API src

Found 8 results.

Other language confidence: 0.9455187738537912

Global Multi-Resolution Land Fraction and Land–Ocean Masks Derived from ESA CCI Water Bodies v4.0

The dataset provides a set of 8 land ocean masks as NetCDF files based on the maps provided by the datasets ESA CCI Water Bodies - v4.0, Ocean-Map-150m-P13Y-2000-v4.0. The masks come as regular lon/lat grids in 0.1°, 0.25°, 0.5° and 1° spatial resolution, each as a version with and without Greenland and the antarctic regions (anything south of 60° S). The data fields of the individual datasets comprise of a) a field with information on the fraction of land in a grid cell, defined by the number of 150m cells of the source dataset lying within a target grid cell, b) a field with a flag (1 or 0) if a a grid cell contains more than 50% land area or not, and c) a field with a flag (1 or 0) if a grid cell contains any land at all. The dataset was generated in the course of the project "Global Gravity-based Groundwater Product (G3P)" to facilitate data production on a regular grid and to mask out oceans. G3P was funded by the EU Horizon 2020 programme in response to the call LC-SPACE-04-EO-2019-2020 “Copernicus evolution – Research activities in support of cross-cutting applications between Copernicus services” under grant agreement No. 870353.

Shallow groundwater level time series and groundwater chemistry survey data from Krycklan catchment

Groundwater can respond quickly to precipitation and is the main contribution to streamflow in most catchments in humid, temperate climates. To better understand shallow groundwater dynamics in a boreal headwater catchment, we installed a network of groundwater wells in two areas in the Krycklan catchment in Northern Sweden. This dataset contains groundwater level data and sampling data from a small headwater catchment (3.5 ha, 54 wells) and a hillslope (1 ha, 21 wells). The dataset is arranged in to subsets, Dataset 1 and 2, the first containing groundwater levels and related information while the second contains information on the chemical sampling procedure and laboratory results. The average wells depth was 274 cm (range: 70 - 581 cm) and recorded the groundwater level variation at a 10-30 min interval between 18. July 2018 – 1. November 2020. Manual water level measurements (0 - 26 per well) during the summer seasons in 2018 and 2019 were used to confirm and re-calibrate the water level logger results. The groundwater level data for each well was carefully processed and quality controlled, using six data labels. The location and depths of the wells are in the file 2022-020_Erdbruegger-et-al_Krycklan_gw_wells.csv and the groundwater levels and classifications 2022-020_Erdbruegger-et-al_Krycklan_gw_levels.csv. The absolute and relative positions of the wells were measured with a high-precision GPS and terrestrial laser scanner (TLS) to determine differences in groundwater levels and thus groundwater gradients (the report of the registration of the point clouds can be found in the files 2022-020_Erdbruegger-et-al_TSL_registration_report_[A/B].rtf). During the summer of 2019, all wells with sufficient water were sampled and analyzed for electrical conductivity, pH, absorbance, anion and cation concentrations, as well as δ18O and δ2H (information on the sampling and the laboratory results can be found in the files 2022-020_Erdbruegger-et-al_Krycklan_gw_chemistry.csv, 2022-020_Erdbruegger-et-al_Field_protocol.csv, 2022-020_Erdbruegger-et-al_Lab_analysis_description.pdf). This combined hydrometric and hydrochemical dataset can be useful to test models that simulate groundwater dynamics and to evaluate subsurface hydrological connectivity. The full description of the data and methods is provided in citation of article XX when available.

Kali Gandaki High Mountain Observatory, Stable Water Isotope database

This dataset was collected during field-based monitoring in the Kali Gandaki River catchment be-tween the years 2013 and 2017. The monitoring aims to understand the hydrological fluxes and feedback with weathering and erosion processes across the mountain range. The Kali Gandaki River sources its water in the North and traverses through the Himalayan Mountain Range, along a north-south transect. The field-based monitoring comprises targeted field campaigns to revisit locations at different years and seasons in order to constrain the annual and intra-annual variability. This is complemented by permanent installations and routine river and rain sampling at two loca-tions, Lete and Purtighat. Lete is situated at the orographic barrier, at ~2500 m asl. and the up-stream catchment integrates the northern part of the Himalayan Range as well as some of the southern edge of the Tibetan Plateau. Purtighat is located further south and integrates the north-ern part as well as south-facing flanks of the Higher and Lower Himalayas. At both locations, auto-mated river monitoring is installed as well as a trained station ward for daily routine sampling. At Lete, rainfall samples are obtained on a daily resolution during the monsoon. This sampling was not feasible at Purtighat for logistic reasons. Instead, rain was sampled daily in Kathmandu. This dataset contains five tables of stable water isotope analysis. One containing grab samples from the Kali Gandaki river in its vicinities and 4 tables with time series sampling from the Kali Gandaki River and from rainfall.

