Journal of Hydrology

Water Quality datsa of the MApocho Upper Basin (Chile)

Shareable data, code, and supplementary figures related to the establishment and calibration of a spatially distributed catchment-scale model of alpine snow dynamics

The data of pore pressures, acceleration and water level in every scenario (total 24 scenarios), collected by the pore pressure, acceleration and water level sensors. They will be used to analyze the variation of pore pressures, acceleration and water level in different shaking conditions. Data Set S1-S24. The data of pore pressures in Scenario 1-24, collected by the pore pressure sensors in settings. It includes 9 pore pressure points, P1-P9 in order(see the position in figure 1). The first and second column is DATE and TIME, from the third to eleventh columns are the PORE PRESSURE data. Note that the collection frequency is 256Hz, so there are 256 numbers in 1 second. Data Set S25-48. The data of acceleration in Scenario 1-24, collected by the acceleration sensors in settings. It includes 5 acceleration points, A1-A5 in order(see the position in figure 1). The first and second column is DATE and TIME, from the third to seventh columns are the ACCELERATION data. Note that the collection frequency is 256Hz, so there are 256 numbers in 1 second. Data Set S49-72. The data of water level in Scenario 1-24, collected by the three water level sensors in settings. It has 3 columns of water level, No.1-No.3 in order(see the position in figure 1). The first and second column is DATE and TIME, from the third to fifth columns are the WATER LEVEL data. Note that the collection frequency is 1Hz, so there is 1number in 1 second undercollection.

These dataset contain the main R codes used in the article.

Our data describe a time series and a spatial analysis of a subtropical creek

Nutrient leaching from agricultural systems is a global threat to waterways. However, indirect aquatic emissions of the potent greenhouse gas nitrous oxide (N2O) from agricultural runoff remain poorly understood. Here, we assess how episodic rain events flushing an agricultural catchment drive nutrients, radon (a natural groundwater tracer) and N2O emissions. We reveal significant nitrate + nitrite (NOX) loads and N2O emissions consistent with N leaching and runoff in Hearnes Lake, Australia. Aquatic nitrogen loads were equivalent to ~15% of applied fertiliser and dominated by NOX (78%). Aquatic N2O (up to 5655% sat.) and atmospheric emissions (up to 2859 µmol m2 day-1) were amongst the highest reported from global waterways. N2O fluxes experienced large variations (~800 fold) between dry and rain periods. Observed N2O emissions and IPCC EF5 N2O emissions were 6.27 N2O-N yr-1 and 26.5 kg N2O-N yr-1, respectively. Estimated N2O emissions ~2 km downstream of farms were 0.79% of DIN loads and 0.004% of applied fertiliser. Aquatic N2O emissions represented ~10% of the total (CO2 + CH4 + N2O) CO2 equivalent emissions which is ~60% higher than the IPCC global N2O average. Episodic rain events rather than groundwater seepage traced by radon were the major drivers of both NOX and N2O, highlighting the importance of detailed sampling approaches to capture extreme variability in heavily fertilised catchments.

1. Model validation Experiments conducted by Seifert and Engesgaard (2012) were used to calibrate our mode 2. Simulations in scenario A & B Simulations in scenario A & B ( In the case of a specific flow rate: 1e-4 m/s)

Geochemical data for the Yangtze Estuary and East China Sea shelf

This data file contains a One-year (2017) data of stable isotopes for water [deuterium (2H) and oxygen-18 (18O)] from River, precipitation, glacial ice, Supra glacial channel and Lake water for Alaknanda river in Satopanth glacier basin at Central Himalayan region, India. There are 275 records with spatial and temporal variation in the basin.

15-minute drip data (sum of drips) for 9 sites at South Glory Cave 2013-2019. Missing data infilled using QuickSampling