Groundwater Availability and Suitability Evaluation for Irrigated Agriculture in Mbale District

The purpose of this study was to evaluate the availability and suitability of groundwater in Mbale district basing on potential, quality and vulnerability to support irrigated agriculture. Groundwater potential was assessed using geospatial technique through AHP model. Thematic layers were prepared namely: land use and land cover, slope, soil, rainfall, lineament density, lithology, geomorphology and drainage density. All layers were integrated using the MLC technique. Ranking of each parameter was performed using WOIA. Weights were assigned to each subject class basing on AHP results. The consistency of the outputs was tested by computation of CR and was at a reasonable acceptable level (0.029 < 0.1). Groundwater potential was delineated basing on the values of groundwater potential index. Groundwater quality was determined using IWQI method. Thirty-three water samples were collected. Five chemical parameters were tested in the laboratory: chloride, electrical conductivity, bicarbonate, sodium and SAR to generate the water quality database using Kriging interpolation technique. Computation of IWQI values for each source was made and was used to generate the IWQI map using the weighted summation. DRATIC method was used to delineate vulnerability of groundwater. Layers were generated using the following parameters: depth to groundwater, net recharge, aquifer media, topography, vadose zone impacts, and hydraulic conductivity. The aquifer vulnerability map was prepared by overlaying layers. Three different vulnerability zones were determined according to DRASTIC scores low (<100), medium (100-140) and high (>140). Integration of maps of quality, vulnerability and potential of groundwater was made using an unsupervised MLC classification method. Groundwater potential was in three zones: very good zone was 26.99 km (12.98%), the good zone was 126.22 km (60.71%) and the poor zone was 54.69 km2 (26.31%). The average annual exploitable groundwater reserves are estimated at 0.026 (MCM/km) in the zone of “very good”, 0.024 MCM/km in the zone of “good” and 0.018 MCM/km in the zone of “poor”. Groundwater quality was in two use restrictions: High Restriction of 90.90% with the IWQI value from 40 to 55 on the area of 188.98 km of the study area and Severe Restriction of 9.10% with IWQI values from 32 to 40 covering of 18.92 km. The study area had a high probability of contamination. Low vulnerability covers an area of 11.23km (5.40%), 77.83km (37.44%) for medium and 118.83km (57.16%) for high. The resulting clustered map was classified into five categories with their respective regions: 17.58% very poor (36.56km), 13.84% poor (28.77km), 12.69% good (26.39km), 31.46% very good (65.39km) and 24.43 % excellent (50.78km). The sub counties in Mbale district that have inadequate and unsuitable groundwater for irrigated agriculture are Budwale, Wanale, Busano, Bubyangu, Bufumbo and Nyondo since they belong to the zones of very poor and poor. The remaining sub counties International Journal for Multidisciplinary Research (IJFMR) E-ISSN: 2582-2160 ● Website: www.ijfmr.com ● Email: editor@ijfmr.com IJFMR23057184 Volume 5, Issue 5, September-October 2023 2 have available and suitable groundwater for irrigated agriculture since they belong to the zones of good, very good and excellent. CHAPTER ONE: INTRODUCTION 1.1 BACKGROUND Globally, present rising requirement for water to cater for cultural, societal and economic requirements will be satisfied by surface water or groundwater sources. Equating sources, groundwater stands extensively distributed and comparatively safer (Guppy et al., 2018). Groundwater is a substantial component of global water cycle and it’s a vital resource for domestic, agricultural and industrial development water globally. (Frappart & Ramillien 2018). Irrigation requirement for the agricultural sector now sums to in excess of 70% of universal water withdrawals and around 85% of universal resource ingestion and the prediction is that groundwater reserves offer 43% of water consumed for irrigation (Liu et al., 2017). Groundwater is more established in North America and South Asia where accessibility is 57% and 54% respectively of total water for irrigation. In the previous thirty years enormous growth in the creation of irrigation water wells has been done for instance they currently control 19 Mha in China, 39 Mha of irrigated land in India, 17 Mha in the United States of America and massive parts of Pakistan and Bangladesh (Angelakιs, et al., 2020). These have enabled key remunerations for masses of moderate agriculturalists as mitigation to increased temperature and rainfall variation (Munyaradzi, et al., 2022). The exclusion to this trend is Tropical Africa with merely 1% of land presently benefitting from irrigation by groundwater (Villholth et al., 2017). Prospective accomplishment of groundwater enlargement aimed at irrigated agriculture is more broadly acknowledged, but currently, the composite factor is the estimation of the availability of natural groundwater storage. Due to this, it has now persisted as a superior issue of concentration for legislators and designers (Ringler et al 2020). In Sub-Saharan Africa, there are diverse water benefactions where 40 million hectares of its land are appropriate for irrigation growth. In the past 3 decades, sub-Saharan Africa has observed amplified public and scholar’s attention in the use of smallscale irrigation in general and groundwater irrigation in specific (Jambo et al., 2021). In Uganda, the total internal renewable water resources (IRWR) are projected at 39 km/yr and groundwater is about 29km/yr (MAAIF, 2020). Though there is this high quantity of IRWR, around 800,000 ha of crops are damaged annually due to climate associated consequences. The damages and losses in the agriculture sector are triggered by rainfall insufficiency and are a threat to agriculture since it’s a daily source of livelihood for over 80% of the population. Currently only 1% of renewable fresh water is used for irrigation in Uganda compared to over 70% global average (MoFPED, 2018). According to the Climate Risk Country Profile report of Uganda by World Bank Group in 2020, the specific climate outlooks of temperature and variable rainfall are on increase in Mbale district and are attributed to climate change. This was confirmed by the survey done by Anna et al in 2021 which revealed that 63% of farmers are affected by drought in Mbale. This has contributed to loss of rural livelihoods and food insecurity (UN, 2015). The Government of Uganda through MAAIF with support from World Bank is implementing the Micro-Scale Irrigation Programme (MAAIF, 2020). However, this program supports only farmers near surface water sources which are inadequate, thereby leaving out 99% of small-scale farmers who have continued to rely on rainfed agriculture (CultivAid, 2021). Groundwater has proved a reliable and accessible water source for irrigation but in Mbale district water depth and variability in quantity and quality are constraints (Wanyama, 2018). Therefore, there is need to International Journal for Multidisciplinary Research (IJFMR) E-ISSN: 2582-2160 ● Website: www.ijfmr.com ● Email: editor@ijfmr.com IJFMR23057184 Volume 5, Issue 5, September-October 2023 3 determine the groundwater quantity, quality and vulnerability with the idea of supporting irrigated agriculture. 1.2 Problem statement According to the survey done by Anna et al in 2021, 63% of farmers are affected by drought in Mbale district. The drought has been manifested by steady rise in temperatures (0.03°C/year), average annual precipitation of 630mm and 560mm in the first and second seasons in the period of 36 years (1980-2015) and less predictableness of commencement of rains (Okiror & Muchunguzi, 2019). This is causing growing difficulties for many crops since agricultural production system is largely rainfed and sensitive to climate change (MAIIF, 2020). This has contributed to loss of rural livelihoods and food insecurity (World Bank, 2020). To mitigate this situation, the Government of Uganda through MAAIF with support from World Bank is implementing the Micro-Scale Irrigation Programme (MAAIF, 2020). However, this program supports only farmers near surface water sources which are inadequate, thereby leaving out 99% of small-scale farmers who have continued to rely on rainfed agriculture (CultivAid, 2021). To increase on the availability of water for irrigation, groundwater can be utilized but its quantity and quality are not known (Wanyama, 2018). Thus, this research aimed at assessing the groundwater availability and suitability by considering potential, quality and vulnerability to contamination in Mbale district. 1.3 Objectives 1.3.1 Main objective To evaluate groundwater availability and suitability for irrigated agriculture in Mbale district. 1.3.2 Specific objectives The study aimed to; 1. Estimate the groundwater potential in Mbale district 2. Analyze groundwater quality in Mbale district 3. Assess the groundwater vulnerability in Mbale district 1.4 Research Questions The study was guided by the following questions; 1. What is the groundwater potential in Mbale district? 2. What is the variability of groundwater in Mbale district quality? 3. What is the extent of groundwater vulnerability to contamination in Mbale district? 4. 1.5 Significance of the Study Groundwater availability and suitability information will be used by government agencies and farmers to promote agriculture in Mbale district as per section 173 of the Uganda Vision 2040 and sections 59 and 140 of the National Development Plan III. This is to contribute to reduction of poverty and hunger which are in line with Sustainable Development Goals 1 and 2 respectively. International Journal for Multidisciplinary Research (IJFMR) E-ISSN: 2582-2160 ● Website: www.ijfmr.com ● Email: editor@ijfmr.com IJFMR23057184 Volume 5, Issue 5, September-October 2023 4 1.6 Scope of the study The study was conducted in Mbale district in Eastern Uganda. It was limited to the determination of groundwater suitability and availability by considering groundwater potential, quality and vulnerability. The period of study was from November 2022 to March 2023. The items outside the study because they would make it have a very large scope are determination of the crops which can grow well in the study area and management of the groundwater quality. 1.7 Conceptual Framework The conceptual framework for the study is presented in Figure 1-1. It’s premised on the fact that groundwater availability and suitability depend on groundwater potential, quality and vulnerability to contamination. These are affected by the independent variables (permanent parameters) and moderating variables (seasonal parameters) as in Figure 1-1. Figure 11: Conceptual Framework for the study Independent Variables • Lineament density • Drainage density • Slope/Topography • Soil • Lithology • Geomorphology • Net recharge • Depth to groundwater • Aquifer media • Soil media • Impact of vadose zone • Hydraulic conductivity • SAR • Electrical concentration • Chloride concentration • Bicarbonates concentration • pH • Sodium concentration Dependent Variable


