The climatic water content is calculated as previously described [1, 2] using the average number of consecutive dry days obtained from precipitation timeseries (MSWEP [3]), potential evapotranspiration [4] (based on WorldClim [5]) and soil information (SoilGrids [6]).
The maps are generated at 0.1° resolution with a global extent of -180 to 180 °E and -60 to 90 °N.

References
[1] Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. “A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness.” Scientific Reports 9, no. 1 (August 20, 2019): 1–10. https://doi.org/10.1038/s41598-019-48571-w.
[2] Bickel, Samuel, and Dani Or. “Soil Bacterial Diversity Mediated by Microscale Aqueous-Phase Processes across Biomes.” Nature Communications 11, no. 1 (January 8, 2020): 1–9. https://doi.org/10.1038/s41467-019-13966-w.
[3] Beck, Hylke E., Eric F. Wood, Ming Pan, Colby K. Fisher, Diego G. Miralles, Albert I. J. M. van Dijk, Tim R. McVicar, and Robert F. Adler. “MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment.” Bulletin of the American Meteorological Society 100, no. 3 (March 2019): 473–500. https://doi.org/10.1175/BAMS-D-17-0138.1.
[4] Jensen, M. E., and H. R. Haise. “Estimating Evapotranspiration from Solar Radiation.” Proceedings of the American Society of Civil Engineers, Journal of the Irrigation and Drainage Division 89, no. 0023 (1963): 15–41.
[5] Fick, Stephen E., and Robert J. Hijmans. “WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas: New Climate Surfaces for Global Land Areas.” International Journal of Climatology 37, no. 12 (October 2017): 4302–15. https://doi.org/10.1002/joc.5086.
[6] Hengl, Tomislav, Jorge Mendes de Jesus, Gerard B. M. Heuvelink, Maria Ruiperez Gonzalez, Milan Kilibarda, Aleksandar Blagotić, Wei Shangguan, et al. “SoilGrids250m: Global Gridded Soil Information Based on Machine Learning.” PLOS ONE 12, no. 2 (February 16, 2017): e0169748. https://doi.org/10.1371/journal.pone.0169748.

The presented database is a set of hydrological, meteorological, environmental and geometric values for Russia Federation for the period from 2008 to 2020.

Database consist of next items:

• Point geometry for hydrological observation stations from Roshydromet network across Russia
• Geometry of the catchment for correspond observation station point
• Daily hydrological values
  • Water level
    • In relative representation (sm)
    • In meters of Baltic system (m)
  • Water discharge
    • as an observed value (qms/s)
    • as a layer (mm/day)
• Daily meteorological values
  • Maximum and minimum daily temperatures (°C) from ERA5 and ERA5-Land
  • Total precipitation (mm/day) from ERA5, ERA5-Land, IMERG v06, GPCP v3.2 and MSWEP
  • Different kind of evaporation (mm/day) corresponded to each variable calculated in GLEAM model
• Set of hydro-environmental characteristics derived from HydroATLAS database

Each variable derived from the grid data was calculated for each watershed, taking into account the intersection weights of the watershed contour geometry and grid cells.

Coordinates of hydrological stations were obtained from resource of Federal Agency for Water Resources of Russia Federation—AIS GMVO

To calculate the contours of the catchment areas, a script was developed that builds the contours in accordance with the rasters of flow directions from MERIT Hydro. To assess the quality of the contour construction, the obtained value of the catchment area was compared with the archival value from the corresponded table from AIS GMVO. The average error in determining the area for 2080 catchments is approximately 2%

To derive values for different hydro-environmental values from HydroATLAS were developed approach which calculate aggregated values for catchment, leaning on type of variable: qualitative (Land cover classes, Lithological classes etc.) Or quantitive (Air temperature, Snow cover extent etc.). Every quantitive variable were calculated as mode value for intersected sub-basins and target catchment, e.g. most popular attribute from sub-basins will describe whole catchment which are they relating. Quantitative values were calculated as mean value of attribute from each sub-basin. More detail could be found in publication.

