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524 results for “Dams”
USFWS Red Bluff Diversion Dam Rotary Screw Trap Juvenile Fish Monitoring Database
The United States Fish and Wildlife Service (USFWS) has conducted direct monitoring of juvenile Chinook Salmon Oncorhynchus tshawytscha passage at the Red Bluff Diversion Dam (RBDD), river kilometer (RKM) 391 on the Sacramento River, in Northern California since 1994 (Johnson and Martin 1997). Martin et al. (2001) developed quantitative methodologies for indexing juvenile Chinook passage using rotary-screw traps (RST) to assess the impacts of the United States Bureau of Reclamation’s (USBR) RBDD Research Pumping Plant. Absolute abundance (passage and production) estimates were needed to determine the level of impact from the entrainment of salmonids and other fish community populations through RBDD’s experimental ‘fish friendly’ Archimedes and internal helical pumps (Borthwick and Corwin 2001). The original project objectives were met by 2000 and funding of the project was discontinued. From 2001 to 2008, funding was secured through a CALFED Bay-Delta Program grant for annual monitoring operations to determine the effects of restoration activities in the upper Sacramento River aimed primarily at winter Chinook Salmon*. The USBR, the primary proponent of the Central Valley Project (CVP), has funded this project since 2010 due to regulatory requirements contained within the National Marine Fisheries Service’s (NMFS) Biological Opinion for the Long-term Operations of the CVP and State Water Project (NMFS 2009 and 2019). The project began sampling in 1994 with (4) 2.4-m diameter RST’s which sampled through March of 2020. From March 25, 2020 through June 25, 2020, in order to protect employee health and safety during the Coronavirus global pandemic (COVID-19), sampling ceased. Just prior to resuming sampling operations in July of 2020, (4) 1.5-m diameter and one 2.4-m diameter RSTs were re-installed across the transect at the RBDD site. This new five-trap configuration provides a solution to sampling a location that has become shallower since the RBDD gates were permanen
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Rio_Grande_below_Elephant_Butte_Dam for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated du
Beaver Dams in Madison and Oneida Counties, New York State, 1994-2022
This point dataset comprises georeferenced (NAD 1983 Zone 18N) digital markers of beaver dam locations in the Counties of Madison and Oneida in New York State in the years 1994, 2003, 2013, and 2022, as determined manually and retroactively through observation of true-color (rgb) composite aerial imagery from those years. The New York State Orthoimagery Database supplied the relevant imagery, and the years were chosen according to the availability of imagery for both counties at the time of data collection. The approximately 40-inch resolution photographs from 1994 were taken as part of the United States Geological Survey’s National Aerial Photography Program, whereas the 6- to 12-inch resolution images from the subsequent years were taken as part of New York’s GIS Coordinate Program. Each point represents a unique beaver dam determined to be present in one or more of the aforementioned years from such land feature characteristics as curvilinearity, perpendicular orientation to streams, proximity to apparent beaver lodges, and other distinguishing contextual clues. The attribute fields to the point data include the smallest unit of watershed in which the dam in question lies or once lay, the approximate location of the dam in terms of a named geographic feature, and one field for each year with a code indicating whether the dam was found to be present that year (1-yes, 0-no). This inventory has been used thus far in an investigation of the changes in dam abundance and distribution over the past three decades.
Annual nutrient loading and yield to Plum Island Estuary, as measured at the Ipswich and Parker Dams
Nutrient concentrations for various forms of N, P, C, as well as suspended sediments, are determined from monthly grab samples taken at the Ipswich and Parker dams. These nutrient concentrations are then used in conjunction with USGS discharge data (recorded at gages in the Parker River at Byfield, MA and the Ipswich River at Ipswich, MA) to calculate annual nutrient loading to the Estuary, coming over each dam. Annual yield is also calculated for both dams.