Scope of the study
The study was conducted in Mbale district in Eastern Uganda. It was limited to the determination of groundwater suitability and availability by considering groundwater potential, quality and vulnerability. The period of study was from November 2022 to March 2023. The items outside the study because they would make it have a very large scope are determination of the crops which can grow well in the study area and management of the groundwater quality.

Conceptual Framework
The conceptual framework for the study is presented in Figure 1-1. It's premised on the fact that groundwater availability and suitability depend on groundwater potential, quality and vulnerability to contamination. These are affected by the independent variables (permanent parameters) and moderating variables (seasonal parameters) as in Figure 1

Geomorphology
Geomorphological maps show topographical patterns related to availability of groundwater its prospects. Relief elements were recognized from DEM (Ibrahim-Bathis & Ahmed, 2016). Prediction of groundwater potential is high near the higher streams that correspond to lower sloping topography (Dinesan et al., 2015). Several publications revealed that some researchers used GIS to localize potential groundwater areas with the incorporation of arithmetical methods for instance Simple Additive Weight (SAW) and Analytical Hierarchy Procedures (AHP) (Yildirim, 2021) and machine learning (Hussein et al., 2020). The amalgamation of GIS technology and remote sensing reduces inaccuracies of hydrogeological data in some respects. Recently, many studies have been applied using index-based models and quantitative methods to assess potential groundwater areas (Gyeltshen et al., 2020). AHP is the most popular and widely used multi-criteria decision analysis (MCDA) technique for zoning groundwater exploration areas (Makonyo & Msabi, 2021). Accordingly, the AHP technique was used for this study to assign the relative importance of each parameter for groundwater zoning. GWPI is a dimensionless measure which supports forecast of the potential of groundwater. The weighted linear matching method is used in the GWPI approximation as in Equation (1 (1) Wi -the normalized weight of the i th thematic layer, Xj -the rank value of each class relative to the j th class, m -the total number of thematic layer and n -the total number of classes in a thematic class.

