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289 results for “hydraulics”
Novel insights into habitat suitability for Amazonian freshwater mussels linked with hydraulic and landscape drivers
<p><span>Novel insights into habitat suitability for two Unionida freshwater mussels, <i>Castalia ambigua</i> Lamarck, 1819 (Hyriidae) and <i>Anodontites elongatus </i><span>(Swainson, 1823) (Mycetopodidae)</span><i>,</i> is presented on the basis of hydraulic variables linked with the riverbed in six 500 m reaches in an eastern Amazonian river basin. Within the reaches, there was strong habitat heterogeneity in hydrodynamics and substrate composition. In addition, we investigated stressors based on landscape modification that are associated with declines in mussel density. We measured hydraulic variables for each 500 m reach, and landscape stressors at two spatial scales (subcatchment and riparian buffer forest). W<span>e used </span><span>the</span> <span>R</span><span>andom </span><span>F</span><span>orest algorithm,</span> a tree-based model, to predict the hydraulic variables linked with habitat suitability for mussels, and to predict which landscape stressors were most associated with mussel density declines. Both mussel species were linked with low substrate heterogeneity and greater riverbed stability (low Froude and Reynolds numbers), especially at high flow (low stream power). Different sediment grain size preferences were observed between mussel species: <i>Castalia ambigua</i> was associated with medium sand, and <i>Anodontites elongatus</i> with medium and fine sand. Declines in mussel density were associated with modifications linked to urbanization at small scales (riparian buffer forest), especially with percent of and distance from rural settlements, distance to the nearest street, and road density. In summary, the high<span> variance </span><span>explained </span><span>in both hydraulic and landscape models</span><span> indicated </span><span>high predictive power, </span><span>suggesting that our findings</span> <span>may be extrapolated </span><span>and used as a baseline to test </span><span><span>hypotheses of habitat suitability </span></span><span><span>in other Amazonian rivers</span></span><span><span> for </span></span><i><span>Castalia ambigua</span></i><span><span> and </span></span><i><span>Anodontites elongatus</span></i><span><span>, and also </span></span><span><span>for</span></span><span><span> other </span></span><span><span>freshwater </span></span><span><span>mussel species</span></span><span>. </span>Our results highlight the urgent need for aquatic habitat conservation to maintain sheltered habitats during high flow as well as mitigate the effects of landscape modifications at the riparian buffer scale, both of which are important for maintaining dense mussel populations and habitat quality.</span></p>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
<p>Hydraulic failure caused by severe drought contributes to aboveground dieback and whole-plant death. The extent to which dieback or whole-plant death can be predicted by plant hydraulic traits has rarely been tested among species with different leaf habits and/or growth forms. We investigated 19 hydraulic traits in 40 woody species in a tropical savanna and their potential correlations with drought response during an extreme drought event during the El Niño–Southern Oscillation in 2015. Plant hydraulic trait variation was partitioned substantially by leaf habit but not growth form along a trade-off axis between traits that support drought tolerance versus avoidance. Semi-deciduous species and shrubs had the highest branch dieback and top-kill (complete aboveground death) among the leaf habits or growth forms. Dieback and top-kill were well explained by combining hydraulic traits with leaf habit and growth form, suggesting integrating life history traits with hydraulic traits will yield better predictions.</p>
Comparison among three different Digital Surface Models and their respective hydraulic outcomes in the flood-prone urban area of Navaluenga (Ávila, Spain)
