dataesr/scientific-paragraphs-categorization huggingface dataset:
As a result, the uncertainty related to the precipitation has a strong impact the estimation of the hydrological contributions to the outflow. With the different precipitation datasets, the ice melt contribution ranges from 29% to 76%, the snowmelt from 14% to 34% and the net rainfall from 7% to 37%. The HAR and Pluvio datasets, which present low annual precipitation amounts, result in higher simulated annual outflow compared to the Geonor dataset due to larger amounts of ice melt. In contrast, the CHIRPS and TRMM datasets overestimate the precipitation and produce too much runoff compared to the observed outflow (bias between 129% and 147%). The Geonor and HAR both give annual outflows similar to the observed outflow but with different contributions for the outflow components (67% of ice melt for HAR and 48% for Geonor).
(ii) Monthly contributions For all the datasets, the production of runoff is higher than the river outflow before the month of July, and lower in the period from August until December. This is due to the variation of the groundwater and englacial storage in the model: liquid water is stored in the soil or glacier reservoirs during the premonsoon and monsoon and then released during postmonsoon and winter  (Mimeau et al. 2018). The Geonor dataset results in the best simulated outflow (NSE and KGE equal to 0. 95 and 0. 91 for daily discharges) except in October 2013 when the simulated discharge is overestimated. This month is marked by the passage of the Phailin typhoon on October 13, which is not accurately reproduced by the model due to a too quick hydrological response to the event. The Geonor dataset results in mixed contributions of net rainfall, snowmelt, and ice melt during the monsoon season, a main contribution from snowmelt during the premonsoon, and a main contribution of ice melt during postmonsoon and winter.
The simulation with the Pluvio dataset leads to an overestimation of the winter and premonsoon discharges with a large contribution of ice melt. This is due to the low amount of snowfall during winter: the snow cover melts rapidly and the glaciers are exposed and contribute significantly already in March to the outflow. With the HAR dataset, the discharges are well simulated (0. 87 NSE and 0. 75 KGE) particularly during low flow periods. The simulations with HAR generate a large amount of ice melt compared to the Geonor dataset and the seasonal repartition of the flow components is very different between the two datasets. HAR results in a large amount of snowfall during winter, which delays the beginning of the ice melt and the rise of the discharges to May and is, thus, consistent with the observed outflow.
However, HAR overestimates the ice melt production during the monsoon season, due to a lack of precipitation to produce snow accumulation at high altitude to limit the glacier melt (Fig. 7 ). Concerning the CHIRPS datasets, the discharges are well simulated outside the monsoon season, but the excess of precipitation during the monsoon season leads to overestimated discharges. The simulations with the CHIRPS 1 and the CHIRPS 2 datasets show the impact of the diurnal cycle of the precipitation on the simulated annual and seasonal outflow. Similar amounts of precipitation with different subdaily distributions give different solid and liquid precipitation repartition and, thus, different amounts of annual outflow: the uniformly distributed dataset (CHIRPS 1) simulates more outflow than the hourly distributed dataset (CHIRPS 2).
The uniform distribution of the precipitation particularly impacts the winter and premonsoon discharges due to an excess of ice melt. The TRMM and CHIRPS 2 datasets give almost the same amount of annual outflow, but different seasonal discharges and contributions to the outflow. Figure  4  shows that TRMM overestimates precipitation during the monsoon season as well as snowfalls during premonsoon. Figure  9  demonstrates that the outflow in this case is mainly controlled by snowmelt during premonsoon, while the melting of ice only begins in June. In postmonsoon and winter the contribution of snow and ice melt remains small.
(iii) Subdaily variations of the flow components Figure  10  shows the diurnal variations of the production of ice melt, snowmelt, net rainfall and total outflow for each precipitation dataset. Except for monsoon net rainfall, the total runoff is produced by snowmelt and ice melt during day time. For all datasets, the diurnal variations of the outflow is not accurately simulated with a systematic overestimation of the midday peak. Particularly during the monsoon season, the model fails to simulate the observed diurnal peak occurring in the evening (between 2100 and 0000 LT). This evening peak is related to the diurnal cycle of the monsoon precipitation with a maximum in the late afternoon/evening as well as by the lag time due to the transfer through the catchment to the outlet after the melting occurring in the middle of the day.