Groundwater vulnerability
Vulnerability means proneness of groundwater to pollutants produced by anthropological activities (Cannon et al., 2013) Assessing inherent vulnerability is similar to assessing the defensive ability of protection layers to the introduction and conveyance of pollutants into groundwater. Vulnerability assessment approaches that use the inherent vulnerability idea include: DRASTIC (depth-to-groundwater, net recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity), AVI (Aquifer Vulnerability Index), and SINTACS (water table depth, effective infiltration, unsaturated zone, soil media, aquifer media, hydraulic conductivity and topographic slope) and are able to distinguish degrees of vulnerability at regional scales where different lithologies exist (Oke,2017). These approaches provide a useful tool for assessing and managing groundwater resources to ensure their sustainability. Further research should focus on the development of more comprehensive and accurate models that can handle uncertainty and predict the potential impacts of climate change on groundwater vulnerability However, DRASTIC model is frequently employed due to its efficacy and simplicity in depicting diverse aquifer configurations (Neshat & Pradhan, 2017). The software possesses a capacity to enable individuals to manage, analyze, and visualize spatial data, rendering it a commendable substitute to other approaches when conducting assessments of groundwater vulnerability (Canora et al. 2022). Furthermore, the DRASTIC model can be readily extrapolated to alternative hydrological catchments given the availability of adequate data and fundamental comprehension of the target region undergoing assessment . Notwithstanding its utility, the DRASTIC method is beset by certain limitations, one of which pertains to its inability to fully integrate pollutant sources and/or the potential impact of future pollution sources on water contamination (Goyal et al. 2021). Moreover, it precludes the assessment of overarching groundwater susceptible areas (Abiy et al., 2016). The DRASTIC approach was formulated based on four underlying suppositions, as postulated by Al-Zabet (2002) which are; the contaminant is initiated at the earth surface, the pollutant is introduced into the groundwater through the process of rainfall infiltration, the velocity of the pollutant is commensurate with that of water and the land area under assessment through the utilization of the DRASTIC model is equal to or exceeds 40 hectares.

Groundwater Quality
Groundwater quality for irrigation is an issue of concern of recent (Ibrahim et al., 2023). Lower or higher chemical fertilization is resulting in groundwater pollution (Nemčić-Jurec & Jazbec 2017). Groundwater quality is a subject to the way of recharging water, precipitation, subsurface and surface water and hydrogeochemical processes in aquifers (Das et al., 2017), land-use land-cover change ). Groundwater quality degrades in twofold, first, due to geochemical reactions in the aquifers and soils and, second, time when it is supplied through improper canals/drainages for irrigation. Therefore, it is necessary to perform a regular assessment of irrigation and drinking water quality . Irrigation demands sufficient water supply of usable quality (Gautam et al., 2013). The suitability of groundwater for irrigation depends on the nature of the mineral elements in the water and their impacts on both the soil and plants (Singh et al. 2015). The excess of salts affects plant's growth by redressing the uptake power of plant due to complex changes arouse out of the osmotic processes (Todd ,1980). Generally, water quality parameters (major cations as Na + , Ca 2+ , Mg 2+ , K + ) and anions Cl − , SO4 2− , HCO3 − ,CO3 2− , NO3 − ) and heavy metals are indicators of drinking water use, while Water Quality Indices (WQI) such as sodium adsorption ratio (SAR), sodium percentage (SSP; %Na), residual sodium carbonate (RSC), residual alkalinity (RA), Irrigation Water Quality Index (IWQI), Kelly's ratio (KR) [or Kelly's index (KI)], permeability index (PI), chloroalkaline indices (CAI1 and CAI2), potential salinity (PS), magnesium hazard (MH) (or magnesium adsorption ratio; MAR), total dissolved solids (TDS) and total hardness (TH) based on primary water quality parameters are frequently used to determine quality of water for irrigation (Gautam et al. 2015).
To determine groundwater appropriateness for irrigated agriculture is accomplished by utilization of the Irrigation Water Quality Index (IWQI), an index which Meireles and others formulated in 2010. When calculating the Irrigation Water Quality Index (IWQI), the foremost factors taken into account are Electrical Conductivities (EC), Sodium (Na + ), Chloride (Cl -), Bicarbonate (HCO3 -), and Sodium Adsorption Ratio (SAR), as these aspects hold significant sway over water quality for the reason of irrigation. This method possesses an important utility and efficiency in evaluating the appropriateness of water quality and disseminating information related to the overall quality of water, as demonstrated by Abbasnia and colleagues in their research conducted in 2018. The application of Geographic Information Systems (GIS) for the purposes of assessment not only facilitates the development of parameter maps that are conducive to intuitive comprehension, but it also heightens the overall rigor, objectivity, and efficiency of the analysis process (Al-Hadithi et al., 2019).

