Estimation of Net Primary Productivity Using CASA Biosphere Model in Hyderabad and Roorkee Region of India

This paper is based on the CASA Biosphere model to estimate NPP for a series of study sites across various ecosystem types. The Carnegie-Ames-Stanford Approach (CASA) is a process-based ecosystem model widely used to estimate Net Primary Productivity (NPP) at regional to global scales. It uses data on environmental variables, such as total solar radiation, temperature, precipitation, Normalized Difference Vegetation Index (NDVI), and information on the ecosystem’s carbon and water balance to estimate and analyse NPP. The CASA model could accurately estimate NPP for the study sites, with good agreement between modeled and observed values. Our results demonstrate the utility of the CASA model for estimating NPP and highlight the importance of accurately modeling ecosystems’ carbon and water balance to estimate NPP, developing more precise models for predicting NPP in different ecosystems. NPP estimates using the CASA Biosphere model for the Roorkee site are much higher than the Hyderabad region.