Interannual variability in net primary production and precipitation.

Primary Production and Precipitation Knapp and Smith (1) suggested that interannual variability in aboveground net primary production (ANPP) is not related to fluctuations in precipitation, based on analysis of data from 11 Long-Term Ecological Research sites across North America. This finding, if applicable to other regions, is crucial to climate change research, because it may necessitate revisions of projections of ecosystem responses to climate change (2, 3). To examine the relationship between variability in net primary production (NPP) and precipitation at a broad scale, a longterm normalized difference vegetation index (NDVI) data set derived from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA), coupled with a historical climate data set, should constitute a useful and powerful data source, because NDVI data are strongly correlated with terrestrial NPP and are frequently used as NPP predictors (4, 5). We used an annual mean NDVI data set over China to quantify temporal NPP variability relative to precipitation variation, and used coefficient of variation (CV) to express the magnitude of interannual variability in NDVI and precipitation. We then calculated CVs of these two variables for each pixel, with a resolution of 0.1° latitude by 0.1° longitude, for five biome groups across China—forest, grassland, desert, alpine vegetation, and cropland (6 )—using 1982 to 1999 NDVI and precipitation data compiled in China (7 ). We assumed that interannual variability in NDVI or NPP was related to temporal variability in precipitation if the correlation between CVs for NDVI or NPP and precipitation were identified as statistically significant. The CV value of NDVI for these five biome groups showed a large spatial variation, with a mean CV of 8.3% for the forest biome group, 10.4% for grasslands, 24.6% for desert areas, 12.7% for alpine vegetation, and 9.3 % for cropland. The largest variation occurred in the desert biome, followed by herbaceous vegetation (grasslands and alpine meadows); forests were the least variable. These results agree with those of Knapp and Smith (1). However, our statistical analysis also showed a significant positive correlation between the CV of NDVI and that of precipitation for all five biome groups (Fig. 1, A to E). The coefficient of correlation (r) was 0.43 for forest, 0.56 for grassland, 0.37 for desert, 0.31 for alpine vegetation, and 0.39 for cropland, with a strong correlation between mean CV of NDVI and that of precipitation for these five biome groups [r 5 0.95, p 5 0.012 (Fig. 1F)]. Moreover, the relationship between CV of NPP estimated based on the Carnegie-Ames-Stanford Approach (CASA) model (8, 9) and that of precipitation revealed trends similar to those implicit in Fig. 1. The r values were estimated at 0.53 for forest, 0.54 for grassland, 0.48 for desert, 0.37 for alpine vegetation, and 0.35 for cropland, with a highly significant correlation between mean NPP CV and mean precipitation CV for these five biome groups (r 5 0.97, p 5 0.005). These results are generally consistent with those of a previous study (10), but disagree with the conclusions of Knapp and Smith (1). Although the data used in the analysis by Knapp and Smith (1) were from the entirety of North America and included different terrestrial biomes, specifically forests, grasslands, and deserts, their study was limited to 11 sites. Considering the small sample size and the large spatial variation of NPP, we suggest that the conclusions of Knapp and Smith need broader investigation. Our results, which are based on remote-sensing approach, suggest that the relationship between interannual variability in NPP and precipitation across China is the opposite of the trends observed by Knapp and Smith (1) in North America.