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Velocity profile data from the Sävar River during ice-covered and open channel conditions

The file includes velocity data taken using an acoustic Doppler current profiler (ADCP) (Sontek M9 sensor) (Sontek, 2018) measured in March and June 2018 at the Sävar River, Sweden. The raw data are found in an Excel file and include the longitudinal flow speed (m/s) from each of the measured water depths. We have exported the data from RiverSurveyorLive software (https://www.sontek.com/softwaredetail.php?RiverSurveyor-LIVE-RSL-34#RSL) and cleaned the files to remove extra information, so that they include only the data we used in reported analyses.These velocity profiles were taken within a larger project to examine differences in hydraulics and sediment transport during ice-covered and open channel flow conditions. Within this project, seismic signals of these geomorphic processes were recorded encompassing the velocity measurement periods (Dietze & Polvi 2019).In winter (March 2018), the measurements were taken via holes drilled through the ice. The ‘moving boat’ method was applied in the RiverSurveyorLive software, but the sensor was kept static during the whole ~5-minute long measurement period in each hole. The velocity measurements for each hole are presented in separate Excel sheets in the file. During summer (June 2018), a similar method was ap-plied—the ADCP sensor was kept static for the same length of time in the same locations as the holes. Note that the winter measurements also had ice cover above them.The starting depth was the depth under the ice-water interface during winter, and at the water-air interface during summer. In the file, the velocity measurement cell closest to the surface is in the column “Cell1 Spd”. This column title refers to the speed (i.e., velocity) in m/s, of the corresponding measurement cell number. “Cell1 loc” refers to the depth of the cell from the surface in meters. Similarly, the near-bed layer velocity is in the column “Cell Spd xx,” with the highest number for that measurement location. Each measure-ment time step is found on a new row. If there is #N/A written in the cell, or the cell is empty, it means that there is no data from the corresponding cell.

Superconducting Gravimeter Data from Aubure - Level 1

The International Geodynamics and Earth Tide Service (IGETS) was established in 2015 by the International Association of Geodesy. IGETS continues the activities of the Global Geodynamics Project (GGP) between 1997 and 2015 to provide support to geodetic and geophysical research activities using superconducting gravimeter (SG) data within the context of an international network. As part of this network, the Aubure station (code AU) was established in 2017 thanks to the financial support of the EQUIPEX CRITEX (https://www.critex.fr/). Continuous time-varying gravity and atmospheric pressure data from AU are integrated in the IGETS data base hosted by ISDC (Information System and Data Centre) at GFZ. The operation and maintenance of the AU instrumentation is done by staff at EOST/ITES in Strasbourg, France. The AU station (longitude: 7.1967 E; latitude: 48.2170 N and elevation: 1151.9 m) is located in the Strengbach catchment in the Vosges mountains, a well instrumented and studied site by the Hydro-Geochemical Observatory of the Environment of Strasbourg (http://ohge.unistra.fr/). The time series of gravity and barometric pressure from iGrav 030 starts in June 2017 and is going on. The time sampling of the raw gravity and barometric pressure data of IGETS Level 1 is 1 minute. For a detailed description of the IGETS data base and the provided files see Voigt et al. (2016, https://doi.org/10.2312/GFZ.b103-16087).

Simulated sensitivity time series and model performance in three German catchments

