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This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).

{"references": ["UNSD Demographic Statistics, available at http://data.un.org", "The World Bank GDP data, available at https://data.worldbank.org/", "UNFCCC: Greenhouse Gas Inventory Data, available at https://unfccc.int/process/transparency-and-reporting/greenhouse-gas-data/what-is-greenhouse-gas-data"]} Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2021-12-03. The dataset is also available via datalad. To obtain the dataset with datalad, see the instructions at https://github.com/mikapfl/unfccc_di_data .

Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper: Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019 Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de). Climate change impact data File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries. File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019). Climate change mitigation cost data The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2]. File 4: REMIND_scenario_results_economic_data.csv File 5: REMIND_scenarios_climate_data.csv Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature. In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios. The first dimension specifies the climate policy regime (delayed action, baseline scenarios): 1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement) The second dimension specifies the technology portfolio and assumptions: x1x Full technology portfolio (used in this study) x2x noCCS: unavailability of CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of solar and wind power limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability of CCS in combination with bioenergy The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.). xx1 0$/tCO2 (baseline) xx2 10$/tCO2 xx3 30$/tCO2 xx4 50$/tCO2 xx5 100$/tCO2 xx6 200$/tCO2 xx7 500$/tCO2 xx8 40$/tCO2 xx9 20$/tCO2 xx0 5$/tCO2 For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price). [1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a. [2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains: 2521 historical floods with impact data (1870-2020); 237 further historical floods with significant impacts, but without precise impact data (1950-2020) Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020). Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders): HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020) HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020) HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020) HANZE_list_of_references.csv - List of all references used in the catalogues HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods Regions_v2010_simplified.zip - Map of subnational regions (v2010) Regions_v2021_simplified.zip - Map of subnational regions (regions v2021) v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected. This work was supported by the German Research Foundation (DFG) through project "Decomposition of flood losses by environmental and economic drivers" (FloodDrivers), project no. 449175973 

CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations. The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: https://github.com/nanophyto/CASCADE/ (zenodo.12797197) Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk v.0.1.2 has some fixes: 1. The wrongly specified S. neapolitana was removed from synonyms.yml (this species is now S. nana)2. Longitudes were corrected for Guerreiro et al., 20233. A double entry for Dimizia et al., 2015 was fixed4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)5. Data from Sal et al., 2013 was re-done, as some species were missing6. Duplicate entries from Baumann et al., 2000 were dropped

Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years We include water levels measured along a transect of groundwater wells in 1999 and 2019, statistical analyses of ground water data, tidal efficiency estimates, vegetation data from 1997, 2005, 2014, and 2018, measures of tide gauge data and sea level rise from the Battery, New York Harbor. We attach the following three groups of files: (1) Files related to data from Piermont Marsh, which includes water levels in wells, tide gauge data collected from the tidal channel, and vegetation data; (2) Files related to analysis of water levels at Piermont Marsh; (3) Files related to analysis of Battery tide gauge data, Battery tide predictions, and precipitation data ## Description of the data and file structure **(1) Files related to data from Piermont Marsh, which includes water levels in wells, tide gauge data collected from the tidal channel, and vegetation data** 1999PiermontWaterlevels.csv 2019PiermontWaterLevels.csv channel_1999.xls channel_2019.xls water_level_elevations.csv Vegetation.xls 1999PiermontWaterlevels.csv and 2019PiermontWaterLevels.csv - Water levels collected at Piermont marsh in groundwater wells, at 0-m, 6-m, 12-m, 18-m, 24-m, 36-m, and 48-m from a tidal channel. The files contain three fields: daytime, well, and elevation. The daytime is the date and time the water level was collected, hours in Eastern Daylight Time -4GMT. The well number refers to its location relative to the tidal channel, with #1 referring to 0-m, #2 referring to 6-m, #3 referring to 12-m, #4 referring to 18-m, #5 referring to 24-m, #6 referring to 36-m, and #7 referring to 48-m. The elevation field refers to the water level in meters relative to the NAVD88 datum. In 1999 water levels were collected 14 April 2019 - 26 May 2019. In 2019, water levels were collected 5 May 2019 - 30 June 2019. channel_1999.xls - This file shows the elevation of water level in the channel. There is a field for date and time, in GMT -4, and water level in meters relative to NGVD29. channel_2019.xls - This file shows the elevation of water level in