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Article

GIS-AHP Ensembles for Multi-Actor Multi-Criteria Site Selection Processes: Application to Groundwater Management under Climate Change

1
Oldenburg-East Frisian Water Board (OOWV), Water Resources Management and Rights, Georgstr. 4, 26919 Brake, Germany
2
IWW Water Centre, Water Resources Management, Justus-von-Liebig-Str. 10, 64584 Biebesheim am Rhein, Germany
*
Author to whom correspondence should be addressed.
Water 2022, 14(11), 1793; https://doi.org/10.3390/w14111793
Submission received: 3 May 2022 / Revised: 25 May 2022 / Accepted: 30 May 2022 / Published: 2 June 2022
(This article belongs to the Section Water and Climate Change)

Abstract

:
A possible adaptation pathway for water suppliers in Germany who face a climatically driven increase in water stress is the development of aquifers which are not used at their full potential. However, identifying suitable sites for aquifer development can go along with severe conflict potential due to the great variety of stakeholders who are involved in the decision-making process. We approach this multi-actor and multi-criteria decision-making problem by developing a geoinformation system-based analytic hierarchy process ensemble (GIS-AHP ensemble). As opposed to the classic GIS-AHP method that yields ratings of site suitability based on a single expert evaluation, the here proposed new GIS-AHP ensemble method respects multiple expert evaluations and allows for quantifying the robustness of yielded site ratings in multi-actor contexts, which helps to mitigate conflict potential. The respectively derived GIS-AHP ensemble site ratings for northwest Germany are successfully checked for plausibility in the framework of the study by using long-established groundwater abstraction areas as indicators for good site conditions. The GIS-AHP ensemble site ratings are further tested regarding their usability for long-term water supply planning by integrating a groundwater recharge scenario under climate change for the period 2020 to 2050. The proposed GIS-AHP ensemble methodology proves useful in the given case study for fostering integrated environmental decision-making and exhibits a high transferability to other, thematically differing site selection problems.

Graphical Abstract

1. Introduction

In addition to socio-economic changes, anthropogenic contamination, and land use changes, climate change represents a central factor to be considered in water supply planning [1,2,3,4]. Current analyses of climate projections for Europe show a differentiated picture. While northwestern Europe tends to expect an increase in heavy rainfall events, winter precipitation, and runoff, southern Europe is projected to experience mainly an increase in droughts and a decrease in annual precipitation and surface runoff [5,6]. Continental Europe lies in between these two extremes and must expect an increase in winter precipitation, a decrease in summer precipitation as well as an increase in heat extremes. Coherently, the anticipated impacts of climate change in Germany for the period 2020 to 2050 include drier and hotter summers, as well as a seasonal increase in water demand and daily demand peaks [7,8,9,10,11,12,13]. Since the vast majority of water demand in Germany is covered by groundwater, a seasonal increase in water demand could force additional groundwater abstractions and inflict elevated stress on heavily exploited groundwater bodies in summer periods [14,15]. This mechanism will likely cause an increase in groundwater level fluctuation and maximum drawdown, despite eventually unchanged long-term average groundwater levels [7,9,14,15,16,17]. Such an increase in groundwater level fluctuations could, in turn, have adverse effects on groundwater-dependent ecosystems, baseflow discharge, rain-fed agriculture, and forestry in areas with shallow groundwater tables. The described impacts could ultimately jeopardize associated sustainable groundwater management objectives as prescribed by the European Water Framework Directive [7,18].
In recent years, there has been a mainly economically driven increase in water demand in large parts of northern Germany [19]. In the supply area of the Oldenburg-East Frisian Water Board (OOWV), which covers the northwestern part of Germany (Figure 1), water demand has continuously increased between the years 2000 and 2018, with an overall increase of approximately 20%. Today the OOWV supplies more than 1 million customers with on average 84 million m3 per annum of drinking water.
An adaption strategy to address the described water demand and availability challenges under climate change is the development of new groundwater abstraction areas in suitable groundwater bodies that are not used at their full potential. Amplifying the spatial distribution of groundwater abstractions could help mitigate a seasonally induced water stress on some heavily exploited aquifers [20] when paired with regionally connected water supply networks—as available in the study area. Such a climate change adaption strategy facilitates opportunities for the efficient mitigation of undesirable local ecological and economic impacts in times of drought, and is, therefore, in accord with the goals of the German National Water Strategy 2021 [21].
The regional selection of new groundwater abstraction areas is a multi-criterial and interdisciplinary decision-making problem that can go along with a severe conflict potential due to a variety of different stakeholder-dependent problem perceptions. The issue of multi-actor conflicts in site selection processes is a widely known decision-making problem [22,23,24,25,26,27,28,29,30,31]. To respect the interdisciplinary and multi-stakeholder dimension of the here discussed site selection problem, a modified version of the widely applied multi-criteria decision support methodology GIS-analytic hierarchy process (GIS-AHP) is proposed [32,33,34,35,36,37]. The core concept of the original GIS-AHP consists of an expert evaluation of the relative importance of different spatially distributed criteria for deciding on a site selection problem. This allows for developing a spatially differentiated but single-disciplinary site suitability rating. The here proposed GIS-AHP ensemble method permits a more differentiated analysis of such site selection problems. Instead of a single expert evaluation, the GIS-AHP ensemble method can respect different, multi-disciplinary expert evaluations, and allows for an associated result robustness analysis which helps to mitigate conflict potential in multi-actor contexts. GIS-AHP ensembles can, therefore, foster required integrated environmental decision-making processes [38,39,40,41,42,43,44,45].
The proposed GIS-AHP ensemble proves useful in the given case study from northern Germany for deciding on potentially suitable new groundwater production sites in a multi-actor context. Overall, the proposed methodology exhibits high transferability and can be applied to site selection problems of any other thematic scope.

