1 Introduction

Previous pertinent studies were reviewed and categorized as pertaining to sustainable water resources management, strategic planning approach, and multi-criteria decision-making models. The review of previous studies provides an insight into existing research gaps and the innovation contributed by this work.

The spatiotemporal variation of precipitation, fluctuations in river flow during the growth period, and the water scarcity during the dry season pose challenges for agricultural water management (Li et al. 2020; Hughes and Farinosi 2020). The increasing population and economic growth may lead to worsening water supply and demand management. Integrated Water Resources Management (IWRM) provides a viable approach to meet water demand and supply management through sustainability concept. The Global Water Partnership defines IWRM as a process which promotes the coordinated development and management of water, land, and related resources, in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems. (Garcia 2008). Crop diversification, crop pattern management, and conjunctive (i.e., surface water and groundwater) water management can be effective in improving agricultural water supply (Conrad et al. 2020; Portoghese et al. 2020). Baccour et al. (2021) analyzed water allocation and agricultural pollution for sustainable water management in the Ebro river basin (Spain).

The management of today’s complex water supply and demand systems relies on assessment models that combine climatic, social, economic, and environmental factors. Yadeta et al. (2020) examined the climate change posed drought and potential of rainy season in Kesem sub-basin, Awash basin, Ethiopia. The latter authors examined the rainfall variability using statistical indicators. The results showed that policy-makers must consider supplementary irrigation for crops in the study area because of the high probability of drought. A participatory modelling based on the system dynamics approach was implemented by Nyam et al. (2021) in the Breede River Catchment, South Africa. The results showed the major causal-relationships that determine the usefulness of the participatory approach in solving challenges related to water management and agricultural development in the catchment. Kim et al. (2021) developed a model using the concept of risk by identifying hazards, exposure, and vulnerability. The vulnerability was classified into two domains, sensitivity and adaptive capacity, and two spheres, natural/built environment and human environment. Water allocation rules among water user groups were evaluated by Rouillard and Rinaudo (2020) considering environmental, economic, and social criteria involving 54 agricultural water user groups across France.

A hydro-economic framework for sustainable water resources management in irrigated agriculture was evaluated by Alamanos et al. (2020), who developed two hydro-economic models for the analysis analysis of challenges regarding data limitations, spatial analysis, and scenario-based problems. The results indicated that the developed model can achieve a balance between simplicity, flexibility, accuracy and robustness. Drisya and Sathish Kumar (2022) applied three water management strategies, i.e., water harvesting, demand reduction, and soil management. In addition, hydrological modeling was carried out to analyze the hydrological responses. All the scenarios showed improvement in the water yield and the soil moisture storage in the study watershed. Climate adaptation alternatives were identified by Acharjee et al. (2020) with stakeholder consultation. The latter authors applied multi-criteria analysis to evaluate and prioritize the proposed options. The researchers recommended that short-term and medium-term planning must focus on opportunities to implement achievable adaptation measures within the local agricultural system. A risk-based two-stage stochastic robust programming model was applied to an agricultural-ecological system to manage agricultural-ecological water resources system in the Heihe River Basin in China. The results showed that the combination of these models can be effective for the optimal water allocation (Youzhi et al. 2021). Robust adaptable plans under climate uncertainties in the agricultural sector were evaluated by Babaeian et al. (2021) using a combination of the Adaptation Pathways (AP) approach and the Soil and Water Assessment Tool (SWAT) to evaluate management strategies under climate uncertainties in the Hablehroud river basin, Iran. Thaler et al. (2020) analyzed two strategic planning approaches in Austrian flood risk management by identifying background conditions to facilitate scaling and replication of catchment regional planning tools in flood-prone areas. A raster-based regional conservation action planning tool was developed for prioritizing local and regional scale conservation actions in heterogeneous landscapes (Thomson et al. 2020). Baskent (2021) proposed a methodology for the assessment of an integrated land management plan. The plan involved a governance option allowing each organization to prepare and implement its own activities according to the rights and responsibilities defined in legal agreements. The water-energy-food nexus index was applied by Karamian et al. (2021) at the farm level for agricultural water management. Psomas et al. (2021) developed an integrated framework combining methods of environmental analysis with multi-criteria decision making for agricultural water management in river basins. The proposed framework combines the driving forces–pressures–state–impacts–responses model with the water–energy–land–food nexus model. The framework recommended strategies for selection by the decision makers and was applied to the Pinios river basin in Greece.

