The Russian River Watershed (RRW) covers about 1,300 square miles (without Santa Rosa Plain) of urban, agricultural, and forested lands in northern Sonoma County and southern Mendocino County, California. Communities in the RRW depend on a combination of Russian River water and groundwater to meet their water-supply demands. Water is used primarily for agricultural irrigation, municipal and private wells supply, and commercial uses - such as for wineries and recreation. Annual rainfall in the RRW is highly variable, making it prone to droughts and flooding from atmospheric river events. In order to better understand surface-water and groundwater issues, the USGS is creating a Coupled Ground-Water and Surface-Water Flow Model (GSFLOW; Markstrom and others, 2008) of the RRW. This model will include climate, geology, surface-water, groundwater, and land-use data. These climate data are temperature, precipitation, solar radiation, and reference evapotranspiration observations from sta ...

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|pollen with a geographic location of Russia, Eastern Europe. The time period coverage is from 12836 to -90 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

CLImate GENerator (CLIGEN) is a stochastic weather generator that produces weather time series for soil erosion modeling and various other applications. The generated time series are statistically similar to observed long-term time series. This gridded CLIGEN parameterization with 0.25° spatial resolution complements existing global coverages by filling in remaining gaps that existed in the northern hemisphere (see the map layer .kmz file with all grid point locations). The coverage is largely represented by Canada, Europe, and Russia and encompasses countries north of ~40°N with no previous known coverage. The CLIGEN inputs may be used to generate daily precipitation, temperature, dewpoint, solar radiation, and wind time series, as well as sub-daily precipitation patterns. The gridded parameterization allows CLIGEN time series to be generated at any point the grid. In particular, the dataset can provide climate drivers for climate-related research in ungauged areas where observed climate records are unavailable. The data are formatted as CLIGEN .par files, which are the only required input for CLIGEN. The files are contained in the "Grid Files" download with n=114,150 files corresponding to the total number of grid points. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign.

Method for quantitative reconstruction of mean July air temperatures (Tjuly). The quantitative reconstruction of mean July air temperatures (TJuly) is based on calibration chironomid data sets for lakes from northern Russia (Nazarova et al., 2015, doi:10.1016/j.gloplacha.2014.11.015). Mean July air temperatures were inferred using a North Russian (NR) chironomid-based temperature inference model (WA-PLS, 2 component; r 2 boot = 0.81; RMSEP boot =1.43 °C) based on a modern calibration data set of 193 lakes and 162 taxa from East and West Siberia (61-75°N, 50-140 °E, T July range 1.8 - 18.8 °C). The mean July air temperature of the lakes for the calibration data set was derived from New et al. (2002, doi:10.3354/cr021001). The TJuly NR model was previously applied to palaeoclimatic inferences in Europe, arctic Russia, East and West Siberia, and demonstrated a high reliability of the reconstructed parameters. The chironomid-inferred TJuly were corrected to 0 m a.s.l. using a modern July air temperature lapse rate of 6 oC km-1. Chironomid-based reconstructions were performed in C2 version 1.7. The chironomid data was square-rooted to stabilize species variance. To assess the reliability of the chironomid-inferred TJuly reconstruction, we calculated the percentage abundances of the fossil chironomids that are rare or absent in the modern calibration data set. A taxon is considered to be rare in the modern data when it has a Hill N2 below 5. Optima of the taxa that are rare in modern data are likely to be poorly estimated. Goodness-of-fit statistics derived from a canonical correspondence analysis (CCA) of the modern calibration data and down-core passive samples with TJuly as the sole constraining variables was used to assess the fit of the analyzed down-core assemblages to TJuly. This method shows how unusual the fossil assemblages are in respect to the composition of the training set samples along the temperature gradient. Fossil samples with a residual distance to the first CCA axis larger than the 90th and 95th percentile of the residual distances of all the modern samples were identified as samples with a 'poor fit' and a 'very poor fit' with the reconstructed variable (TJuly). CCA was performed using CANOCO 5. In the evaluation of goodness-of-fit, the CCA scaling focused on inter-sample distances with Hill's scaling selected to optimize inter-sample relationships.

Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE's ) of Water Resources project develops state-of-the-art daily precipitation and temperature datasets with high-resolution grids for Asia. The datasets are created primarily with data obtained from a rain-gauge-observation network. Product APHRO_RU offers gridded daily precipitation of the northern Eurasia including Russia for 1951-2007.