2. Methodology and Data

2.1. The GIS-AHP Methodology and its Extension to GIS-AHP Ensembles

The classic GIS-AHP method is based on the “Analytic Hierarchy Process” (AHP) which was developed for decision support in operational business management [46,47]. The GIS-AHP methodology is a method for systematic, multi-criteria site suitability assessments within a geographic information system (GIS). There have been numerous applications of the method, including hydrogeological studies [48,49,50,51,52,53,54,55]. The overall goal of the GIS-AHP is to support decisions in working groups and make them comprehensible for other stakeholders, as well as to reveal possible inconsistencies in the decision-making process. Methodological details regarding the AHP and GIS-AHP methods and their limitations have been described extensively in the literature [46,47,56,57]. As the here proposed GIS-AHP ensemble method builds upon the classic GIS-AHP method, a brief outline of the original methodological GIS-AHP procedure is presented in the following (compare also with Figure 2).
First, the evaluation criteria (data sets) that are relevant for the site assessment must be defined. For the subsequent spatial analysis within a GIS, these evaluation criteria must be available as area-wide raster data sets. Consequently, the user will reclassify the value spectrum of each relevant raster data set based on their professional expertise (step I). This reclassification translates each raster cell value of a raster data set into a unitless value, for example from 0 to 10, where a value of 0 could represent insufficient and 10 ideal site conditions concerning the specific criterion (examples presented in the chapter “Rating profiles”). At the end of this process, all selected raster data sets exhibit an interoperable value spectrum that is unitless (in this study, from 0 to 10) and represents the criteria-dependent site suitability.
Consequently, the user prioritizes the evaluation criteria pairwise against each other (Figure 2, step II). Here, only the relative importance of one evaluation criterion over another concerning the site suitability assessment is of importance. By definition, the user has a value spectrum from 1/9 to 9 available for this pairwise prioritization. As an example, consider the matrix in Table 1. The user will go through each cell in Table 1 and apply their expert-knowledge-based opinion of the relative importance of the row criterion against the column criterion concerning the site selection problem. The maximum value of 9 indicates that the row criterion is much more important in determining the site’s suitability than the column criterion. On the contrary, the value 1/9 indicates that the row criterion is much less important than the column criterion. The cells in which a criterion is juxtaposed against itself are assigned, per definition, a value of 1. For example, in Table 1, criterion E obtained a score of 8 when its relative importance was weighted by a user group against the importance of criterion A (per definition, the opposite cell in which criterion A is scored against E receives the reciprocal value of 1/8).
To check whether the user’s entries in the matrix are logically consistent with each other, the ‘Consistency Ratio’ (CR) is calculated (Table 1). The CR can be determined from the respective eigenvectors [47]. By convention, the CR must be <0.1 for the pairwise criteria weighting to be consistent. If this condition is not fulfilled, the user must check their entries for consistency and edit them. The eigenvectors calculated by the procedure represent the weighting factors for each criterion that are of importance for the final site evaluation (example in Table 1). The sum of all eigenvectors is naturally always 1.
In the last step (Figure 2, step III), the GIS-AHP result raster layer is calculated, which, like the reclassified raster data sets, has a value spectrum from 0 to 10. For this purpose, the following formula is applied to each cell, where n is the number of evaluation criteria used, Ki is the respective cell value of the reclassified raster data set (from step I), and Ei is the respective eigenvector (from step II):
GIS AHP Score = i = 1 n K i × E i
The cell size of the resulting raster data set is 1 km × 1 km in the present study.
However, the GIS-AHP procedure described has the shortcoming that it is subject to a more or less subjective evaluation by a specialist expert or single expert group.
To address this shortcoming, the above-described GIS-AHP methodology is extended to a GIS-AHP ensemble methodology. In the framework of the GIS-AHP ensemble methodology, the original GIS-AHP procedure is carried out parallelly by several experts or expert groups after having defined the relevant evaluation criteria.
In the given study, during step I and step II of the procedure, a total of five different site rating profiles was developed by five different expert groups of the OOWV. Each of these groups has a different disciplinary background and societal responsibility for assuring a sustainable drinking water supply in the study area. They have, therefore, different problem perceptions and formulate different site requirements for the same site selection problem (see chapter “Developed rating profiles”).
The five rating profiles served as five different inputs into Equation (1) and yielded five different GIS-AHP result raster layers which form the multi-disciplinary informed GIS-AHP ensemble (Figure 2, step IV). By calculating cell-based averages within the derived GIS-AHP ensemble, it is possible to identify those sites that computationally exhibit the best site conditions according to the inputs of the different expert groups (maxima of cell average). By calculating, cell-wise, the relative standard deviation (STD) of the rating results, it is possible to further quantify the result-agreement across the GIS-AHP ensemble (minima of cell-based relative STD demonstrate a high result agreement). Relative STD is thus used here as an indicator of the robustness of the GIS-AHP ensemble site ratings when considering multiple actors.