Summary of the Literature
The literature review focused on the assessment of groundwater potential for irrigation from different regions of the world. The studies revealed that groundwater is considered an important source of water for irrigation in areas where surface water is erratic or scarce. The review also highlighted the importance of accurate groundwater mapping and modelling for effective utilization of the resource. Overall, the literature review provided valuable insights into the potential of groundwater for irrigation and underscored the need for proper management of these resources. The literature review also covers various studies that have been conducted to assess groundwater quality for irrigation. These studies have identified various sources of contamination including agricultural practices, stormwater runoff, sewage disposal systems and industrial activities. Overall, the literature review on groundwater quality for irrigation highlights the importance of understanding the factors that impact groundwater quality. It also emphasized the need for regular monitoring and management of groundwater resources using several indices methods to ensure their suitability for irrigation use purposes. Groundwater vulnerability for irrigation is a complex issue that is influenced by various factors including geology, hydrology, land use and climate change. Literature review reveals that vulnerability assessments for groundwater for irrigation relies on the use of inherent vulnerability ideas. Overall, the literature suggests continued research and monitoring of groundwater for irrigation is necessary to develop sustainable management strategies and ensure the protection of groundwater resources. ) denote that in Uganda, agriculture is mostly rain-nurtured, is progressively more shaken by climate change and unpredictability demonstrated in unreliable rain configurations, lengthy dry periods, and inundations. As a consequence, farm-level productivity is extremely below the achievable prospective for most crops. In regard with these situations, irrigation is vital in helping farmers against climate change and performs an essential part in evolutions from subsistence to commercial agriculture by guaranteeing perennial production and farm jobs. However, Wanyama and colleagues in 2017 proposed that studies should be conducted to harmonize information on the irrigation potential in the areas of Uganda because numerous sources estimate unlike data and the procedures used do not give inclusive statistics to influence planning for irrigated agriculture. Also Wanyama (2018) indicated that Mbale district had groundwater irrigation potential but the quantity and quality are not known. So the purpose of the study was to address the gap of groundwater quantity and quality so that availability and suitability can be known for proper planning of irrigated agriculture in Mbale district.

CHAPTER THREE: RESEARCH METHODOLOGY 3.0 Research Design
The study was both empirical and quantitative in nature. It involved evaluating groundwater availability and suitability in Mbale district through groundwater potential, quality and vulnerability assessment.

4.1
Research Instruments The research instruments used in the study to achieve the objectives are detailed in Table 3-1. To address objectives 1 and 3 of the study Raster and vector maps which were used as raw data for the study were obtained 5. ArcGIS To address objectives 1, 2 and 3 of the study Analyzed data for groundwater potential, quality and vulnerability to contamination and created availability and suitability map for irrigated agriculture using MLC method.

Study area
The study was carried out in Mbale district, eastern Uganda. It lies at the geographical coordinates of 00⁰57'N 34⁰20'E. It neighbors Mbale City on the north, Manafwa and Bududa districts on eastern side, Butaleja district on the west and Tororo district on the south. The map of Mbale district is shown in Figure  3

Groundwater potential
Groundwater potential was modeled using AHP based weighted overlay index method because of the following reasons: simplicity and easiness to use, outcomes are very easy to comprehend, results are fare well in real world scenarios based on multiple criteria, checks the results for consistency hence preventing errors and it's a solution for personal bias in decision making. It consisted of three key phases. The first phase was preparation of the thematic layers that were appropriate to potential of groundwater. The second phase involved processing of the thematic layers to guarantee even WGS1984 projection and resolution, allocating scores, and weightages. The third and final phase was to determine the ground water potential index (GWPI) by integration of all thematic layers along with indices using the spatial analysis tool in GIS software.

Selection of thematic layers impelling groundwater potential
To identify potential groundwater areas, eight thematic layers were used viz: lineament density, drainage density, geomorphology, lithology, land use land cover, slope, soil, and rainfall are effective factors. The hydrological conditions largely depend on thematic layers and thus affect availability of groundwater. The thematic layers provide a dependable basis for effectively predicting zones of groundwater potential.

Rainfall
Average annual rainfall data of Mbale district in a period of 30 years (i.e., 1991 -2021) was extracted from the Climate Research Unit.

Lithology
The lithology layer was extracted from the US Geological Survey raster map at 1:5,000,000). Groundwater potential can largely be measured by the existence lithology features.

Drainage density
Drainage was taken directly from DEM ASTER (28 x28 m) of Uganda. Drainage density means proportion of total flow length to size of grid extent under consideration (Mogaji et al., 2014). Therefore, a grid (with a cell extent totaling to 10 km 2 ) was designed over the study area and the drainage density index was computed using ArcGIS through Equation (2).
Where: Di (km) -totality of the lengths of watercourses while A is the grid area (km 2 ). Calculated values per grid were plotted midpoint of grid. The coordinates at the center of per grid were then used to construct the drainage density map with the help Kernel density procedure as in Figure 3-6. Drainage density has a negative correlation with groundwater prospect. If drainage density high the probability of groundwater potential is low (Melese & Belay, 2021).

Lineament density
Lineaments were extracted from Uganda's DEM ASTER (28x28 m). Calculation of the lineament density was based the on-grid technique. The lineament density is clearly defined as the ratio of the total length of all recorded lineaments to the area under consideration (Edet et al., 1998). This is shown in Equation Li is the overall length of all lines (km) and A -grid extent (km 2 ). Lineament density is comparative to the area of groundwater replenishment. The rationale for the flow examination is to advance knowledge of affiliation between superficial water infiltration and fault systems, by regulating infiltration and water movement.

