*Article* **Water Supply Delivery Failures—A Scenario-Based Approach to Assess Economic Losses and Risk Reduction Options**

#### **Karin Sjöstrand 1,2,\*, Andreas Lindhe 2, Tore Söderqvist <sup>3</sup> and Lars Rosén <sup>2</sup>**


Received: 15 April 2020; Accepted: 17 June 2020; Published: 19 June 2020

**Abstract:** Access to a reliable water supply is central for a well-functioning society. However, water supply systems are subject to a wide range of threats which may affect their ability to provide water to society. This paper presents a novel risk assessment approach that enables thorough analyses of economic losses and associated uncertainties under a range of water supply disruption scenarios. The purpose is to avoid sub-optimization when prioritizing between risk reduction measures, by integrating the full range of possible outcomes from low to high probability events. By combining risk analysis with cost-benefit analysis, additional information is provided on measures for leveraging investments in managing and reducing the risks. This enables the identification of the most economically profitable risk reduction alternatives and enables decision makers to build strategic capacity for operating in difficult and uncertain futures. The presented approach is exemplified on the island of Gotland, one of the most water scarce areas of Sweden.

**Keywords:** water scarcity; drought; water supply; risk reduction; risk curves; cost-benefit analysis

#### **1. Introduction**

Water supply infrastructure systems are subject to a wide range of threats which may affect their ability to provide water to society. Predicted population growth and hydro-climatic changes are expected to contribute to both an increased probability of water scarcity and more severe societal consequences [1,2]. In addition to threats related to reduced access to and quality of raw water, failures in water provision may also occur due to events related to the treatment systems, e.g., component failures in treatment plants, and related to the distribution systems, e.g., pipe bursts and pump failures. To deal with the uncertainties and the societal impacts that all these threats entail, risk assessment methods need to be integrated in water supply decision making [3,4]. Risk assessments may be performed in different ways, but a common approach is to qualitatively and/or quantitatively estimate and combine the consequences of one or several possible scenarios, typically undesirable events, the probability of occurrence for the scenarios and the uncertainties related to the included factors [5]. Risk-based decision making uses the results of risk assessments to guide and inform decisions on risk reduction measures. It may, for example, involve comparing required resources for implementing potential risk reduction measures with potential benefits of estimated risk reduction. A framing based on risk provides for a better understanding of the severity, distribution and impacts of the full range of possible outcomes [6].

Decision-makers and water supply managers face difficult decisions on resource allocation and prioritizations of risk reduction measures. To support such decisions, effective risk management requires the identification and assessment of a range of representative risk scenarios [7]. The process of summing and showing the interaction between single or individual risks is sometimes referred to as risk aggregation [8]. Moreover, to facilitate rational decision-making, the risk should be expressed in a clear manner, and related uncertainties considered [9]. In this paper, the focus is on economic consequences, and risk is expressed in terms of economic consequences to society arising from water supply disruption events. A disruption in the water provision can lead to economic consequences for the water utility as well as for businesses and residential consumers, and may generate significant economic losses for society [10]. Several different methods have been used to estimate business interruption losses, e.g., input-output models and computable general equilibrium models [11]. The direct economic consequences to commercial and industrial consumers are often estimated by use of importance (or resiliency) factors, i.e., quantitative measurements focusing on the production output during disruption [12]. Residential welfare loss of water supply disruptions can be assessed based on estimates of consumer willingness to pay to avoid such disruptions [13,14]. Short-term disruption events are not evaluated as frequently as long-term disruptions. They may, however, contribute significantly to the total economic losses due to their much higher frequency [10].

According to Uzielli, et al. [15], a quantitative risk assessment should include a quantification of the expected losses, based on the probability for a given event, the economic consequences to society of exposed elements at risk, and their associated vulnerability. However, risk assessments are often complex in nature, and many aspects of the risk may be subject to large uncertainties [16]. It is now common to define risk using uncertainty as a key factor, see e.g., International Organization for Standardization (ISO) [17] and Aven [18]. The importance of considering uncertainties is particularly true for factors affecting high-impact-low-probability risks which, by their very nature, occur only infrequently. Existing statistics may be insufficient to support the risk assessments [19]. Data samples may, for example, be too small, too unreliable, too costly to obtain, or simply unobtainable. In these cases, the only sound option may be to elicit the information needed using expert judgements. The typical way is to elicit judgement from more than one expert and represent the uncertainties by probability distributions [20–22], so that appropriate decisions can be made on risk reduction. The approach proposed in this paper integrates the full range of risk scenarios, while taking the underlying uncertainties into account, to estimate the total risk of the water supply system. This allows for a better understanding of how different factors influence each component of risk and how they, in turn, affect the total risk. It further facilitates a design and prioritization of measures that focus on addressing the total risk rather than individual threats.

The overall aim of this paper is to provide a risk assessment method that enables thorough analyses of risk reduction measures by integrating the full range of possible outcomes from low to high probability events. The purpose of the method is to provide a structured and thorough analysis of the total risk to enable prioritization of possible measures based on, e.g., economic profitability. A key part is also to avoid sub-optimization, where risk reduction measures are prioritized based on individual events. Specific objectives are to: (1) provide a method that enables estimation of economic losses under various levels of water supply disruption events; (2) combine this information with the integrated likelihood function of disruption events to estimate the total risk under existing conditions; (3) analyze and compare the annual benefits and economic profitability of risk reduction measures; and (4) exemplify this method by application on the island of Gotland, Sweden. The proposed approach is a valuable contribution to the water supply reliability literature, in which definition of risk scenarios, uncertainty estimations of input variables, economic valuation of consequences, calculations of the total integral sum of risk over different risk scenarios and calculations of economic profitability through cost benefit analysis (CBA) all are rare.

#### **2. Materials and Methods**

In short, the risk assessment method described in this paper is based on a combination of quantitative risk analysis and CBA [23]. The main steps are: (1) identification of risk scenarios; (2) estimation of factors affecting the risk; (3) characterization of risk; (4) evaluation of risk reduction measures; and (5) performance of uncertainty and sensitivity analyses. The methodology is described in more detail in the following paragraphs.

#### *2.1. Identification of Risk Scenarios*

In this paper, a well-established approach to risk analysis is used where the aim is to answer the following three questions [9]:


To answer these questions, a set of scenarios are defined. The set of scenarios used in a quantitative risk analysis should preferably be complete, finite and disjoint [24]. This means that a nonoverlapping subset of *N* scenarios together should represent all possible risk scenarios for the entire problem so that the total risk *R* is

$$\mathcal{R} = \{(s\_{i\prime}f\_{i\prime}x\_i)\}\tag{1}$$

where *si* is scenario *i, i* = 1, 2, ... , *N*; *fi* is the frequency with which the scenario occurs; and *xi* is the consequence given that scenario *i* occurs. Furthermore, the uncertainties related to the three variables are identified and described quantitatively or qualitatively to enable a thorough description of the risk.

