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48 results for “Hydrologic response”
Macrophyte and microbial mat biomass co-variation along a hydrologic gradient and response to a removal experiment in temporary wetlands Everglades, FL, USA, February 2003 – November 2006
This data package encompasses hydrologic variables, soil depth, hydrologically-regulated macrophyte community types, macrophyte biomass and community structure, and microbial mat biomass that was collected in two observational surveys and one in-situ experimental manipulation in six temporary wetland regions located in the Everglades, FL, USA. The goal of this project was to examine the co-variation in macrophyte and microbial mat biomass along the hydrologic gradient present across wetland regions and to determine the type and strength of interactions occurring between the two communities, which was tested using a biomass (macrophyte or microbial mat) removal experiment. The census observational survey took place at 140 sites from 2003-04-09 to 2004-05-26, which were randomly distributed across the hydrologic gradient present across the six temporary wetland regions. The transect observational survey occurred along six transects and each was deliberately established along the present hydrologic gradient within each region; a total of 254 sites were sampled from 2003-02-19 to 2005-03-04. The experiment took place at three temporary wetland sites with contrasting hydroperiods (3 – 6 months), and four transects were established per site with 24 pairs of control and treatment plots per transect. The removal treatment occurred one year before data collection, and data collection occurred from 2004-06-20 to 2006-11-25. The package includes six datasets, one R code file, and two shape files associated with the R code. Data collection for all datasets is complete. FCE1274_Census_Survey includes hydrologically-regulated macrophyte community type classifications, macrophyte biomass, microbial mat ash-free dry mass, mean soil depth, water depth, mean annual hydroperiod, and vegetation-inferred hydroperiod; each site was sampled once during the survey period and a subset of sites were sampled each year. FCE1274_Transect_Survey includes macrophyte community type classifications
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
Hydrologic response units (base units for PRMS streamflow model), Andrews Experimental Forest, 1993
Hydrologic Response Units are used as base units for the Precipitation-Runoff Modeling System (PRMS) streamflow model. Created by Alok Sikka as part of landscape runoff modeling.
Soil carbon cycling response to hemlock mortality at the Coweeta Hydrologic Laboratory
We studied the impacts of hemlock mortality from infestation by the hemlock woolly adlegid (HWA) on soil carbon cycling at the Coweeta Hydrologic Laboratory. The HWA was first found at Coweeta in 2003. In 2013 and 2014, we re-sampled plots established in an earlier study by Elliott and others. There were 12 20 x 20 m plots: 4 were control hardwood stands, 4 were untreated hemlock communities, and 4 were hemlock that were girdled. We measured soil C and N concentration, soil delta 13 C, exoemzyme activities, root biomass, soil respiration, forest floor mass, and fungal hyphal biomass.
Long term responses of first order streams to allochthonous and detrital manipulations at the Coweeta Hydrologic Laboratory, Otto, NC from 1984 to 2006
This dataset examines the long term effects of litter exclusion, small and large wood removal, and the addition of leaf species of varying detrital quality on organic matter standing crop, export of organic and inorganic particles, and invertebrate abundance and biomass in a high-gradient headwater stream. Pre-treatment and stream reference data are also included. This study was conducted at the Coweeta Hydrologic Laboratory watersheds 53, 54, and 55 from years 1988 through 2006.
Hydrological controls of slope response to precipitation - Code and Data
<p>This repository contains the dataset and codes used in the study of sloping soil response to precipitation through machine learning analysis. The dataset includes synthetic data of precipitation, soil moisture, and groundwater level mimicking field observations conducted in a experimental field. The codes include scripts for data preprocessing, analysis, and visualization. Here you will find: The dataset used to build a random forest (RF) model (01_RF_dataset.csv), the script for building the model (01_RF_model.py) using the sciki-learn library in Python (<a href="https://scikit-learn.org/stable/index.html">https://scikit-learn.org/stable/index.html</a>), the dataset for the cluster analysis (SyntheticData.mat) and the script for the analysis using the k-means clustering technique implemented in Matlab (<a href="https://it.mathworks.com/help/stats/kmeans.html">https://it.mathworks.com/help/stats/kmeans.html</a>).</p><p>The data and the codes in the present repository are part of the research entitled "Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data", published in Hydrology and Earth System Sciences - HESS journal. More details can be found for now in the paper preprint: Roman Quintero DC, Marino P, Santonastaso GF, Greco R (2023). Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data. EGUsphere: 1-41. DOI: 10.5194/EGUSPHERE-2022-1078</p>
Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing
<p><strong><em>Kazmierczak22_data.zip</em></strong><strong> contains the </strong><strong>dataset for the publication </strong><strong>« </strong>Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing » <strong>and </strong><strong>the <em>MATLAB</em> codes used to create the figures appearing in the paper. For more details, please, open the <em>Read me.txt</em> file. </strong></p>
Fig. 13 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 13: The effect of hydrodynamics inside the Y-Cave, the location behind section B-B' (Fig. 2) at 5.5 m of depth, where the unusually coloured sediment sample was collected for analysis.
