The CH-IRP data set: a decade of fortnightly data of δ 2 H and δ 18 O in streamflow and precipitation in Switzerland

. 10 The stable isotopes of oxygen and hydrogen, 2 H and 18 O, provide information on water flow pathways and hydrologic catchment functioning. Here a data set of time series data on precipitation and streamflow isotope composition in Swiss medium-sized catchments, CH-IRP, is presented that is unique in terms of its long-term multi-catchment coverage along an alpine to pre-alpine gradient. The data set comprises fortnightly time series of both δ 2 H and δ 18 O as well as Deuterium excess from streamflow for 23 sites in 15 Switzerland, together with summary statistics of the sampling at each station. Furthermore, time series of δ 18 O and δ 2 H in precipitation are provided for each catchment derived from interpolated datasets from the NISOT, GNIP and ANIP networks. For each station we compiled relevant metadata describing both the sampling conditions as well as catchment characteristics and climate infomation. Lab standards and errors are provided, and potentially problematic measurements are indicated to help the user decide on 20 the applicability for individual study purposes. For the future, it is planned that the measurements will be continued at 14 stations as a long-term isotopic measurement network and the CH-IRP data set will, thus, be continuously be extended. The data set can be downloaded from data repository zenodo

We added links / email contacts to order streamflow data. We also revised carefully the names and IDs of the catchments in the manuscript, and together with the added links they provide enough information to be able to order the corresponding streamflow data for every catchment in our data set.
Please, find also a point by point response to the reviewer comments below.
On behalf of all co-authors,

Reviewer comments
Thank you for implementing the changes and providing additional data.
It is still somewhat bothersome that streamflow data is not directly available (apart from WSL data). It will be really painful for non-german speakers, especially coming from non-European countries, to obtain data from different Swiss authorities. However, I understand that this is beyond your control to some extent. In your reply regarding the streamflow data availability you state that "not all gave us allowance". Please consider the following: -add streamflow data for all catchments where you have the permission and clearly state for which authorities it was not granted We can understand that it might be less comfortable to not receive the streamflow data directly together with the isotopes and precipitation. However, as we already stated in our last reply, if we added the data for some stations and not for others that would be inconsistent and further that would not include data homogenization updates that the agencies perform from time to time.
-for these authorities, provide detailed information where to request data including links to the respective pages to facilitate access We added a link /email address to each agency/office to facilitate access (L222-231).
-add an example letter in German and English in the supplement, addressed to the right authorities including the station names, IDs and all information that is required to obtain the data We know that all staff of the cantonal and federal agencies is capable of understanding English, and we think that a template letter to order data is not necessary.
I understand that this might involve some further changes and additional work, but consider this a huge favor to our community, facilitating open access to a broad international audience.
Please increase the size of the "regime type" legend in Figure 1.
We changed that.