Metadata: Title: Rainfall Erosivity in the WorldDescription: This map provides a complete rainfall erosivity dataset for the whole World based on 3625 precipitation stations and around 60,000 years of rainfall records at high temporal resolution (1 to 60 minutes). Gaussian Process Regression(GPR) model was used to interpolate the rainfall erosivity values of single stations and to generate the R-factor map. In addition, we explore an approach to derive an R-factor based on satellite data.Spatial coverage: WorldPixel size: 30 arc-seconds (~1 km at the Equator).Measurement Unit: MJ mm ha-1 h-1 yr-1Projection: ETRS89 Lambert Azimuthal Equal AreaTemporal coverage: 30-40 years - Predominant in the last decade: 2000 - 2010 Global R-factor The purpose of this study is to assess rainfall erosivity inthe World in the form of the RUSLE R-factor, based on the best available datasets in the Globe. We used the Global Rainfall Erosivity Database (GloREDa) which contains 3,625 precipitation stations from 63 countires in the Globe with temporal resolutions of 1 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 52 years. The average time series per precipitation station is around 16.8 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 30 arc-seconds (~1 km at the Equator). Globally, the mean rainfall erosivity is estimated to be 2,190 MJ mm ha-1 h-1 yr-1 and broadly reflects climatic patterns, with the highest values, (which are 3 three times highergreater than the mean) are found in South America (especially around the Amazon Basin) and the Caribbean countries, Central and parts of east Western Africa and South East Asia. The lowest values are mainly found in mid and high latitude regions such as Canada, the Russian Federation, Northern Europe, Northern Africa, the and Middle East and southern Australia. It should be noted that high rainfall erosivity does not necessarily mean high erosion as factors such as soil characteristics, vegetative cover and land use are also important factors.The new global erosivity map is a critical input to global and continental assessments of soil erosion by water, flood risk and natural hazard prevention. Current global estimates of soil erosion by water are very uncertain, ranging over one order of magnitude (from around 20 to over 200 Pg per year). More accurate global predictions of rill and interrill soil erosion rates can only be achieved when the rainfall erosivity factor is thoroughly computed. GloREDa: Global Rainfall Erosivity Database At global scale, this is the first time ever that an erosivity database of such dimension is compiled. The Global Rainfall Erosivity Database, named hereafter as GloREDa, contains erosivity values estimated as R-factors (refer to the method section) from 3,625 stations distributed in 63 countries worldwide. This is the result of an extensive data collection of high temporal resolution rainfall data from the maximum possible number of countries in order to have a representative sample across different climatic and geographic gradients. GloREDa has three components, which are described in the relevant publication: The Rainfall Erosivity database at European Scale (REDES) 1,865 stations from 23 countries outside Europe (Australia, New Zealand, South Korea, Japan, China, India, Malaysia, Iran, Kuwait, Israel, Turkey, Russian Federation, United States of America, Mexico, Costa Rica, Jamaica, Colombia, Suriname, Chile, Brazil, Algeria, South Africa, Mauritius). 85 stations collected from a literature review (12 countries) The number of GloREDa stations varied greatly among continents. Europe had the largest contribution to the dataset, with 1,725 stations (48% of total), while South America had the lowest number of stations (141 stations or ~4% of total). Africa has very low density of GloREDa stations (5% of the total). In North America and the Caribbean, we collected erosivity values from 146 stations located in 6 countries (Unites States, Canada, Mexico, Cuba, Jamaica and Costa Rica). Finally, Asia and the Middle East were well represented in GloREDa, with 1,220 stations (34% of the total) distributed in 10 countries including the Siberian part of the Russian Federation, China, India, Japan. GloREDa Database: You can also download the updated GloREDa 1.2 includes measured erosivity (R-factor) data for 3939 stations. In addition, we added the monthly component to GloREDa and we calculated the mean monthly R-factor per station. For 94% (3702 stations) of GloREDa, it was possible to add the monthly R-factor values summarizing 44,424 monthly records. This also includes the he derived twelve (12) global monthly erosivity maps. Additional - derived datasets The Global R-factor data and the poin data have contributed to develop additional datasets such as a) Satellite-based R-factor b) Global assessment of storm disaster-prone areas. a) Satellite-based R-factor In addition, we developed two alternatives for erosivity map based on a) satellite-based rainfall data and b) erosivity density concept. We used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity.The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. The global erosivity map and the satellite derived one are publicly available and can be used by other research groups to perform national, continental and global soil erosion modelling. b) Global assessment of storm disaster-prone areas Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. By using measured Ranfall Erosivity Density (RED) for 3,625 raingauges worldwide and applying kriging methodologies, we identify the damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 hm-2 h-1 yr-1, respectively). We have analysed for the first time the spatial pattern of hydrological hazard associated with rainfall erosivity in a global-scale visualisation. The results indicated that about 31% and 19% of the world’s land area have a greater than 50% probability of exceeding the warning and alert thresholds of 1.5 and 3.0 hm-2 h-1 yr-1, respectively. Data The Global erosivity map (GeoTIFF format) at 30 arc-seconds (~1 km) resolution is available for free download in the European Soil Data Centre (ESDAC). The calculated erosivity values per station in GloREDa will become available in the future pending on the agreed copyright issues with data providers. We also share the recenlty developed global erosivity maps based on satellite high resolution temporal data (CMORPH) and the erosivity density concept. GloREDa calcualted erosivity values can be shared in case of scientific collaborations. The point measured data for 3,625 stations can be requested from contact author (for scientific developments). GloREDa has contributed in developing the Global Rainfall Projections for 2050 and 2070. To get access to the all datasets and the code, please compile the request form ; instructions will then follow how to download the datasets. More information about Global Rainfall erosivity in the corresponding section. References A complete description of the methodology and the application in World is described in the paper:Panagos P., Borrelli P., Meusburger K., Yu B., Klik A., Lim K.J., Yang J.E, Ni J., Miao C., Chattopadhyay N., Sadeghi S.H., Hazbavi Z., Zabihi M., Larionov G.A., Krasnov S.F., Garobets A., Levi Y., Erpul G., Birkel C., Hoyos N., Naipal V., Oliveira P.T.S., Bonilla C.A., Meddi M., Nel W., Dashti H., Boni M., Diodato N., Van Oost K., Nearing M.A., Ballabio C., 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports 7: 4175. DOI: 10.1038/s41598-017-04282-8. GloREDa reference: Panagos, P., Hengl, T., Wheeler, I., Marcinkowski, P., Rukeza, M.B., Yu, B., Yang, J.E., Miao, C., Chattopadhyay, N., Sadeghi, S.H. and Levi, Y., et al. 2023. Global Rainfall Erosivity database (GloREDa) and monthly R-factor data at 1km spatial resolution. Data in Brief, 50, Art.no.109482. DOI: 10.1016/j.dib.2023.109482

