008), takes place globally and impacts almost 45 million ha over large irrigated regions such as Bangladesh, India, and Pakistan (Hoffman and Durnford 1999). In the Natore district, located in the northwestern part of Bangladesh, agricultural activity and livelihoods are severely hampered due to WL for several months throughout the year. The agricultural sector (crops, animal farming, forests, and fishing) contributes 14.74% to the gross domestic product (GDP) of Bangladesh and employs ~41% of the labour force, according to the Quarterly Labor Force Survey 2015–2016 (Bangladesh Economic Review 2017). In recent years, this sector has been severely hampered by WL, caused by a combination of human-induced and natural factors, and has become a serious environmental concern within the southwestern region of Bangladesh, according to the Bangladesh Water Development Board (2013). While approximately 1,280 km2 of waterlogged agricultural land had already existed in southwestern Bangladesh (Awal 2014), recently, a vast area in northwestern Bangladesh has also been experiencing severe WL where the total waterlogged area accounts for approximately 11.32 km2. A vast amount of cultivated land became submerged with almost 20,000 houses, surveyed by the Barind Multipurpose Development Authority in 2017–2018 were affected by WL. The impact of WL can be described in three dimensions—the first impact is the immediate loss of life, property, and access to essential services; the second impact involves damage to infrastructure and assets in the affected area; the third impact is the degradation of the quality of life in these areas.

There are many ‘beels’ (or swamps) in the Natore District. The word ‘beel’ refers to a large water body or marshy inland depression with abundant flora and fauna. Among all the beels in Bangladesh, the Chalan Beel is the largest (Hossain et al. 2009). The study area includes two types of WL, natural and man-made. The uncontrolled digging of ponds is a key cause of WL (Yeasmin et al. 2017); additionally, the lack of sufficient surface and subsurface drainage, lack of adequate measures for maintaining the drainage system, and the overirrigation and growth of water-intensive crops (Choubey 1997) contribute to WL problems.

For mapping waterlogged areas, remote sensing (RS) and geographical information system (GIS) techniques, when used jointly, create a new method that is more cost- and time-effective than other techniques (Singh 2016). Through this joint technique, it is possible to investigate the dynamic phenomenon in the study area within a given time period. GIS and RS have crucial advantages and high levels of effectiveness in identifying and monitoring waterlogged areas (Choubey 1997; Dwivedi et al. 2001; Lohani et al. 1999; Singh 2016). High-soil-moisture and waterlogged surface areas can be identified as deep dark grey to light black colours through visual interpretation of satellite images (Brahmabhatt et al. 2000; Mandal and Sharma 2001).

The goals of this study were to identify surface WL in the Natore District through the application of well-planned RS and GIS techniques and to hydrogeologically characterise the actual WL scenario in the study area.

Geographic and hydrogeological settings

Geographic setting

The study area is the Natore District, which has an area of 1,896.05 km2, is located between 24° 21′ 0.00″ N and 89° 04′ 59.88″ E, and consists of six upazilas or subdistricts—Natore Sadar, Bagatipara, Baraigram, Lalpur, Gurudaspur, and Singra (Fig. 1; Rahman et al. 2020). Most parts of the Natore District are plain land.

Fig. 1
figure 1

Location map of the study area showing administrative divisions with rivers

Full size image

The main rivers in the study area are Padma, Baral, Mara Baral, Baranai, Gurh, and Nagar. In addition to the rivers, there are beels, including the Halti, Patul, and Chalan beels. Different waterways, including 47 rivers, flow into the Chalan Beel (Srinivasan et al. 2006). Because the flow contains silt built up in the beel, the size of the beel is reduced (Alam and Hossain 2012). Most surface runoff water in the study area flows towards the Jamuna River located outside the study area towards the east, whereas only a small portion of surface water enters the Padma River, located in the lower part of Lalpur mainly and originating in India as the Ganges River, which meets the Jamuna River.

Natore District is typically composed of swampy and plain areas (Road and Highway Department, Road centerline GPS survey, unpublished report, 2002–2004). The people in this area are mostly involved in cultivation, fishing, livestock, and fruit businesses. Natore fish pond cultures are mostly maintained as semi-intensive systems, and undeveloped land is converted into ponds in an unregulated manner (Yeasmin et al. 2017). WL widely occurs in the Natore District and creates a substantial hazard. Additionally, in the agricultural sectors in the Natore District, farmers have constructed numerous ponds and switched their occupations to aquaculture when necessary, which makes the impact of WL complex and multifaceted.