Files are distributed as follows:

Each file has some connection with the unique identifier of the hydrological observation post. Files in netcdf format (hydrological and meteorological series) are named in response to identifier.

Every file which describe geometry (point, polygon, static attributes) has and column named gauge_id with same correspondence.

• attributes/static_data.csv – results from HydroATLAS aggregation
• geometry/russia_gauges.gpkg – coordinates of hydrological observation stations

  • 	gauge_id	name_ru	name_en	geometry
    0	49001	р. Ковда – пос. Софпорог	r.Kovda - pos. Sofporog	POINT (31.41892 65.79876)
    1	49014	р. Корпи-Йоки – пос. Пяозерский	r.Korpi-Joki - pos. Pjaozerskij	POINT (31.05794 65.77917)
    2	49017	р. Тумча – пос. Алакуртти	r.Tumcha - pos. Alakurtti	POINT (30.33082 66.95957)

• geometry/russia_ws.gpkg – catchments polygon for each hydrological observation stations

  • 	gauge_id	name_ru	name_en	new_area	ais_dif	geometry
    0	9002	р. Енисей – г. Кызыл	r.Enisej - g.Kyzyl	115263.989	0.230	POLYGON ((96.87792 53.72792, 96.87792 53.72708...
    1	9022	р. Енисей – пос. Никитино	r.Enisej - pos. Nikitino	184499.118	1.373	POLYGON ((96.87792 53.72708, 96.88042 53.72708...
    2	9053	р. Енисей – пос. Базаиха	r.Enisej - pos.Bazaiha	302690.417	0.897	POLYGON ((92.38292 56.11042, 92.38292 56.10958...

  • Column ais_diff is corresponded to % error in area definition
• nc_all_q
  • netcdf files for hydrological observation stations which has no missing values on discharge for 2008-2020 period
• nc_all_h
  • netcdf files for hydrological observation stations which has no missing values on level for 2008-2020 period
• nc_all_q_h
  • netcdf files for hydrological observation stations which has no missing values on discharge and level for 2008-2020 period
• nc_concat
  • data for all available geometry provided in dataset

More details on processing scripts which were used for development of this database can be found in folder of GitHub repository where I store results for my PhD dissertation

05.04.2023 – Significant data changes. Removed catchments and related files that have more than ±15% absolute error in calculated area relative to AIS GMVO information. Now these are data for 1886 catchments across the Russia.

17.05.2023 – Significant data changes. Major review of parsing algorithm for AIS GMVO data. Fixed the way of how 0.0xx values were read. Use previous versions with caution.

11.10.2023 – Significant data changes. Added 278 catchments for CIS region from GRDC resource. Calculate meteorological and environmental attributes for each catchment. New folder /nc_all_q_h with no missing observations on discharge and level. Now these are data for 2164 catchments across CIS.

Dataset prepared for manuscript "The effect of surge on riverine flood hazard and impact in deltas globally" (Eilander et al 2020)

This dataset includes water level data and discharge at 3433 river mouth locations globally, including several components of nearshore still water levels based on a model framework for global compound flood simulations. We usedof runoff from tier 2 of the EartH2Observe (E2O) project (Dutra et al 2017, Schellekens et al 2017) with meteorological forcing from ERA-Interim (Dee et al 2011) and MSWEP v1.2 (Beck et al 2017), surge levels from the Global Tide and Surge Reanalysis (GTSR) based on the GTSM model (Muis et al 2016), and tide levels from the FES2012 model (Carrere et al 2012). These runoff and dynamic sea level (surge and tide) data were used to force the global river routing model CaMa-Flood (Yamazaki et al 2011) to simulate riverine water levels.

The accompanying excel file provides an table explaining the data dimensions, variables and metadata.

Version 1.2 (March 2025): Now with longer time series, expanded stream gauge coverage, meteorological data from additional sources, soil moisture time series, and observed rainfall time series from 11,853 rain gauges.

