Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes

Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes | 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 Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes Maria Magdalena Warter, Chris Soulsby, Kati Vierikko, Silvia Martin Muñoz, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7010782/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 7 You are reading this latest preprint version Abstract Blue infrastructure is increasingly implemented in cities as a form of water-related nature-based solutions (aquaNBS), to address ecological and hydrological challenges that threaten urban biodiversity and water security. Nevertheless, the combination of impacts from climate change, multi-faceted consequences of past management, current urban expansion, population growth, and overall urban ecosystem complexity makes it challenging to evaluate the hydrological function of these aquaNBS, and their sensitivity to hydroclimatic and other environmental changes. To enhance adaptation capacity of aquaNBS towards multiple urban and climatic stressors, it is crucial to understand the main hydrologic processes, as well as hydroclimate influences, that determine the functioning of aquaNBS. Stable water isotopes have proven to be a valuable tool in providing integrated understanding of hydrologic functioning over extended spatial scales. While higher frequency isotope data is usually most informative, even limited isotopic data can aid hydrological characterization. We conducted seasonal sampling over the period of one year in 2023/24, across a major hydroclimate gradient across four European cities (Poznań, Berlin, Antwerp, Lisbon). The goal was to identify the dominant physical processes (in terms of water sources, dominant flow paths, and age proxies) linked to the main hydroclimate factors along a continental climate gradient. Comparative analyses of local stable water isotope signatures from different aquaNBS types (i.e. streams, ponds) revealed the strong influence of local hydroclimate, as well as varying water source contributions and mixing processes. The application of transit time proxies, such as tracer damping and young water fraction estimations, suggests ponds to be more sensitive to hydroclimate changes, as evidenced by the strong seasonality in evaporative enrichment and high fractions of young water contributions. In contrast, most streams indicated greater mixing of water sources and longer transit times, suggesting greater resilience to hydroclimate variability. In addition, a comparison between seasonally sampled data and monthly sampling for selected locations in Berlin showed that even relatively coarse temporal data collection, but with more extensive spatial coverage, can be sufficient and still insightful for broader hydrologic characterizations of aquaNBS at larger scales. urban water management transit times urbanization climate adaptation water ages isotope hydrology blue infrastructure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION The multifaceted impacts of climate change and urban development increasingly challenge urban water management. Today, cities have evolved into highly engineered environments, where the natural water cycle has been profoundly altered (Gessner et al., 2014 ; Li et al., 2020 ; Walsh et al., 2005 ). Extensive impervious surfaces on roads and buildings limit infiltration and accelerate runoff, while widespread subsurface storm drain networks can disconnect surface water from groundwater systems, conveying runoff directly into streams, lakes or ponds with little or no attenuation or treatment (Bonneau et al., 2018 ; Burns et al., 2012 ; Ress & James, 2020 ). These hydrologic alterations have generally led to "flashier" urban flow regimes, with larger storm runoff volumes and higher, more frequent peak flow responses to extreme precipitation events. At the same time, the capacity of urban landscapes to store, filter, and slowly release water is substantially reduced, thereby increasing flood risk and reducing recharge (Golden & Hoghooghi, 2018 ; Oswald et al., 2023 ; Walsh et al., 2012 ; Yang et al., 2011 ). Temperature extremes and increased evapotranspiration from urban heat island effects further contribute to water stress, especially during dry periods, leading to more intermittent rivers and ephemeral streams, further reduced groundwater recharge and vegetation water stress (Kuhlemann et al., 2020 ; Ring et al., 2024 ; Warter, Tetzlaff, Marx, et al., 2024a). Given the complexity of the urban water cycle and its different components of engineered and natural hydrology, urban freshwater systems are experiencing chronic ecological, chemical and hydrological stresses (Marx et al., 2023 ; Numberger et al., 2022 ; Richardson & Soloviev, 2021 ). These challenges to sustainable urban water management call for more adaptive approaches beside traditional grey infrastructure of piped storm drains, to ameliorate the negative impacts of urbanization and climate change on urban freshwater resources and aiding the transition towards more resilient urban environments (Bush & Doyon, 2019 ; Davis & Naumann, 2017 ; Krueger et al., 2020 ; Wild et al., 2024 ). Blue infrastructure, such as urban wetlands, ponds, or restored streams and floodplains, are increasingly utilized as water-related (or aquatic) nature-based solutions (aquaNBS), substituting traditional grey infrastructure. By harnessing the multifunctionality of blue infrastructure in urban landscapes, a wide range of environmental and societal challenges related to climate change, human health and well-being, biodiversity and water security can be addressed (Chowdhury et al., 2025 ; Kabisch et al., 2016 ; Pinho et al., 2023 ; van Rees et al., 2023 ). The recognition of the value of blue infrastructure as aquaNBS for enhancing urban ecosystem services and supporting sustainable urban water management has led to widespread ecological restoration efforts of degraded urban freshwater ecosystems (Everard & Moggridge, 2012 ; Hack & Schröter, 2020 ; Lammers et al., 2020 ). By restoring and re-establishing near-natural hydrological functioning, aquaNBS are expected to slow runoff, enhance infiltration and storage, reconnect surface and subsurface flows, and help to reduce pollutant loads (Hack & Schröter, 2020 ; Williams & Filoso, 2023 ). The effectiveness of aquaNBS is governed by local hydrological processes (e.g. water source partitioning, surface and subsurface flow paths, groundwater-surface water interactions, and water residences) and their linkage with hydroclimate and the urban matrix. As aquaNBS can comprise a broad range of catchment interventions, their design and implementation require not only an understanding of the underlying hydrological processes and boundary conditions, but also of their potential evolution under variable and changing climatic conditions. As such, the lack of empirical evidence of the role of hydrological processes in the function and effectiveness of different types of aquaNBS is critical for their successful long-term implementation and management (Lalonde et al., 2024 ; Pinho et al., 2023 ). This poses the risk that aquaNBS may not produce the intended benefits or lead to unintended ecosystem responses with undesirable consequences for water quality, human health and biodiversity (Lalonde et al., 2024 ). Considering the speed of global change, the resilience and ability of aquaNBS to evolve and withstand changing climate conditions not only depends on structural design, but also on their ability to cope with short-term climatic perturbations (e.g., heavy rainfall, droughts) as well as long-term shifts in hydroclimate regimes. Therefore, there is a clear need to better understand how urban water sources and hydroclimate conditions systems affect aquaNBS functioning. Characterizing the dominant hydrological processes in built-up areas remains one of the key challenges of urban hydrological research (Oswald et al., 2023 ). In particular, inter-comparisons of cities across different climate and geographic gradients are important, but difficult, as high-frequency sampling over extended spatial scales is often logistically demanding and impractical. As naturally occurring tracers of the water cycle, the stable water isotopes of δ 18 O and δ 2 H can be an important integrating tool to differentiate contrasting water sources across catchment, regional and global scales and characterize fundamental hydrological processes and water fluxes (Ehleringer et al., 2016 ; Jefferson et al., 2015 ; Tetzlaff et al., 2015 ). Isotopic signatures of different water sources (i.e. precipitation, groundwater, runoff) allow to investigate connectivity between landscapes and freshwater ecosystems, as well as climate-water-ecosystem interactions between natural and anthropogenic systems (Kendall & McDonnell, 1998 ; Kirchner, 2016 ; Soulsby et al., 2015 ). Regional assessments of urban streamflow sources (Kuhlemann et al., 2021 ; Marx et al., 2021 ), groundwater contributions (Vystavna et al., 2019 ), urban water supply dynamics (Bhuiyan et al., 2023 ; Jameel et al., 2018 ), and the impacts of urbanization and climate stress in anthropogenically impacted catchments (Kuhlemann et al., 2020 ; Soulsby et al., 2014 ; Warter, Tetzlaff, Marx, et al., 2024a) have highlighted the value of spatially distributed sampling in cities. In addition, by using relatively simple transit time proxies, such as young water fractions (e.g. the proportion of a water body that is less than ~ 3 months old) mean transit times can be assessed and used to contextualize local hydroclimate, landscape controls and water sources (Hrachowitz et al., 2010 ; Kirchner, 2016 ; Soulsby et al., 2015 ; Von Freyberg, Allen, et al., 2018 ). Especially in urban watersheds, understanding water transit times is important for assessing dominant streamflow generation processes and evaluate potential sensitivities to hydroclimate changes (Morales & Oswald, 2020 ; Warter, Tetzlaff, Marx, et al., 2024b). Clearly, leveraging existing urban blue infrastructures for aquaNBS requires an understanding of local water source dynamics and key hydroclimate drivers. Therefore, we used stable water isotopes as an integrated lens through which to understand the role of hydrology in urban aquaNBS and provide an urgently needed evidence basis of the key hydroclimate and hydrological processes that influence their hydrological functioning. With a geographical focus along a strong hydroclimatic gradient of four European Cities – Poznań (Poland), Berlin (Germany), Antwerp (Belgium) and Lisbon (Portugal), we undertook low-frequency sampling of stable water isotopes in different types of aquaNBS (streams and ponds) at in total 48 locations between February 2023 and March 2024. Concurrently, we used transit time proxies such as young water fractions, to assess flow pathways and transit time processes across urban aquaNBS to evaluate sampling efficiency and suitability of such low-frequency sampling for broader hydrological characterization. More specifically we investigate the following objectives: i) Identify the main water sources and flow paths in urban aquaNBS across a hydroclimate gradient in Europe; ii) Use low-frequency water stable isotope data to infer differences in water residence times across contrasting European cities through simple travel time proxies such as young water fractions; and iii) Assess the influence of hydroclimate variability on hydrological processes in different aquaNBS. 2. METHODOLOGY 2.1. Study sites Four urban case study cities were selected – Poznań, Berlin, Antwerp and Lisbon – set in different hydroclimate contexts across Europe, ranging from continental (Berlin, Poznań), to oceanic (Antwerp) and Mediterranean (Lisbon) climate. Site selection in each city was done using a stratified random sampling approach (12 locations per city), with the goal to have a wide geographic representation of different types of aquaNBS within each urbanized setting. We aimed for diverse urban blue infrastructural features that include artificial and natural pond and stream-based aquaNBS (Fig. 1 ), with different sizes (pond water volume), permanence (perennial, intermittent) and stream orders (2nd, 3rd order). Hydroclimate and land use characteristics of the study cities are summarized in Table 1 (see Table S2 in the Supplementary Material for more information on individual sample sites or Szoszkiewicz et al., 2025). Table 1 Site characteristics of the study locations including mean annual precipitation (in mm/year, long-term average), mean elevation (in meters above sea level (m.a.s.l.), mean annual temperature (T, in °C), mean annual potential evapotranspiration (PET, in mm/yr), the number of samples sites in each city as well as the dominant land use (in %) in each city (Source: Copernicus CORINE Land Cover 2018, Europe, 6-yearly). City Annual precipitation (mm/yr) Elevation (masl) Mean T (°C) Mean PET (mm/yr) Nr. Sampled Sites Dominant land use (%) Arable ° Green * Urban + Poznań 539 85 9.4 450–500 Stream: 6 Pond: 6 25 21 45 Berlin 577 47 12.0 700 Stream: 8 Pond: 4 3 30 55 Antwerp 830 9 11.0 500–600 Streams: na Ponds: 12 8 11 60 Lisbon 774 51 21.0 1100 Stream: 7 Pond: 5 5 26 42 *Green includes urban green spaces, grassland and forests (mixed, broadleaf, coniferous). + Urban includes all urban fabric, roads, industrial units, airports and port areas. ° Arable includes all irrigated and non-irrigated agricultural land and pastures. Briefly, the city of Poznań is located in the lowland central-western part of Poland, in the Warta river basin. The city is rich in small and large urban green spaces and forests. Poznań’s climate is considered a temperate oceanic climate, characterized by cold winters and warmer summers, with mean annual precipitation of around 539 mm/yr (1991–2020 average) and a mean annual PET between 450–500 mm/yr (1991–2020 average) (Bochenek et al., 2024 ). In Poznań, a mixture of small 2nd or 3rd order perennial and intermittent streams was sampled, which primarily provide a means of flash urban flood regulation. Ponds in the city ranged in size from 750 m 2 to 16,000 m 2 , mainly serving as rain water retention ponds from urban runoff. The city of Berlin is located in the NE of Germany. The climate is continental, with a bordering humid oceanic climate. The central area of Berlin is highly urbanized, with large areas of contiguous urban green space and forest (up to 30%). The city has an extensive network of blue infrastructure with first and second order streams and a large number of small surface water bodies (around 700), ranging from small ponds to larger lakes. Annual precipitation is approximately 577 mm/yr (1981–2020 average) (German Weather Service, DWD, 2023 ), distributed throughout the year as frequent, low-intensity frontal winter rains and infrequent heavy convective summer storms. Evapotranspiration often exceeds annual precipitation inputs (PET ⁓700 mm/yr). A mixture of perennial and intermittent 2nd -order streams and artificial medium size ponds (12,000–40,000 m 2 ) were sampled. Each pond performs slightly different functions – from pure rainwater retention to semi-natural swimming ponds and wetland habitats, thus representing a variety of blue urban infrastructure. Due to its proximity to the Atlantic, the city of Antwerp has a strong oceanic climate, characterized by cool winters and warm summers, and frequent, though light, precipitation throughout the year. Annual precipitation is around 830mm/yr, while the mean annual PET is 500–600 mm/yr. The city has a dense network of artificial channels and ponds to support water retention and urban storm runoff capture. As a result, in Antwerp, only rainwater retention ponds were sampled, ranging in size from 153 m 2 to 4.281 m 2 . Small to medium rainwater retention systems are extensively used throughout the city and embedded in the urban matrix due to water regulation laws requiring new developments to retain and infiltrate runoff water on-site. Finally, Lisbon represents a typical Mediterranean climate, where most rainfall occurs in winter and spring, whereas summers are usually hot and dry. Mean annual precipitation is around 770 mm/yr over an average of 78 rain days. Similar to Berlin, potential evapotranspiration vastly exceeds annual precipitation, with an average PET of > 1000mm/yr. The city of Lisbon is highly urbanized, though extended urban green spaces and forested areas (up to 40%) characterize the city. We sampled a mixture of 3rd -order perennial and intermittent streams and small ponds throughout the cities of Lisbon and Almada. Most of the streams were within forested areas, primarily supporting flash flood regulation. Ponds varied in size (280 m 2 to 8,000 m 2 ) and function, serving as biodiversity hotspots and recreational sites as well as supporting flash flood management. 