The data presented in this data release represent observations of postfire debris flows that have been collected from publicly available datasets. Data originate from 13 different countries: the United States, Australia, China, Italy, Greece, Portugal, Spain, the United Kingdom, Austria, Switzerland, Canada, South Korea, and Japan. The data are located in the file called “PFDF_database_sortedbyReference.txt” and a description of each column header can be found in both the file “column_headers.txt” and the metadata file (“Post-fire Debris-Flow Database (Literature Derived).xml”). The observations are derived from areas that have been burned by wildfire and are global in nature. However, this dataset is synthesized from information collected by many different researchers for different purposes, and therefore not all fields are available for each of the observations. Missing information is indicated by the value “-9999” in the ”PFDF_database_sortedbyReference.txt” file. Note that the text file contains special characters and a mix of date-time formats that reflect the original data provided by the authors. The text may not be displayed correctly if it is opened by proprietary software such as Microsoft Excel but will appear correctly when opened in a text editor software.

The data publication contains all heat-flow data of onshore Germany. The data release contains data generated between 1959 and 2020 and constitutes a substantial update and extension compared to the last compilation provided by the Geothermal Atlas from Hurter & Haenel (2002). The data set comprises new heat-flow determinations published after 2002 as well as data from before 2002, which were not included in the Hurter & Haenel atlas. The resulting updated database contains 836 determinations of heat flow at 595 locations from 42 publications. 85% of the reported heat-flow values are determined in boreholes, 5% in mines, and further 9 % are from onshore lake measurements using marine probe sensing techniques. The reporting and storing of the database is following the structure of the IHFC Global Heat Flow Database (Fuchs et al., 2021). A comprehensive description, including field classifications and ex-amples of associated data, is documented there. The IHFC database concept introduces parent elements (providing site-specific information), child elements (i.e. heat-flow values determined at the site and associated meta-data) and further fields providing additional information for the eval-uation of heat-flow quality. Thus, it provides a detailed collection of data and meta-data infor-mation, exceeding the sparse information on coordinates, name and heat-flow value provided in Hurter & Haenel (2002). In our release of the German heat-flow values, we have added fields about the applied quality scoring, the reasoning for inclusion or exclusion of data due to quality, and a descriptive field of the regional tectonic or geological units. For details of this procedure see Fuchs et al. (2022). The associated data description provides the full list of data sources (publications), while the DOI landing page only displays digital versions of articles if available.

Volcanic mass flows, especially lahars and pyroclastic density currents (PDCs) are among the most destructive volcanic hazards due to their speed, mobility, and the often-unexpected behavior these flows exhibit as they interact with topography. Mitigation of risk associated with PDCs and other volcanic mass flows depends upon accurate forecasting of magnitude and frequency, potential inundation areas, and possible runout lengths. Measurements of PDCs and other volcanic mass flows, their deposits, and their properties are used in model development and refinement, provide natural examples for benchmarking and comparing model performance, inform laboratory experiments, serve as useful analogs when local data are sparse, and define input parameter distributions for probabilistic flow modeling. These properties are numerous and varied and include information such as triggering mechanism, flow type, frequency, volume, velocity, runout length, and mobility measurements. Statistical forecasting methods, including event trees, rely upon databases to establish base-rates for flow frequency. To aid these goals, FlowDat: Volcanic Mass Flow Database has been developed. FlowDat is a comprehensive global database of mass flows, including dome-collapse and column-collapse PDCs, ash-cloud surges, ignimbrites, directed blasts, volcanic ice-slurry flows or mixed avalanches, lahars, and volcanic, non-volcanic and extra-terrestrial debris avalanches. This initial version of the database, ver. 1.0, focuses on PDCs. Forthcoming versions will include lahars and volcanic debris avalanches.

This dataset compiles heat flow and temperature gradient data from over 44,000 wells across the United States, along with more than 6,000 related geothermal exploration resources. Originally assembled prior to 2014 for the now-retired National Geothermal Data System (NGDS), the collection includes curated well data, scanned field notes, temperature-depth curves, publications, maps, and other supporting documents. SMU Geothermal Laboratory contributed two different nationwide heat flow databases to the project. One is based on equilibrium temperature measurements (over 14,000 sites) and the other is based on corrected bottom hole temperature (BHT) data from oil and gas industry wells (over 30,000 sites). In addition, scanned field notes and temperature-depth curves were associated with approximately 6,000 specific sites in the heat flow database. Records were corrected and overlapping sites in the equilibrium heat flow database were linked between the original SMU National database and the UND Global Heat Flow database. New or related sites, which were not previously published because they lacked full heat flow content, are now included as gradient only information along with their detailed temperature data to fill in data gaps. Finally, SMU submitted over 920 scanned publications, reports, and maps suitable for full text searching. The dataset is provided in two flat-structured zip archives: one containing the curated well data and another containing related resources. An Excel index file is provided for each archive, allowing filtering by well name, location, and description. Data files are labeled with state or institutional origin where available.

