Groundwater temperature anomalies in central Europe (2024)

Shallow GWT data from 44 205 wells in ten countries in central Europe are the basis for this study. GWT data originate from monitoring networks and are provided by local authorities, environmental agencies or hydrogeological services (table S1). While 11% of the wells are equipped with GWT data loggers, most wells were monitored manually as part of chemical analyses. The highest well densities can be found in France, south-west Germany and Belgium, whereas only few sampled wells are located in Denmark and Slovakia (figure 1(a)). To standardise the data set and to eliminate seasonal GWT variations, data from all wells are averaged over the time span from 2003 to 2017 following the procedure given in Benz et al (2017a). In their approach, each temperature measurement is represented by a vector of a unit length of 1 and directed towards the month of measurement for a clocklike segmentation of the months. The output is the mean of all measurement vectors for one location, known as seasonal radius r, which is equal to zero for uniformly distributed measured data, and equal to one if they were collected in the same month. Following the recommendation by Benz et al (2017a), all wells with a depth ≤60m and r≤0.25, which indicates a bias-free annual mean, are considered for the further analysis (figure S1).

For each well the AHI is defined as the difference between GWT at the well location and the median of surrounding rural background GWTs (GWT_r) [42] (equation (1)). Based on the definition by Benz et al [42], AHI is a measure of the anthropogenic influence on GWTs. Yet, in this study AHI also detects thermal disturbances caused by natural sources, as we apply it to wells in urban as well as rural areas

The input parameters to determine the rural background temperature are the bias-free GWT, geographical elevation and night-time light intensity. Elevation data are extracted from the Global 30 Arc-Second Elevation (GTOPO30) model and downloaded with Google Earth Engine [43]. Night-time lights from Version 4 of the DMSP-OLS Night-time Lights Time Series, processed by NOAA, were also extracted with Google Earth Engine. Since the night light data are only available up to January 2014, a 10 year average (01/2004 to 12/2013) was chosen. Night-time light intensity is expressed as a digital number (DN) running from 0 to 63 indicating an increasing urban activity [44]. All wells with a night-time light of DN<15, an elevation ±90m and within a distance of 47km to the analysed location are considered for the calculation of rural background temperature [42]. To ensure meaningful statistics and to avoid an impact by outliers AHI is only determined, if at least five wells fulfil these criteria.

Based on the bias-free annual mean GWT (12 151 wells) an AHI could be evaluated for 10 656 wells (figure 1(b)). AHI is uniformly distributed over all known measurement depths, proving its independence of depth (figure S4). Its distribution is given in figures S5–S7. Figure 1(c) displays the wells within the top three percentiles, which represent 318 GWT anomalies (AHI_max) in total. 97% of these hot spots are located in Austria, France and Germany, which have the highest AHI well density overall. In Belgium, hot spots exist only in agricultural areas. Slovakia, Switzerland and Luxembourg have only one hot spot in the class artificial and natural, respectively. Czech Republic, Denmark and Netherlands do not show any (figure S8). The hot springs in Austria and Southwest Germany, as well as accumulations of hot spots in the Upper Rhine Graben (URG) and Eastern Germany clearly stand out (figure 1(c)). The URG is a densely urbanised region with multiple industrial areas, while East Germany is widely known for its former coal and ore mining. The minimum values of AHI_max of the classes natural, agriculture and artificial are 2.3K, 1.7K and 3.9K respectively (figure S9).

To illustrate the link between land cover and temperature anomalies, the Level3 CLC classes for wells with an AHI are compared with the CLC classes of the AHI_max wells (figure 2). A shift in the percentages of wells in each land cover class between these two sets is evident. Hence, it becomes apparent for which land cover temperature anomalies appear more frequently. For wells located on natural land cover, the percentage of wells in coniferous and mixed forests decreases from AHI to AHI_max, whereas the percentage of wells associated with transitional woodland-shrub and natural grasslands triples. The latter are therefore more likely to contain GWT anomalies. One explanation is that soil temperatures and/or GWT beneath grass or farming land are typically higher than those beneath a forest, due to differences in incident solar radiation and evapotranspiration [46, 47].

