View data of the frequency at which one unit of currency purchases domestically produced goods and services within a given time period.

M2 Money Velocity: 66 years of historical data from 1959 to 2025.

Money Supply M2 in China increased to 330332.50 CNY Billion in June from 325783.81 CNY Billion in May of 2025. This dataset provides - China Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 In the Euro Area increased to 15736672 EUR Million in May from 15696283 EUR Million in April of 2025. This dataset provides the latest reported value for - Euro Area Money Supply M2 - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Money Supply M2 in Japan increased to 1268407.50 JPY Billion in June from 1267064.60 JPY Billion in May of 2025. This dataset provides - Japan Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 in Brazil increased to 6854638 BRL Million in May from 6765154 BRL Million in April of 2025. This dataset provides - Brazil Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 in Sweden increased to 4991180 SEK Million in May from 4979321 SEK Million in April of 2025. This dataset provides - Sweden Money Supply M2- actual values, historical data, forecast, chart, statistics, economic calendar and news.

It has recently been argued that when the conventional specification of M2 income velocity is extended to include proxies for two types of institutional change, as emphasized by Bordo and Jonung (1987, 1990), corresponding to the processes of monetization and increasing financial sophistication of financial developments, this extended model is stable in the sense that one can reject the null hypothesis of no cointegration against the alternative of a single cointegrating vector. There may be implications that such an equilibrium relation is a structural income velocity of money function. The evidence based on century-long data from 1880 to 1986 presented in this paper about parameter instability of the cointegrating vector of velocity with its determinants for Canada, Norway, Sweden, and the United Kingdom casts doubt on this interpretation. The evidence is based on using formal stability tests. Moreover, it has an eyeball support from the sequential estimates of various parameters of the cointegrating relationship including income and interest semi-elasticities.

Money Supply M2 in the United Kingdom decreased to 3113598 GBP Million in May from 3117847 GBP Million in April of 2025. This dataset provides - United Kingdom Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This is first data set for key experimental data of selected experiments reported in X. Lei (2024), and the second data set is also stored at Mendeley Data (doi: 10.17632/ct25dfrns3.1). Summery of X.Lei (2024): This study investigated the fault nucleation and rupture processes driven by stress and fluid pressure in fine-grained granite by monitoring acoustic emissions (AEs). Through detailed analysis of the spatiotemporal distribution of the AE hypocenter, P-wave velocity, stress-strain, and other experimental observation data under different confining pressures for stress-driven fractures and under different water injection conditions for fluid-driven fractures, it was found that fluid has the following effects: 1) complicating the fault nucleation process, 2) exhibiting episodic AE activity corresponding to fault branching and the formation of multiple faults, 3) extending the spatiotemporal scale of nucleation processes and pre-slip, and 4) reducing the dynamic rupture velocity and stress drop. The experiments also show that 1) during the fault nucleation process, the b-value for AEs decreases from 1-1.3 to 0.5 before dynamic rupture, then rapidly recovers to around 1-1.2 during aftershock activity and 2) the hydraulic diffusivity gradually increases from an initial pre-rupture order of 0.1 m2/s to 10-100 m2/s after dynamic rupture. These results provide a reasonable fault pre-slip model, indicating that hydraulic fracturing promotes shear slip before dynamic rupture, as well as laboratory-scale insights into ensuring the safety and effectiveness of hydraulic fracturing operations related to activities such as geothermal development, evaluating the seismic risk induced by water injection, and further researching the precursory preparation process for deep fluid-driven or fluid-involved natural earthquakes. Potential uses of the data sets include but are not limited to 1) Providing training datasets for machine learning and AI-based technology development, such as developing machine learning models to predict stress accumulation and the remaining time before final fracture. 2) Developing effective methods for identifying weak or low S/N ratio AE signals. The waveform data contain numerous AE events that cannot be accurately located using conventional methods. 3) Inverting source mechanisms and moment tensors. Our research has not yet systematically analyzed the moment tensors of AE events. 4) Conducting more in-depth research on the interaction between fluid migration and rock deformation and fracture. Please cite the associated article as: X. Lei (2024), Fluid-driven fault nucleation, rupture processes, and permeability evolution in Oshima granite — Preliminary results and acoustic emission datasets, Geohazard Mechanics, https://doi.org/10.1016/j.ghm.2024.04.003

