Predicted temperature and precipitation values were generated throughout the state of Massachusetts using a stochastic weather generator (SWG) model to develop various climate change scenarios (Steinschneider and Najibi, 2022a). This data release contains temperature and precipitation statistics (SWG_outputTable.csv) derived from the SWG model under the surface warming derived from the RCP 8.5 climate change emissions scenario at 30-year moving averages centered around 2030, 2050, 2070, 2090. During the climate modeling process, extreme precipitation values were also generated by scaling previously published intensity-duration-frequency (IDF) values from the NOAA Atlas 14 database (Perica and others, 2015) by a factor per degree expected warming produced from the SWG model generator (Najibi and others, 2022; Steinschneider and Najibi, 2022b, c). These newly generated IDF values (IDF_outputTable.csv) account for expected changes in extreme precipitation driven by variations in weather associated with climate change throughout the state of Massachusetts. The data presented here were developed in collaboration with the Massachusetts Executive Office of Energy and Environmental Affairs and housed on the Massachusetts climate change clearinghouse webpage (Massachusetts Executive Office of Energy and Environmental Affairs, 2022). References: Massachusetts Executive Office of Energy and Environmental Affairs, 2022, Resilient MA Maps and Data Center at URL https://resilientma-mapcenter-mass-eoeea.hub.arcgis.com/ Najibi, N., Mukhopadhyay, S., and Steinschneider, S., 2022, Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity: Geophysical Research Letters, v. 49, no. 8, 14 p., https://doi.org/10.1029/2021GL097100. Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O., 2015, NOAA Atlas 14 Volume 10 Version 3, Precipitation-Frequency Atlas of the United States, Northeastern States (revised 2019): NOAA, National Weather Service, https://doi.org/10.25923/99jt-a543. Steinschneider, S., and Najibi, N., 2022a, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf. Steinschneider, S., and Najibi, N., 2022b, Future projections of extreme precipitation across Massachusetts—a theory-based approach: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_IDF_Curves_Dec2021.pdf. Steinschneider, S., and Najibi, N., 2022c, Observed and projected scaling of daily extreme precipitation with dew point temperature at annual and seasonal scales across the northeast United States: Journal of Hydrometeorology, v. 23, no. 3, p. 403-419, https://doi.org/10.1175/JHM-D-21-0183.1.

The Boston Water and Sewer Commission (BWSC) maintains collection sites throughout the city. Those collection sites are equipped with solar powered rain gauges on top of public buildings which log measurements of precipitation and which report data every five minutes. Here you find the link to the Boston Water and Sewer Commission’s interface to the rainfall data, which is updated continually. You can search for rainfall data going as far back as 1999, depending on the year of installation for the various gauges.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

The U.S. Geological Survey has developed tools for projecting twenty-first century climate and hydrologic risk in Massachusetts in collaboration with Cornell University and Tufts University. These tools included a Stochastic Weather Generator (SWG). Output from the SWG is in this data release. The release includes daily precipitation and minimum and maximum air temperature for a 64-year period in the Nashua River watershed (that includes the Squannacook River) in Massachusetts and New Hampshire. There are 100 ensembles from the SWG for warming scenarios of 0 to 8 degrees Celsius in 0.5-degree increments. The SWG data were converted to a format utilized by the Precipitation-Runoff Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms) and input to a PRMS model for the Squannacook River watershed. The PRMS input and output files for the 100 ensembles of each of the 17 warming scenarios are also included in this data release. The 1,700 PRMS output files were utilized by a Stochastic Watershed Modeling tool to correct modeling biases that are inherent with a deterministic model such as PRMS. This data release includes the output from this Stochastic Watershed Model (SWM). For each of the 100 ensembles, the SWM was used to generate 10,000 ensembles, resulting in 1 million ensembles of 64-year periods for each of the warming scenarios. For each ensemble, streamflow characteristics of the annual maximum daily discharge at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval and of the annual 7-day low flow at the 2- and 10-year recurrence interval were determined.