This is the CAMELS-BR dataset (Catchment Attributes and MEteorology for Large-sample Studies – Brazil) accompanying the paper: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.

CAMELS-BR provides daily observed streamflow time series for 4,025 stream gauges, daily observed rainfall for 11,853 rain gauges, daily meteorological time series and 65 attributes for 897 catchments in Brazil.

The daily hydrometeorological time series include (i) observed streamflow accompanied by quality control information, (ii) precipitation extracted from five products, (iii) actual evapotranspiration extracted from three products, (iv) potential evapotranspiration extracted from two products, (v) reference evapotranspiration extracted from one product, (vi) minimum, mean, and maximum temperature extracted from three products, and (vii) soil moisture extracted from two products.

The 65 catchment attributes cover properties such as (i) topography, (ii) climate, (iii) hydrology, (iv) land cover, (v) geology, (vi) soil, and (vii) human intervention.

The data follow the same standards as other CAMELS datasets such as for the United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), and Great Britain (https://doi.org/10.5194/essd-2020-49).

How to cite: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.

Changes in CAMELS-BR version 1.2:

Major changes

Updated streamflow time series up to February 2025 (where available), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Some historical records have changed slightly due to ANA's quality control procedures. For eight gauges (see the readme.txt file), data are merged from 2025 and 2019 records (i.e. from CAMELS-BR version 1.1).

Increased the stream gauge coverage to 4025 stream gauges (including both quality-controlled and non-quality-controlled series), up from 3679 in version 1.1.

Added daily observed rainfall time series for 11853 rain gauges (not catchment averages), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Data include quality flags but are mostly not quality-controlled.

Added a GeoPackage file with coordinates for 11853 rain gauges.

Updated precipitation time series (catchment averages) up to October 2024 (where available). Now derived from: CHIRPS v2.0; CPC; ERA5-Land; MSWEP v2.8; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil – 864 catchments).

Updated actual evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from: GLEAM v4.2a; ERA5-Land; and MGB-SA.

Updated potential evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from GLEAM v4.2a and ERA5-Land.

Added reference evapotranspiration time series (catchment averages). Derived from BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).

Updated daily maximum, mean, and minimum temperature time series (catchment averages) up to October 2024 (where available). Now derived from: CPC; ERA5-Land; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).

Added daily soil moisture time series (catchment averages) up to December 2024 (where available). Computed from GLEAM v4.2a and ERA5-Land.

Improved meteorological data processing. Catchment averages now account for pixel fraction coverage.

Reformatted meteorological time series files. Files now includes data from different products, with columns renamed for clarity.

Hydrological and climatic indices were not updated, despite the new streamflow and meteorological data.

Minor changes

Updated stream gauge coordinates based on ANA's website on 27 February 2025. Coordinates were updated for 73 gauges in the 897 selected catchments and for 298 gauges across all catchments.

Streamflow time series now include quality flag values from 0 to 7 (see the readme.txt file), previously from 0 to 4 in CAMELS-BR version 1.1. Flags from 5 to 7 may be present only in the last few years of data.

Streamflow time series files for the 897 selected gauges now include values in both millimeters per day and cubic meters per second.

Removed streamflow time series with fewer than 180 days of measurement.

Converted gauge and catchment spatial data from Shapefile (.shp) to GeoPackage (.gpkg).

Catchment areas computed by GSIM (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") flagged as "caution" for quality were set to "nan" due to low reliability.

Updated catchment areas computed by ANA (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") to reflect the newest ANA's data from 27 February 2025. Streamflow values in millimeters per day remain unchanged because unit conversions rely on GSIM areas.

Set catchment areas with zero squared kilometers, as computed by ANA, to "nan".

Removed CPC daily mean temperature time series (catchment averages) because they were a simple average of minimum and maximum temperatures. For daily mean temperatures, refer to ERA5-Land data (now included) as they are computed from hourly data.

