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508 results for “nevada”
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
The Sierra Lakes Inventory Project: Non-Native fish and community composition of lakes and ponds in the Sierra Nevada, California
The Sierra Lakes Inventory Project (SLIP) was a research endeavor that ran from 1995-2002 and has supported research and management of Sierra Nevada aquatic ecosystems and their terrestrial interfaces. We described the physical characteristics of and surveyed aquatic communities for > 8,000 lentic water bodies in the southern Sierra Nevada, including lakes, ponds, marshes, and meadows. We also created digital map layers for these water bodies when such layers did not exist. The original objective of SLIP was to describe impacts of non-native fish on lake communities, but SLIP data has subsequently enabled study of additional ecological issues, including regional amphibian declines and their impacts on communities, and impacts of non-native fish on terrestrial species. In addition, these data are being used to develop fish removal efforts to restore aquatic ecosystems and recover endangered amphibians. The SLIP data is stored in a relational database that collectively describes water bodies (e.g., depth, elevation, location), surveys (conditions, effort), and communities (including approximately 170 fish, amphibian, reptile, benthic macroinvertebrate, and zooplankton taxa).
Updated NLL-SSST-coherence earthquake relocation catalog for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.
<p>CSV catalog and plots of NLL-SSST-coherence earthquake relocations of 17583 events M≥1 2020.01.01 to 2021.08.20 for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.</p> <p>This dataset is an update of the relocations presented in the pre-print article:<br> The 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake: relocated seismicity shows rupture of a complete shear-crack system<br> https://eartharxiv.org/repository/view/1904/</p> <p> </p> <p>Plots show map, view from south and view from N73E of NLL-SSST-coherence relocations events with error-ellipsoid, semi-major axis ≤ 5 km. Caption (adapted from https://eartharxiv.org/repository/dashboard/1904/ ; Fig 2):</p> <p>Event color showing hypocenter depth and symbol size proportional to magnitude. Re-picked Mw6.5 hypocenter and its proxy (mean hypocenter of 3 well constrained foreshocks) indicated by small and large, dark red, cross symbols, respectively. SHmin and SHmax show directions of regional minimum and maximum compressive stress, respectively; the intermediate principal stress axis is vertical. Seismic stations shown as dark gray tetrahedrons. Brown lines show faults from the Quaternary fault and fold database for the United States. Background topography image from OpenTopgraphy.org. Red lines show mapped surface ruptures from http://www.nbmg.unr.edu/Geohazards/Earthquakes/MonteCristoRangeEQData.html and https://doi.org/10.1785/0220200371</p>
Nevada and Utah Motus Prioritization Tool Data
Addressing survival and movement of priority migratory avian species of concern along the Pacific Flyway is paramount for their conservation. Yet, the migratory life stage is understudied in many avian species. The Motus radiotelemetry receiver network is an established system for tracking survival and movement of avian species. This network is an international collaborative that successfully identifies stopover site duration, connected migratory routes, post-fledging dispersal and survival, and adult survival and fidelity on a landscape-scale; parameters that cannot be easily estimated using non-tagged birds. While the Motus network is highly connected in eastern North America, the western part of the continent is lagging in coverage and connectivity, limiting the ability to obtain sample sizes large enough to robustly model demographic parameters from tagged birds. Thus, the expansion of the Motus network is a high priority for Pacific Flyway State Agencies. To date, no method exists for determining priority locations for new Motus receiving stations. With collaborations from States and the Canadian Province of British Columbia, we used eBird citizen scientist data to prioritize strategic locations for new Motus receiving stations throughout the Pacific Flyway. We model priority species’ co-occupancy of varying abundance states (i.e., absent, present, abundant, abundant in multiple weeks) with spatially varying Landsat (red and near infrared), water, land cover types, and weather covariates while accounting for variable detection with temporally varying survey effort covariates. Using occupancy model predictions, we identify high-use areas of the Pacific Flyway for establishing new Motus receiving towers that have high probabilities of intercepting high presence and /or abundance of multiple species of interest in a series of predictive occupancy maps. This package contains all the necessary files to recreate the data analysis, print out maps based on predictions
Panorama at Sierra Nevada at night
<p>Pictures of the Sierra Nevada sky station and OSN astronomical observatory for light pollution assessment. Granada, Spain</p>
Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.