Money Supply M0 in the United States decreased to 5648600 USD Million in May from 5732900 USD Million in April of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction : Equations can calculate pulse wave velocity (ePWV) from blood pressure values (BP) and age. The ePWV predicts cardiovascular events beyond carotid-femoral PWV. We aimed to evaluate the correlation between four different equations to calculate ePWV. Methods: The ePWV was estimated utilizing mean BP (MBP) from office BP (MBPOBP) or 24-hour ambulatory BP (MBP24-hBP). We separated the whole sample into two groups: individuals with risk factors and healthy individuals. The e-PWV was calculated as follows:
We calculated the concordance correlation coefficient (Pc) between e1-PWVOBP vs e2-PWVOBP, e1-PWV24-hBP vs e2-PWV24-hBP, and mean values of e1-PWVOBP, e2-PWVOBP, e1-PWV24-hBP, and e2-PWV24-hBP . The multilevel regression model determined how much the ePWVs are influenced by age and MBP values. Results: We analyzed data from 1541 individuals; 1374 ones with risk factors and 167 healthy ones. The values are presented for the entire sample, for risk-factor patients and for healthy individuals, respectively. The correlation between e1-PWVOBP with e2-PWVOBP and e1-PWV24-hBP with e2-PWV24-hBP was almost perfect. The Pc for e1-PWVOBP vs e2-PWVOBP was 0.996 (0.995-0.996), 0.996 (0.995-0.996), and 0.994 (0.992-0.995); furthermore, it was 0.994 (0.993-0.995), 0.994 (0.994-0.995), 0.987 (0.983-0.990) to the e1-PWV24-hBP vs e2-PWV24-hBP. There were no significant differences between mean values (m/s) for e1-PWVOBP vs e2-PWVOBP 8.98±1.9 vs 8.97±1.8; p=0.88, 9.14±1.8 vs 9.13±1.8; p=0.88, and 7.57±1.3 vs 7.65±1.3; p=0.5; mean values are also similar for e1-PWV24-hBP vs e2-PWV24-hBP, 8.36±1.7 vs 8.46±1.6; p=0.09, 8.50±1.7 vs 8.58±1.7; p=0.21 and 7.26±1.3 vs 7.39±1.2; p=0.34. The multiple linear regression showed that age, MBP, and age² predicted more than 99.5% of all four e-PWV. Conclusion: Our data presents a nearly perfect correlation between the values of two equations to calculate the estimated PWV, whether utilizing office or ambulatory blood pressure. Methods This study is a secondary analysis of data obtained from two cross-sectional studies conducted at a specialized center in Brazil to diagnose and treat non-communicable diseases. In both studies, the inclusion criteria were adults aged 18 years and above, referred to undergo ambulatory blood pressure monitoring (ABPM) due to suspected non-treated or uncontrolled hypertension following initial blood pressure measurements by a physician. The combined databases included 1541 people. For the first database, we recruited participants between 28 January and 13 December 2013, and for the second database, between 23 January 2016 and 28 June 2019. Prior to being fitted with an AMBP device and assisted by a trained nurse, all participants signed a written consent form to partake in the research. Later, the nurse collected demographic and clinical data, including any previous reports of clinical cardiovascular disease (CVD), acute myocardial infarction, acute coronary syndrome, coronary or other arterial revascularization, stroke, transient ischemic attack, aortic aneurysm, peripheral artery disease and severe chronic kidney disease (CKD). All subjects had their BP, weight, height, and waist circumference measured and their body mass index (BMI) calculated. Although the ePWV data from the Reference Values for Arterial Stiffness Collaboration originated from cohorts lacking established cardiovascular disease, cerebrovascular disease, or diabetes, we included diabetes, CVD, CKD, smokers, and obese individuals. This choice reflects a sample that more closely resembles what can be seen in everyday Brazilian physician appointments. The study population was divided into two groups: healthy individuals and those with risk factors. Healthy individuals did not present any risk factors and a non-elevated BP (<140 and 90 mmHg). Conversely, the group with risk factors consisted of individuals with elevated BP (≥140 and-or 90 mmHg) or at least one risk factor, such as previous hypertension, dyslipidemia, diabetes, smoking, body obesity (BMI ≥ 30 kg/m2), or an increased waist circumference at risk (waist circumference > 102 cm in males and > 88 cm in females). Blood pressure measurement and ambulatory blood pressure monitoring During the data collection for both studies, office BP (OBP) measurements were conducted following recommended guidelines to ensure accurate pressure values. In the first database, a nurse performed seven consecutive BP measurements utilizing a Microlife device BP3BTOA (Onbo Electronic Co, Shenzhen, China). In the second database, a nurse assistant operated a Microlife device model BP3AC1-1PC (Onbo Electronic Co, Shenzhen, China). This device operated on Microlife Average Mode which takes three measurements in succession and calculates the average BP value. The assistant took two sets of three BP measurements sequentially. All individuals registered twenty-four hours of ABPM using a Dyna-Mapa / Mobil-O-Graph-NG monitor (Cardios, São Paulo, Brazil), equipped with an appropriately-sized cuff on their non-dominant arm. The readings were taken every 20 minutes during the day and every 30 minutes during the night, here understood as the period between going to bed and waking up. We respected all recommended protocols strictly to ensure quality recordings. Calculation of estimated pulse wave velocity The ePWV was calculated using the equations derived from the Reference Values for Arterial Stiffness Collaboration, incorporating age and MBP as follows:
MBP was also calculated as diastolic BP+ 0.4*(systolic BP/diastolic BP). Thus, the values of e1-PWV and e2-PWV were obtained for the total sample, as well as separately for the groups comprising healthy individuals and those with risk factors. We used MBP from OBP (MBPOBP) to calculate e1-PWVOBP and e2-PWVOBP, and MBP of twenty hours BP average (MBP24-hBP) to e1-PWV24-hBP and e2-PWV24-hBP. The Human Research Ethics Committee of Sirio Libanes Hospital and Federal University of the Triângulo Mineiro, provided ethical approval for data collection under protocol numbers 08930813.0.0000.5461 (first database) and 61985316.9.0000.5154 (second database), respectively.