Slope
The slope map of the area was generated from Uganda's ASTER DEM image using ArcGIS 10.2 software. The water volume accessible during refilling and roughness of the topography of watershed are determined from the slope of that basin. Large flow volumes and lower seepage are associated with areas of high vertical angle. So, slope is one of the main factors that disturb the flow and infiltration rate. Weight per slope type was assigned basing on groundwater potential.

Geomorphology
Geomorphology is well-thought-out as the great needed feature for considering the existence, prospect, and flow of groundwater resources. The geomorphological development of a hard rock terrain is mainly organized by tectonic events and denudational practices (Bera et al., 2020). The geomorphology map was extracted from the year 2022 Geomorphology Map at scale of 1:100000

Soil
Soil types in the district play an important part during groundwater rejuvenation and retention capacity. Accordingly, it's a vital factor during delineation of potential groundwater areas (Rehman et al., 2019). The soil map was extracted from the year 1967 Soil Map at scale of 1:250000 3.4.10 Formation of weight for prospecting parameters of groundwater using Analytical Hierarchical Process Analytical Hierarchical Process (AHP) model is Multicriteria decision-making (MCDM) tool that offers answers for complex decision-creating problems, and was first presented by (Saaty, 1980). AHP is an extensively acknowledged model for assigning normalized weight to each layer of groundwater prospecting factor. The concluding weight of each thematic layer was generated from the principal Eigenvalue of the generated matrix. The consistency of the yield was governed by the computed consistency index (CI) and consistency ratio (CR) values (Equation 4&5).
Where: CR is consistency ratio, RI is random consistency index whose values depend on the order of the matrix (Table 3-1), and CI is consistency index which can also be computed using the following formula Where λmax is the principal Eigen value of the matrix and n indicates the quantity of groundwater prospecting factors. The value of CR must be <0.1. The acclimatizing features were likened to one another by pairwise comparison matrix. The inverse ranking method was used to assign a normalized weight for each thematic layer. The potential of groundwater is represented by the rating of 1-9 (Table 3-2), where 1, 2, 3, 4, and 5 for very low, low, medium, high, and very high (Jhariya et al., 2017).

Groundwater Potential Index
The groundwater recharge potential map was produced by bearing in mind the comparative significance of numerous thematic layers and their conforming classes. GWPI, a dimensionless quantitative approach was used to delineate groundwater potential zone (Razandi et al., 2017). Considering all the themes of and features in an integrated layer, the groundwater potential index is computed as Equation 6: - Where: Lt -lithology, Ld -lineament density, Dd -drainage density, S -slope, Rf -rainfall, Ge is Geormophology, LULC is Land use landcover and So is slope. Subscripts 'W' and 'Wf' designate normalized weight of a layer obtained through AHP and the normalized weight of the discrete features of a layer respectively. The GWPI was computed per grid and then plotted in midpoint the grids. The groundwater potential index map was established using the weighted overlay technique and geostatistical analysis was performed. The GWPI values were classified using a quantum classifier. Quantum classification approach, per class contained the similar quantity of features grouped into their respective classes.

Groundwater quality
The study was done by means of the GIS and IWQI method which (Meireles et al., 2010) formulated to evaluate the groundwater quality and decide suitability for irrigation use in Mbale district. The IWQI model assessed groundwater quality using the following parameters: Electrical Conductivities (EC), Sodium (Na + ), Bicarbonate (HCO3 -), Chloride (Cl -), Potassium (K + ), and Magnesium (Mg 2+ ), parameters, that imitate water toxicity, sodicity hazards, and soil salinity to plants. The data was obtained by simple random sampling of functional deep well water sources (boreholes). Simple random sampling was used since each borehole had an equal chance of being selected and an unbiased sample was obtained (Noor, et al., 2022).

Sampling and testing
The district has 163 functional deep boreholes. The final sample size was 33 boreholes determined from Equations 7-11 and the distribution of the sample size is presented in Table 3-4 and Figure 3-11. Each sample was collected from a clean one-liter plastic bottle and water quality testing was done by the Ministry of Water and Environment Mbale regional Laboratory. The results are attached in Appendix 1

Figure 3-3: Sampled boreholes in Mbale district Calculation of the Irrigation Water Quality Index (IWQI)
The EC, Na+, Cl-, HCO3-and SAR parameters suggested by (Meireles et al., 2010) were used to calculate the IWQI. SAR was calculated as the ratio of sodium absorption using the Equation 12: First, the values of cumulative weights (wi) suggested by (Meirales et al., 2010) must be well defined according to their relative importance for IWQ. These are normalized values and their sum is one, as shown in Table 3-5. According to Ayers and Westcot (1994), the Qi value is evaluated in the second step as in Table 3-6. This means a dimensionless number, with a higher value representing better water quality. The Qi value was calculated using Equation 13: Where: qimax is the maximum value of qi for the class, xij is the observed value of the chemical parameters, xinf is the minimum limit of the class to which each parameter belongs; qiamp is the layer amplitude; and xamp is the upper bound of the last layer of each parameter. Finally, the irrigation water quality index (IWQI) is calculated according to the following Equation 14: Where IWQI is a dimensionless index of irrigation water quality in the range from 0 to 100; Qi is a measure of the quality of the parameter, (i th ) a number from (0 to 100) as a function of its concentration; and wi is the normalized weight of the ith parameter. (Meireles et al., 2010) divided the IWQI values for the suitability of irrigation water into five dimensionless parameter categories based on the proposed groundwater quality index determined by the existing groundwater quality index as shown in Table 3-7. The classes were determined based on the problems of salinity risk, reduced water infiltration and toxicity to plants, as suggested by (Bernardo, 1995).  ons of Na⁺, Cl⁻, and HCO₃⁻.
y in order to prevent salt accumulation. Additi onally, it is recommended to apply copious am ounts of water to offset any potential issues stemming from ex cessive salt accumulation.