Introduction 25
There are significant differences in the isotopic contents of seawater, freshwater (Gilfillan, 1934), rain and snow. The isotopic composition in precipitation further depends on meteorological influences such as air temperature, rainfall amount and intensity, and location parameters such as altitude, latitude and distance from the coast (Dansgaard, 1953(Dansgaard, , 1964Epstein, 1956;Friedman, 1953).
When tracing the path of water through a hydrological system such as a catchment, the composition of 30 the stable water isotopes δ 18 O and δ 2 H of precipitation changes by the time it reaches the stream. The isotopic signal in the precipitation is changed along the water flow pathways through a catchment resulting in a temporal delay and a dampened amplitude of the signal in the streamflow. This signal change can be modelled using water transit time distributions or other approaches to consider water ages, and can, hence, help improve the understanding of hydrological functioning of catchments. Event-based 35 isotope sampling has long been the basis for hydrograph separation in hydrological research and allows quantifying pre-event and event water contributions to soil water, streamflow, or groundwater (Christophersen et al., 1990;Klaus and McDonnell, 2013;Sprenger et al., 2019). More extended time series of the isotopic composition of catchment discharge, i.e., streamflow, allow the estimation of water transit times and storage of catchments (McGuire and McDonnell, 2006). Besides their value to develop, 40 calibrate and validate a wide variety of catchment hydrological models, these data sets also have a demonstrated value for catchment intercomparisons in, for instance, Sweden (Lyon et al., 2010), Oregon 3 (McGuire et al., 2005 and different Northern regions (Tetzlaff et al., 2009). McGuire et al. (2005 isotopic data for a three-year period to quantify mean transit times for seven, partly nested, catchments in the Cascades, Oregon, US, and found good relations to topographic indices such as the catchment average 45 of L/G, where L is the distance from the stream and G is the gradient to the stream. In a similar study in Northern Sweden based on 15 snow-dominated subcatchments of the Krycklan catchment, Lyon et al. (2010) found wetlands to be a controlling factor for catchment transit times. Tetzlaff et al. (2009) compiled a data set of 55 catchments in different regions in the northern temperate zone. Their analysis showed that topography is an essential control in catchments with a pronounced topography, whereas the 50 topographic influences are smaller in regions with a flatter topography. In the latter, hydrological soil characteristics become relatively more important.
Here, we present a long-term data set consisting of δ 2 H and δ 18 O values for streamflow and precipitation for 23 catchments in Switzerland and discuss the applicability of the data. The collection of this data set started in 2010 as part of the DROUGHT-CH project (Seneviratne et al., 2013) and is still continuing. 55 2 CH-IRP data set 2.1 δ 2 H and δ 18 O for streamflow 2.1.1 Data sampling The data were collected in 23 catchments with near-natural streamflow in Switzerland. The catchments were selected based on two different criteria and two different temporal sampling resolutions were chosen. 60 The majority of catchments were selected with the focus on studying and comparing low-flow behaviour. Therefore, we selected catchments without major water abstractions or transfers, where the gauging stations provided precise streamflow measurements also during low flows (see 4 https://opendata.swiss/de/dataset/niedrigwasserstatistik-nqstat and Marti and Kan (2003)). These catchments vary in size, mean elevation, topographic characteristics as well as underlying geology (Table  65 1, Figure 1Figure 1). For these catchments the sampling was done fortnightly.
Five catchments belong to the Alptal long-term hydrological research catchments (Alp, Biber, Erlenbach, Lümpenenbach, and Vogelbach). Here, grab samples were collected fortnightly until December 2014 and have been collected weekly starting from January 2015.
Additional isotope data were collected within other research projects in the Alptal catchments ( Figure  70 1Figure 1, inset zoom) during events with higher temporal resolution over a short period or in snapshot campaigns with higher spatial coverage. Fischer et al. (2016) performed isotopic hydrograph separations for several events in five small headwater catchments in the Alptal and found that the event-water fraction depended much more on the event size than on catchment characteristics. These findings contributed to the emerging conceptual understanding of runoff generation in the Alptal (van Meerveld et al., 2018). A 75 general observation for isotopes in the Alptal is the large spatial variation which was found for both, rainfall (Fischer et al., 2017) and groundwater (Kiewiet et al., in press). Rücker et al. (2019) measured the isotopic composition of snowpack outflow to study runoff generation during rain-on-snow events.
Furthermore, in the Alptal a field lab was installed that provides isotope data at high temporal resolution at the outlet of the Erlenbach catchment (von Freyberg et al., 2018). The data collected in these studies 80 are not part of the CH-IRP data set but could be useful for specific research questions.
All samples were taken as grab samples using 100 mL High Density PolyEthylene (HD-PE) bottles. The sampling personnel was instructed to flush the bottle with stream water before taking the sample and to 5 ensure to tightly close the bottle to minimize exchange with the atmosphere and thus to avoid fractionation of the samples. 85

Lab analysis
All liquid water samples were analyzed at the Chair of Hydrology at the University of Freiburg, Germany.
The laboratory regularly participates successfully in IAEA Water Isotope Inter-Comparisons (WICO) (Wassenaar et al., 2018). The samples were analyzed using CRDS laser spectrometers (either Picarro L2120-i or L2130-i, Picarro Inc., Santa Clara, CA, USA) in 'high precision mode'. Samples were filtered 90 via syringe filters (0.45 µm) prior to analysis if they were muddy. Of each sample, 1 mL was filled into autosampler vials. According to the manufacturer handbook, six injections per vial were analyzed with the isotope analyzer and raw data of the first three injections were discarded to keep memory effects from one sample to the next at a minimum. Mean and standard deviation (SD) of the last three injections were calculated. In case there was still a memory effect and the SD was larger than 0.08‰ (in the case of δ 18 O) 95 or larger than 0.30‰ (in the case of δ 2 H), the fourth injection was also discarded and only the last two injections were averaged.
Calibration of the raw data was then conducted using three in-house standards with distinct isotopic compositions, -14,86‰, -9.47‰, and 0.30‰ for  18 O, -107.96‰, -66.07‰, and 1.53‰ for  2 H, referenced to the international VSMOW-SLAP scale (Craig, 1961). The standards were analyzed in 100 triplicates each and averaged. The light and the heavy standards -embracing the samples -were used for a 2-point calibration, the third standard was used for validation. Long-term post-calibration accuracy of the validation standard was ± 0.05‰ for δ 18 O and ± 0.35‰ for δ 2 H. Typically, the nine standards were evenly distributed between 40 samples and thereby additionally used to check for instrument drift. Besides 6 the calibration of the measurements to an international system, a comparison to the standard is useful 105 because it allows an implicit consideration of all corrections for instrumental effects and interferences.
Furthermore, most of the influences are cancelled out since they affect both the sample and the reference standard equally (Gourcy et al., 2005).
All isotope data are expressed in δ-notation calculated following Eq. (1) (after Gonfiantini (1981)): where VSMOW is the Vienna Standard Mean Ocean Water and R is the isotope ratio ( 18 O/ 16 O or 2 H/ 1 H).
During times when there was enough lab capacity, double measurements were performed and the arithmetic average was calculated (for ~50% of the samples).

