DOI: 10.31038/GEMS.2023544

Abstract

Single point adjustment is the special case in triangulation net adjustment where only the coordinates of one point are unknown. In practice, the coordinates of the points established for densification are calculated in the form of mixed resections. Coordinates of the point to be estimated in mixed resection; It is calculated by taking the average of the coordinates found by the method of projecting more than Intersection and Resection. This solution cannot be said to be suitable for error theory. The solution of the problem in accordance with the error theory is to make adjustment calculations as in all problems with excessive measurements. In single point adjustment, the solution becomes easier if there is no edge measure and the directions are equally weighted. In practice, a single point adjustment calculation is made on the clichés developed for this purpose. In this study, we will show how the single point adjustment can be done according to the least squares (LSQ) theory on a cliché.

Keywords

Intersection, Resection, Single point adjustment, Least square

Introduction

There are explanations for single point adjustment in various sources in the literature [1,2]. But the calculation methods in those sources were complex, difficult to understand, and included long calculation steps but their accuracy was also low. The method we propose here is a shorter, more understandable and more accurate method.

Intersection and Resection

Two techniques commonly employed in extending horizontal control surveys and in setting out are intersection and resection [3-8]. Intersection is a method of locating a point without actually occupying it. In Figure 1, points A and B are stations in a control network already surveyed and, in order to coordinate unknown point C which lies at the intersection of the lines from fixed A and B points, angles α and β are observed. Resection is a method of locating a point by taking angle observations from it to at least three known stations in a network . In Figure 2, in order to coordinate unknown point W can be fixed by observing angles α and β subtended at resection point W by control stations fixed D, C and L points.

Figure 1: Intersection

Figure 2: Resection

Solution Of Single-Point Adjustment On Clichés

Let’s now see this single point adjustment step by step on a numerical application. In order to make single point adjustment on the clichés, first chart is arranged for all direction measurements. The procedures to be done to fill the chart and clichés are summarized below in Figure 3.

Figure 3: Numerical Example

In the triangulation network in the figure, fixed points 27, 32, 34, 39 are given with (Y, X) coordinates. The directions indicated by arrows were observed in the wax and given in the chart. Calculate the adjusted coordinates of the 133 point.

In example;

Number of measurements n=16 (number of observed directions)

Unknown number u=7 (2 coordinates + 5 orientation unknowns)

Redundancy measures f=n-u=9

– Firstly, the station point (SP), the point of target (TP), the observed directions (r) columns and the bearing angle (t_o) columns calculated from the coordinates are filled.

– Approximate coordinates (Y_o, X_o) of the point to be estimated are required to calculate the bearing angles about the two point. For this purpose, the point to be estimated from the fixed points (Na: 32 and Nb: 39) two bearing angles.

ϕ_a=Z_o.32 + _r32-133=55.12320 + 42.40162=97.52482

ϕ_b=Z_o.39 + r_39-133=344.96144 + 36.51504=381.47648

The approximate coordinates of the point (133) to be estimated using the cliché are calculated in the cliché and written in the calculation section of the approximate coordinates in the cliché.

– Two different ways are followed to calculate the approximate value (Z_o) of the oriented direction unknowns belonging to the station points. For the calculation of Z_o at fixed points, only the arithmetic mean of the (t_o-r) differences calculated from the directions from the fixed point to the fixed point is taken. At the point to be estimated (adjusted), Z_o is made by taking the arithmetic mean of the (t_o-r) differences to be calculated from all directions. Calculated Z_o values are written in the column they belong to in the chart.

– The oriented direction column is calculated and filled as (α=r + Z_o) for the directions to the points to be estimated from the fixed points and the direction observations made at the point to be estimated.

– The constant term (-∫) column is filled by computing (-∫=to – α) in units of [cc] for directed directions only.

Point adjustment on the cliché:

 – Approximate coordinate calculation section of the cliché was filled in while preparing the chart.

– The point numbers and Y, X coordinates of all fixed points are written in the edge calculation section of the cliché. The coordinate differences and lengths of ∆Y_o=Y_i – Y_o and ∆X_o=X_i – X_o from fixed points to the point to be estimated are calculated and written in the relevant columns.

– In the section of establishment of correction equations; First, the fixed point numbers are written in order up to the double-lined line, and for the values in the p (weight) column, they are filled as p=(u-1)/u . Here u: number of target point at fixed point. The weights here are not the weights of the directions, but the p coefficients come to reduce the orientation unknown at fixed points. The direction coefficients a and b are calculated in the form of

a=63662 ∆Yo/So²                 b=-63662 ∆Xo/So²

in [cc/dm] unit by using the values in the edge calculation section of the cliché and written on the lines they belong to. After the double-lined line, the next section is filled. Again, the fixed point numbers and p weight values ​​are written as 1 this time. The pre-filled and shifted values are written under the b columns in the form of

a’=a – [a]/n             b’=b-[b]/n

– ∫ constant terms are taken from the chart and transferred to the cliché.

