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#spatiotemporal — Public Fediverse posts

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

  1. 💧🌏 Greg Cocks @[email protected] ·

    Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
    --
    doi.org/10.5194/nhess-26-3231- <-- shared #openacess paper
    --
    [part of my old stomping ground as an engineering geologist]
    H/T @Kees Nederhoff
    “Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
    The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
    The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
    [They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”
    #USGS #supercomputing #Hovenweep #HPC #coast #coastal #PNW #Seattle #PacificNorthwest #risk #hazard #riskmanagement #model #modeling #CFRM #deterministic #probabilistic #climatechange #extremeweather #fedscience #WA #KingCounty #PierceCounty #WashingtonState #USA #flood #flooding #compoundflooding #floodmaps #SFINCS #storm #weather #climate #climatechange #rainfall #precipitation #sealevel #sealevelrise #SLR #100yearflood #floodhazardmapping #returnperiods #pluvial #fluvial #spatialanalysis #spatiotemporal #remotesensing #streamgage #history #historicflooding #projections #predictions
    #USGS

    #openacess #usgs #supercomputing #hovenweep #hpc #coast
  2. 💧🌏 Greg Cocks @GregCocks ·

    Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
    --
    doi.org/10.5194/nhess-26-3231- <-- shared paper
    --
    [part of my old stomping ground as an engineering geologist]
    H/T @Kees Nederhoff
    “Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
    The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
    The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
    [They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”

    #openacess #usgs #supercomputing #hovenweep #hpc #coast
  3. 💧🌏 Greg Cocks @[email protected] ·

    Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
    --
    doi.org/10.5194/nhess-26-3231- <-- shared #openacess paper
    --
    [part of my old stomping ground as an engineering geologist]
    H/T @Kees Nederhoff
    “Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
    The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
    The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
    [They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”
    #USGS #supercomputing #Hovenweep #HPC #coast #coastal #PNW #Seattle #PacificNorthwest #risk #hazard #riskmanagement #model #modeling #CFRM #deterministic #probabilistic #climatechange #extremeweather #fedscience #WA #KingCounty #PierceCounty #WashingtonState #USA #flood #flooding #compoundflooding #floodmaps #SFINCS #storm #weather #climate #climatechange #rainfall #precipitation #sealevel #sealevelrise #SLR #100yearflood #floodhazardmapping #returnperiods #pluvial #fluvial #spatialanalysis #spatiotemporal #remotesensing #streamgage #history #historicflooding #projections #predictions
    #USGS

    #openacess #usgs #supercomputing #hovenweep #hpc #coast
  4. 💧🌏 Greg Cocks @[email protected] ·

    Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
    --
    doi.org/10.5194/nhess-26-3231- <-- shared #openacess paper
    --
    [part of my old stomping ground as an engineering geologist]
    H/T @Kees Nederhoff
    “Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
    The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
    The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
    [They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”
    #USGS #supercomputing #Hovenweep #HPC #coast #coastal #PNW #Seattle #PacificNorthwest #risk #hazard #riskmanagement #model #modeling #CFRM #deterministic #probabilistic #climatechange #extremeweather #fedscience #WA #KingCounty #PierceCounty #WashingtonState #USA #flood #flooding #compoundflooding #floodmaps #SFINCS #storm #weather #climate #climatechange #rainfall #precipitation #sealevel #sealevelrise #SLR #100yearflood #floodhazardmapping #returnperiods #pluvial #fluvial #spatialanalysis #spatiotemporal #remotesensing #streamgage #history #historicflooding #projections #predictions
    #USGS

    #openacess #usgs #supercomputing #hovenweep #hpc #coast
  5. 💧🌏 Greg Cocks @[email protected] ·

    Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
    --
    doi.org/10.5194/nhess-26-3231- <-- shared #openacess paper
    --
    [part of my old stomping ground as an engineering geologist]
    H/T @Kees Nederhoff
    “Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
    The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
    The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
    [They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”
    #USGS #supercomputing #Hovenweep #HPC #coast #coastal #PNW #Seattle #PacificNorthwest #risk #hazard #riskmanagement #model #modeling #CFRM #deterministic #probabilistic #climatechange #extremeweather #fedscience #WA #KingCounty #PierceCounty #WashingtonState #USA #flood #flooding #compoundflooding #floodmaps #SFINCS #storm #weather #climate #climatechange #rainfall #precipitation #sealevel #sealevelrise #SLR #100yearflood #floodhazardmapping #returnperiods #pluvial #fluvial #spatialanalysis #spatiotemporal #remotesensing #streamgage #history #historicflooding #projections #predictions
    #USGS