<p>Three different Digital Surface Models (DSMs) generated from LiDAR data are presented. The LiDAR information has been considered as raw data (DSM3) and subjected to some transformations to better represent the urban environment (DSM1). DSM2 is an intermediate state between DSM1 and DSM3. </p> <p>On the other hand, a hydraulic model has been run for each DSM and for two return periods (25 and 500 years), obtaining in all cases the graphical outputs of depths, velocities, Froude numbers and hazard. </p> <p>The different DSMs are named DSM1, DSM2 and DSM3, which can be downloaded in TIN format. The hydraulic outputs associated with the different DSMs can be downloaded in raster format and are named as follows: the Digital Surface Model to which it refers, the return period considered and the type of hydraulic output (depth, velocity, Froude number and hazard).</p> <p>DSM1: Digital Surface Model 1 (TIN format).<br> dsm1_25depth: Depths obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25haz: Hazard obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25veloc: Velocities obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format). <br> dsm1_500depth: Depths obtained when considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500haz: Hazard obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500veloc: Velocities obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).</p> <p>DSM2: Digital Surface Model 2 (TIN format).<br> dsm2_25depth: Depths obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25haz: Hazard obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25veloc: Velocities obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format). <br> dsm2_500depth: Depths obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500haz: Hazard obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500veloc: Velocities obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).</p> <p>DSM3: Digital Surface Model 2 (TIN format).<br> dsm3_25depth: Depths obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25haz: Hazard obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25veloc: Velocities obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format). <br> dsm3_500depth: Depths obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500haz: Hazard obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500veloc: Velocities obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).</p>
Hydraulic fracturing block test experiments in Gabbro & Marble - experiments # GABB-002 & MARB-007
<p>This dataset contains raw, processed and inverted data for 2 hydraulic fracturing tests performed in a Zimbabwe gabbro (GABB-002) and a Carrara Marble (MARB-007) at the Geo-Energy lab @ EPFL.</p> <p>[IMPORTANT NOTE: DUE TO file size-constraint, the raw binary filed containing the acoustic data is not part of this zenodo data set - Please contact Prof. B. Lecampion directly if you are interested in playing with the raw acoustic data file]</p> <p>TEST ID - GABB-002 : Lag/viscosity dominated test in a gabbro</p> <p>TEST ID - MARB-007 : Lag/viscosity dominated test in a carrara Marble</p> <p>The details of these two tests and its analysis is described in details in the following publication:</p> <p><strong>Measurements of the evolution of the fluid lag in laboratory hydraulic fracture experiments in rocks</strong></p> <p>Dong Liu, Brice Lecampion</p> <p>Geo-Energy Laboratory, Gaznat Chair on Geo-Energy, Ecole Polytechique Fédérale de Lausanne, EPFL-ENAC-IIC-GEL, Lausanne, Switzerland</p>
Hydraulic traits are not robust predictors of tree species stem growth during a drought in a wet tropical forest
<p>Severe droughts have led to lower plant growth and high mortality in many ecosystems worldwide, including tropical forests. Drought vulnerability differs among species but there is limited consensus on the nature and degree of this variation in tropical forest communities. Understanding species-level vulnerability to drought requires examination of hydraulic traits since these reflect the different strategies species employ for surviving drought. Here we examined hydraulic traits and growth reductions during a severe drought for 12 common woody species in a wet tropical forest community in Puerto Rico to ask:</p> <p>Q1. To what extent can hydraulic traits predict growth declines during drought? We expected that species with more hydraulicly vulnerable xylem and narrower safety margins would grow less during drought.</p> <p>Q2. How do species successional association relate to levels of vulnerability to drought and hydraulic strategies? We predicted that early- and mid-successional species would exhibit more acquisitive strategies, making them more susceptible to drought than shade-tolerant species.</p> <p>Q3. What are the different hydraulic strategies employed by species and are there trade-offs between drought avoidance and drought tolerance?