dataesr/scientific-paragraphs-categorization huggingface dataset:
Explanatory discharge dataset Two discharge datasets (continuous daily time series) were used as explanatory variables of discrete intermittence observations, with the objective of extrapolating the intermittence frequency over time. The 2011-2017 dataset was composed of 1600 gauging stations distributed across France. Each stream where a HY-DRO gauging station is located has been defined as IRES or perennial. Several definitions of IRES can be found in the literature  (Huxter and van Meerveld, 2012; Eng et al. , 2016; Reynolds et al. , 2015). In this study, we considered stations to be intermittent when five consecutive days with discharge less than 1 L s -1 have been observed during the period of record. The 1989-2017 dataset consisted of 630 gauging stations selected with less than 5 % of missing data (continuous or not) during the period 1989-2017.
Explanatory groundwater-level dataset Because groundwater resources influence stream intermittence, we used available time series of the daily groundwater level available in the ADES database (http://www. ades. eaufrance. fr, last access: 22 May 2018) at sites identified as involved in groundwater-surface water exchanges  (Brugeron et al. , 2012). Similarly to the discharge data, two sets of groundwater-level data with records available over the two periods 2011-2017 and 1989-2017 have been selected. The level of alteration of groundwater levels by water withdrawal is unknown because no information is available at this scale. The 2011-2017 dataset was composed of 750 piezometers with daily groundwater-level data with less than 5 % of missing data (continuous or not). The selection of the 1989-2017 dataset was not easy because few groundwater-level measurements were available in the database before 2000.
For example, only five piezometers met the tolerance limit on missing values considered for the 1989-2017 discharge dataset. In order to extend the dataset and because groundwater levels were less variable than stream discharges, the proportion of permitted gaps was fixed to 20 % between 1989 and 2017. This led us to select 150 piezometers. Thereafter, when the missing data period was less than 10 days, groundwater levels were reconstructed by linear interpolation in order to reduce the proportion of missing values to less than 5 % for the 150 piezometers selected. Figure  3. Strategy of parametric modelling (steps 1-4) developed to predict (step 5) the regional probability of drying (RPoD) by HER2-HR combination in France.
The RPoD LR was then calculated as follows in Eq. (  5 ): RPoD LR (d) = exp(α 2 F (d) + β 2 ) 1 + exp(α 2 F (d) + β 2 ). (5) Models were calibrated against observations available during the same period, 2012-2016, leaving out the year 2017 for an independent validation test. However, for the continuous temporal extrapolations (one over 2011-2017, the other over 1989-2017), two models were built with different piezometers and gauging stations selected as explanatory variables (see Sects. 2. 4 and 2. 5). Thus there are two sets of regression parameters specific to each dataset for both the LLR and LR models, leading to different prediction of RPoD. Finally, in a fifth step, a daily regional probability of drying (RPoD) could be predicted for each HER2-HR combination with both models following analytical formulas (Eqs.
Model robustness: validation using independent datasets We used (1) the POC independent data and (2) the 2017 ONDE year to test the robustness of the LLR and LR model to predict the intermittence frequency (1) in space and (2) over time. Note that when predicting on the POC datasets, a new model was calibrated using only ONDE sites located out of POC streams. For both datasets (POC and ONDE, 2017), the relative performance of the LLR and LR models was compared in multiple ways using both the 2011-2017 and 1989-2017 datasets. The performance of each model was evaluated by the Nash-Sutcliffe efficiency criterion (NSE)  (Nash and Sutcliffe, 1970) : NSE = 1 - N i=1 (RPoD ONDEi -RPoD pri ) 2 N i=1 (RPoD ONDEi -RPoD ONDEi ) 2 , ( 6 ) where RPoD ONDEi is the average proportion of drying statuses over the ONDE sites located in the HER2-HR combination at the ith observation date, RPoD pri is the predicted regional probability of drying at the ith observation date, RPoD ONDEi is the mean of RPoD ONDEi over the period and N is the total number of observations in the ONDE network for each HER2-HR combination.