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Sentinel-1 data stack for Masjed Soleyman Dam
<p>This is a Sentinel-1 sample dataset for the SARvey InSAR time series analysis software.</p> <p>This dataset consists of:</p> <ul> <li> A stack of coregistered SLCs for the Masjed Soleyman Dam and its corresponding geometry data in MiaplPy format. These files serve as the input data for SARvey.<br> SARvey_input_data_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> <li> The final products generated by SARvey for reference.<br> SARvey_final_results_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> </ul> <p><br>A cookbook is available to help you run the software using this dataset.</p> <p> </p>
GeoDAR-TopoCat: Drainage topology and catchment database (TopoCat) for Georeferenced global Dams And Reservoirs (GeoDAR)
<p><strong>Contact</strong>: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)</p> <p> </p> <p><strong>Data description</strong></p> <p>This data can be considered a supplement to the Georeferenced global Dams And Reservoirs (GeoDAR) dataset (doi:10.5281/zenodo.6163413). </p> <p>Here in GeoDAR-TopoCat, the method of TopoCat (doi:10.5281/zenodo.7420810) has been applied on GeoDAR reservoirs in order to construct the drainage topology and catchments for global reservoirs.</p> <p>To avoid ambiguity, please refer to this version of GeoDAR-TopoCat as “<strong>GeoDAR-TopoCat v1.1-1.0</strong>”, where “1.1” specifies the version of GeoDAR reservoirs, whose drainage topology and catchments are constructed using the method in version “1.0” of TopoCat.</p> <p> </p> <p><strong>Relevant datasets</strong></p> <ul> <li>The original GeoDAR v1.1 dataset without topology can be accessed here: doi:10.5281/zenodo.6163413.</li> <li>The TopoCat v1.0 dataset, originally developed based on HydroLAKES v1.0, can be accessed here: doi:10.5281/zenodo.7420810.</li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <p>Description of the attributes of GeoDAR-TopoCat is the same as those of TopoCat v1.0. The unique ID of each GeoDAR reservoir is specified in “id_v11” (consistent with the GeoDAR dataset). Please refer to the attributes of TopoCat and GeoDAR for more details.</p> <p> </p> <p><strong>Data and code availability</strong></p> <p>All datasets are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>).</p> <p>Please refer to GeoDAR and TopoCat datasets for other details and disclaimers.</p> <p> </p> <p><strong>Citation</strong></p> <p>We request anyone who uses GeoDAR-TopoCat to cite <strong>both GeoDAR and TopoCat papers</strong>:</p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869-1899, 2022, <a href="https://doi.org/10.5194/essd-14-1869-2022">https://doi.org/10.5194/essd-14-1869-2022</a>.</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data Discussion, in review, <a href="https://doi.org/10.5194/essd-2022-433">https://doi.org/10.5194/essd-2022-433</a>.</p>
Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project)
<h2><strong>1. SUDOANG PROJECT</strong></h2><p>The SUDOANG project has provided common tools and assessment methods to managers to support the eel conservation in the SUDOE zone (Southern France, Spain and Portugal). One of the goals of the project was to develop an eel abundance and distribution <a href="https://zenodo.org/record/7546419">atlas</a> in the three countries, based on the results of the implementation of Eel Density Analysis (<a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">EDA</a>). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat. To do this, we have created a dataset of "cumulated dam impact" which compiles different ways of calculating cumulated height from the sea.</p><h2><strong>2. SUDOANG DATABASE</strong></h2><p>The dataset on cumulated impact was first derived from information on obstacles collected by the SUDOANG project. Obstacles data for the three countries were imported in the SUDOANG database (<a href="https://sudoang.eu/wp-content/uploads/2020/11/E221_data_collection_storage-1.html#3_data_import_on_physical_obstacles_in_spain_and_portugal">deliverable 2.2.1</a>), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. </p><p>In France the obstacles were compiled from three pre-existing different databases:</p><ul><li>the Referential of flow obstacles (<a href="https://professionnels.ofb.fr/fr/node/367">ROE</a>) ,</li><li>the Information of Ecological Continuity (<a href="https://professionnels.ofb.fr/en/node/731">ICE</a>) and</li><li>the Flow Obstruction Database (Base de Données des Obstacles à l'Ecoulement, (BDOE).</li></ul><p>The data we have integrated into the SUDOANG 1.0.4. database came from a database dump of the 12th September 2019. The inventory includes bridges that have a significant impact on river continuity.</p><p>In Spain, data came from:</p><ul><li>the MITECO Ministry</li><li>the Basque Water Agency (URA) - Basque Country</li><li>the Catalan Water Agency (ACA) - Catalonia</li><li>the University of Girona - Catalonia</li><li>the University of Córdoba - Andalusia</li><li>Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia</li><li>the <a href="https://amber.international">AMBER </a>project</li></ul><p>In Portugal the data came from:</p><ul><li>the Portuguese Water Agency (APA)</li><li>MARE-ULisboa (University of Lisbon)</li><li>CIIMAR, the University of Porto</li><li>the <a href="https://amber.international">AMBER</a> project.