The work presents the systematic evaluation of the acceleration-, velocity- and duration-dependency of the short- and medium-latency stretch reflexes in the ankle plantarflexors. The stretch rflexes are evaluated using EMG measurements and processing with the systematic evaluation performed through thorough stretch perturbation design.In short, we showed that perturbation acceleration, velocity and duration all shape the M1 and M2 response, often via nonlinear or interacting dependencies. Consequently, systematic execution and reporting of stretch reflex and spasticity studies, avoiding uncontrolled parameter interdependence, is essential for proper understanding of the reflex neurophysiology.

Simulation with a global high resolution ocean tide and circulation model (MPI-OM with tides), and an embedded thermodynamic sea-ice model, which is explicitly forced by the full lunisolar tidal forcing of second degree as described by ephemerides. Further, the model is forced by daily climatological wind stress, heat, and fresh water fluxes. The horizontal resolution is about 0.1° and thus, the model implicitly resolves meso-scale eddies and internal waves. !--------------------------------------------------------------------------------------------------------------------------------------------! A simulation of ten years has been performed and the following data is stored: - 2D tidal patterns of sea level - 3D horizontal and vertical velocity fields - barotropic-to-baroclinic tidal energy conversion rates - 3D tidal density perturbations - seasonal sub-sampling of M2 tidal sea level and velocities - daily values of the last year of (u,v,w, T, S) in 100 and 2000 meter depth - monthly mean values of eight years of several ocean variables !--------------------------------------------------------------------------------------------------------------------------------------------! More details on the simulation can be found in: Müller, M., J. Cherniawsky, M. Foreman, and J.-S. von Storch (2012) Global map of M2 internal tide and its seasonal variability from high resolution ocean circulation and tide modelling, Geophysical Research Letters 39, L19607. !--------------------------------------------------------------------------------------------------------------------------------------------!

