Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia
Abstract
:1. Introduction
1.1. Data Scarcity in Central Asia
1.2. Open-Source Data for the Transboundary WFEC Nexus
2. Materials and Methods
3. Results
3.1. Climate
ID | Name | Description | Spatial Extent | Temporal Extent | Resolution (Accuracy) | Data Created/Published | Type of Data | Data Source | Data Provider | Online Link |
---|---|---|---|---|---|---|---|---|---|---|
C01 | GHCN-daily V3 | Daily records of precipitation and temperature station data | Global | At least 30 years of data for each station | Daily | 2012, 2023 | Time series (csv) | Menne et al. [29,30] | NOAA | https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00861/html (accessed on 26 April 2023) |
C02 | Central Asia temperature and precipitation data | Monthly records from station data | Central Asia | Variable length time series between 1879–2003 | Monthly | 2003 | Time series (tab-delimited ASCII) | Williams et al. [31] | NSIDC | https://nsidc.org/data/g02174/versions/1#anchor-1 (accessed 26 April 2023) |
C03 | CHELSA v2.1 | Monthly precipitation and temperature time series | Global | 1979–2018, projected climatologies for selected GCM models for 1981–2010, 2011–2040, 2041–2070, and 2071–2100 | 30 arc-seconds, data quality should be validated prior to use. | 2021 | Raster (tif) | Karger et al. [32,33,34] | WSL | https://chelsa-climate.org/ (accessed on 22 May 2023) |
C04 | WorldClim | Historical monthly weather data downscaled from CRU-TS-4.03 | Global | 1960–2018 | 2.5 arc-minutes/monthly | Raster (tif) | WorldClim [35,36] | WorldClim | https://www.worldclim.org/data/monthlywth.html (accessed on 22 May 2023) | |
C05 | ERA5-Land | Single-level precipitation sum and air temperature at 2 m above ground | Global | 1950–present | 6 arc-minutes/from hourly to monthly | 2023 | Raster (GRIB) | CDS [37] | CDS | https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview (accessed on 26 April 2023) |
C06 | CRU-TS-4.06.01 | High-resolution gridded data of month-by-month variation in climate | Global | 1901–2021 | 30 arc-minutes/monthly | 2023 | Raster (netCDF) | University of East Anglia Climate Research Unit [38] | CEDA | https://data.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.06/data/ (accessed on 26 April 2023) |
C07 | GPM IMERG | Daily precipitation L3 (the successor product of TRMM). | Global | 2000–present | 6 arc-minutes/daily | 2023 | Raster (netCDF) | Huffman et al. [39] | NASA GES DISC | https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/summary (accessed on 26 April 2023) |
C08 | APHRODITE v1801 R1 | Daily precipitation analysis product | Monsoon Asia (incl. Central Asia) | 1998–2015 | 15 arc-minutes/daily | 2018 | Raster (netCDF) | Yatagai et al. [40] | APHRODITE | http://aphrodite.st.hirosaki-u.ac.jp/download/data/search/ (accessed on 26 April 2023) |
C09 | GPCC Full Data Daily Version 2022 | Daily gridded precipitation data | Global | 1982–2020 | 60 arc-minutes/daily | 2022 | Raster (netCDF) | Ziese et al. [41,42] | GPC | https://opendata.dwd.de/climate_environment/GPCC/full_data_monthly_v2022/025/ (accessed on 26 April 2023) |
C10 | CHIRPS | Quasi-global satellite and observation-based precipitation estimates over land | Quasi-global | 1981–near present | 3 arc-minutes/pentad to monthly | 2014 | Raters (netCDF) | Funk et al. [43,44] | Climate Hazard Center, UC Santa Barbara | https://www.chc.ucsb.edu/data (accessed on 26 April 2023) |
C11 | PERSIANN-CDR V1 | Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks | Global | 1982–present (note large data gaps until 1999) | 2.4 arc-minutes/subdaily to annual | 2014 | Raster (netCDF) | Sorooshian et al. [45,46] | NCEI, NOAA | https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00854/html (accessed on 23 May 2023) |