</li></ul><p>In the case of the transboundary river Minho, the data came from:</p><ul><li>CIIMAR, the University of Porto (Portuguese area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li><li>EHEC, the University of Santiago de Compostela (Spanish area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li></ul><h2><strong>3. DATA DESCRIPTION</strong></h2><h3><strong>3.1. Data collected on artificial obstacles</strong></h3><p>Artificial obstacles were classified into 10 types according to the Adaptive Management of Barriers in European Rivers (<a href="https://amber.international">AMBER</a>) project. Some additional types (e.g., penstock pipes) were added to identify other obstacles in national databases that did not fit the AMBER classification (see the list below). Sometimes dams from different branches are connected, creating a dam-network. In those cases, we have only kept the dam(s) in the main course and use a hierarchical classification of the dams to only consider the cumulated height from the sea to the reference dam. We included only obstacles that are presently standing, <i>i.e.,</i> not planned, under construction, or destroyed. Dikes, longitudinal control structures and grates were excluded.</p><p><i>Obstacle classification according to the data collected and the AMBER project:</i></p><ul><li>BR - Bridge: A structure that is built over a river to allow people or vehicles to cross</li><li>CU - Culvert: A tunnel or pipe carrying a stream or open drain under a road or railway</li><li>DI - Dike: An embankment used to hold back water</li><li>DA - Dam: Structure that blocks the river and extends across the river bed to the flood plain</li><li>FO - Ford: A shallow crossing-place in a river</li><li>PP - Penstock: pipe Group of pipes that transport pressurised water from a reservoir (dam) to the turbines installed in a hydro-electric power plant</li><li>RR - Rock ramp: A weir made of rocks</li><li>WE - Weir: Structure across a river that does not extend to the flood plain</li><li>OT - Other: Structure that is not covered by previous definitions</li><li>UN - Unknown: Unknown</li></ul><p>We have projected obstacles on the SUDOANG river network at the nearest point within 300 m. To avoid projecting large obstacles in the wrong location in the southwestern France, SUDOANG experts have reviewed and corrected this information. We have also used an algorithm that extracts the best obstacle height data from the three existing databases in France. In the Iberian Peninsula, data providers validated and corrected obstacle location and height using a Shiny application developed by the project, in which they could directly correct the height of obstacles.</p><p>The variables in the <strong>obstacles </strong>table (csv delimiter ",") are:</p><ul><li><i>op_id</i>: Identifier of the observation place name</li><li><i>op_gis_layername</i>: Original data source</li><li><i>op_placename</i>: Name of the dam</li><li><i>op_op_id</i>: If the dam is linked within a complex (e.g. when there are multiple channels for the same river) the name of the parent dam</li><li><i>id_original</i>: Original id of the dam (in the raw table)</li><li><i>country</i>: Country code ('SP', 'ES' or 'FR')</li><li><i>dp_name</i>: Name of the data provider</li><li><i>obstruction_type_code</i>: Type of obstruction (see table obstruction type code)</li><li><i>obstruction_type_name</i>: Name of the dam</li><li><i>po_obstruction_height</i>: Difference of level of water between the downstream and the upstream part of the dam</li><li><i>po_presence_eel_pass</i>: Presence of a pass suitable for eel (see paper)</li><li><i>po_date_presence_eel_pass</i>: Date of construction of the eel (or eel compatible) pass</li><li><i>fishway_type_code</i>: Code of the fishway type</li><li><i>fishway_type_name</i>: Name of the fishway type</li><li><i>googlemapscoods</i>: Link to google map</li><li><i>x_espg_4326</i>: Longitude (with ESPG 4326)</li><li><i>y_espg_4326</i>: Latitude (with ESPG 4326)</li></ul><h3><strong>3.2. Modeling missing data and estimating the cumulative impact on obstacles</strong></h3><p>For those obstacles missing height information, we have calculated height using a Generalized Linear Models (GLM of log transformed height, <i>family = gaussian, link = identity</i>. In France the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/Dams.Rmd">model</a> was based on river segment slope, river segment median flow and hydrographic basin. In the Iberian Peninsula, we have implemented a simpler <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/E221_data_collection_storage_sp_pt.Rmd">model</a> based on obstacle type, as information about flow or slope was not available for all river segments.