Data from: The microclimate in post-wildfire areas in northern New Mexico: modeling plant stress
Data files
Sep 25, 2026 version files 7.52 GB
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DataLoggerRecordings.csv
357.35 MB
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J_Jemez_class_32613.tif
1.73 GB
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Jemez_DSM.tif
304.75 MB
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Jemez_pointCloud_w_ground.laz
1.77 GB
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L_Luna_class_32613.tif
315.55 MB
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Luna_1m_DSM.tif
11.45 MB
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Luna_pointCloud_w_ground.laz
1.18 GB
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P_Phil_class_32613.tif
434.63 MB
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Phil_pointCloud_w_ground1.laz
1.34 GB
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Philmont_DSM.tif
97.67 MB
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README.md
6 KB
Abstract
Forest canopies regulate understory microclimate, and their loss to high-severity fire can create conditions that exceed the physiological tolerances of juvenile trees of the same species. In the southwestern United States, post-fire tree establishment is often limited, resulting in persistent forest cover loss. We characterized and modeled postfire microclimatic variability to understand how spatial patterns of stress constrain seedling survival. We deployed 125 custom microclimate data loggers at each of three high-severity burn sites in northern New Mexico, recording hourly air temperature, relative humidity, and soil moisture over two growing seasons (2023–2024). Vapor pressure deficit (VPD) was derived from temperature and humidity. Using physiological literature, we then defined moderate, severe, and extreme stress thresholds for each variable. We integrated in situ observations with UAS-derived solar radiation, LiDAR-based topographic indices, and ERA-5 climate data in XGBoost models to generate sub-meter resolution maps of stress metrics, including total stress hours, stress days, and maximum consecutive stress duration (R² = 0.87–0.94). Extreme conditions were common, with temperatures reaching 59.99°C and VPD 17.12 kPa. Moderate stress thresholds were exceeded across 91.5% of site areas, with severe (84.9%) and extreme (58.6%) thresholds also widespread. However, stress magnitude and duration were highly heterogeneous at sub-meter scales. Atmospheric stress was primarily driven by solar radiation, whereas soil moisture stress was weakly correlated with atmospheric variables (r = 0.12–0.15) and instead controlled by profile curvature, catchment area, and relative slope position (61.5% of model influence), all derivable from 1 m LiDAR DEMs. Two sites separated by 150 km exhibited nearly identical stress distributions at similar elevations, suggesting that post-fire microclimate can be predicted from topography. This framework provides a scalable approach to identify low-stress microsites that may support tree regeneration following high-severity fire.
Dataset DOI: 10.5061/dryad.7pvmcvf9j
Description of the data and file structure
Dryad repository for data and materials related to the publication "The microclimate in post-wildfire areas in northern New Mexico: modeling plant stress" by Christopher D. Marsh, Lorraina Rojas, Matthew O'Neil and Matthew Hurteau, in Agricultural and Forest Meteorology, 389, 111399 - https://doi.org/10.1016/j.agrformet.2026.111399
"DataLoggerRecordings.csv" contains all microclimate recordings made throughout the project. This contains errors, and should be used in conjunction with "https://github.com/chmarsh9871/Hourly_spatial_prediction_of_XGBoost_Model_from_DIY_data_loggers"
Spatial data for each site has been saved separately, in 3 formats, DSMs, classified land cover type rasters, and point clouds produced from UAS surveys using from structure-from-motion photogrammetry. DSMs and land cover types were used in the analysis, but we have included point clouds here for completeness.
