dataesr/scientific-paragraphs-categorization huggingface dataset:
This study compares the precipitation derived from each dataset on annual, monthly, and subdaily time scales in order to assess the temporal variations and the liquid and solid phases of the precipitation. Moreover, the uncertainty of the interpolation method used for spatializing in situ data is addressed by estimating different sets of precipitation and temperature altitudinal gradients and analyzing their impact on the average basin precipitation and the snowfall/rainfall distribution. In the second part of this study, the glacio-hydrological model Distributed Hydrological Soil Vegetation Model-Glaciers Dynamics Model (DHSVM-GDM) is used to evaluate the impact of the precipitation uncertainty on the simulated water balance and the estimated contributions of ice and snowmelt to the outflow.
Study area This study focuses on the Pheriche subcatchment of the Dudh Koshi basin (outlet at coordinates 27. 898N, 86. 828E) located in Nepal on the southern slopes of Mt. Everest in the Sagarmatha National Park (SNP; Fig. 1 ). The catchment area corresponds to 146 km 2 , and its elevation extends from 4260 to 8848 m MSL. Further details can be found in  Mimeau et al. (2018). Figure  1c  shows the precipitation as a function of elevation in the upper Dudh Koshi basin. Mean annual precipitation estimated for the period 2000-15 was derived from in situ data collected at meteorological stations maintained by the EvK2-CNR association (www. evk2cnr. org) and from the PAPRIKA-PRESHINE database  (Chevallier et al. 2017). The figure confirms the decrease of the annual precipitation above 2500 m MSL in the Dudh Koshi catchment  (Savéan et al.
a. Available datasets In situ meteorological data are available at hourly time steps at the three meteorological stations located in Pangboche (3950 m MSL), Pheriche (4260 m MSL), and Pyramid (5035 m MSL; Table  1 ). At the Pheriche and Pyramid stations, precipitation was measured with two different types of rain gauges: classical tipping buckets and Geonor T-200 sensors (since December 2012). Classical tipping buckets are known for not correctly measuring solid precipitation  (Wolff et al. 2015). The Geonor rain gauges are designed to measure solid precipitation more accurately, however a correction still needs to be applied on the measured data for potential undercatch. In both locations most of the time the wind speed is lower than 3. 2 m s 21 (third quartile) at Pyramid and 3. 0 m s 21 (third quartile) at Pheriche, median values were 2.
00 and 1. 42 m. s 21 , respectively. For this range of values, the catch efficiency for snowfall of a Geonor T-200B equipped with a single Alter wind shield could be only 65%  (Wolff et al. 2015, their Fig. 5 ). Accurate measurement of snow also depends on the type of snowflake observed during a snowfall  (Thériault et al. 2012). Geonor raw data were corrected from bias using a dynamic correction model, based on air temperature, wind speed and precipitation phase. The correction coefficients were estimated as described in  Førland et al. (1996). For this study, two precipitation datasets were established based on the measurements with the two instruments referred as Pluvio and Geonor datasets. Three other precipitation datasets available at high spatial resolution (,30 km) and daily or subdaily time scale for the Himalayan region were used (Table  2 ).
We considered the High Asia Refined analysis (HAR) atmospheric dataset, which was generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) Model and provides data at an hourly time step and a resolution of 10 km over the Tibetan Plateau from 2001 to 2014  (Maussion et al. 2014). We further used precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). This study makes use of the TRMM 3B42 version 7 product, which provides precipitation data from a combination of remote sensing observations (microwave imager, precipitation radar, visible and infrared scanner) and in situ  Due to the mismatch between the spatial resolutions of the three gridded datasets, particularly TRMM (30 km) and HAR (10 km), and the catchment area (146 km 2 ), we do not intend in this study to evaluate the quality of the datasets for the Pheriche catchment, but to assess the error on the precipitation for a small Himalayan catchment and to quantify the uncertainty of the simulated hydrological response to a realistic range of forcing precipitation.
