Comprehensive Mapping and Classification of Germany’s Drinking Water Protection Areas

Comprehensive Mapping and Classification of Germany’s Drinking Water Protection Areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive Mapping and Classification of Germany’s Drinking Water Protection Areas Kathrin Szillat, Carolin Winter, Jost Hellwig, Kerstin Stahl This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7084780/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Environmental Sciences Europe → Version 1 posted 17 You are reading this latest preprint version Abstract Groundwater supplies up to 65% of drinking water in the European Union and approximately 70% in Germany, making it essential to preserve both its quality and quantity. However, climate change, land use pressures, and socio-economic developments increasingly threaten groundwater resources, posing significant challenges for current and future water supply. To safeguard drinking water sources, water protection areas (WPAs) are designated to mitigate contamination risks. This study introduces the first harmonized, country-wide dataset of all 11,406 designated German WPAs, integrating hydrogeological, land cover, and socio-economic characteristics, to assess groundwater vulnerability. We found these WPAs to cover the full range of the countries hydrogeological characteristics while they have more forest cover fractions than the entire country. A cluster analysis with key characteristics classified all WPAs into 11 distinct characteristic typologies. Comparing the clusters’ groundwater chemical status as per EU Water Framework Directive mapping as an indicator of groundwater vulnerability shows that a complex interplay of hydrogeological conditions, land use patterns, and socio-economic pressures determines the differences. Our study provides a data-driven basis to support sustainable groundwater protection and drinking water resource management across Germany. It stands as exemplary for how to determine a reduced set of WPA types and situations for which to design specific measures. The results also underscore the importance of harmonized WPA designation practices to improve comparability and ensure equitable protection standards across federal states. As the current German drinking water regulation operationalizes the EU Drinking Water Directive, the developed typology may also inform risk-based groundwater protection efforts in other EU member states. Water protection areas drinking water groundwater vulnerability cluster analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background Groundwater is the largest available freshwater resource beneath the Earth’s surface. It plays a crucial role in drinking water supply, agriculture, industry, and in maintaining ecological balance by ensuring baseflow in streams or dilution of pollutants [ 1 ]. It provides up to 65% of drinking water in the European Union (EU) [ 2 ] and roughly 70% in Germany [ 3 ]. Due to natural filtration processes, groundwater often requires minimal treatment before consumption, making it a cost-effective drinking water source. However, increasing pressures from climate change, pollution, competing water uses, and rising demand threaten its quality and quantity. As a result, ensuring acceptable drinking water quality increasingly requires resource mixing and costly treatments. To prevent groundwater pollution and groundwater deterioration, EU member states are required to implement measures aligned with European policies, such as the EU Water Framework Directive (WFD) [ 4 ], the EU Groundwater Directive [ 5 ], and the EU Nitrates Directive [ 6 ]. One key measure is the designation of Water Protection Areas (WPAs), which regulate human activities that endanger water quality. WPAs are designated to mitigate contamination risks from agriculture, industrial pollution, and urban wastewater. In Germany, groundwater protection has a long history, dating back to the early 1930s when land-use restrictions were implemented around municipal wells based on distance and travel time criteria [ 7 ]. Today, the Federal Water Act (WHG) [ 8 ] serves as the legal foundation for WPAs, with state governments responsible for defining protection zones based on regional hydrogeological conditions. Additionally, efforts to delineate catchment areas for groundwater extraction are in progress due to new legislative requirements mandating the delineation and assessment of WPAs by the end of 2025 [ 9 ]. The German regulation transposes the EU Drinking Water Directive [ 10 ] into national law, reflecting the directive's focus on risk-based approaches to safeguard drinking water quality. Previous studies indicate that in 1992, there were 13,050 wellhead protection areas necessary, of which 72% have been designated, 11% are in the process of designation, and for 17%, the process has not been initiated yet [ 7 ]. By 2017, 18,341 drinking water and mineral spring protection areas had been identified, covering 15.4% of Germany’s total area [ 11 ]. Germany’s groundwater resources are shaped by a diverse geological and hydrogeological framework, which exerts a critical influence on both the quantity and quality of drinking water available for public supply. The principal aquifer types can be broadly classified into porous aquifers within unconsolidated sediments and fractured or karstified aquifers in consolidated bedrock formations [ 12 ]. Groundwater from porous aquifers, especially those in Pleistocene and Holocene deposits such as sand, gravel, and clay, is crucial for drinking water supply, particularly in regions like the North German Plain and the Upper Rhine Graben. These aquifers are highly productive and serve as the primary source of public drinking water in these areas. Thick alluvial deposits in the Lower Rhine Basin and the Lower Rhine Plain provide some of the country’s most important groundwater reserves. Additionally, spring water from low mountain ranges with karstified or fissured bedrock, such as the Swabian Jura, Franconian Jura, and the Black Forest, plays an important role in local water supply. However, these aquifers tend to be less productive compared to the porous aquifers in the plains. Groundwater resources of local importance are also found in the central mountain ranges, including the Rhenish Slate Mountains, Harz, Thuringian and Bavarian Forests, Ore Mountains, and Black Forest [ 13 , 14 ]. This hydrogeological diversity in Germany demands region-specific protection measures, such as the designation of WPAs, to safeguard groundwater quality and ensure its continued availability for public use. Despite these protective measures, significant challenges persist in maintaining groundwater quality and quantity. In the EU, approximately 24% of groundwater bodies are classified as having poor chemical status, with 9% facing poor quantitative status [ 2 ]. In Germany, around 33% of groundwater bodies are reported to be in poor chemical status [ 15 ]. This deterioration is driven by multiple stress factors, including land use, climate change, and socio-economic pressures. Forests in WPAs support groundwater recharge and act as natural filters that enhance water quality. However, climate-induced forest dieback can impair these functions and increase nitrate leaching [ 16 ]. Agricultural activities are a major source of diffuse pollution. Intensive crop cultivation, livestock farming, and the excessive application of liquid manure and nitrogenous fertilizers significantly increase the risk of nutrient leaching, particularly nitrate contamination [ 17 – 20 ]. Additionally, pesticide use in agriculture contributes to persistent groundwater pollution, as residues can remain in soils and leach into aquifers over time [ 21 ]. Irrigation practices may further exacerbate stress on groundwater resources by exceeding natural recharge, leading to declining water tables and reduced baseflow [ 22 ]. In urban and industrial areas, groundwater contamination may result from wastewater infiltration, leaky sewage infrastructure, and diffuse pollution pathways, introducing compounds such as pharmaceuticals, heavy metals, and microplastics [ 21 ]. These diverse land use pressures underscore the importance of effective protection measures, such as WPAs, to safeguard groundwater resources. Beyond land use, climate change exacerbates groundwater stress. Climate warming and altered precipitation patterns increase the frequency and severity of droughts and extreme weather events, such as heavy rainfall and flooding [ 23 ]. Across Germany, climate change is projected to reduce groundwater recharge and lower groundwater levels throughout the 21st century. Reinecke et al.[ 24 ] shows that under high-emission scenarios, recharge will decline in many regions due to increased evapotranspiration and more frequent droughts, even in areas where annual precipitation remains stable or slightly increases. Wunsch et al.[ 25 ] further demonstrates that these effects will be most pronounced under RCP8.5, with significant and spatially consistent declines in groundwater levels, particularly in northern and eastern Germany, where existing downward trends are likely to intensify. Both studies highlight that, while some seasonal increases in winter precipitation and recharge may occur, the overall trend will be toward greater variability and longer periods of low groundwater levels, posing substantial challenges for water supply, agriculture, and ecosystem health. Besides long-term gradual shifts, extreme events such as prolonged droughts can further compromise groundwater quantity and quality, making it difficult to meet competing water demands. Recent prolonged droughts in Germany (e.g., 2003, 2011, 2015, 2018, and 2022) caused groundwater depletion and falling water levels [ 26 – 28 ]. Consequently, water supply utilities face growing challenges in maintaining supply security while meeting quality and policy requirements under increasingly severe conditions [ 29 ]. Socio-economic factors contribute to groundwater stress by increasing water demand, particularly in urban areas and tourist hotspots. High population densities in cities lead to high groundwater extraction rates to meet water demand. In Berlin, 150 years of urban growth, industrialization, and shifting water demand have caused significant fluctuations in groundwater levels, creating complex management challenges [ 30 ]. Tourist areas also face fluctuating water demand. During peak seasons, the water use of tourists can far exceed that of local residents, putting additional pressure on local groundwater resources. This seasonal spike in demand complicates long-term water management and raises concerns about fairness and sustainability [ 31 ]. These pressures make groundwater management more difficult and emphasize the need for sustainable water management. Given these multifaceted pressures, assessing the vulnerability of groundwater resources used for drinking water supply requires an integrated approach. Widely used methods such as DRASTIC ( D epth to water table, net R echarge, A quifer media, S oil media, T opography, I mpact of vadose zone, hydraulic C onductivity) [ 32 , 33 ] focus primarily on contamination risks in porous aquifers and consider mainly hydrogeological parameters, limiting their applicability for broader, regional-scale assessments. Current approaches will benefit from integration of environmental and socio-economic dimensions to achieve a more comprehensive understanding of regional vulnerabilities. Incorporating factors such as land use, aquifer type, groundwater extraction patterns, and socio-economic indicators allows for a more comprehensive understanding of regional differences and site-specific pressures. Comprehensive regional assessments, such as those based on WPAs, should complement detailed local assessments to guide and contribute to the development of more targeted and sustainable groundwater management strategies. This study pursues three main objectives. First, we aimed to assemble a comprehensive, country-wide dataset that includes hydrogeological, land cover, and socio-economic characteristics of WPAs in Germany. Second, we aimed to compare the characteristics in WPAs to Germany as a whole. Third, we classified all WPAs into groups with similar characteristics. By integrating hydrogeological, land-cover, and socio-economic dimensions, we aimed to identify regional groundwater vulnerabilities and derive typologies that can inform more targeted governance and resource management strategies. 2. Data and Methods 2.1 Data and Analysis of Drinking water protection area Characteristics We compiled a country-wide dataset of WPAs in Germany based on a range of data sources [ 34 ]. The dataset includes spatial information on WPAs and associated attributes covering hydrogeological, environmental, social, and economic characteristics. Due to variation in legal interpretations of the WHG [ 8 ] across federal states, WPA designations differ in zoning practices. WPAs are generally divided into protection zones, each with specific restrictions to safeguard groundwater quality. The size and location of the protection zones are determined on a case-by-case basis according to the local hydrogeological conditions [ 35 ]. Due to the lack of standardized regulations for WPA designation across federal states and variations in zoning practices, this study considers only the outermost boundary of each WPA. This approach ensures that all designated protection zones, regardless of their specific regulatory restrictions, are encompassed within a unified delineation. For the dataset, we processed data from different sources (Table S1 ). For numerical variables (e.g., population density, water consumption rates) available at the district or city level, we intersected the datasets with the WPA polygons in GIS and computed area-weighted mean values to assign distinct numerical attributes to each WPA polygon. For categorical variables (e.g., aquifer type, land use), the datasets were also intersected, and the dominant value (> 50% of the WPA area) was assigned to each WPA polygon. Descriptive statistics (mean, median, min, max) of all numerical WPA attributes were compared to national values for Germany, using the original data sources. For datasets available only at the district or city level, area-weighted values were calculated. For categorical variables, area shares of each category were calculated for both WPAs and Germany and subsequently compared. 