Team Members:
Person Name | Person role on project | Affiliation |
---|---|---|
Atul Jain | Principal Investigator | University of Illinois, Urbana, United States |
Over the past four decades (1980-present), South and South East Asia (SSEA) have witnessed rapid changes in land cover and land use (LCLUC) due to factors such as population growth, the green revolution, industrialization, and deforestation. These changes in land use have significant impacts on climate, particularly through alterations in continental energy and water surface budgets. While the biogeophysical (BGP) effects of LCLUC have garnered increased attention at regional and global levels in recent decades, their precise implications remain uncertain, influenced by various factors. These include the spatial distribution of land covers, the scale of perturbation (e.g., conversion from forest to agricultural land and vice versa), and the geographical location (e.g., tropics, temperate, or boreal regions).
The primary objective of this research is to employ WRF_ISAM, a process-based regional-scale weather forecasting model, to assess the relative impacts of LCLUC, meteorological/climate datasets, and model parameters on various BGP variables. Specifically, our investigation will center on changes in the surface energy balance (SEB) during boreal summer (JJA) and winter (DJF), alterations in surface albedo (SAL), latent heat (LH) flux, sensible heat flux (SH), surface temperature (Ts), and total runoff from the surface and subsurface flows (Runoff). These variables are crucial for understanding the radiative and non-radiative impacts of LCLUC.
The model will be driven by three distinct sets of Land Cover and Land Use Change (LCLUC) data: Landsat, CCI, and LUH2. Additionally, it will utilize three different sets of reanalysis data, including NASA's MERRA-2, ECMWF's ERA5, and NCEP/NCAR reanalysis datasets. To address uncertainties arising from model parameters, our study will focus on three sets of parameters that are not adequately constrained by observational datasets: surface albedo, water table depth, subsurface runoff, and Roughness length.
Through a series of 63 model experiments spanning the years 2016-2018, we will decompose the individual components of uncertainty, including LCLUCs, reanalysis inputs, and model parameters.
The proposed study aims to identify future research priorities to enhance the quality of the data products and model performance, with the overarching goal of advancing the development of Land Digital Twins. This initiative will contribute to creating comprehensive digital representations of land systems to facilitate better understanding and management of environmental processes and phenomena.