Herein, optimal groundwater pumping solutions based on a variety of energy resources and water storage options are estimated and classi ed. Each energy source and water storage option is rst characterized considering energy, economic, and environmental criteria. A multi-criteria decision making (MCDM) process based on the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) is subsequently applied to identify and classify the optimal groundwater pumping solutions under such a multidimensional framework. An aquifer located in the southeast of Spain is analyzed in a case study to assess the proposed optimal MCDM-based approach. Conventional diesel-based equipment, solar PV power plants, and direct grid connection, as well as three water storage systems|direct pumping, seasonal storage, and annual storage|are identi ed as potential energy sources and water storage options, respectively. Characterization and visualization of these energy and water storage systems, as well as prioritized option results, are also discussed herein. Keywords: Groundwater pumping, Economic-Energy-Environment analysis, Multi-Criteria Decision Making (MCDM), AHP/TOPSIS Method. Nomenclature AHP Analytic Hierarchy Process ANP Analytic Network Process CI Consistency Index CR Consistency Ratio GIS Geographic Information System ELECTRE ELimination and Choice Expressing Reality MCDM Multi-Criteria Decision Making NIS Negative Ideal Solution PIS Positive Ideal Solution PROMETHEE Preference Ranking Organization METHod for Enrichment of Evaluations RES Renewable Energy Sources RI Random Index TOPSIS Technique for Order Performance by Similarity to Ideal Solution PV PhotoVoltaic PVWP PV water pumping 1. Introduction With the relevant integration of renewable energy sources (RES) into most of the sector, renewables currently represent an energy transition opportunity in agriculture [1]. Considerable e orts are being made to improve the eciency of irrigated agriculture [2], where current com- mercially available water pumps mostly run on electricity or diesel-powered equipment [3]. Among the di erent so- lutions, solar photovoltaic (PV) water pumping systems can be used eciently for water pumping in agriculture Corresponding author Email address: angel.molina@upct.es (Angel Molina-Garcia)[4]. Moreover, they play a vital role in reducing the con- sumption of conventional energy sources and their environ- mental impact for water pumping applications [5]. Exam- ples of di erent PV water pumping systems are provided in various studies, mainly as an alternative to traditional diesel pumps [6]. Ouoba et al. describe the sizing and op- timizing of PVWP solutions applied in the tertiary sector at the Faculty of Science and Technology of Mohamme- dia (Morocco) [7]. The optimal size of PV water pumping systems to replace diesel-fueled generators for an exist- ing farm in Oman is determined in [8]. Recently, PVWP technology has been proposed to ful ll the domestic wa- ter requirements of ve isolated houses located in a remote Moroccan area [9]. An analysis of the prevailing conditions for irrigating cassava based on PV solar technology is de- scribed in [10], where solar energy resource availability is also studied. However, PVWP solutions have rarely been implemented, and they are considered as electric energy sources only in remote locations [11, 12], as a part of hy- brid solutions |i.e. integrating fuel cells and solar panels [13], or as standalone solar PV with groundwater pumped- hydro-storage systems [14]. In addition, and according to the authors in [15], the 'cost per watt' is a relevant draw- back to considering PV installations as a major solution for water pumping purposes. Moreover, Sampedro et al. re- cently armed that fossil fuel subsidies are one of the most prominent barriers to tackling climate change, encouraging inecient energy consumption, and diverting investment Preprint submitted to Renewable Energy https: // doi. org/ 10. 1016/ j. renene. 2020. 08. 079 September 1, 2020 away from clean energy sources [16]. Closas and Rap show that most policies and projects promoting PV solar-based groundwater pumping for irrigation through subsidies and other incentives overlook the real nancial and economic costs [17]. PVWP can thus be considered as a promising alternative to conventional pumping systems and a cost- e ective application [18]; mainly in remote o -grid areas of developing countries |such as Algeria [19]| and due to the ongoing in ation of fuel costs leading to a consistent increase in operational and maintenance costs [20]. Under this scenario of di erent possible combinations of resource usage and water storage options for groundwa- ter pumping solutions, the application of Multi-Criteria Decision Making (MCDM) methodologies constitutes a useful tool to prioritize these combinations and estimate the most appropriate