The methodology is applied to a case study in Qatar encompassing open-field agriculture, conventional and hydroponic greenhouses. Nine risk factors are selected to perform the AHP, including temperature, humidity, solar radiation, Soil Quality , groundwater depth, groundwater recharge rate, groundwater salinity and groundwater pH.In this study, a geospatial risk composite indicator that represents the environmental condition of the farming industry within the State of Qatar is developed. The analysis is conducted for three types of agriculture, including: open field, conventional and hydroponic greenhouses, which are subjected to nine exogenous stressors that incorporate: temperature, humidity, solar radiation, soil quality , groundwater depth, groundwater salinity, groundwater recharge rate, and groundwater pH. All required details, such as: growing method and size for the four farms that are identified to consist of a hydroponic greenhouse are derived from public data sources. However, in order to perform the necessary assessment for energy and water consumption, it is assumed that the total production area at the farm locations would be utilized for tomato crop production. All weather data are obtained from the worldwide weather database for the 13 meteorological stations in the State of Qatar. The timescale adopted for this analysis is seasonal, where the time range covers from 2014 to 2019. In addition, the data for groundwater and other soil factors are obtained from literature and previous studies, which is either in the form of hard or soft copies. The water resource used for irrigation is assumedto be sourced from groundwater aquifers in Qatar as illustrated in the GIS map in Fig. 7. Furthermore, four existing hydroponic farms, districts, roads and weather station locations are also illustrated in the GIS map.

The proposed methodological framework uses a novel geospatial nexus approach to support decision-making for resource management in risky environments, such as Qatar. This approach is manifested in the “EWF nexus node” concept developed in this study. Thus, the research approach to be followed for this study is demonstrated in Fig. 2. The methodology begins with a data collection from previous scholarly literature for factors and other details that represent and affect each node. To further demonstrate the “EWF nexus node”, a food-centric system utilizing energy and water resources is used to represent each node. It is also subjected to exogenous risks, grow table hydroponic such as climate, water and land factors. After which, all the collected data for energy, water  and food  resources along with supply chain specifications  which is either in hard copies or electronic formats is mapped using ArcGIS software. The purpose of mapping the different resources on the map of Qatar is to create a fully unified geo-processing platform, also known as an EWF Nexus Atlas for the purpose of this study, thus permitting multiple various scenarios to be conducted on integrated spatial datasets. Scenario analysis examines the possible events that might occur in the future by considering several feasible outcomes using Nexus Atlas. Finally, by developing a composite risk indicator, the quality of each EWF nexus node is assessed.The State of Qatar occupying a total area of 11,571 km² with a population of 2.881 million people is the chosen case study to demonstrate the EWF Node. It is situated in a harsh semi-humid to a semi-arid region with an annual mean rainfall of 125 mm in the northern part of the country, and 49 mm in south Qatar and an average temperature of 23 °C. It experiences scare rainfall with the short rains occurring between April and October while the long rains occur between November and March. The land in Qatar is mainly flat and rocky with only one percent arable farming land. Accordingly, the vegetation is only found in the north, where the country’s irrigated farming areas are located, with desert plants blossoming briefly during the spring rains . Qatar relies on energy-intensive desalination for 99% of its municipal requirements, depleting groundwater aquifers is used for agriculture, and treated water is used for the irrigation of food non-crops. This study focuses on local agricultural businesses, specifically farms that produce tomatoes. In the following subsection, all available information from previous literature related to tomato farms in Qatar is identified.

Currently, there are approximately 1300 farms registered with the Ministry of Municipality and Environment’s  Agricultural Affairs Department, but less than 7% of these farms are commercial businesses. Larger and more established farms such as Al-Sulaiteen Industrial Complex , AGRICO and the Global Farm for Agricultural Supplies produce the majority of the agricultural output distributing to local markets. The aforementioned farms in addition to the Al-Safwa Farm are the four Qatari farms selected for this study and are summarized in Table 1. All four farms utilize hydroponic greenhouses, a high yielding soil-less farming method to grow various types of crops all year around. Hydroponics reduces plant pests without insecticides, and use up to 70% less water than traditional farming . The unique feature within Al-Sulaiteen Agricultural & Industrial Complex  is the existence of two Reverse Osmosis  plants with various capacities to desalinate and treat groundwater prior to distribution and irrigation. The two plants have capacities of 370 m3/day and 700 m3/day, where the product water from both the plants is utilized for greenhouse irrigation and cooling systems . However, AGRICO is a private local Qatari Agricultural Development Company that was founded on the principle of sustainable long-term agricultural production for achieving food security. The farm utilizes two of the most sophisticated air-conditioned hydroponic facility within the State of Qatar in an 20,000 m2 area per greenhouse providing the ability to harvest daily and distribute locally within 24 h . In terms of the yield for tomato crop production, the average yield of tomato per hectare in Qatar using the open field as a growing method is obtained from the Department of Agricultural and Water Research . However, the average yield of tomato grown using a hydroponic greenhouse is estimated based on the previous literature.Once the required industry is identified, the spatial risk factors are collected from different data sources which are either in the form of maps from hard copy books or electronic formats, such as reports and peer-reviewed journal publications. The nine risk factors are: temperature, humidity, solar radiation, soil quality , groundwater depth, groundwater salinity, groundwater recharge rate and groundwater pH. The data is then digitized and integrated into a unified geo-processing platform using ArcGIS software, enabling both visualization and further processing of the risk factors.