According to Rashid (1991), Bangladesh is divided into seven climatic zones; Natore district is mostly found in the western zone where annual rainfall is generally below 150 cm and humidity is <50%. The Bangladesh climate is divided into three distinct seasons of the year: the winter season with much lower humidity (November–February), hot summer season or premonsoon (March–May), and rainy season or monsoon (June–October). The western portion of the country is the driest and hottest in summer among all the subzones. June is the hottest month (mean = 35 °C), and January is the coldest month (mean = 17 °C). In the summer season, the average temperature is higher than 30 °C. Based on weather reports during 2005–2015, the annual rainfall in the study area is 1,221 mm (Ahmed 1997). According to the Köppen-Geiger climatic classification, Bangladesh is mainly in the global tropical zone, but the study area falls into the humid subtropical climate zone criteria (Köppen et al. 2011). Due to the amount of rainfall, the Natore district is often affected by floods, which are generally in the monsoon season (June–October)—for instance, almost 176 km2 of area in the Natore district was affected by floods in 2017 (Reza et al. 2018).

Hydrogeological setting

The study area has geologic units in the Bogra Shelf tectonic framework (Reimann 1993). According to the stratigraphic succession of the Bogra Shelf zone, alluvium composed of sand, silt, and clay reaches a depth of 78 m from the land surface (Table 1). Based on the findings of the United Nations Development Programme (UNDP) and Food and Agriculture Organization (FAO) in 1988, the basic soil types in the study area are (1) calcareous dark grey and brown floodplain soil and (2) noncalcareous dark grey and grey floodplain soil. According to the Institute of Water Modelling (IWM, 2006), the study area consists of three physiographic units: (1) the Barind tract, (2) the Lower Atrai Basin, and (3) the Ganges intensive system. According to the World Reference Base for Soil Resources (FAO-UNESCO 1988), the five soil classes throughout Bangladesh are (1) Entisols, (2) Histosols, (3) Inceptisols, (4) Oxisols, and (5) Ultisols. Among these five soils, the study area is mainly composed of Entisols and Inceptisols (FAO 1988).

Table 1 Stratigraphic succession of the Bogra Shelf zone (Zaher and Rahman 1980)
Full size table

The relationship between soil type and subsurface water flow is crucial because porosity and permeability change with soil type, and this governs groundwater flow direction and alterations of flow rate (Domenico and Schwartz 1997). Based on drill-cutting samples, the lithology of the study area was characterised to more easily appraise the aquifers and their depths. The lithological variation in the study area indicates the depositional environment of the area at a specific time. These data were collected from the Barind Multipurpose Development Authority (BMDA) and Bangladesh Water Development Board (BWBD). The stratigraphy of the study area represents the construction of repeated layers of clay, fine sand, medium sand, coarse sand, and gravel with a very thick clay layer. The clay layer reduces the capability of infiltrating water to reach the saturated zone. The infiltration rate of the study area is low due to the thick clay layer (Mojid et al. 2021).

Mojid et al. (2021) reported that the groundwater level (GWL) responds to rainfall with a moderate lag period. As the water level rises, the risk of WL increases. GWL fluctuation and groundwater flow direction were visualised using groundwater data of the study area that were collected by the BWDB. While the GWL is an important factor in the generation of WL, ground surface elevation and drainage patterns that govern surface flow via gravity are also important. Low surface elevation, soil characteristics, and unmanaged pond digging can worsen WL. GWL data from 1990–2017 were obtained from well stations at various locations in the study area. GWL fluctuation data clearly demonstrate a seasonal groundwater condition during the pre- and postmonsoon seasons.

A major part of the study area comprises recent floodplain alluvium of the Holocene age, a small portion of which was part of the Barind tract from the Pleistocene age. The lithology in the study area mainly consisted of sand, silt, and clay (Brammer 1996). Sea level fluctuations in the late Pleistocene and Holocene greatly impacted sedimentation in the Ganges floodplain (Goodbred and Kuehl 2000). Consequently, the area had a gentle slope towards the south, with evident major drainage channels. The lithology of the flood basin area demonstrated alternating layers of fine sand and clay. Recent floodplain alluvium with surficial sediments of unoxidised clay and silt formed shallow aquifers with good potential for groundwater production. In contrast, a deep aquifer with low seasonal fluctuation indicated a moderate potential for groundwater resources in the area. In detail, the lithology of the study area consists of clay in the shallower part, and variable layers of fine, medium, and coarse sands with gravel in the deeper part (Fig. 2), with an approximately 55-m well depth for the whole study area, according to previous lithology studies (BMDA).