For any questions regarding these data, please contact Christopher Marsh at chmarsh@unm.edu
Files and variables
File: DataLoggerRecordings.csv
Description:
Variables
- : row number
- Site.x: J = Jemez Mountains (Las Conchas fire area), P = Philmont (Ute Park Fire area), L = Luna Fire area
- DateTimeH: DateTime of recordin, rounded to the nearest hour
- Site_DL: Individual data logger ID
- Time: Time of recording
- Date: Date of recording
- Bat_V: Battery voltage remaining
- Bat_%: Battery percentage remaining
- TempC: Recorded temperature in Celsius
- rH: Recorded relative humidity in %
- Lux: Lux recorded in lux values. This measure is problematic as the data logger case caused shadowing on the sensor
- SoilTempC: Recorded soil temperature in Celsius
- SoilMoistureCC: Recorded capacitive measure of soil moisture. Please see Supplementary Materials for calibration methods to reach GWC% from these values
- DL_No: Individual data logger number. This value combined with site produces Site_DL
- DateTime: DateTime in standard format
- SoilM_Perc: Calibrated soil moisture as GWC%
- SoilM_Perc5: Calibrated soil moisture as GWC% rounded to the nearest 5%
- SoilM_Perc10: Calibrated soil moisture as GWC% rounded to the nearest 10%
- Hour: Hour of Day (1-24)
- fie_1: disregard
- row_no: disregard
- Site.y: disregard
- Name: disregard
- Longitude: Longitude of data logger
- Latitude: Latitude of data logger
- Ele: Elevation of data logger in meters
- HLI: Heat Load Index value
- TRI: Topographical Roughness Index value
- TPI: Topographical Position Index value
- ProCurv: Profile Curvature value
- Convergenc: Convergence Index value
- ChanNetDis: Channel network distance value
- ChanNetBas: Channel Network Basin value
- Aspect: Aspect value
- ValleyDept: Valley Depth value
- TotCatchAr: Total Catchment Area Value
- TopoWetnes: Topographical Wetness Index Value
- Slope: Slope value (%)
- RelSlopePo: Relative Slope position value
- May_6: disregard
- UAS_solrad: hourly potential incoming solar radiation value derived from UAS surveys
- system.index: ERA5-Land system index value
- soil_temperature_level_1: ERA5-Land soil temperature value at logger location at time of recording
- surface_solar_radiation_downwards: ERA5-Land solar radiation value at logger location at time of recording
- temperature_2m: ERA5-Land air temperature at 2m at logger location value at time of recording
- timestamp: ERA5-Land time stamp
- total_precipitation_hourly: ERA5-Land total precipitation value at logger location at time of recording
- volumetric_soil_water_layer_1: ERA5-Land volumetric soil water value at logger location at time of recording
File: Jemez_pointCloud_w_ground.laz
Description: Las Conchas fire area point cloud from UAS SfM surveys
File: J_Jemez_class_32613.tif
Description: Classified land cover type from Las Conchas fire area
File: P_Phil_class_32613.tif
Description: Classified land cover type from Ute Park fire area
File: Phil_pointCloud_w_ground1.laz
Description: Ute Park fire area point cloud from UAS SfM surveys
File: L_Luna_class_32613.tif
Description: Classified land cover type from Luna fire area
File: Luna_pointCloud_w_ground.laz
Description: Luna fire area point cloud from UAS SfM surveys
File: Jemez_DSM.tif
Description: Digital Surface Model for the Las Conchas fire area
File: Luna_1m_DSM.tif
Description: Digital Surface Model for the Luna fire area
File: Philmont_DSM.tif
Description: Digital Surface Model for the Ute Park fire area
Code/software
Code written in R using R studio.
For completeness, the workflow used to produce the files is as follows:
Pointclouds provided were processed from 2D imagery using Agisoft Metashape to produce DSMs and Orthomosiacs.
Land cover classification performed using ArcPro supervised classification tool using UAS Orthomosiacs.
Topographical indices processed from DSMs using SAGA-GIS.
To view spatial data, only R or QGIS is required. All SAGA derived topographical indices and ERA5-Land values used in analysis are in "DataLoggerRecordings.csv", so for further analysis, only that file is required.
Access information
Other publicly accessible locations of the data:
Data was derived from the following sources:
