<p>Due to the high variability of climate variables under climate change, t... more <p>Due to the high variability of climate variables under climate change, the assessment of the climate impacts on water management, ecosystems restoration, as well as climate change adaptation requires very detailed climate information regionally and ideally at a local scale. State-of-the-art coupled land-atmosphere numerical models incorporate the water and energy exchange processes in the soil–vegetation–atmosphere continuum in a physically consistent way, thereby their simulations capture the complete evolution of state variables and provide the complex linkages across compartmental boundaries in the Earth system. As an effort to contribute to climate- and water-related research in South Africa, we present a high spatial and temporal resolution climatological atmosphere–land surface–hydrology analysis dataset covering the period 2000-2020. This analysis dataset is dynamically downscaled from ERA5 reanalysis using the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro). This dataset covers the territory of South Africa with a grid resolution of 4 km and a time interval of 1 hour.</p><p>As a result, a comprehensive analysis dataset is provided, including the land surface and atmosphere state conditions, as well as the water flux components for the joint atmospheric-terrestrial water balance. The model performance is evaluated based on in-situ measurement records and remote sensing results. For instance, we evaluate the soil moisture and soil temperature using continuous in-situ measurement over six South Africa locations following a climate gradient, and the spatiotemporal trends of soil moisture are further evaluated using a newly developed radar-retrieved Surface Moisture Index (SurfMI). Biases of simulation results have been identified that should be taken into account in any application.</p>
This is the first time that paleomagnetic secular variation data obtained from South African sedi... more This is the first time that paleomagnetic secular variation data obtained from South African sediment records are used for dating purposes which is the only approach to establish a reliable chronology for recent sediments in this system.
This data repository presents a workflow to derive woody cover information for the Kruger Nationa... more This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication: <em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em> In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code. The data repository consist of the following files: <strong>R-Files:</strong> 1. Script 1: 01_MLR_tune_spatial_final 2. Script 2: 02_MLR_cross_validation_spatial_final 3. ...
A 30.5 m sediment core was recovered from the coastal lake Eilandvlei (EV13), which represents a ... more A 30.5 m sediment core was recovered from the coastal lake Eilandvlei (EV13), which represents a unique high-resolution record of environmental change for southern Africa. For the establishment of a robust chronology, special emphasis was placed on the calibration of radiocarbon (14C) ages obtained from the dating of different material. However, the reliability of 14C ages can be problematic since coastal lakes interact with different source pools providing 14C-depleted ("old") carbon thus causing reservoir effects. The origin of old carbon affecting the EV13 samples was most likely sourced from the Indian Ocean. Two pre-bomb marine molluscan shells were therefore analysed to determine the regional marine reservoir offset (dR), with obtained dR values of 134 ± 38 and 161 ± 38 14C yrs providing the first available data for the south coast of South Africa. However, the application of the resulting average dRmean = 148 ± 27 14C yrs for the calibration of the entire EV13 record underestimates the variable reservoir effects throughout the Holocene. These were possibly caused by past changes in the connectivity between the present lake system and the ocean as well as a varying degree of upwelling in this area. To solve this problem, three sample pairs (each consisting of wood fragments and bulk organic sediment from the same core depth) were dated to calculate the variable past reservoir effects. This approach provided a median basal age of 8920 +200/-250 cal BP. Palaeomagnetic secular variation stratigraphy was used to corroborate the chronology for the topmost 1.5 m of the record (past millennium), thus providing the first Holocene sediment based inclination and declination data from South Africa.
This data set contains measurements of soil texture, i. e. proportion of sand, silt and clay. Dat... more This data set contains measurements of soil texture, i. e. proportion of sand, silt and clay. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5 x 6 m and plots were weeded three times per year.Soil texture was determined from undisturbed soil cores at 27 locations distributed throughout the experimental site before plot establishment. Two of the locations are now reference plots and thus not included in this data set. Soil cores were taken to 100 cm depth and separated in increments with a resolution of ten cm from ground level to 40 cm depth and 20 cm from 40 cm to 100 cm depth. The bulk material was passed through a sieve with 2 mm mesh size and only fine soil was used for the investigation of soil texture. Grain size fractions according to DIN 19683-2 for every sample were then determined at the laboratory for geoecology of Jena University by a combined sieve and hydrometer analysis.Values for each plot were interpolated by ordinary kriging. This data set contains both, the actually measured in may as well as the interpolated values (Date/time start:01/01/2002,Date/time end:31/12/2002).