– In the section of establishment and solution of normal equations; Normal equation coefficients (A, B, C, D, E and F) are calculated from the values in p, a, a’, b, b’, -∫ columns in the upper part of the cliché and written in their places. Unknowns dx, dy are found and adjusted coordinates (Y, X) are calculated by adding them to approximate coordinates. In the upper part of the cliché, using dx, dy values, a.dx, b.dy, a’.dx, b’.dy columns are calculated. Go to the corrections section to the right of the vertical double-lined part of the cliché. In this section, corrections for the direction observations made from fixed points up to the double-lined are calculated from the equation v=a dx + b dy -∫, the corrections for the direction observations made from the point to be estimated point to the next section from the double-lined are from the equation v=a ‘dx + b’ dy -∫. are calculated and written in their places. The weighted sum of squares [pvv] of all corrections is calculated and replaced. As a control, the value of [pvv] is also calculated from the equation [pvv]=F + D dx + E dy. The average error of the unit measure m_o, the average error of the coordinates m_x, m_y, the point position error m_p are calculated.

– Adjusted values section; The columns of point numbers viewed from the estimated point, observed direction and corrections are filled in (corrections were previously calculated). Corrections are added to the observed directions and adjusted directions are calculated so that the first gaze direction is zero. Using the adjusted coordinates of the point to be estimated, the adjusted bearing, adjusted lengths are calculated and written in the columns they belong to. In order to control the point adjustment made with cliché, the following control operations are carried out in the last calculated adjusted values section (Tables 1 and 2).

Table 1: Chart

Table 2: Single Point Adjustments Cliché

Control Steps

1. The two calculated values of [pvv] must be equal within the limit of rounding errors.
2. The sum of corrections must be zero. This total should not be greater than 0.1^cc due to rounding errors.
3. The angles between adjusted directions and the angles between adjusted bearing should be equal. The difference between the angle values calculated in two different ways should not be greater than 0.1^cc.

Warning: The value of estimated point’s coordinates (133) always is exactly correct. But the value of [pvv] is different from its actual value. Because corrections are not calculated for the direction observations made from fixed points in the cliché. Therefore, the calculated values of m_o, m_y, m_x, m_p are not exactly correct. The actual values that should be are: [pvv]=489.48, m_o=7.38 cc, m_x=0.096 dm, m_y=0.107 dm and m_p=0.144 dm.

Conclusion

When this problem is solved in the form of mixed resection without performing point adjustment, there are differences smaller than centimetre between the coordinates of the point 133 found by mixed resection and the coordinates found by adjustment. Although this difference is easily negligible for most practical needs, it is necessary to adjustment it in studies that require high precision.

In this study, we will show how the single point adjustment can be done according to the least squares (LSQ) theory on a cliché. Moreover, with today’s advanced calculation tools, all geodetic problems can be adjusted very quickly in accordance with the error theory by means of appropriate software.

References

1. Allan A, Hollwey J, Maynes J (1968) Practical Field Surveying and Computations, American Elsevier Publishing Co, Inc, New York.
2. Anderson J, Mikhail E (1998) Surveying: Theory and Practice, 7th edition, WCB/McGraw-Hill, New York.
3. Bannister A, Raymond S, Baker R (1984) Surveying, 6th edition, Longman Scientific & Technical, Essex, England.
4. Bektas S (1992) “3D triangulation network” Turkey III. Map of technicians and Services Conference “13-16 April 1992 in Ankara.
5. Bektaş S (1997) Space Resection, Map and Cadastre Engineering Journal, issue: 83, p.72-79, October 1997, Ankara.
6. Bektaş S (2016) Practical Geodesy II. Edition, OMU publications, Samsun.
7. Öztürk,E – Serbetçi M (1989) “Adjustment Calculation Volume II”, KTÜ publications, publication number: 144, page: 310, Trabzon.
8. Uren J, Price WF (1985) Intersection and Resection. In: Surveying for Engineers. English Language Book Society student editions. Palgrave, London.

DOI: 10.31038/GEMS.2023542

Abstract

The formation of a new fluid inclusion type in beryl is shown. Primarily the inclusion was composed of water-rich stishovite, which was transported from the lower mantle into the upper crust crystallization level via a supercritical fluid or melt. After trapping and phase change, the primary homogeneous inclusion split due to drastic density changes into two parts by the high beryl solubility under such conditions.

Keywords

New inclusion type, Beryl, Supercritical fluid, Stishovite, Cristobalite, Raman spectroscopy

Method

All microscopic and Raman spectrometric studies are performed with a petrographic polarization microscope with a rotating stage coupled with the RamMics R532 Raman spectrometer working in the spectral range of 0-4000 cm^-1 using a 50 mW single mode 532nm laser. Details are given in Thomas et al. 2022 [1,2]. Note here that the low-frequency portion of the Raman spectrum is, according to Tuschel (2019) [3], the most efficacious for characterizing, differentiating, and screening polymorphs (here SiC) by Raman spectroscopy. Furthermore, the polarization/orientation (P/O) micro-Raman spectroscopy is complementary to micro-X-ray diffraction. According to Tuschel (2012) [4], this method should be used when X-ray analysis is not practical or possible (micro-needles and mineral globules depth in the sample volume).

Sample

The beryl-quartz sample material comes from the Sauberg mine near Ehrenfriedersdorf in the Erzgebirge region/Germany. Details are given in Thomas (2023) [5]. Noteworthy is that the synchronously grown moissanite whiskers in beryl characterize the beryl-quartz paragenesis.