The dataset is based on the ensemble of climate change experiments conducted by RegCM5 over mid-high latitudes Northern Asia region. In the experiments, the RegCM5 domain encompasses Northeast and Northwest China, Mongolia, Kazakhstan, Eastern Europe Plain and Siberia in Russia, northern part of Japan, and the adjacent areas and oceans. The model is run at 25 km gird spacing, with its standard configuration of 18 vertical sigma layers and model top at 10 hPa. It is driven by three CMIP6 models of EC-Earth3-Veg, MPI-ESM1-2-HR, and NorESM2-MM. The model simulations include two time slices, 1995 to 2014 for the present day climate, and 2080 to 2099 for the future under the emission pathway of SSP2-4.5. For the model configuration, CLM4.5 land surface process model, planetary boundary layer scheme of UW, Tiedtke cumulus convection scheme, resolved scale micro-physics process described by WSM5 from the WRF modeling system, Xu-Randall empirical scheme to represent cloud fraction, and radiation package from the NCAR Community Climate Model CCM3 for atmospheric radiation transfer, are selected. RegCM5 output is post processed into standard CORDEX format. The time scale is 3hr (for selected variables), 6hr (for selected variables), daily mean, and horizontal resolution as 25 km × 25 km, with grid numbers of 307 (east-west)×220 (north-south).The variables included in this dataset：mrro(Total runoff), mrros(Surface runoff), pr(Precipitation), prc(Convective precipitation), ps(Surface air pressure), psl(Sea level pressure), snm(Surface snow melt), snw(Surface snow amount), sund(Duration of sunshine), tas(Near-surface air temperature), tasmax(Daily maximum near-surface air temperature), tasmin(Daily minimum near-surface air temperature), tauu(Surface downward eastward wind stress), tauv(Surface downward northward wind stress), ts(Surface temperature), ta200(Air temperature at 200hPa, similar to later), ta250, ta300, ta400, ta500, ta600, ta700, ta850, ta925, ta1000.Format of file names:

The Russian weather stations included in this analysis are Krenkel Observatory, Nagurskaya, Rudolf Island and Ostrov Victoria. The temperature data from the stations have undergone both manual and automatic quality controls in several stages. The data were initially manually controlled at the weather station by the observers and have later undergone several rounds of manual and automatic quality control including consistency checks and outlier tests. Tests to identify large errors and suspicious observations in the temperature series included logical tests using differences between maximum, minimum and mean temperature. To identify outliers, Grubbs’ criterion was used where values exceeding ±2.5 standard deviation from the monthly mean were marked and examined. A modified Tietjen-Moore test, was sometimes used to test outliers. All suspicious values were examined by experts at AARI (Arctic and Antarctic Research Institute), RIHMI-WDC (All-Russia Research Institute of Hydrometeorological Information - World Data Center) or SPSU (Saint Petersburg State University) who made the final decision on whether to keep or reject the value. The temperature series were also compared to series from neighboring stations to identify possible systematic errors giving shifts in the data series. The homogenized temperature series from Krenkel Observatory also includes data from the weather station Bukhta Thikaya and has been carefully scrutinized as described by Ivanov et al. 2021*.*Svalbard Airport, Ny-Ålesund and Hopen are weather stations intended for forecasting and climate analysis and the data from these stations undergo extensive quality control (QC) when being stored in MET Norway’s database. Quality control has been performed mostly manually until 2005 when an automatic QC routine was put into use that includes several consistency tests such as step tests and threshold tests, in addition to manual inspection of values flagged as suspicious by the system. There have been several changes in instrumentation and location at all three stations leading to breaks in the homogeneity of the series. More details on quality control, station changes, and homogeneity can be found in Førland et al. 2011, Nordli et al. 2015 & 2020, Gjelten et al. 2016, and Hanssen-Bauer et al. 2019. During a time span of nearly thirty years automatic weather stations (AWS) have been in operation on the northern and eastern islands of Svalbard. The instruments and station infrastructure have varied much during those years. During the early years the data were not stored in MET Norway’s database, and there was no quality control. There were also problems with the regularity of the data, in particular many stations were destroyed by polar bears. In 1996, no data of accepted quality reached MET Norway. However, in 2010 a new setup of stations was developed, which improved data quality and significantly reduced the number of missing data. Hence, almost all our work on data control for this study was related to data before the autumn of 2010.

Northern peatlands represent one of the largest carbon pools in the biosphere the carbon they store are increasingly vulnerable to perturbations from climate and land-use change. Meteorological observations directly at peatland areas in Siberia are unique and rare, while peatlands characterized by a specific local climate. This paper presents a hydrological and meteorological dataset collected at the Mukhrino peatland, Khanty–Mansi Autonomous Okrug – Yugra, Russia over the period of 08 May 2010 to 31 December 2019. Hydrometeorological data collected from stations located at the small pine-shrub-Sphagnum ridge and Scheuchzeria-Sphagnum hollow at the ridge–hollow complexes of ombrotrophic peatland. Monitored meteorological variables include air temperature, air humidity, atmospheric pressure, wind speed and direction, incoming and reflected photosynthetically active radiation, net radiation, soil heat flux, precipitation (rain), and snow depth. The gap-filling procedure based on the gaussian process regression model with exponential kernel was developed to obtain a continuous time series. For the record from 2010 to 2019, the average mean annual air temperature site was −1.0 ◦C, with a mean monthly temperature of the warmest month (July) recorded as 17.4 ◦C and for the coldest month (January) −21.5 ◦C. The average net radiation was about 35.0 W m^-2, the soil heat flux was 2.4 and 1.2 W m^-2 for the hollow and the ridge sites, respectively.