2.2. Determination of the Evaluation Criteria and Associated Data Sets

After several discussions, the following five assessment criteria were identified by all involved expert groups to be necessary and sufficient for the regional-scale evaluation of possible new groundwater production sites:
  • Depth to groundwater table (DGT).
  • Groundwater recharge (GWR)
  • Hydrogeological abstraction conditions (transmissivity) (T)
  • Groundwater protection potential (GWP)
  • Officially designated exploitable groundwater reserve per groundwater body (GRB)
All of the above-listed data sets represent secondary raster data that were created in previous studies by the German regional governmental authority, LBEG. In the following, the reasons for choosing each of the above-listed evaluation criteria are outlined.
The evaluation criterion “depth to groundwater table” is critical for assessing the potential environmental impact of groundwater withdrawals and associated groundwater drawdowns at a given site. The basic assumption is that the closer the groundwater table is to the surface, the higher the potential risk of an adverse effect on terrestrial ecosystems, baseflow, rainfed agriculture, or forestry. Accordingly, if the depth of the groundwater table is higher at a given site, the risk of noticeable environmental impacts from local groundwater lowering due to groundwater production is low. The State Office of Lower Saxony for Mining, Energy, and Geology (LBEG) provides data on the long-term average geodetic elevation of the groundwater surface in the uppermost aquifer for the whole area of Lower Saxony [58]. The mentioned data set was converted in the study area into a raster data set of hydraulic heads in meters below ground level (m bgl) via differentiation with an SRTM-based digital terrain model (resolution 90 m × 90 m) (Figure 3A). The hydraulic heads calculated by this method were found to be a sufficient approximation of the criterion “depth to groundwater table” for a regional scale assessment (the resulting resolution of the used data set was 500 m × 500 m). A selective check of the calculated raster data set of the depth to groundwater table showed a satisfactory match between observed and calculated hydraulic heads in the data set for the great majority of the study area.
Groundwater recharge is crucial as an evaluation criterion since high groundwater recharge generally minimizes the radius of drawdown cones and increases the amount of groundwater that can be sustainably exploited. Raster data from the governmental water balance model mGROWA18 were used as a data input for the respective criterion in the study area [59,60] (Figure 3B). For methodological details of the water balance model, check respective sources [59,60,64]. The raster data set depicted in Figure 3B shows the average annual groundwater recharge for the historical period 1980 to 2010. In addition, in a separate step, the raster data set of historical groundwater recharge was replaced by a climate scenario-based mGROWA18 groundwater recharge data set for the period 2020 to 2050 (under representative concentration pathway (RCP) emission scenario 8.5) [59]. The data from the mGROWA18 model were available as raster layers with a resolution of 500 m × 500 m.
The LBEG provides a Lower Saxony-wide map of hydrogeological abstraction conditions in groundwater-bearing rocks [61]. The map is based on an aerial estimate of the total transmissivity of the groundwater-bearing rocks based on strata logs of numerous exploratory and well boreholes (Figure 3C). For the estimate of transmissivity, empirically derived hydraulic conductivity coefficients were assigned to the different lithological units, which were determined by analyzing a large number of pumping tests in the region. The relative classes displayed in the map of Figure 3C represent certain value ranges of the total transmissivity [61]. The better the hydrogeological abstraction conditions, the higher the yield of the aquifer and the better the performance of production wells. The respective raster data set has a resolution of 1000 m × 1000 m.
A high groundwater protection potential of the aquifer covering layers is expected to decrease the risks of possible anthropogenic groundwater contamination, and, therefore, indirectly reduces operational risks during drinking water production. The LBEG provides a Lower Saxony-wide map of the groundwater protection potential [62] (Figure 3D). According to the methodology of the LBEG, groundwater is well protected in areas where aquifer covering layers exhibit low permeabilities and, therefore, impede rapid seepage, and where, at the same time, thick unsaturated zones favor a long residence time of percolating surface water before reaching the groundwater table. In situ groundwater recharge was not explicitly respected in the creation of the data set. The respective raster data set was included as an evaluation criterion with a resolution of 800 m × 800 m.
The exploitable groundwater reserve per groundwater sub-body in millions of m3/a represents the groundwater reserve that can be made use of in a sustainable manner as designated by the regional governmental authorities (LBEG) (Figure 3E) [63]. The assessment of the exploitable groundwater supply reserve is based on geographically delimited groundwater bodies (GBs), which were predominantly delineated from river catchments. Subsequently, these GBs are further subdivided into partial groundwater bodies (PGBs), based on administrative district boundaries. The fundamental parameter for estimating the exploitable groundwater reserve at the PGB level is the amount of annual water recharge in dry years into the PGB. The respective amount is then further reduced considering additional factors, such as naturally occurring groundwater salinization or already approved groundwater withdrawal rights. To meet the objective of safeguarding and preserving groundwater-dependent terrestrial ecosystems and surface waters, ecological deductions are also taken into account.
All additional environmental data displayed in the maps originate from the LBEG. The GIS-AHP ensemble result layers developed in this study were created using Esri ArcGIS Desktop version 10.6.1 (Redlands, CA, USA) and the freely available GIS-AHP toolbox extAHP20 version 2.0.

