## 2 Data  
The study area is India, which is a large country with a total geographical area of 329 million ha. It is located between 7\({}^{\circ}\) and 40\({}^{\circ}\)N and between 68\({}^{\circ}\) and 100\({}^{\circ}\)E. India experiences climatically four distinct seasons: southwest summer monsoon (June to August), northeast winter monsoon (December to February), spring or pre-monsoon (March to May) and autumn post-monsoon (September to November) inter-monosomal periods. The southwest monsoon is essentially warm and humid, and the rainfall received during this period accounts for about 80% of the total annual rainfall of the country. The NE monsoon is cold and dry; however, southeastern parts of the country receive significant rainfall during this period. The two inter-monsoonal periods are mostly dry and moderately warm. The NPP estimates make use of vegetation greenness observations from the AVHRR [19] in conjunction with a light use efficiency model implemented for the MODIS sensor [9]. The algorithm uses fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) derived using AVHRR NDVI and radiative transfer models [20]. Satellite data from multiple AVHRR sensors have been processed consistently, accounting for differences and drift in calibration among the sensors, as well as atmospheric effects from ozone and water vapor. Climate drivers are from the National Center for Environmental Prediction reanalysis. The LUE algorithm first generates an estimate of potential GPP as a function of fPAR, incident radiation and biome specific radiation use efficiencies. Subsequently, the potential GPP is down regulated using daily temperature and soil moisture constraints. An autotrophic respiration component, sum of maintenance and growth components, is computed separately using air temperature, seasonally adjusted Q10 and carbon pools (leaf, root and stem) derived from the satellite-derived LAI. The Normalized Difference Vegetation Index (NDVI) used in the retrieval of our NPP product has been used recently to document the stagnation of food production in water-limited regions of the tropics [21].It was also used to characterize year-to-year variations in vegetation dynamics in Central Asia [22], to study the trends and variability in vegetation structure in a grassland in Canada [23], and to examine vegetation change for a range of agricultural systems in Inner Mongolia, China [24]. The algorithm (called MODIS algorithm) used to obtain our NPP dataset has been validated using Ameriflux data from North America [25], Bigfoot data for North America and Brazil at nine sites representing a range of biomes [26], and global Ecosystem Model-Date Intercomparison (EMDI) NPP dataset [7]. GPP from MODIS algorithm has been also validated for a mixed forest site in the Nepalese Himalayas [27]. These validation studies have indicated that the NPP product used here are reliable. Further, our NPP dataset has been found to represent interannual variability in climate-driven vegetation activity reasonably well [8, 9]. The climate data (temperature and precipitation) at a resolution of 0.5\({}^{\circ}\) are obtained from Climatic Research Unit (CRU) (www.cru.uea.ac.uk/cru/data/hrg) at the University of East Anglia (UEA). The global annual mean of atmospheric CO\({}_{2}\) is obtained from NOAA/ESRL (National Oceanic and Atmospheric Administration/Earth System Research Laboratory) databases (www.esrl.noaa.gov/gmd/ccgg/trends/). The soil moisture data for the top 3.4 m soil ([[http://hydro1.sci.gsfc.nasa.gov/dods/](http://hydro1.sci.gsfc.nasa.gov/dods/)]([http://hydro1.sci.gsfc.nasa.gov/dods/](http://hydro1.sci.gsfc.nasa.gov/dods/))), Global Land Data Assimilation System (GLADS), were acquired as part of the mission of NASA's Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) through the Data and Information Services Center (DISC).