The Storm Event Stream Flow Data Set were collected during storm events from five treatment areas within the Konza Prairie Long-Term Ecological Research (LTER) site located within the northwest quadrant of the FIFE study area. These data were recorded so that the hydrology of the streams draining the tallgrass prairie could be studied. Moreover, these data were collected to determine the effect of burn frequency of a watershed upon runoff. Data are available from June 14, 1985 through December 31, 1987. The V-throated flume and standpipes used at the LTER weirs operated on the principle that the height of the water level in the standpipe at a specific location within a weir of known dimensions can be converted to volume of water in the stream. The change of this instantaneous volume with time could then be used to compute volumetric stream flow. The V-notch, sharp-crested weir used in watershed 1D operated on the principle that water flowing past a point of known dimensions per unit time could be converted through standard equations to volumetric flow.

The BOREAS TE-07 team collected data sets in support of its efforts to characterize and interpret information on the sap flow of boreal vegetation. The heat pulse method was used to monitor sap flow and to estimate rates of transpiration from aspen, black spruce, and mixed wood forests at the SSA-OA, MIX, SSA-OBS, and Batoche sites in Saskatchewan, Canada. Measurements were made at the various sites from May to Oct 1994, May to Oct 1995, and Apr to Oct 1996. A scaling procedure was used to estimate canopy transpiration rates from the sap flow measurements.

This data publication contains the compilation of global heat-flow data by the International Heat Flow Commission (IHFC; http://www.ihfc-iugg.org/) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). The presented data release 2021 contains data generated between 1939 and 2021 and constitutes an updated and extended version of the 2012 IHFC database release (IHFC 2012; later re-published as PANGAEA release: Global Heat Flow Compilation Group, 2013). The 2021 release contains 74,548 heat-flow data from 1,403 publications. 55% of the reported heat-flow values are from the continental domain (n ~ 40,870), while the remaining 45% are located in the oceanic domain (n ~ 33,678). The data are provided in csv and Excel formats. Compared to earlier compilations, which followed the structure defined by Jessop et al. (1976), the new data release was transformed to the recently redefined structure for reporting and storing heat-flow data in the Global Heat Flow Database (Fuchs et al., 2021). Therefore, the notation and structure of the database was adopted, transforming the database field entries defined after Jessop et al. (1976) to the new field structure. Old code notations are not continued and the dataset was cleaned for entries without reporting any heat-flow value. Although successfully transformed, this release marks an intermediate step as the majority of the newly defined database fields have not been filled yet. Filling these fields, checking the existing entries and assessing the quality of each entry are the aim of the upcoming Global Heat Flow Data Assessment Project, for which this data set provides the basis. Consequently, we kindly ask the user to take notice that the current release still suffers similar problems as previously published compilations in terms of data heterogeneity, documentation and quality.

This data set is a compilation of heat flow data of uncertain origin. References as cited in Global Heat Flow Database were incomplete and thus could not be verified. This data compilation contains: data of unknown origin, unpublished data, data which has no full reference information or data which were extracted from other database. The remaining short citation and its related problem are listed in columns 18 and 19.

For the integration of this dataset, several research articles were collected from the catalog of The Global Heat Flow Data Assessment Project. Specially, this data publication encloses all heat-flow data of onshore India. The resulting updated database contains 617 determinations of heat-flow from 36 publications.

The data are presented according to the standards defined by the World Heat Flow Database Project and the International Heat Flow Commission (Fuchs et al., 2023)

General description

SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels. SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide.

The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here: http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/.

The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (http://sapfluxnet.creaf.cat/#team), coordinated by Rafael Poyatos (CREAF, http://www.creaf.cat/staff/rafael-poyatos-lopez), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).

Changelog

Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same.

For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (https://chelsa-climate.org/), replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.

In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.

Variables and units

SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags.

Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: Metadata and data units.

To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (https://github.com/sapfluxnet/sapfluxnetr). In particular, have a look at the package vignettes using R:

remotes::install_github(

'sapfluxnet/sapfluxnetr',

build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")

)

library(sapfluxnetr)

to list all vignettes

vignette(package='sapfluxnetr')

variables and units

vignette('metadata-and-data-units', package='sapfluxnetr')

data flags

vignette('data-flags', package='sapfluxnetr')

Data formats

SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the corresponding vignette.