Achieving sustainability in basins with existing irrigation and drainage networks requires a strategic planning approach according to sustainable development principles (Loucks 2000). The pillars of sustainable development include economic, social and environmental components. Three dimensions of strategic management are process, content, and context that encourage the integration of sustainability into corporate activities and strategies (Baumgartner and Rauter 2016). Strategic planning defines an organizational mission, prioritizes plans, and maximizes potential opportunities and benefits in management (David 2011). SWOT (strengths, weaknesses, opportunities, and threats) analysis is a framework used to evaluate competitive alternatives and to develop strategic planning. SWOT analysis assesses internal and external factors, as well as current and future potential. SWOT analysis was applied to develop strategies for renewable-resources based industries and most suitable approaches (Yang et al. 2018). Petousi et al. (2017) reported SWOT analysis of water resources management, and the results were shared by users, water managers, planners and policy makers. Tziritis et al. (2014) proposed a strategic planning approach at the river basin scale to groundwater quality assessment and evaluation. Abdallah et al. (2020) reported an optimization model for solid waste management strategies. Non-linear mathematical modeling was implemented in the form of a systematic optimization framework to identify the beneficial set of waste to energy management strategies. SWOT analysis was performed to promote the renewable energy sector in Pakistan. The results identified the strategies to develop the renewable energy sector towards sustainability in Pakistan (Kamran et al. 2019).

The analytic hierarchy process (AHP) and Analytic Network Process (ANP) are structured techniques for organizing and analyzing complex decisions (Saaty 1996). Multi-criteria decision making allows the systematic evaluation of SWOT factors (Kajanus et al. 2012). SWOT analysis can be improved by combining it with MCDM (Svekli et al. 2012; Amin et al. 2011). The Analytic Hierarchy Process (AHP) and the Analytical Network Process (ANP) analysis have been combined with SWOT analysis (Kahraman et al. 2007). The risk of floods was assessed using a fuzzy approach in the Kalu-Ganga River basin in Sri Lanka. Prato (2009) reported a study on fuzzy adaptive management of social and ecological capacities in flood protected areas. The main goal of their study was to find suitable strategies for basin management. A multi-criteria approach was reported by Sadr et al. (2020) to assess the relative performance of two types of adaptation strategies: (a) stand-alone strategies (green or grey strategies only), and (b) hybrid strategies (combined green and grey strategies). The results illustrated that the trade-off between adapting to short term pressures and addressing long term challenges. Wang et al. (2020) applied a combination of AHP and SWOT to analyze the growth factors of various energy sources. Their results showed that the economic and socio-political criteria were the dominant factors that influence the growth of renewable energy.

Previous works have demonstrated that a framework based on SWOT analysis in combination with MCDM approach can be effective in assessing the sustainability of agricultural water management strategies in irrigation drainage networks. It is imperative to include sustainability indices within MCDM ranking techniques to achieve sustainability.

Despite numerous studies on sustainable water management and research on sustainability principles, it is factual that sustainable water management at the local scale has received less attention. Studies which have been done by the Organization for Economic Co-operation and Development (OECD) on water sustainability indicators show that analysis at the local levels and scales is necessary to demonstrate the effectiveness of the principles of water sustainability.

The analysis of large-scale water resource systems regarding the resources, stakeholders, reservoirs, small irrigation reservoirs, and water transfer schemes requires the consideration of the interactions between several components (Bozorg-Haddad et al. 2009; Akbari-Alashti et al. 2014). This work develops sustainable strategies for agricultural water management of irrigation and drainage networks. Specifically, this study employs spatial fuzzy strategic planning (SFSP) with MCDM and a conceptual agricultural water use model. This paper poses the management of agricultural water as a spatial analysis of internal and external factors related to agricultural water management. This paper contributes to agricultural water management as follows:

  • Identification and evaluation of the current status of water resources and agricultural water management, and provides a spatial analysis of the strengths, weaknesses, opportunities and threats (SWOT) of irrigation and drainage networks.

  • Development of agricultural water management strategies to ensure efficient use of scarce water in irrigation and drainage networks.