</p> <p>We anticipated that species with greater water storage capacity would have leaves that lose turgor at higher xylem water potential and be less resistant to embolism forming in their xylem (P50). We found a large range of variation in hydraulic traits across species; however, they did not closely capture the magnitude of growth declines during drought. Among larger trees (≥10 cm diameter at breast height—DBH), some tree species with high xylem embolism vulnerability and risk of hydraulic failure experienced substantial declines during drought but this pattern was consistent across species. We found a trade-off among species between drought avoidance (capacitance) and drought tolerating (P50) in this tropical forest community. Hydraulic strategies did not align with successional associations. Instead, some of the more drought-vulnerable species were shade-tolerant dominants in the community, suggesting that a drying climate could lead to shifts in long-term forest composition and function in Puerto Rico and the Caribbean.</p>
Self-propping exists in coal seam hydraulic fractures
<p>This repository contains data used in "Self-propping exists in coal seam hydraulic fractures" submitted by R Li, CZ Qin, SW Wang, and JC Wang.</p>
Characteristic Pressures and Fracture Orientations from Hydraulic Stimulation Project STIMTEC and STIMTEC-X
<p>Characteristic pressure and fracture orientations for each stimulated interval determined in the stimulation campaigns in Reiche Zeche, Freiberg, Germany in the framework of STIMTEC and STIMTEC-X project.</p>
Large leaf hydraulic safety margins limit the risk of drought-induced leaf hydraulic dysfunction in Neotropical rainforest canopy tree species
<p>The sequence of key water potential thresholds from the onset of water stress to mortality, and the timing of stomatal closure with regard to leaf xylem embolism formation are essential to characterizing plant adaptive strategies to drought. This constitutes a critical knowledge gap for tropical rainforest species, which may be less vulnerable to drought than previously thought.</p> <p>We recorded key leaf and stem water potential thresholds, leaf hydraulic safety margins (HSMleaf), leaf stomatal safety margins (SSMleaf) and estimated native embolism levels during a normal-intensity dry season across 18 Neotropical rainforest tree species. We also solved a sequence of key water potential thresholds. Additionally, we provide a cross-biome analysis of SSMleaf encompassing 97 species from four major biomes based on a literature survey.</p> <p>In the studied rainforest species, leaf turgor loss point, used as a surrogate for stomatal closure, typically occurred before the onset of leaf xylem embolism. Most species exhibited positive HSMleaf and SSMleaf, with contrasting values across species and nearly absent embolism levels during the dry season irrespective of the experienced midday leaf water potentials. Our results point out that leaf xylem embolism is not routine for Neotropical rainforest tree species.</p> <p>Based on our proposal of the water potential sequence for tropical rainforest trees, we argue that leaf xylem embolism is a rare event for these species. This was supported by the literature survey, indicating that across biomes, most woody species have rather large SSM<sub>leaf</sub> and that leaves of tropical rainforest trees are not necessarily more vulnerable than in other biomes. However, we found evidence that some tropical rainforest species may be more vulnerable than others to ongoing climate change. Our data provide an opportunity to parametrize tree-based or land-surface models for tropical rainforests.</p>
Soil hydraulic conductivity measurement using Ksat with falling head method
<p>Experimental serie: Soil hydraulic conductivity measurement using Ksat with falling head method.</p> <p>Serie experimental: Medición de conductividad hidráulica saturada de suelo en laboratorio con Ksat con método falling head.</p>
2015/16 El Niño increased water demand and pushed plants from a Mesic tropical montane grassland beyond their hydraulic safety limits
<p>In 2015/16, a strong El Niño event caused anomalously high temperatures and reduced precipitation resulting in Pantropical drought‐induced diebacks and wildfires. Although many studies have documented the El Niño impacts on tropical forests, little we know about its effects on tropical grasslands. Here, we investigated plant drought responses during and after the 2015/16 El Niño event (Jun 2016 to Aug 2017) in 12 species with contrasting drought strategies (tolerance, avoidance and escape) in a Brazilian tropical montane grassland. We tested if (1) the El Niño event induced meteorological drought anomalies, (2) the atmospheric and/or soil drought led to plant water stress and (3) plants showed signs of drought recovery. In contrast to