Working with sfn_data files

To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).

After downloading the entire database, the sapfluxnetr package can be used to: - Work with data from a single site: data access, plotting and time aggregation. - Select the subset datasets to work with. - Work with data from multiple sites: data access, plotting and time aggregation.

Please check the following package vignettes to learn more about how to work with sfn_data files:

Quick guide

Metadata and data units

sfn_data classes

Custom aggregation

Memory and parallelization

Working with text files

We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files.

Data issues and reporting

Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.

Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').

Data access, use and citation

This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.

When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).

SDCC Traffic Congestion Saturation Flow Data for January to June 2023. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

The episodic occurrence of debris flow events in response to stochastic precipitation and wildfire events makes hazard prediction challenging. Previous work has shown that frequency-magnitude distributions of non-fire-related debris flows follow a power law, but less is known about the distribution of post-fire debris flows. As a first step in parameterizing hazard models, we use frequency-magnitude distributions and cumulative distribution functions to compare volumes of post-fire debris flows to non-fire-related debris flows. Due to the large number of events required to parameterize frequency-magnitude distributions, and the relatively small number of post-fire event magnitudes recorded in the literature, we collected data on 73 recent post-fire events in the field. The resulting catalog of 988 debris flow events is presented as an appendix to this article. We found that the empirical cumulative distribution function of post-fire debris flow volumes is composed of smaller events than that of non-fire-related debris flows. In addition, the slope of the frequency-magnitude distribution of post-fire debris flows is steeper than that of non-fire-related debris flows, evidence that differences in the post-fire environment tend to produce a higher proportion of small events. We propose two possible explanations: 1) post-fire events occur on shorter return intervals than debris flows in similar basins that do not experience fire, causing their distribution to shift toward smaller events due to limitations in sediment supply, or 2) fire causes changes in resisting and driving forces on a package of sediment, such that a smaller perturbation of the system is required in order for a debris flow to occur, resulting in smaller event volumes.

This file contains peak-flow data in tab-delimited format for selected streamgages based on data through water year 2022. The file was retrieved from the USGS NWIS peak-flow database (https://nwis.waterdata.usgs.gov/usa/nwis/peak).

in high flow velocity areas like those suitable for tidal applications, turbulence intensity is high and flow variations may have a major impact on tidal turbines behaviour. large boils that can be observed at the sea surface are emitted from the sea floor and may interact with the tidal turbine. these boils have then to be characterized. the reynolds number, based on the rugosity height and mean flow velocity, is rather high in this context: re = 2.5 × 107 . for that purpose, experiments are carried out in a flume tank with re as high as achievable in froude similitude (in the tank: re = 2.5 × 105 and fr = 0.23) in order to study coherent flow structures emitted behind seabed obstacles. the obstacle is here a canonical square wall-mounted cylinder chosen to be representative of specific in-situ bathymetric variations. using piv and ldv measurements, the flow past the cylinder is investigated. the database created is presented in this report.

A highly granular database of nearly 500 capital flow management measures that cover 14 instruments and 49 countries at monthly frequency between 2008 and 2021.

Dayflow is a computer program developed in 1978 as an accounting tool for determining historical Delta boundary hydrology. It presently provides the best estimate of historical mean daily flows: (1) through the Delta Cross Channel and Georgiana Slough; (2) past Jersey Point; and (3) past Chipps Island to San Francisco Bay (net Delta outflow). The degree of accuracy of Dayflow output is affected by the Dayflow computational scheme and the accuracy and limitations of the input data. The input data include the principal Delta stream inflows, Delta precipitation, Delta exports, and Delta gross channel depletions. Both monitored and estimated values are included as described in this Dayflow program documentation. Currently, flows are not routed to account for travel time through the Delta. All calculations involving inflows, depletions, transfers, exports, and outflow are performed using data for the same day. All Dayflow summary reports distributed through January 1985, providing flow data through August 1984, and data for September 1984 reported herein were generated according to the algorithm described in the Computational Scheme section.

This dataset contains daily observations of stream flow from the United States Geological Survey (USGS) and selected United States Army Corps of Engineers gages. A total of 251 stations reported within the SGP99 domain. NOTE - the streamflow data are generally not adjusted for diversions or reservoir storage.

