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<p>Soil moisture plays a crucial role in partitioning surface fluxes. Several studies in past have highlighted the role of soil moisture in Land-Atmosphere (L-A) interactions. Understanding such interactions through regional climate models helps improve the simulation of global and regional hydrological processes. On the contrary, shallow subsurface groundwater also affects soil moisture variations. This calls for an accurate representation of physical processes involved in soil moisture interactions with groundwater. In addition, Shallow groundwater is known to act as a source and sink to the overlying soil layer during dry and wet seasons respectively. In this study, we analyze the impact of two different groundwater models in the Weather Research and Forecast (WRF) model coupled with the Noah-MP land surface model over the Ganga basin, India. Two experiments were carried out, one with the default-free drainage approach (CTL) and another with Miguez-Macho groundwater model (GW). The period of study was between 2008-2014. Preliminary analysis revealed that GW simulations improved soil moisture for the top and bottom-most soil layers. Reduction in temporal dry bias by around 91mm was observed for precipitation during the monsoon season. Dry bias in latent heat flux over the region also improved by 28 W/m<sup>2</sup>. GW run improved soil moisture and precipitation representation compared to CTL run. In summary, our results advocate the need for a better representation of groundwater within coupled regional climate models for improved simulation of hydrological processes</p>

Deforestation can have both biophysical and biochemical effects. In our study we evaluate the impacts of extreme idealized land cover scenarios over Ganga basin, India using an online coupled weather-biosphere model. We present an analysis of the carbon stored, based on introduced afforestation (AFF) and deforestation (DEF) in the Ganga basin of India. WRF-VPRM model simulations were carried out at horizontal resolution of 20 km using optimized downscaling configuration. For DEF case, we found two-fold increase in surface temperatures whereas, AFF scenario exhibited cooling effect. The average carbon sequestration capability for AFF was 0.3 g C m-2 day-1 more than control run (CTL). The CTL simulations exhibited carbon sequestration capability of -0.15 g C m-2 day-1 which for a year accounts for around 59.3 Mt C yr-1. AFF scenario showed relative increase in net sequestration compared to DEF scenario. Most importantly, the model simulations showed that the combined impact of all the vegetation types can increase carbon sequestration rather than just evergreen forest type. Our study highlights the possible effects of land use management practices on atmospheric CO2 variability.