There are several scenario identification methods used within the theory of scenario structuring (TSS), e.g., hierarchical holographic modeling (HHM), failure modes and effects analysis (FMEA), hazard and operations analysis (HAZOP), and anticipatory failure determination (AFD). All methods start by defining a success scenario. The risk scenarios (*si*) can then be identified by decomposing the success scenario into different parts, e.g., in geographical, hydrological, temporal or functional parts, and asking; "What can go wrong in this part?" or "What happens if this parameter changes?". The aggregated risks of all scenarios then determine the total risk of the overall system [24].

#### *2.2. Estimation of Factors A*ff*ecting Risk*

As mentioned above, the risk is defined as a function of a set of scenarios, the frequency with which they occur and the consequences if they occur. When we do not know the frequencies or the consequences with certainty, we can express them by probability distributions so that *R* = - (*si*, *pi*(*fi*), ζ*i*(*xi*)) *,* where *pi* and ζ*<sup>i</sup>* are the probability density functions for the frequency and consequence, respectively. In this study, the following economic consequences are considered: residential welfare losses, businesses losses, and water utility expenditures for upholding water provision (as far as possible) during the disruptions. The water utility expenditures were estimated based on information from the local water utility from previous experiences. In the subsections below it is described how uncertain quantities, of e.g., the return periods and duration of events, were estimated based on formal expert elicitation, and how residential welfare losses and business losses were calculated.

#### 2.2.1. Formal Expert Elicitation

One technique of capturing the probability distributions of uncertain quantities is to elicit this information using a range of experts from different disciplines. In this paper, uncertain quantities, such as return period and duration of events, were estimated by expert elicitation using the Sheffield Elicitation Framework (SHELF) [25]. The SHELF framework elicits a single judicious consensus distribution from the expert group for each uncertain quantity. The process begins by eliciting individual judgements from each expert independently, followed by a group discussion and a group judgement. The parameters estimated in this paper were the lower and upper plausible limits for the uncertain quantity, as well as the median and lower and upper quartiles. The MATCH Uncertainty Elicitation Tool [26] was used to find the best fitted statistical distribution model for the group judgment and to provide direct visual feedback to the expert. The results were reviewed and discussed by the group and when necessary adjusted to fit their final and joint preferences.

#### 2.2.2. Estimation of Household Welfare Losses

One consequence of water disruptions is residential welfare losses. In this paper, this was valued based on estimates of consumer willingness to pay to avoid water supply shortages [13,14]. By integrating the demand curve for water, between baseline consumption and reduced consumption, the daily welfare loss *Wi zjt* for a consumer in region *j* facing a water shortage of *z* at time *t* was calculated as:

$$\mathcal{W}\_j(z\_{jt}) = \frac{\eta}{1+\eta} Y\_{\text{baseline}} Q\_{\text{baseline}} \left[ 1 - \left( \frac{Q\_r(z\_{jt})}{Q\_{\text{baseline}}} \right)^{\frac{1+\eta}{\eta}} \right] \tag{2}$$

where *Ybaseline* is the average water price when no shortage, *Qbaseline* is the average amount of water consumed per capita per day when no shortage, *Qr* is the reduced water consumption, and η is the price elasticity of water demand. The severity of the water shortage was defined as *zjt* - [0,1], where *zjt* = 0 corresponds to no water and *zjt* = 1 corresponds to normal water availability [13].

The average water price on Gotland in 2017 was 12.74 SEK/m3 (100 Swedish Krona (SEK) <sup>≈</sup> 10 USD in October 2019) and the average amount of water consumed was 132 L per capita and per day [27]. There is no price elasticity estimate available for Gotland. Therefore, a mean price elasticity of water demand for developed countries (−0.378) was applied, based on the meta-analysis by Sebri [28] (p. 518). For sensitivity analysis, a price elasticity of −0.2 was used, following a study of household water demand in Sweden [29]. *Qr* was estimated at the SHELF workshops.

#### 2.2.3. Estimation of Business Losses

Another consequence following water disruptions is the economic consequences for commercial and industrial customers due to loss of potable water service. In this paper, the estimation of value added lost for businesses followed the Federal Emergency Management Agency (FEMA) [30] (p. 39) methodology of using local GDP data [31] in combination with water importance factors [32]. It was here assumed that Swedish economic sectors have the same percentage reduction in value added from water supply disruptions as US economic sectors.

#### *2.3. Risk Characterization*

A risk curve for the reference alternative, i.e., the current water supply system, is developed based on the triplets (*si*, *fi*, *xi*). For this, the scenarios must first be arranged in order of increasing consequences, i.e., *x*<sup>1</sup> ≤ *x*<sup>2</sup> ≤ ... *xi* ≤ ... ≤ *xN*, along with corresponding frequencies. Starting with the scenario with the most severe consequences, a cumulative frequency *Fi*, i.e., the frequency of having consequence equal to or greater than *xi*, is calculated as *Fi* = *Fi*+<sup>1</sup> + *fi*. By plotting (*xi*, *Fi*), a staircase function of the analyzed risk scenarios is derived, representing a discrete approximation of the continuous reality. A smoothed risk curve *Rx*, drawn through the staircase (Figure 1), can then be regarded to represent the actual risk [9]. Each point of the curve does not belong to a specific event but instead represents the estimated return period of losses. The integral of the curve, i.e., the area underneath the curve, represents the total expected losses in any given year so that:

$$R\_{tot} = \int\_0^{\mathbf{x}\_N} F(\mathbf{x})d\mathbf{x} \tag{3}$$

where *Rtot* is the total annual risk, *N* is the total number of analyzed scenarios, *x* is the combined economic consequences for the municipality, households and businesses (i.e., *x* = *xMunicipality* + *xHouseholds* + *xBu* sin *esses*), and *F* is the cumulative frequency as a function of consequence *x*. For risk estimation, the continuous function is simplified by the staircase function.

**Figure 1.** Schematic description of staircase and continuous risk functions based on e.g., Kaplan and Garrick [9].

#### *2.4. Evaluation of Risk Reduction Measures*

A risk reduction measure is here defined as any measure that can be applied to reduce the frequency and/or the consequences of the undesirable events. The same scenarios (*si*) used when estimating the risk level of the reference alternative are also used to assess potential risk reduction measures, but the measures' associated frequencies and consequences are applied. For each measure (*a*), a new risk curve is created and thus a new annual total risk. The annual risk reduction, i.e., the annual benefit *Ba*, is calculated as the difference between the risk curve of the reference alternative *R*<sup>0</sup> and the risk curve of the analyzed measure *Ra* as *Ba* = *R*<sup>0</sup> − *Ra*.