1) HOURLY DOWNSCALING Since the hydrological model is forced with precipitation data at a 1 h time step, the TRMM and CHIRPS datasets were converted into hourly data. In the case of the TRMM dataset, the precipitation over 3 h was uniformly distributed among the 1 h time step. Concerning CHIRPS precipitation, two different datasets were established in order to assess the impact of the subdaily distribution of the precipitation on the repartition between solid and liquid phases and on the glacio-hydrological modeling. First, a dataset referred to as CHIRPS 1 was derived by uniformly distributing the precipitation intensity over the entire day. The second dataset, CHIRPS 2, was created using the precipitation intensity observed by the Geonor rain gauges according to P CHIRPSH 5 P CHIRPSJ 3 P ObsH P ObsJ , (1) where P CHIRPSH and P CHIRPSJ are the 1 h and 24 h precipitation in a CHIRPS grid cell and P ObsH and P ObsJ are the 1 h and 24 h precipitation measured by the closest Geonor rain gauge.
2) SPATIAL INTERPOLATION To estimate the Pheriche catchment precipitation from the Geonor and Pluvio datasets, the Pheriche and Pyramid gauges were spatially interpolated by inverse distance weighting (IDW) at a 100-m resolution over the catchment and an hourly altitudinal precipitation gradient was applied in order to take into account the topography (see section 4). The HAR, TRMM, CHIRPS 1, and CHIRPS 2 catchment precipitation were estimated by calculating the weighted average of the datasets grid cells overlapping the catchment domain.
3) SEPARATION OF THE PRECIPITATION PHASES The same temperature distribution was used to separate solid and liquid phases or every precipitation dataset. The temperature distribution is derived from the Pheriche and Pyramid AWS in situ measurements which were interpolated by IDW at 100-m resolution. A daily temperature lapse rate (see section 4) was also applied to take into account the elevation. Precipitation is defined as snowfall if the temperature is below 08C and as rainfall if the temperature is above 28C. Between 08 and 28C precipitation is composed of mixed snowfall with the share of rainfall increasing linearly with temperature.
4) ALTITUDINAL GRADIENTS The precipitation and temperature lapse rates are calculated using in situ data collected at Pangboche (3950 m MSL), Pheriche (4260 m MSL), and Pyramid (5035 m MSL). We analyzed the impact of the imposed temperature and precipitation lapse rates on the catchment precipitation and on the snow and rainfall partitioning by testing different sets of gradients calculated at hourly, daily, monthly, and annual time scales. A benchmark set of lapse rates with a constant precipitation lapse rate equal to 0 and a standard average temperature gradient equal to 20. 00658C m 21 (Barry and Chorley 2009) was used as reference. To retain only significant hourly and daily precipitation lapse rates, the lapse rate is set to zero in cases where the lapse rates between the two pairs of stations (Pangboche-Pheriche and Pheriche-Pyramid) were not of the same sign.
b. Hydro-glaciological modeling 1) NUMERICAL SETUP Glacio-hydrological simulations are run with DHSVM-GDM, a physically based and spatially distributed developed by  Wigmosta et al. (1994)  and  Naz et al. (   2014 ) is a model. The parameterization of snow and glaciers in DHSVM-GDM was modified concerning the snow albedo, the snow transport by avalanches, and the ice melting for debris-covered glaciers. These modifications as well as the applied model parameters are presented in detail in  Mimeau et al. (2018). To describe the topography of the study area an ASTER DEM originally at 30-m resolution was resampled to a 100-m resolution. Data used for the soils, land cover, and glacier description are described in  (Mimeau et al. 2018). Concerning the meteorological forcing, in situ measurements of air temperature, relative humidity, wind speed, incoming shortwave and incoming longwave from the Pheriche and Pyramid AWS were spatially distributed using the IDW method.
To test the sensitivity of the glacio-hydrological modeling to the temperature and precipitation gradients the model was forced with the Geonor precipitation dataset with five sets of precipitation and temperature gradients: hourly, daily, monthly, annual, and benchmark gradients (Table  2 ). For the Geonor and Pluvio datasets, hourly precipitation gradients are used to derive the spatially resolved precipitation. For the HAR, TRMM, and CHIRPS datasets, no precipitation gradient was applied and the precipitation was uniformly distributed over the original grid of each dataset. Concerning temperatures, the same daily temperature lapse rates were applied for the six simulations. Simulations were run at a 1-h time step and 100-m spatial resolution for periods with available meteorological, precipitation, and discharge data.