Fig. 2
figure 2

Lithology of the study area

Full size image

Accordingly, the hydrogeological data identify a confined aquifer system. The presence of layers of fine particles suggests that the porosity and permeability of the study area are insufficient for transporting water (Domenico and Schwartz 1997). Lag time could not be determined in Natore Sadar upazila and Bagatipara because borehole lithology data are unavailable in Singra and Bagatipara, and rainfall data do not exist for Natore Sadar upazila and Bagatipara. Hence, the hydrogeological system of Singra and Bagatipara upazila was inferred through the relation between other upazilas.

Because the western portion of the study area (Natore Sadar, Bagatipara) has higher elevation, with surface water flowing towards the eastern part, any drainage malfunction could easily cause a WL problem in this zone. Moreover, Mojid et al. (2021) mentioned that the western zone had a comparatively long lag period; therefore, it could be assumed that the lithology of Singra was comparatively more porous and permeable. The whole WL scenario can be demonstrated through a conceptual model of the study area, shown in Fig 3, which will help to explain the phenomena more clearly and concisely. Thus, the hydrogeological conceptual model represents the confined aquifer system (stratigraphy of clay, fine sand, medium sand, and coarse sand layers with depth from the surface) and a representation of the associated groundwater features.

Fig. 3
figure 3

Hydrogeological conceptual model of the study area

Full size image

Materials and methods

Data acquisition

This study acquired data using statistical, GIS, and RS techniques and hydrogeological surveys and tests. Climatological data were obtained from the Bangladesh Meteorological Department for the period 1990–2017. LANDSAT 8 satellite images were obtained from the US Geological Survey (USGS) to delineate the waterlogged study area. These images consisted of seven bands with 30-m resolution, where bands 3 and 5 represented the green and near-infrared bands (NIR), respectively. LANDSAT 8 has an operational land imager and a thermal infrared sensor from the Satellite Imaging Corporation. The LANDSAT 8 satellite image for 2019 was processed to create a waterlogged map using ArcGIS 10.7.1. Different imagery data were processed using techniques such as modelling, thresholding, and classification to identify WL-prone areas (Sharma et al. 1996). The most difficult parts to detect during this process were the water bodies. The land and water boundary was difficult to determine because of errors in the analysis of surface water; therefore, the analysis was complex and required a substantial interpretative ability. Slight misinterpretations could lead to erroneous results; thus, the multiple-band approach was more efficient (Goel et al. 2002). A topographic map from the Survey of Bangladesh at a scale of 1:50,000 was used to georeference the satellite images and prepare base maps.

Streams generally receive all rainfall-driven flows, such as groundwater flow, runoff, and flow from a portion of the Earth’s surface. Watersheds or drainage basins, including streams, are divided by topographic barriers (Pidwirny 2006). The delineation of the watershed is also an important factor in recognising drainage basin boundaries and drainage patterns. Watersheds were delineated by computing the flow direction using a digital elevation map (DEM) and ArcGIS (Environmental Systems Research Institute). A DEM with 30-m resolution was downloaded from the USGS website; all the shapefiles for river lines, districts, and upazilas were acquired from the Local Government Engineering Department (LGED); additionally, a contour map was prepared from the DEM, which showed the elevation variation in the area.

Mapping of the waterlogged areas

The normal difference water index (NDWI) is a renowned process in ArcGIS software to precisely indicate surface water and has been used to detect waterlogged areas in the study area. The NDWI was expressed by MacFeeters (1996) as:

$$\textrm{NDWI}=\frac{\left(\textrm{Band}\ 3-\kern0.5em \textrm{Band}\ 5\right)}{\left(\textrm{Band}\ 3+\kern0.5em \textrm{Band}\ 5\right)}$$
(1)

where band 3 refers to green and band 5 refers to near-infrared. The NDWI values range from +1 to –1. Higher NDWI values approaching +1 correspond to a high water content or a water surface, whereas low NDWI values approaching –1 indicate drought conditions. The Landsat images comprised a combination of seven bands. Among these, only bands 3 and 5 were selected because they offer the following advantages (MacFeeters 1996):

  • The green wavelength to detect water features is clearer because it maximises the reflectance of the water features.