other tropical areas, we found that the 2015/16 El Niño event did not strongly affect precipitation in our study site. However, it increased air temperature and vapour pressure deficit, thus pushing all grassland species, even the most drought‐tolerant ones, beyond their hydraulic safety margins during the dry season. Most species showed signs of drought recovery, returning to positive hydraulic margins in the wet season after the El Niño. However, the finding that all evaluated species, regardless of their drought‐response strategy, are already operating close to their hydraulic safe thresholds for stomatal closure and turgor loss suggests that this cool–humid tropical montane grassland is especially vulnerable to meteorological extremes exacerbated by the additive effects of El Niño and climate change.</p>
Data from: Axial conduit widening, tree height and height growth rate set the hydraulic transition of sapwood into heartwood
<p><span>The size-related xylem adjustments required to maintain</span><span> a constant leaf-specific sapwood conductance (<em>K<sub>LEAF</sub></em>) with increasing height (<em>H</em>) are still under discussion. Alternative hypotheses are that: (i) the conduit hydraulic diameter (<em>Dh</em>) at any position in the stem and/or (ii) the number of sapwood rings at stem base (<em>NSWr</em>) increase with <em>H.</em> In addition, (iii) lower stem elongation (</span><em>Δ<span>H</span></em><span>) increases the tip-to-base conductance through inner xylem rings, thus possibly the <em>NSWr</em> contributing to <em>K<sub>LEAF</sub></em>.</span></p> <p><span>A detailed stem analysis showed that </span><em><span>Dh</span></em><span><em> </em>increased with the distance from the apex (<em>DCA</em>) in all rings of a <em>P. abies</em> and a <em>F. sylvatica</em> tree. Net of <em>DCA</em> effect, <em>Dh</em> did not increase with <em>H</em>. Using sapwood traits from a global dataset, <em>NSWr</em> increased with <em>H</em> and decreased with </span><em>Δ<span>H</span></em><span>, and the mean sapwood ring width (<em>SWrw</em>) increased with </span><em>Δ<span>H</span></em><span>. A numerical model based on anatomical patterns predicted the effects of <em>H</em> and </span><em>Δ<span>H</span></em><span> on the conductance of inner xylem rings.</span></p> <p><span>Results suggested the sapwood/heartwood transition depends on both <em>H</em> and </span><em>Δ<span>H</span></em><span>, and is set when the C allocation to maintenance respiration of living cells in inner sapwood rings produces a lower gain in total conductance than investing the same C in new vascular conduits.</span></p>
Coordinated drought responses determine the time to hydraulic failure in five temperate tree species differing in their degree of isohydry
<p>This file contains variables of interest at tree level measured, calculated and presented in the study "Coordinated drought responses determine the time to hydraulic failure in five temperate tree species differing in their degree of isohydry". </p>
Data from: Hydraulic vulnerability of tropical forests is largely independent of water availability
<p>Tropical rainforest woody plants have been thought to have uniformly low resistance to hydraulic failure and to function near the edge of their hydraulic safety margin, making these ecosystems vulnerable to drought; however, this may not be the case. Using data collected at 30 tropical forest sites for three key traits associated with drought tolerance, we show that site-level hydraulic diversity of leaf turgor loss point, resistance to embolism (P<sub>50</sub>), and hydraulic safety margins (HSMs) is high across tropical forests and largely independent of water availability. Species with high HSMs (>1 MPa) and low P<sub>50</sub> values (<-2 MPa) are common across the wet and dry tropics. This high site-level hydraulic diversity, largely decoupled from water stress, could influence which species are favored and become dominant under a drying climate. High hydraulic diversity could also make these ecosystems more resilient to variable rainfall regimes.</p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
Validation of Fracture Caging to Contain Hydraulic Fractures: Timeseries, Videos, and Model Script
<p>The data file include an Excel spreadsheet and two videos for each experimental test.</p> <p>You can start with reading the ReadMeFirst.txt file to understand the whole structure of the dataset.</p> <p>The caging_model.txt file includes python codes to calculate critical flow rates and uncaged fracture radius according to the theory that the authors developed and will be published soon.</p>
