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#wateruse — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #wateruse, aggregated by home.social.

  1. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

  2. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”

    @FAO - Food and Agriculture Organization

  3. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

  4. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

  5. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

  6. Provisional Land Use Data From Water Year 2024 Is Now Available On [CA]DWR Atlas, CNRA Open Data, And SGMA Data Viewer For Public Use
    --
    gis.water.ca.gov/app/CADWRLand <-- shared web-based CDWR datasets / map
    --
    “The collected data is used by various federal, state, and local agencies, academic researchers and private consultants , and can help estimate the amount of water available for agriculture. Using this information, farmers can adapt and make decisions to better manage scarce water supplies more effectively.”
    #GIS #spatial #mapping #California #download #opendata #water #hydrology #datause #datasharing #download #landuse #spatialanalysis #spatiotemporal #wateryear #DWRAtlas #statewide #CNRA #SGMA #publicdata #publicgood #usecase #crops #croplands #cropmapping #counties #countysurvey #CADWR #DWR #groundwater #irrigation #wateruse #watermanagement #federal #state #local #webmapping #agriculture #watersecurity #foodsecurity #watersupply
    #CaliforniaDepartmentOfWaterResources

  7. Provisional Land Use Data From Water Year 2024 Is Now Available On [CA]DWR Atlas, CNRA Open Data, And SGMA Data Viewer For Public Use
    --
    gis.water.ca.gov/app/CADWRLand <-- shared web-based CDWR datasets / map
    --
    “The collected data is used by various federal, state, and local agencies, academic researchers and private consultants , and can help estimate the amount of water available for agriculture. Using this information, farmers can adapt and make decisions to better manage scarce water supplies more effectively.”

  8. Provisional Land Use Data From Water Year 2024 Is Now Available On [CA]DWR Atlas, CNRA Open Data, And SGMA Data Viewer For Public Use
    --
    gis.water.ca.gov/app/CADWRLand <-- shared web-based CDWR datasets / map
    --
    “The collected data is used by various federal, state, and local agencies, academic researchers and private consultants , and can help estimate the amount of water available for agriculture. Using this information, farmers can adapt and make decisions to better manage scarce water supplies more effectively.”
    #GIS #spatial #mapping #California #download #opendata #water #hydrology #datause #datasharing #download #landuse #spatialanalysis #spatiotemporal #wateryear #DWRAtlas #statewide #CNRA #SGMA #publicdata #publicgood #usecase #crops #croplands #cropmapping #counties #countysurvey #CADWR #DWR #groundwater #irrigation #wateruse #watermanagement #federal #state #local #webmapping #agriculture #watersecurity #foodsecurity #watersupply
    #CaliforniaDepartmentOfWaterResources

  9. Provisional Land Use Data From Water Year 2024 Is Now Available On [CA]DWR Atlas, CNRA Open Data, And SGMA Data Viewer For Public Use
    --
    gis.water.ca.gov/app/CADWRLand <-- shared web-based CDWR datasets / map
    --
    “The collected data is used by various federal, state, and local agencies, academic researchers and private consultants , and can help estimate the amount of water available for agriculture. Using this information, farmers can adapt and make decisions to better manage scarce water supplies more effectively.”
    #GIS #spatial #mapping #California #download #opendata #water #hydrology #datause #datasharing #download #landuse #spatialanalysis #spatiotemporal #wateryear #DWRAtlas #statewide #CNRA #SGMA #publicdata #publicgood #usecase #crops #croplands #cropmapping #counties #countysurvey #CADWR #DWR #groundwater #irrigation #wateruse #watermanagement #federal #state #local #webmapping #agriculture #watersecurity #foodsecurity #watersupply
    #CaliforniaDepartmentOfWaterResources

