Abstract. Process-based vegetation models are widely used to predict local and global
ecosystem dynamics and climate change impacts. Due to their complexity, they
require careful parameterization and evaluation to ensure that projections
are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a
wide range of empirical data on European forests to calibrate and evaluate
vegetation models that simulate climate impacts at the forest stand scale. A
particular advantage of this database is its wide coverage of multiple data
sources at different hierarchical and temporal scales, together with
environmental driving data as well as the latest climate scenarios.
Specifically, the PROFOUND DB provides general site descriptions, soil,
climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across
Europe. Moreover, for a subset of five sites, time series of carbon fluxes,
atmospheric heat conduction and soil water are also available. The climate
and nitrogen deposition data contain several datasets for the historic
period and a wide range of future climate change scenarios following the
Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We
also provide pre-industrial climate simulations that allow for model runs
aimed at disentangling the contribution of climate change to observed forest
productivity changes. The PROFOUND DB is available freely as a “SQLite”
relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data
policies of the individual contributing datasets are provided in the
metadata of each data file. The PROFOUND DB can also be accessed via the
ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra
Gonzalez et al., 2020), which provides basic functions to explore, plot and
extract the data for model set-up, calibration and evaluation.
a b s t r a c tRice agricultural practices and hydroperiod dates must be determined to obtain information on water management practices and their environmental effects. Spectral indices derived from an 8-day MODIS composite allows to identify rice phenometrics at varying degrees of success. The aims of this study were (1) to assess the dynamics of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI(1) and NDWI(2)) and Shortwave Angle Slope Index (SASI) in relation to rice agricultural practices and hydroperiod, and (2) to assess the capability for these indices to detect phenometrics in rice under different flooding regimes. Two rice farming areas in Spain that are governed under different water management practices, the Ebro Delta and Orellana, were studied over a 12-year period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). The index time series autocorrelation function was calculated to determine index dynamics in both areas. Secondly, average indices were calculated to identify significant points close to key agricultural and flooding dates, and index behaviors and capacities to identify phenometrics were assessed on a pixel level. The index autocorrelation function produced a regular pattern in both zones, being remarkably homogeneous in the Ebro Delta. It was concluded that a combination of NDVI, NDWI(1), NDWI (2) and SASI may improve the results obtained through each index. NDVI was more effective at detecting the heading date and flooding trends in the Ebro Delta. NDWI(1), NDWI(2) and SASI identified the harvest and the end of environmental flooding in the Delta, and the flooding in Orellana, more effectively. These results may set strong foundations for the development of new strategies in rice monitoring systems, providing useful information to policy makers and environmental studies.
Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand-level, as well as remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction, and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a SQLite relational database or ASCII flat file version (at https://doi.org/10.5880/PIK.2019.008). The data policies of the individual, contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R-package (https://github.com/COST-FP1304-PROFOUND/ProfoundData), which provides basic functions to explore, plot, and extract the data for model set-up, calibration and evaluation.
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