solutions under a multidimensional framework. An MCDM approach is de ned as a selection process from among di erent alternative courses of action, based on a set of criteria, to achieve one or more objec- tives [21, 22, 23]. During the last decade, a large num- ber of MCDM methodologies have been proposed under di erent hypotheses. Some examples include the prefer- ence ranking organization method for enrichment of eval- uations (the PROMETHEE methodology [24]) developed by Brans et al. [25]; the ELimination and Choice Express- ing Reality (ELimination Et Choix Traduisant la REalit e {ELECTRE) from the French school [26]; the Analytic Hi- erarchy Process (AHP) and the Analytic Network Process (ANP) both of which were proposed by Tomas Saaty [27]; and the technique for order performance by similarity to ideal solution (TOPSIS) developed by Hwang and Yoon [28]. Several studies based on an individual or combined application of MCDM techniques have been carried out in the eld of groundwater management. The combination of AHP/ANP methodologies was proposed by Agarwal et alto determine the corresponding weights or coecients of relevance for each criterion, which in uence the eval- uation of potential groundwater zones. The AHP/ANP methodologies were used to delimit the area of potential groundwater in a region of India [29]. Recently, An et al. designed a sustainability assessment methodology to prior- itize groundwater decontamination technologies based on a combined fuzzy logic process from both the AHP and ELECTRE methodologies [30, 31]. Water management strategies have also been analyzed, not only by using the TOPSIS methodology alone [32], but also in combination with the AHP methodology [33]. Other work focused on groundwater pumping solutions has been described in the literature. For example, Liu et al. optimized a pumping well design located in northwestern China by using the TOPSIS method [34]. The potential for groundwater in a semi-arid region of India was also evaluated by combin- ing remote sensing techniques, a Geographical Information System (GIS) computer tool and the AHP methodology [35]. Indeed, GIS has been recently used to analyze dif- ferent resources such as solar, wind, biomass, geothermal, and hydro-power, to determine potential sites within theFukushima Prefecture (Japan) based on geographic, topo- graphic, and land use constraints [36]. Therefore, by con- sidering the eld of management and groundwater pump- ing, the scienti c literature arms that MCDM is a well- known branch of decision making. Indeed, it is a branch of a general class of operations research models that are used to solve decision problems considering several decision cri- teria. However, there is a lack of contributions to evaluate and prioritize di erent groundwater pumping alternatives. Moreover, the results in [37] arm that renewable energy sources (RES) contribute to achieving the '3E' objectives: energy, economic, and environmental goals. These objec- tives have been studied and proposed in other areas, such as waste management [33], or in the role of wind energy in China [38] and Japan [39]; but they have not been taken into account in the eld of agriculture to date. Subse- quently, and from the perspective of the 3E objectives and the evaluation and prioritization of groundwater pumping solutions for irrigation purposes, the main contributions of this paper can be summarized as follows: A multidimensional characterization of di erent en- ergy resources and water storage options based on energy, economic and environmental criteria. An MCDM application based on AHP/TOPSIS tech- niques to prioritize such resources and water storages within a multi-objective framework. The rest of this paper is structured as follows: Section 2 describes the proposed methodology; the case study is described in detail in Section 3; the results are discussed in detail in Section 4; and, nally, the conclusions are given in Section 5. 2. Proposed Methodology 2.1. Overall process The proposed methodology is rstly based on the identi- cation of the groundwater pumping systems based on dif- ferent energy resources and water storage options. These systems are then characterized through a multidimensional criteria scenario to consider energy, economic, and envi- ronmental aspects. This characterization is in line with recent approaches addressed by the authors to discuss the application of renewables for underground water resource purposes [40]. From this preliminary characterization in a multidimensional framework, an MCDM approach is used to provide a classi cation of groundwater pumping sys- tems, with the aim of making the identi cation of optimal solutions easier. Di erent applications under some disci- plines can be found in the literature, such as energy, supply chains, tourism management, construction, risk