Results

During the study of moissanite [SiC] whiskers simultaneously grown in a small beryl-quartz vein related to the Variscan tin deposit, Ehrenfriedersdorf/Erzgebirge, Germany, the author Thomas [5] found a new fluid inclusion type in beryl formed by a necking-of process [6]. That means both inclusions shown in Figure 1 formed from a primarily homogeneous phase, probably stishovite, trapped during the crystal growth of beryl. The density of both minerals, stishovite and cristobalite, is 4.35 vs. 2.20 g/cm³, respectively [7]. It is well known that stishovite formed at the lower crust would never be preserved over geological time at low pressure and temperature [8]. From earlier studies, Thomas and Klemm, 1997 [9] and Thomas et al. 2022 [1,2] follow a trapping temperature of 720°C at a pressure of ≤2 kbar. During cooling, the initially homogeneous inclusion separated itself into two parts by strong density contrast as a result of cooling. The upper inclusion consists of pure methane with a small cristobalite (Crs) crystal, and the lower inclusion is composed of about 65% cristobalite, 17.4% CH₄, and 17.6% H₂O. The solubility of beryl is at or near supercritical conditions extremely high [1,2].

Figure 1: Two supercritical inclusions in beryl (Brl). CH4 – methane, Fl – aqueous liquids phase. Cristobalite (Crs) in both inclusions gives in the Raman spectrum strong lines at 112.6, 229.8. 418.6, and 1072.1 cm^-1, respectively. The numbers 1 to 7 in the lower inclusions show the points at which cristobalite was determined with Raman spectrometry.

The vapor phase consists exclusively of methane (CH₄), and cristobalite (Crs) forms the solid phase of both inclusions. Cristobalite in the methane inclusion is stable under laser light and is metastable in the water-bearing fluid inclusion (creating quartz). With the used Raman spectrometer, in no case hydrogen using the pure rotational lines S₀ (354.8 cm^-1), S₁ (587.4 cm^-1), S₂ (815.0 cm^-1), and S₃ (1024.9 cm^-1) could be determined – see Petrov et al. 2018 [10]. Also, CO₂ could not be determined. The fluid phase is almost pure water. Alkali carbonates are missing. The cristobalite was primarily water-rich stishovite or coesite, giving the supercritical fluid a minimum pressure of 7 GPa. However, if we accept the water content in the lower inclusion primary solved in the primary stishovite, according to Lin et al. (2022) [11], a pressure of about 30 GPa or more is possible (see also Thomas et al. 2022) [1,2]. Figure 2 shows a Raman spectrum of the cristobalite in the CH₄-rich inclusion of Figure 1.

Figure 2: Raman spectrum of cristobalite, nanodiamond, and carbon in the CH4-rich inclusion of Figure 1. The Raman bands at 109.8, 228, 414.3,792, and 1070.5 cm^-1 correspond to cristobalite. The 1328.5 and 1590 cm^-1 bands correspond to nanodiamonds and carbon, respectively.

The strong and stable nanodiamond band at 1328 cm^-1 and the strong carbon band at 1590 cm^-1 are conspicuous and demonstrate that at the origin of the supercritical fluid, its pressure is significantly higher than the pressure at the place of beryl crystallization. Figure 3 shows the Raman spectra of cristobalite (points 1-7) in the lower inclusion part. Here both bands corresponding to nanodiamond and carbon are entirely missing.

Figure 3: Raman spectra of the cristobalite at different measuring points 1-7 in Figure 1

Figure 4 shows the Raman band of pure methane in both inclusion parts. According to Vitkin et al. (2020) [12], methane is ¹²CH₄ rich. Pruteanu et al. (2017) [13] have shown that methane and water are entirely miscible at high pressure and temperature. This condensed state at high pressure and temperature and at least the phase separation at lower PT-data dramatically influence the properties of the supercritical fluid or melt.

Figure 4: Both inclusion parts’ Raman methane spectrum (CH₄)

Conclusion

The here-described supercritical fluid inclusion in beryl is a new type resulting from the interaction of a beryllium-bearing supercritical melt or fluid with the already present Variscan tin mineralization. The solubility of Be as bromelite [BeO] in a supercritical melt or fluid at temperatures higher than 700°C is extreme, as Thomas and Davidson (2010) [14] and Thomas et al. (2022) [1,2] demonstrated. The extreme physicochemical conditions (at a highly activated state) during the beryl crystallization from a supercritical melt or solution favored the simultaneous crystallization of moissanite [SiC] with beryl [5]. The formation of the new inclusion type shows different scenarios for the origin of beryl and simultaneously grown moissanite: ultrahigh-pressure and high-temperature conditions (1000°C and ~30 GPa) generated in the lower mantle region and low-pressure and low-temperature conditions in the upper crust (720°C, ≤2 kbar). Furthermore, the short study shows that supercritical fluids or melts are highly complex in composition and change permanently on the path between the lower mantle and upper crust. Therefore many reactions are far away from any equilibrium. Further studies are necessary to illuminate these complex processes in more detail. One point is essential: which mechanism is responsible for the extreme enrichment of, for example, beryllium? Are near beryl’s crystallization at supercritical conditions a quantum physical entanglement of Be atoms?