1) ANNUAL PRECIPITATION AMOUNTS The six analyzed precipitation datasets show a large variability of annual precipitation amount over the Pheriche catchment (Table  3 ). The Pluvio and HAR datasets indicate less than 470 mm yr 21 of precipitation, whereas the CHIRPS and TRMM datasets result in precipitation rates higher than 960 mm yr 21. The Geonor dataset gives an intermediate rate with 688 mm yr 21. The distribution between solid and liquid precipitation varies between the datasets with the fraction of annual solid precipitation ranging from 60% for the TRMM dataset to 76% for HAR. A large difference between the two datasets from the in situ measurements is found [242 mm (59%) of annual snowfall for the Pluvio dataset versus 533 mm (78%) for the Geonor dataset] related to the undercatch of the solid precipitation by the standard  We also compared the rainfall and snowfall data from the HAR dataset in the Pheriche catchment for the period 2010-14 and found a higher snowfall fraction (83%).
The large fraction of solid precipitation with the HAR dataset compared to the other datasets corroborates other studies finding that WRF with the Thompson microphysics scheme tends to produce too much snowfall (e. g. ,  Hughes et al. 2017). We tested the impact of the temperature thresholds on the rainfall/snowfall repartition by changing the thresholds to 218 and 38C, which are also reliable thresholds according to the existing literature (e. g. ,  L'hôte et al. 2005; Froidurot et al. 2014; Jennings et al. 2018) , and found similar snowfall fractions for every datasets. Concerning the impact of the lapse rate on the catchment precipitation, Table  4  shows that the derived annual amounts range between 575 and 645 mm for the Geonor dataset. Therefore, the uncertainty related to the lapse rate is small compared to the uncertainty related to the source of the precipitation data.
Nevertheless, the lapse rates impact the estimation of very high altitude precipitation (above 6000 m MSL) and, thus, mainly impact the amount of annual solid precipitation ranging from 433 to 488 mm. The application of the hourly lapse rate results in the highest precipitation because positive lapse rates mainly occur during the winter season, which leads to larger amounts of snowfall at very high altitudes. On the opposite, the monthly and annual lapse rates give smaller precipitation amounts than the benchmark dataset. Applying the monthly and annual gradients causes a decrease at higher altitudes, while the precipitation is homogeneous in the benchmark dataset. We also tested the sensitivity of the temperature and precipitation lapse rates separately. The results show that the solid/liquid precipitation distribution is mainly sensitive to the precipitation lapse rate.
The chosen time step for the temperature lapse rate calculation has a limited impact on the solid/liquid precipitation distribution as the mean annual snowfall amount obtained with the hourly, daily, monthly, and annual temperature lapse rates tested separately ranges between 525 and 533 mm. Figure  3  presents the spatial distribution of the precipitation for each dataset for the glaciological year 2012-13, which is the only common year in all datasets. The precipitation datasets show a large variability in their spatial distribution. The interpolated in situ precipitation  of the precipitation occurring during the monsoon season (against 60% with the Geonor dataset). Wagnon et al. (2013)  and  Salerno et al. (2015)  both found a high monsoon precipitation share (respectively 80% and 90%) at the Pyramid station using the EvK2-CNR precipitation measurements with classical tipping buckets and these studies both mentioned that the solid precipitation was underestimated by the tipping buckets.
With the Geonor dataset the monsoon precipitation represents only 58% of the annual precipitation because the Geonor gauges measure more solid precipitation during the winter season (Fig. 4 ). The CHIRPS, HAR, and TRMM datasets also show differences in the seasonal repartition of precipitation. Figure  4  shows that seasonal variations in the HAR dataset are very different from the other datasets as the monsoon precipitation only represents 53% of the annual precipitation in the HAR dataset, whereas it is the main source of precipitation in the other datasets (from 60% up to 85%). Moreover, most of the HAR precipitation occurs between the post and premonsoon seasons, when the temperatures are lower leading to a high proportion of snowfall (76%). The CHIRPS and TRMM data result in particularly high amounts of precipitation during the monsoon season (76% and 77%) with a smaller proportion of snowfall.