  • Assessment and ranking of water-management strategies in the form of structural and non-structural measures in irrigation and drainage networks.

2 Methodology

2.1 Formulation of the Strategic Planning Framework

This paper presents a generalized novel three-stage framework that blends strategic planning with SWOT analysis (Stage 1), a conceptual agricultural water-use model (Stage 2), and fuzzy AHP TOPSIS techniques (Stage 3). Figure 1 depicts the three-stage approach employed in this work. The components of the model are explained next.

Fig. 1
figure 1

Strategic planning framework developed in this work

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2.1.1 SWOT Analysis

A vision for agricultural water management requires a thorough understanding of social, environmental, economic, institutional, and water resources factors. This work identifies internal (strengths and weaknesses) and external (opportunities and threats) factors from the perspective of sustainable agricultural development by applying SWOT analysis. Several data sources are used in the SWOT analysis, including national databases and programs, field visits, and interviews with local and provincial water managers and irrigation water users in the irrigation network. This information was used to develop the internal factor evaluation (IFE) and the external factor evaluation (EFE) matrices. The SWOT matrices are used in conjunction with the space matrix to identify the type of development strategies for sustainable agricultural development It should be noted that the space matrix analysis functions upon two internal and two external strategic dimensions in order to determine the strategic posture (Srinivas et al. 2018). This work’s goal is assisting planners and decision makers in developing sustainable strategies for agricultural water management.

The SWOT analytical matrix provides four general categories of strategies (Babaesmailli et al. 2012). The strategies may overlap, or may be implemented concurrently with each other. The categories are (a) strength-opportunities (SO) strategies that use internal strengths to achieve external opportunities, (b) strength—threats (ST) strategies that use the strengths of the system to maximize strengths and minimize threats, (c) weakness- threats (WT) strategies that reduce internal weaknesses to prevent external threats, and (d) weaknesses-opportunities (WO) strategies that reduce internal weaknesses to take advantage of external opportunities.

2.1.2 Conceptual Model of Agricultural Water Use

The type of available water resources including dam and irrigation network, local rivers, drainage, small irrigation reservoirs and groundwater resources, the crop pattern and quality of soil and water sources vary throughout the study area. Therefore, a database of water-use statistics was prepared to estimate the water use by agricultural lands within the study area. The water use in the agricultural lands is a function of various factors such as the type of water resources, the method of water conveyance and distribution, the irrigation method, the type of crop products, climatic conditions, soil type, management practice, and others. Therefore, estimating the amount of water use in agricultural areas in the study area is beset by complexity. The inputs to the agricultural water use model are the cultivated area and crop pattern of irrigated lands, crop water requirements, surface and ground water withdrawal data, and irrigation efficiencies. Area and crop pattern information were obtained from the most recent agricultural census and from land-use maps based on satellite images. The model calculates water use in each irrigation unit by comparing the water requirements of the crop pattern with the water withdrawals of surface and ground water. The outputs from this model are actual water use from surface and ground water resources, water shortages, and the volumes of return flow.

2.1.3 AHP Fuzzy TOPSIS Approach to Prioritizing Strategies

SWOT analysis is suitable for evaluating agricultural water management. SWOT analysis must determine the relative weights of the factors used to evaluate management strategies, which may introduce subjective biases. For this reason, SWOT analysis has been combined with MCDM techniques (such as AHP) to prioritize the strategies.

The TOPSIS method is used widely for the ranking of problems in the field of water resources management (Simonovic and Verma 2008), in economics and environmental sciences (Xuebin 2009). The TOPSIS method ranks alternatives by determining their distance from the best and worst solutions. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the best (positive ideal) solution and the longest geometric distance from the worst (negative ideal) solution.

The conventional TOPSIS method expresses the judgments of decision-makers with absolute quantities, i.e., as if they were known with certainty. In many instances, however, decision-makers must account for uncertainties in decision-making. Fuzzy logic incorporates uncertainty in decision making. Therefore, this study blends TOPSIS with fuzzy logic to achieve multi-criteria decision making under uncertainty. The AHP Fuzzy TOPSIS steps are as follows:

1st step: Specification of \({\tilde{x }}_{ij},i=\mathrm{1,2},...,n,j=\mathrm{1,2},...,J\)

in which \({\tilde{x }}_{ij}\) denotes the value of the i-th alternative or strategy for agricultural water management with regard to the j-th criterion; \(n\) = the number of management alternatives; and J = the number of decision criteria. The decision matrix \(X\) has elements \({\tilde{x }}_{ij}\).