Reasons for the mismatch between simulated and observed diurnal outflow are discussed in  Mimeau et al. (2018). The comparison between the subdaily variations of the outflow and flow components simulated with the six precipitation datasets show that a poor representation of the diurnal precipitation cycle amplifies the error on the simulated outflow. For instance, outside the monsoon season, the runoff production simulated with the Pluvio dataset is completely different from the results obtained with the other datasets. The wrongly attributed peak of rainfall at noon on the Pluvio dataset leads to a maximum in the rainfall contribution, which is not simulated with the other datasets. The lack of measured solid precipitation also leads to large amounts of ice melt in the middle of the day when the temperature is maximal.
46 m w. e. yr 21 with the annual gradient. The mean annual outflows simulated with the daily, monthly, and annual lapse rates are higher than the annual outflow simulated with the hourly lapse rates due to the larger amounts of ice melt. This shows that the applied spatial distribution also has an impact on the hydrological modeling and the estimation of the flow components. The uncertainty related to the lapse rates is lower than the uncertainty related to the precipitation dataset since the ice melt contribution ranges from 50% to 58% for the different temperature and precipitation lapse rates. For a small high-elevated catchment, the results show that hourly lapse rates are more suitable since the annual lapse rate leads to an underestimation of the winter precipitation.
Conclusions and perspectives In this study we compare the spatial distribution and temporal variability of several precipitation datasets for a small high-elevated catchment and evaluated the impact of the precipitation uncertainty on the glaciohydrological modeling. The precipitation datasets show a large variability particularly in terms of annual amount, seasonal and subdaily variability. First, the comparison between two in situ precipitation datasets measured with different types of rain gauges showed that classical tipping buckets are not able to produce reliable precipitation data during postmonsoon and winter seasons. The classical tipping buckets strongly underestimate the solid precipitation and do not represent correctly the diurnal distribution of the precipitation. Geonor rain gauges seem to measure more realistic winter precipitation.
However, the spatial interpolation of the in situ measurements leads to additional uncertainties: the mean annual catchment precipitation ranges from 575 to 645 mm yr 21 when using different sets of temperature and precipitation altitudinal gradients. Other precipitation datasets used in this study (TRMM, CHIRPS, and HAR) showed a large variability in annual amounts, as well as seasonal and subdaily distributions. The mean annual precipitation estimated on the Pheriche catchment with these gridded precipitation datasets ranges from 410 to 1190 mm yr 21 , and the fraction of annual precipitation occurring during the monsoon season ranges from 53% with the HAR dataset to 84% with the Pluvio dataset. These datasets show different spatial distribution of the annual precipitation with a southnorth gradient for the CHIRPS dataset and a west-east gradient for the HAR dataset.
The comparison between the precipitation datasets also showed that the subdaily distribution of the precipitation has a significant impact on the rainfall and snowfall distribution. The simulations using the glacio-hydrological model DHSVM-GDM demonstrate that the choice of a given precipitation dataset has a very strong impact on the simulated annual outflow as well as the flow components. The annual outflow simulated with the different datasets range from 1035 to 1456 mm for the glaciological year 2012-13 and the ice melt contribution to the outflow range from 28% to 76%. The datasets, which underestimate the annual precipitation (HAR and classical tipping buckets), give higher estimations of the ice melt component. Different precipitation datasets can lead to well simulated discharges, but also to very different estimations of the ice and snowmelt contributions to the outflow (e.
g. , Geonor and HAR). On the opposite, similar precipitation datasets can lead to very different simulated discharges and contributions to the outflow (e. g. , CHIRPS 1 and CHIRPS 2). This means that in a high-altitude region with high precipitation uncertainties the use of a given dataset can lead to large errors on the estimation of the ice and snowmelt contributions to the outflow, even if the discharge is correctly simulated. The diurnal cycle of the precipitation greatly impacts the simulated snow cover and glacier accumulation and, thus, indirectly influence the simulation of ice and snowmelt. This highlights the importance of considering hourly time steps to accurately simulate the glacio-hydrological processes. As a result it is important to evaluate the performance of the precipitation datasets at a subdaily time scale.
