Model outputs to "Langtang Ice Cliff Modelling Study"
<p>This dataset contains all the model results of our study "Supraglacial ice cliffs can substantially increase the mass loss of debris-covered glaciers".</p> <p>The zip-file contains one main folder: "Cliffmodel_Results" (produced using the 3D-backwasting ice cliff model from Buri et al., 2016b, Buri & Pellicciotti, 2018).</p> <p>More raw data upon request.</p> <p> </p> <p><strong>Folder structure of "Langtang_IceCliffModellingStudy.7z":</strong></p> <p>───Cliffmodel_Results<br> ├───LangshishaGlacier<br> │ ├───Output_Cliffs_Langshisha2014_A_3m<br> │ ├───Output_Cliffs_Langshisha2014_B_3m<br> │ ├───Output_Cliffs_Langshisha2014_C_3m<br> │ ├───Output_Cliffs_Langshisha2014_D_3m<br> │ ├───Output_Cliffs_Langshisha2014_E_3m<br> │ ├───Output_Cliffs_Langshisha2014_F_3m<br> │ ├───Output_Cliffs_Langshisha2014_G_3m<br> │ └───Output_Cliffs_Langshisha2014_H_3m<br> ├───LangtangGlacier<br> │ ├───Output_Cliffs_Langtang2014_A_3m<br> │ ├───Output_Cliffs_Langtang2014_B_3m<br> │ ├───Output_Cliffs_Langtang2014_C_3m<br> │ ├───Output_Cliffs_Langtang2014_D_3m<br> │ ├───Output_Cliffs_Langtang2014_E_3m<br> │ ├───Output_Cliffs_Langtang2014_F_3m<br> │ ├───Output_Cliffs_Langtang2014_G_3m<br> │ ├───Output_Cliffs_Langtang2014_H_3m<br> │ ├───Output_Cliffs_Langtang2014_I_3m<br> │ ├───Output_Cliffs_Langtang2014_J_3m<br> │ ├───Output_Cliffs_Langtang2014_K_3m<br> │ ├───Output_Cliffs_Langtang2014_L_3m<br> │ ├───Output_Cliffs_Langtang2014_M_3m<br> │ ├───Output_Cliffs_Langtang2014_N_3m<br> │ ├───Output_Cliffs_Langtang2014_O_3m<br> │ ├───Output_Cliffs_Langtang2014_P_3m<br> │ ├───Output_Cliffs_Langtang2014_Q_3m<br> │ ├───Output_Cliffs_Langtang2014_R_3m<br> │ ├───Output_Cliffs_Langtang2014_S_3m<br> │ └───Output_Cliffs_Langtang2014_T_3m<br> ├───LirungGlacier<br> │ ├───Output_Cliffs_Lirung2014_A_3m<br> │ │ └───intermediateOutput<br> │ └───Output_Cliffs_Lirung2014_B_3m<br> │ └───intermediateOutput<br> └───ShalbachumGlacier<br> ├───Output_Cliffs_Shalbachum2014_A_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_B_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_C_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_D_3m<br> │ └───intermediateOutput<br> └───Output_Cliffs_Shalbachum2014_E_3m<br> └───intermediateOutput<br> </p> <p> </p> <p><strong>File structure within Cliffmodel_Results \ <GLACIERNAME> \ Output_Cliffs_<GLACIERNAME>2014_A_3m:</strong></p> <p><em>DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of 10 matrices for each energy balance flux with average diurnal cycle (rows) and cliff (columns) for specific model period </p> <p><em>geom_ts<TIMESTEP1>_Cliffs_Lirung2014_A_3m.txt</em></p> <p>→Characteristics (Elevation,Slope,Aspect,x,y,ID,VsI,VsL,Vd) for each pixel of all cliffs (specific ID) for specific model period </p> <p><em>MODELSETTINGS_GSM_<GLACIERNAME>_<GLACIERSECTOR>_22Aug_Cliffs_<GLACIER>2014.txt</em></p> <p>→Details on all modelsettings</p> <p><em>PC_Melt_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Melt values for each cliff (columns) and timestep (rows) for specific model period </p> <p><em>PC_Stats_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Summary statistics for each cliff (rows) for specific model period </p> <p><em>perPx_DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of list with per pixel diurnal cycle and standard deviation of each energy flux for specific model period </p> <p><em>SD_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of 10 matrices for each energy balance flux with standard deviation for each hour of the day (rows) and cliff (columns) for specific model period </p> <p><em>Stats_MeanFluxes_tts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Mean energy fluxes per timestep (rows) averaged over all ice cliffs</p> <p><em>Stats_NonGriddedData_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Statistics of non-distributed parameters averaged over all cliffs per timestep (rows)</p>
ShareScore
40/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 8