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FIGURE 3 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 3 Mass–standard length (M–LS) relationships (MLR) determined for exercised () and control () Salmo trutta cohorts over 0–32 weeks from treatment initiation. Each cohort included LS00 individuals (n = 6) as a common origin

FIGURE 1 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 1 (a) Landmark positions () on Salmo trutta parr that were digitised twice and then averaged to minimize measurement error. (b) Shape changes associated with principal components (PCs) 1–3. PCs were derived from a between-group PC analysis of Procrustes superimposed landmarks., Consensus shape with numbered landmark positions;, Shape changes associated with each PC. Shape changes are scaled to observed PC scores: Left hand side shape changes (back outlines) are scaled to the minimum value observed across the sample on each respective PC (shown below the image) and right hand side shape changes (black outlines) are scaled to the maximum value observed across the sample on each respective PC. PC1 describes a change in head size, PC2 describes dorso-ventral arching of the body and PC3 describes changes in overall robustness and body depth

Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)

FIGURE 2 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 2 (a) Principal component (PC) () C00, () C04, () C10, () C20, () C32, () E04, () E10, () E20, and () E32 and (b) linear discriminant (LD) scores for Salmo trutta treatment groups (C, control; E, exercise) across experimental weeks (i.e., age 00 (control sample before treatment initiation) to 32 (32 weeks of treatment); n = 6 individuals per group). PC1 and PC3, derived from a between-group PC analysis of Procrustes superimposed landmarks corrected for the arching artefact (PC2). LD1 and LD2, derived from a LD analysis on the corrected principal component scores. Ellipses demarcate 95% confidence intervals; O, group centroids. N.B. The change of direction for head size on LD1 resulting from a negative association with PC1 (see Table 2)

FIGURE 4 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 4 Box plots showing median (), 25th–75th percentiles () and range () of Salmo trutta condition at length (KÞ for exercised () and control () Salmo trutta cohorts across the experimental period (i.e., age) weeks 4–32 after treatment initiation (n = 6 per group). *, significant differences of pairwise least-squares means between exercised and control cohorts; different lower-case letters (black, exercise; grey, control) denote significant differences of pairwise least-squares means within treatments across the experimental period

opencc-by-4.0Dec 2021View details →

Data published in manuscript "Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO2 and CH4" by Castro-Morales et al.

This data is published in the manuscript: Castro-Morales, K., Canning, A., Körtzinger, A., Göckede, M., Küsel, K., et al. (2022). Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO2 and CH4. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006485. <a href="https://doi.org/10.1029/2021JG006485">https://doi.org/10.1029/2021JG006485</a>. The data contains the water properties and gases data measured at a site in Ambolikha River, meteorological data measured at an eddy covariance tower located in the neighbor floodplain, and data from the analysis of dissolved organic matter in river water samples. The data was collected between 26 June, 2019 and 02 August, 2019. This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_Ambolikha_River.txt" should be read before accessing the data. The authors recommend downloading Version 2.0 because it is the most up to date data. For questions contact the main and corresponding author Dr. Karel Castro-Morales at: karel.castro.morales@uni-jena.de

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Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr

This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.

opencc-zeroJul 2022View details →

opencc-by-4.0Oct 2022View details →

Dataset for the journal article: " In-situ Capillary Pressure and its Interrelationships with Flow Characteristics during Steady-state Three-phase Flow in Water-wet Berea Sandstone"

Dataset 1 contains the summary results of the image analysis performed on a set of three-phase micro-CT  images.  The analysis are:(1) Fluid saturations, (2) Characteristics of Gas clusters, (3) Characteristics of Oil clusters, (4) Pore-fluid occupancy, and (5) In-situ capillary pressure measurements.  Dataset 2 contains the three-phase relative permeability data with the correction analysis.

opencc-by-4.0Feb 2022View details →

WaterGEMS models for flow starved water transmission network

This data contains WaterGEMS models of a Water Transmission Network (WTN) in India. It deploys a newly developed modeling approach, Pressure Driven Analysis for Flow Straved Condition(PDA-FSC). It is used for evaluating the performance of flow-starved WTN.

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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles

Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and α and n, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.

opencc-zeroMay 2024View details →

Fig. 4 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 4. Results of the Geneland analysis. A: Bar plot of posterior probability density according to the number of clusters; B: posterior probability maps for the delimited clusters.

Fig. 3 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 3. Pairwise FST between localities of Telmatobius pefauri obtained using mitochondrial (A) and microsatellite (B) data. The colour scale corresponding to the values of FST is shown to the right of each matrix. Significant (Bonferroni corrected) comparisons showing p <0.05, p <0.01 and p <0.001 are denoted by *, ** and ***, respectively.

Fig. 2 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 2. Median-joining network based on the fragment of the analysed control region. Table 1. Indices of mitochondrial diversity, nuclear diversity, and inbreeding coefficients (FIS) by locality

Fig. 1 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 1. Study area, distribution of Telmatobius pefauri. Localities, 1: Socoroma (Socoroma River); 2: Murmuntani; 3: Copaquilla; 4: Chapiquiña; 5: Belén; 6: Lupica; 7: Saxamar. Localities 2 and 3 belong to the Seco River drainage; localities 4–7 belong to the Tignamar River drainage. Basin limits are indicated with dashed lines. The inset map shows the study area (highlighted by a red box) in relation to South America. SAAD = South American Arid Diagonal.

Fig. 5 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 5. Scatter plot for the first two principal components obtained in the Principal Components Analysis using SSR data.

opencc-by-nc-nd-4.0Nov 2018View details →

Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)

Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors". DOI: 10.1016/j.cej.2018.10.131 Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor. Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination. Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.

opencc-zeroJun 2019View details →

Lac Croche V-notch weir water flow data collected at the Station de biologie des Laurentides (SBL) de l'Université de Montréal, St-Hippolyte QC

These datasets comprise hourly and daily water flow data collected at the Lac Croche v-notch weir at the Station de biologie des Laurentides (SBL) between 2014/04/01 and 2019/05/01.

opencc-by-4.0Dec 2010View details →

Fig. 1 in Distribution of Agonostomus monticola and Brycon behreae in the Río Grande de Térraba, Costa Rica and relations with water flow

Fig. 1. Study area, the main River Térraba and Streams Ojochal, Caña Blancal and Brujo with the respective data recollection points.

opencc-by-4.0Dec 2010View details →

Fig. 7 in Distribution of Agonostomus monticola and Brycon behreae in the Río Grande de Térraba, Costa Rica and relations with water flow

Fig. 7. Turbidity at the main River Térraba and the streams. The marked area is about October, when Térraba was less turbid than streams.