2nd step: normalize the elements of the decision matrix as follows:

$${r}_{ij}=\frac{{\tilde{x }}_{ij}}{\sqrt{\sum_{i=1}^{n}{{\tilde{x }}_{ij}}^{2}}}\quad i=1, 2, \dots , n;\,j=1, 2, \dots , J$$
(1)

which yields the normalized decision matrix \(R=\left[{r}_{ij}\right]\) whose elements are in the range [0,1] when \({\tilde{x }}_{ij}\) >0, whereas the \({\tilde{x }}_{ij}\) s have values over diverse ranges.

3rd step: Calculate the weighted normalized decision-making matrix \(\tilde{V }\) with elements \({\tilde{v }}_{ij}\):

$${\tilde{v }}_{ij}={r}_{ij}\cdot {w}_{j}\quad i=1, 2, \dots , n;\,j=1, 2, \dots , J$$
(2)

The weights \({w}_{j}\) denotes the importance or weight of the j-th criterion, and they are normalized to add to 1: \({w}_{1}+{w}_{2}+\dots +{w}_{J}=1\). The weights are determined with the AHP method (Saaty 2008).

4th step: Define the fuzzy best alternative (\(A^{ * }\)) and the fuzzy worst alternative (\(A^{ - }\)). \({A}^{*}\) and \({A}^{-}\) are given by Eqs. (3) and (4), respectively:

$${A}^{*}=\left\{{{\tilde{v }}_{1}}^{*},{{\tilde{v }}_{2}}^{*},...,{{\tilde{v }}_{J}}^{*}\right\}$$
(3)

in which \({{\tilde{v }}_{j}}^{*}\) denotes the ideal (best) value with respect to the j-th criterion. The \({{\tilde{v }}_{j}}^{*}\) describe the aspired solution to the given problem that may or may not be achievable in practical terms.

$${A}^{-}=\left\{{{\tilde{v }}_{1}}^{-},{{\tilde{v }}_{2}}^{-},\dots ,{{\tilde{v }}_{J}}^{-}\right\}$$
(4)

in which \({{\tilde{v }}_{j}}^{-}\) denotes the worst value with respect to the j-th criterion. The \({{\tilde{v }}_{j}}^{-}\) constitute the possible worst solution to a problem that may or may not occur in practical terms.

5th step: Calculate the (Euclidean) distance of each management strategy or alternative from the best and worst solutions using Eqs. (5) and (6), respectively:

$${D}_{i}^{*}=\sqrt{{\sum }_{j=1}^{n}[{\tilde{v }}_{ij}-{{\tilde{v }}_{1}}^{*}{]}^{2}}i=\mathrm{1,2},...,n$$
(5)
$${D}_{i}^{-}=\sqrt{{\sum }_{j=1}^{n}[{\tilde{v }}_{ij}-{{\tilde{v }}_{j}}^{-}{]}^{2}}i=\mathrm{1,2},...,n$$
(6)

in which \({D}_{i}^{*}\) and \({D}_{i}^{-}\) denote the separation of the i-th alternative from the best and worst solutions, respectively.

6th step: The relative closeness of the i-th alternative to the best solution, CCi, is calculated with Eq. (7):

$$C{C}_{i}=\frac{{{D}_{i}}^{*}}{{{D}_{i}}^{*}+{{D}_{i}}^{-}}i=\mathrm{1,2},...,n$$
(7)

7th step: rank the alternatives based on the CCi values.

The ranking of agricultural water management strategies applies a combination of analytical hierarchy criteria and the fuzzy TOPSIS method as follows:

  • Collect the data and pre-process them.

  • Determine appropriate criteria for the decision-making process.

  • Use a multi-criteria decision-making analysis (Analytical Hierarchy Process, AHP, in this work) to determine the weights of each criterion.

  • Evaluate the alternatives with the fuzzy TOPSIS method and ranking of the alternatives.

