Journal of Hydrology: Regional Studies

This data contains the shape files and groundwater inventory data for the article.

The dataset (n=54) represents the results of analysis for hydrogen and oxygen isotope composition of water with calculated deuterium excess (d-excess: δ^2 H=8δ^18 O+10). The samples were collected during one hydrologic year from November 2015 to November 2016 at Burabay National Nature Park (BNNP), Kazakhstan. Lake water samples (n=30) were collected approximately each month during ice-free period, groundwater (n=13) and streamwater (n=6) samples quarterly. The lake water samples were collected by grab sampling at the shoreline usually at fixed locations (if the location for a given sample for a lake was different from the fixed point it was indicated in comment column and coordinates are provided). The lake water was sampled at approximately monthly intervals, from the start of the open water season (end of April) to the first days of November 2016 (about one week before permanent ice-cover; ice-on). Snow samples (n=2) collected near Lake Shortandy were melted in a sealed container at room temperature. Rainfall samples (n=3) were collected at Kazakh State Hydrometeorological Agency (Kazhydromet) weather station near Ulken Shabakty Lake. The rainfall samples were collected during abundant precipitation events using a large plastic container and immediately transferred into the vials and sealed. Groundwater samples were collected from boreholes (GrdW1-4) using a bailer. Groundwater was sampled during the open water season at approximately three-month intervals (end of April, mid-July, and end of October 2016). Stream samples were collected at approximately the same times as groundwater samples following the same sampling procedure used for lake water samples. The samples were analyzed at the Global Institute for Water Security, McDonnell Watershed Hydrology Laboratory (Saskatchewan, Canada) on a Liquid Water Isotope Analyzer (Los Gatos Research). The analyzer uses liquid water Off-Axis Integrated-Cavity Output Spectroscopy (Off-Axis ICOS) and has an uncertainty of ≤ ±1.0 for δ2H and ±0.2 for δ18O. The following reference materials were used to normalize obtained values to VSMOW international scale: ‘Saskatoon Snow Melt Water’ (SSMW): δ2H= -200.4 ‰, δ18O = -26.1 ‰; and ‘Enriched’: δ2H=3.2 ‰, δ18O=-0.3 ‰). All values are reported as parts per thousand (‰) according to the Vienna Standard Mean Ocean Water - Standard Light Antarctic Precipitation (VSMOW-SLAP) scales. See also the Materials and Methods sections (3.2 and 3.3) of the article. The sampling points and weather station location can also be found in Google Earth geospatial data format file (Burabay isotopes paper.kmz).

Provided data presented in this article data include monthly LDOC per LULC (Table 1), monthly LDOC per site (Table 2), biweekly and monthly stream DOC (Table 3), soil properties such as percent content of clay, silt, sand, total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC) (Table 4), soil hydrological properties (Table 5), rainfall characteristics including rainfall amount and rainfall intensity (Table 6), and rainfall soil storage and runoff (Table 7) for the RRW. All data were used to analyze DOC leaching dynamics and identify their potential implications for stream DOC in the watershed. Additionally, the data can be used for soil and inland water degradation analysis, and therefore for developing a better understanding of the effects of ongoing environmental change on terrestrial and aquatic ecosystem function.

Input files for hourly precipitation at 87 segments of watershed. 4 precipitation data are provided: gauge data, and satellite-based products: TMPA, CMORPH and PERSIANN. HSPF model output files for 6 locations in the watershed which include results for streamflow, and multiple water quality indicators. Output is provided for the gauge-based precipitation forcing simulation and the 3 satellite-based precipitation product simulations.

Basin characteristics for 918 basins in the contiguous United States (US). Basins use US Geological Survey stream gauge IDs that can be cross-referenced on the National Water Information System (waterdata.usgs.gov/nwis). For a description of the characteristics, see paper titled "Regional regression models of percentile flows for the contiguous United States: Expert versus data-driven independent variable selection" currently under review for Journal of Hydrology: Regional Studies. Please contact Geoffrey Fouad at gfouad@monmouth.edu for questions.

1.Data summaryThis dataset is based on water body data, water level data, and DEM data, and is obtained by simulation using the RFim model and U-net model. The dataset contains two sub-datasets (East Dongting Lake and Poyang Lake) and is available in .tif format. The .tif file has two values, i.e., "0" and "1", with "0" indicating unflooded and "1" indicating flooded.2.Data sourcesThe raw data for the simulated reconstruction of the flood inundation dataset come from the Sentinel-1 SAR data and Sentinel-2 MSI data provided by ESA, ALOS 12.5mDEM data provided by the official website of NASA, and water level data provided by the website of River Water Intelligence Table. This data is a phase result of the National Key Research and Development Program for Young Scientists: Intelligent Analysis and Mining Technology and Software for Spatial and Temporal Processes Based on Multi-source Big Data (Project No.: 2021YFF0704400).3.A method of producing or processing dataBased on the water body data, DEM data and water level data, the relationship between water level and flood inundation extent was established using the RFim model and the U-net model, which was then simulated and reconstructed to obtain this dataset.4.Data space projectionWGS845.Description of data qualityThe overall accuracy of the data set is 88% ~ 97% , and the kappa coefficient is 0.601 ~ 0.932. The overall accuracy in East Dongting Lake is 88% ~ 97% with kappa coefficient greater than 0.67, while that in Poyang Lake is 89% ~ 97% with kappa coefficient greater than 0.6. Data quality is good.

R and Python scripts for satellite precipitation data extraction and analysis

This dataset is a compilation of δ18O and δD values in river surface waters of Indian Rivers reported in the literature (n=399) in the pre-monsoon and monsoon season. This includes the Coastal Rivers draining the Western Ghats into the Arabian Sea, Peninsular Rivers flowing into the Bay of Bengal and Himalayan Rivers with the major Indus and Ganges River.