<p><strong>This database provides as polygons the contours of 900 juniper shrubs (<em>Juniperus communis L.</em> and <em>Juniperus sabina L.</em>) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation). </strong></p>
Soil moisture for Sierra Nevada
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Snow cover for Sierra Nevada
<p>The time series contains daily snow cover maps computed with the EURAC algorithm applied to MODIS Terra and Aqua images from 2002 onwards.</p>
Sierra Nevada NDVIs collection from 1984 to 2016
<p>Collection of Normalized Difference Vegetation Index (NDVI) (double data type) of Sierra Nevada Protected Area (PA).</p>
Carbon Storage in Beaver Meadows of the Sierra Nevada, USA
<p><span>The purpose of this study was to examine the effects of active beaver dams on sequestered carbon in subalpine valleys of the Sierra Nevada across a temporal scale. Specifically, carbon stored in floodplain sediments and above and below ground vegetation was measured in 8 beaver meadows to quantify carbon storage.</span></p>
A collection of fully-annotated soundscape recordings from the southern Sierra Nevada mountain range
<p>This collection contains 100 soundscape recordings of 10 minutes duration, which have been annotated with 10,296 bounding box labels for 21 different bird species from the Western United States. The data were recorded in 2015 in the southern end of the Sierra Nevada mountain range in California, USA. This collection has been featured as test data in the 2020 BirdCLEF and Kaggle Birdcall Identification competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>The recordings were made in Sequoia and Kings Canyon National Parks, two contiguous national parks in the southern Sierra Nevada mountain range in California, USA. The focus of the acoustic study was the high-elevation region of the Parks; specifically, the headwater lake basins above 3,000 km in elevation. The original intent of the study was to monitor seasonal activity of birds and bats at lakes containing trout and lakes without trout, because the cascading impacts of trout on the adjacent terrestrial zone remain poorly understood. Soundscapes were recorded for 24 h continuously at 10 lakes (5 fishless, 5 fish-containing) throughout Sequoia and Kings Canyon National Parks during June-September 2015. Song Meter SM2+ units (Wildlife Acoustics, USA) powered by custom-made solar panels were used to obviate the need to swap batteries, due to the recording locations being extremely difficult to access. Song Meters continuously recorded mono-channel, 16-bits uncompressed WAVE files at 48 kHz sampling rate. For this collection, recordings were resampled at 32 kHz and converted to FLAC.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>A total of 100 10-minute segments of audio between July 9 and 12, 2015 from morning hours (06:10-09:10 PDT) from all 10 sites were selected at random. Annotators were asked to box every bird call they could recognize, ignoring those that are too faint or unidentifiable. Every sound that could not be confidently assigned an identity was reviewed with 1-2 other experts in bird identification. To minimize observer bias, all identifying information about the location, date and time of the recordings was hidden from the annotator. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. In this collection, we use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list). Unidentifiable calls have been marked with “????” and were added as bounding box labels to the ground truth annotations. Parts of this dataset have previously been used in the 2020 BirdCLEF and Kaggle Birdcall Identification competition.</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date and timestamp in PDT (UTC-7). As an example, the file “HSN_001_20150708_061805.flac” has sequential ID 001 and was recorded on July 8th 2015 at 06:18:05 PDT. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements </strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection (individual contributors in alphabetic order): Anna Calderón, Thomas Hahn, Ruoshi Huang, Angelly Tovar</p>
Nevada Desert FACE Facility Soil Organic Carbon Data
This data set is the result of soils analysis from the Nevada Desert Free-Air CO2 Enrichment Facility (NDFF) experiment in the Mojave Desert and reports soil organic carbon (%C) and delta 13C stable isotope values. These soils were collected at the end of the NDFF experiment in 2007 and stored at Cornell University until analysis in 2018. Soils were harvested from 6 cover types (5 perennial vegetation covers and unvegetated interspace soils) from 0-100 cm in the soil profile in 20 cm increments. Soils were pretreated for inorganic carbon removal using an acid fumigation technique with HCl. Bulk density from NDFF plots is provided (kg soil* ha ^ -1) so that SOC stocks may be calculated. These data provide the basis for a publication challenging the prevailing idea that arid ecosystems will increase soil organic carbon stocks under long term elevated CO2.