manage- ment, distribution [41]. Therefore, a combination of the AHP and TOPSIS solutions is applied according to the previous contributions discussed in Section 1. In general, a literature review indicates that AHP is a popular ap- proach to cope with MCDM problems [42]. The Analytic 2 Figure 1: Proposed methodology: general overview. Hierarchy Process (AHP) is used for criteria weight cal- culation purposes. Subsequently, the Technique for Order Performance by Similarity to the Ideal Solution (TOP- SIS) allows us to identify and prioritize the optimal alter- natives. The following advantages justify the application of the TOPSIS approach [43]: ( i) TOPSIS logic is ratio- nal and understandable; ( ii) the computation processes of TOPSIS are straightforward; ( iii) the concept of TOP- SIS pursues the best alternatives for each criterion from an easy-to-understand mathematical viewpoint; and ( iv) the relevance weights are incorporated into the compari- son procedures. Figure 1 shows the proposed methodology schematically. 2.2. Criteria selection: multidimensional characterization In line with the aim of the paper, the criteria selec- tion involves a multidimensional characterization of the resources and water storage combinations including eco- nomic, energy and environmental (3E) aspects, as well as additional hydrological and social criteria (see Table 1). It must be noted that all criteria and sub-criteria are char-Table 1: Criteria selection: multidimensional characterization of so- lutions. Criterion Sub-criterion Label Units EconomicEnergy facility investment C1.1.1Euro=ha Water infrastructure investment C1.1.2Euro=ha Maintenance costs C1.2.1Euro=ha Operation costs C1.2.2Euro=ha Sale of surplus energy (retribution) C1.2.3Euro=ha Energy Nominal power value of the facility C2kW=ha Environmental CO2emissions of the groundwater pumping systems C3kCO 2=ha Water Evaporation from the water storage solutions C4m3=ha Social Jobs created in the rural area C5Jobs=ha acterized in quantitative terms according to [44]. This previous work gives the corresponding initial quantitative values for each criterion and sub-criterion. A detailed de- scription of the main criteria is provided as follows: 2.2.1. Economic ( Euro=ha ) This criterion includes economic aspects that decisively in uence the viability of the di erent groundwater pump- ing systems. The economic criterion has been divided into ve sub-criteria: Energy facility investment ( Euro=ha ). This sub- criterion analyses investment data, mainly the energy installation costs, i.e., diesel generator sets, power line connections, and PV modules and their connectors, inverters, etc. Water infrastructure investment ( Euro=ha ). This sub-criterion includes the investment costs of all com- ponents for irrigation and pumping facilities (rafts or irrigation reservoirs, pipelines, pumps, drilling, drains, lters, sensors, and irrigation meters, etc.). Maintenance costs ( Euro=ha ): These expenses refer to the maintenance of both water storage and energy resource facilities, such as the cleaning of lters, the lubrication of moving parts, installation safety, etc. Operations costs ( Euro=ha ). This sub-criterion refers to the expenses derived from the operational costs of the facilities. Fuel costs for diesel equipment and electricity supply costs for systems connected to the grid, are the most relevant costs. Reimbursement for sale of surplus electric energy (Euro=ha ): some combinations can be connected to the grid via a net metering system. Consequently, the sub-criterion of reimbursement for the sale of surplus electric energy must be considered. With the exception of the reimbursement for sale of surplus electric energy sub-criterion, which should be maximized, the rest of the economic sub-criteria are cost criteria and should thus be minimized. 2.2.2. Energy ( kW=ha ) This criterion mainly focuses on analyzing the value of the nominal power of the electrical energy facility. This cri- terion should be minimized |as some energy alternatives 3 require a greater installed power, even under a similar en- ergy demand| to ensure that the electrical supply meets the system requirements. 2.2.3. Environmental ( kgCO 2=ha) This environmental criterion refers to the CO 2emissions of the di erent combinations of groundwater pumping sys- tems. This criterion should be minimized to ensure lower emissions and environmental impact. 2.2.4. Water or hydrological ( m3=ha) Water is a scarce resource in many areas and represents a limiting factor for agriculture. Therefore, the reduction of water losses is a priority issue in irrigation system design. The water or hydrological criterion analyzes the evapora- tion of water that occurs on the surface of the reservoir due to solar radiation. It should be minimized. 