DATASETS:

meteo_MFC_raw.dat - Raw data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). Note: The time step differs during measured period 01.01.2010 – 15.07.2012: 15 minutes; 15.07.2012 – 20.06.2014: 1 hour; 20.06.2014 – 31.12.2020: 30 minutes.

meteo_MFC_qq_1h.dat – Quality controlled data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.

meteo_MFC_gapfilled_1h.dat – Quality controlled and gap-filled hydrometeorological data for the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.

meteo_MFC_raw.dat parameters:

1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).

2. ta_H - Air temperature at 2 m, hollow ( oC ).

3. ta_R - Air temperature at 2 m, ridge ( oC ).

4. rh_H - Relative air humidity at 2 m, hollow ( % ).

5. rh_R - Relative air humidity at 2 m, ridge ( % ).

6. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).

7. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).

8. ws_10m - Wind speed at 10 m ( m s-1 ).

9. wd_10m - Wind direction at 10 m ( deg ).

10. ws_2m - Wind speed at 2 m ( m s-1 ).

11. wd_2m - Wind direction at 2 m ( deg ).

12. stdwd_10m - Standard deviation of wind direction at 10 m for the period of measurement ( m s-1 ).

13. stdwd_2m - Standard deviation of wind direction at 2 m for the period of measurement ( m s-1 ).

14. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).

15. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).

16. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).

17. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).

18. nr_H - Net radiation balance, hollow ( Uncalibrated ).

19. nr_R - Net radiation balance, ridge ( Uncalibrated ).

20. shf_H - Soil heat flux, hollow ( Uncalibrated ).

21. shf_R1 - Soil heat flux, ridge, site 1 ( Uncalibrated ).

22. shf_R2 - Soil heat flux, ridge, site 2 ( Uncalibrated ).

23. ts_2cm_R1 - Soil temperature at 2 cm, ridge, site 1 ( oC ).

24. ts_5cm_R1 - Soil temperature at 5 cm, ridge, site 1 ( oC ).

25. ts_10cm_R1 - Soil temperature at 10 cm, ridge, site 1 ( oC ).

26. ts_20cm_R1 - Soil temperature at 20 cm, ridge, site 1 ( oC ).

27. ts_50cm_R1 - Soil temperature at 50 cm, ridge, site 1 ( oC ).

28. ts_2cm_R2 - Soil temperature at 2 cm, ridge, site 2 ( oC ).

29. ts_5cm_R2 - Soil temperature at 5 cm, ridge, site 2 ( oC ).

30. ts_10cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).

31. ts_20cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).

32. ts_50cm_R2 - Soil temperature at 50 cm, ridge, site 2 ( oC ).

33. ts_2cm_H1 - Soil temperature at 2 cm, hollow, site 1 ( oC ).

34. ts_5cm_H1 - Soil temperature at 5 cm, hollow, site 1 ( oC ).

35. ts_10cm_H1 - Soil temperature at 10 cm, hollow, site 1 ( oC ).

36. ts_20cm_H1 - Soil temperature at 20 cm, hollow, site 1 ( oC ).

37. ts_50cm_H1 - Soil temperature at 50 cm, hollow, site 1 ( oC ).

38. ts_2cm_H2 - Soil temperature at 2 cm, hollow, site 2 ( oC ).

39. ts_5cm_H2 - Soil temperature at 5 cm, hollow, site 2 ( oC ).

40. ts_10cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).

41. ts_20cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).

42. ts_50cm_H2 - Soil temperature at 50 cm, hollow, site 2 ( oC ).

43. T_cont - Temperature at data logger ( oC ).

44. batt_1 - Battery output voltage at data logger 1 ( V ).

45. batt_2 - Battery output voltage at data logger 2 ( V ).

46. batt_2 - Battery output voltage at data logger 3 ( V ).

meteo_MFC_qq_1h.dat and meteo_MFC_gapfilled_1h.dat parameters:

1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).

2. ta_H - Air temperature at 2 m, hollow ( oC ).

3. ta_R - Air temperature at 2 m, ridge ( oC ).

4. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).

5. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).

6. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).

7. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).

8. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).

9. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).

10. alb_H - Albedo PAR, hollow ( [] ).

11. alb_R - Albego PAR, ridge ( [] ).

12. nr_H - Net radiation balance, hollow ( W m-2 ).