Field measurements of mean flow and turbulence parameters at the Kvichak river prior to and after the deployment of ORPC's RivGen hydrokinetic turbine. Data description and turbine wake analysis are presented in the attached manuscript "Wake measurements from a hydrokinetic river turbine" by Guerra and Thomson (recently submitted to Renewable Energy). There are three data sets: NoTurbine (prior to deployment), Not_Operational_Turbine (turbine underwater, but not operational), and Operational_Turbine. The data has been quality controlled and organized into a three-dimensional grid using a local coordinate system described in the paper. All data sets are in Matlab format (.mat). Variables available in the data sets are: qx: X coordinate matrix (m) qy: Y coordinate matrix (m) z : z coordinate vector (m) lat : grid cell latitude (degrees) lon: grid cell longitude (degrees) U : velocity magnitude (m/s) Ux: x velocity (m/s) Vy: y velocity (m/s) W: vertical velocity (m/s) Pseudo_beam.b_i: pseudo-along beam velocities (i = 1 to 4) (m/s) (structure with raw data within each grid cell) beam5.b5: 5th-beam velocity (m/s) (structure with raw data within each grid cell) tke: turbulent kinetic energy (m2/s2) epsilon: TKE dissipation rate (m2/s3) Reynolds stresses: uu, vv, ww, uw, vw (m2/s2) Variables from the Not Operational Turbine data set are identified with _T Variables from the Operational Turbine data set are identified with _TO