C12 | Global aridity and PET database v3 | Potential evaporation and aridity index | Global | Average data from 1970–2000 | 30 arc-seconds | 2022 | Vector (shp) | Zomer et al. [47,48] | CGIAR | https://cgiarcsi.community/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v3/ (accessed on 23 May 2023) |
C13 | SSEpop | Actual evaporation | Global | Annual data from 2003–2021 | Suitable for regional focus | 2020 | Raster (tif) | Senay et al. [49] | USGS | https://earlywarning.usgs.gov/fews/product/466 (accessed on 23 May 2023) |
C14 | GFDLESM4 | Projections of future precipitation and temperature for shared socio-economic pathways | Global | Daily data from 1980 to 2100 | 100 km resolution | 2018 | Raster (netCDF) | Krasting et al. [50] | NOAA | https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4 (accessed on 13 September 2023) |
C15 | IPSL-CM6A-LR | Projections of future precipitation and temperature for shared socio-economic pathways | Global | Daily data from 1980 to 2100 | 250 km resolution | 2018 | Raster (netCDF) | Boucher et al. [51] | IPLS | https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CFMIP.IPSL.IPSL-CM6A-LR (accessed on 13 September 2023) |
C16 | MRI-ESM2.0 | Projections of future precipitation and temperature for shared socio-economic pathways | Global | Daily data from 1980 to 2100 | 250 km resolution | 2019 | Raster (netCDF) | Yukimoto et al. [52] | MRI | https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.MRI.MRI-ESM2-0.historical (accessed on 13 September 2023) |
C17 | UKESM1.0-LL | Projections of future precipitation and temperature for shared socio-economic pathways | Global | Daily data from 1980 to 2100 | 250 km resolution | 2019 | Raster (netCDF) | Tang et al. [53] | MOHC | https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.MOHC.UKESM1-0-LL.esm-piControl (accessed on 13 September 2023) |
3.2. Hydrology
ID | Name | Description | Spatial Extent | Temporal Extent | Resolution (Accuracy) | Data Created/Published | Type of Data | Data Source | Data Provider | Online Link |
---|---|---|---|---|---|---|---|---|---|---|
H01 | HydroRIVERS V1.0 | River network | Global | NA | Suitable for regional focus | 2013 | Vector (shp) | Lehner et al. [73] | WWF HydroSHEDS | https://www.hydrosheds.org/products/hydrorivers (accessed on 6 September 2023) |
H02 | HydroBASINS V1.0 | Basin outlines consistent with river network | Global | NA | Suitable for regional focus | 2013 | Vector (shp) | Lehner et al. [73] | WWF HydroSHEDS | https://www.hydrosheds.org/products/hydrobasins (accessed on 6 September 2023) |
H03 | HydroLAKES V1.0 | Lake outlines | Global | NA | Suitable for regional focus | 2013 | Vector (shp) | Messager et al. [74] | WWF HydroSHEDS | https://www.hydrosheds.org/products/hydrolakes (accessed on 6 September 2023) |
H04 | CA-discharge data set | Geolocations of river gauges in mountainous Central Asia, including basin outlines, discharge, and basin characterization | Mountainous parts of the drainage basins Issy Kul, Chu, Talas, Syr Darya, Amu Darya, Murghab, Harirud | Time series of various lengths between 1915–2012 | Suitable for water balance modelling at basin scale | 2023 | Vector as geopackage (shp/gpkg) | Marti et al. [18] | Zenodo.org | https://www.doi.org/10.5281/zenodo.7743778 (accessed on 24 July 2023) |
H05 | Randolph Glacier Inventory V6.0 | Glacier outlines | Global | 2014 | Suitable for regional focus | 2017 | Vector (shp) | RGI Consortium [75] | Global Land Ice Measurements from Space Initiative (GLIMS) | https://www.glims.org/RGI/ (accessed on 3 April 2023) |
H06 | High Mountain Asia Snow ReanalysisV1 | Snow cover and snow water equivalents | High mountain Asia | 1 October 1999–30 September 2017 | 16 arc-second | 2021 | Raster (netCDF) | Liu et al. [76] | National Snow and Ice Data Center (NSIDC) | https://nsidc.org/data/hma_sr_d/versions/1 (accessed on 3 April 2023) |