A regression model that estimates monthly temperature and precipitation as a function of latitude, longitude, and elevation for the New England area was used to estimate annual growing degree days and precipitation for the state of Massachusetts. For details of the regression model please see the published paper (Ollinger, S.V., Aber, J.D., Federer, C.A., Lovett, G.M., Ellis, J.M., 1995. Modeling Physical and Chemical Climate of the Northeastern United States for a Geographic Information System. US Dept of Agriculture, Forest Service, Radnor, PA, USA).

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

File contains the calculated values for the residual rainfall mass for monthly rainfall data from the Boggabri Post Office Bureau of Meteorology gauge (no. 055007).

Dataset History

The data from the source file was rearranged to facilitate manipulation.

The average rainfall for each month was calculated for the period Jan 1970 to Dec 2012.

The mean monthly rainfall was calculated by averaging all of the monthly rainfall averages (ie the average January rainfall, the average February rainfall etc).

The monthly deviation from the mean was determined as the difference between the monthly rainfall and the average for that month

The cumulative monthly deviation from the mean was then determined for each month in the period Jan 1970 to Dec 2012.

Dataset Citation

Bioregional Assessment Programme (2017) Boggabri PO residual rainfall mass. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/1782e980-21f7-4148-9c49-4efec3c096e0.

Dataset Ancestors

• Derived From Boggabri PO monthly rainfall

This excel contains data for Chapter 2 “Precipitation” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual precipitation at Providence, RI,” (page 64); Figure 2, “Annual precipitation at Worcester, MA,” (page 64); Figure 3, “Annual Palmer Drought Severity Index (PDSI) for Rhode Island,” (page 65); Figure 4, "Annual Palmer Drought Severity Index (PDSI) for Massachusetts," (page 65); Figure 5, "Climate model projection of winter total precipitation in RI or MA to 2100," (page 67); and Figure 6, "Climate model projection of winter annual snowfall in RI or MA to 2100," (page 67). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

IntroductionRainfall patterns are expected to become increasingly erratic as a result of global climate change, with more intense but less frequent rainfall events leading to an increased occurrence of drought events. This process may lead to significant declines in vegetation cover and subsequent increases in soil erosion, consequently accelerating the bury of detached litter by soil deposition and the mixture of residues from different plant species. Responses of litter decomposition to increasing rainfall variability in distribution and subsequent litter mixing or soil cover have scarcely received attention.MethodsTo fill this gap in our knowledge, we analyzed the influence of rainfall variability, soil cover, and litter mixing on shrub-species litter decomposition in a semi-arid shrubland. We explored the effects of redistributing the frequency and amount of precipitation on surface and belowground decomposition of litter from two separate or mixed predominant shrubs.ResultsDecomposition of belowground litter was consistently higher than that of surface litter over the entire field-incubation process. Mass loss significantly decreased in surface litter but not in belowground litter due to the lower frequency and larger amount of precipitation compared to the control treatment. Furthermore, exclusion of 30% precipitation had no significant effects on decomposition of either surface or belowground litter. We observed stronger synergistic effect for belowground litter mixture relative to surface litter mixture of the two shrubs, especially in the hotter months over the 5-month incubation.DiscussionThese findings support that rainfall variability in terms of distribution pattern rather than in the amount controls the litter decomposition on the soil surface in the semi-arid shrubland. Meanwhile, soil burial or litter mixing have greater effects on litter decomposition, individually or jointly. Together, our results highlight the need to consider rainfall distribution variability and incorporate soil-covering and litter-mixing as driving factors of organic matter turnover in drylands.