This paper’s approach first defines the management strategies and the decision- making criteria. In this first stage the hierarchical structure is formed with the first, second, and third levels corresponding to the ultimate goal, decision making criteria, and alternatives, respectively. The weights of the criteria are calculated based on the geometric mean, and the pairwise comparison matrix is calculated based on Saaty’s preferences (Saaty 2008). The fuzzy TOPSIS method is applied to rank the agricultural water management strategies. At this juncture the linguistic variables and associated triangular fuzzy numbers are applied to evaluate the management strategies.

Developing Sustainable Agricultural Water Management Criteria for Decision Making

The evaluation of agricultural water management strategies is made in this study with technical and operational, environmental, socio-political, and economic criteria (Afsordegan 2015). The strategies must be acceptable to the pertinent agencies and relevant stakeholders. Another feature of the management strategies concerns their capacity to ensure environmental protection. Moreover, the technical and operational aspects of the strategies are essential to achieve effective implementation, operation, and maintenance of agricultural projects. After defining the evaluation criteria, they are weighted and the pairwise comparison matrix is calculated based on Saaty’s preferences (Saaty 2008).

3 Study Area

The Sefidroud irrigation and drainage network constitutes this work’s study area (Fig. 2). The Sefidroud irrigation network is one of the largest in Iran. This network covers parts of the three main basins of Iran including Talesh-Talab Anzali, Great Sefidroud, and Sefidroud -Haraz basins. There are regional conflicts over water that affects the Sefidroud irrigation network. The Sefidroud irrigation and drainage network is divided into three irrigation zones, namely, the Markazi, Fumanat, and Shargh irrigation zones.

Fig. 2
figure 2

The location of the study area

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Rice as a basic crop within the Sefidroud irrigation and drainage network where it is essential for food security. About 94% of the total cultivated agricultural land is dedicated to rice fields. Other crops are tea, citrus, and olives, which are of economic importance.

The Sefidroud river is the main source of water supply for the Sefidroud irrigation and drainage network through water releases from Sefidroud dam that are conveyed to the irrigation units through a network of diversion dams and channels. Besides the Sefidroud dam there are other sources of water for the Sefidroud irrigation and drainage network, such as local rivers, farm wastewater, small irrigation reservoirs, and groundwater. The main challenges in the study area are related to the development of agricultural lands in the upstream areas, drought recurrence, climate change, increasing water demand, and the incomplete development of the irrigation network. Furthermore, the need to maintain the role of rice in the study area as a strategic crop in food security of the growing population and increase the income of farmers poses additional challenges to water management in this large irrigation and drainage network.

The management of water distribution and delivery to the Sefidroud network has been impacted by droughts, such as that of 1998–1999. For instance, the intermittent irrigation method used in some parts of the network has been replaced with permanent irrigation, and the amounts of water withdrawals from local rivers, drainages, and groundwater have increased without consideration of the associated environmental impacts.

4 Results and Discussion

This section describes the following topics:

  • Analysis and evaluation of agricultural water consumption in the study area.

  • SWOT analysis of agricultural water management in the study area.

  • Strategy-ranking with multi-criteria analysis.

4.1 Agricultural Water Consumption Analysis

The area of paddy lands in the study area is variable depending of the available water resources, particularly on the amount of delivered water from Sefidroud dam. The total area of paddy fields is cultivated during periods of normal water availability, and a fraction of this area is fallowed in dry years. The data concerning water delivered to the Sefidroud irrigation and drainage network in recent years exhibits significant variations between normal and dry years. The largest volume of water delivered from Sefidroud dam to the Sefidroud irrigation network was about 2.2 billion cubic meters (BCMs) in 1994–1995 and the smallest volume water delivered was about 0.7 billion cubic meters in 1998–1999. The long-term average annual volume of water delivered to irrigation and drainage network is about 1.6 billion cubic meters. The average volume of water delivered to the Sefidroud irrigation and drainage network in recent years is about 1.3 billion cubic meters, which represents a reduction in the amount of water delivered to the network with respect to the long-term average. The difference between the amounts of water delivered to the Sefidroud irrigation and drainage network during normal and dry years is about 700 to 900 million cubic meters (Fig. 3).

