Assessing precipitation, evapotranspiration, and<scp>NDVI</scp>as controls of U.S. Great Plains plant production

Chen, Maosi; Parton, William J.; Hartman, Melannie D.; Grosso, Stephen J. Del; Smith, William K.; Knapp, Alan K.; Lutz, Susan M.; Derner, Justin D.; Tucker, Compton J.; Ojima, Dennis S.; Volesky, Jerry D.; Stephenson, Mitchell B.; Schacht, Walter H.; Gao, Wei

doi:10.1002/ecs2.2889

Cited by 30 publications

(29 citation statements)

References 54 publications

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“…Variation among individual MC2 simulations was likely driven by differences in MAT and MAP projections, but other variables may have been involved. Cumulative April–July precipitation and actual evapotranspiration (AET), as well as the ratio of cumulative April–July AET to potential evapotranspiration rates, are more strongly correlated to interannual changes in ANPP in the Great Plains than is MAP, which was used in these model simulations (Chen et al 2019). Further, CO 2 fertilization has been observed to have the greatest impact on grassland ANPP when it coincides with spring precipitation, which is largely the case throughout the Great Plains (Hovenden et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Potential natural vegetation and NPP responses to future climates in the U.S. Great Plains

2020

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Asymmetric climate projections throughout the U.S. Great Plains may intensify the existing latitudinal temperature gradient and magnify the longitudinal precipitation gradient. These potential changes present a unique challenge to understanding the ecological consequences of future climates in the region. Here we investigate how climate change may affect the spatio-temporal patterns of potential natural vegetation types (PVT) and net primary production (NPP) throughout the 21st century with the global dynamic vegetation model MC2. Simulations were driven by projected climate variables from five global climate models under representative concentration pathway (RCP) 8.5. MC2 simulated C3 and C4 grassland, shrubland, forest, and woodland (shrubland + forest) PVTs, and total NPP for each PVT. The largest increases in woodland and grassland NPP occurred in the Northern Plains (17.5% and 4.7%), followed by the Central Plains (10.6% and 0.0%), while NPP in the Southern Plains remained unchanged compared to historic means (1981-2010). A shift from grassland to woodland in the Northern and Central Plains further affected regional NPP; regional woodland NPP increased 72% and 26% in the Northern and Central Plains, respectively, while regional grassland NPP decreased 18% and 12%, respectively. The most pronounced shift in PVT was associated with increasing, rather than decreasing, mean annual precipitation in the Northern Plains where grassland contracted in response to westward expansion of woodland. C3 grassland was gradually replaced by C4 grassland in the Northern Plains by 2080, and only a trace remained at centuries end. C3 grassland decreased to a trace amount ca. 2060 in the Central Plains, while C4 grassland increased slightly. The relative stability of PVTs in the Southern Plains suggests that species and functional trait diversity may buffer grassland responses to future climates by providing the capacity for species reordering. The asymmetric response of simulated vegetation and NPP to 21st century climate change suggests that the provision of ecosystem services-beef cattle production, carbon sequestration, and grassland bird habitat-will be modified in distinct ways along a latitudinal gradient throughout the Great Plains.

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Section: Discussionmentioning

confidence: 99%

Potential natural vegetation and NPP responses to future climates in the U.S. Great Plains

2020

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“…The equation above, used for the forecasts in this paper, was similar to that of Chen et al (2019). Within the Great Plains, the correlation of ANPP to iNDVI was similar for the warm‐season C 4 ‐dominated southern grassland counties and the cool‐season C 3 ‐dominated northern grassland counties (Chen et al 2019).…”

Section: Methodsmentioning

confidence: 99%

“…The predicted annual ANPP for each county and scenario (ANPP pred, i , g biomass m −2 ) was based on a regional quadratic regression with iNDVI pred ,i . This relationship was determined in previous work that compared pasture‐level iNDVI to measured ANPP for five sites in the Great Plains that span a precipitation gradient and had long‐term ANPP datasets (Chen et al 2019). During the forecast procedure, Grass‐Cast calculated 36 values of ANPP from the i = 1, …, 36 predictions of cumulative growing season NDVI using the following relationship:

{ANPP}_{pred, i} = 24.04 {({iNDVI}_{pred, i})}^{2} ‐ 24.36 \times {iNDVI}_{pred, i} + 16.24

…”

Section: Methodsmentioning

confidence: 99%

“…Grass‐Cast is based on previous work where we established that cumulative April–July actual evapotranspiration (iAET) was the precipitation‐related variable most highly correlated to cumulative growing season (May–September) NDVI (iNDVI) and aboveground net primary production (ANPP; Del Grosso et al 2018, Chen et al 2019). Grass‐Cast predicts ANPP as a function of simulated iAET using previously established county‐level regressions between simulated iAET and iNDVI and regional statistical relationships between iNDVI and measured ANPP (Chen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal grassland productivity forecast for the U.S. Great Plains using Grass‐Cast

et al. 2020

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Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge-trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season aboveground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)-combined with ecosystem modeling and seasonal precipitation forecasts-to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in largerscale decision-making-such as where forage resources for grazing might be more plentiful if a rancher's own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-ofgrowing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass-Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.

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“…Vegetation indices like NDVI are highly correlated to primary production across spatial scales and are commonly used to estimate quantities such as vegetation biomass, productivity and phenology (Pettorelli et al ). For example, satellite NDVI has been used for decades as an estimator of biomass accumulation and aboveground NPP in grassland ecosystems (Tucker et al ; Paruelo et al ; Nestola et al ; Chen et al ). The strong relationship between NDVI, which is calculated as the ratio of red to near‐infrared reflectance, and absorbed photosynthetically active radiation (fPAR) is also well supported in the literature (Pettorelli et al ).…”

Section: Methodsmentioning

confidence: 99%

Sensitivity of primary production to precipitation across the United States

et al. 2020

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Primary production, a key regulator of the global carbon cycle, is highly responsive to variations in climate. Yet, a detailed, continental-scale risk assessment of climate-related impacts on primary production is lacking. We combined 16 years of MODIS NDVI data, a remotely sensed proxy for primary production, with observations from 1218 climate stations to derive values of ecosystem sensitivity to precipitation and aridity. For the first time, we produced an empirically-derived map of ecosystem sensitivity to climate across the conterminous United States. Over this 16-year period, annual primary production values were most sensitive to precipitation and aridity in dryland and grassland ecosystems. Century-long trends measured at the climate stations showed intensifying aridity and climatic variability in many of these sensitive regions. Dryland ecosystems in the western US may be particularly vulnerable to reductions in primary production and consequent degradation of ecosystem services as climate change and variability increase in the future.

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Assessing precipitation, evapotranspiration, andNDVIas controls of U.S. Great Plains plant production

Cited by 30 publications

References 54 publications

Potential natural vegetation and NPP responses to future climates in the U.S. Great Plains

Potential natural vegetation and NPP responses to future climates in the U.S. Great Plains

Seasonal grassland productivity forecast for the U.S. Great Plains using Grass‐Cast

Sensitivity of primary production to precipitation across the United States

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