    #predictions #projections #historicflooding #history #streamgage #remotesensing
  6. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
    --
    doi.org/10.1016/j.envc.2026.10 <-- shared paper
    --
    "ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
    #deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

    #deforestation #probability #machinelearning #algorithms #ai #gazipur
  7. 💧🌏 Greg Cocks @GregCocks ·

    Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
    --
    doi.org/10.1016/j.envc.2026.10 <-- shared paper
    --
    "ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
    ,

    #deforestation #probability #machinelearning #algorithms #ai #gazipur
  8. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
    #Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

    #patagonia #chile #aysen #coyhaique #landcover #mapping
  9. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
    #Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

    #patagonia #chile #aysen #coyhaique #landcover #mapping
  10. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
    #Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

    #patagonia #chile #aysen #coyhaique #landcover #mapping
  11. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
    #Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

    #modeling #earthobservation #satellite #remotesensing #ecologicalconnectivity #habitatquality
  12. 💧🌏 Greg Cocks @GregCocks ·

    Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”

    #patagonia #chile #aysen #coyhaique #landcover #mapping
  13. 💧🌏 Greg Cocks @[email protected] ·

    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

    #irrigatedareas #mapping #gis #spatial #spatialanalysis #spatiotemporal
  14. 💧🌏 Greg Cocks @GregCocks ·

    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

    #irrigatedareas #mapping #gis #spatial #spatialanalysis #spatiotemporal
  15. 💧🌏 Greg Cocks @[email protected] ·

    Watching A #NOAA #Webinar on Flash Droughts
    --
    noaaresearch.webex.com/wbxmjs/ <-- shared NOAA Summer Science Series individual webinar
    --
    drought.gov/what-is-drought/fl <-- shared NOAA overview technical article
    --
    star.nesdis.noaa.gov/star/NOAA <-- subscribe to the NOAA Summer Science Series
    --
    doi.org/10.1038/s41612-024-006 <-- shared paper
    --
    communities.springernature.com <-- shared technical article (derived from paper above)
    H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
    [Flash floods? not TOO hard to conceptualise.
    Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
    "Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
    --
    "Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."
    #water #hydrology #fedscience #publicgood #hydrologicdrought #waterdeficit #spatialanalysis #spatiotemporal #watersecurity #risk #hazard #humanimpacts #streamflow #riverflow #groundwater #surfacewater #climate #weather #climatechange #extremeweather #atmosphere #metrology #regional #global #farming #agriculture #fluvial #pluvial #rainfall #precipitation #cloudcover #energy #heat #temperature #ET #evapotranspiration #farming #agriculture #foodsecurity #waterresources #dynamicsystems #watermanagement #flashdrought #drought #susceptibility #monitoring #prediction #model #modeling
    @noaa

    #noaa #webinar #water #hydrology #fedscience #publicgood
  16. 💧🌏 Greg Cocks @[email protected] ·

    Watching A #NOAA #Webinar on Flash Droughts
    --
    noaaresearch.webex.com/wbxmjs/ <-- shared NOAA Summer Science Series individual webinar
    --
    drought.gov/what-is-drought/fl <-- shared NOAA overview technical article
    --
    star.nesdis.noaa.gov/star/NOAA <-- subscribe to the NOAA Summer Science Series
    --
    doi.org/10.1038/s41612-024-006 <-- shared paper
    --
    communities.springernature.com <-- shared technical article (derived from paper above)
    H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
    [Flash floods? not TOO hard to conceptualise.
    Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
    "Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
    --
    "Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."
    #water #hydrology #fedscience #publicgood #hydrologicdrought #waterdeficit #spatialanalysis #spatiotemporal #watersecurity #risk #hazard #humanimpacts #streamflow #riverflow #groundwater #surfacewater #climate #weather #climatechange #extremeweather #atmosphere #metrology #regional #global #farming #agriculture #fluvial #pluvial #rainfall #precipitation #cloudcover #energy #heat #temperature #ET #evapotranspiration #farming #agriculture #foodsecurity #waterresources #dynamicsystems #watermanagement #flashdrought #drought #susceptibility #monitoring #prediction #model #modeling
    @noaa