The data sets contains the major results of the article “Improving information extraction from model data using sensitivity-weighted performance criteria“ written by Guse et al. (2020). In this article, it is analysed how a sensitivity-weighted performance criterion improves parameter identifiability and model performance. More details are given the in article. The files of this dataset are described as follows. Parameter sampling: FAST parameter sampling.xlsx: To estimate the sensitivity, the Fourier Amplitude Sensitivity Test (FAST) was used (R-routine FAST, Reusser, 2013). Each column shows the values of the model parameter of the SWAT model (Arnold et al., 1998). All parameters are explained in detail in Neitsch et al. (2011). The FAST parameter sampling defines the number of model runs. For twelve model parameters as in this case, 579 model runs are required. The same parameter sets were used for all catchments. Daily sensitivity time series: Sensitivity_2000_2005.xlsx: Daily time series of parameter sensitivity for the period 2000-2005 for three catchments in Germany (Treene, Saale, Kinzig). Each column shows the sensitivity of one parameter of the SWAT model. The methodological approach of the temporal dynamics of parameter sensitivity (TEDPAS) was developed by Reusser et al. (2011) and firstly applied to the SWAT model in Guse et al. (2014). As sensitivity index, the first-order partial variance is used that is the ratio of the partial variance of one parameter divided by the total variance. The sensitivity is thus always between 0 and 1. The sum in one row, i.e. the sensitivity of all model parameters on one day, could not be higher than 1. Parameter sampling: LH parameter sampling.xlsx: To calculate parameter identifiability, Latin Hypercube sampling was used to generate 2000 parameter sets (R-package FME, Soetaert and Petzoldt, 2010). Each column shows the values of the model parameter of the SWAT model (Arnold et al., 1998). All parameters are explained in detail in Neitsch et al. (2011). The same parameter sets were used for all catchments. Performance criteria with and without sensitivity weights: RSR_RSRw_cal.xlsx: • Calculation of the RSR once and RSRw separately for each model parameter. • RSR: Typical RSR (RMSE divided by standard deviation) • RSR_w: RSR with weights according to daily sensitivity time series. The calculation was carried out in all three catchments. • The column RSR shows the results of the RSR (RMSE divided by standard deviation) for the different model runs. • The column RSR[_parameter name] shows the calculation of the RSR_w for the specific model parameter. • RSR_w give weights on each day based on the daily parameter sensitivity (as shown in sensitivity_2000_2005.xlsx). This means that days with a higher parameter sensitivity are higher weighted. In the methodological approach the best 25% of the model runs were calculated (best 500 model runs) and the model parameters were constrained to the most appropriate parameter values (see methodological description in the article). Performance criteria for the three catchments: GOFrun_[catchment name]_RSR.xlsx: These three tables are organised identical and are available for the three catchments in Germany (Treene, Saale, Kinzig). In using the different parameter ranges for the catchments as defined in the previous steps, 2000 model simulation were carried out. Therefore, a Latin-Hypercube sampling was used (R-package FME, Soetaert and Petzoldt, 2010). The three tables show the results of 2000 model simulations for ten different performance criteria for the two different methodological approaches (RSR and swRSR) and two periods (calibration: 2000-2005 and validation: 2006-2010). Performance criteria for the three catchments: GOFrun_[catchment name]_MAE.xlsx: The three tables show the results of 2000 model simulations for ten different performance criteria for the two different methodological approaches (MAE and swMAE) and two periods (calibration: 2000-2005 and validation: 2006-2010).

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.

High-resolution subsidence maps and data for whole Afghanistan, and, in a higher resolution, for Kabul and Ghazni provinces (2015-2022)

We present high-resolution subsidence maps for whole Afghanistan, and, in a higher resolution, for Kabul and Ghazni provinces. These data are complemented by wells water level time-series and precipitation data of Kabul, and time-series of mapped solar panel arrays, mapped Kariz networks, mapped desiccation cracks of 2022, and the Normalized Difference Vegetation Index (NDVI) of Ghazni province. The subsidence maps result from a small-baseline (SBAS) time-series analysis (Lazecky et al., 2020; Morishita et al., 2020; Yunjun et al., 2019) of ~7 years of freely available Sentinel-1 radar data. The monthly wells water level time-series 2013-2022 were digitized from the log data of the Afghan Ministry of Water and Energy. The NDVI data 2015-2022 were derived from Sentinel-2 optical imagery. The solar panel arrays were mapped on high-resolution online Google Earth Pro imagery data in 2016, 2019 and 2022. More details of data processing and methods can be found in (Kakar et al., 2023), currently under review at AGU Water Resources. The jupyter notebook documents the creation of key figures.