  10. Provisional Land Use Data From Water Year 2024 Is Now Available On [CA]DWR Atlas, CNRA Open Data, And SGMA Data Viewer For Public Use
    --
    gis.water.ca.gov/app/CADWRLand <-- shared web-based CDWR datasets / map
    --
    “The collected data is used by various federal, state, and local agencies, academic researchers and private consultants , and can help estimate the amount of water available for agriculture. Using this information, farmers can adapt and make decisions to better manage scarce water supplies more effectively.”
    #GIS #spatial #mapping #California #download #opendata #water #hydrology #datause #datasharing #download #landuse #spatialanalysis #spatiotemporal #wateryear #DWRAtlas #statewide #CNRA #SGMA #publicdata #publicgood #usecase #crops #croplands #cropmapping #counties #countysurvey #CADWR #DWR #groundwater #irrigation #wateruse #watermanagement #federal #state #local #webmapping #agriculture #watersecurity #foodsecurity #watersupply
    #CaliforniaDepartmentOfWaterResources

  11. Analysis Of Colorado River Basin Storage Suggests Need For Immediate Action
    --
    colorado.edu/center/gwc/2025/0 <-- shared UC Boulder School Of Law paper
    --
    usbr.gov/ColoradoRiverBasin/ <-- shared BuRec Colorado River Basin overview page
    --
    usbr.gov/ColoradoRiverBasin/po <-- shared BuRec Colorado River Basin Lake Powell & Lake Mead Operations Post-2026 Update 12/05/24 presentation
    --
    doi.org/10.1029/2022WR033454 <-- shared 2023 paper – “Aridification of Colorado River Basin's Snowpack Regions Has Driven Water Losses Despite Ameliorating Effects of Vegetation”
    --
    #water #hydrology #watermanagement #management #watersecurity #agriculture #ColoradoRiver #ColoradoRiverBasin #storage #waterresources #wateruse #agreement #naturalsupply #usecase #reserve #model #modeling #hydrospatial #future #forecasts #climate #recharge #snowpack #massbalance #conservative #dams #BuRec #reservoir #operations #infrastructure #consumption #magicwater
    @CU Boulder Getches-Wilkinson Center Law Center

  12. Analysis Of Colorado River Basin Storage Suggests Need For Immediate Action
    --
    colorado.edu/center/gwc/2025/0 <-- shared UC Boulder School Of Law paper
    --
    usbr.gov/ColoradoRiverBasin/ <-- shared BuRec Colorado River Basin overview page
    --
    usbr.gov/ColoradoRiverBasin/po <-- shared BuRec Colorado River Basin Lake Powell & Lake Mead Operations Post-2026 Update 12/05/24 presentation
    --
    doi.org/10.1029/2022WR033454 <-- shared 2023 paper – “Aridification of Colorado River Basin's Snowpack Regions Has Driven Water Losses Despite Ameliorating Effects of Vegetation”
    --

    @CU Boulder Getches-Wilkinson Center Law Center

  13. John McPhee’s Short Essay About A 1972 Rockefeller Civil Service Award Winner - Dr. Luna Leopold, The First Chief Hydrologist At The USGS
    --
    jfklibrary.org/archives/other- <--JFK's 1958 speech at the Rockerfeller Civil Service Awards
    --
    usgs.gov/news/featured-story/l <-- shared details of Dr. Luna Leopold from the USGS
    --
    fiddlrts.blogspot.com/2020/04/ <-- shared details of The Patch, by John McPhee
    --
    eos.org/opinions/luna-b-leopol <--shared EOS retrospective of Dr Leopold
    --
    I was reading - with a mug of tea - The Patch (link above) by John McPhee, a decades-old favourite author of mine - and came across this short essay amongst many fine others…
    #spatial #datalover #measurements #metrics #hydrology #water #fedservice #fedscience #LunaLeopold #hydrologist #crossdiscipline #RockefellerCivilServiceAward #USGS #JohnMcPhee #writing #readingforpleasure #mission #opendata #mapping #waterresources #watersecurity #wateruse #watermanagement
    @USGS

