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Los datos pertenecen al trabajo: Gómez, E.J., Delgado, J.A., González, J.M. (2020) Environmental factors affect the response of microbial extracellular enzyme activity in soils when determined as a funciton of water availability and temperature. Ecology and Evolution (Artícle in press), Figure 2. Extracellular glucosidase activity at 20ºC (A) and 60ºC (B) as a function of water activity for different soils. Enzyme activity is presented as percentage of maximum activity for each soil. Error bars represent the standard deviation. Symbols represent different soils: Black square, Galicia; Grey square, Aragón; Black triangle, Salamanca; Grey triangle, Seville; Black circle and dashed line, Cádiz. Figure 3. Extracellular phosphatase activity at 20ºC (A) and 60ºC (B) as a function of water activity for different soils. Enzyme activity is presented as percentage of maximum activity for each soil. Error bars represent the standard deviation. Symbols represent different soils: Black square, Galicia; Grey square, Aragón; Black triangle, Salamanca; Grey triangle, Seville; Black circle and dashed line, Cádiz. Figure 4. Extracellular protease activity at 20ºC (A) and 60ºC (B) as a function of water activity for different soils. Enzyme activity is presented as percentage of maximum activity for each soil. Error bars represent the standard deviation. Symbols represent different soils: Black square, Galicia; Grey square, Aragón; Black triangle, Salamanca; Grey triangle, Seville; Black circle and dashed line, Cádiz., This study was supported by funding through projects from the Spanish Ministry of Economy and Competitiveness (CGL2014-58762-P) and the Regional Government of Andalusia (RNM2529). These projects have been cofunded by FEDER funds., Peer reviewed

Los datos pertenecen al trabajo: Gómez, E.J., Delgado, J.A., González, J.M. (2020) Environmental factors affect the response of microbial extracellular enzyme activity in soils when determined as a funciton of water availability and temperature. Ecology and Evolution (Artícle in press), RDA plot showing the correspondence of water activity giving the optimum enzyme activity and environmental parameters. Capital letters (in black) represent the sampled soils (G, Galicia, P, Aragón; S, Salamanca; C, Sevilla; T, Cádiz). Arrows represent the environmental variables (soil texture, sand and silt content) contributing significantly to explain the variability of water activity resulting in optimum enzyme activity. The distribution of enzyme activities are presented in red: Glu_20, glucosidase activity at 20ºC; Glu_60, glucosidase activity at 60ºC; Pho_20, phosphatase activity at 20ºC; Pho_60, phosphatase activity at 60ºC; Pro_20, protease activity at 20ºC; Pro_60, protease activity at 60ºC. Figure, This study was supported by funding through projects from the Spanish Ministry of Economy and Competitiveness (CGL2014-58762-P) and the Regional Government of Andalusia (RNM2529). These projects have been cofunded by FEDER funds., Peer reviewed

Los datos pertenecen al trabajo: Gómez, E.J., Delgado, J.A., González, J.M. (2020) Environmental factors affect the response of microbial extracellular enzyme activity in soils when determined as a funciton of water availability and temperature. Ecology and Evolution (Artícle in press), Scattered plots showing the relationship between water activity (X-axis), percentage of maximum enzyme activity estimates at 20ºC (left) and 60ºC (right) (proportional to diameter of circles) with environmental variables (Y-axis), specifically, two climaterelated parameters, the annual average of hot days (>30ºC)(A) and the annual average of consecutive days without precipitation (B), and soil-texture through the fraction of sand in the sampled soils (C). Symbol colors indicate the type of enzyme (Dark to light: Glucosidase, Phosphatase, Protease) and the analyzed soil (Greenish, Galicia (G); bluish, Aragón (P); brownish, Salamanca (S); reddish, Sevilla (C); purplish, Cádiz (T))., This study was supported by funding through projects from the Spanish Ministry of Economy and Competitiveness (CGL2014-58762-P) and the Regional Government of Andalusia (RNM2529). These projects have been cofunded by FEDER funds., Peer reviewed

Los datos pertenecen al trabajo: Gómez, E.J., Delgado, J.A., González, J.M. (2020): Persistence of microbial extracellular enzymes in soils under different temperatures and water availabilities Ecology and Evolution (Artícle in press), Examples of the decay curves for extracellular enzymes from mesophiles and thermophiles at 20ºC and 60ºC in a South Spain (Seville) soil under water activity 1 (wet conditions). Red circles, decay of enzyme activity from thermophiles at 60ºC; pink circles, decay of enzyme activity from thermophiles at 20ºC; black triangles, decay of enzyme activity from mesophiles at 20ºC; grey triangles, decay of enzyme activity from mesophiles at 60ºC. Points are average values from triplicates. Error bars indicate a standard deviation, No