This data set includes the materials required to reproduce the figures and tables presented in the study: "Poleward shift of subtropical highs drives Patagonian glacier mass loss". The data consist of:

ASCII files:

1. SMB-components.MAR3v14.1940-2023.PAT.txt: time series of spatially integrated Patagonian glacier annual surface mass balance (SMB) components in Gt per year from ERA5-forced MARv3.14 at 5 km, statistically downscaled to 500 m resolution (1940-2023). SMB components include SMB, total precipitation (PR), snowfall (SF), rainfall (RA), runoff (RU), total melt (ME), refreezing and retention (RF), total sublimation (SU). Note that MARv3.14 does not account for drifting snow erosion (ER).
2. SMB-components.RACMO2.3p2.1979-2023.PAT.txt: time series of spatially integrated Patagonian glacier annual SMB components in Gt per year from ERA5-forced RACMO2.3p2 at 5.5 km, statistically downscaled to 500 m resolution including precipitation adjustment (1979-2023). SMB components include SMB, total precipitation (PR), snowfall (SF), rainfall (RA), runoff (RU), total melt (ME), refreezing and retention (RF), total sublimation (SU), and drifting snow erosion (ER).

Netcdf files:

1. XXXX.1940-2023.MAR3v14.PAT.0.5km.YY.nc: map of annual SMB component XXXX expressed in kg per m² or mm w.e. per year from ERA5-forced MARv3.14 at 5 km, statistically downscaled to 500 m resolution covering Patagonian glaciers and icefields (1940-2023).
2. XXXX.1979-2023.RACMO2.3p2.PAT.0.5km.YY.nc: map of annual SMB component XXXX expressed in kg per m² or mm w.e. per year from ERA5-forced RACMO2.3p2 at 5.5 km, statistically downscaled to 500 m resolution with precipitation adjustment covering Patagonian glaciers and icefields (1979-2023).

SMB components XXXX include: SMB (smb_rec), total precipitation (precip), snowfall, rainfall (precip - snowfall), runoff, total melt (snowmelt), refreezing and retention (refreeze), total sublimation (subl), and drifting snow erosion (sndiv). Note that MAR3v14 does not account for drifting snow erosion. Annual mean glacier near-surface temperature (t2m) is also available from statistically downscaled MARv3.14 and RACMO2.3p2 at 500 m (ºC).

3. PAT_icemask_lon_lat_0.5km.nc: fractional ice mask ranging from 0 (ice-free) to 1 (fully ice-covered), and longitude/latitude of the 500 m grid.

The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 500 m x 500 m.

Additional data: The gridded, daily downscaled SMB data sets from the ERA5-forced MARv3.14 (1940-2023) and RACMO2.3p2 reconstructions (1979-2023) are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be).

Abstract: Patagonian glaciers have been rapidly losing mass in the last two decades, but the driving processes remain poorly known. Here we use two state-of-the-art regional climate models to reconstruct long-term (1940-2023) glacier surface mass balance (SMB), i.e., the difference between precipitation accumulation, surface runoff and sublimation, at about 5 km spatial resolution, further statistically downscaled to 500 m. High-resolution SMB agrees well with in-situ observations and, combined with solid ice discharge estimates, captures recent GRACE/GRACE-FO satellite mass change. Glacier mass loss coincides with a long-term SMB decline (-0.35 Gt yr⁻²), primarily driven by enhanced surface runoff (+0.47 Gt yr⁻²) and steady precipitation. We link these trends to a poleward shift of the subtropical highs favouring warm northwesterly air advections towards Patagonia (+0.14ºC dec⁻¹ at 850 hPa). Since the 1940s, Patagonian glaciers have lost 1,350 ± 449 Gt of ice, equivalent to 3.7 ± 1.2 mm of global mean sea-level rise.

Reference: Noël, B., Lhermitte, S., Wouters, B. et al. Poleward shift of subtropical highs drives Patagonian glacier mass loss. Nat Commun 16, 3795 (2025). https://doi.org/10.1038/s41467-025-58974-1

Code, simulation input files, and postprocessed simulation output data supporting "Impact of Precipitation Mass Sinks on Midlatitude Eddies over a Wide Range of Climates", submitted to Weather and Climate Dynamics. Enclosed README file provides detailed descriptions of the archive contents.

