dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (Geo-SFM).
Developing a tool that can address all these limitations by integrating snow, ice and precipitation information from both satellite and local sources is critical to allow appropriate response to natural hazards to the population  (Wisner et al. , 2004) , and is a focus of this study. Here we present an integrated modeling capability developed to meet the water resources planning needs of this broad, multination region taking full advantage of NASA Earth Science data and modeling products. We developed a hydrologic tool that can be used at basin or sub-basin scale in an easy-to-use graphical user interface framework accessible to most users. The modeling needs were addressed by integrating an enhanced, gridded version of the Utah Energy Balance (UEB) snowmelt model, denoted here as UEB-Grid and the Geospatial Stream Flow Model (GeoSFM) into the Better Assessment Science Integrating point and Nonpoint Sources (BASINS) modeling system.
We downscaled various NASA gridded remote sensing and climate products and combined them with higher-resolution data for use in these models. The resulting tool, publicly available to both the hydrological and cryospheric communities online (http://hspf. com/pub/ HIMALA_BASINS/) is referred here as HIMALA BASINS, and will be maintained in conjunction with the U. S. Environmental Protection Agency's (EPA) open-source BASINS tool. The HIMALA BASINS model grew out of a decade of collaborative work between USAID's Office of Foreign Disaster Assistance, USGS and ICIMOD and was initially funded through the Asia Flood Network, a project designed to produce satellite-derived rainfall data products used to drive hydrological models. Since 2003, the NOAA Climate Prediction Centre's RFE2  (Xie et al.
Modeling framework: HIMALA BASINS In this study, we linked a snow melt model with a stream flow model within a version of the BASINS software developed and maintained at U. S. EPA. BASINS consists of a pre-existing suite of hydrological models and supporting tools, and was chosen for this project due to its implementation using the MapWindow Geographic Information Systems (GIS). HIMALA BASINS incorporates two models: the UEBGrid (for estimating meltwater components) and GeoSFM models (for hydrologic modeling and routing), both of which were implemented as BASINS plugins by AQUA TERRA Consultants, the prime contractor for development and support of BASINS. The HIMALA BASINS version of BASINS is fully available to users via free download at http://hspf. com/pub/HIMALA_ BASINS/. The HIMALA BASINS User's Manual (located in the documentation folder at the same URL) guides the user through each component so that one component builds on another beginning with the preprocessing and downscaling routines, building UEB-Grid-required layers in GeoSFM, running the UEBGrid, and finally running the GeoSFM program.
, 2011; Suarez et al. , 2008)  and National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis  (Wu et al. , 2002). MERRA temperature, wind speed and relative humidity are reported at a height of 2 m above ground, at a spatial resolution of 2/3°longitude by 1/2°latitude, and hourly time resolution. Incoming shortwave and longwave radiation are reported at the  surface, at coarser resolution of 1. 0°by 1. 25°and 3-hourly time step  (Lucchesi, 2012). All MERRA records are available from 1979 to present. MERRA hourly temperature data were averaged into three hour blocks, bilinearly interpolated and projected to 90-m using functions in the R raster library  (Hijmans et al. , 2013). They were also adjusted for elevation differences between the effective elevation determined from the geo-potential height that MERRA used and SRTM DEM elevation using a monthly lapse rate from  Liston and Elder (2006).
We used daily total precipitation estimates from RFE2 data. These records were constructed using four observational input data sources, namely: approximately 280 GTS stations, geostationary infrared cloud top temperature fields, polar orbiting satellite precipitation estimate data from SSM/I, and AMSU-B microwave sensors  (Xie et al. , 2002). Near-real time daily rainfall estimations are available for the Southern Asian domain (70-110°East; 5-35°N orth) at a spatial resolution of 0. 1°by 0. 1°beginning on May 01, 2001. The RFE2 generally underestimates intense rainfall events and overestimates rainfall in rainshadow and arid areas  (Shrestha, 2010). Comparing with gauge data, the RFE2 has daily rainfall bias of À1. 1 mm/day over the period  [2003] [2004] [2005] [2006] , with a root mean square error from gauges over the whole of Nepal of À4.