This set of data are derived from 12 high-resolution ocean model simulations with a realistic paleo-bathymetry. They can be used to investigate the sensitivity of the E–O Southern Ocean to TG deepening and changing wind stress. They also can be used to analyses the zonal momentum budget of the Southern Ocean to interpret the simulated dynamics. The model domain is circumpolar and covers the latitude range between 84°S and 25°S. The model has 1/4 degree horizontal grid spacing and 50 unevenly spaced vertical levels (5000 m). The restoring time scale to 10 days. This dataset includes the final 15-years averaged model simulation outputs. 12 Simulations: katharina38_MediumTG_MediumDP_final: the experiment with 1500m TG and 1000m DP, maximum wind stress at 53°S, 0.1 N/m2. katharina38_MediumTG_MediumDP_5n: the experiment with 1500m TG and 1000m DP, maximum wind stress at 48°S, 0.1 N/m2. katharina38_MediumTG_MediumDP_5s: the experiment with 1500m TG and 1000m DP, maximum wind stress at 58°S, 0.1 N/m2. katharina38_MediumTG_MediumDP_10s: the experiment with 1500m TG and 1000m DP, maximum wind stress at 63°S, 0.1 N/m2. katharina38_MediumTG_MediumDP_final_double_wind: the experiment with 1500m TG and 1000m DP, maximum wind stress at 53°S, 0.2 N/m2. katharina38_MediumTG_MediumDP_10s_double_wind the experiment with 1500m TG and 1000m DP, maximum wind stress at 63°S, 0.2 N/m2. katharina38_300mTG_DeepDP_final: the experiment with 300m TG and 1000m DP, maximum wind stress at 53°S, 0.1 N/m2. katharina38_300mTG_DeepDP_5n: the experiment with 300m TG and 1000m DP, maximum wind stress at 48°S, 0.1 N/m2. katharina38_300mTG_DeepDP_5s: the experiment with 300m TG and 1000m DP, maximum wind stress at 58°S, 0.1 N/m2. katharina38_300mTG_DeepDP_10s: the experiment with 300m TG and 1000m DP, maximum wind stress at 63°S, 0.1 N/m2. katharina38_300mTG_DeepDP_final_double_wind: the experiment with 300m TG and 1000m DP, maximum wind stress at 53°S, 0.2 N/m2. katharina38_300mTG_DeepDP_10s_double_wind the experiment with 300m TG and 1000m DP, maximum wind stress at 63°S, 0.2 N/m2. Different files: in the dataset (acronyms, units, explanations): THETA [℃] is potential temperature. UVEL [m/s] is zonal velocity. UVELSQ [m^2/s^2] is square of zonal velocity. VVEL [m/s] is meridional velocity. VVELSQ [m^2/s^2] is square of meridional velocity. VVELTH [℃ m/s] is meridional transport of potential temperature, which can be used to calculate meridional heat transport. Momentum budget term: momKE [m^2/s^2] is Kinetic Energy in momentum Equation PHIBOT [m^2/s^2] is Bottom Pressure Pot.(p/rho) Anomaly, which can be used to calculate topographic form stress. PHIHYD [m^2/s^2] is Hydrostatic Pressure Pot.(p/rho) Anomaly, which can be used to calculate topographic form stress. TOTUTEND [m/s/day]is Tendency of Zonal Component of Velocity USidDrag [m^2/s^2] is U momentum tendency from Side Drag UBotDrag [m^2/s^2] is U momentum tendency from Bottom Drag Um_Diss [m^2/s^2] is U momentum tendency from Dissipation (Explicit part) Um_Advec [m^2/s^2] is U momentum tendency from Advection terms Um_AdvRe [m^2/s^2] is U momentum tendency from vertical Advection (Explicit part) Um_AdvZ3 [m^2/s^2] is U momentum tendency from Vorticity Advection Um_Cori [m^2/s^2] is U momentum tendency from Coriolis term Um_dPhdx [m^2/s^2] is U momentum tendency from Pressure/Potential gradient Um_Ext [m^2/s^2] is U momentum tendency from external forcing (wind forcing)

This dataset supplements the article: Longitudinal transport of suspended sediment in the Modaomen Estuary of the Pearl River: effects of river, tide, and mouth bar, submitted to Marine Geology

Contact information: Dr. Liu, F., School of Ocean Engineering and Technology, Sun Yat-sen University, Guangzhou, 510275, China.

Email: liuf53@mail.sysu.edu.cn (Liu, Feng)

Brief view of the dataset

Hydrodynamics, suspended sediment concentration and salinity distribution were measured at three fixed stations along the longitudinal direction of the estuary. The stations were located at Guadingjiao (M1), inside the bar (M2), and outside the bar (M3), and sampling was simultaneously conducted at neap tide (NT) from July 31 to August 1 and spring tide (ST) from August 8 to August 9 in 2017. All observation data has been compiled to include data from the surface to the bottom six layers within 26 hours. This directory contains the following datasets.

M10731.xlsx: Current velocity, current direction, salinity and SSC during neap tide at Station M1

M10808.xlsx: Current velocity, current direction, salinity and SSC during spring tide at Station M1

M20731.xlsx: Current velocity, current direction, salinity and SSC during neap tide at Station M2

M20808.xlsx: Current velocity, current direction, salinity and SSC during spring tide at Station M2

M30731.xlsx: Current velocity, current direction, salinity and SSC during neap tide at Station M3

M30808.xlsx: Current velocity, current direction, salinity and SSC during spring tide at Station M3

Three sub-ice-shelf moorings were deployed on Fimbulisen by the Norwegian Polar Institute in December 2009 and have been maintained either each year or every two years. The data consist of temperature, pressure and velocity observations between 09.12.2009 and 31.12.2021 at three mooring sites. All three moorings are hanging below the ice shelf and each is equipped with an upper Aanderaa RCM9 close to the ice base and a lower one close to the bottom. Exact locations and depths are given in each data file.