Summary statistics
The sampling periods for streamflow was about eight years for 14 catchments, about three years for seven 115 catchments and for the remaining two catchments five years and 1.5 years, respectively. There is an overlap of three years with data for almost all catchments (~90%), and for the 14 stations that are still being sampled more than eight years of overlapping data are available. Since the stations were sampled fortnightly the number of samples was between 26 and 224 per station, while for the Alptal catchments the samples were taken weekly from 2015 on and these catchments have a total number of 318 samples 120 each.
We performed a statistical outlier analysis based on z-scores (from a visual inspection of the data sets using qq-plots we assume the data are normally distributed). There were in total 47 outliers according to 7 the z-score in either δ 2 H and δ 18 O or both with an absolute value larger than three, indicating that the value deviates more than three standard deviations from the mean. 125 Isotopic compositions can strongly deviate during high flow conditions compared to mean or baseflow conditions because of larger proportion of event and more recent precipitation, as was found for instance in the high-resolution dataset of the Plynlimon catchment, Wales (Knapp et al., 2019). The time series of δ 2 H in Figure 2Figure 2

Streamflow conditions during sampling
In addition to the values of δ 2 H and δ 18 O and deuterium excess we provide an index of sampling conditions regarding streamflow (sampling Q index). This index was calculated from the sum the streamflow volume on the day of sampling and the previous two days divided by the sum of the long-135 term mean streamflow over the same days of the year. An index larger than one indicates wetter conditions from the long-term mean, an index smaller than one indicates drier conditions than the longterm mean. This information can also be assembled to analyze the frequency of samples taken under certain streamflow conditions as given by the index (Figure 7Figure 7).
Furthermore, we calculated the flow exceedance probabilities on the flow duration curve during the 140 sampling to get an idea if the samples were taken during baseflow, average or high flow conditions. An exceedance probability of 0.95, for instance, would be exceeded 95 % of the time during the year and indicates very low streamflow conditions. The exceedance probabilities were calculated empirically based on the last 20 years of the available record of streamflow (1999 -2018), note that this period contained 8 some pronounced low flow periods (2003, 2011, 2015 and 2018). These computations demonstrated that 145 the percentiles for the sampling times in each catchment span the full range of percentiles, which indicates that samples were taken during baseflow, mean flow and high flow conditions. Most samples were taken during mean flow conditions.
Comparing the mean catchment elevation shows that the isotope values follow the elevation gradient (Figure 5Figure 5 and Figure 6Figure 6) as expected (Dansgaard, 1964). The Riale di Calneggia catchment 150 shows an exception to this general gradient. This catchment in the Canton Ticino in the Southern Alps receives precipitation from the Mediterranean Sea and thus shows a less depleted isotopic signal. The elevational gradient that is visible in the long-term mean values of the time series is also visible in specific seasons (JFM, AMJ, JAS, OND) and is very similar for δ 2 H and δ 18 O.

δ 2 H and δ 18 O for precipitation 155
For isotopes in precipitation, data from the National Network for the Observation of Isotopes in the Water Cycle (ISOT, see Schotterer, 2010) was used to predict precipitation isotopes for the selected catchments.
For the catchments close to the Swiss border also data from the Austrian network (ANIP) as well as the global network (GNIP) were used to allow for a better interpolation. Average gradients for each month were calculated from a representative gradient based on three ISOT stations (Figure 1Figure 1 In order to obtain an estimate of precipitation isotopic signature for any desired location, the deviation of the closest available measurement site from its long term average value of the respective month was combined with the interpolated map of corrected average values for the respective month. A more detailed description of the interpolation method can be found in Seeger and Weiler (2014).
Due to limited availability of precipitation isotope data time series with an appropriate length, the data 170 were only validated qualitatively (Seeger and Weiler, 2014), by comparing the predicted isotope values to limited time series of sites situated in North-Eastern and Central Switzerland. The comparison between the interpolated data and validation data suggested good agreement. However, explorative simulations by Seeger and Weiler (2014) also showed a bias of up to 2‰ for δ 18 O between the interpolated precipitation values and the measured discharge values. This suggests that the interpolated values are well suited to 175 predict the amplitudes of the temporal variations of the precipitation isotopes, while the steep topography of the Swiss Alps might lead to regional inhomogeneities that are not fully captured by the data underlying the interpolation procedure.

