Selecting potential locations for groundwater recharge by means of remote sensing and GIS and weighting based on Boolean logic and analytic hierarchy process

Ardakani, Amir Hossien Hatefi; Shojaei, Saeed; Shahvaran, Ali Reza; Kalantari, Zahra; Cerdà, Artemi; Tiefenbacher, John P.

doi:10.1007/s12665-021-10071-4

Cited by 8 publications

(5 citation statements)

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“…This study will also benefit future groundwater aquifer investigations that will be performed using geophysical methods to delineate their exact locations. An excellent way of finding potential recharge areas is remote sensing and GIS [19,20,87], using different techniques, which is a perfect way to do so [9,36,[88][89][90][91][92]. None of the previous experiments on identifying artificial groundwater recharge potential zones have been conducted in this watershed.…”

Section: Discussionmentioning

confidence: 99%

“…A remote-sensing analysis, with its geographical, spectral, and positional data accessibility spanning huge and complicated areas quickly, is instrumental in evaluating, controlling, and managing groundwater recharge [16][17][18][19][20]. Groundwater potential zoning has been the subject of many studies and various techniques in India and worldwide [18][19][20], and these techniques have been published in multiple academic journals [4][5][6]17,[21][22][23][24][25][26][27][28]. Only a few studies have been conducted in the vast basins across the Indian subcontinent.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

Gururani,

Kumar,

Abed

et al. 2023

Water

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The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km2 area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km2 area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

Gururani,

Kumar,

Abed

et al. 2023

Water

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“…Weighted recharge comes from the map of potential groundwater recharge (PGR map [30,[33][34][35][36][37][38][39]. The PGR map is made by weighting thematic layers, where the recharge behavior is interpreted according to the capacity of each layer.…”

Section: Methodsmentioning

confidence: 99%

“…This work proposes a method for weighted aquifer recharge using the potential groundwater recharge (PGR) map [33,34]. The weighted recharge considers recharge variations spatially depending on the characteristics of the area under study [35], processing and manipulating thematic layers in a geographic information system [30,[36][37][38][39]. The PGR map incorporates spatially distributed information: (i) drainage, (ii) precipitation, (iii) land use, (iv) geological faults, (v) soil type, (vi) slope, and (vii) hydrogeology [30,[33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Comparison between MODFLOW Groundwater Modeling with Traditional and Distributed Recharge

Navarro-Farfán,

García-Romero,

Martínez-Cinco

et al. 2024

Hydrology

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Groundwater models serve the function of predicting and analyzing aquifer behavior. They require input information, such as hydrogeological parameters like hydraulic conductivity and storage coefficient, which are used to calibrate the model, and elementary actions that include recharge and extracted volumes. There are cases in which it is insufficient to know the homogeneous recharge entering through the surface basin, referred to as traditional recharge, since, in many instances, the distribution is altered by changes in land use. For this reason, based on the geomorphological characteristics of the basin, weighting is proposed for sites with greater recharge capacity. The present work shows a solution to the recharge distribution using the potential groundwater recharge (PGR) map, which is formed by weighting spatially distributed information: (i) drainage, (ii) precipitation, (iii) land use, (iv) geological faults, (v) soil type, (vi) slope, and (vii) hydrogeology. A comparison is made between groundwater modeling using traditional recharge and PGR recharge. It is noted that the modeling perform similarly for both recharges, and the errors do not exceed 5% absolute error, which validates the model’s reliability. This manuscript demonstrates how to model and calibrate groundwater in aquifers with scarce information and variable recharge, making it reproducible.

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“…Remote sensing as one of the mapping methods that has no direct and physical contact with the surface or subsurface of roads, if used properly, can quickly survey and monitor large areas with a variety of coarse or fine surface textures (Wang, 2011). Remote sensing is also a good way to study the damage to infrastructures and buildings after natural disasters (Ardakani et al, 2022;Olsen, Raugust, et al, 2013) because it is able to quickly collect information from a wide area of data. However, the use of remote sensing in the study and analysis of road structure or post-crisis access is a new research approach (Schnebele et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

Mansourmoghaddam

Malamiri

Aliabad

et al. 2022

Air, Soil and Water Research

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Prioritization of pathways to perform asphalt pavement operations has always been one of the most important concerns for municipalities, for which, currently there is no specific planning and pattern. In the present study, using (Unmanned Aerial Vehicle) UAV images, a land cover map of the case study was prepared. For this purpose, the accuracy of various object-based classification methods including the Bayes method, the Support Vector Machine (SVM), the K nearest neighbor (KNN), the Decision tree (DT), and the Random tree (RT) was investigated. Findings of the study showed that by increasing heterogeneity in the composition of the studied phenomenon in the image, different classification algorithms offer results different from each other. The obtained results of the accuracy evaluation of classification methods indicate that the SVM method with 80% kappa coefficient and 89% overall accuracy had the best performance compared to other methods. As a result, built-up land covers, bare land, vegetation cover, and paved roads were separated using this method. Then, the exact boundary of pathways was prepared using Google Earth images, and then, using the land-use map prepared from the case study, the roads were divided into two categories: paved and unpaved. To determine the prioritization of unpaved roads for applying asphalt, the proportion of built-up lands (BUL) to bare (non-built-up) lands (BL) was used in each path. Based on the obtained results, 1% of the roads in the case study was placed on a very high level of asphalt, and then 9%, 3%, 49%, 38%, were placed on a high priority to low priority, respectively.

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Selecting potential locations for groundwater recharge by means of remote sensing and GIS and weighting based on Boolean logic and analytic hierarchy process

Cited by 8 publications

References 50 publications

Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

Comparison between MODFLOW Groundwater Modeling with Traditional and Distributed Recharge

The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

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