These data consist of plot-level plant species cover measurements from a precipitation manipulation experiment located in the Spruce Gulch Wildlife and Research Reserve near Boulder, Colorado. This data release consists of absolute percent live foliar cover measurements of all plant species within each plot for the years 2011, 2012, 2013, 2020, 2021, 2022, and 2023. From 2011-2013, plots received one of three precipitation manipulations over the winter (October through March) and summer (April-September): 'reduce' = 50 percent reduction in ambient precipitation, 'increase' = 50 percent increase in ambient precipitation, or 'ambient' = no manipulation of precipitation. Precipitation was reduced through the use of rain-out shelters that blocked precipitation from half of each plot. Precipitation was increased by irrigating plots using water from a local well. The 'increase' precipitation treatments were discontinued after 2013, and thus some winter and summer precipitation treatments for plots were reassigned in 2020-2023.

From 2011-2018 USGS biologists recorded vegetation and biological soil crust (BSC) cover by species and tracked survival of tagged individual plants (388 in total) across 40 locations where paired experimental plots had been installed in 2010. Plant cover was visually estimated using four 75 x 100 cm survey frames. Each site contained a two plots measuring 1.5 by 2.0 meteres: a control plot and a plot covered by a shelter that excluded 35% of incoming precipitation. Plots were selected to represent shallow vs. deep soils, sandstone vs. shale parent material, and dominant plant species on the Colorado Plateau around Moab, Utah. We used an information theoretic approach using generalized linear models to determine the combination of factors that best predicted mortality. We included treatment, year, and species as fixed effects in our first order models to test for treatment effects on mortality while accounting for the influence of interannual-climate variability and species-level differences. Models also included individual plant ID nested within site as random effects to account for pseudo-replication across sites and tagged individuals. We continued with a second set of models by adding abiotic variables including elevation (m), soil depth (shallow or deep), and parent material as additional explanatory variables to the best-fit model.

From 2011-2018 USGS biologists recorded vegetation and biological soil crust (BSC) cover by species and tracked survival of tagged individual plants (388 in total) across 40 locations where paired experimental plots had been installed in 2010. Plant cover was visually estimated using four 75 x 100 cm survey frames. Each site contained a two plots measuring 1.5 by 2.0 meteres: a control plot and a plot covered by a shelter that excluded 35% of incoming precipitation. Plots were selected to represent shallow vs. deep soils, sandstone vs. shale parent material, and dominant plant species on the Colorado Plateau around Moab, Utah. We used an information theoretic approach using generalized linear models to determine the combination of factors that best predicted mortality. We included treatment, year, and species as fixed effects in our first order models to test for treatment effects on mortality while accounting for the influence of interannual-climate variability and species-level differences. Models also included individual plant ID nested within site as random effects to account for pseudo-replication across sites and tagged individuals. We continued with a second set of models by adding abiotic variables including elevation (m), soil depth (shallow or deep), and parent material as additional explanatory variables to the best-fit model.

The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution for 2016 to 2019. A total of 10,906 AIM plots from years 2016 - 2019 were used to train an ensemble of regression tree models (n=5). Besides cheatgrass (Bromus tectorum), other species such as Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus mardritensis L.,Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., Taeniatherum caput-medusae were included in the study. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas.

The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution for 2016 to 2019. A total of 10,906 AIM plots from years 2016 - 2019 were used to train an ensemble of regression tree models (n=5). Besides cheatgrass (Bromus tectorum), other species such as Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus mardritensis L.,Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., Taeniatherum caput-medusae were included in the study. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas.

,This dataset contains water balance data for each year when alfalfa 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). Alfalfa was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field in 1996 through 1999. Irrigation was by linear move sprinkler system. 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. 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), reference "tall crop" 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 alfalfa ET, crop coefficients, crop water productivity reference "tall crop" ET, alternative methods of estimating reference ET from weather data. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield.,,

Cheatgrass (Bromus tectorum) cover data were derived from the Bureau of Land Management’s Assessment Inventory, and Monitoring data and paired with geospatial data representing climate, weather, and disturbances. We derived covariates to capture both the climatic averages (1981-2010) that underlie long-term suitability, hereafter referred to as climate, and conditions during the year of observation, hereafter referred to as weather, that can drive annual variation in invasive grass cover (e.g., fall germination conditions were matched to cheatgrass cover sampled the following spring). Custom variables reflected cheatgrass natural history. Covariates describing geological context (e.g., aspect, elevation, soils), plant communities based on geophysical conditions and natural disturbance regimes, fire history (binary burned or unburned), human disturbance and infrastructure, and management history were used to represent processes that may limit or facilitate cheatgrass invasion

Cheatgrass (Bromus tectorum) cover data were derived from the Bureau of Land Management’s Assessment Inventory, and Monitoring data and paired with geospatial data representing climate, weather, and disturbances. We derived covariates to capture both the climatic averages (1981-2010) that underlie long-term suitability, hereafter referred to as climate, and conditions during the year of observation, hereafter referred to as weather, that can drive annual variation in invasive grass cover (e.g., fall germination conditions were matched to cheatgrass cover sampled the following spring). Custom variables reflected cheatgrass natural history. Covariates describing geological context (e.g., aspect, elevation, soils), plant communities based on geophysical conditions and natural disturbance regimes, fire history (binary burned or unburned), human disturbance and infrastructure, and management history were used to represent processes that may limit or facilitate cheatgrass invasion

This dataset is from a precipitation manipulation experiment conducted at five grassland sites along an elevation gradient near Flagstaff, AZ. The data consist of pre- (1991 - 2015) and post-experimental (2016) treatment plant production and precipitation measurements. The plant production measurements were taken from satellite and hand-held spectroradiometer, in addition to plot-based harvests at the end of growing season.

This dataset is from a precipitation manipulation experiment conducted at five grassland sites along an elevation gradient near Flagstaff, AZ. The data consist of pre- (1991 - 2015) and post-experimental (2016) treatment plant production and precipitation measurements. The plant production measurements were taken from satellite and hand-held spectroradiometer, in addition to plot-based harvests at the end of growing season.