Abstract
Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.
Highlights
Land surface models (LSMs) aim at providing a complete and self-consistent description of the temporal and spatial distribution of water and energy over land (Clark et al, 2015)
The general objectives of this study are (i) to evaluate the simulated water budget components over the Dry Chaco using three different LSMs within the NASA Land Information System (LIS), (ii) to quantify how the simulated water budget components respond when more accurate soil texture and related soil hydraulic properties (SHPs) are implemented or when the static climatological vegetation (LAI and green vegetation fraction (GVF)) indices and land cover map are replaced with interannually varying satellite-based indices and yearly updated land cover maps, and (iii) to identify the remaining deviations in the modeled hydrology compared to different satellite-based and in situ observations of water budget components
The largest ET component is different in each LSM; i.e., EI is largest for CLM, EB for CLSM and EV for NOAH
Summary
Land surface models (LSMs) aim at providing a complete and self-consistent description of the temporal and spatial distribution of water and energy over land (Clark et al, 2015). The output from LSMs is used for many applications, such as the monitoring of water resources, floods and droughts, and their impact on natural hazards, biomass production, ecology or soil salinity. LSMs are an essential part of weather forecast systems and of climate models that simulate past, present and future climate (Pitman, 2003; Clark et al, 2015). They offer ancillary information to decompose, interpolate and extrapolate sparse ground measurements and remote sensing data. The degree to which LSMs can serve these various purposes depends on how well their given structure, forcing
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