2.2.5. Social ( Jobs=ha ) This criterion analyzes the impact of the combinations on the social level. The number of jobs created in the rural area for each combination is determined. Therefore, this criterion should be maximized. 2.3. Analytic Hierarchy Process (AHP): weighting factor estimation The Analytic Hierarchy Process (AHP) [45] is accepted by the research community as a robust and exible MCDM model to address complex decision problems [46, 47]. Fur- thermore, AHP/fuzzy AHP and integrated methods were ranked rst in their number of contributions relevant to sustainable and renewable energies [48]. The AHP consists of three main tasks: ( i) structuring a complex decision as a hierarchy of goals, criteria, and alternatives; ( ii) con- ducting a pairwise comparison of all the elements in each level of the hierarchy with respect to each of the criteria involved in the previous levels; and ( iii) vertically synthe- sizing judgements on the di erent levels of the hierarchy. Therefore, the AHP attempts to estimate the impact of each alternative on the overall hierarchy's objective. The multi-objective index evaluates several impacts, and is a weighted-sum of the technical impacts [49]. In this study, we apply this methodology for criteria weight estimation, assuming that the quanti ed judgements provided by the decision maker for any criteria pair ( Ci,Cj) are the entries in the following n{order matrix: C=C1 C2 C3 ... Cn0 BBBBB@C1 c11C2 c12C3 c13. . . :::Cn c1n c21c22c23::: c 2n c31c32c33::: c 3n ............... cn1cn2cn3::: c nn1 CCCCCA(1) Note that the input cijrefers to the relative signi cance ofCiwith respect to Cj, i.e.,cij(wi=wj). This concept can be further extended and expressed as follows:Table 2: Valuation scale in the pairwise comparison process. Labels Preference verbal judgments ( ivsjcriteria) Saaty's scale (EI) CiandCjare equally important 1 (S+I) Ciis slightly more/less important than Cj 3 - 1/3 (St+I) Ciis strongly more/less important than Cj 5 - 1 /5 (VSt+I) Ciis very strongly more/less important than Cj 7 - 1/7 (Ex+I) Ciis extremely more/less important than Cj 9 - 1/9 cij(wi=wj);8i;j= 1;2;:::;n cii= 1;8i= 1;2;:::;n Ifcij= 6= 0, thencji= 16= ;8i= 1;2;:::;n If the criterion Cibecomes more relevant than Cj, thencij(wi=wj)>1 A positive and symmetric matrix ( C) is then determined, with its main diagonal containing ones. Accordingly, the decision maker provides value judgments to ll in an upper triangular matrix. Moreover, as the Saaty scale indicates [45], the values assigned to each entry cijusually lie within an interval of [1 ;9], or its reciprocal. Table 2 summarizes the decision maker's verbal judgements and the pairwise comparison process considered in our case study. The weight vector is given by the eigen-vector corre- sponding to the maximum eigen-value max of theC- matrix. The Consistency Index ( CI) is then calculated asCI= (max{n)=(n{ 1);max>nif the expert shows a minor inconsistency. The Saaty scale provides the next indicator for the Consistency Ratio ( CR), determined by CR=CI=RI , whereRIis the Random Index estimated as the average value of CIfor random matrices [50, 51]. Table 3 summarizes the Random Index ( RI) for matrix orders from 1 to 15, where nrepresents the number of compared criteria. The CIvalue is subsequently used to quantify the probability that the judgement matrix was randomly estimated [52]. 2.4. TOPSIS Method: prioritization of optimal combina- tions The TOPSIS method, was developed by Ching-Lai Hwang and Kwangsun Yoon [53] and is classi ed as one of the classical MCDM methods [54]. This approach selects an optimal combination according to the farthest distance from the negative ideal solution (NIS) and the shortest distance from the positive ideal solution (PIS). The opti- mal solution provided by TOPSIS constitutes a trade-o between both ideal solutions, as shown in Fig. 2. It re- mains closest to the PIS (labeled as C) and farthest from the NIS (labeled as Band Drespectively). TOPSIS thus considers the distances to both the PIS and the NIS si- multaneously. The method structure is discussed in the following subsections. 2.4.1. Decision matrix: de nition and normalization Ai(i = 1;2;:::;m ) constitutes the alterna- tives/solutions to be prioritized and ordered by the 4 Table 3: Random Index ( RI) for matrix orders (1{15) [51]. n 1{2 3 4 5 6 7 8 9 10 11 12 13 14 15 (RI) 0.00 0.5247 0.8816 1.1086 1.2479 1.3417 1.4057 1.4499 1.4854 1.5140 1.5365 1.5551 1.5713 1.5838 Figure 2: The concepts of PIS and NIS in a TOPSIS approach. Table 4: Decision matrix for the TOPSIS approach. w1w2. . .wj. . .wn C1C2. . .Cj. . .Cn A1x11x12 . . .x1j. . .x1n A2x21x22 . . .x2j. . .x2n . . . . . . . . . . . . . . . . . . . . . Amxm1xm2. . .xmj . . .xmn series of criteria Cj= (j= 1;2;:::;n ). A decision matrix is de ned as summarized in Table 4, where xijrefers to the performance score of alternative Aiwith respect to criteria Cj, andW= [w1;w2;:::;w n] denotes the weighting vector associated with such criteria. The value of each criterion is then normalized through the Euclidean norm in order to homogenize each criterion with the same scale. nij=xijqPm i=1x2 ij(2) The normalized weighted decision matrix ( V) is deter- mined through the following expression: vij=wj nij (3) wherej= 1;2;:::;n ;i= 1;2;:::;m ; andPm j=1wj= 1; corresponding to the weighting vector obtained by the AHP methodology (see Section 2.3). 