2.2 Cluster Analysis by Partitioning Around Medoids Cluster analysis was conducted using the Partitioning Around Medoids (PAM) algorithm, a non-hierarchical clustering method introduced by Kaufman & Rousseuw [ 36 ]. PAM identifies representative data points (medoids), which serve as the center of each cluster. Random medoids initialize the process and are successively updated to minimize the total dissimilarity. The clustering process is based on a dissimilarity matrix computed using Gower’s distance [ 37 ]. This matrix was generated with the daisy function from the cluster package in R [ 38 ]. Gower’s distance accommodates mixed data types. All variables are rescaled between 0 and 1 by subtracting the minimum value of each variable and dividing by its range, ensuring that all variables contribute equally to the final dissimilarity score. The dissimilarity between two WPAs is calculated as the weighted mean of the contributions from each variable. Specifically, the dissimilarity between two WPAs \(\:i\) and \(\:j\) is given by Eq. 1 : $$\:{\text{d}}_{\text{i}\text{j}}=\text{d}\left(\text{i},\text{j}\right)=\:\frac{{\sum\:}_{\text{k}-1}^{\text{p}}{\text{w}\text{k}{\delta\:}}_{\text{i}\text{j}}^{\left(\text{k}\right)}{\text{d}}_{\text{i}\text{j}}^{\left(\text{k}\right)}}{{\sum\:}_{\text{k}-1}^{\text{p}}{\text{w}\text{k}{\delta\:}}_{\text{i}\text{j}}^{\left(\text{k}\right)}})\:$$ 1 where \(\:wk\) represents the weight of the \(\:k\) -th variable, \(\:{\delta\:}_{ij}^{\left(k\right)}\) is a binary indicator that equals 1 if the \(\:k\) -th variable is non-missing for both observations \(\:\:i\) and \(\:j\) and satisfies certain conditions (e.g., non-zero for asymmetric binary variables) or 0 otherwise. \(\:{d}_{ij}^{\left(k\right)}\) is the contribution of the \(\:k\) -th variable to the overall dissimilarity, calculated as the distance between x[ \(\:i,k\) ] and x[ \(\:j,k\) ]. For nominal or binary variables, the contribution \(\:{d}_{ij}^{\left(k\right)}\) is 0 if the values are identical and 1 if they differ. For other variables, \(\:{d}_{ij}^{\left(k\right)}\:\) is the absolute difference between the values, divided by the total range of that variable. Ordinal variables are treated with standard scoring, where they are replaced by their integer codes rather than ranks. Since all individual contributions \(\:{d}_{ij}^{\left(k\right)}\) fall within the range [0, 1], the overall dissimilarity \(\:{d}_{ij}\:\) will also be within this range. If all weights \(\:{wk\delta\:}_{ij}^{\left(k\right)}\) are zero, indicating missing or irrelevant data, the dissimilarity is set to NA. Partitioning methods require a predefined number of clusters \(\:k\) . Each object is assigned to the cluster of its nearest medoid, and cluster memberships are returned. To evaluate clustering quality across different values of \(\:k\) , silhouette indices [ 39 ] were computed. The silhouette width \(\:s\left(i\right)\) evaluates how well a WPA is assigned to its own cluster compared to other clusters (Eq. 2 ): $$\:\text{s}\left(\text{i}\right)=\:\frac{\text{b}\left(\text{i}\right)-\text{a}\left(\text{i}\right)}{\text{m}\text{a}\text{x}(\text{a}\left(\text{i}\right),\text{b}\left(\text{i}\right))}\:\:\:\:\:\:$$ 2 where \(\:a\left(i\right)\) represents the average dissimilarity of a WPA \(\:i\) to all other WPAs in the same cluster, and \(\:b\left(i\right)\) denotes the lowest average dissimilarity of a WPA \(\:i\) to any other cluster. The silhouette width ranges from − 1 to + 1, where values close to + 1 indicate that the observation is well-clustered, values around 0 suggest that the observation lies between two clusters, and negative values imply possible misclassification [ 39 ]. The silhouette of a cluster is a plot of the \(\:s\left(i\right)\) values, ranked in decreasing order, of all its objects \(\:i\) , providing a visual assessment of clustering quality. The optimal number of clusters \(\:k\) was determined using the average silhouette width, with the highest average silhouette width indicating the most appropriate number of clusters. To validate the stability of the identified clusters, we employed the clusterboot function from the fpc package in R [ 40 ]. This method employs bootstrap resampling combined with the Jaccard similarity coefficient to evaluate the robustness of the clustering results. The Jaccard coefficient quantifies the similarity between sets by calculating the ratio of the intersection to the union of the sets. According to Henning [ 40 ], a cluster is considered valid and stable if it achieves a Jaccard similarity value of 0.75 or higher. We applied PAM clustering to all WPAs, excluding those with missing values in the variables (< 1%). We restricted the analysis to variables related to hydrogeological conditions, land cover, and socio-economic characteristics and excluded exclusively administrative information variables. For the numerical variables, we computed a Pearson correlation matrix to restrict our selection to uncorrelated variables (corr < 0.7). For the categorical variables, we chose those with a maximum of four levels and excluded duplicated information as well. This selection process resulted in 15 variables for the cluster analysis, grouped into three main categories: Land cover : land use type (LULC), dominant crop types (Dom_crop), elevation range (Elev_range), livestock (ANTO), irrigation (Irrig) Hydrogeological : type of rock of the uppermost aquifer (Rock), type of porosity (Poros), percolation (Perc), deep groundwater body (DEEP_GW), drought response time (Resp_time) Socio-economic : water costs per m³ (W_cost), groundwater extraction rates (GW_extrac), water consumption rates (W_consum), tourism overnight stays (Tourism_st), population density (Pop_dens) 3. Results 3.1 A new harmonized Dataset of Water protection areas in Germany The dataset includes 11406 WPAs, covering around 15% of Germany’s total area (Fig. 1 ). Political and administrative differences in groundwater protection strategies are reflected in the number and spatial distribution of WPAs across federal states (Fig. 1 ; Table 1 ). These patterns illustrate the varying legal frameworks and zoning practices in WPA designation. For example, Bavaria (BY) has the highest number of WPAs, while they tend to be relatively small in area. In contrast, Baden-Württemberg (BW) exhibits both a high number of WPAs and substantial spatial coverage (Table 1 ). Table 1 Number and area share of WPAs by federal state. Code Federal state Number WPA’s % WPA area to federal state area BY Bavaria 2994 5.4 ST Saxony-Anhalt 125 5.4 BB Brandenburg 352 4.7 HB Bremen 2 9.4 RLP Rhineland Palatinate 921 12.2 SN Saxony 387 7.7 HH Hamburg 6 12.7 NI Lower Saxony 469 15.7 SH Schleswig-Holstein 160 15.7 SL Saarland 47 18.5 TH Thuringia 644 20.3 MV Mecklenburg-Vorpommern 400 16.9 NRW North Rhine-Westphalia 793 21.4 BE Berlin 11 23.4 BW Baden-Württemberg 2330 30.1 HE Hesse 1765 34.8 3.2 Comparison of National and Water protection area Characteristics WPA characteristics differ from the national average, particularly in terms of land cover, hydrogeological conditions, and socio-economic pressures (Fig. 2 ). Land cover in WPAs shows a higher proportion of forest and semi-natural areas, while agricultural land dominates at the national scale (Fig. 2 A). Grassland is more widespread across Germany, whereas WPAs present more forest, small woody features, and artificial surfaces. Crop types, such as cereals and vegetables, show similar distributions (Fig. 2 B). The proportion of irrigated farmland is comparable between WPAs and the national context (Fig. 2 C). In contrast, livestock density and elevation range are lower in WPAs (Figs. 2 D and 2 E). WPAs are more frequently underlain by solid rock in the uppermost aquifer compared to the national average (Fig. 2 F). While the national aquifer distribution is more clearly divided between porous and fissured types, WPAs show a higher share of mixed porosity types (F/P and F/Ka), indicating a greater presence of transitional aquifer conditions (Fig. 2 G). These conditions are accompanied by higher percolation rates in WPAs (Fig. 2 I). Moreover, only a small fraction of WPAs is associated with deep groundwater bodies, suggesting a predominance of shallow groundwater resources (Fig. 2 H). The mean drought response time of WPAs is around 2 months faster than the national average (Fig. 2 J). Regarding socio-economic characteristics, WPAs have higher population densities (Fig. 2 N), tourism overnight stays (Fig. 2 O), groundwater extraction rates (Fig. 2 M), and water consumption rates (Fig. 2 L) relative to the national average. In contrast, average water costs show little variation between WPAs and the national context (Fig. 2 K). 3.3 Clusters of Water protection areas We tested cluster solutions with values of \(\:k\) ranging from 2 to 12. The best solution was found at \(\:k\) = 11, which achieved the highest average silhouette width of 0.51, indicating a reasonably clear structure and separation between clusters (Table S2.). The silhouette plot (Fig. 3 ) shows that most of the WPAs are well-matched to their assigned clusters, as indicated by the overall positive silhouette widths. Notably, two clusters (2 and 9) contain substantially more elements than the others, suggesting dominant patterns or types among these WPAs. Conversely, a few clusters have narrow bars with low or even slightly negative silhouette widths, indicating that they lie near the boundaries between clusters and are less clearly assignable. Overall, the clustering structure reveals meaningful groupings of WPAs. Cluster robustness was further confirmed through a resampling validation procedure with 200 iterations and random subsamples of 50% of the data. This process yielded a Jaccard similarity index of 0.88, supporting the stability of the clustering outcome. The spatial distribution of the optimal 11-cluster solution indicates that all clusters are geographically distributed across Germany (Fig. 4 A). Notably, the main forest regions in Germany are primarily represented by Clusters 2, 6, and 9. For example, the Black Forest in the south and the Erzgebirge in the east are mainly represented by Cluster 2, while the Bavarian Alps and the Pfälzerwald in the southwest are represented by Cluster 9. The Harz region in central Germany and the Swabian Alps are represented by both Clusters 2 and 9. In terms of cluster sizes (number of WPAs in a cluster), the two main clusters (2 and 9) account for around 43% of all WPAs (Fig. 4 B). Those two, along with cluster 6, represent WPAs with non-agricultural land cover, such as forests, semi-natural areas, and urban areas. The remaining clusters represent agriculturally dominated WPAs, which make up around 48% of the total. Specifically, Clusters 3, 5, 7, and 10 represent WPAs dominated by crop-type cereals, comprising around 24% of all WPAs, while Clusters 1, 4, 8, and 11 represent WPAs dominated by crop-type grassland, also making up around 24%. The main categories that distinguish the 11 clusters are the aquifer type (porous vs. fissured/karst), dominant land cover (agriculture vs. forest/other), water costs, and elevation range (Fig. 5 ). Clusters 4, 5, 6, 7, and 8 represent WPAs of porous aquifers with lower water costs per m³ and lower elevation ranges. These clusters tend to have higher groundwater extraction rates, water consumption rates, a higher share of WPAs with deep groundwater bodies, and higher tourism overnight stays and livestock density. They account for about 30% of all WPAs and are mainly located in northern, northeastern, and parts of southern and western Germany. The remaining clusters represent fissured/karst aquifers with higher water costs per m³ and higher elevation ranges. These clusters tend to have lower groundwater extraction rates, lower water consumption rates, a lower share of WPAs with deep groundwater bodies and higher percolation rates. They make up 70% of all WPAs and are located mainly in central, southern, and western Germany. Clusters show different dominant crop types, including cereals, grassland, and other (including forested areas and artificial surfaces). Clusters dominated by cereal cultivation tend to have a higher share of WPAs where vegetables are the dominant crop type, as shown in the column “Vegetables (%)” (Fig. 5 ). Although vegetables are not the dominant crop type in any cluster overall, their presence is most pronounced in agricultural clusters, where irrigation is also more common. 4. Discussion 4.1 Appraisal of the Water protection area Dataset The new harmonized WPA dataset enables a systematic, nationwide comparison of hydrogeological, land use, and socio-economic characteristics across German WPAs that can support a more differentiated understanding of groundwater protection challenges. Our results show that WPAs are predominantly located in regions with favorable hydrogeological conditions for drinking water abstraction. These areas typically feature higher percolation rates, a predominance of productive unconsolidated porous aquifers and moderate topographic reliefs, in agreement with results from [ 12 , 14 ]. In addition, WPAs tend to response to drought around 2 months faster compared to the national average. A higher share of forest and semi-natural areas, combined with lower livestock densities, reduces contamination risks and supports natural recharge functions. Forest ecosystems in particular act as filters and buffers, although recent findings by Winter et al. [ 16 ] suggest that climate-induced forest dieback can impair these functions and lead to increased nitrate leaching. Moreover, WPAs are located in areas with high water demand, characterized by above-average population density, tourism activity, and public water consumption. This underscores the dual role of WPAs in both protecting and securing drinking water supplies. At the same time, these characteristics imply a higher exposure to anthropogenic pressures, necessitating robust, adaptive, and region-specific management strategies. Our findings show that some potential stressors, such as crop type distributions and the extent of irrigated agriculture, do not considerably differ between WPAs and the national average. This similarity raises important questions about the extent to which these potentially high-risk parameters are adequately integrated into current protection and monitoring frameworks. Nitrate remains one of the most persistent threats to groundwater quality [ 41 ]. While it originates from both point and diffuse sources, it mainly originates from agricultural and urban nitrogen inputs [ 19 , 42 ]. Although management measures to reduce nitrate leaching are implemented, their effectiveness is often delayed due to long residence times in the unsaturated zone and aquifers [ 43 , 44 ]. This highlights the importance of early identification and mitigation of high-risk land use types. Additionally, climate change may further exacerbate irrigation demands in agriculture, particularly during more frequent and prolonged drought periods, adding additional stress to vulnerable groundwater systems. While this study includes a broad range of potential stressors to groundwater quality and quantity, it does not capture the full spectrum of risks. The analysis was limited by data availability at the national scale. Information that might be considered additionally includes data from water utilities on actual water abstraction volumes, water volumes from external sources such as long-distance water supplies, information on infrastructure, and quality indicators. These factors remain difficult to assess systematically due to data restrictions, data gaps, and mismatched spatial and temporal resolutions across datasets. Recent studies project a nationwide decline in groundwater recharge and levels throughout the 21st century, with especially pronounced and persistent reductions expected in northern and eastern Germany under high-emission scenarios [ 24 , 25 ]. Although annual precipitation may not decline substantially, rising temperatures, higher evapotranspiration, increasing irrigation demands, and more frequent drought periods are expected to reduce groundwater quantity and increase the variability and duration of low groundwater levels. Those dynamics pose significant challenges for water supply, agriculture, and ecosystems [ 25 ]. Also, long-term groundwater protection is challenging because such future risks are difficult to quantify and are not currently integrated into most WPA delineation practices. A key structural challenge also lies in the heterogeneous designation of WPAs across Germany’s federal states. Differences in zoning criteria and the extent of protected areas reduce comparability, limit transparency, and complicate the implementation of standardized protective measures envisioned under the WFD [ 4 ]. Standardized protection criteria would clearly improve consistency and facilitate assessments like ours. The upcoming legal requirement for comprehensive nationwide WPA delineation by 2025 [ 9 ], introduced in response to the revised EU Drinking Water Directive [ 10 ], offers an opportunity to improve harmonization and to integrate emerging evidence on land use pressures, groundwater demand, and climate-related risks into future-oriented protection strategies across the country and other EU member states. 