2.2. Sampling strategy Seasonal grab sampling was undertaken in each city between February 2023 and March 2024. Although samples were not always collected in the same month, they roughly correspond to a seasonal schedule (see Table S1 in Supplementary Material). This was considered the most efficient sampling strategy feasible as part of such a larger geographical assessment over a short timescale. In Berlin, additional monthly sampling was conducted simultaneously at all locations. Samples were subsequently all sent to Berlin, Germany, for laboratory analysis to ensure quality control and limit errors resulting from different measuring techniques and equipment uncertainties. All liquid samples were filtered (0.2µm cellulose acetate) and decanted into 1.5 mL vials. Samples were analyzed for δ 18 O and δ 2 H using a Picarro L2130-i cavity ring-down water isotope analyzer (Picarro Inc., Santa Clara, CA, USA). The Vienna Standard Mean Ocean Water (VSMOW) was used as a reference. Analytical precision was 0.05‰ standard deviation for δ 18 O and 0.18‰ for δ 2 H. 2.3. Data 2.3.1. Hydroclimate Climate data used in this study – daily precipitation, air temperature and relative humidity – were downloaded for the sample period from open access sources in each country. In Germany, data were obtained from the German Weather Service (DWD) for a weather station in Berlin Dahlem (DWD, 2024 ). In Belgium, daily precipitation and air temperature were obtained from the Flemish Weather service at a station in Wilrijk, approximately 10km south of Antwerp (Waterinfo Vlaanderen, 2024 ). Relative humidity was obtained from a station in Herentals, approximately 30km west of Antwerp. In Poland, data were obtained from the Institute of Meteorology and Water Management for a station in Poznań-Lawica (IMGW-PIB, 2024 ). In Lisbon, data were obtained from SNIRH (Sistema Nacional de Informação de Recursos Hidricos) for a station in Monte Caparica, Almada, approximately 12 km south of Lisbon (SNIRH, 2024 ). 2.3.2. Stable Water Isotopes Global precipitation isotopes (δ 18 O and δ 2 H) for each city were obtained through the Global Network of Isotopes in Precipitation (GNIP), provided by the International Atomic Energy Agency (IAEA) (IAEA & WMO, 2024 ) (see Locations in Fig. 1 ). In each country, we selected a GNIP station closest to the study city. GNIP provides mean monthly values of meteoric precipitation, precipitation amounts and mean monthly temperature measurements. In Poland, we selected the GNIP station in Wroclaw, approximately 180 km south of Poznań, which had monthly data available for precipitation and snow from 2004 to 2009. For Berlin, a station within the city had data from 1978 to 2021 for rainfall and snow. In Belgium only one station had data available, but corresponding precipitation amounts were not recorded. Instead, a station in the Netherlands (Gilze Rijn) was chosen, approximately 70 km northeast of Antwerp, where data were available from 1981 to 1988. In Portugal, a station approximately 9 km from the Lisbon had data available from 2003 to 2017. Globally, the stable isotopic composition of terrestrial waters is characterized by the Global Meteoric Water line (GMWL) (Craig et al., 1963 ). On a local level, meteoric waters often show a more variable composition, resulting in Local Meteoric Water Lines (LMWL). Using δ 18 O - δ 2 H relationship of meteoric precipitation in each city, LMWLs were defined for each city as follows: \(\:LMWL\:Poznan:\:{\delta\:}^{2}H=\:{7.36*\delta\:}^{18}O+7.65\) (R 2 = 0.95) (1) \(\:LMWL\:Berlin:\:{\delta\:}^{2}H=7.80*{\delta\:}^{18}O+7.11\) (R 2 =0.94) (2) \(\:LMWL\:Antwerp:\:{\delta\:}^{2}H=6.86*{\delta\:}^{18}O+2.14\:\) (R 2 =0.94) (3) \(\:LMWL\:Lisbon:\:{\delta\:}^{2}H=6.94{*\delta\:}^{18}O+7.77\:\) (R 2 =0.96) (4) To assess evaporation effects on surface water isotopic composition, we also calculated the line-conditioned excess (lc-excess), which defines the deviation of the relationship between δ 2 H and δ 18 O from that of precipitation (Landwehr & Coplen, 2006 ) as well as deuterium excess (d-excess = δ 2 H – 8* δ 18 O), which reflects the degree of evaporation of water sources, whereby a higher d-excess indicates greater evaporation degree (Dansgaard, 1964 ). Local Evaporation Lines (LEL) were constructed using linear least squares regression of surface water isotope samples from each city. Water bodies undergoing evaporation will exhibit distinctive trends in their isotopic ratios away from the precipitation input and the LMWL, with values typically exhibiting strong linear correlation. They form the characteristic LEL, which deviates from and typically lies below the LMWL. By extrapolating the LEL to the intersection with the LMWL, the surface water origin (input water) can be estimated, and source water pathways - from the origin of atmospheric moisture to evapotranspiration, runoff mechanisms and groundwater recharge - can be assessed (Benettin et al., 2018 ; Evaristo et al., 2015 ; Zanazzi et al., 2020 ) 2.3.3. Transit time proxies Precipitation and surface water isotope data were used to estimate two transit time proxies (TTP) – the damping ratio (DR) and the young water fraction (F yw ). The damping ratio is derived from the ratio of the coefficient of variation of δ 18 O or δ 2 H in streamflow samples to that of δ 18 O or δ 2 H in precipitation. It serves as a semi-quantitative proxy measure to assess general trends and distributions of transit times, with lower damping ratios and higher young water fractions indicating greater influence of more recent precipitation. These can be useful for semi-quantitative characterization of transit times across multiple catchments and larger geographic gradients (Tetzlaff et al., 2009 ). The DR is described as: $$\:DR=\frac{CV\:of\:{\delta\:}^{18}O\:or\:{\delta\:}^{2}H\:in\:streamflow}{CV\:of\:{\delta\:}^{18}O\:or\:{\delta\:}^{2}H\:in\:precipiation\:}$$ 6 where CV represents the coefficient of variation of either δ 18 O or δ 2 H in stream water and precipitation. In general, a higher DR equates to less damping and thus lower transit times, whereas smaller ratios indicate greater damping of precipitation isotopes and thus longer transit times (Tetzlaff et al., 2009 ). Small ratios have also been shown to be related to higher internal catchment storage volumes involved in greater mixing and have an inverse relationship with mean transit times (Soulsby & Tetzlaff, 2008 ). In addition, estimations of young water fractions (F yw ) can provide indications of water ages and transit time behavior. The method developed by Kirchner ( 2016 ) uses simple sine-wave fitting to seasonal tracer input and output, using volume-weighted observed precipitation and surface water isotopes to estimate the fraction of water less than 2–3 months old. Sinusoidal cycles are fitted to precipitation and stream water isotope data, using iteratively reweighted least squares regression (IRLS). Through comparison of sine-wave fitting amplitudes of precipitation and stream/pond isotopes, F yw can be directly estimated from the amplitudes of seasonal cycles. One of the major benefits of this method is that data requirements are modest and it can be performed on sparse and irregularly sampled data sets, making it an ideal candidate for large multi-site datasets of coarser temporal resolution (Kirchner, 2016 ). Young water fractions were computed as outlined in Kirchner ( 2016 ) using public code by Von Freyberg et al. ( 2018 ). All analyses were done using R Studio v.3.4. 3. RESULTS 3.1. Evaporation and seasonality of aquaNBS across European cities In Poznań, total annual precipitation for 2023 was 710 mm/yr, of which ⁓30% fell between June and August (⁓260mm). Annual mean temperature was 10.8°C. In Berlin, late 2022 was relatively dry, with < 80mm between October – December. However, 2023 was an overall wet year, with total annual rainfall of 776.8 mm/yr, which was ⁓30% above of the long-term average. Characteristic of the oceanic climate, Antwerp received a total of 980 mm of precipitation, with frequent and intense rainfall in summer and autumn (> 100mm/month) (Fig. 2c). In Lisbon, late 2022 was relatively wet, with more than 300 mm of rain between October 2022 – January 2023. This was then followed by a dry spring and summer with < 30 mm/month between March and October (Fig. 2d). Table 2 : Summary descriptive statistics of mean historic δ 18 O and δ 2 H concentrations and d-excess for meteoric precipitation from GNIP, and current samples from aquaNBS sites (stream and pond) in each city taken between 2023 and 2024. Precipitation* Streams Ponds City δ 18 O (‰) δ 2 H (‰) d-excess (‰) δ 18 O (‰) δ 2 H (‰) d-excess (‰) δ 18 O (‰) δ 2 H (‰) d-excess (‰) Poznań -6.6 -42.8 11.3 (± 7.6) -8.1 -57.5 7.23 (± 2.8) -7.2 -52.4 5.3 (± 4.3) Berlin -6.9 -50.3 5.5 (± 3.8) -7.7 -56.6 5.5 (± 3.1) -5.1 -41.9 -1.5 (± 5.9) Antwerp -7.3 -47.9 10.4 (± 3.5) n/a n/a - -5.9 -41.7 5.8 (± 7.9) Lisbon -4.3 -22.1 12.5 (± 2.8) -2.4 -19.5 8.4 (± 2.2) -3.5 -15.2 3.8 (± 5.9) *Sampling period for meteoric precipitation from GNIPs: Poznań:2004–2009; Berlin: 1978–2021; Antwerp: 1981–1987; Lisbon: 2003–2017 Table 2 reports a descriptive summary of mean δ-values of historic meteoric precipitation and contemporary surface water samples in each city. Across all cities, isotope signatures showed distinct seasonal patterns, with more depleted signatures during the winter and progressive enrichment during summer, indicating that pond-based aquaNBS were more susceptible to higher evaporation across all cities. Least squares regression of local GNIP precipitation data resulted in highly significant local meteoric water lines in each city characteristic of the local hydroclimate (Fig. 3). The similar slopes of the LMWL in Berlin (7.8) and Poznań (7.36) compared to the GMWL (8.0), indicate the absence of complex kinetic fractionation processes affecting meteoric precipitation. Conversely, the different slopes in Antwerp (6.8) and Lisbon (6.9) highlight the regional differences in moisture cycling across the hydroclimate gradient and more kinetic fractionation processes. In Poznań, the isotopic composition of surface waters showed moderate seasonal variability, with δ 18 O values ranging from − 10.2‰ to -4.6‰ and δ 2 H values from − 71.6‰ to -31.6‰ (Fig. 3a). The more isotopically depleted signatures in stream sites in autumn and winter indicate an influence of depleted winter precipitation on streamflow. Even in the summer, only moderate evaporative enrichment was observed, as inferred from most stream samples plotting close to the GMLW. D-excess values ranged from − 4.1‰ to + 11.4‰, with most negative values in spring. Overall, the sample placement relative to local and global meteoric water lines was characteristic of the mid-latitude continental climate of the Poznań region, with highly seasonal precipitation inputs and moderate evaporation influence during warmer seasons. The LEL intersection with the GMWL further indicates that surface water contributions likely originated from cold-season precipitation and/or cold-season recharge. The largest variability across all water samples was observed in Berlin (Fig. 3b). A clear distinction is evident between stream and pond samples, with pond samples skewed towards more enriched δ 18 O and δ 2 H concentrations throughout the year, indicating significant evaporative fractionation. This trend was also observed in Antwerp, with pond samples exhibiting more enriched and fractionated signals in summer (Fig. 3c). D-excess values ranged from − 14.3‰ to + 9.7‰ and − 23.9‰ to + 16.5‰ in Berlin and Antwerp, respectively, with the most negative values observed in ponds in summer in both cities. Stream samples in Berlin largely plot along the LMWL, with more depleted values observed in winter. The LEL intersections with the LMWLs indicates that cool-season precipitation and recharge were key water sources contributing to streamflow and recharge, especially during winter. In Antwerp, spring, autumn, and winter, samples exhibited overall more depleted signatures, plotting largely along the LMWL, suggesting more modest effects of pond storage on evaporative fractionation as well as the influence of precipitation and groundwater influx. In contrast, Lisbon showed a more limited variability and overall more enriched signatures, compared to the continental climates of Berlin and Poznań. This corresponds to the Mediterranean climate with its strong Atlantic influence (Fig. 3d). More enriched signatures were observed primarily in summer samples of ponds as well as a few stream samples, which were collected in late 2023 and early 2024 following a relatively dry winter. D-excess values ranged from − 8.7‰ to + 10.5‰. The higher intersection of the LEL (-3.8‰) with the LMWL highlights the overall influence of the naturally more enriched precipitation and shallow water storage (i.e. soil water, shallow groundwater, wetlands). A local groundwater sample showed relatively enriched signatures (δ 18 O = -4.1‰ and δ 2 H = -22.9‰), corresponding to the stream samples taken in autumn, winter and spring. 