the channel. There is a field for Date, Time, in GMT -4, absolute pressure in in mbar, temperature in degrees C, and water level in meters relative to NAVD88. water_level_elevations.csv - This csv file includes five fields. The first is "year" or the year collected (1999 or 2019). The second is "well" numbered 1-7. Well 1 is closest to the channel while 7 is the furthest from the channel. #1 referrs to 0-m from the channel, #2 referring to 6-m from the channel, #3 referring to 12-m from the channel, #4 referring to 18-m from the channel, #5 referring to 24-m from the channel, #6 referring to 36-m from the channel, and #7 referring to 48-m from the channel. The datetime field refers to the day and time the measure was made in day/month/year HH:MM AM/PM format. The next field is lunarcyle which refers to whether the measure was made during "spring" or "neap" tidal cycles. Spring was assigned to the tides the week of full or new moons, Neap was assigned to tides the week of the first and last quarter. The last is "elevation" and is the measure of water levels in meters relative to the NAVD88 datum. Vegetation.xls - This Excel file includes four sheets that each refer to a year of vegetation date - 1997, 2005, 2014, and 2017. The first field is "well" which has a number 1 through 7. The well number refers to its location relative to the tidal channel, with #1 referring to 0-m, #2 referring to 6-m, #3 referring to 12-m, #4 referring to 18-m, #5 referring to 24-m, #6 referring to 36-m, and #7 referring to 48-m. There is a field for latitude (lat) and longitude (long), which refers to the location of the shape in UTM, in meters, in the 18N. Cover refers to the plant cover type, area is the area of the polygon in square meters. **(2) Files related to analysis of water levels at Piermont Marsh** Distancefromsurface.R MinNeap_MarshSurface.csv MaxNeap_MarshSurface.csv MinSpring_MarshSurface.csv MaxSpring_MarshSurface.csv PiermontEfficiencyRggplot.csv Tidalefficiency.R The R file Distancefromsurface.R includes calculations of mean and variance of water levels, and as well as production of relevant figures. MinNeap_MarshSurface.csv file has low tide minimum water levels during neap tides (weeks centered on the moons first and third quarter). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at low tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. MaxNeap_MarshSurface.csv file has high tide maximum water levels during neap tides (weeks centered on the moons first and third quarter). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at high tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. MinSpring_MarshSurface.csv file has low tide minimum water levels during spring tides (weeks centered on the new and full moon). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at low tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. MaxSpring_MarshSurface.csv file has high tide maximum water levels during spring tides (weeks centered on the new and full moon). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at high tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. PiermontEfficiencyRggplot.csv - file lists the well number (1-7), distance (a number 1-14, which gives a unique identifier to each combination of well and year), year, which was the year the data was collected. The last field is efficiency. This field refers to the ratio between the change in water level over the course of a tidal cycle in the well to the change in the water level over the course of the tidal cycle at the Battery tide gauge, NYC. Tidalefficiency.R - file that plots and calculates tidal efficiency during 1999 and 2019 at each well. **(3) Files related to analysis of Battery tide gauge data, Battery tide predictions, and precipitation data** MSL_time.R 3348871.csv 3348873.csv Battery.csv Bat_wls.csv monthly.csv sin2.csv predictions.csv tide_l.csv wls.csv MSL_time.R - This R code uses several data files to conduct analysis of change over time in water levels and monthly anomalies in precipitation and water levels. All necessary packages are described. 3348871.csv and 3348873.csv - are weather data from Westchester County airport, station USW00094745 from 1997 to 2001 (3348873.csv) 2017 to 2022 (3348871.csv). The field station lists the station. The field Name is the name of the station, Westchester County Airport. The date is the day data was collected. AWND refers to Average daily wind speed in miles per hour. PGTM refers to peak gust time (hours and minutes, i.e., HHMM). PRCP refers to precipitation in inches, TMAX refers to the maximum daily temperature, in degrees Fahrenheit. TMIN refers to the minimum daily temperature, in degrees Fahrenheit. WDF2 is the direction of fastest 2-minute wind in degrees. WDF5 is the direction of fastest 5-second wind in degrees. WSF2 is the fastest 2-minute wind speed in miles per hour. WSF5 is the fastest 5-second wind speed in miles per hour. Missing data is replaced with -999. Battery.csv - all high tide levels for 1997 through 2022. The two fields are level, referring to high tide water levels in meters relative to the NAVD88 datum. The second field is year. Bat_wls.csv is monthly tide levels from the Battery tide gauge, NY. The year field refers to year including fraction. Mean high water (MHW) refers to monthly mean high water relative to the NAVD88 datum in meters. Mean sea level (MSL) refers to monthly mean sea level relative to the NAVD88 datum in meters. Mean tide level (MTL) refers to monthly mean tide level relative to the NAVD88 datum in meters.. Mean Low Water (MLW) refers to monthly mean low water relative to the NAVD88 datum in meters. monthly.csv - is mean high water and mean sea level from 1980-2022, by month. The field month refers to the month (January =1). MHW is monthly mean high water for all months, relative to the