Monitoring Velocity Changes using Ambient Seismic Noise
SeisMIC (Seismological Monitoring using Interferometric Concepts) is a python software that emerged from the miic library. SeisMIC provides functionality to apply some concepts of seismic interferometry to different data of elastic waves. Its main use case is the monitoring of temporal changes in a mediums Green's Function (i.e., monitoring of temporal velocity changes).
SeisMIC will handle the whole workflow to create velocity-change time-series including: Downloading raw data, Adaptable preprocessing of the waveform data, Computating cross- and/or autocorrelation, Plotting tools for correlations, Database management of ambient seismic noise correlations, Adaptable postprocessing of correlations, Computation of velocity change (dv/v) time series, postprocessing of dv/v time series, plotting of dv/v time-series
Knowledge of groundwater flow is of high relevance for groundwater management and the planning of different subsurface utilizations such as deep geothermal facilities. While numerical models can help to understand the hydrodynamics of the targeted reservoir, their predictive capabilities are limited by the assumptions made in their set up. Among others, the choice of appropriate hydraulic boundary conditions, adopted to represent the regional to local flow dynamics in the simulation run, is of crucial importance for the final modelling result. In this publication we present the hydrogeological models to obtain results to quantify how and to which degree different upper hydraulic boundary conditions and vertical cross boundary fluid movement influence the calculated deep fluid conditions Therefore, we take the central Upper Rhine Graben area as a natural laboratory. The presented three models are set up with different sets of boundary conditions. The Reference Model uses the topography as upper hydraulic pressure surface of 0 kPa. In model S1, a subdued replica of the topography, which was built on the base of hydraulic head measurements is applied as the upper hydraulic boundary condition and in model S2 vertical cross boundary flow is implemented.
Based on our results, we illustrate in the landing paper that for the Upper Rhine Graben specific characteristics of the flow field are robust and insensitive to the choice of imposed hydraulic boundary conditions, while specific local characteristics are more sensitive. Accordingly, these robust features characterizing the first order groundwater flow dynamics in the Upper Rhine Graben include: (i) a regional groundwater flow component descending from the graben shoulders to rise at its centre; (ii) infiltration of fluids across the graben shoulders, which locally rise along the main border faults; (iii) the presence of heterogeneous hydraulic potentials at the rift shoulders. The configuration of the adopted boundary conditions influence primarily calculated flow velocities and the absolute position of the upflow axis within the graben sediments. In addition, the choice of upper hydraulic boundary conditions exerts a direct control on the evolving local flow dynamics, with the degree of influence gradually decreasing with increasing depth.
With respect to regional flow modelling of basin hosted, deep water resources, the main conclusions derived from this study are: (i) the often considered water table as an exact replica of the model topography (Reference Model) likely introduces a source of error in the simulations in regional hydraulic modelling approaches. Here, we show that these errors can be minimized by making use of a water table as upper boundary condition derived from available hydraulic head measurements (model S1). If the study area is part of a supra-regional flow system - like the central Upper Rhine Graben is part of the whole Upper Rhine Graben - the in- and outflow across vertical boundaries need to be considered (model S2).
VERSION HISTORY:-On October 18, 2018 we republished all simulation data for all water (global) sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data.- On November 27, 2018 we republished simulation data for monthly variables swe, soilmoist and rootmoist for impact model PCR-GLOBWB due to an error in the units. Instead of reporting mass per area (kg/m2), values corresponded to mass flux rate (kg/m2/s). Values were thus multiplied by 86400 in order to obtain the correct values in kg/m2. This data caveat was documented in the ISIMIP website (ISIMIP2a: PCR-GLOBWB reported three variables in wrong unit).----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/. For accessing the data set as in http://doi.org/10.5880/PIK.2017.010 before November 27, 2018 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de.----------------------------------------------------------------------------DATA DESCRIPTION:The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors.ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming.The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction).This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors.ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming.The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction).This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2.