Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

Abstract

Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome-BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1-3 m) is sufficient in regions with a short dry season (e.g. 0-2 months), and deeper roots are required in regions with a longer dry season (e.g. 3-5 and 5-10 m for the regions with 3-4 and 5-6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth.

Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado

“…In recent years, attention has been almost singularly focused on fixing the supply side of the problem, implementing deep soil and/or deep roots (Ichii et al, 2007;Baker et al, 2008;Grant et al, 2009;Harper et al, 2010;Verbeeck et al, 2011), root hydraulic redistribution (Lee et al, 2005), unconfined aquifers (Oleson et al, 2008;Fan and Miguez-Macho, 2010;Miguez-Macho and Fan, 2012), or changes to the numerical solution of the Richards equation for soil water fluxes (Zeng and Decker, 2009) to improve seasonal patterns of soil moisture and/or the seasonality of ecosystem metabolism. Despite the attention given to these ecohydrological mechanisms, little is known as to the relative contribution of soil physical versus biological mechanisms mediating supply.…”

Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

“…The failure of the ORCHIDEE vegetation model over these regions needs to be further investigated. As an example, the BIOME-BGC model has been shown to underestimate rooting depth in the Amazon, an important parameter because it controls water stress during the dry season, when plants have access to moisture only in deeper soil (Ichii et al, 2007). A similar deficiency in the ORCHIDEE model was recently shown when assimilating FLUXNET data to optimize the model parameters (Verbeeck et al, 2011).…”

“…A closer analysis of these latter regions shows that the satellite NDVI time series exhibits a significant annual cycle, while the modeled FPAR shows either no cycle or a phase-shifted one. Indeed, in Amazonia, a marked annual cycle has been observed over evergreen forests using MODIS Enhanced Vegetation Index (EVI; Huete et al, 2006;Moreau et al, 2010) or MODIS LAI (Myneni et al, 2007). LAI and EVI are preferred in these studies to FPAR and NDVI as they do not saturate over dense vegetation and then have larger amplitude (Huete et al, 2002).…”

Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

“…We may hypothesize that in places where soil conditions and plant type allow drawing on a large water reservoir, NDVI would be disproportionately affected by long meteorological droughts (reflected by SPEI at long aggregation periods) and not by short droughts. Determining the sensitivity of VHPs to drought of various timescales (as quantified by low SPEI) could be used to map rooting depth of different vegetation types [53,54].…”