3) DIURNAL VARIATION OF PRECIPITATION Figure  5  represents the daily cycle of the precipitation for all datasets and for the different seasons. The two in situ precipitation datasets have very different patterns during winter, premonsoon and postmonsoon. The Geonor shows a broad maximum in the second half of the day with highest values around 1800 local time (LT). The Pluvio dataset shows peaks of precipitation around noon during postmonsoon and winter. This can be explained by the fact that the classical tipping buckets do not register the snow entering the bucket, when the temperature is below 08C. The snow accumulates inside the bucket and melts only later when the ambient temperature rises above 08C during the day. Thus, the peak is mainly caused by the melting of the previously accumulated snow.
This precipitation pattern results in different distributions of liquid and solid precipitation since air temperatures are applied to determine The TRMM dataset has a diurnal pattern similar to the Geonor dataset during the monsoon season with a peak of precipitation in the evening. TRMM clearly overestimates the precipitation during the premonsoon and monsoon seasons, particularly the evening snowfall events during premonsoon. HAR shows diurnal winter and monsoon precipitation patterns different from the other datasets. In winter, HAR shows no particular diurnal variation and higher snowfall intensities than the other datasets. During monsoon season, precipitation intensities are lower, the peak of precipitation occurs in the morning and precipitation at night and during the evening remains limited.
c. Impacts of the precipitation uncertainty on the simulated water balance components Figure  6  shows the annual simulated water balance for the year 2012-13 derived with each precipitation dataset. In all simulations, the sum of water losses is on average 46 mm below the sum of the annual contributions. This difference is essentially due to numerical errors (global model error of 0. 81%), but can also be partly ascribed to the model initialization, despite a 3-yr spinup. The simulated annual outflow is clearly not directly proportional to the annual amount of precipitation. Datasets with high amounts of precipitation (CHIRPS and TRMM) lead to an overestimation of the annual outflow, but this is also the case for the Pluvio dataset FIG. 6. Simulated water balance for the six precipitation datasets for the year 2012-13.
For each dataset, the left column represents the water contributions for runoff (precipitation and ice melt), and the right column represents the water losses (outflow, evapotranspiration, and sublimation). The soil, englacial water, and snow storage variations are either considered as contributions or losses depending on their sign. Observed outflow at the Pheriche station is represented in black with an uncertainty of 15%. with a low amount of precipitation. The annual amount of precipitation impacts the amount of ice melt. Large amounts of precipitation result in a low amount of glacier mass loss, whereas low amounts of precipitation result in large glacier mass losses. The snow and rainfall distribution and the temporal distribution of the precipitation also play major roles in the water balance: datasets with similar amounts of precipitation, but with different amounts of solid precipitation (i.
e. , the Pluvio and HAR datasets) result in different glacier mass losses and annual outflows. Similarly, the CHIRPS 2 dataset has the same annual amount of precipitation than CHIRPS 1, but a different temporal distribution and leads to a lower glacier mass loss and annual outflow. Concerning the losses by evapotranspiration and sublimation, all precipitation datasets lead to similar results with annual values around 150 mm. Results also demonstrate that the simulated soil, snow, and englacial storages show small annual variations and that changes in the stored amounts are negligible for the annual water balance.
EQUIVALENT Figure  7  shows the simulated snow water equivalent (SWE) and snow cover area (SCA) during the glaciological year 2012-13 with respect to the used precipitation dataset. The daily SWE and SCA distributions for the catchment show a large variability, particularly for the SWE where distributions do not always overlap which each other. The two in situ datasets, result in very different SWE and SCA. The Geonor dataset gives a median daily SWE of 235 mm and a median daily SCA of 60% of the catchment area, whereas the Pluvio dataset leads to very low daily SCA and SWE values. Most of the SCA values obtained with the Pluvio dataset range between 20% and 30% and the Pheriche basin is never fully covered by snow, which is not in agreement with the SCA derived from the MODIS images. The differences between the SCA and SWE values obtained with the Geonor and Pluvio datasets are clearly related to different amounts of annual snowfall (Table  3 ).
The datasets CHIRPS 1 and 2 show the impact of the diurnal distribution of the precipitation on the snow cover simulation. CHIRPS 1 has less annual snowfall, which explains that CHIRPS 1 gives smaller daily SWE values than CHIRPS 2. Moreover, as the precipitation is uniformly distributed during the day, the maximum snowfall rates are smaller in the CHIRPS 1 dataset leading to a thinner snowpack and a quicker melting of the fresh snow (see Fig. 5 ). The CHIRPS 2 dataset with the hourly disaggregation using in situ data results in simulated SCA closer to MODIS. The HAR dataset has a low SWE distribution, but a large SCA variability with a mean SCA of 47%. Most of the HAR precipitation occurs during winter and is classified as solid precipitation, which explains the high SCA. Nevertheless, the snow melts quickly, when the temperature increases and the little snowfall during the monsoon season causes the low SWE values and the low minimum SCA.