Fig. 12 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 12: Limestone tablets from the three representative sites after the 1-year exposure period: A) with bioaccumulation at site 1; B) corroded at site 3; C) abraded at site 6 (Fig. 3, Tab. 1).
Fig. 11 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 11: Cave features: A) stalactites in the chamber with the air pocket (section C-C'); B) submerged stalagmites and flowstones with a lack of marine cave biota (section C-C'); C) submerged scallops (asymmetrical, cuspate, oyster-shell-shaped dissolution depressions in the cave walls used as an indicator of flow direction; Murphy, 2012), (section D-D'); D) corroded cave walls (section F-F').
Fig. 5 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 5: The annual variation of temperature along the Y-Cave (from August 23–27, 2003 to July 4/October 8, 2004). Measurement positions are given in Fig. 3 and depths in Table 1.
Fig. 10 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 10: The mass (m.f.) and volume fractions (v.f.) of four sediment categories in the sediment sample collected behind section B-B' (Fig. 2), at 5.5 m of depth inside the Y-Cave; A) detrital terrigenous sediment>4 mm, B) mixed biogenic and terrigenous detritus, C) shells of gastropod Homalopoma sanguineum, D) other biogenic material – shells, tests and skeletons of other marine organisms.
Fig. 4 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 4: Living communities inside the Y-Cave: A) the entrance part of the cave, vertical wall, depth 9 m, biocenosis of semi-dark caves (GSO, see text for explanation of acronym) dominated by numerous sponge species; B) the entrance part of the cave, ceiling, depth 6 m, GSO dominated by scleractinian coral Leptopsammia pruvoti; C) the entrance part of the cave, overhang, depth 7 m, GSO dominated by scleractinian coral Madracis pharensis; D) the entrance part of the cave, vertical wall (near the bottom), depth 9 m, GSO, a large specimen of the orange sponge Agelas oroides dominates the photo; E) the middle part of the cave, in front of the section C-C', bottom, depth 10 m, a massive white specimen of the sponge Chondrosia reniformis; F) the middle part of the cave, between sections C-C' and D-D', vertical wall and overhang, depth 5 m, the transition from GSO to biocenosis of caves and ducts in total darkness (GO, see text for explanation of acronym), the community is dominated by serpulids; G) the middle part of the cave, between sections C-C' and D-D', vertical wall, depth 5 m, transition from GSO to GO, a dense population of brachiopod Novocrania anomala, encrusting sponge Placospongia decorticans and serpulids; H) the end part of the cave, near the section G-G', vertical wall with overhang and horizontal shelf, depth 6 m, GO with scarce calcareous sponges and serpulids (see Fig. 2 for position of the sections).
Fig. 1 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 1: The non-linear relationship between CO and Ca2+ con2 centrations in H 2O-CO2-CaCO3 solution. Each mixture (e.g. C) of the saturated solutions A and B lies on the straight line between them in the zone of undersaturation with respect to calcite, producing an aggressive solution that dissolves the surrounding carbonate (after Gabrovšek & Dreybrodt (2010)).
Fig. 8 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 8: Temperature, salinity and depth profiles recorded with the CTD probe during a dive inside the Y-Cave (August 27, 2003), 1 – vertical profile at the cave opening; 2 – vertical profile inside the cave entrance part; 3 – vertical profile at the turning point, section C-C'; 4 to 6 – vertical profiles in the inner part of the cave: 4 at approximately section E-E', 5 at approximately section G-G' and 6 at approximately section H-H'.
Fig. 3 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 3: The positions of temperature and light intensity data loggers inside the Y-Cave (dark circles - temperature data loggers; white circles - light intensity data loggers). Four loggers with their photosensitive cell facing upwards are marked with a black dot; the remaining cells were positioned to face the entrance of the cave.
Fig. 9 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 9: Variation of light intensity over a period of 11 days (June 19–30, 2006) at representative sites within the Y-Cave (Fig. 3): logger 1 – the entrance of the cave; logger 4 – the central part of the cave; logger 10 – the innermost part of the cave.
Fig. 6 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 6: The comparison of the tidal (solid line) and temperature (dashed line) fluctuation from February 2–8, 2004. Temperature records are from logger 4 (Fig. 3), and tides from the nearest tide gauge in Zadar port.
Fig. 7 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 7: Vertical temperature profiles inside and outside the YCave taken with the CTD probe (August 27, 2003); the profile labelled with a dotted line was taken at approximately section H-H', the profile labelled with a solid line at approximately section G-G'.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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