• Energy Research
• 2021-2025close

1 = present, - = absent, ? questinable occurence

Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.

The field Lake_ID includes a code for each individual lake. Register_ID includes a unique register for each lake and year. Also the year of the projections are detailed together with the thermal variables detailed in Table 1 and the geographic coordinates (ETRS89 UTM 31N). (CSV)

This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).

Climate change greatly affects spring and autumn plant phenology around the world consequently, and significantly impacts ecosystem function and the social economy. However, autumn plant phenology, especially autumn flowering phenology, has not been studied so far. In this study, we examined the spatiotemporal pattern of Osmanthus fragrans phenology, including both leaf phenology (the date of bud-bust, BBD; first leaf unfolding, FLD; and 50% of leaf unfolding, 50 LD) and flowering phenology (the date of first flowering, FFD; peak of flowering, PFD; and end of flowering, EFD). Stepwise multiple linear regressions were employed to analyze the relationships between phenophases and climatic factors in the long term phenological data collected by the Chinese Phenological Observation Network from 1973 to 1996. The results showed that spring leaf phenophases and autumn flowering phenophases were strongly affected by latitude. BBD, FLD, and 50LD of O. fragrans were delayed by 3.98, 3.93, and 4.40 days as per degree of latitude increased, while FFD, PFD and EFD in O. fragrans advanced 3.11, 3.26, and 2.99 days, respectively. During the entire study period, BBD was significantly delayed across the region, whereas no significant trends were observed either in FLD or 50LD. Notably, all flowering phenophases of O. fragrans were delayed. Both leaf and flowering phenophases negatively correlated with growing degree-days (GDD) and cold degree-days (CDD), respectively. BBD and FLD were negatively correlated with total annual precipitation. In addition to the effects of climate on autumn flowering phenology, we found that earlier spring leaf phenophases led to delayed autumn flowering phenophases. Our results suggest that future climate change and global warming might delay the phenological sequence of O. fragrans. Our findings also advanced the flowering mechanism study of autumn flowering plants, and facilitated the accurate prediction of future phenology and climate change.

10-year return levels of precipitation for hourly, 3-hourly, 6-hourly, 12-hourly and 24-hourly durations are calculated based on the 50-member regional climate model (RCM) large ensemble CRCM5-LE at a spatial resolution of 0.11° (~ 12.5km). The return levels thereby provide estimations for areal extreme precipitation for the time periods of near future (NF: 2010-2039), mid future (MF: 2040-2069), and far future (FF: 2070-2099) under the emission scenario RCP 8.5. If compared to in-situ observations, areal reduction factors have to be applied. The return levels are derived for each of the 50 RCM members separately, which induces a range of 50 return level values at each grid cell. The data are stored as txt-files featuring comma separated values in order to ensure an easy access, also for non-scientific applications. The data set includes the median value as well as the 5%- and 95%-quantiles. Additionally, the geographic coordinates (WGS84) are given. Hence, data can be processed via spreadsheet software, GIS applications and scientific program code. More details in: https://doi.org/10.1088/1748-9326/ac0849 The 10-year return levels for historical climate conditions are available at https://doi.org/10.5281/zenodo.3878888 and described in https://doi.org/10.5194/essd-2020-145

Energy supply under various cases.

ABSTRACT The progression of the organic loading rate (OLR) up to a certain limit increases biogas production. The limit and operation range vary according to the configuration of the reactor and are associated with other variables that generate different results with respect to biogas yield (BY) and biogas productiveness (BP). The aim of this study was to investigate the effect of the OLR on the BY and BP from swine manure in continuous stirred tank reactors (CSTRs) and upflow anaerobic sludge blanket reactors (UASBs). In the assay with the CSTR, the best operational condition was at an OLR of 0.7 gVS add L−1 reactor d−1 and a hydraulic retention time (HRT) of 18 days. At this operational condition, 0.8 LN biogas gVS add−1 of BY and 0.6 LN biogas L−1 reactor d−1 of BP were obtained. In the assay with the UASB, the best operational condition was at an OLR of 2.2 gVS add L−1 reactor d−1 and an HRT of two days, and 0.7 LN biogas gvs add−1 of BY and 1.6 LN biogas L−1 reactor d−1 of BP were obtained. The results demonstrate the effects of OLR changes on the biogas production in the CSTR and UASB, avoiding the underutilization or overloading of such equipment and enabling collaboration in projects for power generation from biogas in swine farms.

We have gathered data on the power generation of seven different PV modules from three demonstration sites in Oslo, Touzer, and Sevilla for a comprehensive analysis. This data was sourced from TIGO cloud for the PV modules and Solcast, an open-source platform, for historical weather information. The data set is spanning from May 2021 to November 2023. These datasets are characterized by high-resolution recordings taken every 5 minutes.