H07 | Glacier thickness Farinotti | Glacier thickness on RGI based on inverse modelling | Global | 2014 | Suitable for regional focus | 2019 | Raster (tif) | Farinotti et al. [77] | ETH Zurich | https://www.research-collection.ethz.ch/handle/20.500.11850/315707 (accessed on 3 April 2023) |
H08 | Glacier thickness Millan | Glacier thickness on RGI based on inverse modelling | Global | 2014 | ~50 m | 2022 | Raster (tif) | Millan et al. [78] | SEDOO | https://www.sedoo.fr/theia-publication-products/?uuid=55acbdd5-3982-4eac-89b2-46703557938c (accessed on 3 April 2023) |
H09 | Glacier thinning rates | Glacier thinning rates on RGI | Global | Average rate of change between 2000–2019 | Suitable for regional focus | 2021 | Table (csv) | Hugonnet et al. [79] | SEDOO | https://doi.org/10.6096/13 (accessed on 3 April 2023) |
H10 | Glacier ablation rates | Glacier ablation rates for many of the glaciers with area >2 km2 in High Mountain Asia | High Mountain Asia | Average ablation rate between 2000–2016 | Suitable for regional focus in basins dominated by glacier melt from larger glaciers | 2021 | Table (csv) | Miles et al. [80] | ZENODO | https://doi.org/10.5281/zenodo.3843292 (accessed on 24 July 2023) |
H11 | Projections of glacier melt | Projections of glacier melt under CMIP 6 climate projections | Global | 2000–2100 | Suitable for regional focus | 2023 | Raster (netCDF) | Rounce et al. [81] | National Snow and Ice Data Center (NSIDC) | https://nsidc.org/data/hma2_ggp/versions/1 (accessed on 24 July 2023) |
H12 | HiHydroSoils V2.0 | High resolution (250 m) soil maps hydraulic properties | Global | NA | ~250 m | 2020 | Raster (tif) | FutureWater [82] | FutureWater | https://www.futurewater.eu/projects/hihydrosoil/ (accessed on 24 April 2023) |
H13 | FLO1K | Map of average mean, minimum and maximum river runoff | Global | Averages between 1960–2015 | 30 arc-seconds. Suitable where discharge measurements are unavailable | 2018 | Raster (netCDF) | Barbarossa et al. [83] | Figshare | https://doi.org/10.6084/m9.figshare.c.3890224.v1 (accessed on 3 May 2023) |
H14 | Northern Hemisphere Permafrost–Ground Temperature Map (2000-2016) | Provides modeled mean annual ground temperatures at the top layer of the permafrost | Northern Hemisphere | Based on average temperatures between 2000–2016 | 30 arc-seconds | 2018 | Raster (netCDF) | Obu et al. [84,85] | Arctic Permafrost Geospatial Center | https://doi.org/10.1594/PANGAEA.888600 (accessed on 3 April 2023) |
H15 | Soil Moisture Active Passive (SMAP) | Two datasets (near real-time as well as historic data) providing estimates of global land surface moisture measured by a passive microwave radiometer | Global | Near real-time data, as well as from 2015–today | 36 km2 | 2021/2022 | Raster (HDF5) | O’Neill et al. [86,87] | National Snow and Ice Data Center | https://doi.org/10.5067/NCTT8THPWRTL (accessed on 6 September 2023) https://doi.org/10.5067/LPJ8F0TAK6E0 (accessed on 6 September 2023) |
3.3. Geography and Topography
ID | Name | Description | Spatial Extent | Temporal Extent | Resolution (Accuracy) | Data Created/Published | Type of Data | Data Source | Data Provider | Online Link |
---|---|---|---|---|---|---|---|---|---|---|
T01 | Global Administrative Areas (GADM) | Delineation of country and administrative boundaries | Global | NA | Suitable for regional focus | 2022 | Vector (shp/gpkg) | GADM [58] | GADM | https://gadm.org/data.html (accessed on 22 May 2023) |
T02 | Digital Elevation—Shuttle Radar Topography Mission (SRTM) | Void-filled and non-void-filled options obtained by radar from space | Global | NA | 1 arc-seconds or 3 arc-seconds | 2000/2018 | Raster (tif) | Earth Resources Observation and Science Center (EROS) [94] | United States Geological Survey (USGS)—Earth Resources Observation and Science (EROS) Center | https://doi.org/10.5066/F7PR7TFT (accessed on 22 May 2023) |