To compare the economic profitability of implementing the measures, a CBA [23] was performed. CBA is a structured method to compare the societal costs of an option with its benefits. The estimated risk reductions were included in the CBA as annual benefits [33]. The decision-metric of the CBA is the net present value (NPV), calculated as:

$$NPV\_d = \sum\_{t=0}^{T} \frac{B\_{a,t} - C\_{a,t}}{\left(1 + r\right)^t} \tag{4}$$

*x*

where *a* is the alternative measure, *t* is the time when benefit or cost occur, *T* is the time horizon, *r* is the discount rate, *C* are the costs associated with implementing a risk reduction measure, and *B* is the benefit of risk reduction in relation to the reference alternative. A measure is considered economically profitable when its total benefits to society are larger than its total costs to society, i.e., when its NPV is positive. Three discount rates were used (1.4%, 3.5% and 5%, respectively), reflecting the average discount rate used in the Stern Review on Climate Change [34] and the suggested social and private rates of the Swedish Transportation Administration Guidelines for cost-benefit analysis [35].

#### *2.5. Uncertainty and Sensitivity Analyses*

This paper applies a probabilistic approach with formal uncertainty analysis. As described above, the SHELF Framework was used to elicit information regarding uncertain quantities such as the proportion of households affected in different scenarios and the frequency of events. Probability distributions were assigned to represent each uncertain quantity, and Monte Carlo simulations (10,000 iterations) were used to calculate the annualized risks, risk reductions and NPVs using the risk analysis software @Risk 7.6.0 (Palisade, Ithaca, USA). This provides important additional information for the decision-makers. As Kaplan and Garrick [9] (p. 14) state, a single number is not a big enough

concept to communicate a risk—it takes a whole family of risk curves. The uncertainties in input data can, for example, be used to visualize the resulting mean, minimum and maximum risk curves. The uncertainties can also be transferred in loss exceedance curves, i.e., the probability that the expected loss exceeds a certain value [36]. Since it is hard, and often not possible, to capture all uncertainties in the variables of a risk model, other uncertainty factors are identified, described and discussed using a qualitative approach. The purpose is to provide a transparent decision support that highlights uncertainties that may affect the interpretation of the results.

#### **3. Method Application**

#### *3.1. The Case Study Site*

The case study site was the island of Gotland (3000 km2) in Sweden, located in the Baltic Sea about 100 km east of the mainland and with a population of 58,000. Gotland suffers from low water availability and difficulties in providing enough water to the society. The island's thin soil layers, lack of coherent reservoirs in the limestone bedrock and extensive drainage of arable land, result in an overall low storage capacity of water and a high precipitation run-off [37]. Climate change is expected to further limit the water availability on the island. Longer dry periods are predicted during summers, and the groundwater recharge is expected to decrease due to an increased temperature and the subsequent increase in evaporation and vegetation periods. Currently, about 18 million cubic meter per year is used by households (4 Mm3), animal keeping (1.5 Mm3), tourism (1.3 Mm3), industry (6.1 Mm3) and irrigation (5 Mm3) [38]. A large proportion of the water supply is based on private solutions. For example, only 67% of the households are connected to the public water supply system, which during the summer months to 40% is based on groundwater, 20% on surface water and 40% on desalinated seawater [39].

Gotland is one of the most popular tourist summer destinations in Sweden. In 2016, over 2 million people traveled to Gotland, and the number of guest nights at hotels and other commercial accommodation facilities exceeded 1 million [40]. Hence, there is a large seasonal variation of water demand on the island with the highest demand occurring when the water supplies are at their lowest. In addition to an already constrained water supply situation, the total water demand, i.e., of municipal water provision and other water sources, is expected to increase by about 40% by 2045 with increases of 30% in tourism, 20% in domestic demand, 20% in animal keeping, 15% in industry, and 100% in irrigation [38]. The current water resources on the island cannot meet this projected increase in demand, especially during the summer months. Due to Gotland's insular location there is also no possibility to strengthen the water supply from neighboring municipalities.

#### *3.2. Scenarios and Risk Reduction Measures*

Six scenarios were identified around the question: What can pose a challenge to maintain a continuous municipal water supply provision on Gotland? see Table 1. The scenarios were developed during multiple discussions with the municipality's water supply strategists to represent the range of possible events that may present challenges to the municipal water supply. More detailed information about the scenarios was discussed at the workshops but is confidential for safety reasons.

Based on previous estimates of where, and with how much, the municipality can increase groundwater and surface water abstractions as well as supplement groundwater catchments by managed aquifer recharge (MAR) [37,41], four alternative risk reduction measures were analyzed in this paper, see Table 2. Focus in this paper is hence on improvements in the raw water system. The analyzed measures are site specific, thus they can reduce the risk in the areas in which they are applied but not in areas to which, e.g., the distribution network is not connected. The reason desalination is not further explored is because the municipality has decided to prioritize freshwater (from groundwater, lakes and streams) over seawater for public water supply. Desalination is to be further considered only if the freshwater resources cannot meet demand [42].


#### **Table 1.** Scenario summaries.

**Table 2.** Alternative risk reduction measures.


To estimate uncertain factors affecting identified scenarios and risk reduction measures, three half day SHELF elicitation workshops [25] were held in May and June 2019, with workshop participants ranging from 2 to 6 experts and 1 to 3 workshop facilitators. The workshop participants (6 in total) represented the following areas of expertise: public drinking water management, public water supply strategy, emergency management, environmental expertise, private water supply, and longtime operational water utility staff. For the more frequent events, there was plenty of background information to rely on regarding, e.g., estimation of different cost aspects. For the more infrequent events, the estimations were naturally more speculative.

#### **4. Results**

Details on quantified variables from the SHELF workshops and follow up meetings are provided in Table 3. The table provides the input variables for the calculations of total risk, risk reduction and net present values (NPV), performed by Monte Carlo simulations. For a few events that are expected to occur each year, uncertainties regarding frequency and return period were not quantified. Frequency was generally used as a measurement of occurrence when the estimated time between events was greater than one time per year; otherwise the return period was used.


*Water* **2020** , *12*, 1746



\* Some treatment component costs are recurring every 7 or 10 years [41].

30;

The results and related uncertainties are dependent on the estimated input variables but also on the basic assumptions used to describe the system and the future development. Table 4 provides information on non-quantified uncertainty factors discussed at the SHELF workshops, along with the associated assumptions made. This qualitative analysis of uncertainties is of great importance when interpreting the results.

**Table 4.** Non-quantified uncertainty factors discussed at the Sheffield Elicitation Framework (SHELF) workshops.


The estimated annual risk for the reference alternative *R*<sup>0</sup> is demonstrated in Figure 2 in the form of a staircase to the left and as a risk curve showing the mean and P05 and P95 frequency percentiles to the right. According to calculation results, the low-frequency events are generally associated with larger economic consequences than high-frequency events. However, the annual risk is the lowest for the second least frequent event (Scenario 5) and the highest for the most frequent event (Scenario 6): 425,000 SEK for Scenario 1; 378,000 SEK for Scenario 2; 1,262,000 SEK for Scenario 3; 4,222,000 SEK for Scenario 4; 309,000 SEK for Scenario 5; and 6,321,000 SEK for Scenario 6 (mean values). The total annual risk is estimated at approximately 12,916,000 SEK, ranging from 7,161,000 SEK to 32,370,000 SEK for the 5th and 95th percentiles, respectively (mean values).