The choice of the temperature and precipitation gradients to interpolate in situ precipitation measurements leads to an uncertainty of 15% on the simulated annual outflow and an uncertainty around 8% on the ice melt components. This study shows that, for a small high elevated catchment, the Geonor precipitation data interpolated with an hourly precipitation gradient leads to best results in terms of annual and seasonal outflow as well as a good representation of the snow cover area and glacier mass balance. The spatial resolution of the TRMM and HAR datasets is not adapted for small catchments since it is too coarse for the study area and the precipitation may not be representative for the basin. It would be interesting to reproduce this study over a larger catchment (Dudh Koshi basin) in order to evaluate the uncertainty related to the precipitation dataset at a larger scale.
Moreover, instead of using a gridded precipitation datasets, the glacio-hydrological model could be forces by the output of a high-resolution climate model. High-resolution climate models enable simulating precipitation fluxes at small spatial resolution and have a better representation of the spatial distribution of the precipitation at local scale  (Ménégoz et al. 2013). Collier and Immerzeel (2015)  and  Karki et al. (2017)  both used the Weather Research and Forecasting (WRF) Model at a 1-km resolution to simulate the atmospheric circulation on Himalayan catchments. They found that running the WRF Model at high resolution with an explicit simulation of the convection improves the simulation of precipitation, in particular at high elevation. It also enables a better representation of the spatial pattern, magnitude, and the seasonal and diurnal cycle of the precipitation.
Moreover,  Li et al. (2017)  used precipitation data generated at 300-m spatial resolution with WRF for hydrological simulations of an 800-km 2 basin in the Himalayas. These studies show encouraging results and using WRF simulations at a kilometer or subkilometer scale shows good potential to generate forcing data for glacio-hydrological models in a small and high-elevated catchment such as the Pheriche catchment and, thus, reduces the uncertainty of the precipitation forcing data.
Mean annual basin precipitation obtained with the six precipitation datasets during the period 2010-15 (depending on data availability) and the glaciological year 2012-13. The HAR, CHIRPS 1, CHIRPS 2, and TRMM total precipitation were derived from the weighted average of the datasets grid cells overlapping the catchment domain (see section 2). The two CHIRPS datasets result in rather similar annual precipitation for the year 2012-13, however, the CHIRPS 2 dataset results in a slightly larger fraction of snowfall compared to CHIRPS 1 with fractions of 61% and 57% of solid precipitation.
Acknowledgments. This work has been supported by the  French National Research Agency (ANR)  through the  ANR-13-SENV-0005-04/05 - PRESHINE  project and by a grant from  Labex   OSUG@2020 (investissement d'avenir ,  ANR 10 LABX56 ). It has benefited from data from the  French Service d'Observation GLACIOCLIM  (https://glacioclim. osug. fr/)and from  Ev-K2-CNR  (www. evk2cnr. org). The DHSVM-GDM model was made available by  Chris Frans  ( Department of Civil and Environmental Engineering, University of Washington ). The works of  Yves Lejeune  ( CNRM-GAME ) for correcting precipitation data in the GLACIOCLIM database.
We present the Copernicus in-situ ocean dataset of temperature and salinity (version V5. 2). The 1 ocean subsurface sampling varied widely from 1950 to 2017, as a result of changes in the instrument 2 technology and development of in-situ observational networks (in particular, tropical moorings, ARGO 3 program). The global ocean temperature data coverage on an annual basis grows thus from 10% in 1950 4 (30% for the North Atlantic basin) to 25% in 2000 (60% for the North Atlantic basin) and reaches a plateau 5 exceeding 80% (95% for the North Atlantic Ocean) after the deployment of the ARGO program. The 6 average depth reached by the profiles also increases from 1950 to 2017. The validation framework is 7 presented, and an objective analysis-based method is developed to assess the quality of the dataset 8 validation process.