In contrast, the shift from non-irrigated arable land to pastures in the agricultural class cannot be exclusively explained by physical effects due to vegetation or shielding foliage. According to Herb et al [48], ground surface temperatures (GSTs) beneath grass and land with different plant canopies are similar. A possible explanation for the anomalies is deforestation, which is known to cause subsurface temperature anomalies [49–52], that are detectable at depths of 20–100m [53]. Regarding the temporal and horizontal extent of such temperature anomalies, a lateral spread of several hundred metres over 100years can occur [54]. Nevertheless, one has to notice that AHIs>3K under both natural and agricultural surfaces result from hot springs or local anthropogenic sources, such as contamination caused by landfills, mining or waste water treatment plants.

In the artificial class, the share of discontinuous urban fabric shifts towards industrial areas and continuous urban fabric. Multiple previous studies on SUHIs indicated local hot spots within dense urban areas and industrial sites, which is also evident in our current findings here. Epting et al [41], Menberg et al [23] and Ferguson and Woodbury [55] noticed a strong correlation between the highest underground temperatures and the density of buildings, in particular buildings with heated basem*nts. For the city centre of Cologne and Winnipeg, Zhu et al [56] found an increase in GWT of up to 5K, which compares closely with the median of the AHI_max of artificial surfaces in this study (figure S9). Epting et al [57] observed an increase of GWT up to 6 and 8K in dense industrial and commercial areas of Basel. Single point heat sources in industrial areas were also mentioned by Ferguson and Woodbury [58], Bucci et al [40] and Menberg et al [23].

The outcome of the detailed visual inspection and examination of the surroundings of the 45 artificial AHI_max wells are six LUCs with 20 detailed subclasses (figures 3 and S10). With a mean AHI of 7K, the LUC 'factory' has by far the largest impact on GWT, whereas the mean AHI of 'industry parks' is the smallest with on average 5K. With regard to the share of each utilisation class, most of the hot spots are within 'city' (33%), followed by 'factory' and 'industry park' with 27% and 24%, respectively. In the following, possible heat sources within specific LUCs are discussed.

In the LUC 'industry park', different industrial branches such as plastic, paper, electronic, chemical or machinery construction companies are mixed with office buildings and supermarkets. Here, high GWTs can originate from multiple heat sources such as basem*nts with heating installations, sealed surfaces or injection of cooling water. These interfere with each other and can add up so that the distinct heat source of the groundwater anomaly is difficult to identify. Bucci et al [40] also referred to heat fluxes from buildings into the ground originating from industrial exothermic processes inside the buildings as cause for high GWT above 17 °C in an industrial district close to Turin city.

In the LUC 'waste', one well is close to a landfill with an enclosed waste recycling plant, while the other one is on the premises of a waste disposal facility with detention basins and compensating reservoir. Benz et al [36] also identified a wastewater treatment plant in Osaka, Japan, as a local heat source for increased GWT.

Despite a high thematic accuracy of over 85%, wrong classifications of CLC classes can also occur [59]. Here, two wells in the artificial class are located on farmland and a fruit plantation, and thus actually fall into the agricultural class and the LUC 'farming'.

The LUC 'automotive' refers to wells located at a car workshop, a car race track and car dealer. The common characteristic of the automotive class are sealed surfaces and possible contamination with petroleum hydrocarbons [60].

The high mean AHI and standard deviation of the LUC 'city' stand out and reflect the significant, yet variable impact of the different subclasses and of the corresponding heat sources. High GWT in city centres are due to the interference and superposition of heat input by sealed surfaces and underground structures, as already described in several SUHI studies [23, 36, 38, 40, 41, 56, 61–64]. A conspicuous cluster of wells showing increased GWT were observed close to underground car parks and therefore, classified as separate subclasses. The fringe subclass contains less dense urban areas. A hot spring in Austria, having the highest AHI (27.0 K) of all artificial wells, falls within this subclass and causes the overall high AHI and standard deviation of LUC 'city'. Disregarding this natural temperature anomaly leads to a mean 'city' AHI of 5.0±1.7K.

The LUC 'factory' comprises wells situated on the property of a detached, single factory that is not part of an industrial park. All seven wells in the subclass power plant are at the same location in France, whereas the remaining subclasses are only represented by one well location each. GWT anomalies with temperatures over 30 °C in the vicinity of power plants were also reported by Menberg et al [23].