3. Results

3.1. Rating Profiles

In the following, the five developed rating profiles for executing the GIS-AHP ensemble analyses are presented.
The results from working step I are presented in Figure 4A–E (step I, compare chapter “The GIS-AHP methodology and its extension to GIS-AHP ensembles”). In Figure 4A–E it can be generally seen that all rating profiles agree that the higher the value of the respective evaluation criterion (y-axis), the better the site conditions for groundwater production (x-axis) (0 = insufficient site conditions, 10 = ideal site conditions). However, there were minor differences between individual rating profiles concerning the exact scoring per criterion class.
In turn, the results from working step II, for deriving the weights of each evaluation criteria, provide a quite differentiated picture when comparing the five rating profiles (Figure 4F).
Profile 1 lays its main emphasis on the criterion of hydrogeologic abstraction conditions (~34%) and the exploitable groundwater reserve (also ~34%). Profile 2 weighs the hydrogeologic abstraction conditions strongest (~39%) with the hydraulic head coming second (~25%). Profile 3 has a strong focus on hydrogeologic abstraction conditions (~59%). Profile 4 lays its emphasis on groundwater recharge (~30%) and groundwater protection potential (~44%), and profile 5 has a focus on groundwater protection potential (~37%) and exploitable groundwater reserve (~37). This yields the following average weighting factors across all five rating profiles: T = 29.38%, DGT = 13.93%, GWR = 16.35%, GWP = 22.78%, and GRB = 17.63%.

3.2. GIS-AHP Results per Rating Profile

The site rating results of the individual GIS-AHP ensemble members are shown in Figure 5A–E. A comparison of the results revealed significant differences in some areas, as well as overlapping trends. Site conditions for potential groundwater abstraction in the northeastern part of the study area were predominantly rated as poor to moderate (GIS-AHP score < 6). Southwestern areas, on the other hand, were predominantly rated as good to very good (GIS-AHP score > 6). The differing site ratings reflected the different problem perceptions of the involved expert groups that developed the underlying rating profiles.

3.3. Merging Results from Rating Profiles: GIS-AHP Ensemble Assessment

Figure 6A shows the average of the GIS-AHP score across all five ensemble members (GIS-AHP ensemble average). Areas with a GIS-AHP ensemble score greater than 6, and thus at least good site conditions, were found in four zones (from north to south); in the western part of East Frisia, in the district of Ammerland, in an area extending from the district of Oldenburg to the district of Cloppenburg, and in the southern part of the district of Vechta. A comparison with long-established drinking water abstraction areas (DWAs) showed that the DWAs were almost without exception located in zones that exhibited a GIS-AHP score greater than 6.
Figure 6B presents the spatially distributed relative standard deviation (STD) of the GIS-AHP scores across the five rating profiles. The relative STD was lowest in cells where all rating profiles resulted in a similar GIS-AHP score. Accordingly, the lowest STD was found in the southern parts of the study area, as well as in the district of Ammerland and western East Frisia (Figure 6B).
In addition, in Figure 6C the averaged GIS-AHP ensemble score is presented, if instead of the historical mean annual groundwater recharge (period 1980 to 2010), a simulated annual groundwater recharge under emission scenario RCP 8.5 for the period 2020 to 2050 is considered (calculated with the water balance model mGROWA18 and a selected global/regional climate model ensemble, for details please check source [64]). When comparing results as displayed in Figure 6A,C, it is seen that despite eventually slightly reduced future annual groundwater recharge amounts, the spatially distributed GIS-AHP score values remain nearly unchanged. There are also hardly any differences for the corresponding relative STD (Figure 6B,D).