Analyses of the ocean variability are calculated without taking into account the data 9 quality flags (raw dataset OA), with the near real time quality flags (NRT dataset OA) and with the delayed 10 time mode quality flags (CORA dataset OA). The comparison of the objective analysis variability shows 11 that the near real time dataset managed to detect and to flag most of the large measurement errors, reducing 12 the analysis error bar compared to the raw dataset error bar. It also shows that the ocean variability of the 13 delayed time mode validated dataset is almost exempt from the random error induced variability.
during the 1960s and widely used by navies. Most of the XBT profiles collected during this period are T4 type sensor, measuring temperature above -460 meter depth. The development of the Sippican T-7 instrument with a maximum depth of 1000m slowly increases the number of measurements between 460m and 1000m during the 1980s (see Fig :  2  for the dataset measurements distribution with depth). An instrument capable of measuring conductivity, temperature and pressure (CTD) was developed in the 1960s, allowing an accurate estimation of sea salinity and temperature. The yearly amount of CTD profiles in the CORA dataset then slightly increased reaching a plateau of about 20000 profiles in the early 1990s. During this period, the largest density of profiles is found in the North Atlantic Ocean, with a coverage ratio, calculated on a 3° per 3° grid with a one year time step, increasing from 30% in 1950 to a plateau of 60-70% in the 1970s (Fig:  3 ).
During the 1990 decade, the yearly number of XBT profiles strongly decreases while the number of bottles and CTD profiles slightly increases leading to a more homogeneous but barely smaller dataset. The measurements provided are however deeper than in the previous decade, leading to a better coverage below 500m depth (Figure  2 ). The profile number then exponentially increases since the development of the TAO/RAMA/PIRATA equatorial mooring program throughout the 1990s. During this time, the North Atlantic and the North Pacific Ocean spatial sampling rates decreases, and the global ocean sampling rates barely maintain a plateau at 20%. The ocean sampling rate rapidly increases in the early 2000s thanks to the development of autonomous profilers and the worldwide ARGO program. The global ocean sampling rate reaches 70% before the mid-2000s with a maximum of 85% in the northern Atlantic Ocean.
105 It must be emphasized that a fraction of the profile number sharp increase of the early 2000s results from the data acquisition from high frequency measurement devices such as the ocean drifters, the thermosalinographs (TSGs), both near the ocean surface, or undulating CTDs either towed or untowed (scanfish, seasoar, gliders,. ). Indeed, each undulating CTD profile and each independent TSG or drifter measurement is treated as an independent profile in the CORA dataset, which leads to a more homogeneous dataset file structure. This dataset structure is also 110 adopted for the mooring measurements which in some cases are also collecting data at high frequency. This large Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-144 Manuscript under review for journal Ocean Sci. Discussion started: 21 January 2019 c Author(s) 2019.
Data quality control 115 The measurements collected by the Coriolis data center are distributed to the scientific community with a near real time quality control flag within days from the data reception and with a delayed time mode validation quality control within a year. The Coriolis datacenter validation workflow scheme is given on figure  4. Numerous measurements distributed by Coriolis have however been validated by scientific teams prior to the 120 integration in the Coriolis streamflow. The most important of these datasets are the delayed time mode validated ARGO profiles, the tropical mooring dataset, distributed by PMEL, the sea mammal measurements validated by the MEOP project and the TSG measurements validated by the GO-SUD project. In such cases, the current practice at Coriolis is to retain the flags from the imported database and to run the delayed time mode tests afterwards.
125 The near real time dataset validation tests are mostly taken from the ARGO real time quality control tests  (Wong et al. 2009 ). The goal is to distinguish the spurious measurements from the good measurements and to flag them quickly. The test checks are designed to detect well known types of errors. A global range test and a regional range test are performed to detect obvious errors with respect to known ocean variability. The bounds of those two tests are very large with respect to the known ocean variability to ensure that no bad flag would be incorrectly attributed.