&amp;amp;amp;amp;lt;p&amp;amp;amp;amp;gt;Global biodiversity and ecosystem services are u... more &amp;amp;amp;amp;lt;p&amp;amp;amp;amp;gt;Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African &amp;amp;amp;amp;amp;#8220;Science Partnerships for the Adaptation to Complex Earth System Processes&amp;amp;amp;amp;amp;#8221;. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.&amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;gt;
&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;... more &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;Soil moisture is the dynamic link between climate, soil and vegetation and the dynamics and variation are affected by several often interrelated factors such as soil texture, soil structural parameters (soil organic carbon) and vegetation parameters (e.g. belowground- and aboveground biomass). For the characterization of soil moisture, including its variability and the resulting water and matter fluxes, the knowledge of the relative importance of these factors is of major challenge. Because of the spatial heterogeneity of its drivers soil moisture varies strongly over time and space. Our objective was to assess the spatio-temporal variability of soil moisture and factors which could explain that variability, like soil properties and vegetation cover, in in a long term biodiversity experiment (Jena Experiment).&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;The Jena Experiment consist 86 plots on which plant species richness (0, 1, 2, 4, 8, 16, and 60) and functional groups (legumes, grasses, tall herbs, and small herbs) were manipulated in a factorial design Soil moisture measurements were performed weekly April to September 2003-2005 and 2008-2013 in 0.1, 0.2, 0.3, 0.4, and 0.6 m soil depth using Delta T theta probe.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;The analysis showed that both plant species richness and the presence of particular functional groups affected soil water content, while functional group richness per se played no role. Plots containing grasses was consistently drier than average at the soil surface in all observed years while plots containing legumes comparatively moister, but only up to the year 2008.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;Interestingly, plant species richness led to moister than average subsoil at the beginning of the experiment (2003 and 2004), which changed to lower than average up to the year 2010 in all depths.Shortly after establishment, increased topsoil water content was related to higher leaf area index in species&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#8208;rich plots, which enhanced shading. In later years, higher species richness increased topsoil organic carbon, likely improving soil aggregation. Improved aggregation, in turn, dried topsoils in species&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#8208;rich plots due to faster drainage of rainwater.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;Our decade&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#8208;long experiment shows that besides abiotic factors like texture, soil water patterns are consistently affected by biotic factors such as species…
&amp;lt;p&amp;gt;Due to the high variability of climate variables under climate change, t... more &amp;lt;p&amp;gt;Due to the high variability of climate variables under climate change, the assessment of the climate impacts on water management, ecosystems restoration, as well as climate change adaptation requires very detailed climate information regionally and ideally at a local scale. State-of-the-art coupled land-atmosphere numerical models incorporate the water and energy exchange processes in the soil&amp;amp;#8211;vegetation&amp;amp;#8211;atmosphere continuum in a physically consistent way, thereby their simulations capture the complete evolution of state variables and provide the complex linkages across compartmental boundaries in the Earth system. As an effort to contribute to climate- and water-related research in South Africa, we present a high spatial and temporal resolution climatological atmosphere&amp;amp;#8211;land surface&amp;amp;#8211;hydrology analysis dataset covering the period 2000-2020. This analysis dataset is dynamically downscaled from ERA5 reanalysis using the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro). This dataset covers the territory of South Africa with a grid resolution of 4 km and a time interval of 1 hour.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As a result, a comprehensive analysis dataset is provided, including the land surface and atmosphere state conditions, as well as the water flux components for the joint atmospheric-terrestrial water balance. The model performance is evaluated based on in-situ measurement records and remote sensing results. For instance, we evaluate the soil moisture and soil temperature using continuous in-situ measurement over six South Africa locations following a climate gradient, and the spatiotemporal trends of soil moisture are further evaluated using a newly developed radar-retrieved Surface Moisture Index (SurfMI). Biases of simulation results have been identified that should be taken into account in any application.&amp;lt;/p&amp;gt;
This is the first time that paleomagnetic secular variation data obtained from South African sedi... more This is the first time that paleomagnetic secular variation data obtained from South African sediment records are used for dating purposes which is the only approach to establish a reliable chronology for recent sediments in this system.
This data repository presents a workflow to derive woody cover information for the Kruger Nationa... more This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication: <em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em> In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code. The data repository consist of the following files: <strong>R-Files:</strong> 1. Script 1: 01_MLR_tune_spatial_final 2. Script 2: 02_MLR_cross_validation_spatial_final 3. ...