Model prediction Both models have been calibrated over the period 2012-2016 and were then applied in a fifth step to predict the daily RPoD in France (Fig. 3 ). The RPoD was firstly predicted over the period 2012-2016 in order to identify the most affected regions by flow intermittence using the 2011-2017 datasets. The second application concerned the extrapolation of RPoD in France over a longer period using the 1989-2017 dataset to analyse the temporal variability of flow intermittence at the national level. It should be noted that model predictions only concern streams with a Strahler order of lower than 5 due to the ONDE site location.
Inter-annual intermittence according to the raw discrete ONDE network A total of 1127 ONDE sites have recorded at least one drying event during the period 2012-2016 representing 35 % of the 3300 ONDE sites. From the ONDE database the probability of drying at the country scale was computed as the total number of drying statuses over France divided by the total number of ONDE observations available during statuses the same year (Fig. 4a ). Between 2012 and 2016, the most critical year is 2012, with 15 % of drying statuses followed by 2016 (14 %) and 2015 (14 %) (Fig. 4a ). The years 2013 and 2014 are less affected with only 6 % of drying statuses observed (Fig. 4a ). Drying events mainly occur between July and September, but the evolution of the month's proportion of drying can differ between years (Fig.
Comparison of flow intermittence between the raw ONDE and HYDRO datasets The HYDRO dataset includes 90 gauging stations located on streams considered IRES, which represents only 5. 6 % of the 1600 gauging stations against 35 % for ONDE sites. At the national scale, the number of IRES seems underrepresented in the south-western, central, and north-eastern parts of France and Corsica in comparison with sites experiencing drying in the ONDE network (Fig. 6 ). The number of gauging stations with at least one drying event (discharge < 1 L s -1 ) observed between May and September varies between 79 in 2012 and 47 in 2014 (Table 1). The lowest numbers of gauging stations with drying events are observed in the years 2013 and 2014, while the highest numbers are related to the years 2012, 2015 and 2016.
Regression results The LLR and LR models, calibrated over the period 2012-2016, perform well with the 2011-2017 dataset, with a mean NSE of 0. 8 with the LR model against 0. 7 with the LLR  model (Fig. 7a  and b ). With the LR model, 50 % of the HER2-HR combinations obtain a NSE greater than 0. 8, representing a coverage of 65 % of the French territory, while 33 % of HER2-HR combinations display a NSE higher than 0. 8 (50 % of coverage of France) with the LLR model. Regions with the highest performances are located in sedimentary plains, in the south-east of France and in the Pyrenees Mountains. Conversely, the worst performances are obtained in the mountainous regions of the Alps as well as in the Massif Central. In these regions the size of the HER2 is rather small and the number of ONDE sites, gauging stations and piezometers per HER2-HR combination are certainly too few to derive reliable relations.
Despite pooling, estimating RPoD remains impossible for nine HER2-HR combinations (4. 5 % of coverage of France) because the number of ONDE sites, gauging stations and piezometer sites is insufficient (less than five) to perform the regression analysis. The performance level is lower when the 1989-2017 dataset is used in models: the mean NSE with the LR and LLR models is 0. 7 and 0. 6, respectively (Fig. 7c  and d ). The LR and LLR models lead to a similar performance range. However, the LR model outperforms the LLR model in terms of number of HER2-HR combinations, with NSE greater than 0. 8 (Fig. 7c  and d ). The performance is sensitive to the dataset. As expected, the best results are obtained with the denser network. A decrease in NSE by more than 0. 2 is identified for 5 % of the French territory when the 1989-2017 dataset is used (black areas; Fig.
Comparison to the POC database The observed proportion of drying RPoD POC is rather well simulated by both LLR and LR models with the 2011-2017 explanatory dataset (NSE > 0. 7 except for the year 2011; Fig. 9 ). In addition, the models are able to capture small fluctuations of RPoD POC during the summer period. The best results during the year 2011 are obtained with the LLR model (black curve; Fig. 9 ) and the LR model overestimates RPoD POC by 3 % (dashed grey curve; Fig. 9 ). In 2012, the decline in water levels is more gradual than in 2011 and a marked peak is reached in September with 40 % of RPoD POC (Fig. 9 ). This pattern is well reproduced by both models with a good fit to all observation points (Fig. 9 ). The year 2013 is less affected by drying occurrence and the maximum RPoD POC does not exceed 20 % (Fig.