References

1. Thomas R, Davidson P, Rericha A, Recknagel U (2022) Discovery of stishovite in the prismatine-bearing granulite from Waldheim, Germany: A possible role of supercritical fluids of ultrahigh-pressure origin. Geosciences 12: 1-13.
2. Thomas R, Davidson P, Rericha A, Voznyak DK (2022) Water-rich melt inclusions as “frozen” samples of the supercritical state in granites and pegmatites reveal extreme element enrichment resulting under non-equilibrium conditions. Min. J. (Ukraine), 44, 3-15.
3. Tuschel D (2019) Raman spectroscopy and polymorphism. Spectroscopy 34: 10-21.
4. Tuschel D (2012) Raman crystallography, in theorie and in practice. Spectroscopy 27: 2-6.
5. Thomas R (2023) Growth of SiC whiskers in Beryl by a natural supercritical VLS process. Aspects in Mining & Mineral Science 11: 1292-1297.
6. Roedder E (1984) Fluid inclusions. Reviews in mineralogy. In: Ribbe PH (ed): Mineralogical Society of America 12: 644.
7. Frondel C (1962) The System of Mineralogy. John Wiley and Sons, INC, New York and London, Vol. III Silicate Minerals, 7^th Edition, 334 p.
8. Hemley RJ, Prewitt CT, Kingma KJ (1994) High-pressure behavior of silica. Reviews in mineralogy 29: 41-81.
9. Thomas R, Klemm W (1997) Microthermometric study of silicate melt inclusion in Variscan granites from SE Germany: Volatile contents and entrapment conditions. Journal of Petrology 38: 1753-1765.
10. Petrov DV, Matrosov II, Sedinkin DO, Zaripov AR (2018) Raman spectra of nitrogen, carbon dioxide, and hydrogen in a methane environment. Optics and Spectroscopy 124: 8-12.
11. Lin Y, Hu Q, Meng Y, Walter M, Mao HK (2020) Evidence for the stability of ultrahydros stishovite in Earth’s lower mantle. PNAS 117: 184-189. [crossref]
12. Vitkin V, Polishchuk A, Chubchenko I, Popov E, Grigorenko K, at al. (2020) Raman laser spectrometer: Application to ¹²C/¹³C isotope identification in CH₄ and CO₂ greenhouse gases. Applied Sciences 10: 1-12.
13. Pruteanu CG, Ackland GJ, Poon WCK, Loveday JS (2017) When immiscible become miscible -Methane in water at high pressures. Science Advances 3: 1-5.
14. Thomas R, Davidson P (2010) Hambergite-rich melt inclusions in morganite crystals from the Muiane pegmatite, Mozambique and some remarks on the paragenesis of hambergite. Miner Petrol 100: 227-239.

DOI: 10.31038/GEMS.2023541

Abstract

The demand and investigation of groundwater are occasionally increased by the ever-increasing population and abrupt climatic changes. Groundwater is currently the most important source of fresh water, and many researchers are attempting to cover all aspects of this resource in order to achieve sustainable development. This scientific and academic research and studies are attempting to present a multi-range of techniques and methods focusing on groundwater pollution, potentials, assessment, and prediction. To delineate groundwater potential (GWP) zones in the Lower Omo-Gibe Watershed, Omo-Gibe Basin, Ethiopia, an integrated strategy of remote sensing (RS), geographic information systems (GIS), and multi-criteria decision analysis (MCDA) using analytical hierarchical process (AHP) was used. For this purpose, seven GWP influencing thematic layers comprising lineament density, slope, soil, drainage density, landuse landcover, geology, elevation and rainfall map were used. Scale values for the classes and thematic layers within them were determined using Satty’s AHP and based on expert and literary judgment. The thematic layers have been integrated via their rates using weighted overlay spatial function tool of ArcGIS to provide GWP map. The distribution and extents of different potential groundwater zone are 158.05 km² (21.59%), 35.92 km² (4.91%), 214.77 km² (29.34%), 220.59 km² (30.13%), and 102.72 km² (14.03%) for very good, good, moderate and fair zone respectively. The study’s conclusions can be used to develop a groundwater action plan that will effectively protect the study area’s considerable groundwater resources. With regard to the long-term availability of groundwater, this research will be very beneficial to water management.

