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Tucker 2025, MULTI-SCALE DRIVERS OF INTRA-CATCHMENT VARIABILITY IN DROUGHT RESPONSE: LINKING REMOTE-SENSING, GEOPHYSICAL, AND ECOHYDROLOGICAL DATA
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Created: Jul 21, 2025 at 9:38 p.m. (UTC)
Last updated: Jul 21, 2025 at 10:01 p.m. (UTC)
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Abstract

Quantifying fine-scale controls on tree-stand drought response remains challenging due to the interacting effects of landscape, moisture availability, and vegetation. We investigated drought resistance—the ability of a forest to continue transpiring during drought—and resilience—the ability to rebound post-drought—in a 0.5 km2 subcatchment of the Southern Sierra Critical Zone Observatory (King’s River Experimental Watershed), during and after the 2012-2016 California drought. Using a multi-scale approach, we integrated catchment-wide remote sensing data (LiDAR and Normalized Difference Vegetation Index, NDVI) with tree-scale in situ ecohydrological (sapflow and soil moisture), meteorological (air temperature and vapor pressure deficit), and geophysical (electrical resistivity) data from six stations. We fitted generalized additive models (GAMs), which capture nonlinear relationships, using eight remote-sensing-derived predictors—elevation, slope, aspect, distance to stream, topographic wetness index (TWI), snow depth, canopy height, and baseline NDVI. The intercorrelated variables of elevation, slope, distance to stream, TWI, and baseline NDVI were the strongest predictors of resistance and resilience. Notably, baseline NDVI had approximately the opposite effects on resistance and resilience, highlighting the need to distinguish between drought impacts during versus after drought events. The GAMs explained 51% of the variance in resistance and 39% in resilience, indicating that additional covariates are needed, potentially at the finer, plant-scale. Our in-situ data from the valley bottom indicated the presence of hydrologic refugia—areas that retain higher soil moisture than surrounding terrain—which helped explain the added drought resistance observed there. By combining our ecohydrology and geophysical data, we tracked when trees sourced water from sources other than their root zone (such as internal tree water stores) and identified changes to the active rooting zone extent on the sub-daily scale. During seasonal water deficits, trees accessed deeper rather than broader water stores and increasingly relied on internal reserves until depletion. Stations where internal stores were exhausted more rapidly exhibited lower drought resistance. Altogether, this work emphasizes that accurate predictions of forest drought response—especially in complex montane watersheds—require attention to multi-scale processes.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Southern Sierra Critical Zone Observatory (P304)
North Latitude
37.0576°
East Longitude
-119.1711°
South Latitude
37.0463°
West Longitude
-119.2000°

Temporal

Start Date:
End Date:

Content

ReadMe.txt 

author: Annie Tucker, Kamini Singha
help from: Marc Dumont, Katie Kusold, Joel Singley, Luke Jacobsen, Russell Callahan
location: P304, southern sierra critical zone observatory, California, USA
funding credits: NSF grant #2121659 bedrock critical zone network
timespan: summer 2022 to May 2024
summary: we collected ecohydrology, meterological, and geophysical data from 6 plots and 1 meterological station (MET). All data was collected in 15-min intervals except for the electrical resistivity (~30min intervals)

the 6 ecohydrology stations have IDs: P304_A, P304_B, P304_C, P304_D, P304_E, P304_F
with lat/long:
	P304_A 37.05220717, -119.1842053 
	P304_B 37.04818876, -119.1861060
	P304_C 37.05095145, -119.1883138
	P304_D 37.05264477, -119.1864215
	P304_E 37.04965633, -119.1885004
	P304_F 37.04949724, -119.1832821 
the 1 MET station has lat/long: 37.05249514, -119.1841675 

electrical resistivity (ER) was set up at P304_B, P304_C, and P304_D on a 
cross-shaped array of electrodes
we collected ER data October 2022, June 2023, October 2023, and May 2024 (no data collected
from P304_B from May 2024 due to equipment failure)

methods detailed in Annie Tucker's MS thesis from the Colorado School of Mines, 2025