Morocco MA: Average Precipitation in Depth data was reported at 346.000 mm/Year in 2014. This stayed constant from the previous number of 346.000 mm/Year for 2012. Morocco MA: Average Precipitation in Depth data is updated yearly, averaging 346.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 346.000 mm/Year in 2014 and a record low of 346.000 mm/Year in 2014. Morocco MA: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

This repository holds supplemental figures for the paper "On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process". These include distributions of event-based runoff ratios, scatter plots comparing event-based runoff ratios, initial (antecedent) flows, and rainfall totals, and hydrographs of every qualifying "event" that spans both Daymet and NLDAS forcing data.

This dataset contains water balance data for each year when winter wheat was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Winter wheat was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field in the 1989-1990, 1991-1992, and 1992-1993 seasons. Irrigation was by linear move sprinkler system. Full irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Deficit irrigations were less than full - see crop calendars and irrigation data in these files for details. The weighing lysimeters were used to measure relative soil water storage to 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The water balance data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost fall, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected. The ET data should be considered to be the best values offered in these datasets. Even though ET data are also presented in the "lysimeter" datasets, the values herein are the result of a more rigorous quality control process. Dew and frost accumulation varies from year to year and seasonally within a year, and it is affected by lysimeter surface condition [bare soil, tillage condition, residue amount and orientation (flat or standing), etc.]. Particularly during winter and depending on humidity and cloud cover, dew and frost accumulation sometimes accounts for an appreciable percentage of total daily ET. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on winter wheat ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield. Resources in this dataset:Resource Title: 1989 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1989_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1990 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1990_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1991 Bushland, TX. East Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1991_E_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1992 Bushland, TX. East Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1992_E_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1992 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1992_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1993 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1993_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.

Meteorological data daily averages and daily fluxes for stations located at Governor's Academy and MBL Marshview Farm, Newbury, MA. Data includes air temeprature, precipitation, relative humidity, solar radiation, PAR, wind and air pressure measurements. Years 2000 to 2007 the station was located at Governor's Academy, Newbury, MA and was moved July 30, 2007 to the MBL Marshview Farm field station property where it is currently located.

Year 2017 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Contains the parameters/set-up for a VIC model run along with the input and post-processed output from the model along with a python notebook to recreate the figures in the GRL paper of the same name.

Local weather can influence the growth and development of young birds, either indirectly, by modifying prey availability, or directly, by affecting energetic trade‐offs. Such effects can have lasting implications for life history traits, but the nature of these effects may vary with the developmental stage of the birds, and over timescales from days to weeks. We examined the interactive effects of temperature, rainfall and wind speed on the mass of nestling and fledgling Barn Swallows Hirundo rustica, both on the day of capture and averaging weather across the time since hatching. At the daily timescale, nestling mass was negatively correlated with temperature, but the strength of this association depended on the level of rainfall and wind speed; nestlings were typically heavier on dry or windy days, and the negative effect of temperature was strongest under calm or wet conditions. At the early lifetime timescale (i.e. from hatching to post‐fledging), nestling mass was negatively correlated with temperature at low wind speed. Fledgling body mass was less sensitive to weather; the only weather effects evident were a negative correlation with temperature at the daily scale under high rainfall that became slightly positive under low rainfall. These changes are consistent with weather effects on availability and distribution of insects within the landscape (e.g. causing high concentrations of flying insects), and with the effects of weather variation on nest microclimate. These results together demonstrate the impacts of weather on chick growth, over immediate (daily) and longer term (nestling/fledgling lifetime) timescales. This shows that sensitivity to local weather conditions varies across the early lifetime of young birds (nestling‐fledgling stages) and illustrates the mechanisms by which larger scale (climate) variations influence the body condition of individuals.

Methods For details of colleciton see Facey et al. 2020 https://doi.org/10.1111/ibi.12824

Headings "Female" and "Chick" (here referring to both nestling and fledgling, see under “Groups) refer to identities of individuals derived from ring/band numbers.

Chick = 8-12 days old

Fledgling = 20+ days

Attempt – breeding attempt, second breeding attempt was considered to be any breeding attempt by the same female that followed a successful first breeding attempt.