3.2. Transit time proxies across urban aquaNBS Despite coarse seasonal sampling, differences in the range of damping behavior and water ages were evident when comparing the different aquaNBS across all cities (Fig. 4). Low damping ratios (DR) (< 0.2) - meaning greater damping of isotopic signals, indicates limited rainfall-runoff responses and higher contributions from previously stored water. This was primarily observed in multiple stream locations in Lisbon, Poznań and Berlin (Fig. 4a). In cases where potentially greater damping of δ 2 H compared to δ 18 O was observed, such as in two stream sites in Berlin (DE07, DE09) and some stream sites in Lisbon (PT03, PT05, PT06), this partially also suggests evaporative fractionation effects in these largely intermittent systems as well as more contributions from previously stored water rather than direct contributions through precipitation or runoff. In contrast, higher DR (> 0.6), meaning less damped isotopic signatures and more variable isotopic signatures due to direct rainfall-runoff responses, were mostly observed in ponds in Berlin, Antwerp and Poznań and one stream location in Lisbon (PT07). In terms of water ages, several streams in Berlin, Poznań and Lisbon exhibited lower F yw ( 0.8) could be observed, which signifies an overwhelming dominance of recent precipitation and thus stronger runoff responses, corresponding to the higher DR. In Poznań as well as Berlin, the average F yw for ponds was higher than for streams (Berlin: ponds = 0.45, streams = 0.31; Poznań: ponds = 0.6, streams = 0.43). This indicates that in ponds approximately 50% of the water originates from direct rainfall-runoff responses, making this a key water source to this type of aquaNBS. In contrast, the low DR and low F yw in certain streams in Poznań (PL02, PL05, PL03) indicates that the majority of water in these systems is likely older than 3 months, stemming from previously stored water such as groundwater rather than direct precipitation or runoff. In Lisbon, several F yw estimates based on δ 2 H were much higher than δ 18 O, indicating potentially confounding effects of evaporative enrichment on δ 2 H and potential limitations for using these values when modeling transit times. δ 18 O F yw in Lisbon was on average 0.26 in streams, whereas in ponds, F yw was 0.55, indicating that the water in ponds likely consists of a mixture of recent and older water sources, whereas most stream water appears older than 3 months. Only stream sites exhibited a low DR and F yw (both 3 months, with no discernible influence of evaporative fractionation (Fig. 4c,d). In comparison, in other sites with high DR and F yw (PT12; pond) or low DR but high F yw (PT01; stream), mixing of different water sources is more likely at these sites with more evaporation. Conversely, in Antwerp, F yw estimates for ponds largely exceeded 100%, regardless of whether δ 2 H or δ 18 O was used, indicating that water in ponds consists almost 100% of water < 3 months old, which is in line with their primary function as rainwater retention ponds. A greater number of samples in Berlin allowed us to test the effect of sampling frequency. Comparing F yw estimates using seasonal tracer data with monthly data for sites in Berlin, the effectiveness of transit time proxies to capture seasonal variations became apparent (Fig. 5 ). Except for one location (DE07), F yw estimates of selected stream sites were broadly similar between seasonal and monthly sampled data (Fig. 5 a, b). From a purely statistical standpoint, monthly data produced statistically significant sine-wave fits for the data (see Table 3 ). Nevertheless, the results indicate that even coarser seasonal data still allow meaningful characterizations of transit times, and general inference of transit time distributions. Especially in streams with low isotopic variability (i.e. DE03) due to consistent inflow of less isotopically variable water sources (such as treated wastewater effluent or groundwater), seasonal and monthly sampling revealed very similar results. Ponds showed some differences in F yw estimates, with monthly data producing higher estimates of F yw in all sites. As most ponds are strongly influenced by rainfall-runoff responses, the seasonality and damping of isotope signatures were significantly better captured by monthly sampling data (Fig. 5 c, d) and could be even better characterized with higher frequency data (i.e. weekly or daily) Table 3 Summary of F yw estimates using seasonal and monthly δ 18 O data for streams (DE02, DE03, DE07) and ponds (DE01, DE06, DE08), including significance of model estimates (p-value), mean square error (R 2 ) and adjusted R 2 . Site δ 18 O F yw p-value R 2 R 2 adj RMSE Seas. Mon. Seas. Mon. Seas. Mon Seas. Mon. Seas Mon DE02 0.49 0.55 0.75 0.001 0.42 0.68 -0.72 0.63 DE03 0.09 0.09 0.77 0.018 0.41 0.26 -0.78 0.13 DE07 0.43 ⁓1.0 0.8 0.0002 0.35 0.78 -0.94 0.74 DE01 0.55 0.94 0.33 < 0.01 0.88 0.92 0.66 0.91 0.7 0.3 DE06 0.29 0.43 < 0.01 0.01 0.99 0.95 0.99 0.94 0.017 0.13 DE08 0.6 ⁓1.0 0.54 < 0.01 0.7 0.65 0.1 0.59 1.21 0.88 4. DISCUSSION 4.1. Implications of seasonal water source variability and residence times in urban aquatic NBS Synoptic seasonal isotope sampling of different types of aquaNBS across a hydroclimate gradient of four European cities revealed distinct water source and flowpath dynamics shaped by local hydroclimate conditions. Despite the interest in aquaNBS and their (often unknown) differences in design and size, a functional understanding of the hydrological processes can be aided through stable water isotope analysis to support implementation and management. Most apparent were the differences between stream and pond-based aquaNBS, with ponds in Berlin, Antwerpen and Lisbon showing substantial seasonal differences between warm and cool season samples (Fig. 3). Due to the location in the dry NE of Germany, Berlin pond-based aquaNBS in particular, experienced significant evaporative enrichment. Similarly, the more enriched signatures in Antwerp in summer suggested a greater impact of hydroclimate in urban rainwater retention ponds during warm and dry periods there. A previous study of the same pond systems noted largely shallow groundwater levels in the area, which responded rapidly to precipitation as well as to dry periods (Martín Muñoz et al., 2023 ). Although these systems appear to be linked to groundwater, they still heavily depend on precipitation for recharge, particularly in such high rainfall/low energy environments. Conversely, the limited impact of evaporative enrichment across sites in Poznań indicates less seasonal bias of water sources in aquaNBS, due to more intensive summer precipitation and moderate temperatures, which likely buffer against seasonal extremes in this region. Based on the seasonal patterns in water source dynamics and the importance of rainfall-runoff responses in urban pond-based aquaNBS observed in all cities, new insights emerge regarding the potential impacts of projected changes in the timing and delivery of precipitation. Greater variability in urban water source contributions (i.e. effluent discharge, storm drainage) in anthropogenically altered catchments is known to be linked to increasing hydrological variability (Marx et al., 2021 ; Strokal et al., 2021 ; Wild et al., 2024 ). This warrants consideration when designing aquaNBS concepts, to avoid undesired hydrological outcomes through either increasing flashiness or lack of water inflow. Especially during dry periods, urban rain water retention ponds may become stagnant, and without additional water inflow, can turn eutrophic with extensive and often harmful algae blooms, which pose a risk to water quality and public health (Grogan et al., 2023 ; Warter, Tetzlaff, Soulsby, et al., 2024 ). Further, projected trends of increasing river intermittency across Europe signal future limitations in water permanence for stream-based NBS, especially in low-rainfall/high-energy environments such as the Mediterranean, where an increasing number of zero-flow days is expected to occur earlier in the season (Tramblay et al., 2021 ). Such conditions were already observed in Lisbon during the sampling period in early 2023, where a relatively dry first half of 2023 (Feb-Aug) with only ⁓60 mm of precipitation, resulted in multiple sample locations drying out earlier than expected. Increasing intermittency has also been observed in Berlin and many lowland regions of central and northern Europe, with profound implications to water quality, groundwater recharge and biogeochemical processes (Kleine et al., 2020 ; Smith et al., 2021 ; Wang et al., 2025 ; Wu et al., 2021 ; Ying et al., 2024 ). Arguably, intermittent river and ephemeral stream networks in the Mediterranean and other drought-prone regions are naturally adapted to cycles of flow cessation and dry periods (Datry et al., 2018 ). However, changes in the frequency and length of intermittent periods (i.e. longer, earlier) as well as more frequent extended drought periods can affect initial conditions of intermittent streams beyond the usual drying or low flow conditions(Sarremejane et al., 2022 ). Such climate-induced changes to flow regimes may lead to widespread permanent flow regime shifts from perennial to intermittent or potentially cross irreversible thresholds beyond usual drying conditions (Döll & Schmied, 2012 ; Sarremejane et al., 2022 ). In this way, current flow regimes and potential abiotic implications of climate-driven changes in streamflow permanence must be considered when developing aquaNBS in drought-prone regions. In an urban context, zero-flow conditions are often pre-empted by supplementing declining baseflows with treated effluent discharge, as is the case in some streams in Berlin (Marx et al., 2021 , 2023 ). Stream-based aquaNBS that receive such treated effluent discharge may be more buffered against drought periods, as treated wastewater discharge is modulated to maintain elevated baseflows (Kuhlemann et al., 2020 ; Marx et al., 2021 ; Warter, Tetzlaff, Marx, et al., 2024a) in summer, when most water is consumed by evapotranspiration (Smith et al., 2021 ). However, the augmentation of streamflow with recycled wastewater faces its own unique challenges and potential ecological limitations, which warrant consideration when implementing aquaNBS in such systems (Büttner et al., 2022 ; Plumlee et al., 2012 ; Wild et al., 2024 ). 4.2. Differences in water residence times across urban aquaNBS When sampling frequency is fairly coarse (e.g. seasonal), an open question relates to how this effects the resulting TTPs or F yw . Typically, in urban isotope studies, higher-resolution data (e.g. monthly, fortnightly) are preferred when trying to identify dominant controls on hydrological processes (Bonneau et al., 2018 ; Stevenson et al., 2022 ; von Freyberg et al., 2022 ; Warter, Tetzlaff, Marx, et al., 2024b). However, in this study, we hypothesized that TTP measures using seasonal data may still be powerful enough to characterize the hydrologic functioning of different aquaNBS across Europe. Based on the results, seasonal TTP metrics revealed a clear dichotomy, with pond-based aquaNBS showing greater influence of seasonal precipitation and rainfall-runoff responses, while stream-based aquaNBS exhibited slower, groundwater-influenced dynamics and mixing. This illustrates the hydrological heterogeneities across different aquaNBS in all cities, while also confirming the utility of stable water isotopes for such broader hydrogical characterizations across a hydroclimate gradient. Overall, the comparison between monthly and seasonal data revealed that seasonal data produced new insights into rainfall-runoff responses and residence times for different urban aquaNBS across a major hydroclimate gradient, validating the use of such reconnaissance-style surveys for monitoring. The influence of hydroclimate and urban water sources and flowpaths on residence times is summarized in Fig. 6 . The overall higher F yw in ponds (mean 0.65) reflected a stronger impact of recent rainfall and urban runoff responses, especially in high rainfall/low energy environments like Antwerp, where F yw were > 0.9. Here, a certain homogeneity in runoff processes emerged through shorter transit time dynamics, as would be expected of rapidly responding rainwater retention ponds that quickly capture and store excess water during extreme precipitation events (Fig. 6 a). However, during dry periods, the lack of water inflow and dilution and increased evaporative loss can potentially develop into adverse conditions with widespread implications for water quality and nutrient retention in such aquaNBS. As a result, elevated thermal and chemical stress during extreme events (i.e. flood or drought) - either through increased pollution inflow or lack of dilution – represents a risk to such single-source pond systems. This can limit their ecological potential due to rapid development of toxic algal blooms and more eutrophic conditions due to water pollution (Grogan et al., 2023 ; Oertli & Parris, 2019 ; Warter, Tetzlaff, Soulsby, et al., 2024 ). Across stream-based aquaNBS, lower F yw (mean: 0.3) implied the presence of slower subsurface flow processes and local groundwater influence. One caveat to this conclusion is the overwhelming dominance of treated effluent discharge in some streams in Berlin, which results in extreme damping of the isotope signature and very low F yw estimates (Marx et al., 2021 ; Warter, Tetzlaff, Marx, et al., 2024a; Warter, Tetzlaff, Ring, et al., 2024 ). For general consideration, it can be said that subcatchments with larger F yw tend to have predominately shorter transit times due to more rapid rainfall-runoff responses, thus responding more rapidly to solute input in streamflow and lower nutrient retention capacities (Lutz et al., 2018 ). At the same time, in catchments with lower F yw , other subsurface processes are likely contributing to streamflow (i.e. groundwater), signaling less dependence on rainfall to sustain flow/water levels and a greater resilience to hydroclimate variability and streamflow intermittence. The differences observed underscore the need to match NBS design to local hydrological and climate conditions, such as rainfall intensity and drought frequency, as well as urban modifications like soil compaction, reduced vegetative cover, and catchment fragmentation (Gessner et al., 2014 ; Oswald et al., 2023 ). Thus, when assessing water requirements of different aquaNBS, estimates from TTP can provide valuable insights into inter- and intra-geographical differences in water balance dynamics and nutrient retention; and decision makers should be guided, in part, by evidence derived from collected isotope data. Even at a low frequency, isotope data can yield sufficient information, which provide a possible explanation for heterogeneities in hydrological function across water bodies with diverse water sources and transit time (Jung et al., 2025 ). Hence, we argue that there is great value in pursuing stable isotope assessments in the context of evaluating and monitoring aquaNBS at the urban catchment scale, to better establish hydrological boundary conditions, assess any potential limitations in terms of seasonal water availability or varying urban water source contributions and hydroclimate impacts. 4.3. Implications for future urban aquaNBS The results of this study underscore the value of a more integrated, hydrologically informed approach to the planning and implementation of urban aquaNBS by using reconnaissance-style surveys of stable water isotopes. In densely urbanized areas, the implementation of NBS tends to occur in a piecemeal site-specific way, based on engineering design (e.g. to accommodate runoff from rainfall of a particular return period), with limited cognisance of the local hydrological context (e.g. Miles and Band, 2015 ) and an understanding of cummulative effects (Golden & Hoghooghi, 2018 ). In heavily transformed and managed urban catchments, such as the Panke catchment in Berlin or the Warta River catchment in Poznań, water balance dynamics throughout the catchment and its tributaries are closely linked to land use and water management practices (Marx et al., 2023 ; Sojka, 2022 ). As humans alter flow regimes and runoff patterns in urban catchments through various land use changes and extraction of surface and groundwater, this raises the question whether aquaNBS are able to adapt to changes in the water balance in response to progressive global change or different water management practices (Krauze & Wagner, 2019 ). Especially smaller, independent NBS, such as ponds or micro-reservoirs, are often designed to fit local conditions and support sustainable water management or social or aestethic ecosystem services. As a result, they are at risk of degradation or failure if abandoned, poorly integrated with the ladscape or left unmanaged, which can potentially increase the chances of ecosystem disservices (Lalonde et al., 2024 ). Similarly, restoration of dried-out wetlands or reconnection of floodplains as a form of aquNBS is a challenging task that requires consideration of catchment scale processes and potential stressors to avoid ecosystem disservices and destabilization of the hydrological system (Lalonde et al., 2024 ). As climate variability and anthropogenic pressures intensify, the effectivness and delivery of hydrological and ecological ecosystem services will increasingly depend on their alignment with local water source dynamics and hydroclimate regimes. Understanding whether a system is primarily rainfall-driven, responds to strongly to rainfall-runoff processes, is groundwater-fed or reliant on anthropogenic inflows such as wastewater discharge, will define the performance and resilience of aquaNBS. This may be particularly relevant in lower income countries of the Global South, where urban water infrastructure often exists in close proximity to settlements and a high dependency on local ecosystems for basic needs and livelihoods puts significant pressure on limited water resources (Foster et al., 2020 ; Ogidi, 2024 ). In such settings, aquaNBS can help to reduce water risks to economies and society (Acreman et al., 2021 ). Although aquaNBS are often viewed as ready-to-use units to be implemented in specific locations to address local problems, it is crucial to obtain a fundamental hydrologic process understanding into NBS design appraoches (Pinho et al., 2023 ). In this context, evaluating the implications of different residence times and hydroclimate variability through stable isotopes and TTPs has great value for assessing relevant water partitioning processes and flowpaths in ungauged urban hydrosystems. Further research might explore also explicit linkages to surrounding land use and biodiversity metrics (i.e. eDNA, macrophyte diversity, macrointertebrates) to evaluate the impact of hydrology on ecological ecosystem services – an aspect that is still underappreciated in the context of NBS monitoring (Szoszkiewicz et al., 2024 ; Warter, Tetzlaff, Soulsby, et al., 2024 ). 