</p><p>The cumulated impact of obstacles was assessed by creating a table joining each river segment with all the dams located in the downstream course. Using this, various metrics were computed using different assumptions concerning the effect of obstacles. The heights were power transformed to test for a different effect of obstacle's height (the cumulated effect of two obstacles of 1 m might be different than the cumulated effect of a single obstacle of 2 m), and functions were developed to calculate cumulated obstacle transformed variables. Other variables were also tested. In fact, tests in France have shown that factors such as presence of a fish pass, and eel passability did not improve the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R\#L624">model performance</a>. For this reason, but also because in the Iberian Peninsula this type of information was too limited, we used dam height to model the cumulative height of obstacles at a given river segment. </p><p>The variables in the <strong>cumulated_dam_impact_SUDOANG </strong>table (format Rdata - to be read with the R software, this will load as a data.frame called datadam) are:</p><ul><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li><i>cs_height_08_n</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li><i>cs_height_08_p</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_pps </i>Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values</li><li><i>cs_height_10_n</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula <i>cs_height_10_p</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li><i>cs_height_12_n</i>: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li><i>cs_height_12_p.</i>: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n:</i> Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p.:</i> Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure </li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: duplicate of cumnbdamp</li><li><i>idsegment</i>: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li></ul><h2><strong>4. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.7825552">10.5281/zenodo.7825552 </a>1.0.0 - 2023-04-15 - Initial Upload (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-09-15 - Update provider and names (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-11-08 - Final version (open access)</li></ul><h2><strong>5. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Eel data (<i>Anguilla anguilla</i>) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009</a>)</li></ul><h2><strong>6. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Predicted Beaver Dam Building Capacity in the Minneapolis-St. Paul Metro Area
The Beaver Restoration Assessment Tool (BRAT) (MacFarlane et al., 2015) is a predictive model that integrates hydrology, topography, vegetation, and land use data to predict existing and historical beaver dam building capacity within a watershed. The model was run on the HUC8 Mississippi River Twin Cities Watershed (07010206) in March 2025. The output displayed is a shapefile of the Conservation Restoration Model, which includes existing and historical dam building capacity as well as restoration opportunities.
Conservation and management of migratory fauna and dams in tropical streams of Puerto Rico
1. Compared to most other tropical regions, Puerto Rico appears to have dammed its running waters decades earlier and to a greater degree. The island has more large dams per unit area than many countries in both tropical and temperate regions (e.g., 3x that of the U.S.), and the peak rate of large dam construction occurred two and three decades prior to reported peak rates in Latin America, Asia and Africa.2. Puerto Rico is a potential window into the future of freshwater migratory fauna in tropical regions, given the island's extent and magnitude of dam development and the available scientific information on ecology and management of the island's migratory fauna.3. We review ecology, management and conservation of migratory fauna in relation to dams in Puerto Rico. Our review includes a synthesis of recent and unpublished observations on upstream effects of large dams on migratory fauna and an analysis of patterns in free crest spillway discharge across Puerto Rican reservoirs. Analyses suggest that large dams with rare spillway discharge cause near, not complete, extirpation of upstream populations of migratory fauna. They also suggest several management and conservation issues in need of further research and consideration. These include research on the costs, benefits and effectiveness of simple fish/shrimp passage designs involving simulating spillway discharge and the appropriateness of establishing predatory fishes in reservoirs of historically fishless drainages. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Indirect upstream effects of dams: consequences of migratory consumer extirpation in Puerto Rico