**Figure 2.** Estimated annual risk of the reference alternative for analyzed scenarios in the form of staircase (**a**), and in the form of a risk curve showing the mean values and frequency percentiles P05 and P95 (**b**). Note that the curves are plotted on log-log scales with cumulative frequencies.

The risk curves of the alternative risk reduction measures are shown in Figure 3 along with the risk curve of the reference alternative. The potential risk reduction of the measures is the difference between the risk curve of the reference alternative and those of the measures. The large-scale surface water measure was shown to reduce the total annual risk the most, suggesting a potential reduction of approximately 6 million SEK annually compared to 965,000 SEK for groundwater, 785,000 SEK for MAR, and 307,000 SEK for the small surface water measure (mean values).

**Figure 3.** Risk curves for analyzed risk reduction measures over all scenarios (mean values). Note that the curves are plotted on log-log scale with cumulative frequencies.

The probabilities of each measure being the best option with respect to risk reduction for each individual scenario and combined for all scenarios is shown in Figure 4. The results show that the large-scale surface water measure has the highest probability to be the best option for most individual scenarios and for all scenarios combined. The ranking order of the other measures vary between risk scenarios.

**Figure 4.** Probability that each measure is the best option with respect to risk reduction.

The result from the cost-benefit analysis is shown in Figure 5 displaying that the large-scale surface water measure was the least economically beneficial measure for Scenarios 1 to 5 when analyzed individually, but the most beneficial measure for Scenario 6 and when including the risk reduction for all scenarios combined. The ranking order of the other measures varied somewhat between the analyzed scenarios. The NPV mean values in million SEK for the measures SW small, MAR, GW, and SW large respectively are: −37, −54, −46, and −108 (Scenario 1); −42, −54, −50, and −113 (Scenario 2); −42, −38, −33, and −83 (Scenario 3); −42, −53, −50, and −113 (Scenario 4); −40, −54, −47, and −109 (Scenario 5); −42, −54, −50, and −13 (Scenario 6); and −35, −36 −27 and 24 (all scenarios combined). It is worth noting that the NPVs are based only on implementation costs and the benefits of risk reduction with respect to water supply disruptions. The CBA could therefore be improved by inclusion of other costs and benefits, e.g., relevant ancillary effects. However, the present result is sufficient to highlight the importance of a holistic view based on multiple scenarios when prioritizing between risk reduction measures.

**Figure 5.** Net present values for measure implementation with the annual benefit of risk reduction for each individual risk scenario and for all scenarios combined, over a 50-year time horizon and with 3.5% discount rate (mean values).

The economic benefit of risk reduction is distributed differently across households, businesses and the municipality for the analyzed measures and scenarios. In Scenario 1, the municipality gained 100% of the benefits. In Scenario 2, no measure contributed with any benefit of risk reduction. In Scenario 3, the households gained 100% of the benefits. In Scenario 4, the households gained 100% of the benefits of MAR. The other measures did not contribute to any benefits in that scenario. In Scenario 5, the municipality gained 99.7% of the benefits of the increased groundwater extraction and the smalland large-scale surface water measures, and the households gained 0.3% of the benefits. In Scenario 6, the businesses gained 99.2% of the benefits of the large-scale surface water measure and the households gained 0.8%. No other measure contributed with risk reduction in that scenario.

Results from the two forms of sensitivity analyses performed (based on scenario analysis and Monte Carlo simulations respectively) are provided in Table 5 and Figure 6. Table 5 shows that the ranking order of the measures did not vary much when applying different discount rates. However, the order of the measures varied when applying different price elasticities. Particularly the MAR measure benefited from the −0.2-price elasticity compared to the other measures.

**Table 5.** Ranking order of net present values for analyzed measures when using two different price elasticities of water demand and three different discount rates. Rank 1 = highest net present value (NPV) and Rank 4 = lowest NPV (mean values). The risk reduction of all scenarios combined are used in these calculations.


**Figure 6.** Correlation coefficients (Spearman rank) of the eight most strongly correlated input variables for the total annual risk.

Figure 6 shows the degree to which input variables co-vary with the calculated total risk, expressed using Spearman rank correlation coefficients between −1 and 1. Input variables related to the return periods and duration of the risk scenarios contributed more to the outcome uncertainty than input parameters related to the economic consequences of the scenarios. This holds true also when comparing how the input variables co-vary with the estimated NPVs, i.e., input variables related to return periods and duration of risk scenarios contributed most to the NPV uncertainties.

#### **5. Discussion**

Gotland's drinking water system is vulnerable to supply and demand fluctuations. Insufficient water availability in combination with rainfall deficiencies and large seasonal demand variations pose challenges to the local water utility. In addition, the total water demand on the island is expected to increase by more than 40% over the coming decades [38]. Taken together, the low and varied water availability coupled with other threats to the drinking water system, illustrates the importance of understanding the system risks as well as the benefits of investing in a reliable water supply [14]. In this paper, four potential risk reduction measures were analyzed for Gotland, providing guidance on how efficient the measures are to reduce different types of risks. The risk analysis was combined with cost-benefit analysis to provide information on the measures' economic viability. For Gotland, the large-scale surface water measure (SW large) proved to be the most beneficial measure for reducing the risk in most individual risk scenarios and in all scenarios combined. However, the large-scale surface water measure was the least economically beneficial measure for the individual Scenarios 1 to 5 when comparing NPVs, but the most economically beneficial measure for Scenario 6 and when including the risk reduction of all scenarios combined. This is because the measure has high implementation costs but also a high risk reduction effect on several of the scenarios, and the combined effect of these risk reductions creates a large benefit. The varying ranking order of the measures for Gotland, when analyzing risk reductions for individual scenarios versus all scenarios combined, highlights the importance of a holistic risk assessment, integrating a range of risk scenarios. This is to avoid sub-optimization where measures are prioritized based on individual risk scenarios. By calculating the total risk, the possibility of more than one scenario occurring simultaneously is considered. However, it should be noted that the measures analyzed in this paper focused mostly on improving the raw water system, and little attention was given to improving the treatment system or the distribution system.

The presented method makes use of a non-overlapping subset of risk scenarios, which together should represent all possible scenarios for water supply disruptions. By quantifying the probability of losses caused by the scenarios, a risk curve is produced showing the relationship between frequency and its associated losses. Each point of the curve represents the actual return period of losses, and the curve can hence be used to provide information on how to address the different levels of risk. In the paper, we have chosen to express the risk in terms of expected economic consequences to society arising from disruption events. However, it is important to point out that in other situations there may be reasons to express the risk in other terms, in which case the same method can still be used. It is also important to note that there are limitations in expressing the risk in terms of expected consequences, particularly when it comes to capture events with low probabilities and high consequences [18]. However, we have judged the type of events we consider are the type that can be assessed with expected consequences.

