Winter snow depths for initializing a glacio-hydrological model in high mountain Chile
| Main Authors: | Dr Thomas Edward Shaw, Alexis Caro, Dr Pablo A. Mendoza, Dr Álvaro Ayala, Dr. Francesca Pellicciotti, Dr. Simon Gascoin, Dr. James McPhee |
|---|---|
| Format: | info dataset Journal |
| Terbitan: |
, 2020
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| Online Access: |
https://zenodo.org/record/3613951 |
Daftar Isi:
- The following dataset consists of the forcings, initial conditions, model grids and parameters used to run the TOPKAPI-ETH model (Finger et al., 2011; Ragettli and Pellicciotti, 2012) for the Rio Yeso catchment of central Chile (33.44°S, 69.93°W - Burger et al., 2018). The data and model grids were used to investigate the importance of accurate snow depth maps for initialising the physically-oriented model in a high elevation catchment - For a manuscript submitted to Water Resources Research (WRR) - January 2020. Data and file repository for the submitted article: %------------------------------------------------------------- %------------------------------------------------------------- On the utility of optical satellite winter snow depths for modelling the glacio-hydrological behaviour of a high elevation, Andean catchment. Thomas E. Shaw1, Alexis Caro1,2, Pablo Mendoza3, Álvaro Ayala4, Francesca Pellicciotti5,6, Simon Gascoin7, James McPhee1,3 1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile 2 Univ. Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement (IGE, UMR 5001), Grenoble, France 3 Department of Civil Engineering, Universidad de Chile, Santiago, Chile 4 Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile 5 Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland 6 Department of Geography, Northumbria University, Newcastle, UK 7 CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France %------------------------------------------------------------- %------------------------------------------------------------- The following sub-folders are separated into forcings, grids, initial model conditions, model files and parameters. This file describes briefly the contents of each sub-folder. %------------------------------------------------------------- FORCINGS: CloudCover_TPK.csv - A timeseries of hourly cloud cover fraction (-) derived NASA POWER archives. Discharge_TPK.csv - A timeseries of hourly discharge (m3 s-1) from the outlet station F_TdP. Master_Data_TPK.mat - a Matlab structure (written in version 2017a) for all data availble to the catchment for the considered model period. Precipitation_TPK.csv - A timeseries of hourly precipitation (mm/hr) from AWS TdP. Temperature_TPK.csv - A timeseries of hourly temperature (degC) from AWS TdP. TemperatureGradient_TPK.csv - A timeseries of calibrated temperature gradients based upon forcing from AWS TdP. %------------------------------------------------------------- GRIDS: 42 ascii files for various grids (primary or secondary) use to derive the .TES file (see TOPKAPI-ETH sub-folder) for running the model. Associated projection (.prj) files are given. Naming convention is provided in the manual (see TOPKAPI-ETH sub-folder) except: rdy_SoilDepth.asc - An adjusted top layer soil depth map based upon Ragettli et al. (2012). rdy_debris_v.asc - A debris thickness map for Piramde Glacier and the tongue of Bello Glacier. Values adjusted slightly from Ayala et al. (2016) to account for areas that are not debris, but bedrock (Bello Glacier). %------------------------------------------------------------- INITIAL_CONDITIONS: Sub-folder 'Albedo': Albedo_Pleiades.asc - An albedo map derived from the model spin up and limited to the snow-covered pixels of the Pléiades snow depth map. Sub-folder 'Snow': XXX_snow_mmwe.asc - A snow water equivalent map (mm w.e.) given by the initialisation method 'XXX' (Pléiades, TOPO or DBSM). TPK is derived solely from the model spin up (an input grid not required). XXXeq_snow_mmew.asc - As above, though considering the equal means approach described in the manuscript. TPK included here. Sub-Folder 'SpinUp_State': 201709040000.stt - The system state file that contains