9 ). Curves of both models fit to observations well until the end of August. Note that the LR model is slightly closer to the observations around the peak in September compared to the LLR model. However, the LR model overestimates the RPoD POC at the end of September and in October. When the 1989-2017 dataset is used for explanatory variables, the simulations of RPoD are weakly degraded with both models (Fig. 9d, e, f ). However, the simulated pattern is similar to the observed one. The LLR model outperforms the LR model during the 3 years of validation with the 1989-2017 dataset (black curve; Fig. 9d, e, f ).
Temporal patterns assessment of models between 2012 and 2017 During the calibration period, the LLR and LR models tend to better simulate the RPoD during dry years 2012 and 2016 (NSE = 0. 8 with the LLR and LR models; Table  2 ) than during wet years (e. g. 2014 with NSE < 0. 7). The NSEs are lower during the months of May and June when few drying events are observed, while NSEs are much better during the driest months of August and September. During the validation year of 2017, both models obtain a similar performance over the year independent of datasets (NSE = 0. 7). Monthly NSEs in 2017 follow the same trend as monthly NSEs of the calibration period, with lower NSEs in May (NSEs < 0. 4) and June (NSEs = 0. 5) and higher NSEs in July, August and September (NSEs = 0. 6) with both models independent of datasets.
Modelling of intermittencies severity between 2012 and 2016 Both models have been applied using the 2011-2017 dataset. Figure  11  displays the maximum number of consecutive days (D RPoD > 20 % ) with RPoD higher than 20 % simulated by both the LLR and LR models. The most affected regions are located in the south-east of France and in the sedimentary plains, which is consistent with the spatial pattern obtained from the ONDE observations (Fig. 5 ). The most impacted year followed the same hierarchy: the year 2012 is the most critical year, with 30 % of France displaying D RPoD > 20 % higher than 60 days followed by the year 2015 (20 % of France with D RPoD > 20 % > 60 days) and 2016 (15 % of France with D RPoD > 20 % > 60 days) (Fig. 11 ). The years 2013 and 2014 are weakly affected, with 5 % and 6 % of France with D RPoD > 20 % higher than 60 days, respectively.
Reconstitution of historical regional probability of drying The trend temporal patterns of RPoD predicted by the two models, considering the 1989-2017 dataset, look similar between 1989 and 2016, and the simulated RPoD fit well to RPoD ONDE (Fig. 12 ). The proportion of drying is highly variable over the total simulation period, with alternating dry  (1989 to 1991, 2003 to 2006, 2009 to 2012) and wet (1994 to 1995, 2000 to 2002, 2013 to 2014)  phases. In spite of inter-annual variability, peaks of RPoD occur regularly between August and September, whether in dry years or wet years. This finding is consistent with the preeminence of rainfall-fed river flow regimes with low flows in summer, in France. The highest values of RPoDs (above 35 % over France) are observed in  1989, 1990, 1991, 2003 and 2005  (black curve, Fig.
ONDE network complementarity with conventional flow monitoring network The analysis of the ONDE observations shows that the proportion of rivers undergoing drying is significantly higher (35 %) than that observed with the conventional monitoring (HYDRO database, 8 %). This proportion, although related to a short period of records 2012 and 2016, is consistent with the percentage of 39 % of river segments classified as intermittent by  Snelder et al. (2013). This analysis confirms the under-representation of IRES in the French HY-DRO database, and probably others in other countries (flows are often uncontrolled in IRES). Without gauging stations located on headwaters,  Snelder et al. (2013)  were unable to predict IRES in eastern France (see  Fig. 9, p. 2694). The high density of ONDE sites makes it possible to improve the detection of drying events and lead to better understand the spatial distribution of IRES located at the upstream extent of the hydrographic network.
The ONDE network encompasses various hydrological conditions, which provides a more accurate assessment of inter-annual variability, differentiating between dry years  (2012, 2015 and 2016)  and wet years  (2013, 2014)  with clearly few drying occurrences. The validation of the LR and LLR models against the spatially dense POC database also demonstrates the spatial representativeness of the ONDE network. Thanks to the qualitative information provided and to models such as the statistical models developed here, it is now possible to capture drying events at the regional scale. The ONDE sites are located on small headwater streams which can be very reactive to external disturbances (rainfall deficit, change in air temperature, increase in water withdrawals, etc. ) and by nature are more likely to be IRES.