The large amount of snowfall in the TRMM dataset leads to a median snow coverage of 55% in the catchment. The SWE value is also higher than obtained with the other datasets due to a larger snow accumulation. The comparison between the six precipitation datasets shows that the simulated SWE and SCA are not linearly correlated to the Observed SCA derived from daily MODIS satellite images is also shown. annual amount of snowfall. For example, the Geonor dataset leads to a higher median SWE value compared to the CHIRPS 1 dataset despite the lower amount of snowfall (516 mm for the Geonor dataset versus 578 mm for CHIRPS 1 for the year 2012-13). Similarly, the Pluvio and HAR both result in low median SWE values (lower than 150 mm) with different amounts of annual snowfall (240 mm for the Pluvio dataset versus 406 mm for HAR).
This shows that the seasonal and hourly distribution of the solid precipitation has a large impact on the simulated snow cover. 2) GLACIER COMPONENT Figure  8  illustrates the impact of the precipitation dataset on the simulated glacier mass balance. The simulated glacier mass balance is well correlated to the annual amount of snowfall (R 2 5 0. 7). Nevertheless, the CHIRPS 1 and CHIRPS 2 datasets show that similar snowfall amounts can lead to different simulated glacier mass balances. For instance, for the glaciological year 2012-13, while the annual amounts of snowfall of the CHIRPS 1 and CHIRPS 2 datasets correspond to 578 and 612 mm, the derived glacier mass balances are equal to 21. 55 and 21. 09 m water equivalent (w. e. ) yr 21 , that is, an increase of 6% of the snowfall amount leads to a decrease of 30% of the glacier mass balance.
The large differences between the mass balances simulated with the CHIRPS 1 and CHIRPS 2 datasets shows the impact of the diurnal cycle of the precipitation on the glacier mass balance: the CHIRPS 2 dataset leads to more precipitation during the afternoon and night in the monsoon season (see Fig. 5 ), resulting in larger snow accumulation (Fig. 7 ) and less melting (Fig. 6 ). Figure  8  and Table 5 also show the range of values of glacier mass balances determined with geodetic methods  (Bolch et al. 2012; Gardelle et al. 2013; Nuimura et al. 2015; King et al. 2017; Brun et al. 2017). Only the order of magnitude of these mass balances can be compared to the simulated annual mass balances since the considered areas and the time periods are not the same in all studies. The Pluvio and HAR datasets result in very negative glacier mass balances (with a mean annual mass balances respectively equal to 21.
98 and 21. 52 m w. e. yr 21 ) due to the lack of snow accumulation on the glaciers and the excess of ice melt and differ strongly from observed glacier mass balances. The mass balances simulated 3) RUNOFF COMPONENTS Figure  9  illustrates the simulated annual and monthly discharges and flow components for the glaciological year 2012-13 derived for the six precipitation datasets. Here, the sum of the three flow components (ice melt, snowmelt, and net rainfall) corresponds to the total production of runoff. At a monthly time step, the production of runoff is not necessarily equal to the river outflow because the time of transfer within the catchment is not taken into account.
(i) Annual contributions Like applied precipitation datasets, the simulated annual outflow shows a considerable variability: it ranges FIG. 9. Simulated annual and monthly outflow and flow components with the six precipitation datasets for the glaciological year 2012-13. Observed outflows (black dashed line) are represented with a 15% interval of uncertainty. from 1071 mm for the Geonor dataset to 1456 mm for the CHIRPS 1 dataset. Nevertheless, while the maximum and minimum annual precipitation amounts vary by a factor of 2. 9 (1235 mm for the TRMM dataset and 429 mm for the Pluvio dataset for the year 2012-13), the ratio between the maximum and minimum annual outflow is only on the order of 1. 3. Figures  6  and 9  show that in cases with low annual precipitation amounts (and specifically with low annual snowfall amounts), additional ice melt leads to a compensation in the simulated annual outflows.