For 16 out of the 45 hot spots of the class artificial, we were able to identify potential heat sources summarised into seven classes (table 1). It is important to note that other underground heat sources such as industrial cooling, geothermal applications or sewage pipes are likely [22, 39, 40, 61], but could not be detected with the here proposed method relying on satellite imagery and local knowledge. The highest temperature anomaly is associated with a hot spring in Austria. All remaining temperature anomalies and heat source classes refer to anthropogenic activities. Based on their spatial extent and impact magnitude they can be divided into two groups. The first group consists of heat sources that are scare, but have a large extent, such as contaminations and mining operations. Basem*nts, district heating (DH) networks, swimming pools and underground car parks are the second group. They are rather local sources, but are more frequent in urban environments and therefore also have an extensive impact on GWTs.

The first group, containing the heat sources contamination and mining, exhibits the highest GWT and AHI of all identified anthropogenic heat sources with temperatures of up to 8K warmer than the rural surrounding. The three wells in the class contamination refer to two wells in LUC 'waste', and one well is at a car race track (LUC 'automotive'). Exothermic chemical and biological degradation processes in landfills or contaminated sites can result in higher GWTs [23, 40]. Krümpelbeck [65] reported temperatures up to 60 °C in a landfill. Similar to landfills, exothermic biogeochemical weathering processes, called acid mine drainage (AMD), cause high temperatures in mines and their remote surroundings [66]. Reports by Felix et al [67] and LfULG [68] confirm AMD as heat source of one particular well in the LUC subclass fringe, situated in a hard coal mining district in eastern Germany. Furthermore, they described increased GWT in remote observation wells due to coal seam fires reaching temperatures up to 90 °C within the pithead stocks. The high GWT and AHI of the well in the subclass 'farmland', located in an area in eastern Germany famous for ore mining, could also be associated with AMD.

The second group includes the small scale and local heat sources basem*nts, DH networks, swimming pool and underground car parks. The well linked to warming from basem*nts, is 2m away from a shopping mall in Karlsruhe, Germany. While the AHI of this well is lower than the ones associated with contamination and mining, almost every building in a city has a basem*nt, which typically also hosts the heating installation of the building. Epting and Huggenberger [21], Benz et al [61] and Epting et al [69] also emphasised the large impact of basem*nts on GWT and due to their high heat flux and dominant area, named them as the dominant drivers of SUHIs.

Correlating local DH network plans with well positions, we could classify the heat source of three wells of the subclass city centre as DH. In DH networks, water with temperatures up to 160 °C circulates under high pressure through pipes under many urban areas [70]. Depending on season and type of insulation, heat losses up to 20% occur [71]. Benz et al [61] pointed out that DH pipes are a prominent source of anthropogenic heat fluxes. The time series in figure S11 also clearly demonstrate the impact of DH heat fluxes on a groundwater observation well 3.5m away from the pipe. Regarding the mean GWT at 6m depth, representing the middle of the aquifer, AHI is as high as 8K. Consequently, the heat input by DH pipes, especially in case of a local leakage is not negligible and should be considered more carefully.

Water with lower temperatures than in DH pipes is also released into aquifers by leaking swimming pools. Cracks in the pool or loose tiles can cause leakage rates of 70 m³ d⁻¹ [72]. Another case study about a municipal swimming pool in Montreal reports a leakage rate of 350–700m³ per day into the underlying aquifer [73]. Even if the swimming pool is watertight, the basin releases heat to the subsurface. One of the wells in LUC 'city' is located 4m away from a municipal swimming pool in Germany and the GWT of 16 °C is likely to be influenced by the heat release of the pool. Menberg et al [23] even noticed a GWT of 20 °C for an observation well next to a swimming pool in Frankfurt, Germany. At another municipal swimming pool in Germany, temperatures of 25 °C beneath the swimming pool and increased GWT of 1–3K in the downgradient were measured [74].

In previous SUHI studies, underground car parks were intensively discussed as sources for GWT anomalies [21, 22, 56]. This is in accordance with our findings that reveal underground car parks as the most frequent heat source of temperature anomalies in the class artificial (table 1). Warm, exhausted fumes and a poor ventilation lead to heat accumulation, so air temperature strongly increases in underground car parks. Iskander et al [75] recorded temperatures above 25 °C in summer at the lowest level of an underground car park. We also recorded air temperatures of up to 30 °C in an underground car park and correspondingly high GWT of almost 20 °C in an observation well within this car park (figure S12). The correlation between these two temperatures is obvious and therefore the heat input of underground car parks into the aquifer is evident.