The gauging stations available in the HYDRO database are located on larger streams and their hydrologic response to changes in external factors (environmental or human) is slower and drying occurred with greater inertia under temperate climate. Their uneven distribution across France does not allow us to accurately characterise the inter-annual variability of drying development. Overall, the ONDE network provides very complementary information to conventional flow monitoring, leading to a better understanding of the processes of drying in upstream catchments.
Dependency on spatial gauging network density The performance obtained with the LR and LLR models is slightly better with the 2011-2017 dataset (mean NSE = 0. 75) than those obtained with the 1989-2017 dataset (mean NSE > 0. 65), whose network is less dense. HER2-HR combinations are the most degraded where the number of monitoring stations is the most decreased between the two   datasets. The accuracy of the predictions is dependent on the number of gauging stations, ONDE sites and piezometers available to calibrate the regressions. The highest NSEs are obtained in western sedimentary plains and south-eastern France, where a significant number of streams have dryings regardless of years (Fig. 5 ). The dominant river flow regime in these regions is mainly influenced by precipitation and the lowest water levels are reached in August and September, which corresponds to the monitoring period of the ONDE database.
We have chosen to average the non-exceedance frequencies of flows and groundwater levels in order to increase the monitoring network. If models had been calibrated using only gauging stations, performance will have been globally similar, or slightly better, in some HER2-HR combinations (Fig. 13 ). Therefore, we could not validate the real gain of using groundwater-level data in addition to discharge data. This is certainly due to the dominant proportion of the gauging stations compared to the piezometers. Indeed, in the 2011-2017 dataset, the proportion of gauging stations is greater than 75 % for more than 70 % of HER2-HR combinations, whereas the proportion of piezometers exceeds 70 % in only 5 % of HER2-HR combinations. Groundwaterlevel data thus have a small weight in regressions for this dataset.
However, when more observation data are available, it is likely that including more detailed descriptors of rainfall events and local geology could improve our approach. In France, based on the 2011-2017 dataset, both models suggest the highest values of RPoD along the Mediterranean coast (D RPoD > 20 % > 100 days each year). Rivers in this region are subject to a predominantly pluvial regime (Class 7;  Sauquet et al. , 2008) , i. e. hot and dry summers followed by intense rainfall events in autumn, leading to high flows in November  (Skoulikidis et al. , 2017b). The catchments in this region are small and particularly reactive to environmental changes, making them highly sensitive to flow intermittence. Rivers located in the sedimentary plain in western France are also very impacted by flow intermittence.
The regime is also influenced by precipitation and for the basins subject to intense agriculture significant water abstractions during summer in this region reduce water availability in rivers and in aquifers which are no longer able to support the low water levels, leading to increased flow intermittence. Regarding alteration issues in our datasets, we do not have access to the exact location and the volumes of water withdrawal for irrigation purposes. However, due to their upstream location, water availability is expected to be low, which may limit potential withdrawals and as a consequence flow alteration at ONDE sites. The alteration of groundwater levels is unknown because no information is available. However, in sedimentary plains where agricultural crops dominate the landscape, we are not sure that no human action affects low flows.
Conclusion This paper investigates the spatial and temporal dynamics of the regional probability of drying (RPoD) of headwater streams by taking benefit from qualitative and discontinuous data provided by the ONDE network. Two models based on linear or logistic regressions have been developed and succeeded in reconstructing the temporal dynamics of RPoD. They are based on a strong relationship between the nonexceedance frequencies of discharges and groundwater levels as a function of the proportion of drying statuses observed at ONDE sites per HER2-HR combination. LLR and LR models show similar performance and perform well between 2011 and 2017. The accuracy of predictions is dependent on the number of gauging stations, ONDE sites and piezometers available to calibrate the regressions. Regions with the highest performance are located in the sedimentary plains, where the monitoring network is dense and where the RPoD is the highest.
