Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

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.

Synergistic Effects of High‐Resolution Factors for Improving Soil Moisture Simulations Over China

“…When high‐resolution soil parameters are used (the “S” experiment), the wet bias is significantly reduced with the β factor close to 1 (brown bars in Figure 7e). This finding is consistent with previous studies (De Lannoy et al., 2014; Livneh et al., 2015; Maertens et al., 2021; Singh et al., 2015). However, the “S” experiment does not exhibit a large improvement in the CC and α , thus the KGE is only 0.03 (insignificant) higher than the “Ref” experiment (brown bars in Figures 7b–7d).…”

“…Numerous works have assessed the added value of newly‐developed high‐resolution meteorological forcings and soil parameters to RSM modeling. High‐resolution meteorological forcings have crucial impacts on simulating the mean and variability of RSM (Liu et al., 2023; Meng et al., 2019; Rouf et al., 2021; Zeng et al., 2021), while high‐resolution soil parameters are noted to reduce RSM biases by providing much more precise soil hydraulic properties than that at coarse resolution (De Lannoy et al., 2014; Livneh et al., 2015; Maertens et al., 2021; Singh et al., 2015). Because high‐resolution forcings/parameters typically utilize a larger number of observations and employ more sophisticated data fusion techniques during their production, their added value is not solely derived from their finer spatial resolution but also from the enhanced accuracy of the data (Beven et al., 2014; Singh et al., 2015).…”

“…This indicates that, using the default coarse‐resolution or the updated high‐resolution soil parameters will lead to different results when we quantify the influence of high‐resolution forcings. In addition, there are evidences showing that positive influences of a high‐resolution forcings/parameters on RSM modeling are affected by the LSMs (Alavi et al., 2016; De Lannoy et al., 2014; Maertens et al., 2021). For example, high‐resolution soil texture data sets cannot cause significant reductions in the SM simulation bias when applied to the Catchment LSM unless the soil property parameterizations are optimized (De Lannoy et al., 2014).…”

Effects of a biased LAI data assimilation system on hydrological variables and carbon uptake over Europe

“…Joint updates of LAI and root zone soil moisture as done in LDAS-Monde (Albergel et al, 2017) could alleviate this problem caused by "missing" water to some extent but requires a good estimation of the coupling strength of LAI and soil moisture. The strong effect on the model hydrology might also be model-specific, because the Noah-MP model hydrology is more sensitive to vegetation than other LSMs (Maertens et al, 2021).…”

“…We used the Noah-MP LSM (Niu et al, 2011;Yang et al, 2011) version 4.0.1 with dynamic vegetation as implemented in the NASA Land Information System (LIS; Kumar et al, 2006;Peters-Lidard et al, 2007)). The Noah-MP LSM is based on the Noah LSM, which is widely used for land surface modelling and DA on a regional to global scale (e.g., Rodell et al, 2004;Kumar et al, 2014Kumar et al, , 2019aMaertens et al, 2021). Noah-MP includes a multitude of optional improvements for snow, water, and vegetation modelling.…”