2.4.2. Priorization of solutions The positive ideal solution (PIS) is referred to as A+, and the negative ideal solution (NIS) is represented as A.These expressions can be determined as follows ( i= 1;2;:::;m ): A+=fv+ 1;v+ 2;:::;v+ ng=(max ivij;j2J)formaximizationcriterion (minivij;j2J0)forminimizationcriterion A=fv 1;v 2;:::;v ng=(minivij;j2J)formaximizationcriterion (max ivij;j2J0)forminimizationcriterion ; where theJ-index is associated with the criteria that rep- resent bene ts, and the J0-index is associated with the criteria indicating losses. The separation of each alter- native (PIS and NIS) is obtained through the following expressions: PIS!d+ i=hPm j=1 vijv+ j2i1=2 ;i= 1;2;:::;m: (4) NIS!d i=hPm j=1 vijv j2i1=2 ;i= 1;2;:::;m: (5) The ranking score Riis rst calculated as follows: Ri=d i d+ i+d i;i= 1;2;:::;m: (6) Subsequently, if Ri= 1 thenAi=A+, and, ifRi= 0, thenAi=A. Nevertheless, if the Rivalues are close to one, then the priority for the ithalternative will be higher. The best alternatives according to the ranking score Riare ordered and classi ed in descending order. These results correspond to the case study described in detail in Section 3. 2.5. Additional analysis: homogeneous weighting vector In MCDM approaches based on the TOPSIS technique, it is very common to include an additional analysis con- sidering a homogeneous weighting vector. The TOPSIS methodological process is then executed assuming this ho- mogeneous weighting vector, and not based on the AHP results. Therefore, all criteria take the same relevance as well as their corresponding sub{criteria. The aim of this process is to compare the subsequent classi cation from both analyses: ( i) the expert decision and ( ii) the homo- geneous weight vector decision. Consequently, variations in the list of prioritized alternatives and percentage dif- ferences between both classi cations provide relevant in- formation on the suitability of the expert evaluation with respect to a homogeneous analysis. 5 3. Case Study 3.1. Description of the study area The case study focuses on Aquifer 23, in the Castilla La Mancha Region (eastern part of Spain). The climate char- acteristics of this zone are labeled as Bsk, a typical conti- nental Mediterranean climate (14.4oC, 416mm) consisting of cold winters with periods of frost and signi cantly hot summers. In terms of energy, due to the widespread use of diesel equipment for water extraction, a strong dependence on fossil fuels can be identi ed, not only in this area, but also in agricultural areas that depend on pumping ground- water solutions in the Mediterranean region. For solar re- sources, this Spanish area o ers a representative annual average value of around 4900 kWh=m2per day, with a uniform territorial distribution and variation of less than 5%. Therefore, the availability of this renewable resource on the agricultural land surface provides a remarkable op- portunity to supply energy requirements for groundwater pumping. Three variables represent the main features of the aquifer study area: the aggregated agricultural area, the aquifer depth, and the water requirements. The estimated depth of the aquifer ranges from 10 to 55 m, encompass- ing the depth ranges of most of the plots irrigated by the aquifer. A range from 1500 to 10500 m3=year is consid- ered for water requirements, according to the mosaic of Mediterranean crops involving mostly vines. The aggre- gation of agricultural areas is estimated to be between 1 and 2000ha, which is in line with current crops depend- ing on the aquifer and considering that larger areas would cause excessive pressure losses throughout the grid. 3.2. Energy resources and water storage combinations: identi cation of alternatives As can be seen in Figure 1, alternatives are identi ed through a combination of di erent energy resources and water storage options. Following Section 1, diesel genera- tor, isolated PV power plant, net energy metering schemes for PV installations and direct connection to the grid are selected as potential energy resources. These sources rep- resent the most common solutions currently implemented in most groundwater pumping systems as well as the most promising renewable solutions to be integrated into the agriculture sector. With regard to the water storage op- tions, three di erent possibilities are considered for the case example, according