Elevation and glacier data Elevation data came from the SRTM v. 4 (CGIAR), a hydrologically-sound, void-filled DEM (CGIAR-CSI, 2004). The vertical accuracy of the SRTM DEM in this area, was reported as 31 m ± 10 m  (Racoviteanu et al. , 2013 ). An orthorectified ASTER scene from October 30, 2003 covering the entire Trishuli basin was used as a basis for delineating glacier outlines and variables needed as input in the melt model. The scene had high contrast over glaciers, minimal cloud cover, and was acquired at the end of the ablation season, so it was well-suited for computing a glacier albedo in absence of seasonal snow. Glacier outlines needed for the melt model were derived using semi-automatic methods (band ratios 3 = 4 with a threshold of 2. 0) described in detail elsewhere  (Racoviteanu et al.
, 2007) , these data are used in the model to compute vegetation roughness and overland velocity. The GeoSFM requires soil data for basin characterization and model parameterization. Soil texture and depth, hydraulic conductivity, soil water holding capacity, maximum impervious area, and runoff curve number were estimated from the Digital Soil Map of the World produced by the U. N. Food and Agriculture Organization (FAO) and the U. N. Educational, Scientific and Cultural Organization (UNESCO). Both land cover and soil data are used in GeoSFM to derive runoff curve numbers. These numbers are used to determine the amount of incident precipitation that becomes surface runoff  (Asante et al. , 2007). The UEB requires canopy height, canopy coverage, leaf area index and canopy structure parameters in vegetated areas.
When seasonal snow has melted over debris covered glaciers, melt is generated due to the debris covered glacier albedo, which is generally significantly lower than that of clean glacier ice -which in general would result in larger energy inputs and higher melt rates. Thus, we estimate this approach to work best for glacier ablation areas which are covered by a thin debris cover (less than the critical thickness), where melt is governed by albedo. For this study, the UEB model was reconfigured to run on a distributed grid to explicitly represent spatial variability in the inputs across a basin, and the enhanced model is referred to in this paper as UEBGrid. The new gridded version of the UEB model facilitates coupling with EPA BASINS and the forcing by inputs from NASA remote sensing and earth science data products such as, satellite data (MODIS, ASTER, SRTM), reanalysis data (MERRA) and climate model output (RFE2).
UEBGrid was integrated into the HIMALA BASINS software to facilitate the linking to other models, such as GeoSFM, and to take advantage of BASINS' capability to manage input data and visualize results. UEBGrid has adopted a structured file-based input/output format using text and NetCDF files to facilitate its use and incorporation into the HIMALA BASINS software. The UEB model has never before had a graphical user interface and its incorporation into BASINS here has provided graphical user interface capability for UEB thereby making is accessible to a broader group of users. In UEBGrid, a watershed is divided into a mesh of grid cells and the model runs individually for each grid cell and computes snow or glacier melt. Outflow is then aggregated over sub-basins derived from a Digital Elevation Model (DEM) and used as input into a hydrologic model (in this case, GeoSFM described below, Section 3.
Forcing data for the model includes daily estimates of precipitation and potential evapotranspiration to predict daily streamflow at in-situ gauge stations. GeoSFM consists of four modules-preprocessing, hydrologic analysis, parameter calibration, and post-processing. The components of the GeoSFM that are easily accessed through a series of tabs in HIMALA BASINS are: Terrain Analysis, Basin Characteristics, Basin Response, Rain/Evap Data, Compute Soil Water Balance, Compute Stream Flow, Sensitivity Analysis, Model Calibration, and Output Results. HIMALA BASINS allows access to the other hydrological models currently supported by BASINS, along with extending some of the pre-processed datasets to a global extent to enable users outside of the United States to benefit from the modeling framework.
In this case, the UEBGrid-derived SWIT (total outflow) was selected as the forcing dataset and the PET was automatically estimated using downscaled MERRA air temperature. The soil water balance is then computed using either a linear or a non-linear soil model. For the Langtang case study, the non-linear option was used since it is more suitable for high spatial or temporal resolution analysis and when a model needs to be well calibrated with observed data. Once this is complete, streamflow is computed using one of three routing options (Simple Lag, Diffusion Analog, and Muskingum Cunge). The Muskingum Cunge method was used in this study since it has more parameters and it can diffuse the hydrograph. HIMALA BASINS provides the user not only the option to run both UEBGrid and GeoSFM separately, but to run both models seamlessly in an integrated fashion using the UEBGrid melt output as input for modeling streamflow in GeoSFM.