Figure 11 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 11. Relationships of human presence, horse key summer range (KSR) use, and lion numbers in the Montgomery Pass Wild Horse Territory (MPWHT). Human presence (May– September) is presented as per cent of its highest year. Horse KSR use is presented as per cent of the total population using the KSR. Adult lion numbers are presented as the percentage of the maximum single-year lion number.
Figure 6 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 6. Montgomery Pass Wild Horse Territory (MPWHT) horse use of non-key summer range (KSR) areas. Use outside the MPWHT primarily includes areas east of Basalt and west of Adobe Valley.
Figure 5. Changes from 1987 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 5. Changes from 1987 to 2007 in patterns of the geographic distribution of wild horses in the Montgomery Pass Wild Horse Territory (MPWHT). The central, lightly shaded area is the key summer range (KSR). The black mark within it is the highest elevation in the Territory (elevation decreases 360 degrees around it throughout). Dark shaded areas indicate regular horse use and represent> 90% of the population. Many wild horse populations in the intermountain west accommodate to seasonal conditions, spending winter at lower elevations and summer at higher elevations. The pattern across years in the MPWHT changed from summer horse concentration in the KSR to decreasing return to KSR from winter range. In addition to establishment of decreased KSR use and increased year-round use of historical winter-range areas, expansion of the geographic use areas occurred in the latter, including seasonal use beyond MPWHT map boundaries.
Figure 8 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 8. The relationship of key summer range (KSR) horse use and carrying capacity in the context of the Montgomery Pass Wild Horse Territory (MPWHT) horse population. As KSR horse numbers decreased, use outside the KSR increased. Total population range use is presented as a percentage of KSR carrying capacity. As total population increased across years, it remained <80% of carrying capacity for KSR alone, suggesting that considerable population growth can continue without reaching carrying capacity.
Figure 4 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 4. Relationship of mountain lions and foal survival in the Montgomery Pass Wild Horse Territory (MPWHT). Data are based on annual assessment between May and September. The majority of predation occurred in the key summer range (KSR). Adult lion numbers and foal survival in the MPWHT: foal survival is presented as the ratio of currentyear yearlings to previous-year foals expressed as a percentage, lion numbers are for individual lions documented by telemetry, track and/or sighting. The majority of lions were recorded in the KSR.
Figure 3 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 3. Annual total number of horses in the Montgomery Pass wild horse population. Numbers represent reliable estimate for all range areas (not complete enumeration).
Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:</p><ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>
Outputs of the WiMMed hydrological model for Sierra Nevada (Spain). Sept2015-Aug2022
<p>Ecosystem Services related to flood prevention, aquifer recharge and erosion prevention in SIERRA NEVADA (Spain) were quantified through the WiMMed hydrological model (Watershed Integrated Model in Mediterranean Environments; Herrero et al., 2014). WiMMed is a distributed and physically based model that combines hourly and daily meteorological data with soil hydro-physical properties and land use and land cover information to simulate water balance and flow circulation at basin scale (see Herrero et al. (2014) for details). </p><p>In this study we applied the WiMMed model considering the land use and land cover data for 2020 (according to SIPNA) and the meteorological data from Sept2015 to Aug2022 to evaluate the value of ecosystem services, following the work made by Moreno-Llorca et al. (2020). Specific parameters, expressing the influence of vegetation