Keywords

Lower Omo-Gibe watershed, Omo-Gibe Basin, AHP, GIS, GWP, MCDA

Introduction

Groundwater is a valuable natural resource, and finding the best locations for groundwater recharge, monitoring wells, water supplies, and groundwater quality depends on exploration and wise management of this resource [1]. Given the constantly rising demand for freshwater, groundwater is a crucial freshwater resource for maintaining daily life in urban and suburban areas [2]. The importance of studying groundwater potential zones is based on the quantity, quality, and contamination of groundwater. The main causes of the consistent rise in freshwater demand are population expansion, the need for irrigation in agriculture, and climate change [3]. Groundwater as a substitute supply of fresh water has received serious attention due to the dangers of surface water pollution, rapid climate change, and the likelihood of droughts [4]. In order to keep the ecological balance of the earth system, groundwater is an essential part of the hydrological cycle. By providing freshwater for drinking, agriculture, and industry, it maintains life [5]. Because it is less polluted than surface water sources, which are easily contaminated by human activities like industrial and agricultural operations, groundwater becomes a significant alternative supply of freshwater in areas with a semi-arid temperature and environment [6]. Groundwater potential maps are available in many nations of the world, assisting planners and decision-makers in the development, distribution, and management of groundwater resources [1]. Groundwater accounts for one-third of all freshwater abstractions globally [7]. In many regions of the world where water supplies are limited, groundwater is a crucial natural resource for any economic and social growth [8]. Groundwater supplies are a necessity for all economic activity, both internally and outside. However, a study that combines remote sensing and GIS data can offer a good platform for the converging analysis of massive amounts of data using very advanced cognitive process techniques for groundwater exploration. Nations like Ethiopia are seeing a rise in groundwater demand as a result of the nation’s fast urbanization, population growth, and economic development. In the area under examination, groundwater is the most often used resource for agriculture, animal husbandry, and the provision of drinking water. The Lower Omo-Gibe watershed groundwater potential zone mapping will have a significant impact on the sub-basin. Since the Lower Omo-Gibe watershed is the main source of the Omo River, mapping the underground water would improve the nation’s groundwater resources’ sustainable management. Because of this, the current study is interested in delineating the groundwater potential zones within the study area using remote sensing and GIS technology. Seven determining elements were taken into consideration for the study: slope, landuse landcover, Lineament density, Geology, Elevation, Soil texture, and Drainage density. In order to ensure sustainable management and growth of the resource, groundwater exploration has recently seen a rise in the usage of remote sensing and geographic information systems (GIS). With the development of technology, more scientists are employing remote sensing (RS) and geographic information systems (GIS) to evaluate a basin’s groundwater supplies [5]. Geophysical technology-based groundwater exploration methods are expensive and time-consuming [9]. Due of these problems, humans have been forced to use a variety of technologies that can help them research vast areas in a brief amount of time while using few resources. These techniques, which include remote sensing and GIS, have been applied to find groundwater potential zones. Exploring the groundwater potential zone in varied geological settings is made simple and rapid with this method [10-13] Several scholars have employed the RS and GIS procedures to designate groundwater potential zones all over the world [14-17]. Utilizing RS and GIS, groundwater resources can be found and explored. Geology, geomorphology, slope, soil type, rainfall, land use/land cover, lineament structures, and drainage criteria are all important factors [18,19]. Geospatial technologies offer quick and affordable ways to evaluate spatial data across a range of geoscience disciplines [20]. For long-term resource management, it is essential to be able to locate the groundwater potential zone. It aids policymakers, decision-makers, and planners in making sure the groundwater resource is safeguarded from influences on both quantity and quality. The analytical hierarchy process (AHP) is one of the approaches to multi-criteria decision-making that is most frequently used [11]. AHP can be used to demarcate groundwater potential zones since it is an easy, quick, dependable, and efficient procedure [12]. When it comes to creating sustainable groundwater policies in this district, planners and policymakers will find this research to be a useful resource.

Materials and Methods

Study Area

The Lower Omo-Gibe River Watershed is about 2345.5 km² in area and is situated in the Omo-Gibe Basin, south Ethiopia. Lower Omo-Gibe Watershed (Figure 1) mainly covers South nation and nationality region and south-west Ethiopia region. Mirab Omo, South Omo and Basketo Zones are included in this watershed. Woredas such as Maji, Salamago, South Ari, Jinka town, Boko Dawula, Hamera, Nyngatom, North Ari and Mele koza. It has a southern boundary that is formed by the contained river basin that empties into Kenya’s Lake Turkana. The Lower Omo-Gibe watershed plain is distinguished by the fact that, although being rather flat, it has various beach ridges and depressions. It was formed very recently by the subsequent movements of Lake Turkana. The Omo delta, in the south of the Basin and at the northern end of Lake Turkana has aggraded to such an extent in recent years only some 25 km² of the lake is in Ethiopia.

Figure 1: Location map of the study area

Methods and Data Analysis

The groundwater potential in this study was assessed using seven criteria including soil, lineament density, landuse landcover, slope, drainage density, rainfall, geology, For each criteria, thematic maps were created and utilized to assess the groundwater potential of the study area. In this study, the relationship between the variables that were utilized to forecast groundwater potential during weight calculation and the actual groundwater potential was analyzed.

Slope

In mapping groundwater potential, slope is a key factor [21]. It determines surface runoff and vertical percolation of water; hence, it affects groundwater recharge processes [22]. Slope was inversely related to infiltration [23]. Shuttle Radar Topography Missions (SRTM) 30 m resolution digital elevation model (DEM) was used to generate slope map. The slope map was classified into five classes (Figure 2) based on FAO classification standard (Sheng, 1990).

Figure 2: Slope map

Drainage Density

Drainage density has an impact on both the availability and pollution of groundwater. Lithology has an impact on the drainage system, which is a crucial gauge of infiltration rate. Drainage density has an inverse relationship with permeability [3]. As a result, it is a crucial component in determining the groundwater potential zone obtained from DEM data.  Finding the length of the stream and dividing on to the total area of the basin as shown in the equation of Drainage Density DD=L (length/A (area) and using Archydro program [24]. The drainage-density map (Figure 3) was created by dividing themed river maps produced from a basin-wide flow accumulation (Fac) and stream definition operation [25].

Figure 3: Drainage density map

Geology

The geologic environment has a significant influence on the existence and distribution of groundwater in any terrain [26]. One of the most crucial factors in evaluating groundwater potential zones is geomorphology, which relates to an area’s landform and terrain. It gives information on how different landform traits are distributed as well as information on numerous processes, including temperature changes, geochemical reactions, water flow, freezing and thawing, and more [27]. Geologic and Geomorphological maps created by translating length and measurement into kilometers (km), digitizing with the Arc GIS spatial analyzer application, and then saving the data as shape files. The geological structures were modified to raster file format with the corresponding presentation, and the region’s geological state was assessed. The following formula was used to compute the lineament (fault) density (LL) [28]. The geology of Lower-Omo Gibe watershed (Figure 4) characterized by tertiary extrusive and intrusive rock, tertiary, quaternary and Permian-Carboniferous.