The GeoSFM-simulated streamflow was estimated using eight years of data, October 1, 2003-September 30, 2010 hydrologic year, and was compared to observed gauge stream flow data from Kyangjing station (3920 m) provided by the Nepal Department of Hydrology and Meteorology (DHM), Nepal. Once the calibrated streamflow run was complete, the post-processing module steps were implemented through the calculation of flow statistics, and the creation of flow maps and hydrographs.
The results indicate the high fraction of contribution to water input from glacier melt (62%) even though this is a small fraction of the watershed area. The discharge from GeoSFM driven by UEBGrid inputs compares favorably to the total discharge measured. There are discrepancies in the detail of the hydrograph between modeled and measured. However considering the scarcity of data and the modeling system being totally driven by remote sensing and global or regional climate products the comparisons are reasonable. The augmentation of the GeoSFM model with a capability to capture water melt from snow and glacier ice is important for ICI-MOD and its member countries, since water stored during the winter as ice and snow is a significant contributor to rivers during the spring and summer in many basins in South Asia  (Immerzeel et al.
, 2009 (Immerzeel et al. , , 2010)). Scarcity of data for meteorological parameters necessary to run hydrological models is a problem in regions at risk of natural hazards such as landslides, floods, droughts and food insecurity  (Artan et al. , 2007; Brown and Funk, 2008; Sanyal and Lu, 2004). If downstream communities are not aware of excessive temperatures causing snow and ice melt, or of extreme precipitation events, then they will be poorly prepared for changes in river flow after the fact. Using satellite-derived datasets to drive hydrological models in regions without universally available ground observations helps overcome this problem.
Acknowledgements This work was supported by a grant from the  National Aeronautics and Space Administration's Applied Sciences Program  through its 2008  Decision Support through Earth Science Research Results Grant Number   NNH08ZDA001N-DECISIONS. A. Racoviteanu 's post-doctoral studies were supported by the  Centre National d'Etudes Spatiales (CNES) ,  France. This publication was made possible through partial support provided by the  Office of U. S. Foreign Disaster Assistance, Bureau for Democracy, Conflict and Humanitarian Assistance, U. S. Agency for International Development , under the terms Award No. AID-OFDA-T-11-00002. The opinions expressed in this publication are those of the authors and do not necessarily reflect views of the  U. S. Agency for International Development.
Datasets The two datasets used in this study are the results from the CO 2 survey data from the WOCE '94 A17 section (public data at: http://cdiac. ornl. gov/oceans/woce_ a17c. html carried out in austral autumn and this data has undergone rigorous quality control  (Key et al. , 2010). The data report is available from "http://cdiac. ornl. gov/oceans/ndp_084/" (Ríos et al. , 2005b) , where an offset of -8 µmol kg -1 in the total alkalinity (A T ) data has been reported and corrected for in this study. For a detailed analysis of the WOCE occupation we refer the reader to  Ríos et al. (2010). 2. 2 GEOTRACES-NL (2010/11) measurements.
pH Calculations From DIC, A T and supplementary data (salinity, temperature, pressure, Si(OH) 4 , PO 4 ), the pH and pCO 2 were also calculated in-situ for both datasets using CO2_SYS  (Lewis and Wallace, 1998)  adapted for Matlab (van Heuven, 2011a), applying the acid dissociation constants of Mehrbach et al. , (refit by  Dickson and Millero, 1987) , and the KSO 4 constant of  Dickson (1990). The same calculation was carried out for both the WOCE '94 A17 and GEOTRACES-NL (2010/2011) datasets with the resulting pH reported on the total pH scale.
Deepwater consistency between WOCE and GEOTRACES In a later section, we employ the eMLR method  (Friis et al. , 2005)  to infer ∆C ant between the two cruises. The eMLR method considers various biogeochemical properties (in this case, salinity, DIC, NO 3 , Si(OH) 4 and apparent oxygen utilization (AOU = [O 2 ] sat -[O 2 ] obs ) ) and is particularly sensitive to large scale ("secular") changes in the distributions of these properties, as well as to analytical biases in their measurement. In order to assess the magnitude and distributions of these changes, we gridded the values of salinity, DIC, NO 3 , Si(OH) 4 and AOU of each dataset and subtracted the grids from each other. Grid spacing was every 2 • of latitude, with 80 layers in the vertical direction, with increased density towards the surface.