  • The low reflectance of near-infrared radiation by water features was minimised.

  • There was high reflectance of NIR by terrestrial vegetation and soil features.

Cross-correlation function

A cross-correlation determines the degree of similarity between two sets of data. The cross-correlation function (CCF) was assessed between the peaks of the two datasets. The procedure is not complex and can be shown by the following expression:

$${r}_{xy}=\sum \nolimits_{i=0}^{N-1}{x}_i{y}_i$$
(2)

where N is the number of data points in a series of data, xi is the ith data point of the first series of data, yi is the ith data point of the second series of data, and rxy is the cross-correlation. If one performs a point-by-point multiplication of two datasets, the sum of the products is quantified by their relationship; the number of data points in which the signal is shifted is called the lag (Derrick and Thomas 2004).

Pumping test

Papadopulos and Cooper Jr (1967) derived a solution for a finite-diameter pumping well with wellbore storage in a confined aquifer. The equation assumes that: the areal extent of the aquifer is infinite; the characteristics of the aquifer in the area by pumping are homogeneous, isotropic, and of uniform thickness; the discharge rate of the pump in the aquifer is constant; the well penetrates the aquifer completely by pumping groundwater from the whole thickness of the aquifer through horizontal flow; the aquifer is confined and not leaky; the water flow towards the well is unsteady; and water is released instantaneously from aquifer storage with a decline in the hydraulic head. The drawdown according to the Papadopulos–Cooper equation (Kumar et al. 2013) is given as follows:

$$s=\frac{Q}{4\pi T}\ F\left({U}_{\textrm{w}},\alpha, \varnothing \right)$$
(3)

where s is the drawdown, T is the transmissivity, Q is the pumping rate, and F(Uw, α, Ø) is the Papadopulos–Cooper function. Transmissivity is a property that determines the amount of water moving horizontally through the total thickness of the aquifer. For this function F(Uw, α, Ø), numerical values were determined as follows:

$${U}_{\textrm{w}}=\frac{r^2S}{4 Tt}$$
(4)
$$\alpha =\frac{S{r}_{\textrm{w}}^2}{r_{\textrm{c}}^2}$$
(5)
$$\varnothing =\frac{r}{r_{\textrm{w}}}$$
(6)

where S is the storativity, t is the elapsed time starting from pumping, the subscript ‘w’ stands for ‘at the pumped well’, and rc is the radius of the unscreened part of the well. The drawdown, sw, inside the pumped well with a large diameter (when r = rw) is given by

$${s}_{\textrm{w}}=\frac{Q}{4\pi T}\ W$$
(7)

where W refers to F (Uw, α, 1).

Results

For Bangladesh, premonsoon refers to March–May, and postmonsoon refers to November–February. Thus, April 2019 satellite images were used to observe the premonsoon period, and November 2019 satellite images were used to demonstrate the postmonsoon scenario in the study area. Based on the data in Fig. 4, surface WL situations in the study area during the pre- and postmonsoon seasons in 2019 can be compared. In Fig. 4, blue represents higher NDWI values with high water content or water surface, while red represents low NDWI values and indicates drought conditions. By comparing the WL scenarios of the study area, the WL pattern can be divided into seasonal and perennial surface inundation. The land used for Rabi cultivation is prone to WL but it is available in summer for cultivation because the water has drained, which is called surface inundation (seasonal). Figure 4 displays the surface inundation (seasonal) WL problem in most situations in the study area, with the black and green rectangles clearly illustrating the types of WL zones in the study area. In the premonsoon season (Fig. 4a), most of the land was not waterlogged, which is indicated by the black rectangles, but in the postmonsoon (Fig. 4b), those same locations showed WL, which refers to seasonal WL. The green rectangles show the WL zone in the premonsoon season (Fig. 4a), while Fig. 4b, which represents the scenario of the postmonsoon season, also shows almost the same amount of green rectangles of perennial WL, representing land that remains waterlogged throughout the year. In addition, a significant number of areas have perennial WL situations. This complete identification process was carried out with ArcGIS software.