Brood Size – maximum number of chicks recorded in the nest

Age – hatching to “Day”

Time – hour during which individuals was handled/weighed (24 hour clock)

Day – day of handling/weighed, where day 1 = 1st April

Mass – weighed to the nearest 0.1 g using an electronic balance (Satrue SA-500 http://www.satrue.com.tw/dp2.htm).

Weather data

see Facey et al. 2020 https://doi.org/10.1111/ibi.12824 for details on the origins and handling of weather data.

Temperature (oC) - mean of the daily maximum and daily minimum values

Wind speed (km/h) – daily mean

Rainfall (mm) – total of daily totals.

ABSTRACT The study of surface runoff and transport of pollutants is of major importance, as it helps preventing environmental impacts, and supports planning tasks carried out by public authorities. The goal of this research was to study the dynamics of the transport of material (NaCl) on an impermeable surface, dissolved by the application of water through a rainfall simulator, having as its source of variation three independent variables: slope, rainfall intensity and position of the material. Face-centered experimental design was used, which contemplated different scenarios reproduced in the experiments, where the influence of the three independent variables on the dependent variables (transport time, peak value, peak time and total mass transported) was evaluated, whose responses were monitored at the outlet of the experimental model. The analysis of the results allowed to identify the significant effect of the slope and position of the material on all the dependent variables studied, as well as the quadratic effect of the slope for the transportation time and total mass transported. As for the rainfall intensity, only influence was observed for transportation time, within the study intervals with a level of significance of 95%. The resulting model is capable of explaining: 90% for transport time, 67.9% for peak value, 74.2% for peak time and 62.4% total mass transported.

The input datasets are daily precipitation and minimum and maximum temperature for a period of 64 years for warming scenarios of 0 degrees to 8 degrees Celsius, by 0.5-degree increments for the Squannacook River watershed in Massachusetts. The source of the data is the Stochastic Weather Generator (SWG; Steinschneider and Najibi, 2022) and includes 100 ensembles from the SWG. The daily time-series, space-delimited files cover three subwatersheds within the Squannacook River watershed in a format readable by the Precipitation Runoff-Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms). The input files were input to PRMS, along with the model control and parameter files, to generate the output files. The output files are daily time-series in comma-delimited format of the resulting discharges for the Squannacook River at the mouth of the river and at the Squannacook River near West Groton, Massachusetts streamgage for each of the ensembles of each of the warming scenarios.

[ NOTE – 2022/05/06: this dataset supersedes the earlier versions https://doi.org/10.15482/USDA.ADC/1482548 and https://doi.org/10.15482/USDA.ADC/1526329 ]. This dataset contains 15-minute mean weather data from the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) for all days in each year. The data are from sensors placed at 2-m height over a level, grass surface mowed to not exceed 12 cm height and irrigated and fertilized to maintain reference conditions as promulgated by Allen et al. (2005, 1998). Irrigation was by surface flood in 1989 through 1994, and by subsurface drip irrigation after 1994. Sensors were replicated and intercompared between replicates and with data from nearby weather stations, which were sometimes used for gap filling. Quality control and assurance methods are described by Evett et al. (2018). Data from a duplicate sensor were used to fill gaps in data from the primary sensor using appropriate regression relationships. Gap filling was also accomplished using sensors deployed at one of the four large weighing lysimeters immediately west of the weather station, or using sensors at other nearby stations when reliable regression relationships could be developed. The primary paper describes details of the sensors used and methods of testing, calibration, inter-comparison, and use. The weather data include air temperature (C) and relative humidity (%), wind speed (m/s), solar irradiance (W m-2), barometric pressure (kPa), and precipitation (rain and snow in mm). Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The mean annual precipitation is ~470 mm, the 20-year pan evaporation record indicates ~2,600 mm Class A pan evaporation per year, and winds are typically from the South and Southwest. The climate is semi-arid with ~70% (350 mm) of the annual precipitation occurring from May to September, during which period the pan evaporation averages ~1520 mm. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data. See the README for details of each data resource.