4.2 Typical Vulnerability profiles The cluster analysis revealed 11 distinct typologies of WPAs across Germany, each representing a characteristic combination of natural and anthropogenic factors influencing groundwater vulnerability. These typologies represent vulnerability profiles shaped by hydrogeology, land use, and socio-economic pressures. Understanding these profiles is essential for developing tailored protection strategies, particularly under increasing stress from diffuse pollution, overexploitation, and climate extremes. To evaluate whether there might be cluster-specific vulnerabilities, we used the chemical status of groundwater bodies from the WFD assessment as an indicator for groundwater vulnerability [ 45 ]. Note that this can only be regarded as a rough exemplary indicator due to the very simplified mapping approach. Across Germany, 36% of the total WPA area (corresponding to 25% of all WPAs) is located in groundwater bodies classified as being in poor chemical status. Cluster-specific shares range from 8–62%, indicating disparities in contamination risk (Fig. 6 ). Higher contamination levels (35–62%) are observed in clusters dominated by porous aquifers (Clusters 5, 4, 8, 6, and 7), which are mostly located in northern, northeastern, and parts of southern and western Germany. Porous aquifers are generally more vulnerable to contamination due to their higher permeability [ 46 ]. Additionally, these clusters are characterized by high groundwater extraction and consumption rates, lower water prices, lower elevation ranges, and are often associated with intensive agriculture. In contrast, lower contamination shares (8–32%) occur in clusters with fractured or karst aquifers (Clusters 2, 9, 1, 11, 3, and 10), which are mainly found in central and southern Germany. Our results show that groundwater quality is linked to a combination of hydrogeological, land use, and socio-economic factors. Negative correlations with poor chemical status are observed for variables such as aquifer type (porous vs. fractured/karst), percolation rates, elevation ranges, and water costs. This indicates that regions with fissured or karst aquifers, higher percolation rates, steeper topography, and higher water prices are associated with better groundwater quality. Conversely, positive correlations are observed with groundwater bodies containing deep layers, high groundwater extraction and consumption rates, intensive agriculture, and longer drought response times. Particularly vulnerable are areas where cereal and vegetable cultivation dominate, likely due to high fertilizer and pesticide application. For example, Cluster 5 stands out as the most vulnerable one. The cluster is characterized by intensive agriculture with a high share of irrigated vegetable production. Similarly, a study by Dieser et al. [ 47 ] reports that groundwater nitrate pollution in Germany is directly linked to the use of fertilizers and manure in intensive agricultural areas. Moreover, the demand for irrigation and associated groundwater extraction is expected to rise under climate change conditions [ 48 ], potentially exacerbating pressure on groundwater resources in agricultural landscapes. However, not all agriculturally dominated clusters show elevated contamination risks. Clusters 1 and 11, both dominated by grassland, differ in elevation, percolation rates, and drought response times. Cluster 1, with a lower elevation range, has a faster drought response, higher percolation rates, and lower livestock density and experiences slightly lower contamination. Clusters 4 and 8 both have high livestock density, but Cluster 4, with higher percolation, faster drought response time and lower water consumption rates, exhibits a lower number of WPAs in a bad chemical status (20%) compared to Cluster 8 (40%). This analysis highlights that groundwater vulnerability is not determined by land cover alone, but rather by the complex interplay of hydrogeological conditions, land use patterns, and socio-economic pressures. Environmental drivers, including land use change, urbanization, agriculture, and industrial activities, can significantly intensify groundwater vulnerability by introducing contaminants and degrading water quality [ 49 ]. These cumulative pressures reflect the need for targeted management strategies. Several cluster comparisons illustrate how similar land cover or hydro-climatic conditions can lead to divergent vulnerability profiles. For example, forest and semi-natural land cover dominate both Clusters 2 and 9, while Cluster 2 is distinguished by higher percolation rates, greater tourism intensity, and shorter drought response times. Despite their similar land use, the described differences result in lower contamination levels for Cluster 2 (8%) compared to Cluster 9 (15%). In contrast, Clusters 3 and 10 share intensive agricultural land use but differ in elevation, drought response times, and population density. Despite these differences, both report comparable groundwater quality, implying that socio-economic pressures such as high population density and extraction rates may offset the benefits of more favorable natural settings. Moreover, Clusters 6 and 7 experience similar hydro-climatic conditions but differ markedly in their contamination levels (35% vs. 55%). This contrast reflects differences in land cover and socio-economic pressures. Cluster 6 has greater forest cover and higher population density, while Cluster 7 is dominated by agricultural land use and exhibits higher groundwater extraction rates. This highlights how land cover and socio-economic factors can influence groundwater quality despite similar hydro-climatic settings. Overall, these examples demonstrate how nuanced differences in natural or anthropogenic conditions can affect groundwater vulnerability. Drought response time, which indicates the time lag between precipitation anomalies and observed changes in groundwater levels, adds another dimension to vulnerability that may become even more relevant in the future. Across the clusters, the average drought response time ranges from 4 to 26 months. Besides decreased recharge rates, droughts might induce a lack of nitrate dilution and a reduction in nitrogen retention due to reduced plant uptake and denitrification rates in dry soils [ 50 ]. Thereby, droughts might not only be a stressor for groundwater quantity but also for its quality. Our study underscores the complex and heterogeneous nature of WPA vulnerability across Germany and the need for differentiated, context-specific protection strategies. The cluster typologies developed in this study provide a practical framework for understanding groundwater stress and identifying priority areas for intervention. Vulnerability assessments play a key role in this process by identifying potential risks and informing strategies for the sustainable management of groundwater resources [ 51 ]. By identifying high-risk areas, these assessments enable more effective prioritization of management efforts and resource allocation [ 1 ]. Several overarching strategies emerge as central to safeguarding groundwater resources. These include regulating groundwater abstraction in high-demand regions, enhancing infiltration and groundwater recharge, reducing nutrient inputs, particularly from livestock and intensive agriculture, maintaining ecological buffer zones such as forests and grasslands, and incorporating socio-economic stressors into water management plans. The cluster typologies help translate these general strategies into context-specific actions. For instance, Cluster 5 requires controls on groundwater extraction, nutrient reduction, and active engagement with agricultural stakeholders. In contrast, Clusters 2 and 9 should prioritize the preservation of forested land cover and the enhancement of natural recharge processes. Unlike widely used vulnerability assessment tools such as the DRASTIC method [ 32 , 33 ], which focus primarily on hydrogeological properties, our cluster typology integrates both environmental and socio-economic dimensions. This broader scope enhances its utility for adaptive groundwater management in the context of climate change and land use pressures. By capturing the variability and site-specific pressures, the typologies support more comprehensive vulnerability assessments. Consequently, WPA-based assessments can meaningfully complement detailed local analyses to inform the development of targeted, sustainable groundwater management strategies. By identifying both high-risk (e.g., Cluster 5) and more resilient areas (e.g., Clusters 2 and 9), the typologies provide a robust, evidence-based foundation for prioritizing protection efforts. Embedding these differentiated strategies into national and regional policy frameworks will be essential to ensure the long-term sustainability of drinking water resources in Germany. At the same time, the relevance of these findings extends beyond the German context. The updated national regulation on delineation and assessment of WPAs [ 9 ] represents the implementation of the EU Drinking Water Directive [ 10 ], which mandates a preventive, risk-based approach to the protection of drinking water across all member states. The typology and vulnerability assessment developed in this study may therefore provide a transferable framework for supporting similar assessments and management strategies across the EU. Its integration of hydrogeological, environmental, and socio-economic stressors makes it particularly well suited for the complex, site-specific conditions faced throughout Europe. Together, these insights offer a robust foundation for targeted, sustainable groundwater protection within Germany and potentially across the EU. 5. Conclusion WPAs are exposed to a complex set of climatic, land use, and socio-economic influences. The new harmonized dataset of WPAs compiled in this study enables a consistent nationwide analysis of WPA characteristics and the identification of potential stressors affecting groundwater vulnerability. By integrating previously fragmented data sources into a unified and comprehensive dataset, this study provides a robust foundation for groundwater protection and management. The comparative analysis with national-level characteristics revealed that WPAs are strategically designated in regions with favorable hydrogeological conditions and high public water demand, emphasizing their critical role in safeguarding drinking water resources. Furthermore, the identification of eleven distinct WPA clusters, each representing characteristic combinations of hydrogeological, environmental, and socio-economic factors, facilitates the development of targeted management strategies that address specific characteristic-dependent vulnerabilities. These generalized typologies illustrate that certain WPAs are particularly susceptible to stressors such as drought and nitrate pollution due to their characteristics. Adopting a more standardized approach to WPA designation and protection criteria could enhance comparability across federal states, ultimately strengthening long-term groundwater resilience and supporting equitable water resource management under changing environmental and socio-economic conditions. The new German regulation on drinking water catchment delineation [ 9 ] implements the EU Drinking Water Directive [ 10 ], which mandates risk-based and preventive approaches to water source protection. The presented typology and methodological approach for identifying vulnerability patterns in groundwater-dependent catchments provide a transferable framework that may support the implementation of similar assessments and management strategies in other European countries. Overall, the harmonized dataset and classification approach developed in this study offers valuable tools for anticipating groundwater risks and supporting adaptive protection strategies, both in Germany and beyond. Abbreviations BW Baden-Württemberg BY Bavaria DRASTIC D epth to water table, net R echarge, A quifer media, S oil media, T opography, I mpact of vadose zone, hydraulic C onductivity EU European Union PAM Partitioning Around Medoids WFD Water Framework Directive WHG Federal Water Act WPA Water protection area Declarations Availability of data and materials: The dataset supporting the conclusions of this article is available in the FreiDok repository, DOI: 10.6094/UNIFR/263651, https://freidok.uni-freiburg.de/data/263651 [34]. Acknowledgements: We sincerely thank the Environmental Agencies of the Federal States of Germany for the provision of WPA boundaries. We also gratefully acknowledge all institutions and authors who provided geodata and statistical information used to derive the various characteristics in the dataset, including the Federal Institute of Geosciences and Natural Resources (BGR), the Federal Agency for Cartography and Geodesy (BKG), the Thünen Institute, the Federal and State Statistical Offices, Eurostat, the European Environment Agency (EEA), and the various research groups and authors cited throughout the documentation. We further thank the members of the StressRes project team for their valuable support and constructive discussions throughout the development of this study, especially apl. Prof. Dr. Jens Lange (Chair of Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Germany) for his valuable input. Funding: The Federal Ministry of Research, Space and Technology (BMFTR) is funding the StressRes project within the LURCH funding measure as part of the federal research program on water “Wasser: N”. Wasser: N contributes to the BMFTR “Research for Sustainability’ (FONA) Strategy. Author information Authors and Affiliations: Chair of Environmental Hydrological Systems, Faculty of Environment and Natural Resources, University of Freiburg, Germany Authors' contributions: KSz, CW, JH and Kst conceptualized the study, designed, and revised the methodology. Investigation and data curation was conducted by KSz. KSz conducted the formal analysis, visualization and wrote the original draft. All authors reviewed and edited the manuscript. Corresponding author: Correspondence to Kathrin Szillat ( [email protected] ) Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests: Kerstin Stahl is a Guest Editor of the Collection “Insights from the BMBF LURCH Initiative on Sustainable Groundwater Management”. All other authors declare that they have no competing interests. References Taylor RG, Scanlon B, Döll P et al. (2013) Ground water and climate change. Nature Clim Change 3:322–329. https://doi.org/10.1038/nclimate1744 EEA (2022) Europe's groundwater: a key resource under pressure. Publications Office of the European Union. https://doi.org/10.2800/50592 Umweltbundesamt (2024) Grundwasser. https://www.umweltbundesamt.de/themen/wasser/grundwasser. Accessed 15 Jan 2025 European Parliament and European Council (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy European Parliament and European Council (2006) Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration European Council (1991) Directive 91/676/EEC of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources Schleyer R, Milde G, Milde K (1992) Wellhead Protection Zones in Germany: Delineation, Research and Management. Water & Environment J 6:303–310. https://doi.org/10.1111/j.1747-6593.1992.tb00754.x BMUKN (2009) Gesetz zur Ordnung des Wasserhaushalts (Wasserhaushaltsgesetz – WHG) BGBl. I Seite 2585 vom 31.07.2009, das zuletzt durch Artikel 1 des Gesetzes vom 19. Juni 2020 (BGBl. I Seite 1408) geändert worden ist BMUKN (2023) Verordnung über Einzugsgebiete von Entnahmestellen für die Trinkwassergewinnung (Trinkwassereinzugsgebieteverordnung: TrinkwEGV ) BGBl. 2023 I Nr. 346 vom 11.12.2023 European Parliament and European Council (2020) Directive (EU) 2020/2184 of the European Parliament and of the Council of 16 December 2020 on the quality of water intended for human consumption BMU/ UBA (2017) Wasserwirtschaft in Deutschland: Grundlagen, Belastungen, Maßnahmen. https://www.umweltbundesamt.de/publikationen/wasserwirtschaft-in-deutschland-grundlagen. Accessed 02 May 2025 Hölting B, Coldewey WG (eds) (2013) Hydrogeologie: Einführung in die Allgemeine und Angewandte Hydrogeologie, 8. Auflage. Springer Berlin Heidelberg, Berlin, Heidelberg Umweltbundesamt (2008) Grundwasser in Deutschland. https://www.umweltbundesamt.de/sites/default/files/medien/publikation/long/3642.pdf. Accessed 02 Jul 2025 BMUKN (2003) Hydrologischer Atlas von Deutschland. 