Figure 4: Geology map

Elevation

Elevation of the catchment ranges from 333 to 3277 m above sea level (Figure 5). High elevation was found in west and north edge of the study area and decrease toward east and south of the study area except topographically irregular and hilly areas. A higher weight is given for lower elevated zones and lower weight was assigned for highly elevated areas.

Figure 5: Elevation map

Landuse Landcover

One of the key determining criteria to pinpoint areas with potential for groundwater recharge is the landuse and landcover map [29]. Landuse and landcover have an impact on hydrological processes such evapotranspiration, surface runoff, and infiltration. Areas with vegetation cover have high groundwater potential than built-up areas that inhibits the infiltration of water into the subsurface [30]. From land use classes, built-up area has the lowest weight compared to other classes and crop land has the highest weight [31]. Landuse landcover offers essential details on infiltration, moisture, groundwater, and surface water in addition to suggestions on groundwater requirements.  The primary coverings of the land surface by human activity or vegetation are referred to as landuse and landcover, respectively (natural or planted). The land-use type in the study area in accordance with the order of infiltration includes barren land, built up land, crop land, grass land, lichen mosses, open water, shrubs cover area, tress cover area and vegetation aquatic (Figure 6). The landuse landcover layer was obtained from the Ethiopia Sentinel2 LULC 2022. The boundaries in the layer were then visually evaluated using Landsat8 satellite images. Next, the land-use classes changed to the USGS standard classifications. Finally, the layer was entered into the GIS model after the necessary edits.

Figure 6: Landuse landcover

Lineament Density

Lineaments are tectonic-derived linear, rectilinear, and curvilinear features that are visible in satellite photography. These lineaments typically display linearity and curvilinerities in vegetation, drainage, drainage patterns, soil tonality, relief, and tonality in soil in satellite data [32]. In particular, in the lineaments that are not filled by soil, the lineaments may operate as a channel for water to seep into the earth and migrate through the aquifer system due to linear features including fault, fractures, and geological connections between various lithologies. Since it is assumed that the rate of water infiltration increases with increasing lineament density, the lineament density (Figure 7) of a region might indirectly indicate the groundwater potential [33]. These lineaments are mapped with the help of satellite data and can be correlated with faults, fractures, joints, bedding planes and geological contacts which are useful for the groundwater potential study [34].

Figure 7: Lineament density map

Soil

The type of soil texture present in an area has a great impact on the availability of groundwater. Highly porous soils allow easy percolation and infiltration of surface water into the subsurface, and this is ideal for a favourable groundwater potential. Soil properties influence the relationship between runoff and infiltration rates, which in turn control the degree of permeability [35]. Soil texture raster data of the Lower Omo-Gibe Watershed was obtained by HWSD (harmonized world soil database) Version 1.2 at 900 m resolution. Lower Omo-Gibe Watershed consists of five major soil textural groups (Figure 8), such as Eutric Nitosols, Eutric Cambisols, Calcaric Fluvisols, Chromic Vertisols and Haplic Yermosols. Due to their difference in infiltration and porosity, weight of soil texture is assigned on the basis of their infiltration rate.

Figure 8: Soil map

Rainfall

The slope gradient and rainfall distribution have a direct impact on the infiltration rate and the potential for groundwater potential zones [22]. As indicated in Figure 9, the annual rainfall was categorized into five classes: very high (1410-1520 mm), high (1300-1400 mm), moderate (1180-1290 mm), low (1070-1170 mm), very low (946-1060 mm) and extremely very low (831-945 mm). Rainfall generally decreases from east to west and similar to elevation, with annual rainfall varying between 831 mm and 1520 mm.

Figure 9: Annual rainfall map

GIS Based AHP Method

GIS Geographic Information System

A software and hardware combination is a created instrument that has been extensively employed in groundwater potential zones and assessment. A method for limiting the number of target regions for hydrogeo-physical study is provided by the combined RS and GIS analysis [36]. Following site appraisal and selection for sustainable groundwater management, site-specific ground geophysical methods like vertical electrical sounding (VES) and very low frequency electromagnetic (VLF-EM) profiling may be used. In the GIS and RS spatial analysis and modeling, thematic layers for soil, lineament density, drainage density, geomorphology, slope, and LULC are integrated. In terms of groundwater prospect, weights ranging from 1 to 4 were allocated to the classes in order of increasing.

AHP Analytical Hierarchy Process

Developing an Analytical Hierarchy Process (AHP) Model

Making a decision hierarchy is the first step in an AHP analysis. AHP divides the issue into a hierarchy. As adopted by [37] first level of the hierarchy is the goal of groundwater potential zone identification. The second level in the hierarchy indicates the parameters that used to decide the potential zone and the third level is about an alternative to select groundwater potential zone as very good, good, moderate and fair. The benefit of AHP is that it simplifies issue solving by better understanding how to break down challenges. To make sure that all requirements and potential alternatives were followed, it is also feasible to request the participation of specialists [37,38].