Rational decision-making requires that the risks, along with other costs and benefits, are properly accounted for in the decision-making process [9]. However, evaluations of alternative measures and their effects will always comprise uncertainties. In this paper, the uncertainties of input variables were represented by probability distributions, and the uncertainty of the outcomes were calculated by means of Monte Carlo simulations. This approach allows us to study the uncertainty in the results and the likelihood of each outcome. It also facilitates sensitivity analysis, e.g., using Spearman rank correlation coefficients, to study how uncertainties of specific input variables contribute to the uncertainties in the results. Such information can for example be used to support decisions on which input variables to prioritize for further research and/or data collection in order to reduce uncertainties in results. However, it is practically impossible to cover all aspects of real systems [24]. Hence, the assigned probabilities are conditioned on several assumptions and simplifications. For assumptions and simplifications not to be overlooked in the risk management and decision-making processes, these variables are included in the analysis using a qualitative approach as suggested by e.g., Aven [18] (p. 630). This approach highlights basic assumptions that, for example, affect the estimated input variables. If the analysis would have been based on another understating of the system and its development, the results would of course have been different. Hence, the qualitative uncertainty analysis provides transparency and is of great importance when interpreting the result. Further, some discrete uncertainties, such as discount rates and price elasticities, are analyzed by use of scenario analysis [43]. This comprehensive handling of uncertainties demonstrates a structured and transparent way of expressing risk so that water utilities can use estimates of failure rates and welfare losses over a range of disruption scenarios to identify the measures that will lead to the lowest economic losses for society, and hence improve water supply planning and risk management.

It should be emphasized that despite the abundant information provided by the risk assessment approach, its most important contribution may be that it initiates a process in which aspects otherwise likely overlooked or ignored are openly addressed. For example, definition of risk scenarios, uncertainty estimations of input variables, economic valuation of consequences, calculations of the total integral sum of risk over different risk scenarios, and calculations of economic profitability through CBA are all rare in water supply reliability studies. However, we did not consider combinations of risk reduction measures or effects on other externalities, such as health issues or agricultural production. Hence, the provided method can be improved by enabling assessments of measure combinations and inclusion of other relevant costs and benefits.

#### **6. Conclusions**

The main conclusions of this paper are:


**Author Contributions:** Conceptualization, methodology and resources, L.R., A.L. and K.S.; software, formal analysis, investigation, project administration, visualization, writing—original draft preparation, K.S.; data curation, K.S., A.L. and T.S.; funding acquisition, L.R. and K.S.; supervision, A.L. and L.R.; validation and writing—review and editing, K.S., A.L, T.S. and L.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 754412; Region Västra Götaland; and the Swedish Research Council Formas contract no 942-2015-130.

**Acknowledgments:** This research was performed within the DRICKS center for drinking water research coordinated by the Chalmers University of Technology. The authors would like to thank Mikael Tiouls and Lars Westerlund, as well as all other workshop participants, at Region Gotland for contributing with Gotland-specific expertise on scenarios and societal effects.

**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Analysis of Barriers and Opportunities for Reclaimed Wastewater Use for Agriculture in Europe**

#### **Enrique Mesa-Pérez and Julio Berbel \***

Water, Environmental and Agricultural Resources Economics (WEARE), Universidad de Córdoba, 14001 Córdoba, Spain; emesa@uco.es

**\*** Correspondence: berbel@uco.es

Received: 15 June 2020; Accepted: 7 August 2020; Published: 17 August 2020

**Abstract:** This paper presents an analysis of the perception regarding reclaimed wastewater reuse in agriculture conducted in the European Union regions. The analysis is based upon a SWOT framework and applies a cluster analysis to reduce the dimension of the responses enabling an assessment of the different perceptions of water reuse. More than one hundred key actors identified among the regions participated in the evaluation of the relevance of aspects identified. The results indicate some groups of countries according to natural conditions (water scarcity) and the strategic role of agriculture as a key factor to determine agent's perceptions and attitudes. The results indicate that the forthcoming EU regulation of water reuse should focus in the problems of the perceived high cost of reclaimed water for farmers and the sanitary risk perception for irrigated crops by consumers as the critical points for fostering the use of reclaimed water in agriculture and the need for regional implementation of the global regulatory framework.

**Keywords:** water reuse; reclaimed water; SWOT analysis; cluster analysis

#### **1. Introduction**

Arid regions of the world usually have a demand for water that exceeds available resources. The use of reclaimed water is frequently mentioned as a "win–win" solution [1,2]. Previous experience in implementing reclaimed water for agricultural irrigation is satisfactory, especially in water-scarce areas [3], such as Spain, California, Australia [4], Jordan [5], or Italy [6]. Nevertheless, there are still some barriers and obstacles that should be reviewed by [7]. Therefore, water reuse "is considered vital to alleviate the demand on existing but limited water supplies and is gaining impetus throughout the world" [8], also as an alternative water resource to fight droughts and water scarcity [9].

Nevertheless, this opinion should be taken into consideration as wastewater is part of the hydrological cycle and its use in a closed basin where resources are already overallocated (as it is frequent in many regions) may increase exploitation of resources [10]. Additionally, the financial cost or the greenhouse gas emissions should also be considered. The main governance instrument in the EU is the Water Framework Directive (2000/60/EC) (WFD) [11]; the WFD has been successful slowing down the deterioration of water status and reducing (mainly point source) chemical pollution, regarding urban wastewater, 88% of EU wastewaters are subject to secondary treatment although water reuse is still low in the EU [12,13].

The EU included reclaimed water as part of the circular economy. As it is considered in the literature [14], the resources efficiency strategy and several regulations are developed with the aim to foster the use of reclaimed water. Water quantity and quality, including reclaimed water, is regulated by the EU mainly through the following: Water Framework Directive (2000/60/EC) [11], the Urban Waste Treatment Directive (91/271/ECC), the Scheme for Fertilizers (EC2003/2003) [15], or the Nitrates Directive (91/676/EEC) [16]. Closely related to EU water regulation is the Common Agricultural Policy provisions 2014–2020 [17] and the Marine Directive [18]. Additionally, the EU also influences water reuse by strategic documents such as Commission communication on Water Scarcity and Droughts [19], Blueprint for Safeguarding European Waters [20], and the Circular Economy strategy [21]. Finally, several international initiatives like the Sustainable Development Goals included in the UN 2030 Agenda for Sustainable Development include fostering the use of reclaimed water within its goals.

However, the keystone in the implementation of reclaimed water for irrigation is the development of the "Regulation EU-2020/741 Minimum Requirements for Water Reuse" (European Commission, 2018) [21]. This regulation has been recently approved by the EU Parliament and seeks the homogenization of reclaimed water quality standards and water risk management systems for all the EU countries. There is a general agreement about water reuse brings benefits [22,23], but the proposed regulation should be adapted to varying conditions in each of the EU regions [2]. Consequently, a specific strategy should be used to foster reclaimed water in each region. This paper tries to answer this issue, analyzing the perception of the opportunities and barriers that several European regions face in the implementation of reclaimed water for agricultural irrigation.