T03 | HydroSHEDS V1.0 | Hydrological conditioned DEM and other DEM-based products (flow direction, flow accumulation, land mask grid) | Global | NA | 3, 15, 30 arc-seconds and 5, 6 arc-minutes | 2007/2008 | Raster (tif) | Lehner et al. [95] | WWF HydroSheds | https://www.hydrosheds.org/downloads (accessed on 22 May 2023) |
T04 | Copernicus Global Land Service | Land cover data of 23 classes, including transitions of land cover classes over time capturing land cover changes | Global | Annual between 2015—2019 | ~100 m (Mapping accuracy is just over 80%) | 2020 | Raster (tif) | Buchhorn et al. [96] | Copernicus Global Land Service | https://land.copernicus.eu/global/products/lc (accessed on 6 September 2023) |
T05 | Land cover classification gridded maps | Global maps categorizing the land surface into 22 classes, defined by the FAO Land Cover Classification System | Global | Annual between 1992—2020 | ~300 m | 2019 | Raster (netCDF4) | Copernicus Climate Change Service [90] | Copernicus Climate Change Service | https://doi.org/10.24381/cds.006f2c9a (accessed on 17 May 2023) |
3.4. Geomorphology
ID | Name | Description | Spatial Extent | Temporal Extent | Resolution (Accuracy) | Data Created/Published | Type of Data | Data Source | Data Provider | Online Link |
---|---|---|---|---|---|---|---|---|---|---|
G01 | Global lithological map (GLiM) | Lithological map with three-level classification system for rock types | Global | - | Suitable for regional focus | 2012 | Vector (shp) | Moosdorf and Hartmann [98,100] | Commission for the Geological Map of the World | https://ccgm.org/en/product/world-lithology-map/ (accessed on 24 July 2023) |
G02 | Generalized Geology of the Former Soviet Union | Geological map showing geology, oil and gas fields, and geologic provinces | Former Soviet Union | - | Suitable for regional focus | 1999 | Vector (shp) | Persits et al. [101] | United States Geological Survey (USGS) | https://certmapper.cr.usgs.gov/data/apps/world-maps/ (accessed on 5 May 2023) |
G03 | Soil Erosion | Assessment of global soil erosion using the RUSLE method | Global | 2001, 2012 | ~25 km | 2017/2019 | Raster (tif) | Borrelli et al. [102,103] | Joint Research Centre of the European Commission | https://esdac.jrc.ec.europa.eu/content/global-soil-erosion (Available upon request) (accessed on 5 May 2023) |
G04 | Landslide Hazard | Global landslide hazard map containing rainfall and earthquake-induced landslide hazards | Global | - | ~1 km | 2020/2021 | Raster (tif) | The World Bank [104] | The World Bank | https://datacatalog.worldbank.org/search/dataset/0037584 (accessed on 5 February 2023) |
3.5. Ecology
ID | Name | Description | Spatial Extent | Temporal Extent | Resolution (Accuracy) | Data created/Published | Type of Data | Data Source | Data Provider | Online Link |
---|---|---|---|---|---|---|---|---|---|---|
E01 | Freshwater Ecoregions | Delineation of 426 freshwater conservation units with distinct freshwater communities | Global | NA | Suitable for global and regional focus | 2008 | Vector (shp) | Abell et al. [123] | The Nature Conservancy and World Wildlife Fund 2019 | www.feow.org (accessed on 2 May 2023) |
E02 | Key Biodiversity Areas | Areas contributing significantly to biodiversity | Global | Updated regularly | Suitable for global, regional, and national focus | 2016 | Vector (shp) | IUCN [134] | Bird Life International (2022) | www.keybiodiversityareas.org/kba-data (Available upon request) (accessed on 2 May 2023) |
E03 | IUCN Red List of Freshwater species | Distribution ranges of freshwater species | Global | Updated regularly | Suitable for global and regional focus | 2021 | Vector (shp) | IUCN [135] | IUCN | www.iucnredlist.org/resources/spatial-data-download (accessed on 2 May 2023) |
