These data were compiled to allow further understanding of how aboveground net primary production of different plant functional types in ecosystems along an elevation gradient in the southwestern U.S. respond to extreme changes in warm-season precipitation (drought and water addition) associated with the North American Monsoon. The objectives of the study were to 1) determine how primary production responds to warm-season precipitation extremes over time; 2) compare production sensitivities to warm-season precipitation (slopes of production – precipitation relationships) across an elevation gradient; 3) evaluate whether the sensitivity of production differed under extreme dry and wet years compared to ambient precipitation. These data represent aboveground net primary production and associated warm-season (May - September) precipitation measurements from 2015 - 2020 during a precipitation manipulation experiment carried out across a desert scrubland, desert grassland, juniper savanna, ponderosa pine meadow, and mixed conifer meadow. These data were collected on or adjacent to the San Francisco Peaks near Flagstaff, Arizona by the U.S. Geological Survey using field measurements. These data can be used to better understand how production of different plant functional types respond to changes in warm-season precipitation in the aforementioned ecosystem types.

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

,Thirty-six years of aboveground net primary productivity (ANPP) data collected across a topographic sequence in the semiarid shortgrass steppe of North America to examine patterns and drivers of spatiotemporal variability in ANPP. ANPP data were collected from the 6,500 ha USDA-Central Plains Experimental Range (CPER), which is part of the Long-Term Agroecosystem Research (LTAR; 2012-present; https://ltar.ars.usda.gov/) network, a former Long-Term Ecological Research station (LTER, 1983-2012), and located in the shortgrass steppe of north-central Colorado, USA. Additional information and referenced materials about many of the long-term studies initiated on the CPER can be found: https://dx.doi.org/10.25675/10217/81141.,The topography at the CPER is characterized by gently rolling hills, and the topographic positions for data collection were focused along a catena in one of the most common ecological sites on the CPER, Loamy Plains (ID: R067BY002CO; NRCS, 2020). The plant community included four herbaceous plant functional types (PFTs): 1) perennial, warm-season, C4 grasses (primarily Bouteloua gracilis [Willd. ex Kunth] Lag ex Griffiths and B. dactyloides [Nutt.] J.T. Columbus), 2) perennial, cool-season, C3 grasses (primarily Pascopyrum smithii [Rydb] A. Love and Hesperostipa comata [Trin. & Rupr.] Barkworth ssp. comata), 3) cool-season, annual grass (Vulpia octoflora [Walter] Rydb.), and 4) forbs (primarily Sphaeralcea coccinea [Nutt.] Rydb.). Shrubs, subshrubs, and cactus were present but do not represent a large component of total ANPP and were not included in this study.,Daily precipitation data were obtained from a long-term (1979-2018) precipitation gauge associated with the National Atmospheric Deposition program (Site ID: NTN-CO22; http://nadp.slh.wisc.edu/), located on site. Missing precipitation data were gap-filled using CPER headquarters data (1939-2018), or from the Soil Climate Analysis Network (SCAN) rain gauge (1997-2018, Site Number: 2017; https://wcc.sc.egov.usda.gov/), depending on proximity and temporal overlap. Following gap-filling, precipitation data were omitted if >10% of the time series was missing for each focal time period (e.g. fall or spring).,,

These data are daily climate, water balance, and soil moisture data for 270 plots in the National Park Service (NPS) Southern Colorado Plateau Network (SCPN) Inventory & Monitoring (I&M) network. Climate data was collected from a gridded, daily climate dataset, Daymet (https://daymet.ornl.gov/). Climate, alongside field-collected soils (SoilDepthsByPlot.csv) and vegetation information, were then used to drive a point based, daily, multi soil-layer, ecosystem water-balance model, SOILWAT2 (https://github.com/DrylandEcology/SOILWAT2). SCPN plots were established to capture the range of ecosystem conditions present in this network. Plant communities of the SCPN are a vital sign for this region, enhancing habitat, stabilizing soils, and moderating hydrology. However, these ecosystems are water-limited, and many plant and ecosystem processes are driven by the amount of water available in the soil profile (i.e. soil moisture). These data provide daily observations of gridded climate and predicted measures of water-balance (ie. transpiration, evaporation, etc.) and soil moisture availability for the last 38 years for 270 NPS plots and can be used to provide insight into plant and ecosystem processes.

These data are daily climate, water balance, and soil moisture data for 270 plots in the National Park Service (NPS) Southern Colorado Plateau Network (SCPN) Inventory & Monitoring (I&M) network. Climate data was collected from a gridded, daily climate dataset, Daymet (https://daymet.ornl.gov/). Climate, alongside field-collected soils (SoilDepthsByPlot.csv) and vegetation information, were then used to drive a point based, daily, multi soil-layer, ecosystem water-balance model, SOILWAT2 (https://github.com/DrylandEcology/SOILWAT2). SCPN plots were established to capture the range of ecosystem conditions present in this network. Plant communities of the SCPN are a vital sign for this region, enhancing habitat, stabilizing soils, and moderating hydrology. However, these ecosystems are water-limited, and many plant and ecosystem processes are driven by the amount of water available in the soil profile (i.e. soil moisture). These data provide daily observations of gridded climate and predicted measures of water-balance (ie. transpiration, evaporation, etc.) and soil moisture availability for the last 38 years for 270 NPS plots and can be used to provide insight into plant and ecosystem processes.

These data are daily climate, water balance, and soil moisture data for 270 plots in the National Park Service (NPS) Southern Colorado Plateau Network (SCPN) Inventory & Monitoring (I&M) network. Climate data was collected from a gridded, daily climate dataset, Daymet (https://daymet.ornl.gov/). Climate, alongside field-collected soils (SoilDepthsByPlot.csv) and vegetation information, were then used to drive a point based, daily, multi soil-layer, ecosystem water-balance model, SOILWAT2 (https://github.com/DrylandEcology/SOILWAT2). SCPN plots were established to capture the range of ecosystem conditions present in this network. Plant communities of the SCPN are a vital sign for this region, enhancing habitat, stabilizing soils, and moderating hydrology. However, these ecosystems are water-limited, and many plant and ecosystem processes are driven by the amount of water available in the soil profile (i.e. soil moisture). These data provide daily observations of gridded climate and predicted measures of water-balance (ie. transpiration, evaporation, etc.) and soil moisture availability for the last 38 years for 270 NPS plots and can be used to provide insight into plant and ecosystem processes.

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

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.