to the irrigation crop require- ments: direct pumping without any water tank storage, seasonal water storage, and annual water storage. Four di erent energy resources and three water storage op- tions are thus identi ed, giving a total of twelve di erent groundwater pumping systems to be characterized and pri- oritized from a multidimensional framework according to Table 1 and Section 2.2. Table 5 summarizes the alter- natives and the corresponding energy resources and water storage options. In addition, Figure 3 shows graphicallyTable 5: Energy resources and water storage combinations: identi - cation of alternatives. Alternative Label Energy resource Water storage option Alternative 1 A1 Diesel generator Annual water tank storage Alternative 2 A2 Diesel generator Seasonal water tank storage Alternative 3 A3 Diesel generator Direct pumping Alternative 4 A4 Isolated PV power plant Annual water tank storage Alternative 5 A5 Isolated PV power plant Seasonal water tank storage Alternative 6 A6 Isolated PV power plant Direct pumping Alternative 7 A7 Net energy metering { PV Annual water tank storage Alternative 8 A8 Net energy metering { PV Seasonal water tank storage Alternative 9 A9 Net energy metering { PV Direct pumping Alternative 10 A10 Direct connected to the grid Annual water tank storage Alternative 11 A11 Direct connected to the grid Seasonal water tank storage Alternative 12 A12 Direct connected to the grid Direct pumping the di erent groundwater pumping systems to be consid- ered in line with the overall process general scheme pro- vided in Figure 1. With regard to the water storage options, the annual storage systems include a reservoir with a sucient capac- ity to store the water required at the time of irrigation for a year; thus, they require a low amount of energy in comparison with the other alternatives but have signi - cant water requirements due to their water evaporation losses. In the case of seasonal water storage facilities, such reservoirs only store, ahead of the irrigation season, the amount of water demanded during the irrigation season. The size and cost of hydraulic infrastructure is consider- ably lower than that of annual water storage solutions, but more energy facilities are required. The direct pumping option lacks storage systems since water is pumped only when required. Therefore, the cost of water infrastructure is considerably reduced at the expense of increasing energy installation costs. These alternatives require more pump- ing points to avoid depletion of the well when the water demand per unit of time is very high. This is the most common case, especially in small individual facilities. Based on the proposed methodology described in Sec- tion 2, as well as the di erent energy resource and water storage options summarized in Table 5 |where the alter- natives are labeled as Ai,i= 1;2;:::;n ; withn= 12|, Figure 4 graphically represents a multiobjective framework based on the proposed methodology described in Section 2, as well as the di erent energy resource and water stor- age options summarized in Table 5, where the alternatives are labeled as Ai,i= 1;2;:::;n ; withn= 12. It is based on the di erent criteria de ned in Section 2.2, and the ap- plication of such criteria on each alternative/solution in a multidimensional analysis. The total number of criteria is consequently considered for the decision making process, asCj,j= 1;2;:::;m , withm= 9 according to Table 1. Finally, in order to carry out the weight estimations and criteria based on the expert survey and AHP methodology, a questionnaire is prepared and provided to the experts. This is a pseudo{Delphi technique, as the members take part in the decision process but do not interact with each other at any time. This questionnaire is based on Saaty's scale (Table 2), and is intended to perform a pairwise com- parison. Comparisons between pairs of criteria at the same 6 Figure 3: Case study: identi cation of alternatives. level in the hierarchy, determined via the AHP methodol- ogy, are also conducted. 4. Results 4.1. AHP: weighting factor estimation In order to verify the consistency of the AHP method, the consistency ratio ( CR) is determined by each expert. This value was lower than 0.1, which demonstrates the con- sistency of the method. Therefore, it is not necessary to re- vise the judgments of the experts. A comparison between the expert ranking (priorization of expert alternatives) and the homogeneous decision ranking is also included in this work. Table 6 shows the weight vector for both the homo- geneous and expert approaches, according to the hierarchy of criteria described in Figure 4 and the criteria selection de ned in Table 1. From the weighting vector data given in Table 6, it can be initially deduced that economic as- pects are mostly relevant for the experts in comparison to the rest of the criteria. The TOPSIS methodology is then applied with the homogeneous weight vector to determine a di erent ranking of alternatives, which is subsequently compared to the expert ranking results. 