Fig. 3
figure 3

The volume of water delivered to the Sefidroud irrigation and drainage network from 1988–1989 through 2009–2010 water years (MCM = 106 m3; BCM = 109 m3)

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The cultivated areas with paddy fields in the Sefidroud irrigation and drainage network has been estimated from land use maps (Fig. 4) at about 157,000 hectares in dry years and about 179,000 hectares in normal (or average) precipitation years.

Fig. 4
figure 4

The Landuse map of the study area

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The area of irrigated land in the irrigation management zones of Sefidroud irrigation network are presented in Figs. 5a, b in normal and dry years.

Fig. 5
figure 5

a. Spatial distribution of irrigated land in the study area. b. The area of irrigated land in the irrigation management zones of Sefidroud irrigation network

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As can be seen in these Figures, the most changes of irrigated land between normal and dry years in the study area is related to Markazi irrigation zone and the next ranks are related to Fumanat and Shargh irrigation zones.

This indicates the strong dependence of Markazi irrigation zone on water supply from Sefidroud dam. It should be noted that the changes in irrigated land are mainly related to paddy fields, which are visible due to the dependence of the irrigation management zones of Sefidroud irrigation and drainage network on water supply from Sefidroud dam. As can be seen under normal conditions, the highest area of ​​irrigated land in the study area is equal to 79,529 hectares related to Markazi irrigation zone and the lowest area of​​ irrigated land related to Fumanat irrigation zone is equal to 51,815 hectares. Similarly, in dry climatic conditions, the highest area of ​​irrigated land is related to the Markazi irrigation zone equal to 70,412 hectares and the lowest area of ​​irrigated land related to the ​​Fumanat irrigation zone is equal to 46,069 hectares.

Accordingly, agricultural water use has been estimated in normal and dry years from long-term statistical analysis of water delivered to the Sefidroud irrigation and drainage network. The annual volumes of agricultural water use in the Sefidroud irrigation and drainage network during normal and dry conditions are listed in Table 1. It can be seen in Table 1 the cultivated area of paddy fields in Sefidroud irrigation and drainage network is about 179,181 hectares under normal conditions, and about 157,612 hectares under dry climatic conditions. The total annual water use of cultivated area in Sefidroud irrigation and drainage network is about 1.8 billion cubic meters under normal climatic conditions, of which about 1707 million cubic meters (95%) are surface water and 90 million cubic meters (5%) are groundwater. Of the total volume of surface water use about 1.4 billion cubic meters are from the Sefidroud dam and related canals, 260 million cubic meters from local rivers and farm wastewater, and about 47 million cubic meters from small irrigation reservoirs. The average volume of water use in the 191,141 hectares of irrigated lands of the Sefidroud irrigation and drainage network equals 9404 cubic meters per hectare under normal climatic conditions.

Table 1 Summary of the current status of agricultural water use based on the water resources of the Sefidroud irrigation and drainage network
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The total annual water use in the cultivated area in the Sefidroud irrigation and drainage network is estimated at 1.5 billion cubic meters under dry conditions, of which 1386 million cubic meters (93%) are surface water and 107 million cubic meters (7%) from groundwater. Of the total volume of surface water use 939, 390, and 58 million cubic meters are from Sefidroud dam and related channels, from local rivers and farm wastewater, and from small irrigation reservoirs, respectively. The average unit use of water in the Sefidroud irrigation and drainage network is about 8808 cubic meters per hectare under dry conditions in the 169,752 hectares of cultivated land (Figs. 6a, b).

Fig. 6
figure 6

a. Description of agricultural water consumption in different irrigation zones of the Sefidroud irrigation network (Normal Condition) b. Description of agricultural water consumption in different irrigation zones of the Sefidroud irrigation network (Dry Condition)

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A comparison of agricultural water use from surface water resources (i.e., from the Sefidroud dam and irrigation network, local rivers and farms waste water, small irrigation reservoirs) and groundwater use in the irrigation zones of the Sefidroud irrigation network including Shargh, Markazi and Fumanat under normal and dry climatic conditions is depicted in Tables 5 and 6, respectively. Figures 7a, b display the spatial distribution of water use in the Sefidroud irrigation and drainage network from surface and groundwater sources under normal and dry conditions, respectively. It can be seen that the water from groundwater, local rivers, and farm wastewater increases during the periods of water scarcity. Specifically, the water from local rivers and farm wastewater has been increased by 50% under deficit conditions compared to the normal condition. This increase is mainly from farm wastewater, which is a common supplementary source especially during drought periods in the irrigation network. Using more farm wastewater poses environmental risks due to the presence of pollutants such as nitrates and heavy metals, and by domestic and industrial effluents in the wastewater.