This data set only contains the data published in the two publications mentioned below.

Note: The full hourly data consisting of temperature, salinity, dissolved oxygen, currents, and pressure for all six instruments is available here.

Quality

The raw data have been processed and quality controlled. All velocities have been corrected for magnetic declination. Due to instrument failures, there are some gaps in the data, and due to sensor drifts, most salinity data had to be discarded.

Related papers

Lauber, J., Hattermann, T., de Steur, L., Darelius, E., Auger, M., Nøst, O.A., & Moholdt, G. (2023). Warming beneath an East Antarctic ice shelf due to increased subpolar westerlies and reduced sea ice. Nature Geoscience, 16, 877-885. https://doi.org/10.1038/s41561-023-01273-5

Lauber, J., de Steur, L., Hattermann, T., & Darelius, E. (2024). Observed Seasonal Evolution of the Antarctic Slope Current System off the Coast of Dronning Maud Land, East Antarctica. Journal of Geophysical Research: Oceans, 129(4), e2023JC020540. https://doi.org/10.1029/2023JC020540

Versions

Version 1.0 (03.07.2023): First upload of data.

Version 1.1 (11.10.2023): An error in the time vector has been fixed. The time is now given in days since 1.1.1950 ( this was wrongly 1.1.1050 in v1.0).

Version 2.0 (22.01.2024): Temperature, velocity, and pressure have been added until 31.12.2021 for M1_lower and M3_lower.

Version 3.0 (07.08.2024): Data have been re-processed using updated routines. The main temperature sensors at all instruments were thought to have recorded only noise since mooring deployment in 2009. However, after the most recent fieldwork in December 2023, these sensors were found to have worked just fine with the right calibration coefficients applied. The time-mean offset between the temperature of the main and (previously used) secondary sensor is smaller than the instrument accuracy of 0.05°C. In this version of the data, the published temperature is the one measured by the main sensor for all instruments except M3_lower. For M3_lower, the primary temperature was taken until it started drifting (23.10.2014). After this date, the secondary temperature was taken, but the mean offset to the main temperature (0.0227 °C, calculated before the main sensor started drifting) was removed.

ObjectiveFlywheel resistance training (FRT) is a training modality for developing lower limb athletic performance. The relationship between FRT load parameters and barbell squat loading remains ambiguous in practice, resulting in experience-driven load selection during training. Therefore, this study investigates optimal FRT loading for specific training goals (maximal strength, power, muscular endurance) by analyzing concentric velocity at varying barbell 1RM percentages (%1RM), establishes correlations between flywheel load, velocity, and %1RM, and integrates force-velocity profiling to develop evidence-based guidelines for individualized load prescription.MethodsThirty-nine participants completed 1RM barbell squats to establish submaximal loads (20–90%1RM). Concentric velocities were monitored via linear-position transducer (Gymaware) for FRT inertial load quantification, with test–retest measurements confirming protocol reliability. Simple and multiple linear regression modeled load-velocity interactions and multivariable relationships, while Pearson’s r and R2 quantified correlations and model fit. Predictive equations estimated inertial loads (kg·m2), supported by ICC (2, 1) and CV assessments of relative/absolute reliability.ResultsA strong inverse correlation (r = −0.88) and high linearity (R2 = 0.78) emerged between rotational inertia and velocity. The multivariate model demonstrated excellent fit (R2 = 0.81) and robust correlation (r = 0.90), yielding the predictive equation: y = 0.769–0.846v + 0.002 kg.ConclusionThe strong linear inertial load-velocity relationship enables individualized load prescription through regression equations incorporating velocity and strength parameters. While FRT demonstrates limited efficacy for developing speed-strength, its longitudinal periodization effects require further investigation. Optimal FRT loading ranges were identified: 40–60%1RM for strength-speed, 60–80%1RM for power development, and 80–100% + 1RM for maximal strength adaptations.