5. CONCLUSION The successful implementation of water-related nature-based solutions (aquaNBS) for urban water management requires consideration of the underlying hydrological processes and hydroclimate interactions within a catchment context. In this study, we showed that through seasonal sampling of stable water isotope surveys, the main water sources and flowpaths within different types of aquaNBS could be well characterized across a major hydroclimate gradient. The application of transit time proxies, such as tracer damping and young water fraction estimations, has shown that ponds were potentially more sensitive to hydroclimate changes, as evidenced by the strong seasonality in evaporative enrichment and high fractions of young water contributions. In contrast, most streams indicated greater mixing of water sources and longer transit times, suggesting potentially greater resilience to hydroclimate variability. In addition, a comparison between seasonally sampled data and monthly sampling for selected locations in Berlin showed that even relatively coarse temporal data collection on a seasonal basis, but with more extensive spatial coverage, can be insightful for broader hydrologic characterizations of aquaNBS at larger scales and provide crucial understanding of local hydrological processes and hydroclimate sensitivities. This has further implications for evaluating risks to water quality, urban water management and various ecological and social ecosystem services. The study fills an important knowledge gap regarding the fundamental understanding of aquaNBS and highlights the potential to use stable water isotopes as a monitoring framework to support the design and implementation of urban aquaNBS. Declarations The authors declare no competing interests. Clinical trial number: not applicable Acknowledgements We thankfully acknowledge Franziska Schmidt from the IGB Isotope Lab for the isotope analysis and sample handling. Funding This study was funded through BiodivRestore for the Binatur project (BMBF No. 16WL015). DT received funding through the German Research Foundation (DFG) as part of the Research Training Group “Urban Water Interfaces” (UWI, GRK2032/2). DT also acknowledges funding from the Wasserressourcenpreis 2024 of the Rüdiger Kurt Bode Stiftung. Research was also partially funded by the Einstein Stiftung Berlin, MOSAIC project Grant/Award Number: EVF-2018-425. Polish team members were also funded through the Binatur project, which is partially financed by the National Science Centre (Poland) UMO-2021/03/Y/NZ8/00100. Lisbon team members were also funded through the Binatur project, which is financed by FCT (10.54499/2020.03415.CEECIND/CP1595/CT0006, 10.54499/DivRestore/0001/2020). CA was supported by Fundação para a Ciência e a Tecnologia (FCT): 2022.08532.CEECIND/CP1715/CT0006. PP was supported through funding from FCT: (Grant No: 10.54499/UIDB/00329/2020,10.54499/Water4All/0009/2023 and 10.54499/DivRestore/0001/2020). Data Availability Stable water isotope data will be available on request. Metadata is stored on the IGB Freshwater Research and Environmental Database (FRED) under doi: 10.18728/igb-fred-984.0. CRediT author contribution statement: Conceptualization: DT, KV Methodology: MMW, DT, CS Investigation: MMW, DG, MS, SMM, VDC Formal Analysis: MMW Data Curation: MMW, DG, CM, SMM, MS Writing-Original Draft: MMW Writing – Review & Editing: DT, CS, KV, DG, VDC, MS, CA, PP, SMM Visualization: MMW Supervision: DT, CS Funding acquisition: DT, KV References Acreman, M., Smith, A., Charters, L., Tickner, D., Opperman, J., Acreman, S., Edwards, F., Sayers, P., & Chivava, F. (2021). 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Supplementary Files SUPPLEMENTARYMATERIALv2.docx Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 24 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 03 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 30 Jun, 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. 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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-7010782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485356534,"identity":"b739ea31-89a9-45b0-b9f5-de83477c95b4","order_by":0,"name":"Maria Magdalena Warter","email":"data:image/png;base64,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","orcid":"","institution":"Leibniz Institute of Freshwater Ecology and Inland Fisheries","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Magdalena","lastName":"Warter","suffix":""},{"id":485356535,"identity":"5ef562fb-996f-4f16-8757-c4a2b24b9cdf","order_by":1,"name":"Chris Soulsby","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Soulsby","suffix":""},{"id":485356536,"identity":"78d5907e-47ec-4c39-b0f7-93181dca8d1e","order_by":2,"name":"Kati Vierikko","email":"","orcid":"","institution":"Finnish Environment Institute, Built Environment Solutions Unit","correspondingAuthor":false,"prefix":"","firstName":"Kati","middleName":"","lastName":"Vierikko","suffix":""},{"id":485356537,"identity":"c4445867-86f1-45c8-afee-9b2388cf693e","order_by":3,"name":"Silvia Martin Muñoz","email":"","orcid":"","institution":"University of Antwerp","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"Martin","lastName":"Muñoz","suffix":""},{"id":485356538,"identity":"eef025be-e1a8-48bb-ac8d-e0a4a0943217","order_by":4,"name":"Daniel Gebler","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Gebler","suffix":""},{"id":485356539,"identity":"d791db8e-dc2a-4cb8-9f19-623149822908","order_by":5,"name":"Mariusz Sojka","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mariusz","middleName":"","lastName":"Sojka","suffix":""},{"id":485356540,"identity":"d65f29de-71d3-42e5-99f6-04318dbfd9fe","order_by":6,"name":"Vladimíra Dekan Carreira","email":"","orcid":"","institution":"University of Lisbon","correspondingAuthor":false,"prefix":"","firstName":"Vladimíra","middleName":"Dekan","lastName":"Carreira","suffix":""},{"id":485356541,"identity":"215e2249-8c50-4c17-83e1-60b649893ffd","order_by":7,"name":"Cristina Antunes","email":"","orcid":"","institution":"University of Lisbon","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Antunes","suffix":""},{"id":485356542,"identity":"4c7cac7f-c0a6-4004-803a-4ed6eb510bf7","order_by":8,"name":"Pedro Pinho","email":"","orcid":"","institution":"University of Lisbon","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Pinho","suffix":""},{"id":485356543,"identity":"d876458e-c480-48a3-aa93-cf52a4c38fa6","order_by":9,"name":"Dörthe Tetzlaff","email":"","orcid":"","institution":"Leibniz Institute of Freshwater Ecology and Inland Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Dörthe","middleName":"","lastName":"Tetzlaff","suffix":""}],"badges":[],"createdAt":"2025-06-30 12:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7010782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7010782/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-14882-x","type":"published","date":"2025-12-06T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86848815,"identity":"8eb88200-4937-46da-aa9a-373fe9845de5","added_by":"auto","created_at":"2025-07-16 09:22:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1489179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOverview of urban case study sites in each city showing land use distribution (incl. urban, green space, arable land, and water bodies). Sample points are denoted with symbols (triangle = pond, filled circle = stream). Pictures of two representative sites in each city are shown.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/15cc3d9517efaf1ab19814c9.png"},{"id":86847334,"identity":"528a9b00-b0d7-49ad-aeba-f84ca7dfdc0e","added_by":"auto","created_at":"2025-07-16 09:06:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1519565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDaily precipitation (in mm) and mean daily temperature (in °C) for the period between October 2022 to March 2024. Sample dates are indicated with colored horizontal bars.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/5d8ede11fa3620ba61e8a01f.png"},{"id":86847336,"identity":"23a71f37-6e7e-4eee-8dcc-a418a222f018","added_by":"auto","created_at":"2025-07-16 09:06:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2246193,"visible":true,"origin":"","legend":"\u003cp\u003eDual isotope plots of sampled stable water isotopes for different seasons from different aquaNBS (streams or ponds) in a) Poznań, b)Berlin, c)Antwerp, and d) Lisbon. Global (black line) (GMWL) and local (red dashed line) (LMWL) meteoric water lines are indicated. Symbols denote the different type of aquaNBS (stream = filled circle; pond = triangle) and colors denote seasons. Local evaporation lines are shown in blue for each city. Meteoric precipitation is indicated in grey (from GNIP).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/3d084e5a32b0a0dc68052d38.png"},{"id":86848816,"identity":"57f81a6c-f577-4a08-ab8f-cd1d046be0ed","added_by":"auto","created_at":"2025-07-16 09:22:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1590833,"visible":true,"origin":"","legend":"\u003cp\u003eMean transit time proxy measures with 1:1 line as red dashed line. a) Damping ratio and b) Fyw for all aquaNBS sites. c) and d) Comparison of water age metrics based on δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH data.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/5e099e2d6a4e16655a726748.png"},{"id":86847338,"identity":"b78ac010-ce0b-45f6-af24-7b2d4529a0d6","added_by":"auto","created_at":"2025-07-16 09:06:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1659877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of young water fraction estimates using a) and c) seasonal or b) and d) monthly δ\u003c/em\u003e\u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eO data for three stream sites (a and b) and c) and three pond sites (c and d) in Berlin. Sinusoidal cycles were fitted to precipitation (grey) and seasonal or monthly isotopes from aquaNBS sites using iteratively weighted least squares regression (IRLS) for estimates of young water fractions (F\u003c/em\u003e\u003csub\u003e\u003cem\u003eyw\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/f6470c1a5b695dc6a79800be.png"},{"id":86848337,"identity":"6264e769-dbee-47bb-afd2-035fdf7c44a4","added_by":"auto","created_at":"2025-07-16 09:14:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97573,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation illustrating the influence of urban water sources on residence time responses in different urban aquaNBS in high rainall/low energy (left) and low rainfall/high energy (right) environments.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/e57bacfb71a16e94f00ea9d6.png"},{"id":97723889,"identity":"8aee3990-1498-4c10-826d-b6bca9aa2612","added_by":"auto","created_at":"2025-12-08 16:09:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8424055,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/bff92b97-f312-4244-a496-a7500a3f1f34.pdf"},{"id":86847330,"identity":"3fdbecaf-8012-4800-8a93-59cb9a5c8ccd","added_by":"auto","created_at":"2025-07-16 09:06:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30265,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALv2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7010782/v1/87951a3b6cb311cb313afd9d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe multifaceted impacts of climate change and urban development increasingly challenge urban water management. Today, cities have evolved into highly engineered environments, where the natural water cycle has been profoundly altered (Gessner et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Walsh et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Extensive impervious surfaces on roads and buildings limit infiltration and accelerate runoff, while widespread subsurface storm drain networks can disconnect surface water from groundwater systems, conveying runoff directly into streams, lakes or ponds with little or no attenuation or treatment (Bonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Burns et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ress \u0026amp; James, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These hydrologic alterations have generally led to \"flashier\" urban flow regimes, with larger storm runoff volumes and higher, more frequent peak flow responses to extreme precipitation events. At the same time, the capacity of urban landscapes to store, filter, and slowly release water is substantially reduced, thereby increasing flood risk and reducing recharge (Golden \u0026amp; Hoghooghi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Oswald et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Walsh et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Temperature extremes and increased evapotranspiration from urban heat island effects further contribute to water stress, especially during dry periods, leading to more intermittent rivers and ephemeral streams, further reduced groundwater recharge and vegetation water stress (Kuhlemann et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ring et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Warter, Tetzlaff, Marx, et al., 2024a). Given the complexity of the urban water cycle and its different components of engineered and natural hydrology, urban freshwater systems are experiencing chronic ecological, chemical and hydrological stresses (Marx et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Numberger et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Richardson \u0026amp; Soloviev, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These challenges to sustainable urban water management call for more adaptive approaches beside traditional grey infrastructure of piped storm drains, to ameliorate the negative impacts of urbanization and climate change on urban freshwater resources and aiding the transition towards more resilient urban environments (Bush \u0026amp; Doyon, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Davis \u0026amp; Naumann, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Krueger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wild et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBlue infrastructure, such as urban wetlands, ponds, or restored streams and floodplains, are increasingly utilized as water-related (or aquatic) nature-based solutions (aquaNBS), substituting traditional grey infrastructure. By harnessing the multifunctionality of blue infrastructure in urban landscapes, a wide range of environmental and societal challenges related to climate change, human health and well-being, biodiversity and water security can be addressed (Chowdhury et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kabisch et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pinho et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; van Rees et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The recognition of the value of blue infrastructure as aquaNBS for enhancing urban ecosystem services and supporting sustainable urban water management has led to widespread ecological restoration efforts of degraded urban freshwater ecosystems (Everard \u0026amp; Moggridge, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hack \u0026amp; Schr\u0026ouml;ter, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lammers et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By restoring and re-establishing near-natural hydrological functioning, aquaNBS are expected to slow runoff, enhance infiltration and storage, reconnect surface and subsurface flows, and help to reduce pollutant loads (Hack \u0026amp; Schr\u0026ouml;ter, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Williams \u0026amp; Filoso, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe effectiveness of aquaNBS is governed by local hydrological processes (e.g. water source partitioning, surface and subsurface flow paths, groundwater-surface water interactions, and water residences) and their linkage with hydroclimate and the urban matrix. As aquaNBS can comprise a broad range of catchment interventions, their design and implementation require not only an understanding of the underlying hydrological processes and boundary conditions, but also of their potential evolution under variable and changing climatic conditions. As such, the lack of empirical evidence of the role of hydrological processes in the function and effectiveness of different types of aquaNBS is critical for their successful long-term implementation and management (Lalonde et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pinho et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This poses the risk that aquaNBS may not produce the intended benefits or lead to unintended ecosystem responses with undesirable consequences for water quality, human health and biodiversity (Lalonde et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Considering the speed of global change, the resilience and ability of aquaNBS to evolve and withstand changing climate conditions not only depends on structural design, but also on their ability to cope with short-term climatic perturbations (e.g., heavy rainfall, droughts) as well as long-term shifts in hydroclimate regimes. Therefore, there is a clear need to better understand how urban water sources and hydroclimate conditions systems affect aquaNBS functioning.