Quality characteristics for 21586 river flow time series from 13 datasets worldwide. The 13 datasets are: the Global Runoff Database from the Global Runoff Data Center (GRDC), the Global River Discharge Data (RIVDIS; Vörösmarty et al., 1998), Surface-Water Data from the United States Geological Survey (USGS), HYDAT from the Water Survey of Canada (WSC), WISKI from the Swedish Meteorological and Hydrological Institute (SMHI), Hidroweb from the Brazilian National Water Agency (ANA), National data from the Australian Bureau of Meteorology (BOM), Spanish river flow data from the Ecological Transition Ministry (Spain), R-ArcticNet v. 4.0 from the Pan-Arctic Project Consortium (R-ArcticNet), Russian River data (NCAR-UCAR; Bodo, 2000), Chinese river flow data from the China Hydrology Data Project (CHDP; Henck et al., 2010, 2011), the European Water Archive from GRDC - EURO-FRIEND-Water (EWA), and the GEWEX Asian Monsoon Experiment (GAME) – Tropics dataset provided by the Royal Irrigation Department of Thailand. Quality characteristics are based on availability, outliers, homogeneity and trends: overall availability (%), longest availability (%), continuity (%), monthly availability (%), outliers ratio (%), homogeneity of annual flows (number of statistical tests agreeing), trend in annual flows, trend in one month of the year.

Bodo, B. (2000) Russian River Flow Data by Bodo. Boulder CO: Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Retrieved from http://rda.ucar.edu/datasets/ds553.1/

Henck, A. C., Huntington, K. W., Stone, J. O., Montgomery, D. R. & Hallet, B. (2011) Spatial controls on erosion in the Three Rivers Region, southeastern Tibet and southwestern China. Earth and Planetary Science Letters 303(1–2), 71–83. doi:10.1016/j.epsl.2010.12.038

Henck, A. C., Montgomery, David R., Huntington, K. W. & Liang, C. (2010) Monsoon control of effective discharge, Yunnan and Tibet. Geology 38(11), 975–978. doi:10.1130/G31444.1

Vörösmarty, C. J., Fekete, B. M. & Tucker, B. A. (1998) Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS). doi:10.3334/ornldaac/199

In order to improve the operation efficiency and market competitiveness, how to optimize the ticket pricing strategy of high-speed railway to match the dynamic supply-demand relationship was an urgent problem to be studied. Taking differentiated passenger demand and supply trains as the research object, the space-time service network based on train timetable was constructed. The generalized cost formula and travel utility formula of passenger travel were proposed, which contained economy, rapidity, convenience, comfort, and route correlation cost. A multi-objective dynamic pricing model was proposed. The model aimed at maximize the corporate revenue and maximize passenger travel benefit, and was solved by large neighborhood search heuristic algorithm and path size logit assignment based on capacity constraint-passenger flow increment accurate algorithm. Based on real data, the Shandong circular high-speed railway case compared the average total revenue under different ticket price adjustment ranges and the ticket price for different classes of trains under different OD levels. The case proved the practicability of dynamic pricing adjustment strategy considering train classification, which could provide a reference for the ticket price management of high-speed railway.

Overview: Streamflow is part of the dynamic hydrologic system that supports a range of water dependent activities in Whatcom County, including farming, fishing and recreation. The relationship between streamflow, groundwater recharge and groundwater discharge, precipitation, fish habitat and crop production is critical for understanding how best to manage water to meet those needs. Streamflow is the best characterized, and most easily measured, component of the dynamic hydrologic system, and as such, is the primary metric used in modeling the water budget. For example, facilitating development of water management options that improve streamflow in the late summer is one of the reasons for developing the Lower Nooksack Water Budget.

The Lower Nooksack Water Budget project included a review of available streamflow measurements made available since WRIA 1 Water Management Project Phase III Task 1 (2002). For the streamflow database update (Lower Nooksack Water Budget Project, Task 2), the project team compiled available information, data, and maps of the stream data collected in the Lower Nooksack study area, as well as WRIA 1 upstream boundary conditions, and updated the database of measured streamflow from 1999 to 2011. The updated streamflow database will be used to calibrate and validate the Topnet-WM hydrologic model, and to calculate water budgets for each of the Lower Nooksack drainages; data will be available in an ASCII format for all other drainages but will not be formally summarized.

The following section begins with the list of streamflow gages in WRIA 1, followed by an explanation of how the data were used for model inputs. This information was used along with other Lower Nooksack Water Budget technical components to calculate the hydrologic model outputs working with the WRIA 1 Joint Board’s existing hydrologic model and supporting technical tools.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.