Fig. 4
figure 4

Map of waterlogged areas in the a premonsoon and b postmonsoon seasons

Full size image

Delineation of drainage patterns compared with topographic contours

A drainage map shows the watershed of an area, the direction of flow, and the elevation from which water flows into a stream, river, or lake. Hydraulically, surface water is commonly connected to groundwater (Winter et al. 1998). Water flows from the upper surface elevation to the lower surface elevation because of gravitational energy; hence, combining the contour map and drainage map helped to easily locate any errors in the map.

As shown in Fig. 5, the elevation of the study area is not great; the highest elevation is 20 m above sea level (asl) and the lowest is 10 m asl. Some large-surface-area beels are located in the study area. During the study period, a large portion of the eastern and adjacent part of the Chalan Beel was at a lower elevation, and hence became waterlogged during the monsoon season, a condition that persisted until the premonsoon season (Fig. 5). The areas of elevation within 10–14 m asl were mostly waterlogged in the eastern area during the study period because of topographic conditions. The waterlogged areas were found to be mostly natural, but some were human-made unregulated dug ponds designed to block stream paths to support aquaculture (Yeasmin et al. 2017). The pervious areas are occupied by buildings and infrastructure, haphazardly arranged; drainage paths were also responsible for creating the present scenario in the study area. Most high-risk WL areas presented flood and WL hazards, which could be severely destructive (Pandey et al. 2010).

Fig. 5
figure 5

Drainage map and topographic contour lines of the study area

Full size image

Subsurface-water scenario

Groundwater level is a critical factor in WL. As the GWL rises, the risk of WL increases, so an excellent way to identify subsurface water scenarios is to collect GWL data (Ghumman et al. 2011). GWL (presented as depth to water) data during 2011–2012 were collected from different well stations at various locations in the study area. GWL fluctuations clearly demonstrated the groundwater condition during the study period. The GWL and rainfall data of the wells from the six upazilas of Natore District were collected and compared (Fig. 6).

Fig. 6
figure 6

Location of the wells used in the cross-correlation

Full size image

Figure 7 shows the interactions between GWLs and rainfall amount in the study area. Fig. 7a shows the scenarios representing long lag time, and Fig 7b shows short lag time. The Fig 7a graphs represent the impact of the highest rainfall on the GWL, which is a long period of time called the ‘long lag time’; in contrast, Fig 7b graphs show a short period of time for the GWL to rise after the highest rainfall, corresponding to the ‘short lag time’. Water level fluctuations during 2011–2012 with respect to rainfall demonstrated that rainfall-driven water reached the water table within a certain period or lag time (Mojid et al. 2021). Based on the proportional relation of GWL and rainfall, GWL came close to the surface when the amount of rainfall was higher; however, some places in the study area exhibited a delay or lag time in water movement relative to the GWL. It was inferred that there was a short lag time in the Singra upazila through cross-correlation (Fig. 7).

Fig. 7
figure 7

Comparison of groundwater level (GWL) and rainfall with a long and b short lag times

Full size image

Cross-correlation between rainfall and groundwater level

The lag time could be determined more specifically and quantitatively using cross-correlation and can be represented by a diagram. This cross-correlation analysis was conducted in different locations (Baraigram, Lalpur, Gurudaspur, and Singra) in the study area from 2011 to 2012. Figure 8a shows the lag times of the two monitoring wells in Baraigram and the three monitoring wells in Lalpur. Cross-correlation analysis revealed that most of the wells exhibited a lag time of ~5 months, which indicated the time difference between the highest rainfall and the water level nearest to the surface in the Baraigram and Lalpur upazilas. Figure 8b shows a lag time of 1–3 months for three monitoring wells in the Singra region and two monitoring wells in the Gurudaspur region. Considering groundwater recharge scenarios, a long lag time is caused by slow groundwater recharge and other anthropogenic factors and contributes to significant GWL fluctuations. In contrast, a short lag time, for example at the Singra upazila, is created by rapid groundwater recharge and small GWL fluctuation and has a good proportional relationship with rainfall; therefore, it could be assumed that the lithology of Singra was more porous and permeable. Mojid et al. (2021) also mentioned that the western zone includes Natore District in Bangladesh. A high CCF indicated that the relationship between rainfall and groundwater was quite high and proportional. The lag time of other upazilas could not be determined because of insufficient data.