Groundwater Quality GIS Database Generation and Analysis
The outcomes garnered from the examination of chemicals in the samples were imported into the Geographic Information System (GIS) platform with the aid of Microsoft Excel, which facilitated the generation of a comprehensive water quality database for the district. Subsequently, a spatial distribution map was produced with the utilization of the weighted sum tool.

Groundwater vulnerability modelling
The study utilized the DRASTIC model as introduced by Aller and colleagues (1987) to examine the susceptibility of groundwater to potential contamination. The DRASTIC model comprises seven (7) parameters, namely Depth to Groundwater, Recharge, Aquifer Media, Soil Media, Topography, Impact of Vadose Zone Media, and Hydraulic Conductivity of the Aquifer, as represented by the acronym. Such parameters have been assessed in order to arrive at a comprehensive understanding of groundwater vulnerability. In the process of assessing the DRASTIC index (DVI), each parameter was allocated a numerical rating ranging from 1 (representing the lowest pollution potential) to 10 (representing the highest pollution potential). This rating was subject to the specific value of the parameter found at the location under consideration, as indicated in Table 3-9. Each parameter was assigned a weighting factor, which ranged from 1 to 5, in accordance with their relative influence in impacting the pollution potential, as depicted in Table 3-8. The process of calculating the DVI entails the multiplication of each parameter weight by the site rating, followed by the summation of these values as represented in Equation 15.
Where, Dr = Rating for the depth to water table , Dw = Weight assigned to the depth to water table, Rr = Rating for aquifer recharge , Rw = Weight for aquifer recharge , Ar = Rating assigned to aquifer media , Aw = Weight assigned to aquifer media, Sr = Rating for the soil media, Sw = Weight for the soil media, Tr = Rating for topography (slope), Tw = Weight assigned to topography, Ir = Rating assigned to impact of vadose zone, Iw = Weight assigned to impact of vadose zone, Cr = Rating for rates of hydraulic conductivity, Cw = Weight given to hydraulic conductivity. The higher the DRASTIC index value, the greater the groundwater pollution potential and aquifer vulnerability. The Drastic index was classified into three classes: Impact of vadose zone media (I) 5 Hydraulic Conductivity of the aquifer (C) 3 The parameters employed in the DRASTIC methodology were derived from various sources, including the topographical data obtained from the Uganda Digital Elevation Model (DEM), the Climatic Research Unit (CRU), the United States Geological Survey (USGS), as well as results of hydrogeological field surveys at the district level, which included water level measurements. In order to input the collected data into Arcview 10.6 GIS and avail the benefits of versatile data storage, manipulation, and analysis capabilities at varying scales and formats, a database was established as per Voudouris (2009). Upon entry into the database, it became plausible to formalize all information into data layers that conformed to the WGS 1984 coordinate system. These layers were subsequently calibrated in order to generate thematic maps. The process of constructing the aquifer vulnerability map involved the utilization of a Weighted Sums overlay method to overlap various layers. Based on the DRASTIC scoring system, three distinct levels of vulnerability were identified: low (scores below 100), medium (scores ranging from 100 to 140) and high (scores exceeding 140). The term "water table" is commonly used to refer to the vertical level of the water surface within an unconfined aquifer. Lower water table levels are associated with an increased likelihood of contaminat ion, while deeper water table levels correspond to a reduced probability for the occurrence of contamination. The determination of the depth to the uppermost part of a confined aquifer is achieved through the utilization of the "depth to water" concept. This statement denotes the quantity of water that infiltrates the land surface per unit area and eventually percolates into the water The term "zone of aeration" describes the uppermost layer of the vadose zone, which is subject to significant biological activity and weathering. The soil's properties exhibit a pronounced influence on the magnitude of infiltration recharge that can transpire into the subsurface. The term "slope" pertains to the incline or decline of the terrain elevation. Assisting a pollutant in remaining on the surface within a specific location for an extended period of tim e enables its gradual infiltration into that area. The term is denoted as material situated in the unsaturated zone. The confining layers situated above an aquifer are utilized in a confined aquifer, as the medium that bears the greatest influence. Such a zone is known to be considerably restrictive.

5
Hydra ulic conduc tivity (C) (GPD/ ft2) 1-100 100-300 300-700 700-1,000 1000-2,000 >2,000 The notion of aquifer transmissivity pertains to the capacity of an underground layer of permeable rock or sediment to facilitat e water movement, whereby the rate of groundwater flow is governed by a specific hydraulic gradient. The substance contained in the subterranean water system. 3

Integration groundwater potential, quality, and vulnerability raster maps
The variance and covariance of class signatures were measured through the employment of MLC, which simultaneously assigned individual cells to their corresponding signed classes within the signature file. The maps depicting the quality, potential, and vulnerability of groundwater were obtained through raster analysis and subsequently utilized as input in the generation of Iso (Iterative Self-Organizing) cluster signatures via the MLC algorithm. The present algorithm operates under an unsupervised classification method, wherein a user-defined count of one-dimensional categories are created for cells within the multidimensional realm of a multi-band raster. Henceforth, the iterative procedure for assessing the identified class mean values proceeds until definite user specified quantities of iterations are input, or until more than two percent of the cells exhibit variation. "The transition from one cluster to another can be observed in relation to the updated mean within the range of repetitions." The Machine Learning Classifier (MLC) calculates the probability of a cell being a member of a specified class for each class. The underlying logic of this weight and probability approach is predicated upon the Bayesian selection framework. The determination of the probability values for each cell and class is executed by means of the computation of the means and covariance matrix of each class, which are stored in the signature file. The Iso raster map output was categorized into five distinct classifications that corresponded to the degree of suitability and accessibility of groundwater for the purpose of irrigated agriculture.