\u003c/p\u003e\u003cp\u003eCharacterizing the dominant hydrological processes in built-up areas remains one of the key challenges of urban hydrological research (Oswald et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In particular, inter-comparisons of cities across different climate and geographic gradients are important, but difficult, as high-frequency sampling over extended spatial scales is often logistically demanding and impractical. As naturally occurring tracers of the water cycle, the stable water isotopes of δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH can be an important integrating tool to differentiate contrasting water sources across catchment, regional and global scales and characterize fundamental hydrological processes and water fluxes (Ehleringer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jefferson et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tetzlaff et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Isotopic signatures of different water sources (i.e. precipitation, groundwater, runoff) allow to investigate connectivity between landscapes and freshwater ecosystems, as well as climate-water-ecosystem interactions between natural and anthropogenic systems (Kendall \u0026amp; McDonnell, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Kirchner, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Soulsby et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegional assessments of urban streamflow sources (Kuhlemann et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marx et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), groundwater contributions (Vystavna et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), urban water supply dynamics (Bhuiyan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jameel et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the impacts of urbanization and climate stress in anthropogenically impacted catchments (Kuhlemann et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soulsby et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Warter, Tetzlaff, Marx, et al., 2024a) have highlighted the value of spatially distributed sampling in cities. In addition, by using relatively simple transit time proxies, such as young water fractions (e.g. the proportion of a water body that is less than ~\u0026thinsp;3 months old) mean transit times can be assessed and used to contextualize local hydroclimate, landscape controls and water sources (Hrachowitz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kirchner, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Soulsby et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Von Freyberg, Allen, et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Especially in urban watersheds, understanding water transit times is important for assessing dominant streamflow generation processes and evaluate potential sensitivities to hydroclimate changes (Morales \u0026amp; Oswald, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Warter, Tetzlaff, Marx, et al., 2024b).\u003c/p\u003e\u003cp\u003eClearly, leveraging existing urban blue infrastructures for aquaNBS requires an understanding of local water source dynamics and key hydroclimate drivers. Therefore, we used stable water isotopes as an integrated lens through which to understand the role of hydrology in urban aquaNBS and provide an urgently needed evidence basis of the key hydroclimate and hydrological processes that influence their hydrological functioning. With a geographical focus along a strong hydroclimatic gradient of four European Cities \u0026ndash; Poznań (Poland), Berlin (Germany), Antwerp (Belgium) and Lisbon (Portugal), we undertook low-frequency sampling of stable water isotopes in different types of aquaNBS (streams and ponds) at in total 48 locations between February 2023 and March 2024. Concurrently, we used transit time proxies such as young water fractions, to assess flow pathways and transit time processes across urban aquaNBS to evaluate sampling efficiency and suitability of such low-frequency sampling for broader hydrological characterization. More specifically we investigate the following objectives: i) Identify the main water sources and flow paths in urban aquaNBS across a hydroclimate gradient in Europe; ii) Use low-frequency water stable isotope data to infer differences in water residence times across contrasting European cities through simple travel time proxies such as young water fractions; and iii) Assess the influence of hydroclimate variability on hydrological processes in different aquaNBS.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study sites\u003c/h2\u003e\u003cp\u003eFour urban case study cities were selected \u0026ndash; Poznań, Berlin, Antwerp and Lisbon \u0026ndash; set in different hydroclimate contexts across Europe, ranging from continental (Berlin, Poznań), to oceanic (Antwerp) and Mediterranean (Lisbon) climate. Site selection in each city was done using a stratified random sampling approach (12 locations per city), with the goal to have a wide geographic representation of different types of aquaNBS within each urbanized setting. We aimed for diverse urban blue infrastructural features that include artificial and natural pond and stream-based aquaNBS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with different sizes (pond water volume), permanence (perennial, intermittent) and stream orders (2nd, 3rd order). Hydroclimate and land use characteristics of the study cities are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (see Table S2 in the Supplementary Material for more information on individual sample sites or Szoszkiewicz et al., 2025).\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\u003e\u003cem\u003eSite characteristics of the study locations including mean annual precipitation (in mm/year, long-term average), mean elevation (in meters above sea level (m.a.s.l.), mean annual temperature (T, in \u0026deg;C), mean annual potential evapotranspiration (PET, in mm/yr), the number of samples sites in each city as well as the dominant land use (in %) in each city\u003c/em\u003e\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e\u003cem\u003e(Source: Copernicus CORINE Land Cover 2018, Europe, 6-yearly).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAnnual precipitation (mm/yr)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003cp\u003e(masl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean T\u003c/p\u003e\u003cp\u003e(\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean PET (mm/yr)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNr. Sampled Sites\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e\u003cp\u003eDominant land use (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003eArable\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026deg;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eGreen\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e\u003cem\u003eUrban\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoznań\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e450\u0026ndash;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eStream: 6\u003c/p\u003e\u003cp\u003ePond: 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBerlin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eStream: 8\u003c/p\u003e\u003cp\u003ePond: 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntwerp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e500\u0026ndash;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eStreams: na\u003c/p\u003e\u003cp\u003ePonds: 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLisbon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eStream: 7\u003c/p\u003e\u003cp\u003ePond: 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Green includes urban green spaces, grassland and forests (mixed, broadleaf, coniferous).\u003c/p\u003e\u003cp\u003e+ Urban includes all urban fabric, roads, industrial units, airports and port areas.\u003c/p\u003e\u003cp\u003e\u0026deg; Arable includes all irrigated and non-irrigated agricultural land and pastures.\u003c/p\u003e\u003cp\u003eBriefly, the city of Poznań is located in the lowland central-western part of Poland, in the Warta river basin. The city is rich in small and large urban green spaces and forests. Poznań\u0026rsquo;s climate is considered a temperate oceanic climate, characterized by cold winters and warmer summers, with mean annual precipitation of around 539 mm/yr (1991\u0026ndash;2020 average) and a mean annual PET between 450\u0026ndash;500 mm/yr (1991\u0026ndash;2020 average) (Bochenek et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Poznań, a mixture of small 2nd or 3rd order perennial and intermittent streams was sampled, which primarily provide a means of flash urban flood regulation. Ponds in the city ranged in size from 750 m\u003csup\u003e2\u003c/sup\u003e to 16,000 m\u003csup\u003e2\u003c/sup\u003e, mainly serving as rain water retention ponds from urban runoff.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe city of Berlin is located in the NE of Germany. The climate is continental, with a bordering humid oceanic climate. The central area of Berlin is highly urbanized, with large areas of contiguous urban green space and forest (up to 30%). The city has an extensive network of blue infrastructure with first and second order streams and a large number of small surface water bodies (around 700), ranging from small ponds to larger lakes. Annual precipitation is approximately 577 mm/yr (1981\u0026ndash;2020 average) (German Weather Service, DWD, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), distributed throughout the year as frequent, low-intensity frontal winter rains and infrequent heavy convective summer storms. Evapotranspiration often exceeds annual precipitation inputs (PET ⁓700 mm/yr). A mixture of perennial and intermittent 2nd -order streams and artificial medium size ponds (12,000\u0026ndash;40,000 m\u003csup\u003e2\u003c/sup\u003e) were sampled. Each pond performs slightly different functions \u0026ndash; from pure rainwater retention to semi-natural swimming ponds and wetland habitats, thus representing a variety of blue urban infrastructure.\u003c/p\u003e\u003cp\u003eDue to its proximity to the Atlantic, the city of Antwerp has a strong oceanic climate, characterized by cool winters and warm summers, and frequent, though light, precipitation throughout the year. Annual precipitation is around 830mm/yr, while the mean annual PET is 500\u0026ndash;600 mm/yr. The city has a dense network of artificial channels and ponds to support water retention and urban storm runoff capture. As a result, in Antwerp, only rainwater retention ponds were sampled, ranging in size from 153 m\u003csup\u003e2\u003c/sup\u003e to 4.281 m\u003csup\u003e2\u003c/sup\u003e. Small to medium rainwater retention systems are extensively used throughout the city and embedded in the urban matrix due to water regulation laws requiring new developments to retain and infiltrate runoff water on-site.\u003c/p\u003e\u003cp\u003eFinally, Lisbon represents a typical Mediterranean climate, where most rainfall occurs in winter and spring, whereas summers are usually hot and dry. Mean annual precipitation is around 770 mm/yr over an average of 78 rain days. Similar to Berlin, potential evapotranspiration vastly exceeds annual precipitation, with an average PET of \u0026gt;\u0026thinsp;1000mm/yr. The city of Lisbon is highly urbanized, though extended urban green spaces and forested areas (up to 40%) characterize the city. We sampled a mixture of 3rd -order perennial and intermittent streams and small ponds throughout the cities of Lisbon and Almada. Most of the streams were within forested areas, primarily supporting flash flood regulation. Ponds varied in size (280 m\u003csup\u003e2\u003c/sup\u003e to 8,000 m\u003csup\u003e2\u003c/sup\u003e) and function, serving as biodiversity hotspots and recreational sites as well as supporting flash flood management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Sampling strategy\u003c/h2\u003e\u003cp\u003eSeasonal grab sampling was undertaken in each city between February 2023 and March 2024. Although samples were not always collected in the same month, they roughly correspond to a seasonal schedule (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supplementary Material). This was considered the most efficient sampling strategy feasible as part of such a larger geographical assessment over a short timescale. In Berlin, additional monthly sampling was conducted simultaneously at all locations. Samples were subsequently all sent to Berlin, Germany, for laboratory analysis to ensure quality control and limit errors resulting from different measuring techniques and equipment uncertainties. All liquid samples were filtered (0.2\u0026micro;m cellulose acetate) and decanted into 1.5 mL vials. Samples were analyzed for δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH using a Picarro L2130-i cavity ring-down water isotope analyzer (Picarro Inc., Santa Clara, CA, USA). The Vienna Standard Mean Ocean Water (VSMOW) was used as a reference. Analytical precision was 0.05\u0026permil; standard deviation for δ\u003csup\u003e18\u003c/sup\u003eO and 0.18\u0026permil; for δ\u003csup\u003e2\u003c/sup\u003eH.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Hydroclimate\u003c/h2\u003e\u003cp\u003eClimate data used in this study \u0026ndash; daily precipitation, air temperature and relative humidity \u0026ndash; were downloaded for the sample period from open access sources in each country. In Germany, data were obtained from the German Weather Service (DWD) for a weather station in Berlin Dahlem (DWD, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Belgium, daily precipitation and air temperature were obtained from the Flemish Weather service at a station in Wilrijk, approximately 10km south of Antwerp (Waterinfo Vlaanderen, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Relative humidity was obtained from a station in Herentals, approximately 30km west of Antwerp. In Poland, data were obtained from the Institute of Meteorology and Water Management for a station in Poznań-Lawica (IMGW-PIB, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Lisbon, data were obtained from SNIRH (Sistema Nacional de Informa\u0026ccedil;\u0026atilde;o de Recursos Hidricos) for a station in Monte Caparica, Almada, approximately 12 km south of Lisbon (SNIRH, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Stable Water Isotopes\u003c/h2\u003e\u003cp\u003eGlobal precipitation isotopes (δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH) for each city were obtained through the Global Network of Isotopes in Precipitation (GNIP), provided by the International Atomic Energy Agency (IAEA) (IAEA \u0026amp; WMO, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (see Locations in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In each country, we selected a GNIP station closest to the study city. GNIP provides mean monthly values of meteoric precipitation, precipitation amounts and mean monthly temperature measurements. In Poland, we selected the GNIP station in Wroclaw, approximately 180 km south of Poznań, which had monthly data available for precipitation and snow from 2004 to 2009. For Berlin, a station within the city had data from 1978 to 2021 for rainfall and snow. In Belgium only one station had data available, but corresponding precipitation amounts were not recorded. Instead, a station in the Netherlands (Gilze Rijn) was chosen, approximately 70 km northeast of Antwerp, where data were available from 1981 to 1988. In Portugal, a station approximately 9 km from the Lisbon had data available from 2003 to 2017.