changes in the hydrological processes of the study area, were considered, namely on evapotranspiration, interception, infiltration, overland flow and soil erodibility. Aquifer recharge (mm/m2/year) was calculated as the total volume of water moving from the soil into the aquifer and becoming groundwater. For that, the model firstly interpolates the precipitation at the cell scale (Herrero et al., 2009), and then calculates rainfall/snowfall partition, reproduces the interception from the vegetation, calculates the snow accumulation and melting (Herrero et al., 2009), and separates surface runoff from infiltration on the ground surface. Vertical and horizontal soil water movement was reproduced by a two-layer soil approach, using Darcy-Buckingham law with Mualem-vanGenuchten parameterization (Muñoz Carpena and Ritter Rodriguez, 2005). Evapotranspiration extract water from soil using a parameterization based on potential evapotranspiration and soil water content (Herrero et al., 2014). Water percolating through the second layer of soil becomes aquifer recharge. Soil erosion prevention (T/ha/year) was calculated by considering the inverse of soil loss by water flow concentration (rill processes) and raindrop impacts (interrill processes). WiMMed uses the variation of different parameters that link soil loss, with changes in vegetation cover and land uses, as described in (Millares et al., 2019). Changes on soil erodibility were estimated from vertical distribution of root biomass, by adapting empirical models (e.g. Gale and Grigal, 1987; Jackson et al., 1996) to Mediterranean environments reported previously (Martinez- Fernandez et al., 1995). From these estimations, distributed soil erodibility was calculated from the empirical model proposed by Flanagan and Livingstone (1995). The calibration and validation of the WiMMed model in Sierra Nevada has been conducted through a series of studies that analysed each hydrological process in the area and designed and corrected each WiMMed module, pertaining to snow (Herrero et al., 2009), soil (Aguilar and Polo, 2011), baseflow (Millares, 2008; Millares et al., 2009), river flow (Pérez-Palazón et al., 2014), or soil loss and sediment transportation (Bergillos et al., 2016; Millares et al., 2020).</p><p><strong>INPUT DATA</strong></p><p>The input data used in the hydrological simulations were:</p><ul><li>Digital elevation model from national remote sensing program PNOA-LIDAR MDT02 and the topographic features calculated by WiMMed from the DEM: surface drainage system, river delineation, slope, aspect, sky view factor and horizon (sky obstruction in 8 directions).</li><li>Meteorological data from more than 50 weather stations in the area: hourly/daily rainfall (mm), hourly and daily temperature (oC), daily solar radiation (MJ/m2), average daily wind speed (m·s−1), average daily relative humidity (%), average daily barometric pressure (hPa).</li><li>Physico-chemical and hydraulic properties of the soil selected from the available spatial database performed by Rodríguez (2008), in which thematic maps were obtained for Andalusia at a 250-m resolution: hydraulic conductivity (mm·h−1), saturation and residual moisture values (mm·mm−1), air-entry matric potential (mm), retention parameter of the van Genuchten (dimensionless) and soil thickness (mm).</li><li>Land cover and land use information from SIPNA 2020.</li><li>Aquifer regions and information from hydrogeological atlas of Andalusia (ITGE-Junta de Andalucía, 1998; Castillo, 2008).</li></ul><p><strong>OUTPUT DATA</strong></p><p>The results contained in this database are raster files in UTM ETRS89 30S, with a spatial resolution of 30x30 meters, for the whole SIerra Nevada. The raster files are Esri-ASCII ArcGIS (.asc) grids with 3846 columns (X) and 2099 rows (Y). There are different time scales for each variable. The prefix of the file indicates this time scale, namely "Ano" for annual maps, "mes" for monthly maps and "Tot" for the whole simulation. The suffix indicates the variable of interest:</p><ul><li>Pre: Accumulated precipitation (solid + liquid) in mm</li><li>T_m: Mean temperature in ºC</li><li>P_n: Accumulated snowfall in mm</li><li>ErT: Accumulated total erosion (rill + interrill) in kg/m2</li><li>ET0: Accumulated potential evapotranspiration in mm</li><li>EvC: Accumulated real evaporation from canopy (intercepted precipitation) in mm</li><li>EvN:Accumulated real sublimation from snow in mm</li><li>EvS: Accumulated real evapotranspiration ration from soil in mm</li><li>Exp: Accumulated direct runoff in mm</li><li>Fus: Accumulated snowmelt in mm</li><li>HSol1: Instantaneous soil moisture in surface layer 1 (upper 25 cm) in mm</li><li>HSol2: Instantaneous soil moisture in deep layer 2 in mm</li><li>Inf: Accumulated infiltration from surface into soil in mm</li><li>Per: Accumulated aquifer recharge (from soil to groundwater) in mm</li><li>Qlat: Accumulated lateral flow (horizontal movement of water between cells) in mm</li><li>Tmn: Minimum temperature in ºC</li><li>Tmx: Maximum temperature in ºC</li></ul><p>There are also some other grid files (Tot_XXX.asc) related to the initial and final conditions of the state variables or internal conditions of the model.</p><p><strong>References</strong></p><p>Aguilar, C., Polo, M.J., 2011. Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale. Hydrol. Earth Syst. Sci. 15, 2495–2508. doi: 10.5194/hess-15-2495-2011.</p><p>Bergillos, R.J., Rodríguez-Delgado, C., Millares, A., Ortega-Sánchez, M., Losada, M.A., 2016. Impact of river regulation on a Mediterranean delta: assessment of managed versus unmanaged scenarios. Water Resour. Res. 52 (7), 5132–5148.</p><p>Castillo, A. 2008. Manantiales de Andalucía. Agencia Andaluza del agua, Consejería de Medio Ambiente, Junta de Andalucía, Sevilla, 410 pp.</p><p>Herrero, J., Polo, M.J., Moñino, A., Losada, M.A., 2009. An energy balance snowmelt model in a Mediterranean site. J. Hydrol. 371 (1-4), 98–107.</p><p>Herrero, J., Millares, A., Aguilar, C., Egüen, M., Losada, M.A., 2014. Coupling spatial and time scales in the hydrological modelling of mediterranean regions: WiMMed, in: CUNY Academic Works. In: Presented at the International Conference on Hydroinformatics, p. 8. ITGE-Junta de Andalucía: Atlas Hidrogeológico de Andalucía. Madrid, 216 pp., ISBN: 84-7840-351-5, available at: http: //aguas.igme.es/igme/publica/libros1 HR/libro110/lib110.htm, last access: 18 March 2012, 1998</p><p>Millares, A., 2008. Integración del caudal base en un modelo distribuido de cuenca. Estudio de las aportaciones subterráneas en ríos de montaña. University of Granada.</p><p>Millares, A., Polo, M.J., Losada, M.A., 2009. The hydrological response of baseflow in fractured mountain areas. Hydrol. Earth Syst. Sci. 13 (1261–1271), 2009.</p><p>Millares, A., Díez-Minguito, M., Moñino, A., 2019. Evaluating gullying effects on modeling erosive responses at basin scale. Environ. Modell. Software 111, 61–71. Millares, A., Herrero, J., Bermúdez, M., Leiva, J.F., Cantalejo, M., 2020. Long-term modelling of soil loss and fluvial transport processes in a mountainous semi-arid basin, southern Spain, in: River Flow 2020 - Twentieth International Conference on Fluvial Hydraulic. Delf, Netherlands.</p><p>Moreno-Llorca, R., Vaz, A. S., Herrero, J., Millares, A., Bonet-García, F. J., & Alcaraz-Segura, D. 2020. Multi-scale evolution of ecosystem services' supply in Sierra Nevada (Spain): An assessment over the last half-century. <i>Ecosystem Services</i>, <i>46</i>, 101204.</p><p>Muñoz Carpena, R., Ritter Rodriguez, A., 2005. Hidrología Agroforestal. Mundiprensa.</p><p>Pérez-Palazón, M. J., Pimentel, R., Herrero, J., & Polo-Gómez, M. J. 2014. Analysis of snow spatial and temporary variability through the study of terrestrial photography in the Trevelez river valley. In <i>Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI</i> (Vol. 9239, pp. 358-368). SPIE.</p><p>Rodríguez, J. A. 2008. Sistema de Inferencia Espacial de Propiedades Físico-Químicas e Hidráulicas de los Suelos de Andalucía. Herramienta de Apoyo a la Simulación de Procesos Agro-Hidrológicos a Escala Regional. Informe Final. Empresa Pública Desarrollo Agrario y Pesquero, Consejería de Agricultura y Pesca, Sevilla.</p><p> </p><p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.