Rainfall
The map of the rainfall for the district was classified into five classes as shown in Figure 4-1 in units of cm/year. The minimum and maximum rainfall was 390 and 1128 mm/year respectively. The uppermost score was assigned to the southern areas getting the highest rainfall, whereas the rainfall magnitude decreased towards the northern side of the district and the assigned rating also decreased towards the north.

Lineament density
Map of flow density illustrates that central part of the district was considered as excellent and promising groundwater area (Figure 4-4).

Slope
For the rank assignment effect, slope was classified into 5 classes. The maximum rating was allocated to flat terrain with slope values from 0 to 5.69, and the ratings decrease as the slope value increases. The maximum slope values ranged from 38.9 to 80.6 with the lowest of 0.03 being found in western, central and southern parts, as shown in Figure 4-5.  tropical rainforest. Subsistence farmland has been considered the most suitable recovery area since it promotes the filtration of rainwater and irrigation water.       Melese and Belay's study in Muga watershed, Abay basin in Ethiopia in 2022 found that about 60% of the area had moderate to very good groundwater potential for irrigation while 39% of the area had poor to no potential. Obuobie and others' study in north-eastern Ghana in 2013 found that only 7% of the area had good potential for irrigation while 25% of the area had moderate potential and 68% had poor potential. Ganapuram and others' in Musi basin in Prriyabrata in 2009 in the semi-arid region of India had similar results to Mbale district with 15% of the area having very good potential, 65% having good potential and 20% ha poor potential. Markos and others conducted a study in 2021 in the Adilo catchment in Ethiopia, which showed that around 13% of the area had very good groundwater potential for irrigation, 56% had good potential and 31 had good potential

International Journal for Multidisciplinary Research (IJFMR)
In conclusion Mbale district has relatively high percentages of the areas with good and very good groundwater potential for irrigation compared to other regions studied. While the percentage of poor potential is also relatively high, the overall availability of groundwater is promising for irrigation development in the region.

Groundwater quality 4.2.1 Salinity Hazard
The EC of the samples collected in the district was determined and the spatial distribution map is as in Figure 4-10. Overall, there is a difference in EC that arrays from 106.5μmhos/cm to 932μmhos/cm attributed to predominant anthropological activities in the research area. In agreement with Rao and others' (1986), the high EC value was due to reduced osmotic movement of vegetation which inhibits absorption of water and nutrients.

Toxicity and Miscellaneous Effects
The chloride concentration is offered as a constraint that determines the exact toxicity of the ions. Chloride values ranged from 6.2 mg/L to 245 mg/L. The spatial distribution of chloride concentration is shown in Figure 4-13. Chloride concentrations were observed to be moderately high. Chloride is needed in minimal quantities by plants, but is toxic to sensitive plants at elevated concentrations, as described by Bauder et al., 2003 in Table 4-5.

Bicarbonate and pH
The pH parameter was found to have different influence ranges for sensitive crops. The pH value ranged from 5.2 -7.9 in studied samples, as in

Irrigation Water Quality Index
SAR, HCO3 -, EC, Na+ and Clparameters were used during computation of IWQI. maps of spatial distribution were organized per parameter and integrated. The integration created the IWQI index map after geostatistical analysis (Figure 4-16). The analysis of IWQI map was categorized into two classes that depict water use restrictions and they were High Restriction (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55) and Severe Restriction (32-40). 90.90% (188.98km 2 ) of the study area was under high restriction water use and 9.10% (18.92km 2 ) was under severe restriction water use. Results from several studies done are similar to the study. For instance, Supriyanto and others' (2022) study in Polinella, India found that the groundwater quality in the area was mostly suitable for irrigation, but there were some limitations due to high levels of salinity and alkalinity in certain areas. The study in Gaza strip found that the groundwater in the area was contaminated with nitrate and other pollutants from agricultural activities and sewage (Gharbia et al., 2021). Likewise, Al-Hadith and others (2019) study in Baghdad, Iraq found that the groundwater in the area was contaminated with nitrate and other pollutants from agricultural and industrial sources. Eid and

Figure 4-16: Irrigation Water Quality Index (IWQI) map
others (2023) Souf Valley's study in Algeria found that the groundwater quality in the area was generally poor due to high levels of salinity, but that some improved agricultural practices could still allow for use of the water. Rawat and others' (2018) study in Kanchipuram district, Tamil Nadu, India found that the groundwater quality in the area was generally good for irrigation, but that there were concerns regarding overuse and depletion of resources.
However, it is clear that the groundwater quality for irrigation in Mbale district falls primarily in high restriction use category, indicating limitations on its use for agriculture. This is consistent with other studies globally that have found water resources to be limited or of poor quality for irrigation. It is important to note that water quality deterioration are global concerns, and sustainable management practices are needed to ensure the availability of adequate and safe water resources for future use. Further research and monitoring are necessary to track changes in water quality and develop effective management practices.