This paper contributes to identifying regions with similar barriers and opportunities to implement reclaimed water in agriculture. We suggest that it is necessary for the implementation of specific strategies adapted to each regions' characteristics if a satisfactory reclaimed water implementation in agricultural irrigation is sought.

The paper continues as follows, firstly with the material and methods employed in the development of the research; secondly, with the cluster analysis; thirdly, with the results discussion; and finally, with the conclusions.

#### **2. Materials and Methods**

This research is based on the empirical work made during the European Project H2020 SUWANU-Europe [24], which proposes an exploratory analysis of the opportunities and barriers facing the use of reclaimed water in agriculture. To achieve it, this paper proposes a Cluster Analysis to know the similarities among the regions participating in the project: Belgium, Bulgaria, France, Germany, Greece, Italy, Portugal, and Spain. SUWANU-Europe departed from the results of the previous EU project (SUWANU) [19], which were used to support our analysis. The research design includes the survey of the relevant stakeholders (farmers, private sector, drinking water suppliers, wastewater suppliers, national and local administration, research institutions, and Non-Governmental Organizations (NGOs)) in the eight countries. In Appendix B is attached the table with the resume of key actors provided in the deliverable 2.1 of SUWANU [25].

#### *2.1. Study Area*

Regions included in the survey belong to eight European countries carefully selected to promote the adoption of water reuse strategies. The eight regions were selected following criteria of high technological development, Braunschweig; high water consumption in agriculture, Thessaloniki; high contribution of agriculture to regional economy, Andalusia and Plovdiv; total employment, Thessaloniki and Plovdiv; existing legislation, Andalusia; water stress, Thessaloniki, Tuscany, Antwerp, Limburg, and Andalusia; and high levels of rural population, Occitan, Santarem, Plovdiv, Thessaloniki, and Andalusia [2,4,24,26]. These regions belong to Belgium, Bulgaria, France, Germany, Greece, Italy, Portugal, and Spain. Table 1 illustrates the regional differences regarding urban wastewater treatment plants (WWTP) and related variables.

Regions under analysis differ in size and population. For that reason, we use data available in Table 1 such as the number of WWTP or the total discharge of wastewater allowed, to characterize reclaimed wastewater potential availability. Reclaimed water potential availability in these countries supports the idea of considering it as an alternative water resource, i.e., in some water abundant regions, such as Belgium the volume of treated water exceeds agriculture water demand.


**Table 1.** Data insight for regions.

Source: SUWANU Europe Deliverable 1.1. resume table [27] and (\*) Total water abstraction/Renewables resources. Data from EUROSTAT [28].

#### *2.2. Material and Research Design*

The material consists in the responses to a large survey conducted from May to July 2019, in the eight EU member states' regions. Aspects analyzed in the survey are categorized following the SWOT framework dimensions (Strength, Weakness, Opportunity, and Threat).

The proposed structure makes a more flexible comparison of aspects identified among the different regions for two main reasons. Firstly, not all regions have the same concern and expectations about the use of reclaimed water for irrigation. Consequently, aspects identified in each region can vary, making the comparison difficult. This classification respects those singularities and allows the aspects characterization following proposed categories. Secondly, whether all regions follow the same classification, the evaluation of the different aspects will show which categories received more attention in each region making results comparable.

Key actors were identified by the regional group from among members of all sectors related to the topic of reclaimed water in agriculture (policymakers, farmers' representatives, water technology companies, wastewater treatment suppliers, government institutions, and research institutions). Each one of the partners identified its regional key actors.

The identification of aspects involved in fostering reclaimed water for irrigation consisted of a three-step process. The first phase consisted in determining whether aspects identified in the previous EU project [29] were still relevant and proposing new aspects not included that could be relevant nowadays. Secondly, a design phase is conducted using different methods such as workshops, key actor interviews, and brief surveys to key actors. The aim of this phase is the final identification of all the aspects influencing reclaimed water implementation. Finally, the third step consisted in arranging the different aspects pointed in previous phases within SWOT framework dimensions (Strengths, Weakness, Opportunities, or Threats) and the categories explained in Figure 1. This process included a discussion about some results that considered an aspect as a strength or an opportunity at the same time, varying in relation to each key actor's opinion.

**Figure 1.** SWOT analysis dimensions and aspects classification proposed.

#### *2.3. Aspects Evaluation*

Although the use of SWOT analysis originated in business analysis, it also received uses outside this domain [9], and use of SWOT analysis to identify factors influencing the implementation of reclaimed water has already been made [8]. This paper focused on the evaluation to know the most relevant aspects influencing reclaimed water fostering for agricultural irrigation. The aim of this evaluation is the identification of the most relevant aspects in each region and the comparison of the results among the different regions. The classification proposed in Figure 1 will allow us to compare which groups of aspects have more relevance.

To evaluate aspects relevance, the methodology proposed is a Likert scale from 1 to 5. The Likert scale allows us to evaluate the agreement or disagreement for a series of statements [30,31] and is recommended the use of 5 levels (1 not relevant to 5 very important). This scale allows a neutral option, rate 3, for respondents without a clear answer about a question [31]. Most countries follow a 1–5 scale, although France and Germany use a scale 1–10 that was later converted to a 1–5 scale with the aim to compare results.

The methodology to evaluate aspects relevance also varies from one to another country. The most common tool used was an online survey sent to key users by email in Bulgaria, Greece, Italy, Portugal, and Spain. However, Belgium, France, and Germany evaluated the relevance of the different aspects surveying key actors directly during a workshop. Aspects identified and a preliminary analysis of the main results are available in SUWANU Europe Deliverable 2.1. [25]. In this research, we analyzed the compared results from the different regions following categories explained above (See Figure 1) trying to know which specific characteristics affect the implementation of reclaimed water as an alternative water resource.

#### **3. Results**

Generally, SWOT analysis makes a statistical description of the responses with an "expert opinion" for interpretation of the results. Our proposal is innovative as we will use cluster analysis to get some insight into the survey since we have eight countries with different objective characteristics (water scarcity, agricultural demand, etc.) and socioeconomic conditions.

Table 2 shows the results of the survey following the categories classification and SWOT dimensions explained in Figure 1. The higher the value, the more relevant is the aspect. For example, in Belgium, the most relevant categories are product-related strengths; in Bulgaria, strengths related with market-related issues; in France, market-related weaknesses and opportunities; in Germany, market-related opportunities; in Greece, issues about market-related strengths are the most relevant; in Italy, market-related strengths; in Portugal, market-related weaknesses; and in Spain, market-related strengths. This information will be analyzed more in detail following the cluster analysis results.


**Table 2.** Country average value for each for category for SWOT critera.

Source: Own elaboration with data from SUWANU Europe SWOT Analysis. (1 means: no relevant; 5 means: very relevant).