NAVD88 datum, and MSL is monthly mean sea level relative to the NAVD88 datum. sin2.csv is the monthly mean sea level at the Battery tide gauge (1980-2022), with a 1 year rolling window median smooth added. There are three fields, month, MSL, and year. Month is the number of months elapsed since January 1961. MSL is the monthly mean sea level in meters, relative to the NAVD88 datum, with a one year smoothing function applied. Year refers to the observation month, expressed in years and the fraction of years so January 1980 would be 1980, while February 1980 is depicted as 1980.083. predictions.csv - tide predictions for the Battery tide gauge, New York City. Fields are y, which stands for year, represented by year, including fractions representing months. High_p is the highest predicted tide of the month, in meters relative to the NAVD88 datum. MHW_p is the predicted mean high tide for the month relative to the NAVD88 datum. MLW_p is the predicted mean low tide for the month relative to the NAVD88 datum. MTL_p is the predicted mean tide level for the month relative to the NAVD88 datum. High_1 is the highest actual tide of the month, in meters relative to the NAVD88 datum. MHW_a is the actual mean high tide for the month relative to the NAVD88 datum. MLW_a is the actual mean low tide for the month relative to the NAVD88 datum. MTL_a is the actual mean tide level for the month relative to the NAVD88 datum. tide_l.csv is a file with the monthly mean high water (MHW_l), monthly mean tide level (MTL_l), and mean low water (MLW_l) for 1960 -2021. wls.csv is a file that has monthly water levels from 1999 to 2019, listing year (as a fraction, not just an integer for month), Highest, as the highest tide of the month in meters relative to the NAVD88 datum. MHW refers to the mean high water during the month in meters relative to the NAVD88 datum. MTL refers to the mean tidal level during the month in meters relative to the NAVD88 datum. MLW refers to the mean low water during the month in meters relative to the NAVD88 datum. ## Sharing/Access information Data was derived from the following external sources: * Vegetation shapefiles for the Hudson River NERR for 1997, 2005, and 2014, were obtained through personal request to Sarah Fernald, *Reserve Manager and Research Coordinator.* Files should be available through the Reserve website, although the link is not functional at this time: * The 2018 vegetation shapefiles were obtained from under the heading, [Hudson River Estuary tidal wetlands](https://data.gis.ny.gov/datasets/ee2723393f894e929dbd6dbdc84770de_0/explore?location=41.308770%2C-73.842410%2C9.10). * We acknowledge the NYS DEC Hudson River Estuary Program, NYS DEC Hudson River National Estuarine Research Reserve, and Cornell Institute for Resource Information Sciences for collection and curation of the Hudson River NERR vegetation data. * Tide gauge data and tide predictions for the Battery, NY were obtained from NOAA tides and currents website: * Precipitation data was obtained from the National Centers for Environmental Information, NOAA: . The station for which data was obtained was the Westchester County airport, station USW00094745. ## Code/Software We provide three R files, which we ran using R version 4.3.1 (2023-06-16), in R Studio 2022.02.1, Build 461. All required packages are described in the .R files. Distancefromsurface.R - This R code utilizes four data files that include low tides during spring tides, low tides during neap tides, high tides during spring tides, and high tides during neap files to compare average and variance in low and high tide water levels during 1999 and 2019 relative to the marsh surface and relative to the NAVD88 datum. Code is also included to produce plots. Tidalefficiency.R - file that plots and calculates tidal efficiency during 1999 and 2019 at each well. MSL_time.R - This R code uses several data files to conduct analysis of change over time in water levels and monthly anomalies in precipitation and water levels. Hydrological measurements were collected during the spring and summer of 1999 and 2019 in Piermont Marsh (coordinates 41.0361°, -73.9105°). These measurements covered a transect that was laid out perpendicular to a tidal channel. The objective of this study was to compare the current tidal flooding and groundwater table levels with the data from 1999. The goal was to assess the differences in tidal hydrology between these two distinct time periods, which also differed in terms of marsh and water level elevations. To determine groundwater levels and tidal flooding across the marsh, we installed seven water level loggers along a gradient, ranging from the tidal channel to the upland area. We constructed wells by suspending pressure transducers within 7.5 cm diameter perforated PVC pipes lined with screening to prevent sediment from entering the well. These wells were positioned one meter below the marsh surface, 0.6 meters above the soil surface, vented to the atmosphere, and only the section below the soil surface was perforated. Additionally, we installed concrete collars at the marsh surface around the wells to prevent preferential water flow down the well sides. These seven wells were placed along the original transect, perpendicular to the creek, with increasing distances (0 meters, 6 meters, 12 meters, 18 meters, 24 meters, 36 meters, and 48 meters). We installed and monitored the wells from May 5 to June 30, 2019, and from April 6 to May 26, 1999. In 2019, we measured the absolute elevation of the top of each well using RTK-enabled static GPS measurements from Leica GNSS GS14 rover units and static measurements with an AX1202 GG base station unit to reference water levels to the NAVD88 vertical datum. We measured reference water levels each time data was collected, which involved determining the distance