Current global multi-source merged precipitation datasets can facilitate better utilization of the complementary nature of gauge-, satellite-, and reanalysis-based precipitation estimates, particularly for capturing precipitation variability. However, merging these datasets at high resolutions of 1-hourly and 0.1° on a full global scale remains a substantial challenge for the scientific community owing to high spatiotemporal heterogeneities. This study proposed a merging-and-calibration framework to optimally integrate the advantages of gauge-, satellite-, and model-based precipitation estimates, focusing on precipitation occurrences and providing a new fully Global multi-source Merging-and-Calibration Precipitation dataset (GMCP: 1-hourly, 0.1°, global, 2000–Present). The main conclusions included: (1) GMCP generally outperformed the input datasets, ERA5-Land, GSMaP-MVK, and IMERG-Late, across various spatiotemporal scales, both in regional statistics and extreme precipitation systems; (2) GMCP significantly outperformed IMERG-Final, calibrated by gauge analysis at the monthly scale, with the improvements in correlation coefficient (CC), root mean square error (RMSE), and Heidke skill score (HSS) by approximately 66.67%, 39.25%, and 26.83%, respectively, from 2016 to 2020 over the Continental United States (CONUS); (3) compared to the state-of-the-art multi-source merged product with a daily gauge correction scheme, MSWEP V2 (3-hourly and 0.1°), GMCP demonstrated the notable improvements with an approximately 20% enhancement in accurately capturing the precipitation occurrences against approximately 67, 000 rain gauges over Mainland China in 2016; (4) in comparison to another well-known multi-source merged quasi-global daily and 0.05° precipitation product, CHIPRS integrating the gauge-, satellite-, and reanalysis-based precipitation estimates, GMCP also demonstrated the notable improvements at the daily scale, achieving the increases in CC, RMSE, and HSS by around 57.45%, 38.18%, and 75.76%, respectively, against approximately 67, 000 rain gauges over Mainland China in 2016; and (5) this framework was suitable for generating the fully global precipitation datasets at 1-hourly and 0.1° scales, significantly mitigating the inherent shortcomings of each input dataset, with GMCP demonstrating the great potential as a valuable resource for worldwide scientific research and societal applications.

Hydrological variables (evapotranspiration, precipitation, runoff, and terrestrial water storage) are distributed through several datasets that do not have the same characteristics (spatial and temporal coverage, spatial and temporal resolution). This dataset aims at providing a coherent gathering of all datasets available as of January 2021, covering at least the period from 2003 to 2014, and from 50°S to 50°N.

This file contains:

14 datasets for evapotranspiration (ERA5-Land, FLUXCOM, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GLEAM, JRA55, MERRA2, MOD16, and SEBBop)

11 datasets for precipitation (CPC, CRU, ERA5-Land, PGF, GPCC, GPCP, GPM, JRA55, MERRA2, MSWEP, and TRMM)

11 datasets for runoff (ERA5-Land, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GRUN, JRA55, and MERRA2)

2 datasets for terrestrial water storage (GRACE CSR mascons, and GRACE JPL mascons)

Each dataset is given as the original version (on a regular grid) and also as a post-treated file averaged for 189 river basins (whose borders are also provided).

The dataset ‘Rainfall Associated with Flood Fatalities in the Euro-Mediterranean Region (1980–2020)’ links rainfall extracted from the MSWEP database to flood fatalities recorded in FFEM-DB. The FFEM-DB provides detailed information on each fatality, including geographical locations. The dataset includes rolling maximum precipitation values for multiple durations (3h, 6h, 12h, 24h, and 48h) and daily rainfall (5-day window) at each FF location, allowing for a detailed assessment of rainfall intensity leading to fatal flood events. The data support the findings of the peer-reviewed article: Papagiannaki, K., Kotroni, V., Lagouvardos, K., Diakakis, M. & Kyriakou, P. Rainfall patterns and their association with flood fatalities across diverse Euro-Mediterranean regions over 41 years. npj Nat. Hazards 2, 39 (2025).

https://doi.org/10.1038/s44304-025-00095-2