Groundwater Vulnerability 4.3.1 Depth to groundwater
The recorded depths to the groundwater table experienced a variation from 38.45 meters to 70.34 meters, as depicted in Figure 4-17. The depth to groundwater was then categorized and assigned rates g from 1 (minimum influence on vulnerability) to 10 (maximum influence on vulnerability) as in Figure 4-18.

Net recharge
The study demonstrates that the net recharge exhibited variability within the range of 351.98 to 819.76m per annum, as visualized in Figure 4-19. The map depicting net recharge was subsequently classified and allocated values ranging from 1(indicating negligible effect on vulnerability) to 10 (indicating substantial effect on vulnerability), as demonstrated in Figure 4-20.

Aquifer media
Aquifer media refers to either consolidated or unconsolidated geological formations that effectively serve as a medium for water storage, transportation and extraction. The primary aquifer was restricted beneath strata comprised of fresh granite and bedded sandstone, as depicted in

Drastic Vulnerability Index
Scores of DRASTIC varied from 96 to 156, bearing in mind the determined ratings and weightings. The vulnerability was categorized as low (less than 100) occupying an area of, medium (100 to140) and high (greater than 140) in regard to data from hydrogeological surveys (Figure 4-31). 11.23km 2 (5.40%) had low vulnerability, 77.83km 2 (37.44%) had medium vulnerability and 118.83km 2 (57.16%) had high vulnerability. The groundwater vulnerability results in Mbale district show that the majority of the area (57.16%) has a high vulnerability to contamination which highlights the need for proper management and protection of groundwater resources in the region, particularly related to irrigation practices.

Figure 4-31: Drastic Vulnerability Index map
Comparing these results with studies done in other regions, Assef and others' (2022) study in Doornfontein area, Johannesburg, South Africa showed a high vulnerability of 60% of the area. Machar and others (2017) found out medium to high vulnerability in two sub districts in Banda Aceh city, Indonesia with 45.5% and 31.8% of the area respectively being vulnerable. Abad and others (2017) found that the Zanjan Plain in Iran had medium, with 28% of the areas being vulnerability. Also, Hamed and others in 2022 in Central Erbil Basin in Iraq found groundwater vulnerability was high, with 45% of the area being vulnerable.
Overall, the results indicate that groundwater vulnerability is a significant issue across different regions, with some areas being more vulnerable than others. In the context of irrigation, it is crucial to consider the impact of agricultural activities on groundwater quality and to use appropriate measures to protect groundwater resources from contamination. The results of these studies suggest a need for better management and protection of groundwater resources to ensure sustainable use for irrigation.

Groundwater availability and suitability
An unsupervised MLC classification method integrated quality, vulnerability and potential maps of groundwater to form availability and suitability map. The integrated map (Figure 4-32) was categorized to five classes with their corresponding areas: 17.58% very poor (36.56km 2 ), 13.84% poor (28.77km 2 ), 12.69% good (26.39 km 2 ), 31.46% very good (65.39km 2 ) and 24.43% excellent (50.78km 2 ). The sub counties in Mbale district that have inadequate and unsuitable groundwater for irrigated agriculture are Budwale, Wanale, Busano, Bubyangu, Bufumbo and Nyondo since they belong to the zones of very poor and poor. The remaining sub counties have available and suitable groundwater for irrigated agriculture since they belong to the zones of good, very good and excellent. Comparing this to the studies done in various regions around the world, it appears that the results are somewhat consistent with the findings in Algeria and Iraq where ground water availability and suitability for agriculture are generally good with some areas of lower suitability (Kadri et al., 2022;Al Maliki et al., 2020). However, the results in North China plain indicate challenges with groundwater quality with less than a third of the area suitable for irrigation (Guo et al., 2021).

Figure 4-32: Groundwater Availability and Suitability Map
The study in United Arab Emirates showed relatively low groundwater availability with less than 10% of the area suitable for irrigation (Murad, 2014). Bari's study in 2021 in India showed similar trends as Mbale district with a significant portion (29.03%) having poor groundwater suitability for irrigation.
Overall, while the results in Mbale district are positive compared to other regions, there is still room for improvement in ensuring adequate groundwater availability and suitability for irrigation. Managing groundwater resources, promoting sustainable agriculture practices and investing in infrastructure to enhance irrigated agriculture could help address this challenge.

CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusions
Based on the results obtained through unsupervised Multivariate Cluster (MLC) classification method, it can be concluded that the groundwater availability and suitability for irrigation purposes in Mbale district is relatively good. Approximately 44% of the groundwater potential is classified as very good or good, while just a quarter is classified as poor. This indicates that there is a potential for irrigation using groundwater in the area.
However, it is important to note that the quality of groundwater has a significant impact on its suitability for irrigation. In this area, approximately 91% of the groundwater falls under the high restriction use category, indicating that it may not be suitable for irrigation without strict management practices. Additionally, the vulnerability of groundwater is medium to high in over 90% of the area, indicating that further monitoring and management of the groundwater resources is necessary to ensure sustainable utilization.
Overall, these results suggest that while groundwater may be a potential source for irrigation in the area, careful consideration of both the groundwater quality and vulnerability is essential before making decisions.

Recommendations
The following interventions should be implemented to make the study effective: • Further research and monitoring are necessary to track changes in water quality and develop effective management practices. • There is need for better management and protection of groundwater resources to ensure sustainable use for irrigation. • There is need to promote sustainable agricultural practices and invest in infrastructure to enhance irrigated agriculture • Study should be carried out in Mbale district to determine the crops to be grown in each region