13. nr_R - Net radiation balance, ridge ( W m-2 ).

14. shf_H - Soil heat flux, hollow ( W m-2 ).

15. shf_R1 - Soil heat flux, ridge, site 1 ( W m-2 ).

16. shf_R2 - Soil heat flux, ridge, site 2 ( W m-2 ).

17. ws_10m - Wind speed at 10 m ( m s-1 ).

18. wd_10m - Wind direction at 10 m ( deg ).

19. ws_2m - Wind speed at 2 m ( m s-1 ).

20. wd_2m - Wind direction at 2 m ( deg ).

21. wU_10m - U component of wind at 10 m ( m s-1 ).

22. wV_10m - V component of wind at 10 m ( m s-1 ).

23. wU_2m - U component of wind at 2 m ( m s-1 ).

24. wV_2m - V component of wind at 2 m ( m s-1 ).

25. prs - Atmospheric pressure ( hPa ).

26. sdp - Snow depth ( cm ).

27. prc - Liquid precipitations ( mm ).

Arctic sea-ice loss is emblematic of an amplified Arctic water cycle and has critical feedback implications for global climate. Stable isotopes (δ18O, δ2H, d-excess) are valuable tracers for constraining water cycle and climate processes through space and time. Yet, the paucity of well-resolved Arctic isotope data preclude an empirically derived understanding of the hydrologic changes occurring today, in the deep (geologic) past, and in the future. To address this knowledge gap, the Pan-Arctic Precipitation Isotope Network (PAPIN) was established in 2018 to coordinate precipitation sampling at 19 stations across key tundra, subarctic, maritime, and continental climate zones. Here, we present a first assessment of rainfall samples collected in summer 2018 (n = 281) and combine new isotope and meteorological data with sea ice observations, reanalysis data, and model simulations. Data collectively establish a summer Arctic Meteoric Water Line where δ2H = 7.6⋅δ18O–1.8 (r2 = 0.96, p < 0.01). Mean amount-weighted δ18O, δ2H, and d-excess values were −12.3, −93.5, and 4.9‰, respectively, with the lowest summer mean δ18O value observed in northwest Greenland (−19.9‰) and the highest in Iceland (−7.3‰). Southern Alaska recorded the lowest mean d-excess (−8.2%) and northern Russia the highest (9.9‰). We identify a range of δ18O-temperature coefficients from 0.31‰/°C (Alaska) to 0.93‰/°C (Russia). The steepest regression slopes (>0.75‰/°C) were observed at continental sites, while statistically significant temperature relations were generally absent at coastal stations. Model outputs indicate that 68% of the summer precipitating air masses were transported into the Arctic from mid-latitudes and were characterized by relatively high δ18O values. Yet 32% of precipitation events, characterized by lower δ18O and high d-excess values, derived from northerly air masses transported from the Arctic Ocean and/or its marginal seas, highlighting key emergent oceanic moisture sources as sea ice cover declines. Resolving these processes across broader spatial-temporal scales is an ongoing research priority, and will be key to quantifying the past, present, and future feedbacks of an amplified Arctic water cycle on the global climate system.