∆C ant from eMLR The multi-linear regression approach to estimating anthropogenic CO 2 invasion was introduced by  Wallace (1995). It involves using a number of biogeochemical properties, known to be related to DIC, to obtain a model of the observed DIC. As the relationships between DIC and these properties are expected not to change over time, the same statistical relationships can be applied to a second dataset of later date. Differences between the thus "predicted" DIC and the observed DIC are attributed to the invasion of anthropogenic CO 2. In the extended version (eMLR), which is applied here, the DIC and the difference between parameter coefficients is assumed to be predictive of the difference in C ant between the two cruises: ∆C eMLR ant = DIC MLR2, t2 -DIC MLR1, t1 (1) = (a 2 -a 1 ) + (b 2 -b 1 )SiO 2 t2 + (c 2 -c 1 )NO 3 t2 + (d 2 -d 1 )AOU t2 + (e 2 -e 1 )S t2 + (f 2 -f 1 )T t2.
( 2 ) Using a stepwise regression the most significant terms for the WOCE A17 '94 dataset were determined as, in order of decreasing importance: S, SiO 2 , NO 3 , T , and AOU. These properties predict DIC with an R 2 value of 0. 99 and a rmse of 6. 66 µmol kg -1. For the GEOTRACES-NL (2010/2011) dataset, the order of importance was: S, SiO 2 , NO 3 , T and AOU, with an R 2 of 0. 98 and rmse of 9. 87 µmol kg -1 (Fig. 2 ). The eMLR regressions were applied along isopycnals intervals, as the preferred method of water movement from the surface into the ocean interior is along surfaces of constant density, it thus follows that waters occupying the same density band share a common formation history and can be described by a single equation. Isopycnal bands were chosen based on temperature-salinity plots of the water masses and the amount of data occupying each interval.
The coefficients and accompanying statistics from each isopycnal interval are displayed in Table  1. The residuals of each fit are shown in Fig. 2 , with the 2011 dataset showing a maximum of 7 µmol kg -1 , in the surface layer and an overall average of 2. 1 µmol kg -1 in the deeper waters (> 2000 dbar). In comparison, the WOCE '94 A17 dataset shows a water column average residual of 3. 17.
These equations quantify the resistance to change of [CO 2 ] (γDIC), [H + ] (βDIC) and Ω (ωDIC) in a water mass to changes in DIC. The concentrations used for the calculations were obtained from CO2SYS  (Lewis and Wallace, 1998, adapted  for Matlab by van Heuven, 2011a) using the same input conditions as previously mentioned (Sect. 2. 2. 3). [CO 2 ] + [HCO - 3 ] + [CO 2- 3 ], ( 6 ) Alk C = [HCO - 3 ] + 2[CO 2- 3 ], ( 7 ) P = 2[CO 2 ] + [HCO - 3 ], ( 8 ) S = [HCO - 3 ] + 4[CO 2- 3 ] + [H + ] + [OH -] + {[H + ][B(OH) - 4 ]/Khb + [H + ]} (.
e. temperature, NO 3 and Si(OH) 4 ). However, the conceivably variable carbon export between the two time periods (due to variably dominant phytoplankton groups) may have lead to variation in the chemical stoichiometry, which could not be accounted for. To corroborate our observations of surface ∆C ant obtained using eMLR by another approach, we follow  Lee et al. (1997)   For T ≥ 19 • C the gradient between temperature and NDIC is almost identical for all three datasets, varying from 6. 90 to 7. 02, which would result in a maximum NDIC difference of 2. 6 µmol kg -1 at 29. 3 • C (the maximum temperature recorded of both datasets). These temperatures cover the tropics to the mid-latitudes, reaching 40 • S. The resulting equations, from the WOCE '94 A17 and GEOTRACES-NL (2010/2011) datasets, show an increasing intercept of NDIC of 19 (2160-2141) µmol kg -1 from 1994 to 2011, which is consistent with the increase anticipated solely by increasing atmospheric pCO 2 (+20 µmol kg -1 ).