  14. John McPhee’s Short Essay About A 1972 Rockefeller Civil Service Award Winner - Dr. Luna Leopold, The First Chief Hydrologist At The USGS
    --
    jfklibrary.org/archives/other- <--JFK's 1958 speech at the Rockerfeller Civil Service Awards
    --
    usgs.gov/news/featured-story/l <-- shared details of Dr. Luna Leopold from the USGS
    --
    fiddlrts.blogspot.com/2020/04/ <-- shared details of The Patch, by John McPhee
    --
    eos.org/opinions/luna-b-leopol <--shared EOS retrospective of Dr Leopold
    --
    I was reading - with a mug of tea - The Patch (link above) by John McPhee, a decades-old favourite author of mine - and came across this short essay amongst many fine others…
    #spatial #datalover #measurements #metrics #hydrology #water #fedservice #fedscience #LunaLeopold #hydrologist #crossdiscipline #RockefellerCivilServiceAward #USGS #JohnMcPhee #writing #readingforpleasure #mission #opendata #mapping #waterresources #watersecurity #wateruse #watermanagement
    @USGS

  15. John McPhee’s Short Essay About A 1972 Rockefeller Civil Service Award Winner - Dr. Luna Leopold, The First Chief Hydrologist At The USGS
    --
    jfklibrary.org/archives/other- <--JFK's 1958 speech at the Rockerfeller Civil Service Awards
    --
    usgs.gov/news/featured-story/l <-- shared details of Dr. Luna Leopold from the USGS
    --
    fiddlrts.blogspot.com/2020/04/ <-- shared details of The Patch, by John McPhee
    --
    eos.org/opinions/luna-b-leopol <--shared EOS retrospective of Dr Leopold
    --
    I was reading - with a mug of tea - The Patch (link above) by John McPhee, a decades-old favourite author of mine - and came across this short essay amongst many fine others…
    #spatial #datalover #measurements #metrics #hydrology #water #fedservice #fedscience #LunaLeopold #hydrologist #crossdiscipline #RockefellerCivilServiceAward #USGS #JohnMcPhee #writing #readingforpleasure #mission #opendata #mapping #waterresources #watersecurity #wateruse #watermanagement
    @USGS

  16. John McPhee’s Short Essay About A 1972 Rockefeller Civil Service Award Winner - Dr. Luna Leopold, The First Chief Hydrologist At The USGS
    --
    jfklibrary.org/archives/other- <--JFK's 1958 speech at the Rockerfeller Civil Service Awards
    --
    usgs.gov/news/featured-story/l <-- shared details of Dr. Luna Leopold from the USGS
    --
    fiddlrts.blogspot.com/2020/04/ <-- shared details of The Patch, by John McPhee
    --
    eos.org/opinions/luna-b-leopol <--shared EOS retrospective of Dr Leopold
    --
    I was reading - with a mug of tea - The Patch (link above) by John McPhee, a decades-old favourite author of mine - and came across this short essay amongst many fine others…
    #spatial #datalover #measurements #metrics #hydrology #water #fedservice #fedscience #LunaLeopold #hydrologist #crossdiscipline #RockefellerCivilServiceAward #USGS #JohnMcPhee #writing #readingforpleasure #mission #opendata #mapping #waterresources #watersecurity #wateruse #watermanagement
    @USGS

  17. John McPhee’s Short Essay About A 1972 Rockefeller Civil Service Award Winner - Dr. Luna Leopold, The First Chief Hydrologist At The USGS
    --
    jfklibrary.org/archives/other- <--JFK's 1958 speech at the Rockerfeller Civil Service Awards
    --
    usgs.gov/news/featured-story/l <-- shared details of Dr. Luna Leopold from the USGS
    --
    fiddlrts.blogspot.com/2020/04/ <-- shared details of The Patch, by John McPhee
    --
    eos.org/opinions/luna-b-leopol <--shared EOS retrospective of Dr Leopold
    --
    I was reading - with a mug of tea - The Patch (link above) by John McPhee, a decades-old favourite author of mine - and came across this short essay amongst many fine others…

    @USGS