Nepal-FRES: GDGT and temperature data

This data package accompanies the study of branched glycerol dialkyl glycerol tetraether (brGDGT) source-elevation signals after the 2015 Gorkha earthquake and the 2021 Melamchi flood in the Nepal Himalaya.

HydroShare resource: https://www.hydroshare.org/resource/66ebdb06fcb24d27b5c51426921324a0

Suggested citation: Chen, C.-M., Peterse, F., West, A. J., Yedema, Y. W., Li, G. K., Chamlagain, D., & Feakins, S. J. (2026). Nepal-FRES: GDGT and Temperature Data. HydroShare. http://www.hydroshare.org/resource/66ebdb06fcb24d27b5c51426921324a0

Contents

• data/gdgt/sample_measurements.csv: sample location, type, depth, TOC, GDGT totals, MBT′₅Me, BIT, and analysis groups for 56 soil and 15 sediment samples.
• data/gdgt/compound_concentrations.csv: compound-level concentrations normalized to extracted dry mass.
• data/gdgt/compound_peak_areas.csv: integrated peak areas, including C46 internal standard. These are not raw chromatograms.
• data/gdgt/laboratory_quantification_metadata.csv: extracted dry mass, split factor, and internal-standard information used for quantification.
• data/gdgt/calibration_summary_melamchi.csv and calibration_residuals_melamchi.csv: depth-specific Melamchi MBT′₅Me–elevation statistics and residuals.
• data/temperature/logger_exports/: 35 air and soil temperature series from 10 monitoring stations.
• data/temperature/summaries/: daily, monthly, annual, and elevation-gradient summaries.
• data/figure3/: regional and Melamchi Valley Figure 3 calibration data, regression statistics, residuals, sediment observations, and site-cluster bootstrap source-elevation estimates. The sensitivity_combined_under50/ subdirectory contains Figure S6 and the combined surface-plus-shallow (<50 cm) sensitivity results.
• data/calibration_diagnostics/: individual-versus-cluster bootstrap comparisons, bootstrap slope summaries, site-level influence statistics, and highest-elevation-site sensitivity tests.
• metadata/: field definitions.
• code/: scripts used to prepare temperature summaries and reproduce the regression tables.

GDGT data

Sample identifiers are the join key among GDGT tables. Soil depth groups are surface (0–4 cm), shallow (5–49 cm), and deep (50–200 cm). Sediment groups distinguish 2015 deposits, 2021 flood deposits, and the non-flood Yangri Khola sample NP22-12.

The analytical split was made before internal-standard addition. The internal-standard amount records the amount added to the analyzed aliquot (152 ng where recorded), while dry-mass normalization uses the mass of the extracted sample. total_gdgt_ng_g_dry equals the sum of iso- and branched-GDGT totals. Compound-level missing values are blank.

Temperature data

Timestamps are Nepal Standard Time (UTC+05:45). Records are converted to equal-weight hourly means; daily means require at least six valid hourly observations. Monthly and annual summaries weight retained daily means by available hours. Partial-year coverage fields must be considered when interpreting annual means. The 772 m station includes an air logger and a 10 cm soil logger; NP23-11 is the corresponding 10 cm GDGT soil sample, but no surface-soil sample is available at this station.

Coordinates and elevations are reported as supplied with the field records; horizontal and vertical datum metadata are not available. No additional temperature outlier filter or instrument-calibration correction was imposed.

Figure 3 analysis

Regional calibrations use eligible soil observations east or west of 85°E. A separate Melamchi Valley calibration uses soils collected in this study. Each calibration fits the three soil-depth groups separately. Source-elevation estimates use 5,000 site-cluster bootstrap refits. Sampling sites are resampled with replacement within each depth group, all observations from a selected site are retained together, and calibration-residual draws first weight sites equally. Western surface soils contain only two observations at one elevation and therefore do not support a regression or source-elevation estimate. The Eastern Section and Melamchi Valley deep-soil bootstrap slope distributions include values near and above zero; their inverse estimates are therefore treated as sensitivity tests rather than stable quantitative source-elevation constraints.

Figure S6 provides an additional regional sensitivity analysis in which surface and shallow soils are pooled into one <50 cm calibration. Observations collected at exactly 50 cm remain assigned to the deep group and are excluded from this pooled model. The associated CSV files report the combined regression statistics and untruncated sediment source-elevation estimates with 95% predictive intervals.

The Figure 3 regional compilation includes measurements from this study and the following open datasets:

• Märki, L. (2020). Data for: Molecular Tracing of Riverine Soil Organic Matter From the Central Himalaya. ETH Zurich Research Collection. https://doi.org/10.3929/ethz-b-000431464 (CC BY 4.0).
• van der Veen, I. (2020). Research data to: Validation and calibration of soil δ2H and brGDGTs along (E–W) and strike (N–S) of the Himalayan climatic gradient. Mendeley Data, version 1. https://doi.org/10.17632/cvdnf723k5.1 (CC BY 4.0).

Reused records retain source attribution in the data tables. Modifications consist of field standardization, geographic/depth grouping, regression, and inverse source-elevation calculations.

License

Original data, metadata, and code produced by this study are released under the Creative Commons Attribution 4.0 International license (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/. Reuse requires appropriate attribution and an indication of modifications. Third-party records from Märki (2020) and van der Veen (2020) retain their original CC BY 4.0 attribution and source citations.

Reproduction

Install the versions in code/requirements.txt, then run:

text python code/prepare_temperature_station_summaries.py python code/analyze_temperature_lapse_rate.py python code/recompute_melamchi_calibration.py python code/recompute_figure3.py python code/plot_combined_under50_sensitivity.py --data-dir data/figure3 --output data/figure3/sensitivity_combined_under50

The Excel workbook is a formatted snapshot; the accompanying CSV files are the machine-readable analysis tables.