<p>Large dams degrade the integrity of a wide variety of ecosystems, yet direct downstream effects of dams have received the most attention from ecosystem managers and researchers. We investigated indirect upstream effects of dams resulting from decimation of migratory freshwater shrimp and fish populations in Puerto Rico, USA, in both high- and low-gradient streams. In high-gradient streams above large dams, native shrimps and fishes were extremely rare, whereas similar sites without large dams had high abundances of native consumers. Losses of native fauna above dams dramatically altered their basal food resources and assemblages of invertebrate competitors and prey. Compared to pools in high-gradient streams with no large dams, pool epilithon above dams had 9 times more algal biomass, 20 times more fine benthic organic matter (FBOM), 65 times more fine benthic inorganic matter (FBIM), 28 times more carbon (C), 19 times more nitrogen (N), and 4 times more non-decapod invertebrate biomass. High-gradient riffles upstream from large dams had 5 times more FBIM than did undammed riffles but showed no difference in algal abundance, FBOM, or non-decapod invertebrate biomass. For epilithon of low-gradient streams, differences in basal resources between pools above large dams vs. without large dams were considerably smaller in magnitude than those observed for pools in high-gradient sites. These results match previous stream experiments in which the strength of native shrimp and fish effects increased with stream gradient. Our results demonstrate that dams can indirectly affect upstream free-flowing reaches by eliminating strong top-down effects of consumers. Migratory omnivorous shrimps and fishes occur throughout the tropics, and the consequences of their declines upstream from many tropical dams are likely to be similar to those in Puerto Rico. Thus, ecological effects of migratory fauna loss upstream from dams encompass a wider variety of species interactions and
Minocqua Dam Daily Meteorological Data at North Temperate Lakes LTER 1978 - current
Meteorological measurements are being gathered at a site at the Minocqua Dam for these purposes: 1) to supplement the data from the raft on Sparkling Lake and 2) to provide standard meteorological measurements for the North Temperate Lakes site. The following parameters are measured and stored as daily values: 1) maximum air temperature, 2) minimum air temperature, 3) precipitation, 4) snowfall, and 5) snowdepth. Snowdepth data begin in 1996. Precipitation data are summed for 5- minute intervals during periods of detectable precipitation. Data are reported at 7am each day for the previous 24 hours. E.g, data for June 5 are for period 7am June 4 to 7am June 5. Sampling Frequency: data averaged to daily values Number of sites: 1
Minocqua Dam Monthly Meteorological Data at North Temperate Lakes LTER 1905 - current
Minoqua Dam, Wisconsin. Meteorological measurements are being gathered at a site at the Minocqua Dam for these purposes: 1) to supplement the data from the raft on Sparkling Lake and 2) to provide standard meteorological measurements for the North Temperate Lakes site. The following parameters are measured and stored as monthly values: 1) mean daily air temperature, 2) mean maximum air temperature, 3) mean minimum air temperature, 4) total precipitation, and 5) total snowfall. Sampling Frequency: data averaged to monthly values Number of sites: 1
PIE LTER nutrient samples collected by Sigma Autosampler between 2001 and 2017 in three headwater sites of contrasting land use, and at the Parker and Ipswich River Dams as they enter into the Plum Island Sound estuary, Massachusetts.
Total organic nitrogen, total organic phosphorus, and nitrate concentrations collected frequently by Sigma autosampler (or volunteers in winter) from 5 sites. Sites include three headwater sites of contrasting land use (CC= Forest, SB = suburban, CS = wetland) and at the mouth of the Ipswich and Parker Rivers where they flow into the estuary.
PIE LTER Year 2017, 15 minute measurements of conductivity, water temperature at the Ipswich River head of tide, Sylvania Dam in Ipswich, MA.
Year 2017, continuous measurements, every 15 minutes were made of conductivity, water temperature in the Ipswich River behind the head of tide Sylvania Dam in Ipswich, MA. The datalogger was retrieved in August, 2017 and returned to Dr. Green.
Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.
<p>Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.</p>
Dataset of Georeferenced Dams in South America (DDSA) v1.0.2
<p><strong>Recommended citation</strong></p> <p>Paredes-Beltran, B., Sordo-Ward, A., and Garrote, L.: Dataset of Georeferenced Dams in South America (DDSA), Earth Syst. Sci. Data, 13, 213–229, https://doi.org/10.5194/essd-13-213-2021, 2021.</p> <p><strong>Updated version 1.0.2:</strong></p> <p>We present version 1.0.2 to the DDSA database, the improvements made to version 1.0.1 are described below:</p> <ol> <li>Supplementary table 1: Future Dams in South America has been updated and now 574 future projected dams in South America, 61 under construction for 2020 and 513 planned projects for the future.</li> </ol> <p><strong>Updates made in version 1.0.1:</strong></p> <p>Version 1.0.1 to the DDSA database, includes improvements made to version 1.0.0, which are described below:</p> <ol> <li>New hydrological information attributes have been included: <ol> <li>Aridity index</li> <li>Residence time</li> <li>Degree of regulation.</li> </ol> </li> <li>A shapefile of watersheds for each dam has been included.</li> <li> <ol> </ol> Supplementary table 1: Future Dams in South America has been included.</li> </ol> <p><strong>Use of the dataset</strong></p> <p>Before using the dataset, please notify us (be.paredes@alumnos.upm.es; be.paredes@uta.edu.ec) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using this dataset.