Table 2 The results of the analytical hierarchical process (AHP)
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Fig. 7
figure 7

a. Spatial distribution of water use from surface and groundwater sources in the Sefidroud irrigation network (normal precipitation condition). (m3/ha = m3/hectare). b. Spatial distribution of water use from surface and groundwater sources in the Sefidroud irrigation network (dry condition). (m3/ha = m3/hectare)

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The volume of groundwater is equal to 90 × 106 m3 annually under normal conditions, which increases to 107 × 106 m3 annually under dry condition. This produces a 20% rise on the demands imposed on groundwater withdrawn from wells under dry conditions. A comparison between surface and groundwater use in the irrigation management zones of Sefidroud irrigation network are displayed through Figs. 8a–d.

Fig. 8
figure 8

a. Comparison between surface and groundwater use in Shargh irrigation zone (106 m.3). b. Comparison between surface and groundwater use in Fumanat irrigation zone (106 m.3). c. Comparison between surface and groundwater use in Markazi irrigation zone (106 m3). d. Comparison between surface and groundwater use in Sefidroud irrigation network (106 m3)

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4.2 SWOT Analysis of Agricultural Water Management

SWOT analysis has been applied successfully to complex water resources management (Tziritis et al. 2014). This paper applies SWOT analysis because of its capacity to incorporate present conditions (through strengths and weaknesses) and future conditions (through opportunities and threats) which are pertinent in the Sefidroud irrigation network, which is undergoing rapid changes in terms of land use change and upstream water resources development plans. SWOT analysis minimizes weakness and threats by converting weakness into strengths, and threats are converted into opportunities. Strengths and opportunities are exploited to optimize agricultural water resources management (Wickramasinghe and Takano 2009).

SWOT assumes that strengths and weaknesses are internally-related, while opportunities and threats are due to external influences. The separation between internal and external factors is made by the physical boundary of the Sefidroud irrigation and drainage network. Thus, the factors under the control and management of Sefidroud irrigation and drainage network are considered internal factors (IFs), and the others are considered external factors (EFs).

This study relies on knowledge and information gathered through interviews with relevant experts as the main input for the SWOT analysis. The viewpoints of the decision-making experts’ committee members with expertise in the technical and operational aspects in the study area were gathered to improve the formulation of agricultural water management strategies. The strengths and opportunities factors are scored in the range of 3–4, and the weaknesses and threats factors are scored in the range of 1–2 based on work by David (2011). It is noteworthy that if the final score of the internal or external factors exceeds 2.5 then the strengths/opportunities overcome the weaknesses/threats, and vice versa.

The results of SWOT analysis for the agricultural water management in the Sefidroud irrigation network are presented in Tables 7 and 8. Table 7 lists the internal SWOT factors and weighted ratings of factors. Table 8 lists the external SWOT factors and weighted ratings of factors. The internal and external factors of agricultural water management include 10 strengths (S1-S10), 14 weaknesses (W1-W14), 6 opportunities (O1-O6), and 6 threats (T1-T6). The final scores of the internal and external factors were equal to 2.9 and 2.73, respectively. According to the literature (David 2011) this work’s results indicate that the most attractive strategic type was SO (aggressive). The possible strategies are formed as a combination of two related strategies. For example, if the main strategy type is “SO” the relevant strategies are a combination of the “ST” and “WO” strategic types (David 2011). Based on this rule 17 strategies within 3 strategic groups were formulated by matching the IFs against the EFs based on SWOT analysis, and they are listed in Table 9. The next step is to rank the set of water management strategies with the AHP Fuzzy TOPSIS approach.

Table 3 The weighted fuzzy decision making matrix for strategies
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Table 4 The ranking of strategies based on the CCi index
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4.3 Strategy-ranking with the Fuzzy AHP TOPSIS Approach

The weights of the sustainable criteria related to agricultural water management were calculated based on the opinions of experts in the field of agricultural water management and applying the AHP. The calculated weights are listed in Table 2. The inconsistency ratio (IR) for the pairwise comparison matrix is determined that shows the consistency of results. The inconsistency index for a pairwise comparison matrix was introduced by Saaty (1996).