4.2. Priorization of optimal combinations: expert results From the participation of experts previously discussed, the TOPSIS application gives a prioritization of the alter- natives described in Section 3.2. Consequently, an iden- ti cation of the most ecient and optimal groundwater pumping systems in a multidimensional analysis is then provided by the expert results. With this aim, a total of six experts were selected, who were completely consistent in their opinions. These experts included PhD engineers with relevant backgrounds in renewable energy technolo- gies and agriculture and hydrology experience, as well as post-graduates focused on renewable energy sources. By considering the vector [ X= aggregated agricul- tural areas, Y= aquifer depth, Z= irrigation crop requirements], the optimal energy resource and water stor- age alternatives selected by the experts for each value of aquifer depth ( m), aggregated agricultural surface ( ha 103) and crop water requirements ( m3=ha103) are then estimated. With the resulting data, a 4D graphic of the op- timal alternatives for each [ X;Y;Z ] value is given. Figure5 shows the corresponding optimal alternatives selected by the experts depending on the aquifer depth ( m), the aggregated agricultural surface ( ha103) and the crop wa- ter requirements ( m3=ha103). Using a color legend for each di erent alternative ( Ai), this 4D-graph o ers infor- mation on recommended groundwater pumping systems in terms of optimal energy resource and water storage op- tions depending on the aquifer depth ( m), the aggregated agricultural surface ( ha103) and the water requirements (m3=ha103). By considering these results, it can be af- rmed that direct water pumping connected to the grid (labeled as A12in Section 3.2) is the most likely alterna- tive selected by the experts. Moreover, this selection is almost independent of the aquifer depth, aggregated agri- cultural area, and water requirements, as can be seen in Figure 5 where the A12alternative is marked in bold in the legend. In addition, Figures 6 and 7 summarize the second and third most preferable and ecient alternatives selected by the experts. Both the direct PV net energy metering and isolated PV power plant solutions (labeled as A9and A6 respectively in Section 3.2) are considered to be the next most valuable alternatives. Therefore, alternatives based on diesel installations are excluded by the experts from the most preferred solutions. In addition, these three op- timal alternatives avoid water storage requirements; thus, direct water pumping options were selected by the experts as the most preferable choices. It can then be concluded that water storage options are less attractive in terms of energy, economic, and environmental factors. However, when we considered the same energy solution but included water storage requirements, this option was ranked lower by most of the experts. Therefore, the consideration of any water storage solution entails a lower value for such alternatives. With regard to conventional diesel solutions, which are currently some of the most commonly used solutions in the agricultural sector, it is noted that, by considering the expert results, the diesel direct pumping solution is exclu- sively preferred under conditions of low aquifer depth and small agricultural area, independent of the water require- ment range (between 0 and 12 m3=(ha103)). Therefore, despite its negative environmental impact and energy de- pendence, the diesel solution is considered the most prefer- 7 Figure 4: Hierarchy of Criteria: energy resources and water storage option alternatives ( Ai). Table 6: Weight vector comparison: homogeneous and expert approaches. Economic Energy Environmental Water Social C.1.1.1 C.1.1.2 C.1.2.1 C.1.2.2 C.1.2.3 C.2 C.3 C.4 C.5 TOTAL Homogeneous 0.0500 0.0500 0.0333 0.0333 0.0334 0.2000 0.2000 0.2000 0.2000 1 Experts 0.1262 0.1218 0.0169 0.0328 0.0381 0.2372 0.1605 0.1794 0.0867 1 Figure 5: First optimal alternatives selected by the experts. 8 Figure 6: Second optimal alternatives selected by the experts. Figure 7: Third optimal alternatives selected by the experts. 9 able alternative only for very low aquifer depths and small agricultural areas, which is not in line with usual aquifer depths and water requirements. Figure 8 shows the rank- ing position of the Direct Diesel Pumping alternative (Al- ternative A3) based on aquifers of di erent depths, di er- ent aggregated agricultural areas, and water requirements of various crops. A color scale is used proportional to the sphere size, depending on the ranking position of the repre- sented Direct Diesel Pumping alternative. As can be seen, Alternative A3is ranked between positions 6 and 7, assum- ing that position 1 is the most valuable alternative. Only for very low-depth aquifers and a small aggregated agricul- tural area, A3is considered the most suitable groundwater pumping solution by the experts; for such a cases, they experts move A3from positions 6 and 7 to position 1. 