E04 | Global EPTO Database | Comprehensive table of Ephemeroptera, Plecoptera, Trichoptera, and Odonata (EPTO) occurrence records | Global | 1951–2021 (94% with complete date) | Suitable for global, regional, and national focus | 2023 | Table (csv) with coordinates and catchment IDs | Grigoropoulou et al. [136] | IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries | https://fred.igb-berlin.de/data/package/829 (accessed on 2 May 2023) |
E05 | Living Planet Index Database | Time-series of population abundance data for vertebrate species (public version) | Global | 1970–2021 | Varying | 2022 | Table (csv) of species with yearly abundance metrics and site coordinates | Living Planet Index [137] | Zoological Society of London and WWF 2022 | www.livingplanetindex.org (accessed on 2 May 2023) |
E06 | Free-Flowing Rivers | Global river network including a connectivity status assessment on the reach scale | Global | NA | Suitable for global and regional focus | 2019 | Vector (gdb) | Grill et al. [129,138] | Grill and Lehner (2019) | https://doi.org/10.6084/m9.figshare.7688801 (accessed on 2 May 2023) |
E07 | World Database on Protected Areas | Global Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) | Global | Updated regularly | Suitable for global, regional, and national focus | 2023 | Vector (shp) | UNEP-WCMC and IUCN [139] | Protected Planet | www.protectedplanet.net (accessed on 2 May 2023) |
E08 | Ramsar sites | Global point information of Ramsar Sites | Global | NA | Suitable for global and regional focus | 2021 | Table (csv) with coordinates or Vector (shp) | Ramsar [140] | Ramsar Sites Information Service | https://rsis.ramsar.org (accessed on 2 May 2023) |
E09 | Global Environmental Stratification (GEnS) | High-resolution bioclimate map of the world | Global | NA | 30 arc-seconds | 2018 | Raster, Vector (tif, shp) | Metzger [132,133] | M. Metzger | https://datashare.ed.ac.uk/handle/10283/3089 (accessed on 2 May 2023) |
3.6. Anthropogenic Uses
ID | Name | Description | Spatial Extent | Temporal Extent | Resolution (Accuracy) | Data Created/Published | Type of Data | Data Source | Data Provider | Online Link |
---|---|---|---|---|---|---|---|---|---|---|
A01 | Global Energy Observatory—Hydro PowerPlants | Consolidated and processed dataset of hydropower plants | Global | NA | Suitable for regional focus | Varied, modified in 2018 | Vector (shp) and table (xls) | Global Energy Observatory [147] | Global Energy Observatory (GEO) | https://globalenergyobservatory.org/list.php?db=PowerPlants&type=Hydro (accessed on 19 May 2023) |
A02 | Global Georeferenced Database of Dams (GOODD) | Location of >38,000 dams and associated watersheds | Global | NA | Suitable for regional focus. Older structures are mostly complete, newer ones are incomplete | 2020 | Vector (shp) | Mulligan et al. [148] | Global Dam Watch | https://www.globaldamwatch.org/ (accessed on 19 May 2023) |
A03 | Irrigation canals by OpenStreetMap (OSM) | Location of OSM irrigation channels | Global | -- | Suitable for regional focus (incomplete) | 2020 | Vector (shp) | OpenStreetMap contributors [149] | OpenStreetMap | https://www.openstreetmap.org/#map=6/40.388/68.994 (accessed on 20 November 2023) |
A04 | Electricity network by OpenStreetMap | OpenStreetMap electricity network | Global | -- | Suitable for regional focus (incomplete) | 2020 | Vector (shp) | OpenStreetMap contributors [149] | OpenStreetMap | https://www.openstreetmap.org/#map=6/40.388/68.994 (accessed on 20 November 2023) |
A05 | AQUASTAT | Data on harvested area, crop yields, renewable water resources, and agricultural water withdrawal | Global | 1964–2020 | Suitable for regional and national focus | 1993 | Table (xls) | FAO [146] | FAO | https://tableau.apps.fao.org/views/ReviewDashboard-v1/country_dashboard?%3Aembed=y&%3AisGuestRedirectFromVizportal=y (accessed on 20 May 2023) |