4. Discussion

4.1. Advantages of the GIS-AHP Ensembles over the Classic GIS-AHP Approach

The results of the present study are spatially distributed site suitability ratings for groundwater abstraction in northwestern Germany as developed by five different expert groups (Figure 6A). Those results were assembled into a single GIS-AHP ensemble by cell-wise computation of averages and the relative standard deviation across the single GIS-AHP result layers. We argue that extending the classic GIS-AHP method by this means allows, for the first time, (1) respecting multiple problem perceptions and requirements in GIS-AHP assessments in a transparent way, and (2) mitigating associated conflict potential in multi-actor contexts (Figure 6B). Classic GIS-AHP assessments yield a single expert-evaluation-based indicator for site suitability. GIS-AHP ensembles, in turn, yield a site suitability indicator based on several expert evaluations (site rating average) and, additionally, provide an indicator of the resulting agreement between those expert evaluations (relative STD) (Figure 6A–D). Areas that exhibit a low relative STD evince high result robustness within the GIS-AHP ensemble and highlight areas with low conflict potential for yielding decisions across involved stakeholders. Particularly in multi-actor contexts, GIS-AHP ensembles therefore provide advantages over classic GIS-AHP assessments. Yet, GIS-AHP ensembles exhibit the same high thematic transferability as the classic GIS-AHP methodology.

4.2. Discussion of Results

Based on the site suitability ratings and the result robustness derived by the GIS-AHP ensemble, potential groundwater abstraction sites can be ranked and prioritized in the given study, respecting inputs from multiple stakeholders with different problem perceptions. According to the site rating results and the relative STD, the most promising sites with the lowest conflict potential across involved stakeholders are sub-areas of the following districts (Figure 6A,B): Aurich/Wittmund, Ammerland, Oldenburg, and in southern parts of Cloppenburg.
However, the respective site ratings were elaborated using current recharge conditions (period 1980 to 2010). As aquifer development and groundwater production projects are typically planned and executed over decades, it must be assessed whether the GIS-AHP ensemble site ratings hold their validity under possible future climatic changes. For this purpose, we integrated a groundwater recharge scenario into the assessment as simulated for the period 2020 to 2050 under emission scenario RCP 8.5 (Figure 6C). As can be seen in Figure 6C, the respective site rating results are largely confirmed under eventual future climatic conditions, which substantiates their suitability for long-term water supply planning (Figure 6C).
Secondary criteria, as displayed in Figure 6E, could be considered qualitatively for a more detailed site prioritization on a local level. Secondary criteria are the presence of nature reserves, possible groundwater quality hazards such as oil and gas production, or an existing geogenic impaired groundwater salinization [65].
The here derived results allow for efficiently targeting future investments for carrying out in situ hydrogeological investigations. Thereby, they help to reduce the overall costs for the development of new groundwater production sites.

4.3. Uncertainties and Plausibility of Results

As with all GIS-AHP approaches, uncertainties of site ratings depend largely on the input data quality which underlies the assessment. The raster data sets used as input for the GIS-AHP analysis in this study are exclusively official governmental data that were derived by well-defined and documented methodologies. The data quality of these secondary data sets can thus be regarded as reliable (see comments on this in the chapter “Determination of the evaluation criteria and associated data sets”). The orientating prospection of possible new groundwater abstraction areas, for which the respective input data are used, takes place on a regional scale (tens of kilometers), which far exceeds the resolution of the underlying raster data sets (resolution of several hundred meters). As a result, inaccuracies in the input data sets that might cause difficulties at the local level do not carry much weight at a regional scale. The highest uncertainties in the data are associated with complex geological conditions, as they exist especially in the southern part of the district of Vechta. In any case, the data used represent the best possible existing data for the region, based on which a decision can be made.
The plausibility of the site ratings derived by the GIS-AHP ensemble has been demonstrated by the high level of agreement between the ubication of existing groundwater production areas and areas with an average GIS-AHP ensemble score greater than 6, predicting at least good site conditions (Figure 6A). This is because the vast majority of existing drinking water production areas can be assumed to be established in areas where detailed hydrogeological investigations found that the respective site is indeed well suited for groundwater production. Existing drinking water production areas can, therefore, be taken as an indicator of good groundwater abstraction site conditions. Thus, the results of the presented GIS-AHP site rating are plausible on a regional scale.