Other data types used include, districts and roads, industries, water resources and weather station locations. As part of the database development, three main toolsets form Spatial Analyst Toolbox within ArcGIS are applied to the maps: extraction, interpolation and map algebra. The process of database development is initiated by applying the Extraction tool on three existing maps, namely groundwater depth, groundwater recharge rate, and solar radiation . The tool extracted a subset of cells  from a raster  by using their spatial location, ensuring that all required locations are identified by using mask raster . This function processes only locations that fall within the mask, while all other locations outside the mask are assigned to “NoData” in the output raster. A sample map for extraction operation is illustrated in Fig. 3The second tool used is the interpolation, where the values of certain sampled points are interpolated and predicted into the continuous surface for all locations in an output raster dataset by using the inverse distance weighted  technique. The representation of the continuous surface of the output raster dataset indicates some measure, such as temperature, humidity, groundwater and soil data. A sample map for the interpolation operation is illustrated in Fig. 3.  original map from literature;  ArcGIS map after extraction by mask;  interpolated map with sample points. Fig. 4. In this case study, all maps with digitalized sampled points , groundwater salinity and groundwater pH are interpolated into a raster file known as GIS maps. For the other three maps , two steps where applied, extraction of origin maps, followed by digitalization and interpolation for sampled points. The last tool utilized in ArcGIS is Map Algebra. It is a simple and powerful tool that performs spatial analysis by constructing expressions in an algebraic language. Subsequently, in order to create and run few Map Algebra expressions, the Raster Calculator tool is applied to all output raster datasets . The purpose of using the Raster Calculator tool is to apply multiple Spatial Analyst tools and operators on nine inputs in a single-line algebraic expression . Hence, once all nine maps are digitalized and converted into shape and layer files within ArcGIS as illustrated in Fig. 5, the Map Algebra is applied to the three different scenarios in order to generate risk maps for each; open-field agriculture, conventional greenhouse and hydroponic greenhouse. For open field and conventional greenhouse, the nine rasters that represent the risk factors are considered in order to generate an overall risk map. However, only seven rasters, excluding soil factors, are reflected into the hydroponic greenhouse risk map. Using Map Algebra, all rasters are combined through multiplying each raster with its relative weight generated from the Analytic Hierarchy Process and then summing the results generated from the multiplication.The assignment of weights that represent the importance of various risk factors is conducted using a multi-criteria decision making method, known as Analytical Hierarchy Process , grow table in order to generate the composite geospatial risk indicators.

According to Saaty , AHP is a multi-attribute decision-making method that is commonly used in dealing with complex decisions and scenarios. This technique enables the decomposition of a problem into a hierarchical structure, thus ensuring that the integration of both quantitative and qualitative aspects of a problem are considered into the evaluation process. Within the hierarchy, the AHP allows evidence and data, experience, knowledge, insight, and intuition to be implemented logically and thoroughly. In particular, AHP as a weighting method allows decision-makers to derive weights rather than assign them arbitrarily . The evaluation process begins by defining all indicators that affect the goal, as illustrated in Fig. 6. The basis of AHP is an ordinal pairwise comparison of attributes. Comparisons are made between the pairs of individual indicators for a given goal by identifying which of the two is highly important and by how much. The preference is expressed on a semantic scale of 1 to 9 , where the preference of 1 implies equality between the two individual indicators, whilst a preference of 9 implies that the individual indicator is 9 times more crucial than the other one. In this study, it is assumed that all four farms technologically evolved from open-field agriculture, into conventional greenhouses, and finally into a hydroponic greenhouse, which is the current status of all four farms. Thus, the AHP is applied for three different scenarios; hydroponic greenhouse, conventional greenhouse and open field agriculture. The information which is related to the importance of each factor in comparison to one and another is obtained from previous literature. For the hydroponic greenhouse, there are 21 pairwise comparisons for the seven risk factors, as illustrated in Table 2. The risk factors are: temperature, humidity, solar radiation, groundwater depth, groundwater recharge rate, groundwater salinity and groundwater pH. In Table 2, the factors listed on the left are each compared with each factor listed on top so as to evaluate which factor is more important with respect to the goal of selecting highly risky farms. Then, the normalized relative weight of each factor, as detailed in Table 3, is obtained by summing each column and dividing each unit by the total sum of the columns. Finally, averaging across each row is performed in order to determine the normalized principal eigenvector or priorities of each individual factor/indicator. The same procedures are applied for conventional greenhouses and open fields. However, in these two cases, the two additional factors associated with the amount of concentration within soil are considered in the pairwise comparisons. The pairwise comparisons between nine risk factors and normalized relative weight representing each factor for conventional greenhouse and open field agriculture are detailed in Appendix B. Once the eigenvalues are determined, it is then possible to ascertain the consistency of the comparison matrix and validate assumptions and results by using Consistency Index and Consistency Ratio equations with a Random Consistency Index that is derived from Saaty’s values to represent a small problem as detailed in Appendix A – Table A2.