jacobqs/MST-Himalaya: Data release

This data set contains data used in Skavang, J. (2023) Assessing the Shyft Modelling Framework in Nepal: Impact of Snow Routines and Terrain Representation on Simulated Water Balance Components. University of Oslo, Department of Geosciences. The dataset contains: The WATCH Forcing Data methodology applied to ERA5 data set (WFDE5), is a meteorological forcing dataset for land surface and hydrological models. The dataset includes a bias-corrected reconstruction of eleven near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (Cucchi et al. 2020). The data set is derived from the ERA5 reanalysis product that has been re-gridded to a 0.5° x 0.5° resolution. The data has been adjusted using an elevation correction and monthly-scale bias based on Climatic Research Unit (CRU) data (for temperature, diurnal temperature range, cloud-cover, wet days number and precipitation fields) and Global Precipitation Climatology Centre (GPCC) data (for precipitation fields only). Furthermore, correction has been done for varying aerosol-loading, and separate precipitation gauge observations. The dataset covers the period 1979-2019, and has a hourly temporal resolution. The dataset is distributed through the Copernicus Climate Change Service (C3S) Data Store as monthly files in netCDF format. The netCDF format is self-describing data that machine-independent. The format supports creation, access, and sharing of array-oriented scientific data. The data is downloaded at https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.20d54e34?tab=form. The WFDE5 data is distributed under the CCA 4.0 License. Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020. The topographical and land cover data sets used in this project includes the SRTM 1 Arc-Second Global from the NASA's Shuttle Radar Tomography Mission (NASA-SRTM) that provides a digital elevation model (DEM) with approximately 30 meters resolution. The DEM is on the GeoTIFF/TIFF format and can be downloaded at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1?qt-science_center_objects=0#qt-science_center_objects. Earth Resources Observation And Science (EROS) Center (2017) Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global. U.S. Geological Survey. DOI: 10.5066/F7PR7TFT Furthermore, the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Snow Cover Daily L3 Global 500 m SIN Grid v6.1 dataset was used. The MODIS product MOD10A1 provides snow cover, snow albedo and quality assessment (QA) with a spatial resolution of 500 m x 500 m. The data has a daily temporal resolution. The MOD10A1 covers the period 24 February 2000 to present. The Terra MODIS snow products have been validated under both ideal and non-ideal conditions (Aalstad et al. 2020; D. K. Hall and Riggs 2007). The MODIS instrument onboard the Terra (2000-present) and Aqua (2002-present) satellites provides multispectral imagery in the visible shortwave infrared (VSWIR) wavelength spectrum (Aalstad et al. 2020). The optical sensors measures the reflected shortwave radiation from the upper 5-10 cm in the snowpack in multiple bands of the VSWIR sprectrum. These bands can be combined to give a spectral signature of the snow so that snow-covered area, albedo, snow grain size and and impurity concentration can be measured (Aalstad et al. 2020). MODIS has a daily revisit period with a a ground sampling distance (GSD) of 500 meters (Aalstad et al. 2020). The NASA MODIS snow cover products compare favourably with other products in terms of quality and resolution (D. K. Hall and Riggs 2007). The snow-cover is found using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow-cover detections (NSIDC 2023). The datasets come in HDF-EOS formatted data files, and can be downloaded at https://modis.gsfc.nasa.gov/data/dataprod/mod10.php. The MODIS data provided here is pre-processed by a method described by Aalstad, K. et al. (2020), and includes snow-covered area, snow cover duration, mean snow-covered area and mean snow cover duration for the Budhi Gandaki catchment in the period 2000-10-01 to 20015-09-30. The pre-processed data has a daily resolution and sinusoidal projection, and a netCDF format. Only data from the Terra satellite is being used (MOD10A1). The NASA snow-cover product is distributed by the National Snow and Ice Data Center (NSIDC) (D. Hall et al. 2006; D. K. Hall, Riggs G., et al. 2015). The fSCA and fSCD used in this study, is retrieved from MODIS using methods described in (Aalstad et al. 2020). The pre-processed data set contains fractional snow-covered area for each pixel in the domain with a daily resolution, as well as statistics such as mean fSCA and snow-cover duration (fSCD). The data set includes the water years 2000/2001-2014/2015 (2000-10-01 to 2015-09-30). The fSCA value range from 0 to 1, where 1 means that the pixel is completely snow-covered and 0 means not snow-covered. The fSCD is the sum of daily fSCA within a given water year. A fSCD equal to 365 indicates that the pixel is snow-covered for the entire water year (i.e. a glacier), while a fSCD equal to 0 indicates that a pixel is never snow-covered. The projection of the pre-processed and native MODIS data is sinusoidal. The pre-processed MODIS data has not been published before, but is distributed under the CC BY 4.0 License through this work. Hall, D. K., G. A. Riggs, and V. V. Salomonson. 2006. MODIS/Terra Snow Cover 5-Min L2 Swath 500m. Version 5. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. http://dx.doi.org/10.5067/ACYTYZB9BEOS. The Land Cover Classification System (LCCS) Land Cover Map Fine Resolution V2.3 Global dataset from the GlobCover Portal provides global composites and land cover maps. The GlobCover products have been processed by the European Space Agency (ESA) and by the Université Catholique de Louvain using input observations from the 300 m MERIS sensor on board on the ENVISAT satellite mission. The land cover maps covers December 2004 - June 2006 and January - December 2009. The surface reflectance mosaic products are projected in a Platé-Carré projection (WGS84 ellipsoid) with a 1/360° pixel resolution. The land cover classes as defined by a set of classifiers. The data format is “.tif”, and is downloaded from: http://due.esrin.esa.int/page_globcover.php. No license is provided, but the data may be used for educational and/or scientific purposes without any fee on the condition that ESA is credited, and the Université Catholique de Louvain is used as the source of the GlobCover product. ESA & UCLouvain (2010) GlobCover 2009. ESA. http://due.esrin.esa.int/page_globcover.php A shapefile for the Budhi Gandaki catchment is used to make maps. This shapefile contains the catchment outline, catchment id and the catchment area. The file is the same the data set used in Bhattarai 2020. There were no license information available for the shape file. The file was allowed to share. River network data is used for the catchment map. The river network data is a vectorised line network of all the global rivers that have a catchment area of at least 10 km of a river flow of more than 0.1 m3/s (Lehner and Grill 2013). HydroRIVERS is a free and open-source database for scientific, educational and commercial use. The downloaded zip data contains a documentation file (.pdf) and a shapefile that can be visualised in QGIS. Version 1.0 of HydroRIVERS is downloaded. The HydroRIVERS is freely available at www.hydrosheds.org for scientific, commercial and educational use. The data is distributed under a license described in https://data.hydrosheds.org/file/technical-documentation/HydroSHEDS_TechDoc_v1_4.pdf in Appendix A: “Hydrosheds version 1 - License Agreement”. Lehner, B., Grill G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. https://doi.org/10.1002/hyp.9740 The regular grid cell data produced by Bhattarai 2020, and is shared under the CCA4.0 License at: https://zenodo.org/record/3567830#.ZHXsZS9Bzuw. TINs for the Budhi Gandaki catchment are made using the Rasputin software. The TINs are created using the Rasputin software (Silantyeva et al. 2023). The Rasputin software can convert a point set of coordinates to a TIN using a Digital Elevation Model (DEM) and an outline of the catchment in a wkt-file (Bhattarai, Silantyeva, et al. 2020) using Delaunay triangulation methods (Silantyeva et al. 2023). This is done by converting a DEM into simplified triangulated meshes. The triangulation routine in Rasputin is based on the CGAL Delaunay method (Silantyeva et al. 2023; Yvinec 2023). The 2D Delaunay triangulation in Rasputin is non constrained, meaning that the Delaunay triangulation is purely based on the position of a given set of vertices disregarding how they ought to be connected by edges (Silantyeva et al. 2023). The land types are designated to each TIN by determining the position of the middle point in the land cover data set. In this study, Rasputin uses the GlobCov 2009 data set with a 300 m resolution for the land types. Only one land type is assigned to each triangle, which differs from the regular grid cells that have fractional land cover types (Silantyeva et al. 2023). In addition to creating the TINs, the software also calculates physical parameters for each TIN facet (such as slope, aspect and area) from the geometry (Bhattarai, Silantyeva, et al. 2020). The TIN generated from Rasputin is in a XDMF format and can be visualised using various visualisation tools or in a H5 format that can be parsed and visualised using Python. In this study, the H5 format will be used to parse the TINs for Shyft and to visualise the TINs. The Rasputin software is freely available under GNU GPL v3.0 license. The TINs are not published before, but not shared as a part of this work under the CC4 License at https://github.com/jacobqs/MST-Himalaya/tree/main/shyft_workspace/shyft-data/budhi_gandaki/tin_archive as a “.h5” file and “.xdmf” file. GeoJSON polygons of countries are downloaded from datahub.io. These polygons are used in figures where country boundaries are illustrated. The data is in the GeoJSON format and is licensed under the Open Data Commons Public Domain Dedication and License (PDDL) v1.0. Glacier polygons are downloaded from the NASA Earth Data Explorer in a shapefile format (Li et al. 2021). The glacier polygons are globally glacier polygons based on the Ralph Glacier Inventory (RGI) v6.0. The RGI glacier polygon data is provided under an CC BY 4.0 License at https://www.glims.org/RGI/ RGI Consortium, 2017. Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 6. [Indicate subset used]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: https://doi.org/10.7265/4m1f-gd79 Daily stream flow data from the period 2000-2015 is obtained from The Department of Hydrology and Meteorology, DHM (DHM 2022). The flow gauging station is located at Arguhat bazaar (485 m.a.s.l.) at 28.043611 N and 84.816389E. It has a drainage area of 4270 km2 (DHM 2022). The station has been operating since 28 November 1963. To get observed river discharge data, please contact Innovation PostDoctoral Researcher Olga Silantyeva at University of Oslo: [e-mail]. For more info, please refer to: Skavang, J. (2023) Assessing the Shyft Modelling Framework in Nepal: Impact of Snow Routines and Terrain Representation on Simulated Water Balance Components. University of Oslo.