5. Conclusions

We propose GIS-AHP ensembles as an extension of the classic GIS-AHP method for solving multi-criteria site selection problems in multi-actor contexts. As opposed to the classic GIS-AHP method, it is shown in the given case study that GIS-AHP ensembles are capable of respecting a variety of multi-disciplinary problem perceptions and requirements. GIS-AHP ensembles yield two central spatially distributed indicators for steering site selection problems, namely, ensemble averages of site suitability, and an associated relative standard deviation. Both indicators together allow for identifying best-suited sites that exhibit, at the same time, a low conflict potential for achieving decisions across involved stakeholder groups. GIS-AHP ensembles are thus particularly suitable for multi-criteria site selection problems where interdisciplinary and/or interinstitutional stakeholders wish to reach an agreement. The given case study demonstrates that GIS-AHP ensembles can yield plausible results and can be a useful extension of the classic GIS-AHP method. They provide a framework for fostering integrated environmental decision-making. Yet, GIS-AHP ensembles exhibit the same high thematic transferability as the classic GIS-AHP methodology.
The GIS-AHP ensemble method has the following advantages over the classic GIS-AHP methodology:
  • It integrates multi-disciplinary problem perceptions and requirements in the site rating process.
  • It enables the assessment of the site rating agreement in interdisciplinary contexts.
  • It provides a transparent framework for achieving compromises for site selection in multi-stakeholder contexts.

Author Contributions

Conceptualization, K.W.S. and U.S.; methodology development, K.W.S. and U.S.; validation and formal analysis, K.W.S. and C.K.; writing and original draft preparation, K.W.S. and C.K.; visualization, K.W.S.; supervision, U.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 869171—B-WaterSmart.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Restrictions apply to the availability of these data. Data were obtained from the State Office for Mining, Energy, and Geology, Lower Saxony, Germany (LBEG), and are available from the corresponding author with the permission of the latter.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the study area, overall extension approximately 7800 km2.
Figure 1. Location of the study area, overall extension approximately 7800 km2.
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Figure 2. Flow chart of the GIS-AHP ensemble methodology; steps I to III represent the procedure for executing the classic GIS-AHP and step IV represents its extension to the here proposed GIS-AHP ensemble methodology.
Figure 2. Flow chart of the GIS-AHP ensemble methodology; steps I to III represent the procedure for executing the classic GIS-AHP and step IV represents its extension to the here proposed GIS-AHP ensemble methodology.
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Figure 3. Raster data sets used as rating criteria: (A) groundwater hydraulic heads based on [58], (B) mean annual groundwater recharge from 1980 to 2010 [59,60], (C) hydrogeological abstraction conditions [61], (D) groundwater protection potential [62], and (E) exploitable groundwater reserve per groundwater sub-body [63].
Figure 3. Raster data sets used as rating criteria: (A) groundwater hydraulic heads based on [58], (B) mean annual groundwater recharge from 1980 to 2010 [59,60], (C) hydrogeological abstraction conditions [61], (D) groundwater protection potential [62], and (E) exploitable groundwater reserve per groundwater sub-body [63].
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Figure 4. Characteristics of the developed rating profiles (0 = insufficient site conditions, 10 = ideal site conditions): (A) reclassification of groundwater recharge, (B) reclassification of hydraulic head, (C) reclassification of the exploitable groundwater reserve, (D) reclassification of hydrogeological abstraction conditions, (E) reclassification of groundwater protection potential, and (F) calculated weighting factors from the pair-wise prioritization of the five rating criteria.
Figure 4. Characteristics of the developed rating profiles (0 = insufficient site conditions, 10 = ideal site conditions): (A) reclassification of groundwater recharge, (B) reclassification of hydraulic head, (C) reclassification of the exploitable groundwater reserve, (D) reclassification of hydrogeological abstraction conditions, (E) reclassification of groundwater protection potential, and (F) calculated weighting factors from the pair-wise prioritization of the five rating criteria.
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Figure 5. Results of the GIS-AHP rating (GIS-AHP-Score) per rating profile (0 = insufficient site conditions, 10 = ideal site conditions): (A) profile 1 with focus on T and GRB, (B) profile 2 with focus on T and DGT, (C) profile 3 with focus on T, (D) profile 4 with focus on GWP and GWR, and (E) profile 5 with focus on GRB and GWP.
