Publications

We used long‐term observations of grassland aboveground net plant production (ANPP, 1939–2016), growing seasonal advanced very‐high‐resolution radiometer remote sensing normalized difference vegetation index (NDVI) data (1982–2016), and simulations of actual evapotranspiration (1912–2016) to evaluate the impact of Pacific Decadal Oscillation (PDO) and El Niño–Southern Oscillation (ENSO) sea surface temperature (SST) anomalies on a semiarid grassland in northeastern Colorado. Because ANPP was well correlated ( R 2 = 0.58) to cumulative April to July actual evapotranspiration (iAET) and cumulative growing season NDVI (iNDVI) was well correlated to iAET and ANPP ( R 2 = 0.62 [quadratic model] and 0.59, respectively), we were able to quantify interactions between the long‐duration (15–30 yr) PDO temperature cycles and annual‐duration ENSO SST phases on ANPP. We found that during cold‐phase PDOs, mean ANPP and iNDVI were lower, and the frequency of low ANPP years (drought years) was much higher, compared to warm‐phase PDO years. In addition, ANPP, iNDVI, and iAET were highly variable during the cold‐phase PDOs. When NINO‐3 (ENSO index) values were negative, there was a higher frequency of droughts and lower frequency of wet years regardless of the PDO phase. PDO and NINO‐3 anomalies reinforced each other resulting in a high frequency of above‐normal iAET (52%) and low frequency of drought (20%) when both PDO and NINO‐3 values were positive and the opposite pattern when both PDO and NINO‐3 values were negative (24% frequency of above normal and 48% frequency of drought). Precipitation variability and subsequent ANPP dynamics in this grassland were dampened when PDO and NINO‐3 SSTs had opposing signs. Thus, primary signatures of these SSTs in this semiarid grassland are (1) increased interannual variability in ANPP during cold‐phase PDOs, (2) drought with low ANPP occurring in almost half of those years with negative values of PDO and NINO‐3, and (3) high precipitation and ANPP common in years with positive PDO and NINO‐3 values.

No abstract is provided for this article.

Results are provided of a project to derive improved products from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data record for land investigations. As part of this project, a prototype AVHRR processing system has been developed. This paper describes the different components of this system, which include radiometric in-flight vicarious calibration for the visible and near infrared channels, geometric correction and atmospheric correction as pre-processing steps. The processed data are then stored in a new intermediate data format, which enables flexible compositing approaches. The system generates surface reflectance and vegetation index products as well as new higher order products of reflectance at 3.75 mum and active fires. A comparison of a significant sample of data with widely used precursor AVHRR products is presented to evaluate the processing chain and the improvements it provides.

Several climate models indicate that in a 2 × CO 2 environment, temperature and precipitation would increase and runoff would increase faster than precipitation. These models, however, did not allow the vegetation to increase its leaf density as a response to the physiological effects of increased CO 2 and consequent changes in climate. Other assessments included these interactions but did not account for the vegetation down‐regulation to reduce plant's photosynthetic activity and as such resulted in a weak vegetation negative response. When we combine these interactions in climate simulations with 2 × CO 2 , the associated increase in precipitation contributes primarily to increase evapotranspiration rather than surface runoff, consistent with observations, and results in an additional cooling effect not fully accounted for in previous simulations with elevated CO 2 . By accelerating the water cycle, this feedback slows but does not alleviate the projected warming, reducing the land surface warming by 0.6°C. Compared to previous studies, these results imply that long term negative feedback from CO 2 ‐induced increases in vegetation density could reduce temperature following a stabilization of CO 2 concentration.

No abstract is provided for this article.

Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.

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.