    #noaa #webinar #water #hydrology #fedscience #publicgood
  17. 💧🌏 Greg Cocks @[email protected] ·

    Watching A #NOAA #Webinar on Flash Droughts
    --
    noaaresearch.webex.com/wbxmjs/ <-- shared NOAA Summer Science Series individual webinar
    --
    drought.gov/what-is-drought/fl <-- shared NOAA overview technical article
    --
    star.nesdis.noaa.gov/star/NOAA <-- subscribe to the NOAA Summer Science Series
    --
    doi.org/10.1038/s41612-024-006 <-- shared paper
    --
    communities.springernature.com <-- shared technical article (derived from paper above)
    H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
    [Flash floods? not TOO hard to conceptualise.
    Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
    "Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
    --
    "Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."
    #water #hydrology #fedscience #publicgood #hydrologicdrought #waterdeficit #spatialanalysis #spatiotemporal #watersecurity #risk #hazard #humanimpacts #streamflow #riverflow #groundwater #surfacewater #climate #weather #climatechange #extremeweather #atmosphere #metrology #regional #global #farming #agriculture #fluvial #pluvial #rainfall #precipitation #cloudcover #energy #heat #temperature #ET #evapotranspiration #farming #agriculture #foodsecurity #waterresources #dynamicsystems #watermanagement #flashdrought #drought #susceptibility #monitoring #prediction #model #modeling
    @noaa

    #noaa #webinar #water #hydrology #fedscience #publicgood
  18. 💧🌏 Greg Cocks @GregCocks ·

    Watching A on Flash Droughts
    --
    noaaresearch.webex.com/wbxmjs/ <-- shared NOAA Summer Science Series individual webinar
    --
    drought.gov/what-is-drought/fl <-- shared NOAA overview technical article
    --
    star.nesdis.noaa.gov/star/NOAA <-- subscribe to the NOAA Summer Science Series
    --
    doi.org/10.1038/s41612-024-006 <-- shared paper
    --
    communities.springernature.com <-- shared technical article (derived from paper above)
    H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
    [Flash floods? not TOO hard to conceptualise.
    Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
    "Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
    --
    "Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."

    @noaa

    #noaa #webinar #water #hydrology #fedscience #publicgood
  19. 💧🌏 Greg Cocks @[email protected] ·

    National Water Availability Assessment Data Companion Launches Interactive Map
    --
    water.usgs.gov/nwaa-data/ <-- shared USGS resource link
    --
    water.usgs.gov/nwaa-data/inter <-- shared USGS webmap
    --
    H/T @USGS NWDC
    “The National Water Availability Assessment Data Companion (NWDC) delivers national-scale modeled water data underlying the National Water Availability Assessment Report. The NWDC will be continuously updated to include new data used in future National Water Availability Assessment Reports, with planned reports in 2026 and 2030.
    The NWDC also serves information on underlying model methodologies, strengths, and limitations to enable proper use of the data…
    USGS scientific teams develop NWDC models to analyze and represent the complexities of water systems. These models fill gaps where USGS observations are unavailable, covering the conterminous United States (lower 48 states) and soon extending to Alaska, Hawaii, and Puerto Rico.
    All NWDC datasets currently cover past conditions over multiple decades, and are standardized to 12-digit [WBD] hydrologic unit code (HUC12) watersheds and monthly timesteps…”
    #opendata #monitoring #spatialanalysis #spatiotemporal #fedscience #publicgood #water #hydrology #waterresources #watermanagement #change #model #modeling #USA #NationalWaterAvailabilityAssessment #NWDC #CONUS #USGS #USGS_water
    @USGS

    #opendata #monitoring #spatialanalysis #spatiotemporal #fedscience #publicgood
  20. 💧🌏 Greg Cocks @GregCocks ·