It’s one of the techniques employed the most in MCDA approaches, as was indicated in the introduction, this concept has been developed by [39]. The AHP evaluates a collection of evaluation criteria as well as a set of alternate options before selecting the best one with the most weight [40]. It is a strategy for assessing the relative weights of the criteria, different optimization strategies for each parameter, and generating an average rating of the variations that were generated using the predetermined criteria (selection criteria identification) at a certain stage [2]. The AHP is frequently used to establish priorities utilizing expert recommendations and evaluations, reduce issue confusion and complexity, and facilitate decision-making. There should be at least two criteria and two substitutes in every study that has been examined by AHP in order to determine each factor’s weight [41]. Since these criteria were given varying degrees of importance, education and expertise are particularly important for thorough evaluation and analysis. It’s possible to employ both qualitative and quantitative criteria [42]. After using AHP within the MCDA, a decision matrix was produced in each study that was reviewed. Field experiences and literature reviews were used to weigh several parameters on a flexible scale from 1 to 5. Then using a few formulae to get the system’s normalized weight and consistency ratio (CR) (Figure 10) [38].

Figure 10: Methodology flow chart for the present study

Figure 11: Groundwater potential map

Result and Discussion

Landuse Landcover

Land use map of the study area (Figure 6) consists of several classes, viz., Bare area 112 km², Built up area 143 km², Cropland 435 km², Grassland 268.5 km², Lichen mosses 112 km², Open water 97 km², Shrubs cover areas 289 km², Tree cover areas 667 km² and Vegetation aquatic 222 km² as shown in Figure 6 and Table 1. Land use is one of the important criteria for identifying groundwater potential zones. Suitable weights were given to each class and their areal extents were calculated using “Zonal Statistics” in GIS (Table 1).

Table 1: Area covered under different range of parameter of Landuse landcover

Geology

Geology is also one of the input parameter factors in delineating the groundwater potential zones. Four different lithologies have been observed in the study area (Figure 4). Their areal extent has been determined using “Zonal Statistics” in geographic information system domain (Table 2). Geology of Lower Omo-Gibe watershed is dominated by tertiary extrusive and intrusive rock 758 (32.32%), tertiary (397 km², 16.93%), quaternary (845 km², 36.03%) and Permian-Carboniferous (345.5 km², 14.73%).

Table 2: Area covered under different range of parameter of Geology

Slope

The slope of the study area has been estimated from Digital Elevation Model (DEM) data as shown in Figure 2 and Table 3. Flat or gentle slope is suitable for increasing the infiltration amount from rainfall. Otherwise, in steep slope area where the infiltration capacity is very minimum whereas runoff is maximum. From the analysis, slope ranges from 0-5 percent rise covers high area coverage which is 31.29% (734 km²), 5-10 slope categories covers 17.67% (414.5 km²), 10-15 slope  categories covers 13.69% (321 km²), 15-20 slope categories covers 9.46% (222 km²) and >20 slope categories covers 27.88% (654 km²).

Table 3: Area covered under different range of parameter of Slope

Soil Type

Soil type is also one of the most determinant factors in estimating the groundwater. Ten different soil types have been observed in the study area (Figure 8). Their areal extent has been determined using “Zonal Statistics” in geographic information system domain (Table 4). Soil type of Bahir dar zuria is dominated by vertisoil with an aerial extent of 1045.19 km² (67.78%), followed by luvisoil (284.31 km², 18.08%), and other units together cover in the study area as indicated in Figure 8 and Table 4. Based on the hydraulic properties of the soil, the soil classes were accordingly ranked using AHP.

Table 4: Area covered under different range of parameter of soil type

Lineament Density

Lineaments are the natural linear feature like faults, joints and fractures, which can visualize and interpreted directly from remotely sensed data. As shown in the Table 5, lineament density class ranked as very low, low, medium, high and very high with the areal extent of 0-0.83 Km/km², 0.84-1.66 Km/km², 1.67-2.35 Km/km², 2.36-2.93 Km/km² and 2.94-3.77 Km/km² respectively. The total area falling in different range of lineament density is given in Figure 7 and Table 5 which show that most part of the study area have very low lineament density. So very low lineament density indicates very low infiltration rate, whereas the high lineament density areas indicate high infiltration (Table 5) thus a potential zone for groundwater development.

Table 5: Area covered under different range of parameter of Lineament

Drainage Density

Drainage density is a significant parameter for evaluating the groundwater potential zones. The areas having high drainage density have less potential for groundwater recharge, and the areas with low drainage density have a high potential for groundwater recharge. Hence, the areas having low drainage density are favorable for high groundwater potentiality. So, higher weights were assigned to the areas having low drainage density. The drainage density was classified into five categories viz. very low (0.08-091 km/km²), low (0.92-1.67 km/km²), moderate (1.68-2.74 km/km²), high (2.75-3.43 km/km²) and very high (3.44-4.22 km/km²). The highest area was under low density with 225.5 km², followed by very low with 388 km², moderate 510 km², high 589 km², and very high 633 km² as shown in Figure 3 and Table 6.

Table 6: Area covered under different range of parameter of Slope

Rainfall

Rainfall is one of the most important sources of groundwater recharge through percolation in the groundwater system. The average annual rainfall in the study area is grouped into six classes namely extremely very low (831-945 mm), very low (946-1060 mm), low (1070-1170 mm), moderate (1180-1290 mm), high (1300-1400 mm) and very high (1410-1520 mm), covering the area of about 410 km² (17.48%), 370 km² (15.77%), 366 km² (15.60%), 433 km² (18.46%), 376.5 km² (16.05%) and 390 km² (16.63%), respectively as shown in Figure 9 and Table 7. Rainfall distribution along with the slope gradient directly affects the infiltration rate of runoff water hence, increases the possibility of groundwater potential zones.