Fig. 8
figure 8

Cross-correlation between groundwater level and rainfall with a long and b short lag times

Full size image

Hydrogeological characteristics

Pumping tests were conducted at 17 pumping wells for 5–100 min to determine the hydraulic parameters of the alluvial layers (Table 2). A constant pumping rate of 0.029–0.085 m3/s was used. Because of the confined aquifer system, with the upper finer particle layer and the lower coarser particle layer, the Papadopulos–Cooper method was selected to interpret the pumping test, considering hydrogeological conditions and the corrected drawdown of pumping wells (Cheong et al. 2008). The drawdown values, well radius, and pumping discharge were used as input parameters to obtain the values of transmissivity and storativity, which further helped to acquire hydraulic conductivity values (Table 3). Pumping test analysis was conducted using AQTESOLV pro software. The theoretical curves of the pumping test are shown in Fig. 9.

Table 2 Drawdowns (m) at the 17 pumping wells with discharge in the study area
Full size table
Table 3 Hydraulic parameters determined by the pumping test
Full size table
Fig. 9
figure 9

Drawdown vs. elapsed time with theoretic curves based on the Papadopulos–Cooper method

Full size image

Hydraulic conductivity (K) is the capability of a fluid (e.g., water) to move through the pore spaces or fractures in media and depends on factors such as the intrinsic permeability of the material, density and viscosity of the fluid, and the degree of saturation. K values were calculated by dividing the transmissivity by the saturated thickness (Tables 4 and 5). In the long-lag-time areas, K values (49.37–76.24 m/day) were quite low compared to the K values (74.74–117.79 m/day) of the short-lag-time areas (Tables 4 and 5).

Table 4 Estimated hydraulic conductivity values for long-lag-time scenarios
Full size table
Table 5 Estimated hydraulic conductivity values for short-lag-time scenarios
Full size table

Potential of implementing the managed aquifer recharge technique

Local newspapers and electronic media have reported that farmers in parts of the Natore District in Bangladesh drill boreholes in waterlogged parts of various beels to reduce WL. This technology is locally called ‘house boring’. One house boring can infiltrate at a rate of 1,200 L/h, as reported in a newspaper (Daily Sunshine 2022). Later, the local government stopped this activity because of contamination concerns; moreover, its initiation showed that groundwater harvesting could solve the WL problem. Further study is needed regarding the possibility of harvesting waterlog water and discharging it into groundwater as a safe implementation of managed aquifer recharge (MAR).

Conclusions

The WL problem is critical in agricultural areas in southeastern Asia such as Bangladesh, India, and Pakistan. This study first characterised WL in the Natore District (study area), Bangladesh, with a combination of well-planned RS, GIS techniques and hydrogeological interpretation, and then evaluated the WL problem and its impacts in the Natore District. Field surveys and satellite image interpretation confirmed the existence of WL issues in the study area. In most areas, the water level reached a maximum of 2 m below the surface in the postmonsoon season and a maximum of nearly 10 m below the surface in the premonsoon season, revealing a significant fluctuation in the water level between the pre- and postmonsoon seasons.

The pre- and postmonsoon scenarios of the waterlogged area indicated (1) seasonal and (2) perennial WL types. Groundwater recharge scenarios were classified as long and short lag times. Rainfall, which is the only water source, played the most important role in GWL fluctuations. The long lag time caused slow groundwater recharge and contributed to significant GWL fluctuation, with lower K values of 49.37–76.24 m/day. In contrast, the short lag time hastened groundwater recharge, creating a good proportional relationship with rainfall and reducing GWL fluctuation, with higher K values of 74.74–117.79 m/day. The increase in the human-made pond area (an increase of approximately 11.50 km2 from 2001 to 2016) greatly disturbed the natural drainage pattern of the study area. People create artificial ponds by blocking drainage channels for aquaculture. As a result, upstream water cannot flow and spread to various localities.

The WL phenomenon in the Natore District of Bangladesh is a serious issue. A depressed areas can be easily waterlogged, and human-induced problems created by digging ponds and constructing barriers interrupt natural channels and make them more vulnerable. WL results from excessive water, whereas some places suffer from water scarcity; therefore, proper water management plays a vital role in improving agriculture through efficient irrigation practices and can support better uses of water resources with more potential throughout the country. Hence, the appropriate synchronization of water resources may comprehensively solve this problem. Groundwater harvesting could solve the WL problem with the safe implementation of MAR techniques.