This preliminary analysis shows that the perception of reclaimed water differs considerably according to each region's characteristics. We want to process this information and try to find similarities and differences that explain the perception of SWOT dimensions among the different regions to know the barriers and opportunities that reclaimed water is facing within each region. Consequently, this research drives a cluster analysis to evaluate which regions face similar barriers or opportunities in implementing reclaimed water for agricultural irrigation. For that reason, we simplify the results (see Table 3) to identify the type of barriers or opportunities the regions are facing. We calculated the average values of the aspects following the classification explained in Figure 1.


**Table 3.** Categories average evaluation.

Source: Own elaboration.

The analysis of agents' response is difficult to carry out based exclusively on descriptive statistics; therefore, we try some multivariate techniques whose primary purpose is to group objects based on the characteristics they possess. We select cluster analysis because it tries to identify internal homogeneity within the aspects of a group (cluster) and an external heterogeneity between each cluster [32].

We also analyze the differences among the regions following SWOT characteristics; on the one hand we pay attention to the prevalence of positive or negative aspects among the countries (see Table 4).


**Table 4.** Difference positive minus negative aspects SWOT analysis.

In this analysis, we can observe the prevalence of positive or negative aspects among the regions under analysis. On the one hand, Germany and Spain's key actors give more importance to positive issues in the three categories. On the other hand, Bulgaria, France, and Italy give a more positive relevance to two over three categories, and finally, Belgium, Greece, and Portugal give a higher negative relevance to two over three categories. This analysis could suggest that fostering reclaimed water could be "easier" in Spain or Germany than in Portugal or Bulgaria.

On the other hand, we provide an analysis of the prevalence of internal or external aspects among the countries. SWOT analysis evaluates internal aspects (strengths and weaknesses) and external aspects (opportunities and threats); consequently, we try to show which aspects are more relevant in each region. This analysis' results are provided in Table 5:

**Table 5.** Internal–external SWOT analysis.


This analysis suggests that internal aspects are more relevant than external ones in France, Italy, Portugal, and Spain, while external aspects are more relevant in Belgium, Bulgaria, Germany, and Greece; we will discuss these results in the following part of the paper together with cluster analysis results.

Finally, cluster analysis is an exploratory data mining technique applied to the whole survey trying to force objects (responses in our case, regardless of the country of origin) to fall into the same group (called a cluster) following a similar definition of distance [32]. Our degrees of freedom "a priori" are eight countries by 12 groups: 4 SWOT dimensions × 3 categories (see Figure 1). We apply principal components analysis to reduce the dimensionality of the space of answers, although the results show that the Kaiser Meyer-Olkin (KMO) is lower to 0.6, recommending the use of original data [32]. Consequently, according to Hair [32], a logical basis is needed to determine the variables

to apply cluster. For that reason, this research concludes the proper variables are "market-related, product-related, and social and governance".

According to the results of cluster analysis (see Figure 2), we may identify two cluster groups: (a) Belgium, Portugal, Germany, Greece, and Bulgaria and (b) Italy, Spain, and France. The next section makes a deeper analysis of the perception in these four groups and tries to analyze results.

**Figure 2.** Cluster analysis result.

#### **4. Discussion**

This paper seeks similarities and differences among the barriers and opportunities perceived by key actors of eight EU regions. We conducted a SWOT analysis with the key actors' groups established for each one of the regions participating in the project. The first step was the identification of the relevant aspects. The SWOT analysis and the evaluation of the aspects were supported by a cluster analysis to identify similarities and differences among the regions. Following the categories proposed above (market-related, product-related, and social and governance), cluster analysis results in two groups: (a) Belgium, Bulgaria, Germany, Greece, and Portugal and (b) France, Italy, and Spain. An in-depth analysis of the aspects identified within the countries of each group is conducted.

Providing an in-depth analysis of the first cluster group (BE, BU, GE, GR, PT), we focus on the relevance of each category observed in Appendix A, Table A1 (see a resume in Table 6). We also provide a heatmap where the most relevant issues are colored red and the less green in Appendix A, Table A3. This first cluster key actors seem to agree about the high relevance of product-related issues. This can be a reflection of the potential use of reclaimed water supported in the existence of technological and technical conditions to treat wastewater (especially in Germany). However, in the same way, there exist some regions where product-related is also considered a weakness (Portugal and Bulgaria), or weakness and a threat (Belgium). Paying attention to the specific aspects identified by the key actors of those regions, we can identify risks for implementing reclaimed water, e.g., energy cost, the lack of infrastructure to distribute reclaimed water from the WWTP to the crops, or the necessity to learn from most advanced countries (see Cyprus and Israel). It can be observed a kind of consensus about the cost

of implementing water reuse and the cost of reclaimed water itself being aspects that should be faced by the public administration within these countries.


**Table 6.** Belgium, Bulgaria, Germany, Greece, and Portugal categories average evaluation.

Trying to understand how to face the cost management issues identified in Portugal, Belgium, or Bulgaria, we can observe that Germany shows just the opposite. German key actors may give higher relevance to product-related issues and market-related opportunities with comments such as "The potential self-financing business model of AV-BS (region of Braunschweig, Germany) where water fees paid by customers to support the system", or, the most relevant opportunity, "irrigation free of pollutants". These aspects are similar to the market-related issues identified in Belgium and Bulgaria, where the cost of reclaimed water for agricultural irrigation is considered a weakness. Moreover, these countries only identified one aspect as product-related strengths, e.g., "knowledge and technology about reclaimed water treatment". Consequently, as we explained just above, Belgium and Bulgaria give more relevance to product-related weaknesses than strengths. Nevertheless, the rest of the regions in this cluster, Germany, Greece, and Portugal, agreed in considering product-related aspects as a strength. These regions considered the existence of previous success stories and technology available, an issue that will facilitate the implementation of reclaimed water. However, it also seems relevant that product-related issues are considered as a threat in Portugal, Belgium, and Greece. In the case of Belgium, this is clear (see above), but in the case of Portugal and Greece, although these countries' key actors considered the existence of technology and technical conditions good to support reclaimed water implementation, they also suggested that the potential nanoparticles could require intensive treatment that threatens the use of reclaimed water. Besides, in the case of Portugal, the lack of infrastructure was not only considered a weakness but also a threat to overcome in the future.

It can be concluded that product-related issues are the most relevant in this cluster, positively such as in Germany or negatively like in the rest of the regions. The position regarding costs is the main difference between these regions. Paying attention to German product-related issues, they are considered the most relevant concerning strengths and opportunities dimensions. The technical experience of AV-SB (the German regional water company) in water reuse and the 4th wastewater treatment technology developed can be considered the solution for the high cost of reclaimed water that is perceived in the other countries. They have previous experience in reusing 20 hm3 out 30 hm3 wastewater discharge, and consequently, their cost is lower, but a relevant reason to understand this difference can be that Braunschweig is a small region, with only two WWTPs in comparison with the other, bigger regions with more WWTPs.