The corresponding 2.5D electrical forward and inverse problem is solved on an unstructured mesh with tetrahedral elements using BERT (Boundless Electrical Resistivity Tomography) (Rucker et al. 2006, Gunther et al. 2006) and pyGIMLi (Generalized Inversion and Modeling Library) (Rucker et al. 2017)., Surface electrical resistivity tomography (ERT) is a widely used tool to study seawater intrusion (SWI). It is noninvasive and offers a high spatial coverage at a low cost, but it is strongly affected by decreasing resolution with depth. We conjecture that the use of CHERT (cross-hole ERT) can partly overcome these resolution limitations since the electrodes are placed at depth, which implies that the model resolution does not decrease in the zone of interest. The objective of this study is to evaluate the CHERT for imaging the SWI and monitoring its dynamics at the Argentona site, a well-instrumented field site of a coastal alluvial aquifer located 40 km NE of Barcelona. To do so, we installed permanent electrodes around boreholes attached to the PVC pipes to perform time-lapse monitoring of the SWI on a transect perpendicular to the coastline. After two years of monitoring, we observe variability of SWI at different time scales: (1) natural seasonal variations and aquifer salinization that we attribute to long-term drought and (2) short-term fluctuations due to sea storms or flooding in the nearby stream during heavy rain events. The spatial imaging of bulk electrical conductivity allows us to explain non-trivial salinity profiles in open boreholes (step-wise profiles really reflect the presence of fresh water at depth). By comparing CHERT results with traditional in situ measurements such as electrical conductivity of water samples and bulk electrical conductivity from induction logs, we conclude that CHERT is a reliable and cost-effective imaging tool for monitoring SWI dynamics., This work was funded by the project CGL2016-77122-C2-1-R/2-R of the Spanish Government.We would like to thank SIMMAR (Serveis Integrals de Manteniment del Maresme) and the Consell Comarcal del Maresme in the construction of the research site. This project also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 480 Agreement No 722028. Author Albert Folch is a “Serra-Húnter Fellow”., Name of raw data files: chert_argentona_1.txt chert_argentona_2.txt chert_argentona_3.txt chert_argentona_4.txt chert_argentona_5.txt chert_argentona_6.txt chert_argentona_7.txt chert_argentona_8.txt chert_argentona_9.txt chert_argentona_10.txt chert_argentona_11.txt chert_argentona_12.txt chert_argentona_13.txt chert_argentona_14.txt chert_argentona_15.txt chert_argentona_16.txt chert_plus_surfaceert_argentona.txt Content of raw data files: List of electrodes X and Z positions (with topography), and list of quadripoles (a, b, m, n) with measured resistance (r), error (err), geometric factor (k), and apparent resistivity (rhoa). This files are formatted to go through the BERT program for 2.5D ERT modeling and inversion. The “chert_plus_surfaceert_argentona.txt” raw file combines cross-hole and surface ERT data from Sep 8th, 2015. Name of inverse data files: chert_argentona_sigma_model_1.vector chert_argentona_ sigma_model_2. vector chert_argentona_ sigma_model_3. vector chert_argentona_ sigma_model_4. vector chert_argentona_ sigma_model_5. vector chert_argentona_ sigma_model_6. vector chert_argentona_ sigma_model_7. vector chert_argentona_ sigma_model_8. vector chert_argentona_ sigma_model_9. vector chert_argentona_ sigma_model_10. vector chert_argentona_ sigma_model_11. vector chert_argentona_ sigma_model_12. vector chert_argentona_ sigma_model_13. vector chert_argentona_ sigma_model_14. vector chert_argentona_ sigma_model_15. vector chert_argentona_ sigma_model_16. Vector chert_plus_surfaceert_argentona.vector (Reference Model for Time-lapse Inversion), Peer reviewed

Los datos pertenecen al trabajo: Gómez, E.J., Delgado, J.A., González, J.M. (2020): Persistence of microbial extracellular enzymes in soils under different temperatures and water availabilities Ecology and Evolution (Artícle in press), Figure 2. Decay rates as a function of water availability and temperature for extracellular enzymes from mesophiles and thermophiles at three different soils. Extracellular enzymes: A, B and C (upper row), glucosidases; D, E and F (central row), phosphatases; G, H and I (lower row), proteases. Left column (A, D and G), Seville soil (South Spain); Center column (B, E and H), Cadiz soil (South Spain); Right column (C, F and I), North Spain soil. Symbols: in red, decay of extracellular enzymes from thermophiles at 60ºC; in blue, decay of extracellular enzymes from mesophiles at 20ºC; in black, decay of extracellular enzymes from thermophiles at 20ºC. Points resulted from the average of triplicated samples. Error bars indicate a standard deviation, Figure 3. NMDS ordination of decay rates as a function of temperature, water availability and the soils for glucosidase (A), phosphatase (B) and protease (C) activities. Water activity is shown with brownish filled circles from dark to light in decreasing levels of water activity. Decays at 20ºC and 60ºC by enzymes from mesophilic (M20, down-pointing triangles, and M60, up-pointing triangles, respectively) and thermophilic (T20, squares, and T60, diamonds, respectively) microoorganisms are shown as unfilled symbols for each studied soil (Huesca soil [North Spain] in green, Cadiz soil [South Spain] in blue, Seville soil [South Spain] in red)., No