  • Consistency index C.I. (Saaty 1996):

$$C.I.=\frac{{\lambda }_{max}-n}{n-1}$$
(8)

In Eq. (8), \({\lambda }_{max}\) is the maximum eigenvalue of the pairwise matrix. According to Saaty, a pairwise comparison matrix with C.I. equal to or lower than 0.10 is sufficiently consistent. However, C.I. varies with \(n\), which is why Saaty (2008) introduced a more suitable measure of consistency, i.e., the consistency ratio C.R.:

  • Consistency ratio C.R.:

$$C.R. = \frac{C.I.}{{R.I.}}$$
(9)

The R.I. in Eq. (9) is the random inconsistency which is an average C.I. of random matrices (generated by the Monte Carlo method) of order n.

A pairwise comparison matrix with C.R. equal to or lower than 0.10 is sufficiently consistent. Notably, R.I. was found to converge to the value 1.58 with increasing n (Saaty 2008). This fact drew some criticism of the C.R. (and C.I.) because the random inconsistency should increase with increasing n (the larger a random matrix is, the more “mess” it contains) (Saaty 1996).

The main strategies were defined: St-1: development and rehabilitation of the physical structure of water resources system of Sefidroud irrigation network; St-2: improvement of operation management and maintenance of the Sefidroud irrigation network; St-3: wastewater management; and St-4: inter-basin water transfer within the Sefidroud irrigation network from the internal water resources of the system. These strategies were ranked with the AHP fuzzy TOPSIS approach.

The weighted fuzzy decision-making matrix was calculated using the weights of criteria obtained with the AHP method. The weighted fuzzy decision-making matrix is listed in Table 3, where St-1 to St-4 denote the strategies or alternatives (agricultural water management strategies) and C1 to C4 denote the decision-making criteria. Table 3 shows that the elements \({\tilde{v }}_{ij}\) for all values i and j, are normalized in the interval [0,1]. The fuzzy best and worst solutions are listed in Table 3, also.

The strategies were ranked based on their distances from the best and worst solutions based on the \({CC}_{i}\) indices computed with Eq. (7). Calculations results related to the \({CC}_{i}\) index are presented in Table 4. These results show that the combination of structural and non-structural strategies (the SO, ST, and WO strategies) with \({CC}_{i}\) index equal to 0.248 was top ranked in terms of sustainable agricultural water management. The ranking of strategies based on the \({CC}_{i}\) index is listed in Table 4.

5 Concluding Remarks

This work presented spatial fuzzy strategic planning (SFSP) in combination with a MCDM approach to rank agricultural water management strategies. A spatial combination analysis of internal and external factors related to agricultural water management including strengths, weaknesses, opportunities and threats (SWOT) analysis matrix was applied in this work. The strategies were ranked with AHP and Fuzzy TOPSIS. The AHP method was used to determine the criteria weights, and the fuzzy TOPSIS model ranked the agricultural water management strategies in the study area.

This study applied linguistic variables that are converted to triangular fuzzy numbers to account for the uncertainty in the decision-making process. A model of decision making with respect to identified agricultural water management strategies was developed based on a defined set of criteria. This paper’s model combines the concepts of fuzzy logic, the analytical hierarchy process (AHP), and the fuzzy TOPSIS method to resolve multi-criteria decision making under uncertainty. The analytical hierarchy process was applied for determining the weights in the decision-making process, and the TOPSIS method in a fuzzy environment was applied to obtain the ranking of strategies. The results indicate that a combination of structural and non-structural strategies (SO, ST, and WO strategies) are the top ranked strategy. The strategies offer the best path to improve agricultural sustainably and reduce social tensions.

The next step for implementing the sustainable agricultural water management strategies in the Sefidroud irrigation network is to apply structural and non-structural measures in selected modern and traditional irrigation zones having the highest priority for assistance. Agricultural water management strategies are likely to vary across irrigation zones. The applicability of this paper’s methodology transcends the specifics of its study area. In fact, the proposed spatial fuzzy strategic planning (SFSP) in combination with MCDM and a conceptual agricultural water consumption model presented in this study could be adapted and applied in other large-scale irrigation and drainage networks.