Angiosperms follow a convex trade-off to optimize hydraulic safety and efficiency
<p><span>Intervessel pits are considered to function as valves that avoid embolism spreading and optimize efficient transport of xylem sap across neighbouring vessels. Hydraulic transport between vessels would therefore follow a safety-efficiency trade-off, which is directly related to the total intervessel pit area (A<sub>p</sub>), inversely related to the pit membrane thickness (T<sub>PM</sub>), and driven by a pressure difference.</span></p> <p><span>To test this hypothesis, we modelled the relative transport rate of gas (k<sub>a</sub>) and water (Q) at the intervessel pit level for 23 angiosperm species, and correlated these parameters with the water potential at which 50% of embolism occurs (</span><span>Ψ</span><sub><span>50</span></sub><span>). We also measured k<sub>a</sub> for 10 species using pneumatic measurements.</span></p> <p><span>The pressure difference across adjacent vessels, and estimated values of k<sub>a </sub>and Q were related to </span><span>Ψ</span><sub><span>50</span></sub><span>, following a convex safety-efficiency trade-off based on modelled and experimental data. Minor changes in T<sub>PM</sub> and A<sub>p </sub>exponentially affected the pressure difference and flow, respectively. </span></p> <p><span>Our results provide clear evidence that a xylem safety-efficiency trade-off is not linear, but convex due to flow across intervessel pit membranes, which represent mesoporous media within microporous conduits. Moreover, the convex nature of long-distance xylem transport may contribute to an adjustable fluid balance of plants, depending on environmental conditions. </span></p>
Scripts and Data for "Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region"
<p>Matlab scripts for processing and showing the coupled ELM-MOSART coupled simulations for Pantanal region.</p> <p>domain_lnd_Pantanal_default.nc, MOSART_Pantanal_default_c211116.nc, and surfdata_Pantanal_default_c220520.nc are the domain file, MOSART input file, and ELM surface dataset, respectively. </p> <p><a href="https://zenodo.org/api/files/6be468b6-cbfa-4ef4-bb10-0f15812be9bd/Pantanal_half_calibration_CLMCRUNCEPv7.sh">Pantanal_half_calibration_CLMCRUNCEPv7.sh</a> is the bash script to run E3SM with coupled ELM-MOSART configuration. ANd detailed instruction of running E3SMV2 can be found at: https://e3sm.org/model/running-e3sm/e3sm-quick-start/ (last access: Aug 2023).</p> <p>Pantanal_GSIM.zip contains the observed streamflow that used in this study, which is downloaded from <a href="https://doi.pangaea.de/10.1594/PANGAEA.887470">https://doi.pangaea.de/10.1594/PANGAEA.887470</a> (last access: Aug 2023). The reference is Gudmundsson, Lukas; Do, Hong Xuan; Leonard, Michael; Westra, Seth (2018): The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data, 10(2), 787-804, https://doi.org/10.5194/essd-10-787-2018.</p> <p>BFI3.mat is the baseflow index from GSCD and processed to the study domain. The GSCD dataset was download from <a href="http://www.gloh2o.org/gscd/">http://www.gloh2o.org/gscd/</a> (last access: Aug 2023). The reference is Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour Res, 49, 7843-7863, <a href="https://doi.org/10.1002/2013WR013918">https://doi.org/10.1002/2013WR013918</a>, 2013.</p> <p>runoff_uncertianty.mat contains the annual runoff time series from GRUN, LORA, and GFRF that processed to the study domain. The GRUN runoff dataset was downloaded from <a href="https://doi.org/10.6084/m9.figshare.9228176">https://doi.org/10.6084/m9.figshare.9228176</a> (last access: Aug 2023). The LORA runoff dataset was downloaded from <a href="https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml">https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml</a> (last access: Aug 2023). The reference is Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851-870, 10.5194/hess-23-851-2019, 2019. The GRFR runoff was downloaded from <a href="http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/">http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/</a> (last access: Aug 2023). The reference is Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. d. H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-level 3-hourly River Flood Reanalysis (1980-2019), B Am Meteorol Soc, 1-49, 10.1175/BAMS-D-20-0057.1, 2021.</p> <p>GLAD_Pantanal_half.mat, GLAD_Pantanal_8th.mat are the processed surface water fraction from GLAD at half and 8th spatial resoution. Specifically, The GLAD surface water dynamics was downloaded from <a href="https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects">https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects</a> (last access: Aug 2023). The reference is Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A., Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sens Environ, 243, 111792, <a href="https://doi.org/10.1016/j.rse.2020.111792">https://doi.org/10.1016/j.rse.2020.111792</a>, 2020.</p>