A 30.5 m sediment core was recovered from the coastal lake Eilandvlei (EV13), which represents a ... more A 30.5 m sediment core was recovered from the coastal lake Eilandvlei (EV13), which represents a unique high-resolution record of environmental change for southern Africa. For the establishment of a robust chronology, special emphasis was placed on the calibration of radiocarbon (14C) ages obtained from the dating of different material. However, the reliability of 14C ages can be problematic since coastal lakes interact with different source pools providing 14C-depleted ("old") carbon thus causing reservoir effects. The origin of old carbon affecting the EV13 samples was most likely sourced from the Indian Ocean. Two pre-bomb marine molluscan shells were therefore analysed to determine the regional marine reservoir offset (dR), with obtained dR values of 134 ± 38 and 161 ± 38 14C yrs providing the first available data for the south coast of South Africa. However, the application of the resulting average dRmean = 148 ± 27 14C yrs for the calibration of the entire EV13 record underestimates the variable reservoir effects throughout the Holocene. These were possibly caused by past changes in the connectivity between the present lake system and the ocean as well as a varying degree of upwelling in this area. To solve this problem, three sample pairs (each consisting of wood fragments and bulk organic sediment from the same core depth) were dated to calculate the variable past reservoir effects. This approach provided a median basal age of 8920 +200/-250 cal BP. Palaeomagnetic secular variation stratigraphy was used to corroborate the chronology for the topmost 1.5 m of the record (past millennium), thus providing the first Holocene sediment based inclination and declination data from South Africa.
This data set contains measurements of soil texture, i. e. proportion of sand, silt and clay. Dat... more This data set contains measurements of soil texture, i. e. proportion of sand, silt and clay. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5 x 6 m and plots were weeded three times per year.Soil texture was determined from undisturbed soil cores at 27 locations distributed throughout the experimental site before plot establishment. Two of the locations are now reference plots and thus not included in this data set. Soil cores were taken to 100 cm depth and separated in increments with a resolution of ten cm from ground level to 40 cm depth and 20 cm from 40 cm to 100 cm depth. The bulk material was passed through a sieve with 2 mm mesh size and only fine soil was used for the investigation of soil texture. Grain size fractions according to DIN 19683-2 for every sample were then determined at the laboratory for geoecology of Jena University by a combined sieve and hydrometer analysis.Values for each plot were interpolated by ordinary kriging. This data set contains both, the actually measured in may as well as the interpolated values (Date/time start:01/01/2002,Date/time end:31/12/2002).
&amp;amp;amp;amp;lt;p&amp;amp;amp;amp;gt;Global biodiversity and ecosystem services are u... more &amp;amp;amp;amp;lt;p&amp;amp;amp;amp;gt;Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African &amp;amp;amp;amp;amp;#8220;Science Partnerships for the Adaptation to Complex Earth System Processes&amp;amp;amp;amp;amp;#8221;. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.&amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;gt;
&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;... more &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;Soil moisture is the dynamic link between climate, soil and vegetation and the dynamics and variation are affected by several often interrelated factors such as soil texture, soil structural parameters (soil organic carbon) and vegetation parameters (e.g. belowground- and aboveground biomass). For the characterization of soil moisture, including its variability and the resulting water and matter fluxes, the knowledge of the relative importance of these factors is of major challenge. Because of the spatial heterogeneity of its drivers soil moisture varies strongly over time and space. Our objective was to assess the spatio-temporal variability of soil moisture and factors which could explain that variability, like soil properties and vegetation cover, in in a long term biodiversity experiment (Jena Experiment).&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;The Jena Experiment consist 86 plots on which plant species richness (0, 1, 2, 4, 8, 16, and 60) and functional groups (legumes, grasses, tall herbs, and small herbs) were manipulated in a factorial design Soil moisture measurements were performed weekly April to September 2003-2005 and 2008-2013 in 0.1, 0.2, 0.3, 0.4, and 0.6 m soil depth using Delta T theta probe.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;The analysis showed that both plant species richness and the presence of particular functional groups affected soil water content, while functional group richness per se played no role. Plots containing grasses was consistently drier than average at the soil surface in all observed years while plots containing legumes comparatively moister, but only up to the year 2008.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;Interestingly, plant species richness led to moister than average subsoil at the beginning of the experiment (2003 and 2004), which changed to lower than average up to the year 2010 in all depths.Shortly after establishment, increased topsoil water content was related to higher leaf area index in species&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#8208;rich plots, which enhanced shading. In later years, higher species richness increased topsoil organic carbon, likely improving soil aggregation. Improved aggregation, in turn, dried topsoils in species&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#8208;rich plots due to faster drainage of rainwater.&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;Our decade&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#8208;long experiment shows that besides abiotic factors like texture, soil water patterns are consistently affected by biotic factors such as species…
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