    National Water Availability Assessment Data Companion Launches Interactive Map
    --
    water.usgs.gov/nwaa-data/ <-- shared USGS resource link
    --
    water.usgs.gov/nwaa-data/inter <-- shared USGS webmap
    --
    H/T @USGS NWDC
    “The National Water Availability Assessment Data Companion (NWDC) delivers national-scale modeled water data underlying the National Water Availability Assessment Report. The NWDC will be continuously updated to include new data used in future National Water Availability Assessment Reports, with planned reports in 2026 and 2030.
    The NWDC also serves information on underlying model methodologies, strengths, and limitations to enable proper use of the data…
    USGS scientific teams develop NWDC models to analyze and represent the complexities of water systems. These models fill gaps where USGS observations are unavailable, covering the conterminous United States (lower 48 states) and soon extending to Alaska, Hawaii, and Puerto Rico.
    All NWDC datasets currently cover past conditions over multiple decades, and are standardized to 12-digit [WBD] hydrologic unit code (HUC12) watersheds and monthly timesteps…”

    @USGS

    #opendata #monitoring #spatialanalysis #spatiotemporal #fedscience #publicgood
  21. 💧🌏 Greg Cocks @[email protected] ·

    Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

    #earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports
  22. 💧🌏 Greg Cocks @[email protected] ·

    Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

    #earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports
  23. 💧🌏 Greg Cocks @[email protected] ·

    Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

    #earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports
  24. 💧🌏 Greg Cocks @[email protected] ·

    Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

    #workflow #hydrogeomorphology #disaster #spatiotemporal #spatialanalysis #earthobservation
  25. 💧🌏 Greg Cocks @GregCocks ·

    Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”

    #earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports
  26. 💧🌏 Greg Cocks @[email protected] ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
    --
    H/T @CLIM4cities
    #urbanclimate #downscaling #landsurfacetemperature #LST #AI #machinelearning #scaleinvariance #residualcorrection #Sentinel #Landsat #satellite #remotesensing #earthobservation #CLIM4cities #UrbanClimate #ClimateServices #MachineLearning #ClimateAdaptation #heatwave #temperature #ontheground #Copenhagen #Denmark #impervioussurface #asphalt #roof #concrete #albedo #heatabsorption #mitigation #urban #urbancentre #treecover #vegetation #urbanheatisland #planning #design #hotspots #monitoring #spatialanalysis #spatiotemporal #model #modeling #usecase #operational #climatechange #extremeweather #evidencebased #adapation #sustainability #urbanplanning #climateresilience #EssentialClimateVariable #ECV
    @+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

    #urbanclimate #downscaling #landsurfacetemperature #lst #ai #machinelearning
  27. 💧🌏 Greg Cocks @GregCocks ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
    --
    H/T @CLIM4cities

    @+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

    #urbanclimate #downscaling #landsurfacetemperature #lst #ai #machinelearning
  28. 💧🌏 Greg Cocks @[email protected] ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
    --
    H/T @CLIM4cities
    #urbanclimate #downscaling #landsurfacetemperature #LST #AI #machinelearning #scaleinvariance #residualcorrection #Sentinel #Landsat #satellite #remotesensing #earthobservation #CLIM4cities #UrbanClimate #ClimateServices #MachineLearning #ClimateAdaptation #heatwave #temperature #ontheground #Copenhagen #Denmark #impervioussurface #asphalt #roof #concrete #albedo #heatabsorption #mitigation #urban #urbancentre #treecover #vegetation #urbanheatisland #planning #design #hotspots #monitoring #spatialanalysis #spatiotemporal #model #modeling #usecase #operational #climatechange #extremeweather #evidencebased #adapation #sustainability #urbanplanning #climateresilience #EssentialClimateVariable #ECV
    @+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

    #urbanclimate #downscaling #landsurfacetemperature #lst #ai #machinelearning
  29. 💧🌏 Greg Cocks @[email protected] ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
    --
    H/T @CLIM4cities
    #urbanclimate #downscaling #landsurfacetemperature #LST #AI #machinelearning #scaleinvariance #residualcorrection #Sentinel #Landsat #satellite #remotesensing #earthobservation #CLIM4cities #UrbanClimate #ClimateServices #MachineLearning #ClimateAdaptation #heatwave #temperature #ontheground #Copenhagen #Denmark #impervioussurface #asphalt #roof #concrete #albedo #heatabsorption #mitigation #urban #urbancentre #treecover #vegetation #urbanheatisland #planning #design #hotspots #monitoring #spatialanalysis #spatiotemporal #model #modeling #usecase #operational #climatechange #extremeweather #evidencebased #adapation #sustainability #urbanplanning #climateresilience #EssentialClimateVariable #ECV
    @+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