This dataset is composed of the input files of the numerical models for simulating Amposta fault stability during different phases of the project and poromechanical pressure changes induced by shear slip. The input files of each numerical simulation are included in a folder. The names of the folders and the description of the model are: - “1_Oil_production”: simulation of oil production and 7 years of post-production to quantify the changes in the Amposta fault stability - “2_Overpressure_plus_buoyancy_cushion”: simulation of the cushion gas injection considering both the effect of pore pressure increase and buoyancy to quantify the changes in the Amposta fault stability - “3_Overpressure_cushion”: simulation of the cushion gas injection considering only the effect of pore pressure increase (without gravity) to quantify the changes in the Amposta fault stability - “4_Buoyancy_cushion”: simulation of the cushion gas injection considering only the buoyancy of the gas to quantify the changes in the Amposta fault stability - “5_Buoyancy_full_capacity”: simulation of the gas storage at its maximum capacity considering only the buoyancy of the gas to quantify the changes in the Amposta fault stability - “6_Poromechanical_pressure_changes”: simulation of the pore pressure changes induced by shear slip and its effects in the short and long term., Each folder includes a file with the name of the folder and ended as “_gen.dat” that contains the input data of the model, including material properties, initial and boundary conditions and the time intervals. There is also a file ended as “_gri.dat” that includes the information on the mesh. The “root.dat” includes the name of the model. To run simulations, just execute the Code_Bright executable “Cb_vX_Y.exe” in a folder that contains the three input files and the executable, where X and Y denote the used version of the executable., Peer reviewed

A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer. The data presented are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data., A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer. The data presented are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data., Peer reviewed

(1) Five Fiels with Data obtained by computing numerical simulations. DSA results were performed in a linked code of TRACONF (Carrera et al. 1993) and CHEPROO (Bea et al. 2009). WMA results were performed by a self-made code based on TRACONF_CHEPROO original code. - Data in "FIG_4_half_domain_inj": SHEET 1 "u_data (fig 4b)": cross section of component at diferent node columns SHEET 2 "r_data (fig 4a)": cross section of reaction rates at diferent node columns - Data in "FIG_5a_CAL_equil_accur": Accuracy performance for WMA and DSA for CAL equilibrium case. Check the Numerical dispersion effect between models of just different time steps A3: Data case information A19: DSA data I19: WMA data - Data in "FIG_5b_CAL_kin_accur": Accuracy performance for WMA and DSA for CAL kinetic case. Check the Numerical dispersion effect between models of just different time steps Idem than previous - Data in "TABLE_3a_CAL_equil_cpu": Comparison between CPU performance between DSA and WMA algorithms on CAL EQUILIBRIUM case A4: CPU computed from numerical simulations A15: Obtain the constants of each step calculation A23: CPU calculated analiticaly (NOT shown in this versiona as a Figure) - Data in "TABLE_3b_CAL_kin_cpu": Comparison between CPU performance between DSA and WMA algorithms on CAL KINETIC case Idem than previous (2) Two files with computed models: - Data in "EXAMPLE_of_M": Easy 1D example of 5 nodes to understand the difference between classic (sheet "classic ADE solution") solutions and WMA solution (sheet "WMA solution") One can set the value (between 0 and 1) of B5 cell at first sheet "Data" to see how the matrix M looks (cells M3:Q7) - Data in "APPENDIX_C_RT_Pulse_2d_WMA": Comparison between analytical solution (De Simoni et al., 2005) and WMA results of 2D model after pulse injection of mass M, Five files with data obtained by computing numerical simulations and two files with computed numerical models, Peer reviewed

Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. WMA results were performed by a self-made code modifying the original TRACONF (Carrera et al. 1993) linked with CHEPROO (Bea et al. 2009). MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010) - Data in "Fig 3": Comparison of acccuracy performances between RW, WMA and MAWMA methods. A1: Information data about the problem A8: Dissipation rate results A23: Dispersion results - Data in "Analytical_solution (Dentz and Carrera 2007)": Calculation of the analytic solution of aparent dispersion, Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. WMA results were performed by a self-made code modifying the original TRACONF (Carrera et al. 1993) linked with CHEPROO (Bea et al. 2009). MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010), Peer reviewed