It can also be concluded that technology and technical issues to foster the use of reclaimed water for agriculture exist, and key actors within this cluster agreed about it. Nevertheless, energy costs or distribution costs should be overcome. Other aspects also received attention in this cluster. It can be observed how social and governance is considered a relevant threat in Belgium, Bulgaria, and Greece. On the one hand, Belgium and Bulgaria highlight that the new regulation will imply a high cost in implementing reclaimed water. On the other hand, Greece's key actors are more concerned about the public perception itself, e.g., "disagreement between various parties" or "uncertainty in the public ... ". Portugal considered social and governance issues more a strength than a threat, e.g., their key actors highlight the existence of information programs and a perception of safety in using reclaimed water for agriculture. Finally, as explained above, Germany's key actors did not consider social and governance a relevant category, indeed one of the most relevant aspects identified is the no existence of water scarcity in the region.

Tables 4 and 5 illustrate the point that product-related issues are evaluated negatively in all the regions except for Germany, at the time that market-related issues are evaluated positively among the regions with the exception of Portugal (the most relevant category is market-related weakness, due to distribution costs). Being classified as market-related or product-related, this group is characterized by being concerned about the cost of implementing, distributing, and storing reclaimed water. In the case of Germany, the country is characterized by being able to drive this issue for the last years.

Regarding the second cluster, regions (FRA, ITA, ESP) give relevance to social and governance and market-related strengths (see Table 7). They perceive that the most relevant aspects are related to social and governance issues. This situation shows that society is concerned with water scarcity problems and considered that reclaimed water could help to fight it. Nevertheless, it is important to inform society properly, because threats about public perceptions also received higher attention, even when the new European Regulation implementation, the existence of reclamation standards, and good communication with users are considered a relevant strength to face the use of reclaimed water.

**Table 7.** France, Italy, and Spain categories average evaluation.


In Appendix A, Table A2, it can be observed the evaluation of the different aspects' categories. France, Italy, and Spain give more relevance to internal than external aspects and they all agree to evaluate positively product-related issues (see Tables 4 and 5). It seems that key actors are optimistic about the implementation of reclaimed water in these regions. Paying attention to aspects identified as social and governance strength, the most common relevant category among this cluster, it can be observed that key actors considered the existence of an EU regulation such a quality guarantee to achieve public support. This characteristic opposes to the other cluster, where the EU regulation quality requirements were considered as an "extra cost". Besides, there exists an agreement about water scarcity and the necessity to seek alternative water sources. Consequently, the need for constant water flow for irrigation, the higher water demand for agricultural uses, and the existence of WWTP can lead to the consideration of reclaimed water as a proper alternative water resource. The difference between Greece and these countries can be motivated in the smaller number of WWTP and the greater availability of water regarding irrigated areas (see Table 1).

Finally, other aspects also received a higher score by key actors. For example, both Italy and Spain considered market-related issues as a strength. Aspects identified are related to the existence of quality standard, of constant water flow, or the environmentally friendly consideration of reclaimed water. All these aspects are related to the social and governance issues commented in the previous paragraph. In the case of France, market-related issues are considered an opportunity. The existence of big cities in the coastal areas and the increasing population support this evaluation. This aspect is also the most relevant in Germany, an issue that is supported by previous literature [2]. Finally, social and governance is also evaluated as a threat in France and Spain and as a weakness in Italy. In the case of France and Spain, the lack of a proper communication policy can result in consumers and wholesalers refusing to consume products irrigated with reclaimed water. The same happens in Italy, but in this case, the lack of public support is considered a weakness.

It can be concluded that this cluster is more optimistic than the first one. Although costs are also considered, more attention is paid to social and governance aspects. The motivation can result from a water scarcity situation and the higher water demand for agricultural irrigation. However, the need to communicate properly the benefits of irrigating with reclaimed water is also relevant for the environment and human health. For that reason, the new EU regulation is considered an opportunity within these regions because it is considered a quality guarantee to avoid the distrust from consumers and food chain actors.

The groups that cluster analysis have shown can be seen as counterintuitive as they include only three southern countries (ES, FR, IT) meanwhile Greece and Portugal belong to the other cluster. The relative abundance of water in Portugal and the smaller amount of WWTP in Greece may be an explanation. Besides water abstraction (all uses) divided by available renewable resources in Portugal is closer to Northern countries than to neighboring Spain. Additionally, Italy and Spain have a competitive, export-oriented food industry, which may explain also the differentiation from other countries. Consequently, the relative water scarcity and the competitiveness of agribusiness may explain these results, although further research is required.

#### **5. Conclusions**

This paper provides an analysis that identifies the main opportunities and barriers faced by reclaimed water based upon cluster methodology and the interpretation of results. Although regions' hierarchy of topics varies, the global perception is that (a) high cost of reclaimed water for farmers and (b) social fear of products irrigated with reclaimed water should be the keystone of the EU strategy to foster the use of reclaimed water in agriculture.

In our research, we have detected that the perception of key actors varies according to the degree of water scarcity and the importance of irrigated agriculture. France, Italy, and Spain focus on water costs and the necessity to achieve consumer acceptance. Other countries without serious scarcity concerns focus on social governance issues to foster collaboration between farmers and the food chain. Policymakers should consider the impact of new EU regulation and support farmers in the financing of operation, at least in the initial stages, in order to strengthen the risk assurance system that will make transparency and social trust possible. Stronger involvement of regional or basin authorities will be probably the more efficient mechanism to promote water reuse avoiding farers and consumer resistance.

The analysis contributes to identifying the main barriers and opportunities that reclaimed water faces in its implementation process among the different regions. Consequently, when the European Commission seeks the approval of reclaimed water specific legislation, these differences should be considered. As this research concludes, not all the regions considered reclaimed water as an alternative water resource at the same level. In some cases, this is because the cost of water distribution is higher or maybe because there is not enough to achieve public support. Consequently, our opinion, based upon this evidence, is that there is a need for implementing different strategies in the different regions if a satisfactory reclaimed water implementation in agricultural irrigation is sought.

Further research could include other regions within the EU to obtain a complete landscape of reclaimed water barriers and opportunities.

**Author Contributions:** Both authors declare they worked equally in the development of this research. Both authors contributed to the development of the paper similarly. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is part of the EU project SUWANU-European, which is a Thematic Network funded by the EC under the H2020 program (contract number: 818088).

**Acknowledgments:** Authors want to acknowledge the support of participating experts and partners through all the Project SUWANU and assume the responsibility for all statements contained in this document.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. Tables SWOT Analysis Evaluation**


**Table A1.** Belgium, Bulgaria, Germany, Greece, and Portugal aspects evaluation.

1,2,3 represent the aspects with a higher relevance, according to key actors' evaluation per region.



1,2,3 represent the aspects with a higher relevance, according to key actors' evaluation per region.


**Table A3.** Aspects Relevance Heatmap by Country.

\* Countries belonging to cluster two. Colors represent the less relevant aspects (green) and the most relevant aspects (red), following the average relevance achieved in the survey.

#### **Appendix B**

**Table A4.** Resume of key actors participating in the SWOT analysis, original from D2.1 SUWANU-Europe.


#### **References**


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