Data file format
δ 2 H and δ 18 O in streamflow are provided as one ASCII.txt file for each station. Additionally to these time 180 series each of the files contains the Deuterium excess, the streamflow conditions preceding the sampling (Q sampling index and streamflow percentiles) as well as the z-scores indicating if a sample might be a statistical outlier, assuming the data are normally distributed. All files contain further information for each sample whether double measurement was performed in the lab comments indicating for instance special sampling conditions (ice etc.) or storage-related issues that could alter the isotopic composition 185 due to fractionation (e.g., sample bottle not closed tightly).
For each data file for streamflow data there is a corresponding ASCII.txt file for catchment precipitation.
These contain the interpolated δ 2 H and δ 18 O in precipitation for the catchment as well as the source data that were used to derive the interpolated values.

Associated data 190
The data set is complemented by daily precipitation and air temperature for each catchment as well as the shape-files for the topographical catchment boundaries. The underlaying data for the areal precipitation sums and the areal air temperature averages were extracted from the gridded data products 'Rhires' and 'Tabs' from MeteoSwiss, respectively. The gridded data were masked with the shape-file of the catchment and the arithmetic mean of each grid cell value for each day was calculated to obtain mean areal air 195 temperature and precipitation for grid cells that were only partially within the catchment boundaries their value was included in the mean assigning a weight according to the percentage intersecting the mask.
These data are provided together with the isotopic data as separate ASCII.txt files. The shapefiles of the catchment boundaries are also provided. These were extracted from a data set comprising the topographic catchment boundaries of gauged Federal stations (FOEN, https://data.geo.admin.ch/ch.bafu.hydrologie-200 hydromessstationen/ch.bafu.hydrologie-hydromessstationen_einzugsgebiete.zip).

Data application and outlook
This data set with isotope data from precipitation and streamflow allows the estimation of mean transit times. With these, catchment water storage (mobile storage) can be estimated and may be related to the sensitivity to droughts (Staudinger et al. 2017). From this data set also young water fractions can be 205 calculated and for instance, using the data presented here, Von Freyberg et al. (2018) assessed how sensitive the young water fraction is to both hydro-climatic forcing and catchment properties. Using the δ 18 O values in precipitation and streamflow for 12 catchments of the presented data set Allen et al. (2019) assessed whether summer or winter precipitation is overrepresented in streamflow, relative to its proportion of total precipitation. Also parts of this data set (composition of isotopes in precipitation) were 210 used to re-investigate the relationship between transit times and catchment topography (Seeger and Weiler, 2014).
In 2019 we were still collecting data for 14 sites and it is planned to continue these observations. A longterm sampling will allow for more robust estimations of storages and young water, and hence for a more robust reassessment of the mentioned studies. The growing dataset will also provide opportunities for a 215 closer look at catchment transit times and storages. The data set will, for instance, allow us to compare different conditions such as dry or wet years or the effects of extreme events.
Additional data that may be useful for potential applications are time series of streamflow time series and shapefiles of the catchments, which are both provided by the Swiss FOEN, meteorological time series, which are provided by MeteoSwiss, and a digital elevation model of Switzerland, which is provided by 220 Swisstopo.

Author contribution
MS, JS, KS, MW designed the sampling net, BH and MS did the lab analysis and maintained the data bank. SS interpolated the isotopes in precipitation. MS wrote the first draft of the manuscript. All authors 235 contributed to the discussion and revised the submitted manuscript.

Competing interests
The authors declare that they have no conflict of interest. 240 Wassenaar, L. I., Terzer-Wassmuth, S., Douence, C., Araguas-Araguas, L., Aggarwal, P. K. and Coplen, T. B.: Seeking excellence: An evaluation of 235 international laboratories conducting water isotope analyses by isotope-ratio and laser-absorption spectrometry, Rapid Commun. Mass Spectrom., 32(5), 393-406, doi:10.1002/rcm.8052, 2018. 335 Table 1 Catchment characteristics: ID is the identification number that is used throughout the paper. FOEN ID is the identification number that is used by the Swiss Federal Office of the Environment (FOEN), if there is no FOEN ID the station is maintained on a Cantonal level or by the WSL and indicated with the acronym of the respective organization, gauge coordinates are given using the official Swiss reference system CH1903+, aquifer productivity is given as relative catchment area with low, varying and high productivity (Bitterli, 2004 19   The colors indicate the regime type to which the catchments were assigned. Note the different x-axis scaling for the nival catchments. Figure 5 Median and ranges (10th and 90th percentile) of δ2H for the full sampling period (left) and separately for the seasons (right) of the samples for each catchment against the mean catchment elevation. 360 The colors indicate the regime type to which the catchments were assigned. Figure 6 Median and ranges (10th and 90th percentile) of δ18O for the full sampling period (left) and separately for the seasons (right) of the samples for each catchment against the mean catchment elevation.
The colors indicate the regime type to which the catchments were assigned.