3. Lieferung. https://geoportal.bafg.de/dokumente/had/52GroundwaterYields.pdf. Accessed 08 May 2025 Umweltbundesamt (2022) Chemischer Zustand des Grundwassers. https://www.umweltbundesamt.de/themen/wasser/grundwasser/zustand-des-grundwassers/chemischer-zustand-des-grundwassers. Accessed 02 Jul 2025 Winter C, Müller S, Kattenborn T et al. (2025) Forest Dieback in Drinking Water Protection Areas—A Hidden Threat to Water Quality. Earth's Future 13. https://doi.org/10.1029/2025EF006078 Strebel O, Duynisveld W, Böttcher J (1989) Nitrate pollution of groundwater in western Europe. Agriculture, Ecosystems & Environment 26:189–214. https://doi.org/10.1016/0167-8809(89)90013-3 Foster SSD (2000) Assessing and controlling the impacts of agriculture on groundwater : from Barley Barons to Beef Bans. QJEGH 33:263–280. https://doi.org/10.1144/qjegh.33.4.263 Böhlke J-K (2002) Groundwater recharge and agricultural contamination. Hydrogeology Journal 10:153–179. https://doi.org/10.1007/s10040-001-0183-3 Bijay-Singh, Craswell E (2021) Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem. SN Appl Sci 3. https://doi.org/10.1007/s42452-021-04521-8 Lapworth DJ, Baran N, Stuart ME et al. (2012) Emerging organic contaminants in groundwater: A review of sources, fate and occurrence. Environ Pollut 163:287–303. https://doi.org/10.1016/j.envpol.2011.12.034 Siebert S, Burke J, Faures JM et al. (2010) Groundwater use for irrigation – a global inventory. Hydrol Earth Syst Sci 14:1863–1880. https://doi.org/10.5194/hess-14-1863-2010 Intergovernmental Panel on Climate Change (2023) Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/9781009157896 Reinecke R, Müller Schmied H, Trautmann T et al. (2021) Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study. Hydrol Earth Syst Sci 25:787–810. https://doi.org/10.5194/hess-25-787-2021 Wunsch A, Liesch T, Broda S (2022) Deep learning shows declining groundwater levels in Germany until 2100 due to climate change. Nat Commun 13:1221. https://doi.org/10.1038/s41467-022-28770-2 Hellwig J. (2019) Grundwasserdürren in Deutschland von 1970 bis 2018. Korrespondenz Wasserwirtschaft 12:567–572 Luo S, Tetzlaff D, Smith A et al. (2024) Long-term drought effects on landscape water storage and recovery under contrasting landuses. Journal of Hydrology 636:131339. https://doi.org/10.1016/j.jhydrol.2024.131339 Ebeling P, Musolff A, Kumar R et al. (2024) Groundwater head responses to droughts across Germany. https://doi.org/10.5194/egusphere-2024-2761 Blauhut V, Stahl K, Falasca G (2020) Dürre und die öffentliche Wasserversorgung in Baden-Württemberg: Folgen, Umgang und Wahrnehmung. Wasserwirtsch 110:31–36. https://doi.org/10.1007/s35147-020-0744-9 Frommen T, Moss T (2021) Pasts and Presents of Urban Socio-Hydrogeology: Groundwater Levels in Berlin, 1870−2020. Humboldt-Universität zu Berlin Becken S. (2014) Water equity – Contrasting tourism water use with that of the local community. Water Resources and Industry:9–22. https://doi.org/10.1016/j.wri.2014.09.002. Aller L, Lehr JH, Petty R et al. (1987) Drastic: A Standardized System to Evaluate Groundwater Pollution Potential using Hydrogeologic Setting. Journal Geological Society of India 29:23–37. https://doi.org/10.17491/jgsi/1987/290112 Sinreich M. (2009) Konzept der Vulnerabilität im Grundwasserschutz: Anwendung auf die Verhältnisse der Schweiz. Gas Wasser Abwasser gwa:109–117 Szillat K, Winter C, Hellwig J et al. (2025) Water protection areas (WPAs) in Germany: dataset. Albert-Ludwigs-Universität Freiburg BMUKN (2024) Trinkwasserschutzgebiete: Einführung und Situation. https://www.bundesumweltministerium.de/themen/wasser-und-binnengewaesser/trinkwasser/trinkwasser-trinkwasserschutzgebiete. Accessed 03 Jul 2025 Kaufman L, Rousseuw PJ (1991) Finding Groups in Data: An Introduction to Cluster Analysis. Biometrics 47:788. https://doi.org/10.2307/2532178 Gower JC (1971) A General Coefficient of Similarity and Some of Its Properties. Biometrics 27:857. https://doi.org/10.2307/2528823 Maechler M., Rousseeuw P., Struyf A., Hubert M., Hornik K., Studer M., Roudier P., Gonzalez J., Kozlowski K., Schubert E., Murphy K. (2021) cluster: Cluster Analysis Basics and Extensions: R package version 2.1.2 Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7 Henning C. (2024) fpc: Flexible Procedures for Clustering: R package version 2.2-13 EEA (2018) European waters: Assessment of status and pressures 2018. EEA Report No 7. https://doi.org/10.2800/303664 Wakida FT, Lerner DN (2005) Non-agricultural sources of groundwater nitrate: a review and case study. Water Res 39:3–16. https://doi.org/10.1016/j.watres.2004.07.026 Tomer MD, Burkart MR (2003) Long-term effects of nitrogen fertilizer use on ground water nitrate in two small watersheds. J Environ Qual 32:2158–2171. https://doi.org/10.2134/jeq2003.2158 Winter C, Lutz SR, Musolff A et al. (2021) Disentangling the Impact of Catchment Heterogeneity on Nitrate Export Dynamics From Event to Long‐Term Time Scales. Water Resources Research 57. https://doi.org/10.1029/2020WR027992 BfG (2024) Water body for WFD (Wasserkörper-DE). https://geoportal.bafg.de/inspire/download/AM/waterBodyForWFD/datasetfeed.xml. Accessed 14 Nov 2024 Todd D. K., Mays L. W. (2004) Groundwater Hydrology, 3rd edn. John Wiley & Sons, 2004 Dieser M, Zieseniß S, Mielenz H et al. (2023) Nitrate leaching potential from arable land in Germany: Identifying most relevant factors. J Environ Manage 345:118664. https://doi.org/10.1016/j.jenvman.2023.118664 Bender, S., Butts, M., Hagemann, S., Smith, M., Vereecken, H. & Wendland, F (2017) Der Einfluss des Klimawandels auf die terrestrischen Wassersysteme in Deutschland. Eine Analyse ausgesuchter Studien der Jahre 2009 bis 2013. Report 29 Foster SSD, Chilton PJ (2003) Groundwater: the processes and global significance of aquifer degradation. Philos Trans R Soc Lond B Biol Sci 358:1957–1972. https://doi.org/10.1098/rstb.2003.1380 Winter C, Nguyen TV, Musolff A et al. (2023) Droughts can reduce the nitrogen retention capacity of catchments. Hydrol Earth Syst Sci 27:303–318. https://doi.org/10.5194/hess-27-303-2023 Kundzewicz, Z. W., Döll, P. (2009) Will groundwater ease freshwater stress under climate change? Hydrological Sciences Journal 54:665–675. https://doi.org/10.1623/hysj.54.4.665 Additional Declarations Competing interest reported. Kerstin Stahl is a Guest Editor of the Collection “Insights from the BMBF LURCH Initiative on Sustainable Groundwater Management”. All other authors declare that they have no competing interests. Supplementary Files SupplementInformation.docx Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Environmental Sciences Europe → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 11 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7084780","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486337727,"identity":"f4a5bd22-c17c-4815-ba57-85be64d4df9a","order_by":0,"name":"Kathrin Szillat","email":"data:image/png;base64,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","orcid":"","institution":"University of Freiburg","correspondingAuthor":true,"prefix":"","firstName":"Kathrin","middleName":"","lastName":"Szillat","suffix":""},{"id":486337728,"identity":"a0dfe658-9595-4c13-b345-e12df11740ba","order_by":1,"name":"Carolin Winter","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Carolin","middleName":"","lastName":"Winter","suffix":""},{"id":486337732,"identity":"8f18973b-15e4-4b9c-8d22-fa3f8b647388","order_by":2,"name":"Jost Hellwig","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Jost","middleName":"","lastName":"Hellwig","suffix":""},{"id":486337735,"identity":"d2d59856-a155-4a96-b6ca-a998d46adcad","order_by":3,"name":"Kerstin Stahl","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Kerstin","middleName":"","lastName":"Stahl","suffix":""}],"badges":[],"createdAt":"2025-07-09 14:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7084780/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7084780/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12302-025-01233-3","type":"published","date":"2025-10-22T16:16:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87021401,"identity":"15ef40b3-f0f2-473b-8f91-f8949feb7e4d","added_by":"auto","created_at":"2025-07-18 11:16:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":645010,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of WPAs across Germany.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/6d7836e47d00ad17d4d04378.png"},{"id":87020595,"identity":"1dcc985c-3a37-44b1-950b-8c4b20263300","added_by":"auto","created_at":"2025-07-18 11:08:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252987,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of WPA and national characteristics: land cover (A–D), elevation (E), hydrogeology (F–J), and socio-economic indicators (K–O). Panels A-C and F-H show the relative distribution of categories (% of area). Panel E displays the range of elevation values (maximum–minimum). All remaining panels (D, I–O) represent area-weighted mean values.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/78d0a8e640edf75726e14bde.png"},{"id":87021400,"identity":"b6c2bcd7-7f3b-42e7-a9f0-5bc8776c2d35","added_by":"auto","created_at":"2025-07-18 11:16:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10971,"visible":true,"origin":"","legend":"\u003cp\u003eSilhouette plot for the PAM partitioning in 11 clusters\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/c531c2a2da92b8f1f5897181.png"},{"id":87020619,"identity":"8623a463-7f73-4138-9ae1-6914bf0a6479","added_by":"auto","created_at":"2025-07-18 11:08:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2186473,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of clusters of WPAs across Germany. A) A map depicting the spatial distribution of clusters; B) The number of WPAs in each cluster.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/18e2dee582040097fbd3b418.png"},{"id":87020601,"identity":"61f167b7-e053-4a77-a96b-f1d016c1cedf","added_by":"auto","created_at":"2025-07-18 11:08:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":185344,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing median values per cluster with hierarchical clustering of rows and columns. Categorical variables (cat.) are scaled as follows: dominant land use (low = agriculture, high = forest/semi-natural areas); dominant crop type (low = cereals, medium = grassland, high = forest/other); aquifer type (low = unconsolidated rock (porous), high = consolidated rock (fissured/karst)). The symbol (%) indicates that the values represent the share of each category within the cluster.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/83c25988aad1f3ea84af5c5e.png"},{"id":87020602,"identity":"bc6b41aa-9b83-4e01-9e1c-838be57e0c15","added_by":"auto","created_at":"2025-07-18 11:08:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25012,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of WPAs per cluster in poor chemical status based on WFD [4]\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/88824845a3eeee90e9477c69.png"},{"id":94490183,"identity":"33451cea-fba7-41f1-82e1-7d5b5ebda6d7","added_by":"auto","created_at":"2025-10-27 17:08:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3568553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/bf49cad0-ba0f-41a2-8db2-4f9e6936ac42.pdf"},{"id":87020598,"identity":"7f8ca107-eef7-4159-8958-705c3c5c14cd","added_by":"auto","created_at":"2025-07-18 11:08:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33028,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7084780/v1/30223d8d7959b3e88637da78.docx"}],"financialInterests":"Competing interest reported. Kerstin Stahl is a Guest Editor of the Collection “Insights from the BMBF LURCH Initiative on Sustainable Groundwater Management”. All other authors declare that they have no competing interests.","formattedTitle":"Comprehensive Mapping and Classification of Germany’s Drinking Water Protection Areas","fulltext":[{"header":"1. Background","content":"\u003cp\u003eGroundwater is the largest available freshwater resource beneath the Earth\u0026rsquo;s surface. It plays a crucial role in drinking water supply, agriculture, industry, and in maintaining ecological balance by ensuring baseflow in streams or dilution of pollutants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It provides up to 65% of drinking water in the European Union (EU) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and roughly 70% in Germany [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to natural filtration processes, groundwater often requires minimal treatment before consumption, making it a cost-effective drinking water source. However, increasing pressures from climate change, pollution, competing water uses, and rising demand threaten its quality and quantity. As a result, ensuring acceptable drinking water quality increasingly requires resource mixing and costly treatments.\u003c/p\u003e\u003cp\u003eTo prevent groundwater pollution and groundwater deterioration, EU member states are required to implement measures aligned with European policies, such as the EU Water Framework Directive (WFD) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the EU Groundwater Directive [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the EU Nitrates Directive [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One key measure is the designation of Water Protection Areas (WPAs), which regulate human activities that endanger water quality. WPAs are designated to mitigate contamination risks from agriculture, industrial pollution, and urban wastewater. In Germany, groundwater protection has a long history, dating back to the early 1930s when land-use restrictions were implemented around municipal wells based on distance and travel time criteria [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Today, the Federal Water Act (WHG) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] serves as the legal foundation for WPAs, with state governments responsible for defining protection zones based on regional hydrogeological conditions. Additionally, efforts to delineate catchment areas for groundwater extraction are in progress due to new legislative requirements mandating the delineation and assessment of WPAs by the end of 2025 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The German regulation transposes the EU Drinking Water Directive [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] into national law, reflecting the directive's focus on risk-based approaches to safeguard drinking water quality. Previous studies indicate that in 1992, there were 13,050 wellhead protection areas necessary, of which 72% have been designated, 11% are in the process of designation, and for 17%, the process has not been initiated yet [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. By 2017, 18,341 drinking water and mineral spring protection areas had been identified, covering 15.4% of Germany\u0026rsquo;s total area [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGermany\u0026rsquo;s groundwater resources are shaped by a diverse geological and hydrogeological framework, which exerts a critical influence on both the quantity and quality of drinking water available for public supply. The principal aquifer types can be broadly classified into porous aquifers within unconsolidated sediments and fractured or karstified aquifers in consolidated bedrock formations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Groundwater from porous aquifers, especially those in Pleistocene and Holocene deposits such as sand, gravel, and clay, is crucial for drinking water supply, particularly in regions like the North German Plain and the Upper Rhine Graben. These aquifers are highly productive and serve as the primary source of public drinking water in these areas. Thick alluvial deposits in the Lower Rhine Basin and the Lower Rhine Plain provide some of the country\u0026rsquo;s most important groundwater reserves. Additionally, spring water from low mountain ranges with karstified or fissured bedrock, such as the Swabian Jura, Franconian Jura, and the Black Forest, plays an important role in local water supply. However, these aquifers tend to be less productive compared to the porous aquifers in the plains. Groundwater resources of local importance are also found in the central mountain ranges, including the Rhenish Slate Mountains, Harz, Thuringian and Bavarian Forests, Ore Mountains, and Black Forest [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This hydrogeological diversity in Germany demands region-specific protection measures, such as the designation of WPAs, to safeguard groundwater quality and ensure its continued availability for public use. Despite these protective measures, significant challenges persist in maintaining groundwater quality and quantity. In the EU, approximately 24% of groundwater bodies are classified as having poor chemical status, with 9% facing poor quantitative status [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Germany, around 33% of groundwater bodies are reported to be in poor chemical status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This deterioration is driven by multiple stress factors, including land use, climate change, and socio-economic pressures.