130 A spike test and a gradient test are performed to detect measurement spikes in the temperature and salinity fields. The test thresholds are set large enough to lower the number of incorrect spike detections corresponding to a sharp, yet correct, thermocline or halocline. The stuck value test aims to detect temperature or salinity profiles with a constant value within the vertical reported inaccurately. A second step in the near real time quality control is performed daily on the ARGO profilers distributed by Coriolis 135 using an objective mapping detection method  (Gaillard et al. 2009). Following the framework developed by  Bretherton et al. (1976) , the residual of the objective analysis depends on the covariance from data point to data point. This second check step aims thus to detect measurements departing from other data in its vicinity.
The correlation scale in the objective analysis varies with depth and latitude. Spurious detections can however occur when profiles located on both sides of a frontal zone are within a correlation radius. Therefore, detected profiles 140 are visually checked by a PI to distinguish erroneous measurements from possible measurements. Lastly, a quality control based on altimetry comparisons is also performed on a quarterly basis to improve the real time validated dataset  (Guinehut et al. 2009). A PI investigation is also performed on profiles flagged as suspicious by comparison with altimetric sea level.
Depth check, Stability Check, Vertical check The depth check, stability check and vertical check have initially been developed by the UK Met-Office for the EN4 dataset validation. They have been added to the CORA validation framework after a collaborative comparison of the two dataset validation methods with the UK Met-Office team. This study has shown that most of the profiles 170 flagged in EN4 and not in CORA were detected by these three tests and that applying a visual control to the profiles detected in this way results in more accurate flags. The tests have been described in  Ingleby and Huddleston, 2007. The stability test detects density inversions for profiles where both temperature and salinity are available. The density inversions with 0 > d\rho >-0. 03 kg. m3 are dismissed. Both temperature and salinity are visualized for profiles with larger density inversion.
215 A third validation is performed with the ISAS objective analysis tool, following the method developed by  Gaillard et al, (2009). This validation test is similar to the validation performed by the Coriolis datacenter in near real time with the ARGO floats. The scope of the test is however extended to other profiles (XBT, CTD, etc. ) and to ARGO profiles which have been updated in the Coriolis database too late to be part of the near real time validation. With   the number of measurements available in the summed grid point and (, )the total number of measurements involved in the calculation of a grid point value. The values of   and   are set to 5° longitude and latitude in order to include enough grid points with data in this 275 averaging. To reduce the calculation time of the analysis, each (, ) calculation is performed on a 20 per 20 grid points windows.
The objective analysis is performed at three steps of the global dataset. A first analysis is performed on a raw dataset, considering all available profile measurements. All the QC flags are considered good. A second analysis 280 is performed on the same data profiles considering the QC available on NRT mode. A third one is performed on the same profiles considering the QC available on delayed time mode. The ocean data coverage is sometimes insufficient to perform the monthly objective analysis on the whole ocean. A striking feature is the corresponding spike visible in the NRT analysis and in the raw dataset analysis in late 2010, which suggest that major data errors have not been flagged in the dataset during the NRT validation. Further exploration of this anomaly has shown that a fraction of the larger error bar in the NRT analysis is caused by an 345 issue in the update of delayed time mode processed ARGO profiles.
In a few cases when salinity measurements present large drifts, the ARGO PIs can decide that the salinity drift is too high to be adjusted. In these cases, the PI provides to the global DAC a delayed time version of the profiles with an adjusted temperature field, but with a practical salinity field filled with fillvalues and a salinity QC field filled with "4" values (bad measurement status). In some cases, the Coriolis data center had updated the profiles by getting the temperature adjusted field 350 but without creating a salinity adjusted field. The available salinity field and QC in the Coriolis datacenter is therefore the original salinity field which might not have been flagged at "4". In this study, a handful of these profiles, often associated with large salinity drifts (for instance salinity values on the order 20 PSU in the Indian Ocean) have produced large error bars in the NRT analysis fields.