Rapid evolution of the hydraulic architecture of the Wenchuan rupture zone
<p>This data set are used for generating key figures (Figure 2, 4, 6, 7, 8) for the manuscript "<strong>Rapid evolution of the hydraulic architecture of the Wenchuan rupture zone</strong>". The "Waterlevel-1.csv" is the original water level data that used in the analysis. While the "Pha_data.csv" is the tidal analysis result of the water level data. And the "result_data.csv" is the result of the fault-guided aquifer tidal response model of Guo et al. (2021). "wenchuan permeability.csv" is the result of temporal evolution of fault thickness, permeability of fault zone damage zone and host rock. "wenchuan fault fig2 fig8.ipynb" and "wenchaun fault result fig 467.ipynb" are python code to generate the key figures of the manuscript. </p>
Spatial and temporal variation in toxicity and inorganic composition of hydraulic fracturing flowback and produced water
<p>Hydraulic fracturing for oil and gas extraction produces large volumes of wastewater, termed flowback and produced water (FPW), that are highly saline and contain a variety of organic and inorganic contaminants. In the present study, FPW samples from ten hydraulically fractured wells, across two geologic formations were collected at various timepoints. Samples were analyzed to determine spatial and temporal variation in their inorganic composition. Results indicate that FPW composition varied both between formations and within a single formation, with large compositional changes occurring over short distances. Temporally, all wells showed a time-dependent increase in inorganic elements, with total dissolved solids increasing by up to 200,000 mg/L over time, primarily due to elements associated with salinity (Cl, Na, Ca, Mg, K). Toxicological analysis of a subset of the FPW samples showed median lethal concentrations (LC<sub>50</sub>) of FPW to the aquatic invertebrate <em>Daphnia magna</em> were highly variable, with the LC<sub>50</sub> values ranging from 1.16% to 13.7% FPW. Acute toxicity of FPW significantly correlated with salinity, indicating salinity is a primary driver of FPW toxicity, however organic components also contributed to toxicity. This study provides insight into spatiotemporal variability of FPW composition and illustrates the difficulty in predicting aquatic risk associated with FPW.</p>
Data from: Interactions between beech and oak seedlings can modify the effects of hotter droughts and the onset of hydraulic failure
<p><span>Mixing species with contrasting resource use strategies could</span><span> reduce forest vulnerability to extreme events. Yet, how species diversity affects seedling hydraulic responses to heat and drought, including mortality risk, is largely unknown.</span></p> <p><span>Using open-top chambers, </span><span>we assessed how, over several years, species interactions (monocultures vs. mixtures) modulate heat and drought impacts on the hydraulic traits </span><span>of juvenile European beech and pubescent oak. Using modelling, we estimated species interaction effects on timing to drought-induced mortality and the underlying mechanisms driving these impacts. </span></p> <p><span>We show that mixtures mitigate adverse heat and drought impacts for oak (less negative </span><span>leaf water potential, higher stomatal conductance, and delayed stomatal closure) but enhance them for beech (lower water potential and stomatal conductance, narrower leaf safety margins, faster tree mortality). Potential underlying mechanisms include oak's larger canopy and higher </span><span>transpiration</span><span>, allowing for quicker exhaustion of soil water in mixtures.</span></p> <p><span>Our findings highlight that diversity has the potential to alter the effects of extreme events, which would ensure that some species persist even if others remain sensitive. Among the many processes driving diversity effects, differences in canopy size and transpiration associated to the stomatal regulation strategy seem the primary mechanisms driving mortality vulnerability in mixed seedling plantations.</span></p>
ScienceDex guides
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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.