Scientific Personnel V. E. Romanovsky, S. S. Marchenko, R.R. Muskett Partner Organizations: Alaska Ecoscience, USA Alfred Wegener Institute, Germany Centre d'etudes Nordiques, Department de Geographie, Universite Laval, Quebec, Canada Danish Meteorological Institute, Denmark Institute of Earth Cryosphere, Russia Institute of Northern Engineering, UAF Interdisciplinary Centre on Climate Change and Department of Geography & Environmental Management, University of Waterloo, Canada International Arctic Research Center, UAF International Permafrost Association, USA Melinkov Permafrost Institute, Russia Moscow Institute of Geography, Russia Academy of Sciences National Center for Atmospheric Research, USA NASA Goddard Space Flight Center, USA Scenarios Network for Alaska Planning (SNAP), UAF Stokholm University, Sweden University of Delaware, USA University of New Hampshire, USA Water Environment Research Center, UAF Local Collaborators: Jorgenson, M.T., Alaska Ecoscience, AK Kholodov, A.L., Geophysical Institute, UAF Daanen, R., Institute of Northern Engineering, UAF Kanevskiy M., Institute of Northern Engineering, UAF Shur, Y., Institute of Northern Engineering, UAF Walsh, J., International Arctic Research Center, UAF Fresco, N., Scenarios Network for Alaska Planning, School of Natural Resources & Agricultural Sciences, UAF Rupp, S., Scenarios Network for Alaska Planning, School of Natural Resources & Agricultural Sciences, UAF Walter-Anthony, K., Water Environmental Research Center, UAF International Collaborators: Christensen, J., Danish Meteorological Institute, Denmark Comiso, J., NASA Goddard Space Flight Center, Oceans and Ice Branch, USA Duguay, C. R., University of Waterloo, Canada Frolking, S., Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA Georgiadi, A., Moscow Institute of Geography, Russian Academy of Sciences Groisman, P., National Climatic Data Center, USA Hachem, S., Université Laval, Québec, Canada Hubberten, H.-W., Alfred Wegener Institute, Potsdam, Germany Harden Jennifer, US Geological Survey, Menlo Park, CA, USA Kattsov, V., Voeikov Main Geophysical Observatory, Russia Kuhry, P., Stockholm University, Sweden Lawrence, D., National Center for Atmospheric Research, USA Malkova, G., Institute of Earth Cryosphere, Russia Pavlova, T., Voeikov Main Geophysical Observatory, Russia Rawlins, M., University of New Hampshire, USA Rinke, A., Alfred Wegener Institute, Potsdam, Germany Romanovskii, N., Moscow State University, Russia Saito, K., Japan Agency for Marine-Earth Science Technology, Japan Shiklomanov, N., University of Delaware, USA Shiklomanov, A., University of New Hampshire, USA Shkolnik, I.M., Voeikov Main Geophysical Observatory, Russia Schirrmeister L, Alfred Wegener Institute, Potsdam, Germany Schuur A.G. Edward, University of Florida, Gainesville, FL, USA Stendel, M., Danish Meteorological Institute, Denmark Wisser, D., Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA Zheleznyak, M., Melnikov Permafrost Institute, Russia Funding: NSF Grants OPP ARC-0652838 [ARC-0520578 and ARC-0632400] NASA (NNOG6M48G), Alaska EPSCoR (NSF) The State of Alaska Study Sites Permafrost Freshwater Interactions Alaska, Canada, Russia Permafrost Observatories?Thermal state of permafrost in Russia and Central Asia Permafrost Freshwater Interactions Project continues investigations began during the Thermal State of Permafrost (TSP) Project with renewed and expanded collaboration. Our efforts focus and expand on permafrost and hydrology changes through geophysical modeling and remote sensing (satellite geodesy). During TSP in cooperation with above mentioned Russian partners a large number of existing boreholes have been identified for possible measurements (candidate sites). Many of these have metadata files on the IPA coordinated GTN-P website. Additional sites will be added to the web site. New boreholes over the next several years are planned. A total of 320 boreholes, located in Russia, Kazakhstan, and Mongolia were considered from the point of view of possibility for continuous geothermal observations (see Figure). Boreholes cover all types of permafrost, from continuous to sporadic, both on the plains and in the mountains. Active (sites where regular observations were carried out recently and are intended to continue in the future), candidate (where equipment for long-term observations can be installed soon), potential (equipment for long-term observation is planned to be installed during the project) and historical (there are some existing data but now these sites are unavailable for observations for different reasons) boreholes were selected. In order to standardize all investigations within the framework of the Project the “Manual for monitoring and reporting temperature data in permafrost boreholes” was d... Visit https://dataone.org/datasets/148eeac1-59d6-4e13-9130-fbe1c2a0b66c for complete metadata about this dataset.