\u003c/p\u003e\u003cp\u003eGlobally, the stable isotopic composition of terrestrial waters is characterized by the Global Meteoric Water line (GMWL) (Craig et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). On a local level, meteoric waters often show a more variable composition, resulting in Local Meteoric Water Lines (LMWL). Using δ\u003csup\u003e18\u003c/sup\u003eO - δ\u003csup\u003e2\u003c/sup\u003eH relationship of meteoric precipitation in each city, LMWLs were defined for each city as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LMWL\\:Poznan:\\:{\\delta\\:}^{2}H=\\:{7.36*\\delta\\:}^{18}O+7.65\\)\u003c/span\u003e\u003c/span\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.95) (1)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LMWL\\:Berlin:\\:{\\delta\\:}^{2}H=7.80*{\\delta\\:}^{18}O+7.11\\)\u003c/span\u003e\u003c/span\u003e (R\u003csup\u003e2\u003c/sup\u003e=0.94) (2)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LMWL\\:Antwerp:\\:{\\delta\\:}^{2}H=6.86*{\\delta\\:}^{18}O+2.14\\:\\)\u003c/span\u003e\u003c/span\u003e (R\u003csup\u003e2\u003c/sup\u003e=0.94) (3)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LMWL\\:Lisbon:\\:{\\delta\\:}^{2}H=6.94{*\\delta\\:}^{18}O+7.77\\:\\)\u003c/span\u003e\u003c/span\u003e (R\u003csup\u003e2\u003c/sup\u003e=0.96) (4)\u003c/p\u003e\u003cp\u003eTo assess evaporation effects on surface water isotopic composition, we also calculated the line-conditioned excess (lc-excess), which defines the deviation of the relationship between δ\u003csup\u003e2\u003c/sup\u003eH and δ\u003csup\u003e18\u003c/sup\u003eO from that of precipitation (Landwehr \u0026amp; Coplen, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) as well as deuterium excess (d-excess\u0026thinsp;=\u0026thinsp;δ\u003csup\u003e2\u003c/sup\u003eH \u0026ndash; 8* δ\u003csup\u003e18\u003c/sup\u003eO), which reflects the degree of evaporation of water sources, whereby a higher d-excess indicates greater evaporation degree (Dansgaard, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1964\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLocal Evaporation Lines (LEL) were constructed using linear least squares regression of surface water isotope samples from each city. Water bodies undergoing evaporation will exhibit distinctive trends in their isotopic ratios away from the precipitation input and the LMWL, with values typically exhibiting strong linear correlation. They form the characteristic LEL, which deviates from and typically lies below the LMWL. By extrapolating the LEL to the intersection with the LMWL, the surface water origin (input water) can be estimated, and source water pathways - from the origin of atmospheric moisture to evapotranspiration, runoff mechanisms and groundwater recharge - can be assessed (Benettin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Evaristo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zanazzi et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3. Transit time proxies\u003c/h2\u003e\u003cp\u003ePrecipitation and surface water isotope data were used to estimate two transit time proxies (TTP) \u0026ndash; the damping ratio (DR) and the young water fraction (F\u003csub\u003eyw\u003c/sub\u003e). The damping ratio is derived from the ratio of the coefficient of variation of δ\u003csup\u003e18\u003c/sup\u003eO or δ\u003csup\u003e2\u003c/sup\u003eH in streamflow samples to that of δ\u003csup\u003e18\u003c/sup\u003eO or δ\u003csup\u003e2\u003c/sup\u003eH in precipitation. It serves as a semi-quantitative proxy measure to assess general trends and distributions of transit times, with lower damping ratios and higher young water fractions indicating greater influence of more recent precipitation. These can be useful for semi-quantitative characterization of transit times across multiple catchments and larger geographic gradients (Tetzlaff et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The DR is described as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:DR=\\frac{CV\\:of\\:{\\delta\\:}^{18}O\\:or\\:{\\delta\\:}^{2}H\\:in\\:streamflow}{CV\\:of\\:{\\delta\\:}^{18}O\\:or\\:{\\delta\\:}^{2}H\\:in\\:precipiation\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere CV represents the coefficient of variation of either δ\u003csup\u003e18\u003c/sup\u003eO or δ\u003csup\u003e2\u003c/sup\u003eH in stream water and precipitation. In general, a higher DR equates to less damping and thus lower transit times, whereas smaller ratios indicate greater damping of precipitation isotopes and thus longer transit times (Tetzlaff et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Small ratios have also been shown to be related to higher internal catchment storage volumes involved in greater mixing and have an inverse relationship with mean transit times (Soulsby \u0026amp; Tetzlaff, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, estimations of young water fractions (F\u003csub\u003eyw\u003c/sub\u003e) can provide indications of water ages and transit time behavior. The method developed by Kirchner (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) uses simple sine-wave fitting to seasonal tracer input and output, using volume-weighted observed precipitation and surface water isotopes to estimate the fraction of water less than 2\u0026ndash;3 months old. Sinusoidal cycles are fitted to precipitation and stream water isotope data, using iteratively reweighted least squares regression (IRLS). Through comparison of sine-wave fitting amplitudes of precipitation and stream/pond isotopes, F\u003csub\u003eyw\u003c/sub\u003e can be directly estimated from the amplitudes of seasonal cycles. One of the major benefits of this method is that data requirements are modest and it can be performed on sparse and irregularly sampled data sets, making it an ideal candidate for large multi-site datasets of coarser temporal resolution (Kirchner, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Young water fractions were computed as outlined in Kirchner (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) using public code by Von Freyberg et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). All analyses were done using R Studio v.3.4.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Evaporation and seasonality of aquaNBS across European cities\u003c/h2\u003e\n\u003cp\u003eIn Poznań, total annual precipitation for 2023 was 710 mm/yr, of which ⁓30% fell between June and August (⁓260mm). Annual mean temperature was 10.8\u0026deg;C. In Berlin, late 2022 was relatively dry, with \u0026lt;\u0026thinsp;80mm between October \u0026ndash; December. However, 2023 was an overall wet year, with total annual rainfall of 776.8 mm/yr, which was ⁓30% above of the long-term average. Characteristic of the oceanic climate, Antwerp received a total of 980 mm of precipitation, with frequent and intense rainfall in summer and autumn (\u0026gt;\u0026thinsp;100mm/month) (Fig.\u0026nbsp;2c). In Lisbon, late 2022 was relatively wet, with more than 300 mm of rain between October 2022 \u0026ndash; January 2023. This was then followed by a dry spring and summer with \u0026lt;\u0026thinsp;30 mm/month between March and October (Fig.\u0026nbsp;2d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: \u003cem\u003eSummary descriptive statistics of mean historic \u0026delta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eO and \u0026delta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eH concentrations and d-excess for meteoric precipitation from GNIP, and current samples from aquaNBS sites (stream and pond) in each city taken between 2023 and 2024.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePrecipitation*\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eStreams\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePonds\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eH (\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ed-excess\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO (\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eH (\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ed-excess\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eH (\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ed-excess\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoznań\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-42.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.3 (\u0026plusmn;\u0026thinsp;7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-57.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.23 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-52.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.3 (\u0026plusmn;\u0026thinsp;4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBerlin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-50.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.5 (\u0026plusmn;\u0026thinsp;3.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-56.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.5 (\u0026plusmn;\u0026thinsp;3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-41.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.5 (\u0026plusmn;\u0026thinsp;5.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntwerp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-47.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.4 (\u0026plusmn;\u0026thinsp;3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-41.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.8 (\u0026plusmn;\u0026thinsp;7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLisbon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.5 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-19.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4 (\u0026plusmn;\u0026thinsp;2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-15.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8 (\u0026plusmn;\u0026thinsp;5.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003e*Sampling period for meteoric precipitation from GNIPs: Poznań:2004\u0026ndash;2009; Berlin: 1978\u0026ndash;2021; Antwerp: 1981\u0026ndash;1987; Lisbon: 2003\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;2 reports a descriptive summary of mean \u0026delta;-values of historic meteoric precipitation and contemporary surface water samples in each city. Across all cities, isotope signatures showed distinct seasonal patterns, with more depleted signatures during the winter and progressive enrichment during summer, indicating that pond-based aquaNBS were more susceptible to higher evaporation across all cities. Least squares regression of local GNIP precipitation data resulted in highly significant local meteoric water lines in each city characteristic of the local hydroclimate (Fig.\u0026nbsp;3). The similar slopes of the LMWL in Berlin (7.8) and Poznań (7.36) compared to the GMWL (8.0), indicate the absence of complex kinetic fractionation processes affecting meteoric precipitation. Conversely, the different slopes in Antwerp (6.8) and Lisbon (6.9) highlight the regional differences in moisture cycling across the hydroclimate gradient and more kinetic fractionation processes.\u003c/p\u003e\n\u003cp\u003eIn Poznań, the isotopic composition of surface waters showed moderate seasonal variability, with \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values ranging from \u0026minus;\u0026thinsp;10.2\u0026permil; to -4.6\u0026permil; and \u0026delta;\u003csup\u003e2\u003c/sup\u003eH values from \u0026minus;\u0026thinsp;71.6\u0026permil; to -31.6\u0026permil; (Fig.\u0026nbsp;3a). The more isotopically depleted signatures in stream sites in autumn and winter indicate an influence of depleted winter precipitation on streamflow. Even in the summer, only moderate evaporative enrichment was observed, as inferred from most stream samples plotting close to the GMLW. D-excess values ranged from \u0026minus;\u0026thinsp;4.1\u0026permil; to +\u0026thinsp;11.4\u0026permil;, with most negative values in spring. Overall, the sample placement relative to local and global meteoric water lines was characteristic of the mid-latitude continental climate of the Poznań region, with highly seasonal precipitation inputs and moderate evaporation influence during warmer seasons. The LEL intersection with the GMWL further indicates that surface water contributions likely originated from cold-season precipitation and/or cold-season recharge.\u003c/p\u003e\n\u003cp\u003eThe largest variability across all water samples was observed in Berlin (Fig.\u0026nbsp;3b). A clear distinction is evident between stream and pond samples, with pond samples skewed towards more enriched \u0026delta;\u003csup\u003e18\u003c/sup\u003eO and \u0026delta;\u003csup\u003e2\u003c/sup\u003eH concentrations throughout the year, indicating significant evaporative fractionation. This trend was also observed in Antwerp, with pond samples exhibiting more enriched and fractionated signals in summer (Fig.\u0026nbsp;3c). D-excess values ranged from \u0026minus;\u0026thinsp;14.3\u0026permil; to +\u0026thinsp;9.7\u0026permil; and \u0026minus;\u0026thinsp;23.9\u0026permil; to +\u0026thinsp;16.5\u0026permil; in Berlin and Antwerp, respectively, with the most negative values observed in ponds in summer in both cities. Stream samples in Berlin largely plot along the LMWL, with more depleted values observed in winter. The LEL intersections with the LMWLs indicates that cool-season precipitation and recharge were key water sources contributing to streamflow and recharge, especially during winter. In Antwerp, spring, autumn, and winter, samples exhibited overall more depleted signatures, plotting largely along the LMWL, suggesting more modest effects of pond storage on evaporative fractionation as well as the influence of precipitation and groundwater influx.\u003c/p\u003e\n\u003cp\u003eIn contrast, Lisbon showed a more limited variability and overall more enriched signatures, compared to the continental climates of Berlin and Poznań. This corresponds to the Mediterranean climate with its strong Atlantic influence (Fig.\u0026nbsp;3d). More enriched signatures were observed primarily in summer samples of ponds as well as a few stream samples, which were collected in late 2023 and early 2024 following a relatively dry winter. D-excess values ranged from \u0026minus;\u0026thinsp;8.7\u0026permil; to +\u0026thinsp;10.5\u0026permil;. The higher intersection of the LEL (-3.8\u0026permil;) with the LMWL highlights the overall influence of the naturally more enriched precipitation and shallow water storage (i.e. soil water, shallow groundwater, wetlands). A local groundwater sample showed relatively enriched signatures (\u0026delta;\u003csup\u003e18\u003c/sup\u003eO = -4.1\u0026permil; and \u0026delta;\u003csup\u003e2\u003c/sup\u003eH = -22.9\u0026permil;), corresponding to the stream samples taken in autumn, winter and spring.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Transit time proxies across urban aquaNBS\u003c/h2\u003e\n\u003cp\u003eDespite coarse seasonal sampling, differences in the range of damping behavior and water ages were evident when comparing the different aquaNBS across all cities (Fig.