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The dataset covers biotic and abiotic data from the aquatic habitat of a population of the sediment-rooted macrophyte Myriophyllum spicatum in the temperate climate region (The Netherlands). The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh and dry weight), shoot length, seasonal short-term growth rates of shoots, relevant environmental parameters and weather data. This dataset includes the 2-year experimental biotic (macrophyte biomass and growth data) and environmental data (water quality data, sediment data). A second file includes the statistical data. A third file includes the weather data.

Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.

In the current study, we examine the Indonesian government's watershed management program, which was established in 2001. In 2005, the Coordination Team for Rescue of Water Resources (CTRWR) was established to execute the program on a national level. However, at the time, field implementation was a sectoral interest due to the lack of program integration. To this end, the Indonesian government promoted integrated watershed management in 2009, which since then has been implemented by all stakeholders (in Top–Down management form), with application limited to preparing and planning documents. This is mainly driven by the stakeholders’ lack of understanding with regard to watershed systems as integrated management units. Field implementation results have not yet been realized, including the promotion of community-based watershed management (through Bottom–Up management). The purpose of our research was to determine the index numbers by measuring the level of cooperation between watershed management workers based on the Village Watershed Model (VWM) specifically surface water which includes six variables: planning, participation, institutional, fund sharing, gender, and management systems. The method used was an ordinal measure with the Likert scale. Our data showed successful watershed management, in which five of the six VWM variables—planning, participation, institutional, fund sharing, and management systems—were in the “good” category with indices ranging from 73.08 to 78.27. The gender variable index (69.12) was in the “medium” category.

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format TL;DR: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010). Method Description The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff. The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full). Data coverage This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). The countries and their acronyms for both technologies included in this dataset are: Country Run-of-River Storage Austria AT_ROR AT_STO Belgium BE_ROR BE_STO Bulgaria BG_ROR BG_STO Switzerland CH_ROR CH_STO Cyprus CZ_ROR CZ_STO Germany DE_ROR DE_STO Denmark DK_ROR Estonia EE_ROR Greece EL_ROR EL_STO Spain ES_ROR ES_STO Finland FI_ROR FI_STO France FR_ROR FR_STO Great Britain GB_ROR GB_STO Croatia HR_ROR HR_STO Hungary HU_ROR HU_STO Ireland IE_ROR IE_STO Italy IT_ROR IT_STO Luxembourg LU_ROR Latvia LV_ROR the Netherlands NL_ROR Norway NO_ROR NO_STO Poland PL_ROR PL_STO Portugal PT_ROR PT_STO Romania RO_ROR RO_STO Sweden SE_ROR SE_STO Slovenia SI_ROR SI_STO Slovakia SK_ROR SK_STO Data structure description The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following: first column (or A) contains the row number Label: unlabeled Contents: interger range [1,262968] second column (or B) contains the valid-time Label: T1h Contents represent time with text as [DD/MM/YYYY HH:MM]) column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR) Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.). DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. The raw hydro data was generated as part of 'Evaluating sediment Delivery Impacts on Reservoirs in changing climaTe and society across scales and sectors (DIRT-X)', this project and therefor, Jing hu, received funding from the European Research Area Network (ERA-NET) under grant number 438.19.902. Laurens P. Stoop received funding from the Netherlands Organization for Scientific Research (NWO) under Grant No. 647.003.005.

Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L-1 selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 �� 17 mgSe L-1 day-1) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L-1 day-1, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856��26 mgSe-1day-1gbiomass-1, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H2Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (���80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 ��m. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.

This dataset contains data collected during experiment on foulant fractionation of seawater in reverse electrodialysis (RED). For explanation of the experimental setup we refer you to the published paper. It is being made public both to act as supplementary data for publication and in order for other researchers to use this data in their own work.

Data used to obtain the results of the research paper entitled: "Heatwaves, elevated temperatures, and a pesticide cause interactive effects on multi-trophic levels of a freshwater ecosystem", published in the journal "Environmental Pollution". The data derives from an indoor (micro-) cosm experiment in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the effects of two different temperature scenarios (i.e., elevated temperature and reoccurring heatwaves) in combination with the fungicide carbendazim.

Projections of Sea Level Rise (SLR) under RCP 4.5 and RCP 8.5 (AR5) along the Mekong Coast, Published1 by the Ministry of Natural Resources and Environment (MONRE), Hanoi, Vietnam. Projections of Mekong River discharge during the dry season under RCP 4.5 and RCP 8.5 at Kratie, Cambodia. The data contains the cumulative, minimum and maximum dry season (January-1st to April-30th) discharge from 5 different climate models. PCR-GLOBWB2 was run at 5 arc-min spatial resolution and forced with the data based on output from five ISIMIP CMIP5 global climate models (HadGEM2-ES, GFDL-ESM2, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M). 1. Ministry of Natural Resources and Environment (MONRE), V. Climate change and sea level rise scenarios for Vietnam, Ministry of Natural Resources and Environment. (2016). 2. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018). {"references": ["Sutanudjaja et al. (2018)", "Ministry of Natural Resources and Environment (2016)"]}