Note: This is copied/pasted from the attached Word document. That version may be easier to read.Datasets used in analyses for “Drought response strategies of vascular epiphytes in isolated pasture trees in a Costa Rican tropical montane landscape”. There are 4 combined sap flow/microclimate hourly datasets, 2 microclimate daily datasets, and datasets containing PV curve results, water potential, and leaf traits.Sap flow references: (Burgess et al., 2001; Clearwater et al., 2009)PV curve references: (Williams et al., 2017)Leaf trait references: (Gotsch et al., 2022)Common variables that appear in multiple datasets:· Site (Upper = Ortega, Middle = Guindon, Lower = Zelmi)· Species: 4 letter species code· Ind: Non-unique plant identifier, must be combined with Site and Species for a unique key.· Timestamp: Hourly as y-m-d h:m:s or daily as y-m-d· Season: Dry or Wet· Succulent: Sample comes from leaf succulent species, yes or no· Type: Growth form of sampled individual (Woody single stem, woody multi stem, or herbaceous)Sap velocity and microclimate datasets explained· Sap_L4: The complete dataset after cleaning, baselining, and averaging to hourly intervals· Sap_L4a: L4 averaged to the level of species within site· Sap_L5: L4 filtered to the specific wet/dry weeks of interest· Sap_L5a: L5 averaged to the level of species within siteSap velocity and microclimate variables:· EpiID: Unique epiphyte identifier consisting of Site letter, tree identifier, and number from 1-16.· Sap.vel: Sap velocity (cm/hr)· LWSup: Leaf wetness (g/m2)· Solar: Solar radiation (W/m2)· VPD: Vapor pressure deficit (Kpa)· VWC.rel: Relative volumetric water content (Proportion)· Precipitation: Water input to pluviometers (mm)PV curve variables:· TLP: Turgor loss point (MPa)· totE: Bulk modulus of elasticity (MPa)· CTarea: Hydraulic capacitance before TLP, normalized by fresh area (g m-2 MPa-1)Water potential variables:· Month (Feb, Mar, or Apr)· Midday: Water potential taken at midday (MPa)· Predawn: Water potential taken at predawn (MPa)· Difference: Difference between midday and predawn measurements (MPa)· TLP_mean: Mean turgor loss point of the species within site, calculated from PV curve data (MPa)· Buffer: Difference between the Midday value and the TLP_mean (MPa)Leaf traits:· gmin: Minimum stomatal conductance (mmol m-2 s-1)· LDMC: Leaf dry matter content (g g-1)· SLA: Specific leaf area (cm2 g-1)· Tough: Leaf toughness (g mm-2)· SD: Stomatal density (# mm-2)· LT: Leaf thickness (mm)· LWC: Leaf water content (%)· HT: Hydrenchymal thickness (mm)ReferencesBurgess SSO, Adams MA, Turner NC, Beverly CR, Ong CK, Khan AAH, Bleby TM. 2001. An improved heat pulse method to measure low and reverse rates of sap flow in woody plants (Tree Physiology 21 (589-598)). Tree Physiology 21: 1157.Clearwater MJ, Luo Z, Mazzeo M, Dichio B. 2009. An external heat pulse method for measurement of sap flow through fruit pedicels, leaf petioles and other small-diameter stems. Plant, Cell and Environment 32: 1652–1663.Gotsch SG, Williams CB, Bicaba R, Cruz-de Hoyos R, Darby A, Davidson K, Dix M, Duarte V, Glunk A, Green L, et al. 2022. Trade‑offs between succulent and non‑succulent epiphytes underlie variation in drought tolerance and avoidance. Oecologia.Williams CB, Reese Næsborg R, Dawson TE. 2017. Coping with gravity: The foliar water relations of giant sequoia. Tree Physiology 37: 1312–1326.