Publications

Budakoti, S; Chauhan, T; Murtugudde, R; Karmakar, S; Ghosh, S (2021). Feedback From Vegetation to Interannual Variations of Indian Summer Monsoon Rainfall. WATER RESOURCES RESEARCH, 57(5), e2020WR028750.

Abstract
Interannual variations of Indian summer monsoon rainfall (ISMR) are modulated by external forcings such as El Nino Southern Oscillation, Indian Ocean Dipole, and the Atlantic Nino. Vegetation over land responds to variations in ISMR, but the feedback from vegetation to ISMR variability has not been fully explored yet. To address this gap, we perform two simulations with the regional Weather Research and Forecasting model coupled to the Community Land Surface Model (WRF-CLM) for the period of 2004-2018. We use the same boundary forcing from ERA-interim reanalysis for the two experiments, but with two different vegetation prescriptions, (1) observed, interannually varying Leaf Area Index (LAI), obtained from satellite images/data (VAR-LAI); and (2) climatological Leaf Area Index from the same product, to suppress interannual LAI variations (CLIM-LAI). We find that the correlation coefficient of simulated total seasonal rainfall with the observed data is higher for VAR-LAI simulation as compared to CLIM-LAI. To elicit causality among eco-hydro-climatological variables, we develop a network based on information theory, i.e., a process network. We find that LAI plays a major role in influencing precipitation in the network through evapotranspiration. The number of links originating from LAI and evapotranspiration increases during drought years, making the eco-hydro-climatological network denser. Our findings indicate that the ISMR predictions and projections need to represent the time-varying LAI to fully capture the varying feedbacks from evolving vegetation to the atmosphere especially during drought years. Plain Language Summary Indian Summer Monsoon Rainfall (ISMR) has strong interannual variations impacting land processes, including vegetation. Here, we address an unanswered science question: Can these interannual variations of vegetation impact the variability of ISMR in turn through feedbacks? To test this hypothesis, we perform numerical experiments using a coupled regional landatmosphere model and find that representing varying vegetation improves the simulations of ISMR. We further use the information theory-based process network and find a higher number of information links originating from land to atmospheric variables, during the drought years. The interannual variations of ISMR are conventionally predicted based on large-scale dynamics neglecting region-specific local land processes. Here, we show the importance of regional land-atmosphere feedbacks to the interannual variations of ISMR.

DOI:
10.1029/2020WR028750

ISSN:
0043-1397