****************************
file name: BCZN_SSCZO_P304_Tucker_Ecohydrology_Raw.csv
columns:
	timestamp
	plot
	SoilMoisture_15cm
		Decagon/METER EC5, USA
		measures volumetric water content (m3/m3)
		installed 15 cm below ground surface
	SoilMoisture_30cm
		Decagon/METER EC5, USA
		measures volumetric water content (m3/m3)
		installed 30 cm below ground surface
	air_temp_2m_C
		HOBO U23
		measures air temperature in degrees Celsius (°C)  
		sensor housed in radiation shield, installed 2 m above ground surface 
	rh_percent
		HOBO U23
		measures relative humidity in %
		sensor housed in radiation shield, installed 2 m above ground surface 
	e_sat_Pa
		calculated  
		derived from air_temp_2m_C using Tetens equation  
		units: Pascals (Pa)
	vpd_kpa
		calculated  
		vapor pressure deficit = (1 - RH/100) × e_sat_Pa  
		converted to kilopascals (1 kPa = 1000 Pa)  
		units: kilopascals (kPa)
	SapFlow_probe1, SapFlow_probe2, SapFlow_probe3, SapFlow_probe4
		Implex Gen 2 sapflow sensor
		Data is NOT raw. It has been corrected for misalignment and filtered for outliers
		units are cm/hr, so it's a sap velocity
		4 white-fir trees (1-4) were instrumented with sapflow probes at each plot
		see MS Thesis for lots more sapflow details


****************************
file name: BCZN_SSCZO_P304_Tucker_SnowDepth_Raw.csv
snow depth sensor used = Judd Communications, USA
columns:
	timestamp
	Air_TempF_Avg
	Depth
		snow depth
	plot

****************************
file name: BCZN_SSCZO_P304_Tucker_SnowDepth_Processed.csv
columns:
	TBD

****************************
file name: BCZN_SSCZO_P304_Tucker_MET_Raw.csv
columns:
	timestamp
	air_temp_2m_C  
		HOBO U23  
		measures air temperature in degrees Celsius (°C)  
		sensor housed in radiation shield, installed 2 m above ground surface 
	air_pressure_kpa  
		HOBO U23
		measures atmospheric pressure in kilopascals (kPa)  
		sensor housed in radiation shield, installed 2 m above ground surface 
	rh_percent 
		HOBO U23  
		sensor housed in radiation shield, installed 2 m above ground surface 
		measures relative humidity in percent (%)  
		relative humidity calculated from dry bulb temperature and dew point 
	e_sat_Pa
		calculated  
		derived from air_temp_2m_C using Tetens equation  
		units: Pascals (Pa)
	vpd_kpa
		calculated  
		vapor pressure deficit = (1 - RH/100) × e_sat_Pa  
		converted to kilopascals (1 kPa = 1000 Pa)  
		units: kilopascals (kPa)
	wind_speed_ms
		Davis Instruments
		average wind speed over 15-min
		units: meters per second
	wind_gust_ms
		Davis Instruments
		max wind speed over 15-min
		units: meters per second
	wind_direction_deg
		Davis Instruments
		direction of wind in degrees from north
	plot
		data in this table is only from the MET station

****************************
folder: BCZN_SSCZO_P304_Tucker_ER_Raw


file name: ibutton_soil_temperature_data_degC.csv
iButton Technology device monitored soil temperature at 15 and 30-cm deep for 
temperature correcting the ER
columns:
	timestamp
	Value
		temp in deg C
	plot
	depth
	survey
		indicates the field event


file names: [plot]_elec1CS.csv, [plot]_elec2AS.csv
	files detail surveyed electrode locations for the 1-cross slope transect
	and 2-along slope transect survey lines


file name: lines-elec.csv
	file details idealized survey array positions for code, if helpful


file name: [plot]_ER_[survey year]_[survey month].csv
	output file from IRIS Syscal Pro device detailing ER data and quality measurements
	look to IRIS for what each column header means, or reach out to Annie


****************************
file name: BCZN_SSCZO_P304_Tucker_ER_Processed_RootZone.csv
BCZN_SSCZO_P304_Tucker_ER_Processed_RootZone.pkl
	python pickle file
	contains inverted, temperature-corrected electrical resistivity models with the 
	root zone area selected. the root zone area was identified by the highest 
	standard deviation in the conductivity time series. See MS thesis for details

BCZN_SSCZO_P304_Tucker_ER_Processed_RootZone.xlsx
	contains the same information as pickle file but in excel format

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
U.S. National Science Foundation Collaborative Research: How roots, regolith, rock and climate interact over decades to centuries ? the R3-C Frontier 2121659

How to Cite

Tucker, A., K. Singha (2025). Tucker 2025, MULTI-SCALE DRIVERS OF INTRA-CATCHMENT VARIABILITY IN DROUGHT RESPONSE: LINKING REMOTE-SENSING, GEOPHYSICAL, AND ECOHYDROLOGICAL DATA, HydroShare, http://www.hydroshare.org/resource/2424579caa904330bd99da9d29daeb96

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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