\u003c/p\u003e\u003cp\u003eForests in WPAs support groundwater recharge and act as natural filters that enhance water quality. However, climate-induced forest dieback can impair these functions and increase nitrate leaching [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Agricultural activities are a major source of diffuse pollution. Intensive crop cultivation, livestock farming, and the excessive application of liquid manure and nitrogenous fertilizers significantly increase the risk of nutrient leaching, particularly nitrate contamination [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, pesticide use in agriculture contributes to persistent groundwater pollution, as residues can remain in soils and leach into aquifers over time [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Irrigation practices may further exacerbate stress on groundwater resources by exceeding natural recharge, leading to declining water tables and reduced baseflow [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In urban and industrial areas, groundwater contamination may result from wastewater infiltration, leaky sewage infrastructure, and diffuse pollution pathways, introducing compounds such as pharmaceuticals, heavy metals, and microplastics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These diverse land use pressures underscore the importance of effective protection measures, such as WPAs, to safeguard groundwater resources.\u003c/p\u003e\u003cp\u003eBeyond land use, climate change exacerbates groundwater stress. Climate warming and altered precipitation patterns increase the frequency and severity of droughts and extreme weather events, such as heavy rainfall and flooding [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Across Germany, climate change is projected to reduce groundwater recharge and lower groundwater levels throughout the 21st century. Reinecke et al.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] shows that under high-emission scenarios, recharge will decline in many regions due to increased evapotranspiration and more frequent droughts, even in areas where annual precipitation remains stable or slightly increases. Wunsch et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] further demonstrates that these effects will be most pronounced under RCP8.5, with significant and spatially consistent declines in groundwater levels, particularly in northern and eastern Germany, where existing downward trends are likely to intensify. Both studies highlight that, while some seasonal increases in winter precipitation and recharge may occur, the overall trend will be toward greater variability and longer periods of low groundwater levels, posing substantial challenges for water supply, agriculture, and ecosystem health. Besides long-term gradual shifts, extreme events such as prolonged droughts can further compromise groundwater quantity and quality, making it difficult to meet competing water demands. Recent prolonged droughts in Germany (e.g., 2003, 2011, 2015, 2018, and 2022) caused groundwater depletion and falling water levels [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consequently, water supply utilities face growing challenges in maintaining supply security while meeting quality and policy requirements under increasingly severe conditions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSocio-economic factors contribute to groundwater stress by increasing water demand, particularly in urban areas and tourist hotspots. High population densities in cities lead to high groundwater extraction rates to meet water demand. In Berlin, 150 years of urban growth, industrialization, and shifting water demand have caused significant fluctuations in groundwater levels, creating complex management challenges [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Tourist areas also face fluctuating water demand. During peak seasons, the water use of tourists can far exceed that of local residents, putting additional pressure on local groundwater resources. This seasonal spike in demand complicates long-term water management and raises concerns about fairness and sustainability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These pressures make groundwater management more difficult and emphasize the need for sustainable water management.\u003c/p\u003e\u003cp\u003eGiven these multifaceted pressures, assessing the vulnerability of groundwater resources used for drinking water supply requires an integrated approach. Widely used methods such as DRASTIC (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eD\u003c/span\u003eepth to water table, net \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eR\u003c/span\u003eecharge, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003equifer media, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eS\u003c/span\u003eoil media, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003eopography, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eI\u003c/span\u003empact of vadose zone, hydraulic \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003eonductivity) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] focus primarily on contamination risks in porous aquifers and consider mainly hydrogeological parameters, limiting their applicability for broader, regional-scale assessments. Current approaches will benefit from integration of environmental and socio-economic dimensions to achieve a more comprehensive understanding of regional vulnerabilities. Incorporating factors such as land use, aquifer type, groundwater extraction patterns, and socio-economic indicators allows for a more comprehensive understanding of regional differences and site-specific pressures. Comprehensive regional assessments, such as those based on WPAs, should complement detailed local assessments to guide and contribute to the development of more targeted and sustainable groundwater management strategies.\u003c/p\u003e\u003cp\u003eThis study pursues three main objectives. First, we aimed to assemble a comprehensive, country-wide dataset that includes hydrogeological, land cover, and socio-economic characteristics of WPAs in Germany. Second, we aimed to compare the characteristics in WPAs to Germany as a whole. Third, we classified all WPAs into groups with similar characteristics. By integrating hydrogeological, land-cover, and socio-economic dimensions, we aimed to identify regional groundwater vulnerabilities and derive typologies that can inform more targeted governance and resource management strategies.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data and Analysis of Drinking water protection area Characteristics\u003c/h2\u003e\u003cp\u003eWe compiled a country-wide dataset of WPAs in Germany based on a range of data sources [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The dataset includes spatial information on WPAs and associated attributes covering hydrogeological, environmental, social, and economic characteristics. Due to variation in legal interpretations of the WHG [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] across federal states, WPA designations differ in zoning practices. WPAs are generally divided into protection zones, each with specific restrictions to safeguard groundwater quality. The size and location of the protection zones are determined on a case-by-case basis according to the local hydrogeological conditions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Due to the lack of standardized regulations for WPA designation across federal states and variations in zoning practices, this study considers only the outermost boundary of each WPA. This approach ensures that all designated protection zones, regardless of their specific regulatory restrictions, are encompassed within a unified delineation.\u003c/p\u003e\u003cp\u003eFor the dataset, we processed data from different sources (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For numerical variables (e.g., population density, water consumption rates) available at the district or city level, we intersected the datasets with the WPA polygons in GIS and computed area-weighted mean values to assign distinct numerical attributes to each WPA polygon. For categorical variables (e.g., aquifer type, land use), the datasets were also intersected, and the dominant value (\u0026gt;\u0026thinsp;50% of the WPA area) was assigned to each WPA polygon. Descriptive statistics (mean, median, min, max) of all numerical WPA attributes were compared to national values for Germany, using the original data sources. For datasets available only at the district or city level, area-weighted values were calculated. For categorical variables, area shares of each category were calculated for both WPAs and Germany and subsequently compared.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Cluster Analysis by Partitioning Around Medoids\u003c/h2\u003e\u003cp\u003eCluster analysis was conducted using the Partitioning Around Medoids (PAM) algorithm, a non-hierarchical clustering method introduced by Kaufman \u0026amp; Rousseuw [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. PAM identifies representative data points (medoids), which serve as the center of each cluster. Random medoids initialize the process and are successively updated to minimize the total dissimilarity. The clustering process is based on a dissimilarity matrix computed using Gower\u0026rsquo;s distance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This matrix was generated with the \u003cem\u003edaisy\u003c/em\u003e function from the cluster package in R [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Gower\u0026rsquo;s distance accommodates mixed data types. All variables are rescaled between 0 and 1 by subtracting the minimum value of each variable and dividing by its range, ensuring that all variables contribute equally to the final dissimilarity score. The dissimilarity between two WPAs is calculated as the weighted mean of the contributions from each variable. Specifically, the dissimilarity between two WPAs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e is given by Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{d}}_{\\text{i}\\text{j}}=\\text{d}\\left(\\text{i},\\text{j}\\right)=\\:\\frac{{\\sum\\:}_{\\text{k}-1}^{\\text{p}}{\\text{w}\\text{k}{\\delta\\:}}_{\\text{i}\\text{j}}^{\\left(\\text{k}\\right)}{\\text{d}}_{\\text{i}\\text{j}}^{\\left(\\text{k}\\right)}}{{\\sum\\:}_{\\text{k}-1}^{\\text{p}}{\\text{w}\\text{k}{\\delta\\:}}_{\\text{i}\\text{j}}^{\\left(\\text{k}\\right)}})\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:wk\\)\u003c/span\u003e\u003c/span\u003e represents the weight of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e-th variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{ij}^{\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e is a binary indicator that equals 1 if the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e-th variable is non-missing for both observations\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e and satisfies certain conditions (e.g., non-zero for asymmetric binary variables) or 0 otherwise. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}^{\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e is the contribution of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e-th variable to the overall dissimilarity, calculated as the distance between x[\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i,k\\)\u003c/span\u003e\u003c/span\u003e] and x[\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j,k\\)\u003c/span\u003e\u003c/span\u003e]. For nominal or binary variables, the contribution \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}^{\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e is 0 if the values are identical and 1 if they differ. For other variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}^{\\left(k\\right)}\\:\\)\u003c/span\u003e\u003c/span\u003eis the absolute difference between the values, divided by the total range of that variable. Ordinal variables are treated with standard scoring, where they are replaced by their integer codes rather than ranks. Since all individual contributions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}^{\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e fall within the range [0, 1], the overall dissimilarity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003ewill also be within this range. If all weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{wk\\delta\\:}_{ij}^{\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e are zero, indicating missing or irrelevant data, the dissimilarity is set to NA.\u003c/p\u003e\u003cp\u003ePartitioning methods require a predefined number of clusters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e. Each object is assigned to the cluster of its nearest medoid, and cluster memberships are returned. To evaluate clustering quality across different values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e, silhouette indices [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] were computed. The silhouette width \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e evaluates how well a WPA is assigned to its own cluster compared to other clusters (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{s}\\left(\\text{i}\\right)=\\:\\frac{\\text{b}\\left(\\text{i}\\right)-\\text{a}\\left(\\text{i}\\right)}{\\text{m}\\text{a}\\text{x}(\\text{a}\\left(\\text{i}\\right),\\text{b}\\left(\\text{i}\\right))}\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the average dissimilarity of a WPA \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e to all other WPAs in the same cluster, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the lowest average dissimilarity of a WPA \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e to any other cluster. The silhouette width ranges from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, where values close to +\u0026thinsp;1 indicate that the observation is well-clustered, values around 0 suggest that the observation lies between two clusters, and negative values imply possible misclassification [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The silhouette of a cluster is a plot of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e values, ranked in decreasing order, of all its objects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, providing a visual assessment of clustering quality. The optimal number of clusters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e was determined using the average silhouette width, with the highest average silhouette width indicating the most appropriate number of clusters.\u003c/p\u003e\u003cp\u003eTo validate the stability of the identified clusters, we employed the clusterboot function from the fpc package in R [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This method employs bootstrap resampling combined with the Jaccard similarity coefficient to evaluate the robustness of the clustering results. The Jaccard coefficient quantifies the similarity between sets by calculating the ratio of the intersection to the union of the sets. According to Henning [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], a cluster is considered valid and stable if it achieves a Jaccard similarity value of 0.75 or higher.\u003c/p\u003e\u003cp\u003eWe applied PAM clustering to all WPAs, excluding those with missing values in the variables (\u0026lt;\u0026thinsp;1%). We restricted the analysis to variables related to hydrogeological conditions, land cover, and socio-economic characteristics and excluded exclusively administrative information variables. For the numerical variables, we computed a Pearson correlation matrix to restrict our selection to uncorrelated variables (corr\u0026thinsp;\u0026lt;\u0026thinsp;0.7). For the categorical variables, we chose those with a maximum of four levels and excluded duplicated information as well. This selection process resulted in 15 variables for the cluster analysis, grouped into three main categories:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eLand cover\u003c/em\u003e: land use type (LULC), dominant crop types (Dom_crop), elevation range (Elev_range), livestock (ANTO), irrigation (Irrig)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eHydrogeological\u003c/em\u003e: type of rock of the uppermost aquifer (Rock), type of porosity (Poros), percolation (Perc), deep groundwater body (DEEP_GW), drought response time (Resp_time)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eSocio-economic\u003c/em\u003e: water costs per m\u0026sup3; (W_cost), groundwater extraction rates (GW_extrac), water consumption rates (W_consum), tourism overnight stays (Tourism_st), population density (Pop_dens)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 A new harmonized Dataset of Water protection areas in Germany\u003c/h2\u003e\u003cp\u003eThe dataset includes 11406 WPAs, covering around 15% of Germany\u0026rsquo;s total area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Political and administrative differences in groundwater protection strategies are reflected in the number and spatial distribution of WPAs across federal states (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These patterns illustrate the varying legal frameworks and zoning practices in WPA designation. For example, Bavaria (BY) has the highest number of WPAs, while they tend to be relatively small in area. In contrast, Baden-W\u0026uuml;rttemberg (BW) exhibits both a high number of WPAs and substantial spatial coverage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber and area share of WPAs by federal state.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFederal state\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber WPA\u0026rsquo;s\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% WPA area to federal state area\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBavaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaxony-Anhalt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrandenburg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBremen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRhineland Palatinate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaxony\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHamburg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower Saxony\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSchleswig-Holstein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaarland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThuringia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMecklenburg-Vorpommern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorth Rhine-Westphalia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBerlin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaden-W\u0026uuml;rttemberg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHesse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparison of National and Water protection area Characteristics\u003c/h2\u003e\u003cp\u003eWPA characteristics differ from the national average, particularly in terms of land cover, hydrogeological conditions, and socio-economic pressures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Land cover in WPAs shows a higher proportion of forest and semi-natural areas, while agricultural land dominates at the national scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Grassland is more widespread across Germany, whereas WPAs present more forest, small woody features, and artificial surfaces. Crop types, such as cereals and vegetables, show similar distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The proportion of irrigated farmland is comparable between WPAs and the national context (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In contrast, livestock density and elevation range are lower in WPAs (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). WPAs are more frequently underlain by solid rock in the uppermost aquifer compared to the national average (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). While the national aquifer distribution is more clearly divided between porous and fissured types, WPAs show a higher share of mixed porosity types (F/P and F/Ka), indicating a greater presence of transitional aquifer conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). These conditions are accompanied by higher percolation rates in WPAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Moreover, only a small fraction of WPAs is associated with deep groundwater bodies, suggesting a predominance of shallow groundwater resources (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). The mean drought response time of WPAs is around 2 months faster than the national average (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). Regarding socio-economic characteristics, WPAs have higher population densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN), tourism overnight stays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eO), groundwater extraction rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM), and water consumption rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL) relative to the national average. In contrast, average water costs show little variation between WPAs and the national context (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Clusters of Water protection areas\u003c/h2\u003e\u003cp\u003eWe tested cluster solutions with values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e ranging from 2 to 12. The best solution was found at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e = 11, which achieved the highest average silhouette width of 0.51, indicating a reasonably clear structure and separation between clusters (Table S2.). The silhouette plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows that most of the WPAs are well-matched to their assigned clusters, as indicated by the overall positive silhouette widths. Notably, two clusters (2 and 9) contain substantially more elements than the others, suggesting dominant patterns or types among these WPAs. Conversely, a few clusters have narrow bars with low or even slightly negative silhouette widths, indicating that they lie near the boundaries between clusters and are less clearly assignable. Overall, the clustering structure reveals meaningful groupings of WPAs. Cluster robustness was further confirmed through a resampling validation procedure with 200 iterations and random subsamples of 50% of the data. This process yielded a Jaccard similarity index of 0.88, supporting the stability of the clustering outcome.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial distribution of the optimal 11-cluster solution indicates that all clusters are geographically distributed across Germany (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Notably, the main forest regions in Germany are primarily represented by Clusters 2, 6, and 9. For example, the Black Forest in the south and the Erzgebirge in the east are mainly represented by Cluster 2, while the Bavarian Alps and the Pf\u0026auml;lzerwald in the southwest are represented by Cluster 9. The Harz region in central Germany and the Swabian Alps are represented by both Clusters 2 and 9. In terms of cluster sizes (number of WPAs in a cluster), the two main clusters (2 and 9) account for around 43% of all WPAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Those two, along with cluster 6, represent WPAs with non-agricultural land cover, such as forests, semi-natural areas, and urban areas. The remaining clusters represent agriculturally dominated WPAs, which make up around 48% of the total. Specifically, Clusters 3, 5, 7, and 10 represent WPAs dominated by crop-type cereals, comprising around 24% of all WPAs, while Clusters 1, 4, 8, and 11 represent WPAs dominated by crop-type grassland, also making up around 24%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe main categories that distinguish the 11 clusters are the aquifer type (porous vs. fissured/karst), dominant land cover (agriculture vs. forest/other), water costs, and elevation range (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Clusters 4, 5, 6, 7, and 8 represent WPAs of porous aquifers with lower water costs per m\u0026sup3; and lower elevation ranges. These clusters tend to have higher groundwater extraction rates, water consumption rates, a higher share of WPAs with deep groundwater bodies, and higher tourism overnight stays and livestock density. They account for about 30% of all WPAs and are mainly located in northern, northeastern, and parts of southern and western Germany. The remaining clusters represent fissured/karst aquifers with higher water costs per m\u0026sup3; and higher elevation ranges. These clusters tend to have lower groundwater extraction rates, lower water consumption rates, a lower share of WPAs with deep groundwater bodies and higher percolation rates. They make up 70% of all WPAs and are located mainly in central, southern, and western Germany. Clusters show different dominant crop types, including cereals, grassland, and other (including forested areas and artificial surfaces). Clusters dominated by cereal cultivation tend to have a higher share of WPAs where vegetables are the dominant crop type, as shown in the column \u0026ldquo;Vegetables (%)\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although vegetables are not the dominant crop type in any cluster overall, their presence is most pronounced in agricultural clusters, where irrigation is also more common.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Appraisal of the Water protection area Dataset\u003c/h2\u003e\u003cp\u003eThe new harmonized WPA dataset enables a systematic, nationwide comparison of hydrogeological, land use, and socio-economic characteristics across German WPAs that can support a more differentiated understanding of groundwater protection challenges. Our results show that WPAs are predominantly located in regions with favorable hydrogeological conditions for drinking water abstraction. These areas typically feature higher percolation rates, a predominance of productive unconsolidated porous aquifers and moderate topographic reliefs, in agreement with results from [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, WPAs tend to response to drought around 2 months faster compared to the national average. A higher share of forest and semi-natural areas, combined with lower livestock densities, reduces contamination risks and supports natural recharge functions. Forest ecosystems in particular act as filters and buffers, although recent findings by Winter et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] suggest that climate-induced forest dieback can impair these functions and lead to increased nitrate leaching. Moreover, WPAs are located in areas with high water demand, characterized by above-average population density, tourism activity, and public water consumption. This underscores the dual role of WPAs in both protecting and securing drinking water supplies.\u003c/p\u003e\u003cp\u003eAt the same time, these characteristics imply a higher exposure to anthropogenic pressures, necessitating robust, adaptive, and region-specific management strategies. Our findings show that some potential stressors, such as crop type distributions and the extent of irrigated agriculture, do not considerably differ between WPAs and the national average. This similarity raises important questions about the extent to which these potentially high-risk parameters are adequately integrated into current protection and monitoring frameworks. Nitrate remains one of the most persistent threats to groundwater quality [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. While it originates from both point and diffuse sources, it mainly originates from agricultural and urban nitrogen inputs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Although management measures to reduce nitrate leaching are implemented, their effectiveness is often delayed due to long residence times in the unsaturated zone and aquifers [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This highlights the importance of early identification and mitigation of high-risk land use types. Additionally, climate change may further exacerbate irrigation demands in agriculture, particularly during more frequent and prolonged drought periods, adding additional stress to vulnerable groundwater systems.\u003c/p\u003e\u003cp\u003eWhile this study includes a broad range of potential stressors to groundwater quality and quantity, it does not capture the full spectrum of risks. The analysis was limited by data availability at the national scale. Information that might be considered additionally includes data from water utilities on actual water abstraction volumes, water volumes from external sources such as long-distance water supplies, information on infrastructure, and quality indicators. These factors remain difficult to assess systematically due to data restrictions, data gaps, and mismatched spatial and temporal resolutions across datasets. Recent studies project a nationwide decline in groundwater recharge and levels throughout the 21st century, with especially pronounced and persistent reductions expected in northern and eastern Germany under high-emission scenarios [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although annual precipitation may not decline substantially, rising temperatures, higher evapotranspiration, increasing irrigation demands, and more frequent drought periods are expected to reduce groundwater quantity and increase the variability and duration of low groundwater levels. Those dynamics pose significant challenges for water supply, agriculture, and ecosystems [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Also, long-term groundwater protection is challenging because such future risks are difficult to quantify and are not currently integrated into most WPA delineation practices. A key structural challenge also lies in the heterogeneous designation of WPAs across Germany\u0026rsquo;s federal states. Differences in zoning criteria and the extent of protected areas reduce comparability, limit transparency, and complicate the implementation of standardized protective measures envisioned under the WFD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Standardized protection criteria would clearly improve consistency and facilitate assessments like ours. The upcoming legal requirement for comprehensive nationwide WPA delineation by 2025 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], introduced in response to the revised EU Drinking Water Directive [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], offers an opportunity to improve harmonization and to integrate emerging evidence on land use pressures, groundwater demand, and climate-related risks into future-oriented protection strategies across the country and other EU member states.