Table 7: Area covered under different range of parameter of Rainfall

Derived Priorities (Weights) for the Parameters

The AHP method was applied in the current work to determine the weight for each data layer. One of the most widely used techniques for determining the weights of criterion in the Multi-Criteria Decision Making is AHP (MCDM) [38]. To identify the purpose, criteria, and relationship between them, the hierarchical structure of the subject being studied is created as the first stage in the AHP approach. Initially, the AHP approach involved conducting a pairwise comparison of the alternatives using specially created questions that followed the format of the standard AHP questionnaires. In this study, opinions from a range of experts were gathered regarding the potential for groundwater, including hydrologists, hydrogeologists, environmentalists, and GIS experts. The overall method is as described in the following Figure 9. The result of AHP shows that geology has the highest weight (38.39%) followed by slope, which accounts for 23.27%, drainage density 14.54%,  landuse landcover 11.40%, lineament density 7.04% and soil 5.35% of the weight given to each parameter (Tables 1 and 2). When the judgements matrix is consistent and the computed weight has been normalized by multiplying the derived weight of all parameters by 100%, it is then possible to rank the features of each parameter for the purpose of a weighted overlay. Features of each thematic layer were ranked on the basis of influence of groundwater potential within each parameter (Table 8) [38,43].

Table 8: Comparison matrix and significance weightage value of the influential factors

Groundwater Potential Zone of the Watershed

To choose the best location for future groundwater development and management, it is crucial to integrate several groundwater regulating variables for discovering possible potential groundwater resources. Using the WLC of the GIS spatial analysis tool, all created thematic maps were integrated based on their weights and rates. The calculations of all the layers went into creating the GWP map. A prospective groundwater zone map was created by weighing the various theme layers and their specific features see Figure 10. The potential groundwater zone of the study area revealed five distinct zones, namely very good, good, moderate and fair. The distribution and extents of different potential groundwater zone are 158.05 km² (21.59%), 35.92 km² (4.91%), 214.77 km² (29.34%), 220.59 km² (30.13%), and 102.72 km² (14.03%) for very good, good, moderate and fair zone respectively see Table 3. Study area have very high to high potential zone in southern, south west and central portion, while the west and eastern portion exhibits medium to low groundwater potentials. The distribution is more or less a reflection of drainage density, lineament, slope, and soil patterns in addition to geomorphic and geological elements, according to analysis of the groundwater potential map (Table 9).

Table 9: Groundwater potential zone

The region appears to have a great potential for irrigation because there are large expanses of irrigable land with fertile soil available. The watershed lower areas get volcanic sediments that are moved from the watershed upper portions due to its geographic location. The area is seen as one where food security is a worry, despite having a lot of potential for irrigation agriculture due to its good soil, irrigable acreage, and high capacity for groundwater. I consequently suggest that concerted activities by the district and regional governments are required to improve the living conditions of persons who reside in the catchment. I have found in my study that the watershed may be abundant in water resources, particularly groundwater. Groundwater was found to have a high potential in nearly half of the basin. Despite the aforementioned limitation, I am confident that the results can be used to identify solutions for utilizing the local water resources and resolving the issue that the residents have been experiencing. I advise that a thorough investigation be done to determine the quantity and quality of water that is available in order to determine whether it can be used for the intended purpose. Due to the availability of substantial tracts of irrigable land with fertile soil, the area appears to have a high potential for irrigation. Due to its geographical location, the watershed lower portions receive volcanic sediments that are transferred from the watershed top portions. Despite having a lot of potential for irrigation agriculture due to its good soil, irrigable land, and high capacity for groundwater, the region is considered one of the locations where food security is a concern. In order to better the living conditions of those living in the watershed, I therefore propose that coordinated actions by the district and regional governments are needed.

Conclusion

In this study, groundwater potential zone evaluation using remote sensing, GIS, and MCDM methodologies has been successfully used and proven. The generation of thematic layers, the application of AHP to get the weights, and overlay analysis to determine the groundwater potential zone are the three steps of the process. Thematic layers were created by digitizing existing maps using a GIS and remotely sensed satellite image data. Utilizing the AHP, utility weights for the substitutes were provided. The identification of the groundwater potential zone uses an overlay analysis of multiple thematic maps and weighting. Based on remote sensing, GIS, and MCDA methodologies, four categories of groundwater potential zones have been identified in this study region. This particular study deals with the assessment of groundwater potential zones (GWPZs) using geographical information systems and remote sensing techniques in the Lower Omo-Gibe watershed, Omo-Gibe Basin, Ethiopia. The thematic layers influencing GWPZs such as geology, landuse landcover, slope, lineament density, drainage density, soil properties (soil texture, soil type), and elevation maps were getting prepared with the available data, i.e., conventional data, satellite image, and assigned individual weights along with their subclass divisions depending on literature and expertise decision. The distribution and extents of different potential groundwater zone are 158.05 km² (21.59%), 35.92 km² (4.91%), 214.77 km² (29.34%), 220.59 km² (30.13%), and 102.72 km² (14.03%) for very good, good, moderate and fair zone respectively.

Data Availability

Data used in this research are available upon request.

Conflicts of Interest

The author declares that they have no conflicts of interest or personal relationships that could have influenced the work reported in this paper.

Acknowledgments

The authors acknowledge the Ethiopian Ministry of Water, Irrigation and Energy, and the National Metrological Agency for providing the necessary data which is helpful to accomplish this research work.

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