</p> <p><strong>Description</strong></p> <p>Dams and their reservoirs generate major impacts on society and the environment. In general, its relevance relies on facilitating the management of water resources for anthropogenic purposes. However, dams could also generate many potential adverse impacts related to safety, ecology or biodiversity. These factors, and the additional effects that climate change could cause in these infrastructures and their surrounding environment, highlight the importance of dams and the necessity for their continuous monitoring and study. There are several studies examining dams both at regional and global scale, however, those that include the South America region focus mainly on the most renowned basins (primarily the Amazon basin), most likely due to the lack of records on the rest of the basins of the region. For this reason, a consistent database of georeferenced dams located in South America is presented: Dataset of georeferenced dams in South America DDSA. It contains 1,010 entries of dams with a combined reservoir volume of 1,017 cubic kilometres and it is presented in form of a list describing a total of 24 attributes that include the dams name, characteristics, purposes and georeferenced location. Also, hydrological information on the dams’ catchments is also included: catchment area, mean precipitation, mean near-surface temperature, mean potential evapotranspiration, mean runoff, catchment population, catchment equipped area for irrigation, aridity index, residence time and degree of regulation. Information was obtained from public records, governments records, existing international databases and from extensive internet research. Each register was validated individually and geolocated using public access online map browsers and then, hydrological and additional information was derived from a hydrological model computed using the HydroSHEDS dataset. With this database, we expect to contribute to the development of new research in this region.</p> <p><strong>Content</strong></p> <p>The files included in the Dataset of georeferenced dams in South America DDSA are:</p> <ul> <li><strong>1.</strong> Dam Information</li> <li><strong>2.1.</strong> Dam Hydrological Information - Catchment Area</li> <li><strong>2.2.</strong> Dam Hydrological Information - Catchment Mean Monthly Near Surface Temperature</li> <li><strong>2.3. </strong>Dam Hydrological Information - Catchment Mean Monthly Precipitation</li> <li><strong>2.4.</strong> Dam Hydrological Information - Catchment Mean Monthly Potential Evapotranspiration</li> <li><strong>2.5. </strong>Dam Hydrological Information - Catchment Mean Monthly Runoff</li> <li><strong>2.6.</strong> Dam Hydrological Information - Catchment Population</li> <li><strong>2.7. </strong>Dam Hydrological Information - Catchment Eqquiped Area for Irrigation</li> <li><strong>2.8. </strong>Dam Hydrological Information - Aridity Index</li> <li><strong>2.9. </strong>Dam Hydrological Information - Residence Time</li> <li><strong>2.10. </strong>Dam Hydrological Information - Degree of Regulation</li> <li><strong>3.</strong> Dataset Attribute Description</li> <li><strong>4.</strong> Dataset Data Source</li> <li><strong>5.</strong> Dataset in KMZ format </li> <li><strong>6.</strong> Dataset in SHAPEFILE format (dams)</li> <li><strong>7. </strong>Dataset in SHAPEFILE format (dams catchments)</li> <li><strong>8. </strong>Supplementary Table 1: Future Dams in South America v1.01</li> </ul>
Landslides from Space - Bento Rodrigues Dam Failure, Brazil (5th November 2015)
<p>On 5th November 2015, an iron ore tailings dam in Bento Rodrigues suffered a failure. About 60 million cubic meter of iron waste flowed down the valley. Two villages were partly destroyed and the drinking water supply of a few hundred thousand people were effected. The river Doce will be affected by this disaster for many decades.</p> <p>The pre-event acquisition is from 5th November 2015 (Landsat-8) and the post-event acquisition is from 26th December 2015 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel and Landsat data (2015) </em></p>
Data from: Understanding the Influence of Check Dam and Season on Habitat Use to Develop Habitat Suitability Criteria for Overwintering Tadpoles of Nanorana spp.
<p>Dataset for the article: Understanding the Influence of Check Dam and Season on Habitat Use to Develop Habitat Suitability Criteria for Overwintering Tadpoles of <em>Nanorana</em> spp.</p> <p>See readme.txt for details.</p>
The longer arc of channel recovery post-dam removal
<p>This repository includes all data and analyses referenced in the paper "The Longer Arc of Channel Recovery Post-Dam Removal" by Fields et al. (submitted 2024). The R files used to plot figures and complete additional data analysis are also included. </p> <p>This project is a follow-up to earlier work at the same site by Fields et al. (2021) "A Mechanistic Understanding of Channel Evolution following Dam Removal" (10.1016/j.geomorph.2021.107971). Data for that paper is available on the CUAHSI database (http://www.hydroshare.org/resource/ ae0589f6a2e54effb5514d126ecb6908). </p> <p>If any neccessary data is missing or if you require more of our data for your analsyses please contact Jordan Fields (jordan.f.fields@gmail.com). </p>
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