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Typical Vulnerability profiles\u003c/h2\u003e\u003cp\u003eThe cluster analysis revealed 11 distinct typologies of WPAs across Germany, each representing a characteristic combination of natural and anthropogenic factors influencing groundwater vulnerability. These typologies represent vulnerability profiles shaped by hydrogeology, land use, and socio-economic pressures. Understanding these profiles is essential for developing tailored protection strategies, particularly under increasing stress from diffuse pollution, overexploitation, and climate extremes. To evaluate whether there might be cluster-specific vulnerabilities, we used the chemical status of groundwater bodies from the WFD assessment as an indicator for groundwater vulnerability [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Note that this can only be regarded as a rough exemplary indicator due to the very simplified mapping approach. Across Germany, 36% of the total WPA area (corresponding to 25% of all WPAs) is located in groundwater bodies classified as being in poor chemical status. Cluster-specific shares range from 8\u0026ndash;62%, indicating disparities in contamination risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Higher contamination levels (35\u0026ndash;62%) are observed in clusters dominated by porous aquifers (Clusters 5, 4, 8, 6, and 7), which are mostly located in northern, northeastern, and parts of southern and western Germany. Porous aquifers are generally more vulnerable to contamination due to their higher permeability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, these clusters are characterized by high groundwater extraction and consumption rates, lower water prices, lower elevation ranges, and are often associated with intensive agriculture. In contrast, lower contamination shares (8\u0026ndash;32%) occur in clusters with fractured or karst aquifers (Clusters 2, 9, 1, 11, 3, and 10), which are mainly found in central and southern Germany.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur results show that groundwater quality is linked to a combination of hydrogeological, land use, and socio-economic factors. Negative correlations with poor chemical status are observed for variables such as aquifer type (porous vs. fractured/karst), percolation rates, elevation ranges, and water costs. This indicates that regions with fissured or karst aquifers, higher percolation rates, steeper topography, and higher water prices are associated with better groundwater quality. Conversely, positive correlations are observed with groundwater bodies containing deep layers, high groundwater extraction and consumption rates, intensive agriculture, and longer drought response times.\u003c/p\u003e\u003cp\u003eParticularly vulnerable are areas where cereal and vegetable cultivation dominate, likely due to high fertilizer and pesticide application. For example, Cluster 5 stands out as the most vulnerable one. The cluster is characterized by intensive agriculture with a high share of irrigated vegetable production. Similarly, a study by Dieser et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] reports that groundwater nitrate pollution in Germany is directly linked to the use of fertilizers and manure in intensive agricultural areas. Moreover, the demand for irrigation and associated groundwater extraction is expected to rise under climate change conditions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], potentially exacerbating pressure on groundwater resources in agricultural landscapes. However, not all agriculturally dominated clusters show elevated contamination risks. Clusters 1 and 11, both dominated by grassland, differ in elevation, percolation rates, and drought response times. Cluster 1, with a lower elevation range, has a faster drought response, higher percolation rates, and lower livestock density and experiences slightly lower contamination. Clusters 4 and 8 both have high livestock density, but Cluster 4, with higher percolation, faster drought response time and lower water consumption rates, exhibits a lower number of WPAs in a bad chemical status (20%) compared to Cluster 8 (40%).\u003c/p\u003e\u003cp\u003eThis analysis highlights that groundwater vulnerability is not determined by land cover alone, but rather by the complex interplay of hydrogeological conditions, land use patterns, and socio-economic pressures. Environmental drivers, including land use change, urbanization, agriculture, and industrial activities, can significantly intensify groundwater vulnerability by introducing contaminants and degrading water quality [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These cumulative pressures reflect the need for targeted management strategies. Several cluster comparisons illustrate how similar land cover or hydro-climatic conditions can lead to divergent vulnerability profiles. For example, forest and semi-natural land cover dominate both Clusters 2 and 9, while Cluster 2 is distinguished by higher percolation rates, greater tourism intensity, and shorter drought response times. Despite their similar land use, the described differences result in lower contamination levels for Cluster 2 (8%) compared to Cluster 9 (15%). In contrast, Clusters 3 and 10 share intensive agricultural land use but differ in elevation, drought response times, and population density. Despite these differences, both report comparable groundwater quality, implying that socio-economic pressures such as high population density and extraction rates may offset the benefits of more favorable natural settings. Moreover, Clusters 6 and 7 experience similar hydro-climatic conditions but differ markedly in their contamination levels (35% vs. 55%). This contrast reflects differences in land cover and socio-economic pressures. Cluster 6 has greater forest cover and higher population density, while Cluster 7 is dominated by agricultural land use and exhibits higher groundwater extraction rates. This highlights how land cover and socio-economic factors can influence groundwater quality despite similar hydro-climatic settings. Overall, these examples demonstrate how nuanced differences in natural or anthropogenic conditions can affect groundwater vulnerability.\u003c/p\u003e\u003cp\u003eDrought response time, which indicates the time lag between precipitation anomalies and observed changes in groundwater levels, adds another dimension to vulnerability that may become even more relevant in the future. Across the clusters, the average drought response time ranges from 4 to 26 months. Besides decreased recharge rates, droughts might induce a lack of nitrate dilution and a reduction in nitrogen retention due to reduced plant uptake and denitrification rates in dry soils [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Thereby, droughts might not only be a stressor for groundwater quantity but also for its quality.\u003c/p\u003e\u003cp\u003eOur study underscores the complex and heterogeneous nature of WPA vulnerability across Germany and the need for differentiated, context-specific protection strategies. The cluster typologies developed in this study provide a practical framework for understanding groundwater stress and identifying priority areas for intervention. Vulnerability assessments play a key role in this process by identifying potential risks and informing strategies for the sustainable management of groundwater resources [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. By identifying high-risk areas, these assessments enable more effective prioritization of management efforts and resource allocation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Several overarching strategies emerge as central to safeguarding groundwater resources. These include regulating groundwater abstraction in high-demand regions, enhancing infiltration and groundwater recharge, reducing nutrient inputs, particularly from livestock and intensive agriculture, maintaining ecological buffer zones such as forests and grasslands, and incorporating socio-economic stressors into water management plans. The cluster typologies help translate these general strategies into context-specific actions. For instance, Cluster 5 requires controls on groundwater extraction, nutrient reduction, and active engagement with agricultural stakeholders. In contrast, Clusters 2 and 9 should prioritize the preservation of forested land cover and the enhancement of natural recharge processes.\u003c/p\u003e\u003cp\u003eUnlike widely used vulnerability assessment tools such as the DRASTIC method [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which focus primarily on hydrogeological properties, our cluster typology integrates both environmental and socio-economic dimensions. This broader scope enhances its utility for adaptive groundwater management in the context of climate change and land use pressures. By capturing the variability and site-specific pressures, the typologies support more comprehensive vulnerability assessments. Consequently, WPA-based assessments can meaningfully complement detailed local analyses to inform the development of targeted, sustainable groundwater management strategies. By identifying both high-risk (e.g., Cluster 5) and more resilient areas (e.g., Clusters 2 and 9), the typologies provide a robust, evidence-based foundation for prioritizing protection efforts. Embedding these differentiated strategies into national and regional policy frameworks will be essential to ensure the long-term sustainability of drinking water resources in Germany. At the same time, the relevance of these findings extends beyond the German context. The updated national regulation on delineation and assessment of WPAs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] represents the implementation of the EU Drinking Water Directive [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which mandates a preventive, risk-based approach to the protection of drinking water across all member states. The typology and vulnerability assessment developed in this study may therefore provide a transferable framework for supporting similar assessments and management strategies across the EU. Its integration of hydrogeological, environmental, and socio-economic stressors makes it particularly well suited for the complex, site-specific conditions faced throughout Europe. Together, these insights offer a robust foundation for targeted, sustainable groundwater protection within Germany and potentially across the EU.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWPAs are exposed to a complex set of climatic, land use, and socio-economic influences. The new harmonized dataset of WPAs compiled in this study enables a consistent nationwide analysis of WPA characteristics and the identification of potential stressors affecting groundwater vulnerability. By integrating previously fragmented data sources into a unified and comprehensive dataset, this study provides a robust foundation for groundwater protection and management. The comparative analysis with national-level characteristics revealed that WPAs are strategically designated in regions with favorable hydrogeological conditions and high public water demand, emphasizing their critical role in safeguarding drinking water resources. Furthermore, the identification of eleven distinct WPA clusters, each representing characteristic combinations of hydrogeological, environmental, and socio-economic factors, facilitates the development of targeted management strategies that address specific characteristic-dependent vulnerabilities. These generalized typologies illustrate that certain WPAs are particularly susceptible to stressors such as drought and nitrate pollution due to their characteristics.\u003c/p\u003e\u003cp\u003eAdopting a more standardized approach to WPA designation and protection criteria could enhance comparability across federal states, ultimately strengthening long-term groundwater resilience and supporting equitable water resource management under changing environmental and socio-economic conditions. The new German regulation on drinking water catchment delineation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] implements the EU Drinking Water Directive [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which mandates risk-based and preventive approaches to water source protection. The presented typology and methodological approach for identifying vulnerability patterns in groundwater-dependent catchments provide a transferable framework that may support the implementation of similar assessments and management strategies in other European countries. Overall, the harmonized dataset and classification approach developed in this study offers valuable tools for anticipating groundwater risks and supporting adaptive protection strategies, both in Germany and beyond.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"311\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003eBaden-W\u0026uuml;rttemberg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003eBavaria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRASTIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003e\u003cu\u003eD\u003c/u\u003eepth to water table, net \u003cu\u003eR\u003c/u\u003eecharge, \u003cu\u003eA\u003c/u\u003equifer media, \u003cu\u003eS\u003c/u\u003eoil media, \u003cu\u003eT\u003c/u\u003eopography, \u003cu\u003eI\u003c/u\u003empact of vadose zone, hydraulic \u003cu\u003eC\u003c/u\u003eonductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003eEuropean Union\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003ePartitioning Around Medoids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWFD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003eWater Framework Directive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003eFederal Water Act\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWPA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 231px;\"\u003e\n \u003cp\u003eWater protection area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is available in the FreiDok repository, \u0026nbsp;DOI: 10.6094/UNIFR/263651, https://freidok.uni-freiburg.de/data/263651 [34].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the Environmental Agencies of the Federal States of Germany for the provision of WPA boundaries. We also gratefully acknowledge all institutions and authors who provided geodata and statistical information used to derive the various characteristics in the dataset, including the Federal Institute of Geosciences and Natural Resources (BGR), the Federal Agency for Cartography and Geodesy (BKG), the Th\u0026uuml;nen Institute, the Federal and State Statistical Offices, Eurostat, the European Environment Agency (EEA), and the various research groups and authors cited throughout the documentation. We further thank the members of the StressRes project team for their valuable support and constructive discussions throughout the development of this study, especially apl. Prof. Dr. Jens Lange\u0026nbsp;\u003cem\u003e(Chair of Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Germany)\u003c/em\u003e for his valuable input.