Multi-approach Gravity Field Models from Swarm GPS data

Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE and GOCE satellite missions, the geophysical community has identified the need for the continued monitoring of its time-variable component with the purpose of estimating the hydrological and glaciological yearly cycles and long-term trends. Currently, the GRACE-FO satellites are the sole provider of this data, while previously the GRACE mission collected these data for 15 years. Between the GRACE and GRACE-FO data periods lies a gap spanning from July 2017 to May 2018, while the Swarm satellites have collected gravimetric data with its GPS receivers since December 2013. This project aims at providing high-quality gravity field models from Swarm data that constitute an alternative and independent source of gravimetric data, which could help alleviate the consequences of the 10-month gap between GRACE and GRACE-FO, as well as the short gaps in the existing GRACE and GRACE-FO monthly time series. The geodetic community has realized that the combination of the different gravity field solutions is superior to any individual model. This project exploits this fact and delivers to the highest quality monthly-independent gravity field models, resulting from the combination of 4 different gravity field estimation approaches. All solutions are unconstrained and estimated independently from month to month. Preliminary comparison with GRACE data has demonstrated that the signal in the Swarm gravity field models is restricted to degrees 12-15 and below, while the temporal correlations decrease considerably above degree 10. The 750km smoothed models are suitable to retrieve the global annual temporal variations of Earth's gravity field and the agreement with GRACE over large basins (e.g. Amazon, Congo-Zambezi, Ganges-Brahmaputra) is within 1cm RMS in terms of Equivalent Water Height. The global RMS relative to a bias, trend, an annual and semi-annual model derived from GRACE over deep ocean areas (those roughly 1000km from shorelines) is under 1mm geoid height during periods of low ionospheric activity. More information about this project can be found at https://www.researchgate.net/project/Multi-approach-gravity-field-models-from-Swarm-GPS-data and ESA's Swarm DISC (the Data, Innovation and Science Cluster) Website (https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/activities/scientific-projects/disc#MAGF). This project is funded by ESA via the Swarm DISC, Sub-Contract No. SW-CO-DTU-GS-111.

HydReSGeo: Field experiment dataset of surface-sub-surface infiltration dynamics acquired by hydrological, remote sensing, and geophysical measurement techniques

This dataset comprises data of an interdisciplinary pedon-scale irrigation experiment at a grassland site near Karlsruhe, Germany, including pedo-hydrological, geophysical, and remote sensing data. The objective of this experiment is to monitor soil moisture dynamics during a well-defined infiltration process with a combination of direct and non-invasive techniques.Overall, the quantification of soil water dynamics and, in particular, its spatial distributions is essential for the understanding of land-atmosphere interactions. However, the precise measurement of soil water dynamics and its spatial distribution in a continuous manner is a challenging task. Pedo-hydrological monitoring techniques rely on direct, point-based measurement with buried probes for soil water content and matric potential. Non-invasive remote sensing (RS) and geophysical measurement techniques allow for spatially continuous measurements on different spatial scales and extents. This experiment provides a basis for the analyses of signal coherence between the measurement techniques and disciplines. It contributes to forthcoming developments of monitoring setups and modeling approaches to landscape-water dynamics.For direct monitoring, an array of time-domain reflectometry (TDR) probes and tensiometers was used. As non-invasive techniques, we applied a ground-penetrating radar (GPR), a hyperspectral snapshot sensor, a long-wave infrared (LWIR) sensor, and a hyperspectral field spectroradiometer. We provide the data in nearly raw format, including information about the site properties and calibration references. The data are organized along with the different sensors and disciplines. Thus, the distinct sensor data can also be used independently of each other. In addition, exemplary scripts for reading and processing the data are included.

Global High-Resolution Terrestrial Water Storage Anomalies through a Dynamic Soft-Constrained Deep Learning Paradigm

This dataset provides a comprehensive, high-resolution global record of monthly Terrestrial Water Storage Anomalies (TWSA) from April 2002 to December 2022. It was generated to address the spatial resolution limitations of raw satellite gravimetry observations from the GRACE and GRACE-FO missions, offering a product suitable for regional and basin-scale hydrological analysis. The dataset was simulated using a novel deep learning framework. This model spatially downscales the low-resolution (~300 km) JPL GRACE/GRACE-FO Mascon solutions (Watkins et al., 2015; Wiese et al., 2016; Landerer et al., 2020; Wiese et al., 2023) to a high resolution of 50 km (0.5-degree grid). The core of the methodology is a dynamic soft-constrained paradigm, where the model is simultaneously guided by the observational accuracy of GRACE/GRACE-FO data and the high-resolution spatial patterns from the WaterGAP Global Hydrology Model (Müller Schmied et al., 2023) and ERA5 reanalysis data (Hersbach et al., 2023). The influence of these constraints is dynamically weighted at each training step based on the evolving correlation between the model's prediction and the high-resolution inputs, ensuring an optimal simulation of observational integrity and high-resolution detail.

Hierarchical Clustering with Visualizations (hc-viz)

This repository offers a straightforward implementation of hierarchical clustering coupled with interactive visualizations. The visualization depicts the clusters on a map and an interactive dendrogram to users. The interactive visualization allows users to explore the hierarchical cluster structure and navigate different levels of granularity of the dendrogram.

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