\u0026nbsp;4). Low damping ratios (DR) (\u0026lt;\u0026thinsp;0.2) - meaning greater damping of isotopic signals, indicates limited rainfall-runoff responses and higher contributions from previously stored water. This was primarily observed in multiple stream locations in Lisbon, Poznań and Berlin (Fig.\u0026nbsp;4a). In cases where potentially greater damping of \u0026delta;\u003csup\u003e2\u003c/sup\u003eH compared to \u0026delta;\u003csup\u003e18\u003c/sup\u003eO was observed, such as in two stream sites in Berlin (DE07, DE09) and some stream sites in Lisbon (PT03, PT05, PT06), this partially also suggests evaporative fractionation effects in these largely intermittent systems as well as more contributions from previously stored water rather than direct contributions through precipitation or runoff. In contrast, higher DR (\u0026gt;\u0026thinsp;0.6), meaning less damped isotopic signatures and more variable isotopic signatures due to direct rainfall-runoff responses, were mostly observed in ponds in Berlin, Antwerp and Poznań and one stream location in Lisbon (PT07).\u003c/p\u003e\n\u003cp\u003eIn terms of water ages, several streams in Berlin, Poznań and Lisbon exhibited lower F\u003csub\u003eyw\u003c/sub\u003e (\u0026lt;\u0026thinsp;0.2), indicating that in these systems older water sources (i.e. groundwater) likely dominate (Fig.\u0026nbsp;4b). Conversely in ponds, higher F\u003csub\u003eyw\u003c/sub\u003e (\u0026gt;\u0026thinsp;0.8) could be observed, which signifies an overwhelming dominance of recent precipitation and thus stronger runoff responses, corresponding to the higher DR. In Poznań as well as Berlin, the average F\u003csub\u003eyw\u003c/sub\u003e for ponds was higher than for streams (Berlin: ponds\u0026thinsp;=\u0026thinsp;0.45, streams\u0026thinsp;=\u0026thinsp;0.31; Poznań: ponds\u0026thinsp;=\u0026thinsp;0.6, streams\u0026thinsp;=\u0026thinsp;0.43). This indicates that in ponds approximately 50% of the water originates from direct rainfall-runoff responses, making this a key water source to this type of aquaNBS. In contrast, the low DR and low F\u003csub\u003eyw\u003c/sub\u003e in certain streams in Poznań (PL02, PL05, PL03) indicates that the majority of water in these systems is likely older than 3 months, stemming from previously stored water such as groundwater rather than direct precipitation or runoff.\u003c/p\u003e\n\u003cp\u003eIn Lisbon, several F\u003csub\u003eyw\u003c/sub\u003e estimates based on \u0026delta;\u003csup\u003e2\u003c/sup\u003eH were much higher than \u0026delta;\u003csup\u003e18\u003c/sup\u003eO, indicating potentially confounding effects of evaporative enrichment on \u0026delta;\u003csup\u003e2\u003c/sup\u003eH and potential limitations for using these values when modeling transit times. \u0026delta;\u003csup\u003e18\u003c/sup\u003eO F\u003csub\u003eyw\u003c/sub\u003e in Lisbon was on average 0.26 in streams, whereas in ponds, F\u003csub\u003eyw\u003c/sub\u003e was 0.55, indicating that the water in ponds likely consists of a mixture of recent and older water sources, whereas most stream water appears older than 3 months. Only stream sites exhibited a low DR and F\u003csub\u003eyw\u003c/sub\u003e (both \u0026lt;\u0026thinsp;0.2), indicating potentially longer transit times in these aquaNBS sites and an influence of water sources\u0026thinsp;\u0026gt;\u0026thinsp;3 months, with no discernible influence of evaporative fractionation (Fig.\u0026nbsp;4c,d). In comparison, in other sites with high DR and F\u003csub\u003eyw\u003c/sub\u003e (PT12; pond) or low DR but high F\u003csub\u003eyw\u003c/sub\u003e (PT01; stream), mixing of different water sources is more likely at these sites with more evaporation. Conversely, in Antwerp, F\u003csub\u003eyw\u003c/sub\u003e estimates for ponds largely exceeded 100%, regardless of whether \u0026delta;\u003csup\u003e2\u003c/sup\u003eH or \u0026delta;\u003csup\u003e18\u003c/sup\u003eO was used, indicating that water in ponds consists almost 100% of water\u0026thinsp;\u0026lt;\u0026thinsp;3 months old, which is in line with their primary function as rainwater retention ponds.\u003c/p\u003e\n\u003cp\u003eA greater number of samples in Berlin allowed us to test the effect of sampling frequency. Comparing F\u003csub\u003eyw\u003c/sub\u003e estimates using seasonal tracer data with monthly data for sites in Berlin, the effectiveness of transit time proxies to capture seasonal variations became apparent (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Except for one location (DE07), F\u003csub\u003eyw\u003c/sub\u003e estimates of selected stream sites were broadly similar between seasonal and monthly sampled data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). From a purely statistical standpoint, monthly data produced statistically significant sine-wave fits for the data (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Nevertheless, the results indicate that even coarser seasonal data still allow meaningful characterizations of transit times, and general inference of transit time distributions. Especially in streams with low isotopic variability (i.e. DE03) due to consistent inflow of less isotopically variable water sources (such as treated wastewater effluent or groundwater), seasonal and monthly sampling revealed very similar results. Ponds showed some differences in F\u003csub\u003eyw\u003c/sub\u003e estimates, with monthly data producing higher estimates of F\u003csub\u003eyw\u003c/sub\u003e in all sites. As most ponds are strongly influenced by rainfall-runoff responses, the seasonality and damping of isotope signatures were significantly better captured by monthly sampling data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec, d) and could be even better characterized with higher frequency data (i.e. weekly or daily)\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eSummary of F\u003c/em\u003e\u003csub\u003e\u003cem\u003eyw\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eestimates using seasonal and monthly \u0026delta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eO data for streams (DE02, DE03, DE07) and ponds (DE01, DE06, DE08), including significance of model estimates (p-value), mean square error (R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) and adjusted R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSite\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026delta;\u003csup\u003e18\u003c/sup\u003eO F\u003csub\u003eyw\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRMSE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSeas.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMon.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSeas.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMon.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSeas.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMon\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSeas.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMon.\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSeas\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMon\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDE02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDE03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDE07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e⁓1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDE01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDE06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDE08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e⁓1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Implications of seasonal water source variability and residence times in urban aquatic NBS\u003c/h2\u003e\u003cp\u003eSynoptic seasonal isotope sampling of different types of aquaNBS across a hydroclimate gradient of four European cities revealed distinct water source and flowpath dynamics shaped by local hydroclimate conditions. Despite the interest in aquaNBS and their (often unknown) differences in design and size, a functional understanding of the hydrological processes can be aided through stable water isotope analysis to support implementation and management.\u003c/p\u003e\u003cp\u003eMost apparent were the differences between stream and pond-based aquaNBS, with ponds in Berlin, Antwerpen and Lisbon showing substantial seasonal differences between warm and cool season samples (Fig.\u0026nbsp;3). Due to the location in the dry NE of Germany, Berlin pond-based aquaNBS in particular, experienced significant evaporative enrichment. Similarly, the more enriched signatures in Antwerp in summer suggested a greater impact of hydroclimate in urban rainwater retention ponds during warm and dry periods there. A previous study of the same pond systems noted largely shallow groundwater levels in the area, which responded rapidly to precipitation as well as to dry periods (Mart\u0026iacute;n Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although these systems appear to be linked to groundwater, they still heavily depend on precipitation for recharge, particularly in such high rainfall/low energy environments. Conversely, the limited impact of evaporative enrichment across sites in Poznań indicates less seasonal bias of water sources in aquaNBS, due to more intensive summer precipitation and moderate temperatures, which likely buffer against seasonal extremes in this region.\u003c/p\u003e\u003cp\u003eBased on the seasonal patterns in water source dynamics and the importance of rainfall-runoff responses in urban pond-based aquaNBS observed in all cities, new insights emerge regarding the potential impacts of projected changes in the timing and delivery of precipitation. Greater variability in urban water source contributions (i.e. effluent discharge, storm drainage) in anthropogenically altered catchments is known to be linked to increasing hydrological variability (Marx et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Strokal et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wild et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This warrants consideration when designing aquaNBS concepts, to avoid undesired hydrological outcomes through either increasing flashiness or lack of water inflow. Especially during dry periods, urban rain water retention ponds may become stagnant, and without additional water inflow, can turn eutrophic with extensive and often harmful algae blooms, which pose a risk to water quality and public health (Grogan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Warter, Tetzlaff, Soulsby, et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther, projected trends of increasing river intermittency across Europe signal future limitations in water permanence for stream-based NBS, especially in low-rainfall/high-energy environments such as the Mediterranean, where an increasing number of zero-flow days is expected to occur earlier in the season (Tramblay et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such conditions were already observed in Lisbon during the sampling period in early 2023, where a relatively dry first half of 2023 (Feb-Aug) with only ⁓60 mm of precipitation, resulted in multiple sample locations drying out earlier than expected. Increasing intermittency has also been observed in Berlin and many lowland regions of central and northern Europe, with profound implications to water quality, groundwater recharge and biogeochemical processes (Kleine et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ying et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Arguably, intermittent river and ephemeral stream networks in the Mediterranean and other drought-prone regions are naturally adapted to cycles of flow cessation and dry periods (Datry et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, changes in the frequency and length of intermittent periods (i.e. longer, earlier) as well as more frequent extended drought periods can affect initial conditions of intermittent streams beyond the usual drying or low flow conditions(Sarremejane et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such climate-induced changes to flow regimes may lead to widespread permanent flow regime shifts from perennial to intermittent or potentially cross irreversible thresholds beyond usual drying conditions (D\u0026ouml;ll \u0026amp; Schmied, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sarremejane et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this way, current flow regimes and potential abiotic implications of climate-driven changes in streamflow permanence must be considered when developing aquaNBS in drought-prone regions.\u003c/p\u003e\u003cp\u003eIn an urban context, zero-flow conditions are often pre-empted by supplementing declining baseflows with treated effluent discharge, as is the case in some streams in Berlin (Marx et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Stream-based aquaNBS that receive such treated effluent discharge may be more buffered against drought periods, as treated wastewater discharge is modulated to maintain elevated baseflows (Kuhlemann et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marx et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Warter, Tetzlaff, Marx, et al., 2024a) in summer, when most water is consumed by evapotranspiration (Smith et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the augmentation of streamflow with recycled wastewater faces its own unique challenges and potential ecological limitations, which warrant consideration when implementing aquaNBS in such systems (B\u0026uuml;ttner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Plumlee et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wild et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Differences in water residence times across urban aquaNBS\u003c/h2\u003e\u003cp\u003eWhen sampling frequency is fairly coarse (e.g. seasonal), an open question relates to how this effects the resulting TTPs or F\u003csub\u003eyw\u003c/sub\u003e. Typically, in urban isotope studies, higher-resolution data (e.g. monthly, fortnightly) are preferred when trying to identify dominant controls on hydrological processes (Bonneau et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stevenson et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; von Freyberg et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Warter, Tetzlaff, Marx, et al., 2024b). However, in this study, we hypothesized that TTP measures using seasonal data may still be powerful enough to characterize the hydrologic functioning of different aquaNBS across Europe. Based on the results, seasonal TTP metrics revealed a clear dichotomy, with pond-based aquaNBS showing greater influence of seasonal precipitation and rainfall-runoff responses, while stream-based aquaNBS exhibited slower, groundwater-influenced dynamics and mixing. This illustrates the hydrological heterogeneities across different aquaNBS in all cities, while also confirming the utility of stable water isotopes for such broader hydrogical characterizations across a hydroclimate gradient. Overall, the comparison between monthly and seasonal data revealed that seasonal data produced new insights into rainfall-runoff responses and residence times for different urban aquaNBS across a major hydroclimate gradient, validating the use of such reconnaissance-style surveys for monitoring. The influence of hydroclimate and urban water sources and flowpaths on residence times is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe overall higher F\u003csub\u003eyw\u003c/sub\u003e in ponds (mean 0.65) reflected a stronger impact of recent rainfall and urban runoff responses, especially in high rainfall/low energy environments like Antwerp, where F\u003csub\u003eyw\u003c/sub\u003e were \u0026gt;\u0026thinsp;0.9. Here, a certain homogeneity in runoff processes emerged through shorter transit time dynamics, as would be expected of rapidly responding rainwater retention ponds that quickly capture and store excess water during extreme precipitation events (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). However, during dry periods, the lack of water inflow and dilution and increased evaporative loss can potentially develop into adverse conditions with widespread implications for water quality and nutrient retention in such aquaNBS. As a result, elevated thermal and chemical stress during extreme events (i.e. flood or drought) - either through increased pollution inflow or lack of dilution \u0026ndash; represents a risk to such single-source pond systems. This can limit their ecological potential due to rapid development of toxic algal blooms and more eutrophic conditions due to water pollution (Grogan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oertli \u0026amp; Parris, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Warter, Tetzlaff, Soulsby, et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAcross stream-based aquaNBS, lower F\u003csub\u003eyw\u003c/sub\u003e (mean: 0.3) implied the presence of slower subsurface flow processes and local groundwater influence. One caveat to this conclusion is the overwhelming dominance of treated effluent discharge in some streams in Berlin, which results in extreme damping of the isotope signature and very low F\u003csub\u003eyw\u003c/sub\u003e estimates (Marx et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Warter, Tetzlaff, Marx, et al., 2024a; Warter, Tetzlaff, Ring, et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For general consideration, it can be said that subcatchments with larger F\u003csub\u003eyw\u003c/sub\u003e tend to have predominately shorter transit times due to more rapid rainfall-runoff responses, thus responding more rapidly to solute input in streamflow and lower nutrient retention capacities (Lutz et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, in catchments with lower F\u003csub\u003eyw\u003c/sub\u003e, other subsurface processes are likely contributing to streamflow (i.e. groundwater), signaling less dependence on rainfall to sustain flow/water levels and a greater resilience to hydroclimate variability and streamflow intermittence.