Figure 5. Results of the GIS-AHP rating (GIS-AHP-Score) per rating profile (0 = insufficient site conditions, 10 = ideal site conditions): (A) profile 1 with focus on T and GRB, (B) profile 2 with focus on T and DGT, (C) profile 3 with focus on T, (D) profile 4 with focus on GWP and GWR, and (E) profile 5 with focus on GRB and GWP.
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Figure 6. Results from the GIS-AHP ensemble: (A) average of GIS-AHP ratings from all five ensemble members together with long-established groundwater abstraction and protection areas, marked by deep blue lines (0 = insufficient site conditions, 10 = ideal site conditions), (B) standard deviation (STD) of the GIS-AHP scores across all five ensemble members, (C) average GIS-AHP scores across all five ensemble members under altered groundwater recharge condition for the period 2020 to 2050 (existing groundwater abstraction and protection areas are marked by deep blue lines), (D) relative standard deviation (STD) of GIS-AHP scores across all five ensemble members under altered groundwater recharge conditions for the period 2020 to 2050, and (E) GIS-AHP-scores from Figure 6A with other relevant environmental data.
Figure 6. Results from the GIS-AHP ensemble: (A) average of GIS-AHP ratings from all five ensemble members together with long-established groundwater abstraction and protection areas, marked by deep blue lines (0 = insufficient site conditions, 10 = ideal site conditions), (B) standard deviation (STD) of the GIS-AHP scores across all five ensemble members, (C) average GIS-AHP scores across all five ensemble members under altered groundwater recharge condition for the period 2020 to 2050 (existing groundwater abstraction and protection areas are marked by deep blue lines), (D) relative standard deviation (STD) of GIS-AHP scores across all five ensemble members under altered groundwater recharge conditions for the period 2020 to 2050, and (E) GIS-AHP-scores from Figure 6A with other relevant environmental data.
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Table 1. Synthetic example of the paired prioritization of evaluation criteria, including the resulting criteria weightings (eigenvector).
Table 1. Synthetic example of the paired prioritization of evaluation criteria, including the resulting criteria weightings (eigenvector).
Evaluation Criteria
ABCDE Eigenvector
A11/51/51/81/8 0.03
B5111/21/2 0.15
C5111/81/8 0.08
D82811 0.37
E82811 0.37
Sum1
Consistency Ratio0.08
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Scheihing, K.W.; Kübeck, C.; Sütering, U. GIS-AHP Ensembles for Multi-Actor Multi-Criteria Site Selection Processes: Application to Groundwater Management under Climate Change. Water 2022, 14, 1793. https://doi.org/10.3390/w14111793

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Scheihing KW, Kübeck C, Sütering U. GIS-AHP Ensembles for Multi-Actor Multi-Criteria Site Selection Processes: Application to Groundwater Management under Climate Change. Water. 2022; 14(11):1793. https://doi.org/10.3390/w14111793

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Scheihing, Konstantin W., Christine Kübeck, and Uwe Sütering. 2022. "GIS-AHP Ensembles for Multi-Actor Multi-Criteria Site Selection Processes: Application to Groundwater Management under Climate Change" Water 14, no. 11: 1793. https://doi.org/10.3390/w14111793

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