Abstract:There is considerable uncertainty in the magnitude and direction of changes in precipitation associated with climate change, and ecosystem responses are also uncertain. Multiyear periods of above- and below-average rainfall may foretell consequences of changes in rainfall regime. We compiled long-term aboveground net primary productivity (ANPP) and precipitation (PPT) data for eight North American grasslands, and quantified relationships between ANPP and PPT at each site, and in 1-3 year periods of above- and … Show more
“…This is likely because δ s reflects gradients of different vegetation types across precipitation, while δ t only includes the short-term temporal response of fluxes to precipitation variability 14 . The legacy effect of previous year’s precipitation on current-year’s production may also contribute to the lower δ t 25,26 . The sensitivity of GPP to precipitation decreases from dry grass/shrub ecosystem to wet forest ecosystems, also consistent with independent ecosystem-scale measurements 13,15 .Fig.…”
Understanding the sensitivity of ecosystem production and respiration to climate change is critical for predicting terrestrial carbon dynamics. Here we show that the primary control on the inter-annual variability of net ecosystem carbon exchange switches from production to respiration at a precipitation threshold between 750 and 950 mm yr−1 in the contiguous United States. This precipitation threshold is evident across multiple datasets and scales of observation indicating that it is a robust result and provides a new scaling relationship between climate and carbon dynamics. However, this empirical precipitation threshold is not captured by dynamic global vegetation models, which tend to overestimate the sensitivity of production and underestimate the sensitivity of respiration to water availability in more mesic regions. Our results suggest that the short-term carbon balance of ecosystems may be more sensitive to respiration losses than previously thought and that model simulations may underestimate the positive carbon–climate feedbacks associated with respiration.
“…This is likely because δ s reflects gradients of different vegetation types across precipitation, while δ t only includes the short-term temporal response of fluxes to precipitation variability 14 . The legacy effect of previous year’s precipitation on current-year’s production may also contribute to the lower δ t 25,26 . The sensitivity of GPP to precipitation decreases from dry grass/shrub ecosystem to wet forest ecosystems, also consistent with independent ecosystem-scale measurements 13,15 .Fig.…”
Understanding the sensitivity of ecosystem production and respiration to climate change is critical for predicting terrestrial carbon dynamics. Here we show that the primary control on the inter-annual variability of net ecosystem carbon exchange switches from production to respiration at a precipitation threshold between 750 and 950 mm yr−1 in the contiguous United States. This precipitation threshold is evident across multiple datasets and scales of observation indicating that it is a robust result and provides a new scaling relationship between climate and carbon dynamics. However, this empirical precipitation threshold is not captured by dynamic global vegetation models, which tend to overestimate the sensitivity of production and underestimate the sensitivity of respiration to water availability in more mesic regions. Our results suggest that the short-term carbon balance of ecosystems may be more sensitive to respiration losses than previously thought and that model simulations may underestimate the positive carbon–climate feedbacks associated with respiration.
“…Although long‐term ANPP measurements exist for a number of grassland sites across the North American GP (Petrie et al. ), remote sensing provides spatially extensive data for > 30 yr. Satellite‐derived normalized difference vegetation index or NDVI (Tucker ) measurements can be used to predict grassland daily to seasonal net carbon exchange and gross primary production (GPP) (Gilmanov et al. , Morgan et al.…”
Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation‐related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi‐year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite‐derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April‐to‐July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April‐to‐July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May‐to‐September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county‐level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county‐level NRCS ANPP. Accounting for how different precipitation‐related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision‐making by land managers and use of grassland ANPP for biofuels.
“…, Petrie et al. ). To better understand these multiyear events, a growing area of research focuses on experimentally manipulating sustained extreme high and low PPT in a field setting (Fay et al.…”
Section: Introductionmentioning
confidence: 99%
“…, Gherardi and Sala , Petrie et al. ). Vegetation is also sensitive to temporal PPT patterns and to extreme PPT events, which fluctuate over time periods of days to multiple years (Jentsch and Beierkuhnlein , Collins et al.…”
Climate change is predicted to impact ecosystems through altered precipitation (PPT) regimes. In the Chihuahuan Desert, multiyear wet and dry periods and extreme PPT pulses are the most influential climatic events for vegetation. Vegetation responses are most frequently studied locally, and regional responses are often unclear. We present an approach to quantify correlation of PPT and vegetation responses (as Normalized Difference Vegetation Index [NDVI]) at the Jornada ARS‐LTER site (JRN; 550 km2 area) and the surrounding dryland region (from 0 to 500 km distance; 400,000 km2 study area) as a way to understand regional similarity to locally observed patterns. We focused on fluctuating wet and dry years, multiyear wet or dry periods of 3–4 yr, and multiyear wet periods that contained one or more extreme high PPT pulses or extreme low rainfall. In all but extreme high PPT years, JRN PPT was highly correlated (r > 0.9) to PPT across the regional study area (0–500 km distance; high correlation from 25th to 75th percentiles) and was highly correlated across a greater PPT range subregionally (0–200 km distance; high correlation from 10th to 90th percentiles). In contrast, the statistical distribution of JRN NDVI was less similar to that of regional NDVI. Yet, local‐regional NDVI similarity increased during multiyear periods to a maximum of >90% similarity for 10th–90th percentiles in a number of years. Thus, local‐regional heterogeneity in PPT and vegetation responses is reduced in both multiyear wet and dry periods, with the largest changes in climatic forcing and responses during multiyear wet periods. These wet and dry events support greater similarity between local‐regional PPT and vegetation response patterns. We conclude that site‐based research on multiyear periods can be extended to anticipate larger regional responses, and illustrate the opportunity to enhance understanding of future PPT change through increased focus on multiyear periods.
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