A06 | Crop Calendar | Global crop planting/harvesting dates for 19 major crops; combined data from Food and Agriculture Organization (FAO) and United States Department of Agriculture (USDA) | Global | NA | 5 arc-minutes or 0.5 arc-degrees | 2010 | Raster (netCDF, ArcINFO ASCII) | Sacks et al. [150,151] | Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, USA, | https://sage.nelson.wisc.edu/data-and-models/datasets/crop-calendar-dataset/ (accessed on 21 June 2023) |
A07 | MapSPAM | Crop production indicators for 42 crop types, including physical area, harvest area, production, and yield | Global | 2000, 2005, 2010 | 5 arc-minutes | 2010 | Raster (csv) | MapSPAM (CGIAR, FAO, World bank etc.) [152] | Harvard database | https://www.mapspam.info/data/ (accessed on 19 February 2023) |
A08 | Harmonized World Soil database (HWSD) v 1.2 | Dataset of harmonized soil properties | Global | 30 arc-seconds | 2009 | Raster (mdb) | FAO, IIASA, ISRIC-World Soil Information, Institute of Soil Science-Chinese Academy of Sciences (ISSCAS), and the JRC [153] | FAO | https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (accessed on 19 May 2023) | |
A09 | Gridded Population of the World (GPW) v4 | Gridded population counts aggregated from national and sub-national levels | Global | 2000, 2005, 2010, 2015, 2020 | 30 arc-seconds | 2018 | Raster (tif, ASCII; netCDF4) | Center for International Earth Science Information Network—CIESIN—Columbia University [154] | Center for International Earth Science Information Network (CIESIN)- Socioeconomic Data and Applications Center (SEDAC) | https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11 (accessed on 19 May 2023) |
A10 | Human Development Index (HDI) | An index for assessing human developments considering three dimensions: (i) a long and healthy life, (ii) the status of knowledge, and (iii) the standard of living | Global | 1990–2021 | Suitable for regional focus | 2020 | Table (csv, xls) | United Nations Development Programme (UNDP) [155] | UNDP | http://hdr.undp.org/en/content/download-data (accessed on 19 May 2023) |
4. Discussion
4.1. Data Updates and Integration
4.2. Real-Time Applications
4.3. WFEC Nexus Application in Central Asia: The Case of Hydropower
4.4. Data Limitations
4.5. Promoting Data Sharing and Fostering Collaboration
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Background | Main Institutions * | Role in the Project | n | WFEC Nexus Expertise |
---|---|---|---|---|
Engineering | TUM, BOKU, ILF, SJE, | Hydropower planning and construction, analysis of hydropower potential | 14 | Energy |
Hydrology | HSOL | Modelling of water resources and climate change effects | 2 | Water (hydrology), climate |
Ecology | BOKU, TIIAME, EV-INBO | Fish biodiversity and ecology assessments | 9 | Water (ecology) |
Water and land resources management | IWMI | Cross-border WFEC Nexus analyses with a focus on agricultural aspects | 3 | Food, water (management) |
Spatial analysis | CARTIF, IWMI | Benefits and trade-off analyses in the context of the WFEC Nexus | 7 | Nexus modelling |
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De Keyser, J.; Hayes, D.S.; Marti, B.; Siegfried, T.; Seliger, C.; Schwedhelm, H.; Anarbekov, O.; Gafurov, Z.; López Fernández, R.M.; Ramos Diez, I.; et al. Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia. Water 2023, 15, 3482. https://doi.org/10.3390/w15193482
De Keyser J, Hayes DS, Marti B, Siegfried T, Seliger C, Schwedhelm H, Anarbekov O, Gafurov Z, López Fernández RM, Ramos Diez I, et al. Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia. Water. 2023; 15(19):3482. https://doi.org/10.3390/w15193482
Chicago/Turabian StyleDe Keyser, Jan, Daniel S. Hayes, Beatrice Marti, Tobias Siegfried, Carina Seliger, Hannah Schwedhelm, Oyture Anarbekov, Zafar Gafurov, Raquel M. López Fernández, Ivan Ramos Diez, and et al. 2023. "Integrating Open-Source Datasets to Analyze the Transboundary Water–Food–Energy–Climate Nexus in Central Asia" Water 15, no. 19: 3482. https://doi.org/10.3390/w15193482