The Russian weather stations included in this analysis are Krenkel Observatory, Nagurskaya, Rudolf Island and Ostrov Victoria. The temperature data from the stations have undergone both manual and automatic quality controls in several stages. The data were initially manually controlled at the weather station by the observers and have later undergone several rounds of manual and automatic quality control including consistency checks and outlier tests. Tests to identify large errors and suspicious observations in the temperature series included logical tests using differences between maximum, minimum and mean temperature. To identify outliers, Grubbs’ criterion was used where values exceeding ±2.5 standard deviation from the monthly mean were marked and examined. A modified Tietjen-Moore test, was sometimes used to test outliers. All suspicious values were examined by experts at AARI (Arctic and Antarctic Research Institute), RIHMI-WDC (All-Russia Research Institute of Hydrometeorological Information - World Data Center) or SPSU (Saint Petersburg State University) who made the final decision on whether to keep or reject the value. The temperature series were also compared to series from neighboring stations to identify possible systematic errors giving shifts in the data series. The homogenized temperature series from Krenkel Observatory also includes data from the weather station Bukhta Thikaya and has been carefully scrutinized as described by Ivanov et al. 2021*.*Svalbard Airport, Ny-Ålesund and Hopen are weather stations intended for forecasting and climate analysis and the data from these stations undergo extensive quality control (QC) when being stored in MET Norway’s database. Quality control has been performed mostly manually until 2005 when an automatic QC routine was put into use that includes several consistency tests such as step tests and threshold tests, in addition to manual inspection of values flagged as suspicious by the system. There have been several changes in instrumentation and location at all three stations leading to breaks in the homogeneity of the series. More details on quality control, station changes, and homogeneity can be found in Førland et al. 2011, Nordli et al. 2015 & 2020, Gjelten et al. 2016, and Hanssen-Bauer et al. 2019. During a time span of nearly thirty years automatic weather stations (AWS) have been in operation on the northern and eastern islands of Svalbard. The instruments and station infrastructure have varied much during those years. During the early years the data were not stored in MET Norway’s database, and there was no quality control. There were also problems with the regularity of the data, in particular many stations were destroyed by polar bears. In 1996, no data of accepted quality reached MET Norway. However, in 2010 a new setup of stations was developed, which improved data quality and significantly reduced the number of missing data. Hence, almost all our work on data control for this study was related to data before the autumn of 2010.

This data set contains Northern Hemisphere surface pressure and precipitation data prepared by the U.S.S.R. The surface pressure grids include average monthly pressures for 1873-1981 and climatological normals by month for 1873-1973 and 1931-1960. Precipitation grids include percentages of normal for all Januarys and all Julys for 1891-1979.

The Russian River Watershed (RRW) covers about 1,300 square miles (without Santa Rosa Plain) of urban, agricultural, and forested lands in northern Sonoma County and southern Mendocino County, California. Communities in the RRW depend on a combination of Russian River water and groundwater to meet their water-supply demands. Water is used primarily for agricultural irrigation, municipal and private wells supply, and commercial uses - such as for wineries and recreation. Annual rainfall in the RRW is highly variable, making it prone to droughts and flooding from atmospheric river events. In order to better understand surface-water and groundwater issues, the USGS is creating a Coupled Ground-Water and Surface-Water Flow Model (GSFLOW; Markstrom and others, 2008) of the RRW. This model will include climate, geology, surface-water, groundwater, and land-use data. These data are solar radiation data from three stations in the Russian River watershed for January 1989 through September 2017. These data were used to compute monthly mean solar radiation values in order to calibrate parameters used in the Russian River Integrated Hydrologic Model (RRIHM).

This data publication, the Fire Emission Inventory - Northern Eurasia (FEI-NE), consists of a high spatial resolution (500 meter ? 500 meter) dataset of daily black carbon (BC) emissions from forest, grassland, shrubland, and savanna fires in Northern Eurasia from 2002 to 2015. BC emissions were estimated using land cover maps and detected burned areas based on MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing products, the Forest Inventory Survey of the Russian Federation, the IPCC Tier-1 Global Biomass Carbon Map for the year 2000, and cover type specific BC emission factors. The data publication includes land cover type, fuel loading, and fuel consumption which are input for the model used to estimate BC emissions. These data provide daily emission sources for the assessment of the transport and deposition of BC on Arctic ice and snow.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Subgrid processes occur in various ecosystems and landscapes but, because of their small scale, they are not represented or poorly parameterized in climate models. These local heterogeneities are often important or even fundamental for energy and carbon balances. This is especially true for northern peatlands and in particular for the polygonal tundra, where methane emissions are strongly influenced by spatial soil heterogeneities. We present a stochastic model for the surface topography of polygonal tundra using Poisson–Voronoi diagrams and we compare the results with available recent field studies. We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. Hydraulic interconnectivities and large-scale drainage may also be investigated through percolation properties and thresholds in the Voronoi graph. […]