Conversely, the worst performances are obtained in the mountainous regions. Finally, both models have been used to reconstruct historical RPoD between 1989 and 2016 and suggest the highest values of mean RPoD (> 35 %) in  1989-1991, 2003 and 2005. This is consistent with other published studies, but the high density of ONDE sites makes it possible to improve the detection of drying events and lead to better capturing of the spatial distribution of IRES located at the upstream extent of the hydrographic network. Moreover, the duration of drying is of importance for ecologists and the prediction of a daily RPoD provides one key driver for the composition and persistence of aquatic species. From a methodological point of view, our method relating discrete drying observation obtained by citizen science networks to continuous daily gauging data seems robust across the highly diverse (climate and topography) regions of France, and provides good predictions in an independent region excluded from the calibration process (PoC).
Acknowledgements. The authors wish to thank  Anne van Loon  and  Catherine Sefton  for their valuable comments, suggestions and positive feedback on the manuscript. The research project was partly funded by the  French Agency for Biodiversity (AFB, formerly ONEMA). This study is based upon works from  COST Action CA15113 (SMIRES, Science and Management of Intermittent Rivers and Ephemeral Streams , www. smires. eu),supported by  COST (European Cooperation in Science and Technology). Edited by: Laurent Pfister Reviewed by:  Catherine Sefton  and  Anne Van Loon.
Acknowledgements. Part of this project was written while T. O. -B. was working at the  BCAM-Basque Center for Applied Mathematics  and was supported by the  Basque Government  through the  BERC 2014-2017 program  and by  Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa  excellence accreditation SEV-2013-0323. Now, T. O. -B. is supported by a public grant as part of the  Investissement d'avenir project , reference  ANR-11-LABX-0056-LMH ,  LabEx LMH. L. V. is supported by an  ERCEA Advanced Grant   2014 669689 -HADE , by the  MINECO  project  MTM2014-53850-P  and  MINECO Severo Ochoa  excellence accreditation SEV-2013-0323.
3 This article is primarily based on a brief session of six weeks of fieldwork conducted in early 2016 as part of a preparatory exploration for a larger ongoing research project on urban popular religion in India which focuses on wayside shrines. During this time, I conducted unstructured interviews with devotees and caretakers of several wayside shrines, and spend many hours in participant observation documenting the shrines presented here and many others. However, my interest in this phenomenon started as a photographic exploration of wayside shrines which began the first time I set foot in India in 2005. The reader might thus expect detailed ethnographic data from each site. However, due to the current project being in its early stages, I can only hope to be able to enrich the data in future publications.
05 threshold. To further scrutinize the statistical significance of our data, we used the bootstrap technique with 10 6 replications on both the averaged data (table  S1 ) and raw data (see separate Excel file in other Supplementary Materials). The results of these analyses are given in the "Further statistical analysis" section (see the Supplementary Materials) and, in conformity with the t tests, indicate that the 3 to 4 ppm difference between the lunar and terrestrial data sets is statistically significant. Our results contrast with those of Young et al. (5) , undertaken on a somewhat smaller data set than the present one, which concluded that lunar and terrestrial rocks are indistinguishable with respect to D 17 O at a level of ±5 ppm. Our results indicate a dichotomy in terrestrial D 17 O values, with no significant difference between lunar rocks and terrestrial olivine (both high-and low-3 He/ 4 He varieties), but a statistically significant difference of 3 to 4 ppm when basaltic samples are included in the terrestrial data set.
The terrestrial olivine mineral separate data discussed in this paper have been presented previously  (19). However, as discussed below, the data have been recalibrated to VSMOW using more recent isotopic values for the Open University's laboratory standard gas (O 2 -8). Details of collection locations, composition, and mineralogy of these samples have been presented previously  (19). Lunar whole rock and mineral separates analyzed in this study include samples from all the Apollo landings: Apollo 11 (10020 and 10044), Apollo 12 (12016 and 12040), Apollo 14 (14053), Apollo 15 (15016, 15059, 15386, and 15555), Apollo 16 (62237 and 67435), and Apollo 17 (70017, 70035, 71055, 72155, and 78235). All of the major lunar rock suites are also covered by the samples analyzed in this study: high-and low-Ti Mare basalts, olivine normative basalts, KREEP basalts, and highland suite lithologies.