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Federal Ministry of Research, Space and Technology (BMFTR) is funding the StressRes project within the LURCH funding measure as part of the federal research program on water \u0026ldquo;Wasser: N\u0026rdquo;. Wasser: N contributes to the BMFTR \u0026ldquo;Research for Sustainability\u0026rsquo; (FONA) Strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChair of Environmental Hydrological Systems, Faculty of Environment and Natural Resources, University of Freiburg, Germany\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKSz, CW, JH and Kst conceptualized the study, designed, and revised the methodology. Investigation and data curation was conducted by KSz. KSz conducted the formal analysis, visualization and wrote the original draft. All authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Kathrin Szillat ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKerstin Stahl is a Guest Editor of the Collection \u0026ldquo;Insights from the BMBF LURCH Initiative on Sustainable Groundwater Management\u0026rdquo;. All other authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaylor RG, Scanlon B, D\u0026ouml;ll P et al. (2013) Ground water and climate change. Nature Clim Change 3:322\u0026ndash;329. https://doi.org/10.1038/nclimate1744\u003c/li\u003e\n\u003cli\u003eEEA (2022) Europe\u0026apos;s groundwater: a key resource under pressure. Publications Office of the European Union. https://doi.org/10.2800/50592\u003c/li\u003e\n\u003cli\u003eUmweltbundesamt (2024) Grundwasser. https://www.umweltbundesamt.de/themen/wasser/grundwasser. Accessed 15 Jan 2025\u003c/li\u003e\n\u003cli\u003eEuropean Parliament and European Council (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy\u003c/li\u003e\n\u003cli\u003eEuropean Parliament and European Council (2006) Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration\u003c/li\u003e\n\u003cli\u003eEuropean Council (1991) Directive 91/676/EEC of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources\u003c/li\u003e\n\u003cli\u003eSchleyer R, Milde G, Milde K (1992) Wellhead Protection Zones in Germany: Delineation, Research and Management. Water \u0026amp; Environment J 6:303\u0026ndash;310. https://doi.org/10.1111/j.1747-6593.1992.tb00754.x\u003c/li\u003e\n\u003cli\u003eBMUKN (2009) Gesetz zur Ordnung des Wasserhaushalts (Wasserhaushaltsgesetz \u0026ndash; WHG) BGBl. I Seite 2585 vom 31.07.2009, das zuletzt durch Artikel 1 des Gesetzes vom 19. Juni 2020 (BGBl. I Seite 1408) ge\u0026auml;ndert worden ist\u003c/li\u003e\n\u003cli\u003eBMUKN (2023) Verordnung \u0026uuml;ber Einzugsgebiete von Entnahmestellen f\u0026uuml;r die Trinkwassergewinnung (Trinkwassereinzugsgebieteverordnung: TrinkwEGV ) BGBl. 2023 I Nr. 346 vom 11.12.2023\u003c/li\u003e\n\u003cli\u003eEuropean Parliament and European Council (2020) Directive (EU) 2020/2184 of the European Parliament and of the Council of 16 December 2020 on the quality of water intended for human consumption\u003c/li\u003e\n\u003cli\u003eBMU/ UBA (2017) Wasserwirtschaft in Deutschland: Grundlagen, Belastungen, Ma\u0026szlig;nahmen. https://www.umweltbundesamt.de/publikationen/wasserwirtschaft-in-deutschland-grundlagen. Accessed 02 May 2025\u003c/li\u003e\n\u003cli\u003eH\u0026ouml;lting B, Coldewey WG (eds) (2013) Hydrogeologie: Einf\u0026uuml;hrung in die Allgemeine und Angewandte Hydrogeologie, 8. Auflage. Springer Berlin Heidelberg, Berlin, Heidelberg\u003c/li\u003e\n\u003cli\u003eUmweltbundesamt (2008) Grundwasser in Deutschland. https://www.umweltbundesamt.de/sites/default/files/medien/publikation/long/3642.pdf. Accessed 02 Jul 2025\u003c/li\u003e\n\u003cli\u003eBMUKN (2003) Hydrologischer Atlas von Deutschland. 3. Lieferung. https://geoportal.bafg.de/dokumente/had/52GroundwaterYields.pdf. Accessed 08 May 2025\u003c/li\u003e\n\u003cli\u003eUmweltbundesamt (2022) Chemischer Zustand des Grundwassers. https://www.umweltbundesamt.de/themen/wasser/grundwasser/zustand-des-grundwassers/chemischer-zustand-des-grundwassers. Accessed 02 Jul 2025\u003c/li\u003e\n\u003cli\u003eWinter C, M\u0026uuml;ller S, Kattenborn T et al. (2025) Forest Dieback in Drinking Water Protection Areas\u0026mdash;A Hidden Threat to Water Quality. Earth\u0026apos;s Future 13. https://doi.org/10.1029/2025EF006078\u003c/li\u003e\n\u003cli\u003eStrebel O, Duynisveld W, B\u0026ouml;ttcher J (1989) Nitrate pollution of groundwater in western Europe. Agriculture, Ecosystems \u0026amp; Environment 26:189\u0026ndash;214. https://doi.org/10.1016/0167-8809(89)90013-3\u003c/li\u003e\n\u003cli\u003eFoster SSD (2000) Assessing and controlling the impacts of agriculture on groundwater : from Barley Barons to Beef Bans. QJEGH 33:263\u0026ndash;280. https://doi.org/10.1144/qjegh.33.4.263\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;hlke J-K (2002) Groundwater recharge and agricultural contamination. Hydrogeology Journal 10:153\u0026ndash;179. https://doi.org/10.1007/s10040-001-0183-3\u003c/li\u003e\n\u003cli\u003eBijay-Singh, Craswell E (2021) Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem. SN Appl Sci 3. https://doi.org/10.1007/s42452-021-04521-8\u003c/li\u003e\n\u003cli\u003eLapworth DJ, Baran N, Stuart ME et al. (2012) Emerging organic contaminants in groundwater: A review of sources, fate and occurrence. Environ Pollut 163:287\u0026ndash;303. https://doi.org/10.1016/j.envpol.2011.12.034\u003c/li\u003e\n\u003cli\u003eSiebert S, Burke J, Faures JM et al. (2010) Groundwater use for irrigation \u0026ndash; a global inventory. Hydrol Earth Syst Sci 14:1863\u0026ndash;1880. https://doi.org/10.5194/hess-14-1863-2010\u003c/li\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change (2023) Climate Change 2021 \u0026ndash; The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/9781009157896\u003c/li\u003e\n\u003cli\u003eReinecke R, M\u0026uuml;ller Schmied H, Trautmann T et al. (2021) Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study. Hydrol Earth Syst Sci 25:787\u0026ndash;810. https://doi.org/10.5194/hess-25-787-2021\u003c/li\u003e\n\u003cli\u003eWunsch A, Liesch T, Broda S (2022) Deep learning shows declining groundwater levels in Germany until 2100 due to climate change. Nat Commun 13:1221. https://doi.org/10.1038/s41467-022-28770-2\u003c/li\u003e\n\u003cli\u003eHellwig J. (2019) Grundwasserd\u0026uuml;rren in Deutschland von 1970 bis 2018. Korrespondenz Wasserwirtschaft 12:567\u0026ndash;572\u003c/li\u003e\n\u003cli\u003eLuo S, Tetzlaff D, Smith A et al. (2024) Long-term drought effects on landscape water storage and recovery under contrasting landuses. Journal of Hydrology 636:131339. https://doi.org/10.1016/j.jhydrol.2024.131339\u003c/li\u003e\n\u003cli\u003eEbeling P, Musolff A, Kumar R et al. (2024) Groundwater head responses to droughts across Germany. https://doi.org/10.5194/egusphere-2024-2761\u003c/li\u003e\n\u003cli\u003eBlauhut V, Stahl K, Falasca G (2020) D\u0026uuml;rre und die \u0026ouml;ffentliche Wasserversorgung in Baden-W\u0026uuml;rttemberg: Folgen, Umgang und Wahrnehmung. Wasserwirtsch 110:31\u0026ndash;36. https://doi.org/10.1007/s35147-020-0744-9\u003c/li\u003e\n\u003cli\u003eFrommen T, Moss T (2021) Pasts and Presents of Urban Socio-Hydrogeology: Groundwater Levels in Berlin, 1870\u0026minus;2020. Humboldt-Universit\u0026auml;t zu Berlin\u003c/li\u003e\n\u003cli\u003eBecken S. (2014) Water equity \u0026ndash; Contrasting tourism water use with that of the local community. Water Resources and Industry:9\u0026ndash;22. https://doi.org/10.1016/j.wri.2014.09.002.\u003c/li\u003e\n\u003cli\u003eAller L, Lehr JH, Petty R et al. (1987) Drastic: A Standardized System to Evaluate Groundwater Pollution Potential using Hydrogeologic Setting. Journal Geological Society of India 29:23\u0026ndash;37. https://doi.org/10.17491/jgsi/1987/290112\u003c/li\u003e\n\u003cli\u003eSinreich M. (2009) Konzept der Vulnerabilit\u0026auml;t im Grundwasserschutz: Anwendung auf die Verh\u0026auml;ltnisse der Schweiz. Gas Wasser Abwasser gwa:109\u0026ndash;117\u003c/li\u003e\n\u003cli\u003eSzillat K, Winter C, Hellwig J et al. (2025) Water protection areas (WPAs) in Germany: dataset. Albert-Ludwigs-Universit\u0026auml;t Freiburg\u003c/li\u003e\n\u003cli\u003eBMUKN (2024) Trinkwasserschutzgebiete: Einf\u0026uuml;hrung und Situation. https://www.bundesumweltministerium.de/themen/wasser-und-binnengewaesser/trinkwasser/trinkwasser-trinkwasserschutzgebiete. Accessed 03 Jul 2025\u003c/li\u003e\n\u003cli\u003eKaufman L, Rousseuw PJ (1991) Finding Groups in Data: An Introduction to Cluster Analysis. Biometrics 47:788. https://doi.org/10.2307/2532178\u003c/li\u003e\n\u003cli\u003eGower JC (1971) A General Coefficient of Similarity and Some of Its Properties. Biometrics 27:857. https://doi.org/10.2307/2528823\u003c/li\u003e\n\u003cli\u003eMaechler M., Rousseeuw P., Struyf A., Hubert M., Hornik K., Studer M., Roudier P., Gonzalez J., Kozlowski K., Schubert E., Murphy K. (2021) cluster: Cluster Analysis Basics and Extensions: R package version 2.1.2\u003c/li\u003e\n\u003cli\u003eRousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20:53\u0026ndash;65. https://doi.org/10.1016/0377-0427(87)90125-7\u003c/li\u003e\n\u003cli\u003eHenning C. (2024) fpc: Flexible Procedures for Clustering: R package version 2.2-13\u003c/li\u003e\n\u003cli\u003eEEA (2018) European waters: Assessment of status and pressures 2018. EEA Report No 7. https://doi.org/10.2800/303664\u003c/li\u003e\n\u003cli\u003eWakida FT, Lerner DN (2005) Non-agricultural sources of groundwater nitrate: a review and case study. Water Res 39:3\u0026ndash;16. https://doi.org/10.1016/j.watres.2004.07.026\u003c/li\u003e\n\u003cli\u003eTomer MD, Burkart MR (2003) Long-term effects of nitrogen fertilizer use on ground water nitrate in two small watersheds. J Environ Qual 32:2158\u0026ndash;2171. https://doi.org/10.2134/jeq2003.2158\u003c/li\u003e\n\u003cli\u003eWinter C, Lutz SR, Musolff A et al. (2021) Disentangling the Impact of Catchment Heterogeneity on Nitrate Export Dynamics From Event to Long‐Term Time Scales. Water Resources Research 57. https://doi.org/10.1029/2020WR027992\u003c/li\u003e\n\u003cli\u003eBfG (2024) Water body for WFD (Wasserk\u0026ouml;rper-DE). https://geoportal.bafg.de/inspire/download/AM/waterBodyForWFD/datasetfeed.xml. Accessed 14 Nov 2024\u003c/li\u003e\n\u003cli\u003eTodd D. K., Mays L. W. (2004) Groundwater Hydrology, 3rd edn. John Wiley \u0026amp; Sons, 2004\u003c/li\u003e\n\u003cli\u003eDieser M, Zieseni\u0026szlig; S, Mielenz H et al. (2023) Nitrate leaching potential from arable land in Germany: Identifying most relevant factors. J Environ Manage 345:118664. https://doi.org/10.1016/j.jenvman.2023.118664\u003c/li\u003e\n\u003cli\u003eBender, S., Butts, M., Hagemann, S., Smith, M., Vereecken, H. \u0026amp; Wendland, F (2017) Der Einfluss des Klimawandels auf die terrestrischen Wassersysteme in Deutschland. Eine Analyse ausgesuchter Studien der Jahre 2009 bis 2013. Report 29\u003c/li\u003e\n\u003cli\u003eFoster SSD, Chilton PJ (2003) Groundwater: the processes and global significance of aquifer degradation. Philos Trans R Soc Lond B Biol Sci 358:1957\u0026ndash;1972. https://doi.org/10.1098/rstb.2003.1380\u003c/li\u003e\n\u003cli\u003eWinter C, Nguyen TV, Musolff A et al. (2023) Droughts can reduce the nitrogen retention capacity of catchments. Hydrol Earth Syst Sci 27:303\u0026ndash;318. https://doi.org/10.5194/hess-27-303-2023\u003c/li\u003e\n\u003cli\u003eKundzewicz, Z. W., D\u0026ouml;ll, P. (2009) Will groundwater ease freshwater stress under climate change? Hydrological Sciences Journal 54:665\u0026ndash;675. https://doi.org/10.1623/hysj.54.4.665\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-sciences-europe","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eseu","sideBox":"Learn more about [Environmental Sciences Europe](http://enveurope.springeropen.com)","snPcode":"12302","submissionUrl":"https://submission.nature.com/new-submission/12302/3","title":"Environmental Sciences Europe","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Water protection areas, drinking water, groundwater, vulnerability, cluster analysis","lastPublishedDoi":"10.21203/rs.3.rs-7084780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7084780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater supplies up to 65% of drinking water in the European Union and approximately 70% in Germany, making it essential to preserve both its quality and quantity. However, climate change, land use pressures, and socio-economic developments increasingly threaten groundwater resources, posing significant challenges for current and future water supply. To safeguard drinking water sources, water protection areas (WPAs) are designated to mitigate contamination risks. This study introduces the first harmonized, country-wide dataset of all 11,406 designated German WPAs, integrating hydrogeological, land cover, and socio-economic characteristics, to assess groundwater vulnerability. We found these WPAs to cover the full range of the countries hydrogeological characteristics while they have more forest cover fractions than the entire country. A cluster analysis with key characteristics classified all WPAs into 11 distinct characteristic typologies. Comparing the clusters\u0026rsquo; groundwater chemical status as per EU Water Framework Directive mapping as an indicator of groundwater vulnerability shows that a complex interplay of hydrogeological conditions, land use patterns, and socio-economic pressures determines the differences. Our study provides a data-driven basis to support sustainable groundwater protection and drinking water resource management across Germany. It stands as exemplary for how to determine a reduced set of WPA types and situations for which to design specific measures. The results also underscore the importance of harmonized WPA designation practices to improve comparability and ensure equitable protection standards across federal states. As the current German drinking water regulation operationalizes the EU Drinking Water Directive, the developed typology may also inform risk-based groundwater protection efforts in other EU member states.\u003c/p\u003e","manuscriptTitle":"Comprehensive Mapping and Classification of Germany’s Drinking Water Protection Areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 11:08:00","doi":"10.21203/rs.3.rs-7084780/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-26T14:30:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T17:29:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T13:40:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T15:00:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T11:47:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317624930428931294013796341532999296371","date":"2025-07-19T18:05:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314724201686033616277748808092165811148","date":"2025-07-16T12:36:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287312619172026460805421885754497551347","date":"2025-07-15T09:03:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T13:28:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281374403779430027573341653976525845352","date":"2025-07-14T11:49:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302038508775883347041790414724593639789","date":"2025-07-14T10:42:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270062944638729225356396850977013202779","date":"2025-07-14T08:33:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122445517540927991569691298415887125430","date":"2025-07-14T08:30:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T08:09:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T08:06:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T13:49:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Sciences Europe","date":"2025-07-09T14:22:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-sciences-europe","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eseu","sideBox":"Learn more about [Environmental Sciences Europe](http://enveurope.springeropen.com)","snPcode":"12302","submissionUrl":"https://submission.nature.com/new-submission/12302/3","title":"Environmental Sciences Europe","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ddab428-d940-4184-a82a-0ee462c7b8ce","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:22:11+00:00","versionOfRecord":{"articleIdentity":"rs-7084780","link":"https://doi.org/10.1186/s12302-025-01233-3","journal":{"identity":"environmental-sciences-europe","isVorOnly":false,"title":"Environmental Sciences Europe"},"publishedOn":"2025-10-22 16:16:07","publishedOnDateReadable":"October 22nd, 2025"},"versionCreatedAt":"2025-07-18 11:08:00","video":"","vorDoi":"10.1186/s12302-025-01233-3","vorDoiUrl":"https://doi.org/10.1186/s12302-025-01233-3","workflowStages":[]},"version":"v1","identity":"rs-7084780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7084780","identity":"rs-7084780","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}