Analytical work was carried out in the Laboratoire Magmas et Volcans (Clermont-Ferrand). Minerals were imaged by cathodoluminescence using a Jeol JSM-5910 MEB and U-Pb analyses were conducted in situ by LA ICP-MS with an Agilent 7500ICP-MS coupled to a Resonetics M-50E 193 nm ArF Excimer laser system. Ablation spot diameters of 26 µm and 7 µm with repetition rates of 3 Hz and 1 Hz were used for zircon and monazite, respectively. Data were corrected for U-Pb fractionation and the mass bias by standard bracketing with repeated measurements of the GJ-1 zircon  (Jackson et al. 2004 ) and the Moacyr monazite standards  (Gasquet et al. 2010). Repeated analyses of the same standards treated as unknowns were used to control the reproducibility and accuracy of the corrections. Data reduction was carried out with the GLITTER ® software package developed by Macquarie Research Ltd.
Acknowledgements. Pierre Bouilhol ,  Philippe-Hervé Leloup  and  Oscar Laurent  are thanked for comments on earlier versions of this work and stimulating discussions. JHS was financially supported by the  Spanish  grant  CGL2008-02864  and the Andalusian grant  RNM1595. Reviews from  Jean-Louis Vigneresse  and  František Holub  helped to improve the quality of the manuscript. Editorial handling by  Miroslav Štemprok  and  Vojtěch Janoušek  was greatly appreciated. Electronic supplementary material. The sample descriptions and locations are available online at the Journal web site (http://dx. doi. org/10. 3190/jgeosci. 155).
The Argo-derived data product (Fig. 3a ) reveals a relatively weak stratification over the Southern Ocean. The stratification is significantly stronger than the observations for the CNTRL case (Fig. 3b ) over the Southern Ocean, but relatively weak for the WSTIR case (Fig. 3c ). The stratification maximum is shallower for WSTIR than it is for observations over the Southern Ocean, but is shallower still for CNTRL. We consider next the amplitude of the seasonal cycle of sea surface temperature (SST), considered for a climatology constructed for years 2000-2006. This is considered first in Fig. 3d  for the observational product derived using the method of  Reynolds and Smith (1994) , which includes remote sensing information. Over large parts of the Southern Ocean, the amplitude of the seasonal cycle of SST is on the order of 2 • C.
It is worth noting that whereas both WSTIR and the data product exhibit a local maximum in outgassing of CO 2 along the divergence region around 60 • S, the CN-TRL case exhibits a local maximum in uptake over this region. Lenton et al. (2013)  demonstrated in their Southern Ocean synthesis study that ocean carbon models contain significant biases in their representation of the seasonal cycle of airsea CO 2 fluxes over the Southern Ocean. One of their main conclusions was that identifying uncertainties related to specific processes operating over the seasonal cycle should be a priority for future Southern Ocean carbon cycle research.
Carbon and oxygen fluxes The climatological seasonal cycle of air-sea CO 2 flux, integrated south of 45 • S, is shown in Fig. 6a  for CNTRL, WSTIR, and for the data product derived from  Takahashi et al. (2009). Observations show that ocean CO 2 uptake, relative to the annual mean, occurs in austral summer and outgassing occurs during austral winter. There are significant differences in phase, magnitude, and shape between CNTRL and WSTIR. It is nevertheless clear that this sensitivity is less than the CMIP5 model spread in evidence in Fig. 15b  of  Anav et al. (2013). production and enhances the biological drawdown of dissolved inorganic carbon (DIC) in the ocean's surface layer earlier in the season for CNTRL than for WSTIR. Inclusion of the ad hoc wind stirring parameterization delays the onset of the winter reversal from outgassing to ingassing by 3 to 4 months (Fig.
Chlorophyll and blooms Remotely sensed surface chlorophyll from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, http://oceancolor. gsfc. nasa. gov/) is used here as a metric to evaluate the modeled seasonal cycle and ecological effects the wind stirring parameterization. A seasonal climatology was constructed over the period 1998-2006 to address both the timing of maximum seasonal chlorophyll concentrations and the timing of bloom onset. To create the climatology, SeaWiFS data were first put on the same grid as the ocean model. Modeled chlorophyll and regridded SeaWiFS data were then averaged to 8-day mean fields. In order to focus on local comparisons and minimize extrapolation errors due to missing data where storms and other phenomena have obscured data retrieval, the model was sampled only at space-time coordinates where SeaWiFS observations are available.