Terrestrial water storage on the South American continent: Data from numerical simulations, observations, and deep learning

In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA) in the Amazonas region for the time period 2003-2019. TWSAs were used as target (output) values of the neural network and were derived from the Land Surface Discharge Model (LSDM, Dill, 2008). The corresponding input values were calculated by spatially smoothing the TWSA fields with a 600 km Gaussian filter. After training the neural network over the time period of 2003 to 2018, its performance was tested and compared to LSDM for the subsequent year 2019.

Compound flood drivers for northwestern Europe in high-resolution EURO-CORDEX Simulations

This dataset comprises time series of 6-hourly surges and the daily streamflow records simulated from hydrodynamic-hydrologic modelling to quantify the compound effects of surges and peak river discharge over northwestern Europe. We simulate the surge height (m) and river discharge (m3/s) at the vicinity of the coast in the reference (1981–2005) and projected (2040–2069) periods using time series of high-resolution (0.11⁰, which is about 12 km) regional dynamically downscaled meteorological forcings from the World Climate Research Program CORDEX (COordinated Regional Climate Downscaling EXperiment) framework (Nikulin et al., 2011) (https://esg-dn1.nsc.liu.se/search/esgf-liu/) for Europe, forced by five host (or parent)-GCMs from the CMIP5 project. Given data availability, we use meteorological forcing dataset from SHMI’s Rossby Centre regional atmospheric model (RCA4; Strandberg et al., 2015) driven by five host GCMs participating in CMIP5, i.e., CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, IPSL-IPSL-CM5A-MR, MOHC-HadGEM2-ES, and MPI-M-MPI-ESM-LR. For each host GCM, the first ensemble member (r1i1p1) of climate realization has been used except the ICHEC-EC-EARTH model, r12i1p1 realization has been used. All simulations have the same physical version (p1) and initialization method (i1) but differ in initial states (i.e., r1 and r12). After 2005, the future scenarios diverge, and we investigate projected change in compound flood climatology during 2040 – 2069 using business as usual RCP8.5 scenario to cover extremes. While we simulate surge at 33 tide gauges using hydrodynamic model Delft3D (Delft3D-FLOW, 2014), the simulation of discharge from 39 stream gauges is performed using the global hydrological and water use model, WaterGAP 2.2d (Müller Schmied et al., 2014). Since we are mostly interested in the meteorological phenomena that drive the compound flood mechanism, we focus on modeling of surges and do not simulate tides. The individual datasets of the surge and discharge time series for each host GCMs in the GCM-RCM chains are available in the sub-folders ‘Discharge’ and ‘Surge’ under the zip-file ‘CF_drivers’.

Soil chemical, physical and hydrological characteristics in two agroforestry systems in Malawi

The described dataset was the result of a field effort consisting of several campaigns to assess the influence of carbon increase as a result of agroforestry treatments on soil hydrological characteristics and water fluxes at two sites in Malawi. At the sites, two experimental trials have been established which differ in age and soil characteristics, while climatic conditions are roughly comparable. At both sites we focused on control plots of maize and agroforestry treatments including Gliricidia sepium (Jacq.) Walp. as the tree component. The dataset contains soil characteristics such as texture, porosity, carbon and nitrogen concentrations, carbon density fractions, dispersible clay proportions, soil hydraulic conductivity and water retention curves. To assess the differences in water fluxes between treatments and sites, we installed soil moisture and matric potential sensors and a small weather station at the sites and monitored the fluxes over the course of about three months. The resulting time series are also part of the dataset, as well as some measurements of maize heights. The file structure of the dataset as well as details on the sites, sampling procedures, measurements and methodology are included in the data description.

European Catchment Climate Reanalysis Data

This dataset contains catchment average time series of five meteorological or hydrological parameters for 3872 hydrometric stations across Europe from 1960-2010. The parameters are: rainfall, soil moisture saturation, snowmelt, snow cover and convective conditions. All parameters have a daily resolution and were derived from a 0.11x0.11° reanalysis dataset. Daily averages were calculated from the pixels within each catchment, weighted by the fraction of pixel area that lies within the respective catchment. This dataset was originally created for the classification of floods by their generating process, but is also suitable for different hydrological studies.The dataset consists of two types of files:(1) The station metadata, which contains latitude, longitude, catchment area and an ID for each hydrometric station.(2) The five time series datasets, which contain one value for each station ID and each day from 1960-01-01 to 2010-12-31.

Hydrological, pedological, dendrological and meteorological measurements in a blackberry-alder agroforestry system in South Africa

The described dataset resulted from a joint multidisciplinary measurement campaign in an agroforestry system in the Western Cape region in South Africa. Five participating institutions measured a range of environmental variables to characterise the influence of windbreak trees onto water fluxes, nutrient distribution and microclimate in the adjacent blackberry field. The dataset contains spatially collected soil characteristics, a soil profile description, time series of meteorological measurements as well as soil moisture and matric potential, information on soil hydraulic properties of the soil determined in the laboratory and windbreak characteristics and shape from a point cloud derived from terrestrial LiDAR scanning.

1