from the top of the well to the water surface and converting it to elevation relative to the NAVD88 datum. To relate marsh elevation to water elevations, GPS surveys were conducted along the transect using a Leica GNSS GS14 rover unit. In 1999, elevation control for the wells and water levels was similarly measured using survey-grade GPS. We compared changes in the marsh water table with significant potential hydrological and vegetation changes that have occurred over the past 20 years. We calculated the rates of change in monthly water levels at Battery, NY for the period from 1999 to 2019 using two different methods. We modeled changes over time in monthly highest water levels, mean high water (MHW), mean tide level (MTL), and mean low water (MLW) using an ordinary least squares regression model with ARIMA errors to account for the autoregressive structure of tide data. We removed the annual cycle first using a curve with a 1-year periodicity. The ARIMA errors model was fitted using the "auto.arima" function from the "forecast" package. We calculated the squared correlation of fitted values to actual values to produce a pseudo-r2. For comparison, we calculated trends using ordinary least squares regression for the 1999-2019 period, although it's important to note that the temporal autocorrelation likely results in underestimated uncertainty. We obtained vegetation maps from the HRNERR for 1997, 2005, 2014, and 2018 to help assess changes in the coverage of plant species over time, as these changes could impact evapotranspiration and water table patterns. A 20-meter buffer zone was created around each well location, and the composition of vegetation within this buffer zone was quantified using QGIS version 3.30.2. While four time-points may not be sufficient for statistically identifying trends, we analyzed the changes observed. To put the measurement time periods in context and ensure that our selected seasons were not anomalous, we compared water levels in spring 1999 and 2019 relative to the astronomical cycles driving interannual sea level variability using data from the Battery tide gauge. We also compared spring high tide levels in 1999 and 2019 with surrounding years. The main astronomical cycles thought to influence tides include the 18.6-year lunar nodal cycle and the 4.4-year subharmonic of the 8.85-year lunar perigee cycle. As our 1999 and 2019 measurements were collected during slightly different time periods (April/May 1999 vs. May/June 2019), we also examined mean monthly water levels (1980-2022) from the NOAA Battery tidal gauge to identify potential artifacts. We obtained rainfall data from spring 1999 and 2019 from the nearest precipitation monitoring station (Westchester airport) to determine whether the measurements were made during an unusually wet or dry period. The sampling periods were 20 years apart, so they occurred at approximately the same point in the 18.6-year lunar nodal cycle. Pressure transducer data was processed using HOBOware Pro (Version 3.7.16, Onset Computer Corporation, Bourne, MA) with reference water levels collected in the field. The data were corrected for atmospheric pressure using the HOBOware barometric compensation assistant, using data from the Hudson River National Estuarine Research Reserve. Raw water elevation data from 1999 was analyzed in conjunction with the 2019 data. Water level data from 1999 were converted from the NVGD29 to NAVD 88 datum using NOAA VDatum v4.0.1 prior to analysis. Well seven's transducer experienced three brief malfunctions from May 30 to June 3, 2019, resulting in inaccurate elevation measurements for a total of 19.5 hours. These data were excluded from the analysis. In 1999, well seven also experienced malfunctions, which were corrected by Montalto into smoothed six-hour increments using average water elevation measurements and calculated error, calibrated using regression. No other well transducers appeared to have malfunctioned. Groundwater hydrology plays an important role in coastal marsh biogeochemical function, in part because groundwater dynamics drive the zonation of macrophyte community distribution. Changes that occur over time, such as sea level rise and shifts in habitat structure are likely altering groundwater dynamics and eco-hydrological zonation. We examined tidal flooding and marsh water table dynamics in 1999 and 2019 and mapped shifts in plant distributions over time, at Piermont Marsh, a brackish tidal marsh located along the Hudson River Estuary near New York City. We found evidence that the marsh surface was flooded more frequently in 2019 than in 1999, and that tides were propagating further into the marsh in 2019, although marsh surface elevation gains were largely matching that of sea level rise. The changes in groundwater hydrology that we observed are likely due to the high tide rising at a rate that is greater than that of mean sea level. In addition, we reported on changes in plant cover by P. australis, which has displaced native marsh vegetation at Piermont Marsh. Although P. australis has increased in cover, wrack deposition and plant die off associated Superstorm Sandy allowed for native vegetation to rebound in part of our focus area. These results suggest that climate change and plant community composition may interact to shape ecohydrologic zonation. Considering these results, we recommend that habitat models consider tidal range expansion and groundwater hydrology as metrics when predicting the impact of sea level rise on marsh resilience.

Fortuna station (Centro de Investigaciones Jorge L. Arauz)TowerLocation: 8�� 43.340'N, 82�� 14.241'WSolar Radiation, Pyranometer, Interval max/min/avgLocated in the highlands of the Chiriqui Province, in western Panama.There are three sensor locations: north clearing, south clearing, and a 15m tower.