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Spatial counterfactuals of the July 2021 flood in the Ahr valley, Germany

This dataset comprises gridded precipitation fields, simulated hourly discharge values and simulated inundation areas and depths in the Ahr catchment in Germany for the reference scenario of the July 2021 flood and 25 spatial counterfactuals. The precipitation dataset contains the observed gridded E-OBS precipitation field and 25 counterfactuals shifted by one cell. Subsequently, the reference scenario and spatial counterfactuals are used as atmospheric forcing for the mesoscale hydrological model mHM set up and calibrated for the Ahr catchment, Germany. The model simulates hourly discharge series at seven gauge locations (Müsch, Kirmutscheid, Niederadenau, Denn, Kreuzberg, Altenahr, Bad Bodendorf) from which the event peak flows and flood event volumes can be derived. These discharge data is used as boundary condition for the RIM2D hydrodynamic inundation model which simulates inundation areas and maximum inundation depths along the Ahr valley between Müsch and Sinzig for the reference scenario and spatial counterfactuals.

3D regional models to test influences of hydraulic boundary conditions on the deep fluid flow in the central Upper Rhine Graben

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).

ISIMIP2a Simulation Data from Water (global) Sector (V. 1.1)

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

40-year record of radon, water temperature, and discharge rate from a hot spring in China

A high-fidelity radon record covering nearly 40 years from the hot spring site of BangLazhang (BLZ), Southwestern China allows to study multi-year periodicities. At BLZ, radon dissolved in water (Radon), water temperature (WT), and spring discharge rate (DR) were measured daily from 1976 until 2015. Barometric pressure, regional rainfall, galactic cosmic rays (GCR flux is modulated by solar wind and thus a proxy for solar activity), and regional seismicity from the same period were considered to identify potentially influencing factors controlling the changes in radon [Yan et al., 2017]. Various wavelet techniques indicate that the long-period radon concentration is characterized by a quasi-decadal (8-11 years) cycle, matching well with the concurrent periodicity in water temperature, spring discharge rates.The BLZ hot spring monitoring site is maintained and operated by the China Earthquake Administration of Yunnan Province. Water from the spring is sampled once daily and measurements of radon have been performed routinely in a laboratory since 1976 April 6. The sample time is designated to occur at 8 o’clock in the morning in order to reduce the effect of daily variations. The radon concentration has been measured with three types of radon measurement instruments during the past 40 years. From 1976 April 6, to 1982 June 5, a FD-105 type radon gas detector was used, reporting the radon concentration in Eman. Eman is converted to the metric unit Bq/L using the relationship 1 Eman = 3.7 Bq/L. From 1982 June 6 to 2012 April 11, a FD-105K type electrometer (manufactured by Shanghai Electronic Instrument, co.) was used, the measurements given in Bq/L. Since 2012 April 12, a FD-125 type Radon & Thorium analyzer, manufactured by Beijing Nuclear Instrument Factory, sponsored by CNNC (China National Nuclear Corporation), has been used.Water sampled from the spring is degassed by bubbling air and transported into a chamber, where the radon concentration is measured in a ZnS cell connected to a photomultiplier detector, and a scintillation counter. The measurement precision of the instruments is 0.1 Bq/L. A solid radium source (226Ra) with a known radioactive radon content is used for the calibration of the water radon under normal working conditions. This source is used to measure and calculate the calibration value of the instrument.In addition to radon, water temperature and spring discharge rate are measured at the spring site when the water is sampled for radon. Temperature is measured using a mercury thermometer with a resolution of 0.1°C. Discharge rate is measured using the stopwatch capacity method, i.e., the required time per unit volume of water is measured. Barometric pressure has been measured since 1997. Regional rainfall data were downloaded through the CPC Merged Analysis of Precipitation (CMAP) for the same period to evaluate its possible influence on radon in the present study.

ISIMIP2a Simulation Data from Water (global) Sector

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.

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