information on the equiblibrium state of catchment (as read by the model upon initialisation). Running the model with a spinup shuld call upon this file within the command prompt. %------------------------------------------------------------- PARAMETERS: Sub-folder 'Calibration' TPK_ParameterAllocation - A Matlab script for the establishing the Monte Carlo parameter simulation and running the model n times. The current script is considered for soil parameters. Sub-folder 'Sensitivity' TPK_Sensivity_Analysis - A Matlab script for establishing the upper and lower boundaries of parameter/forcing sensitivities for a one-at-a-time analysis. %------------------------------------------------------------- RESULTS: Model_Output_Comparison.mat - A matlab file with output grids and vectors for model intercomparisons (i.e. Pléiades (Pléiades-Uncertainty and Pléiades+Uncertainty), TOPO, TPK, DBSM + equal means equivalents). Files are: All_S - Daily snow mass balance grids (mm w.e.) Bias_Month - Monthly bias (row) of modelled vs measured streamflow at F_aP site for each model run (column). Date_Daily - Numeric date of daily grids DateTPK - Numeric date of hourly model simulations Gla_Map - Daily cumulative glacier modelled mass balance grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation GMB_Bello - Cumulative modelled mass balance (mm w.e.) of Bello Glacier AWS grid cell GMB_Piramide - Cumulative modelled mass balance (mm w.e.) of Piramide Glacier AWS grid cell GMB_Yeso - Cumulative mass modelled balance (mm w.e.) of Yeso Glacier AWS grid cell KGE_Month - KGE values per month (row) and for each model run (column) M3AP - Measured streamflow at F_aP M3TP - Measured streamflow at F_TdP MeltG_Avg_all - Mean hourly ALL-glacier melt rate (mm w.e./hr) for each model run (column) MeltS_Avg_all - Mean hourly catchment-wide melt rate (mm w.e./hr) for each model run (column) MOD_SnowCC - Daily MODIS snow cover fraction Model_Name - .... well, its the name of the model run :=) MODIS_SLE - The calculated Snow Line Elevation (m a.s.l.) for each daily MODIS scene PlanetSLE - As above, but for PlanetScope images (17 days total) Planet_SnowObs - The numeric dates of the equivalent PlanetSLE data Q_Mod_aP - The modelled hourly streamflow at F_aP Q_Mod_TdP - The modelled hourly streamflow at F_TdP Q_Prc_Month - The percentage difference in monthly (row) modelled-measured streamflow by model run (column) R_Month - Correlation values per month (row) and for each model run (column) RelVar_Month - Relative variance per month (row) and for each model run (column) Snow_Map - Daily snow water equivalent grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation SnowCC - The hourly modelled snow cover fraction for the catchment for each model run (column) TPKSLE - The daily modelled TOPKAPI-ETH model SLE from each model run (column) %------------------------------------------------------------- TOPKAPI-ETH: FIUME - A generic file type that is called by the model to ID the name of the study site. I this case 'rdy'. rdy.TES - A vectorised file of all grids required by the model to run. rdy.TPK - The TPK parameter and command file. This is adjusted to change input parameters and forcing files etc. The current file is the optimised version for this catchment. TManual_Aug2013.pdf - A PDF instruction file (semi-complete) for the model written by Stefan Rimkus (2013). The naming conventions and grid names are given here. CITED MATERIAL REGARDING THE MODEL Burger, F., Ayala, A., Farias, D., Shaw, T. E., Macdonell, S., Brock, B., McPhee, J., Pellicciotti, F. (2018a). Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment: understanding the role of debris cover in glacier hydrology. Hydrological Processes, SI-Latin(January), 1–16. https://doi.org/10.1002/hyp.13354 Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., & Burlando, P. (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resources Research, 47(7), 1–14. https://doi.org/10.1029/2010WR009824 Ragettli, S., & Pellicciotti, F. (2012). Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters. Water Resources Research, 48(3), n/a-n/a. https://doi.org/10.1029/2011WR010559