\u003c/p\u003e\u003cp\u003eThe differences observed underscore the need to match NBS design to local hydrological and climate conditions, such as rainfall intensity and drought frequency, as well as urban modifications like soil compaction, reduced vegetative cover, and catchment fragmentation (Gessner et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oswald et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, when assessing water requirements of different aquaNBS, estimates from TTP can provide valuable insights into inter- and intra-geographical differences in water balance dynamics and nutrient retention; and decision makers should be guided, in part, by evidence derived from collected isotope data. Even at a low frequency, isotope data can yield sufficient information, which provide a possible explanation for heterogeneities in hydrological function across water bodies with diverse water sources and transit time (Jung et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Hence, we argue that there is great value in pursuing stable isotope assessments in the context of evaluating and monitoring aquaNBS at the urban catchment scale, to better establish hydrological boundary conditions, assess any potential limitations in terms of seasonal water availability or varying urban water source contributions and hydroclimate impacts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Implications for future urban aquaNBS\u003c/h2\u003e\u003cp\u003eThe results of this study underscore the value of a more integrated, hydrologically informed approach to the planning and implementation of urban aquaNBS by using reconnaissance-style surveys of stable water isotopes. In densely urbanized areas, the implementation of NBS tends to occur in a piecemeal site-specific way, based on engineering design (e.g. to accommodate runoff from rainfall of a particular return period), with limited cognisance of the local hydrological context (e.g. Miles and Band, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and an understanding of cummulative effects (Golden \u0026amp; Hoghooghi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In heavily transformed and managed urban catchments, such as the Panke catchment in Berlin or the Warta River catchment in Poznań, water balance dynamics throughout the catchment and its tributaries are closely linked to land use and water management practices (Marx et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sojka, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As humans alter flow regimes and runoff patterns in urban catchments through various land use changes and extraction of surface and groundwater, this raises the question whether aquaNBS are able to adapt to changes in the water balance in response to progressive global change or different water management practices (Krauze \u0026amp; Wagner, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Especially smaller, independent NBS, such as ponds or micro-reservoirs, are often designed to fit local conditions and support sustainable water management or social or aestethic ecosystem services. As a result, they are at risk of degradation or failure if abandoned, poorly integrated with the ladscape or left unmanaged, which can potentially increase the chances of ecosystem disservices (Lalonde et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, restoration of dried-out wetlands or reconnection of floodplains as a form of aquNBS is a challenging task that requires consideration of catchment scale processes and potential stressors to avoid ecosystem disservices and destabilization of the hydrological system (Lalonde et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs climate variability and anthropogenic pressures intensify, the effectivness and delivery of hydrological and ecological ecosystem services will increasingly depend on their alignment with local water source dynamics and hydroclimate regimes. Understanding whether a system is primarily rainfall-driven, responds to strongly to rainfall-runoff processes, is groundwater-fed or reliant on anthropogenic inflows such as wastewater discharge, will define the performance and resilience of aquaNBS. This may be particularly relevant in lower income countries of the Global South, where urban water infrastructure often exists in close proximity to settlements and a high dependency on local ecosystems for basic needs and livelihoods puts significant pressure on limited water resources (Foster et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ogidi, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In such settings, aquaNBS can help to reduce water risks to economies and society (Acreman et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough aquaNBS are often viewed as ready-to-use units to be implemented in specific locations to address local problems, it is crucial to obtain a fundamental hydrologic process understanding into NBS design appraoches (Pinho et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this context, evaluating the implications of different residence times and hydroclimate variability through stable isotopes and TTPs has great value for assessing relevant water partitioning processes and flowpaths in ungauged urban hydrosystems. Further research might explore also explicit linkages to surrounding land use and biodiversity metrics (i.e. eDNA, macrophyte diversity, macrointertebrates) to evaluate the impact of hydrology on ecological ecosystem services \u0026ndash; an aspect that is still underappreciated in the context of NBS monitoring (Szoszkiewicz et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Warter, Tetzlaff, Soulsby, et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThe successful implementation of water-related nature-based solutions (aquaNBS) for urban water management requires consideration of the underlying hydrological processes and hydroclimate interactions within a catchment context. In this study, we showed that through seasonal sampling of stable water isotope surveys, the main water sources and flowpaths within different types of aquaNBS could be well characterized across a major hydroclimate gradient. The application of transit time proxies, such as tracer damping and young water fraction estimations, has shown that ponds were potentially more sensitive to hydroclimate changes, as evidenced by the strong seasonality in evaporative enrichment and high fractions of young water contributions. In contrast, most streams indicated greater mixing of water sources and longer transit times, suggesting potentially greater resilience to hydroclimate variability. In addition, a comparison between seasonally sampled data and monthly sampling for selected locations in Berlin showed that even relatively coarse temporal data collection on a seasonal basis, but with more extensive spatial coverage, can be insightful for broader hydrologic characterizations of aquaNBS at larger scales and provide crucial understanding of local hydrological processes and hydroclimate sensitivities. This has further implications for evaluating risks to water quality, urban water management and various ecological and social ecosystem services. The study fills an important knowledge gap regarding the fundamental understanding of aquaNBS and highlights the potential to use stable water isotopes as a monitoring framework to support the design and implementation of urban aquaNBS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thankfully acknowledge Franziska Schmidt from the IGB Isotope Lab for the isotope analysis and sample handling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded through BiodivRestore for the Binatur project (BMBF No. 16WL015). DT received funding through the German Research Foundation (DFG) as part of the Research Training Group “Urban Water Interfaces” (UWI, GRK2032/2). DT also acknowledges funding from the Wasserressourcenpreis 2024 of the Rüdiger Kurt Bode Stiftung. Research was also partially funded by the Einstein Stiftung Berlin, MOSAIC project Grant/Award Number: EVF-2018-425. Polish team members were also funded through the Binatur project, which is partially financed by the National Science Centre (Poland) UMO-2021/03/Y/NZ8/00100. Lisbon team members were also funded through the Binatur project, which is financed by FCT (10.54499/2020.03415.CEECIND/CP1595/CT0006, 10.54499/DivRestore/0001/2020). CA was supported by Fundação para a Ciência e a Tecnologia (FCT):\u0026nbsp;2022.08532.CEECIND/CP1715/CT0006. PP was supported through funding from FCT: (Grant No: 10.54499/UIDB/00329/2020,10.54499/Water4All/0009/2023 and 10.54499/DivRestore/0001/2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStable water isotope data will be available on request. Metadata is stored on the IGB Freshwater Research and Environmental Database (FRED) under doi: 10.18728/igb-fred-984.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author contribution statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: DT, KV\u003c/p\u003e\n\u003cp\u003eMethodology: MMW, DT, CS\u003c/p\u003e\n\u003cp\u003eInvestigation: MMW, DG, MS, SMM, VDC\u003c/p\u003e\n\u003cp\u003eFormal Analysis: MMW\u003c/p\u003e\n\u003cp\u003eData Curation: MMW, DG, CM, SMM, MS\u003c/p\u003e\n\u003cp\u003eWriting-Original Draft: MMW\u003c/p\u003e\n\u003cp\u003eWriting – Review \u0026amp; Editing: DT, CS, KV, DG, VDC, MS, CA, PP, SMM\u003c/p\u003e\n\u003cp\u003eVisualization: MMW\u003c/p\u003e\n\u003cp\u003eSupervision: DT, CS\u003c/p\u003e\n\u003cp\u003eFunding acquisition: DT, KV\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcreman, M., Smith, A., Charters, L., Tickner, D., Opperman, J., Acreman, S., Edwards, F., Sayers, P., \u0026amp; Chivava, F. 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Using stable isotopes to determine the water balance of Utah Lake (Utah, USA). \u003cem\u003eHydrology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(4), 1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/hydrology7040088\u003c/span\u003e\u003cspan address=\"10.3390/hydrology7040088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"urban water management, transit times, urbanization, climate adaptation, water ages, isotope hydrology, blue infrastructure","lastPublishedDoi":"10.21203/rs.3.rs-7010782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7010782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBlue infrastructure is increasingly implemented in cities as a form of water-related nature-based solutions (aquaNBS), to address ecological and hydrological challenges that threaten urban biodiversity and water security. Nevertheless, the combination of impacts from climate change, multi-faceted consequences of past management, current urban expansion, population growth, and overall urban ecosystem complexity makes it challenging to evaluate the hydrological function of these aquaNBS, and their sensitivity to hydroclimatic and other environmental changes. To enhance adaptation capacity of aquaNBS towards multiple urban and climatic stressors, it is crucial to understand the main hydrologic processes, as well as hydroclimate influences, that determine the functioning of aquaNBS. Stable water isotopes have proven to be a valuable tool in providing integrated understanding of hydrologic functioning over extended spatial scales. While higher frequency isotope data is usually most informative, even limited isotopic data can aid hydrological characterization. We conducted seasonal sampling over the period of one year in 2023/24, across a major hydroclimate gradient across four European cities (Poznań, Berlin, Antwerp, Lisbon). The goal was to identify the dominant physical processes (in terms of water sources, dominant flow paths, and age proxies) linked to the main hydroclimate factors along a continental climate gradient. Comparative analyses of local stable water isotope signatures from different aquaNBS types (i.e. streams, ponds) revealed the strong influence of local hydroclimate, as well as varying water source contributions and mixing processes. The application of transit time proxies, such as tracer damping and young water fraction estimations, suggests ponds to be more sensitive to hydroclimate changes, as evidenced by the strong seasonality in evaporative enrichment and high fractions of young water contributions. In contrast, most streams indicated greater mixing of water sources and longer transit times, suggesting greater resilience to hydroclimate variability. In addition, a comparison between seasonally sampled data and monthly sampling for selected locations in Berlin showed that even relatively coarse temporal data collection, but with more extensive spatial coverage, can be sufficient and still insightful for broader hydrologic characterizations of aquaNBS at larger scales.\u003c/p\u003e","manuscriptTitle":"Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 09:06:53","doi":"10.21203/rs.3.rs-7010782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-24T09:57:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T03:50:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97926906831353092658012234706815257462","date":"2025-08-25T18:48:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T11:47:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T22:37:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T22:36:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-06-30T12:50:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a747de69-19ac-445d-b572-de809a66e182","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:01:47+00:00","versionOfRecord":{"articleIdentity":"rs-7010782","link":"https://doi.org/10.1007/s10661-025-14882-x","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-12-06 15:57:49","publishedOnDateReadable":"December 6th, 2025"},"versionCreatedAt":"2025-07-16 09:06:53","video":"","vorDoi":"10.1007/s10661-025-14882-x","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14882-x","workflowStages":[]},"version":"v1","identity":"rs-7010782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7010782","identity":"rs-7010782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}