4.3. Priorization of optimal combinations: homogeneous weighting vector results Complementary to Section 4.2 and as described in Sec- tion 2.5, the TOPSIS methodology is also applied with a homogeneous weight vector to determine a di erent rank- ing of alternatives compared to the expert ranking results. From these results, Figure 9 shows the rst optimal alter- natives selected by the homogeneous weight vector, which di ers from the expert selection. In this case, seasonal pumping based on the PV net-energy metering (labeled as Alternative A8) is estimated to be the optimal alternative for most deep aquifers, aggregated agricultural areas, and crop irrigation con gurations. In a similar way, Figures 10 and 11 present, respectively, the second and third best optimal alternatives determined from the homogeneous weight vector, which correspond to direct pumping net- balance and seasonal pumping isolated PV power plant, respectively. As previously discussed, seasonal pumping alternatives are prioritized by the homogeneous weight vector approach. Moreover, due to the decreased eco- nomic criterion weight in comparison to the expert de- cision vector (see Table 6), alternatives with higher in- vestment costs (including PV installations), either isolated or connected to the grid, increase their ranking positions compared to lower investment cost solutions |mainly al- ternatives based on direct grid connections without any additional facilities. Finally, in an attempt to compare the expert and ho- mogeneous selection of alternatives, Table 7 compares the ranking position order of the alternatives for both ap- proaches. The nomenclature is in line with the identi ca- tion of alternatives described in Section 3.2; nevertheless, a brief description of each alternative is also included in the table. In quantitative terms, the alternatives vary, on av- erage, between positions 3 and 4 (see the column Ranking di erences in Table 7, which indicate the relative change of ranking position with respect to the expert ranking). Consequently, seasonal solutions emerge as preferable al- ternatives to direct pumping systems. Diesel solutions (the alternatives from A1toA3), are less preferred by the homo-geneous weight vector, mainly due to the greater relevance of the environmental criterion, according to Table 6. 5. Conclusions A multidimensional analysis, including economic, en- ergy, and environmental (3E) criteria, is proposed to char- acterize di erent groundwater pumping solutions based on energy resources and water storage options. The AHP/TOPSIS method is used to prioritize the groundwa- ter pumping alternatives depending on the corresponding water storage conditions (annual, seasonal, or direct) and the selected energy resourced (PV, diesel, or grid). A real aquifer located in the southeast of Spain is considered in a case study, in which twelve di erent groundwater pumping alternatives are de ned. By applying the MCDM process using a group of experts, a comparison between expert ranking and homogeneous weight vector ranking is also conducted. Based on the expert decisions, alternatives that avoid any water storage are preferred over those that include annual or seasonal water storage options, which re- quire additional facilities. Consequently, water storage ini- tiatives are less preferable than direct groundwater pump- ing solutions. The most optimal and prioritized alterna- tives based on the multidimensional criteria are ( i) direct grid pumping solutions, ( ii) direct PV net energy meter- ing, and (iii) direct isolated PV power plant pumping. With regard to diesel alternatives, despite their negative environmental and energy dependence impact, they are preferred by the experts under very low aquifer depth con- ditions (between 0 and -15 m) and small agricultural areas (between 0 and 200 ha), being independent of the crop water requirements. The proposed methodology can be applied and extrapolated to other water requirements and aquifer conditions, as well as di erent energy resources. Other resources based on renewables and their integration and evaluation in the groundwater pumping problem are currently under analysis for future publication by the au- thors using an MCDM process. Acknowledgments This work is partially supported by the Spanish Min- istry of Economy and Competitiveness (MINECO), ref- erence TIN2017{86647{P. The authors also acknowledge the support of the Fundaci on S eneca (Region of Murcia, Spain) through the Grant 19882{GERM{15.