    #urbanclimate #downscaling #landsurfacetemperature #lst #ai #machinelearning
  30. 💧🌏 Greg Cocks @[email protected] ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
    --
    H/T @CLIM4cities
    #urbanclimate #downscaling #landsurfacetemperature #LST #AI #machinelearning #scaleinvariance #residualcorrection #Sentinel #Landsat #satellite #remotesensing #earthobservation #CLIM4cities #UrbanClimate #ClimateServices #MachineLearning #ClimateAdaptation #heatwave #temperature #ontheground #Copenhagen #Denmark #impervioussurface #asphalt #roof #concrete #albedo #heatabsorption #mitigation #urban #urbancentre #treecover #vegetation #urbanheatisland #planning #design #hotspots #monitoring #spatialanalysis #spatiotemporal #model #modeling #usecase #operational #climatechange #extremeweather #evidencebased #adapation #sustainability #urbanplanning #climateresilience #EssentialClimateVariable #ECV
    @+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

    #ecv #essentialclimatevariable #climateresilience #urbanplanning #sustainability #adapation
  31. 💧🌏 Greg Cocks @[email protected] ·

    A Global Systematic Review Of The Effects Of Hydromorphological Floodplain Restoration On Biodiversity
    --
    doi.org/10.1111/1365-2664.70485 <-- shared paper
    --
    zenodo.org/records/20561534 <-- shared open data
    --
    doi.org/10.1038/s43247-026-034 <-- shared paper
    --
    adriadapt.eu/adaptation-option <-- shared technical article
    --
    americanrivers.org/threats-sol <-- shared overview technical article
    --
    #water #hydrology #literaturereview #global #hydromorphology #river #floodplain #restoration #biodiversity #habitat #ecosystem #ecology #naturebasedsolutions #flood #flooding #risk #hazard #birds #fish #invertebrates #plants #amphibians #microorganisms #smallmammals #spatialanalysis #spatiotemporal #management #planning #policy #riverfloodplain #conservation #results #summary #effectivness

    #water #hydrology #literaturereview #global #hydromorphology #river
  32. 💧🌏 Greg Cocks @GregCocks ·

    A Global Systematic Review Of The Effects Of Hydromorphological Floodplain Restoration On Biodiversity
    --
    doi.org/10.1111/1365-2664.70485 <-- shared paper
    --
    zenodo.org/records/20561534 <-- shared open data
    --
    doi.org/10.1038/s43247-026-034 <-- shared paper
    --
    adriadapt.eu/adaptation-option <-- shared technical article
    --
    americanrivers.org/threats-sol <-- shared overview technical article
    --

    #water #hydrology #literaturereview #global #hydromorphology #river
  33. 💧🌏 Greg Cocks @[email protected] ·

    A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”
    #Calabria #Italy #massmovement #records #landslides #geology #engineeringgeology #spatiotemporal #spatialanalysis #rainfall #precipitation #extremeweather #trigger #monitoring

    #calabria #italy #massmovement #records #landslides #geology
  34. 💧🌏 Greg Cocks @GregCocks ·

    A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”

    #calabria #italy #massmovement #records #landslides #geology
  35. 💧🌏 Greg Cocks @[email protected] ·

    A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”
    #Calabria #Italy #massmovement #records #landslides #geology #engineeringgeology #spatiotemporal #spatialanalysis #rainfall #precipitation #extremeweather #trigger #monitoring

    #calabria #italy #massmovement #records #landslides #geology
  36. 💧🌏 Greg Cocks @[email protected] ·

    A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”
    #Calabria #Italy #massmovement #records #landslides #geology #engineeringgeology #spatiotemporal #spatialanalysis #rainfall #precipitation #extremeweather #trigger #monitoring

    #calabria #italy #massmovement #records #landslides #geology
  37. 💧🌏 Greg Cocks @[email protected] ·

    A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”
    #Calabria #Italy #massmovement #records #landslides #geology #engineeringgeology #spatiotemporal #spatialanalysis #rainfall #precipitation #extremeweather #trigger #monitoring

    #monitoring #trigger #extremeweather #precipitation #rainfall #spatialanalysis
  38. 💧🌏 Greg Cocks @[email protected] ·

    Statistical Characterization Of High Flow Volumes Across The Conterminous United States Supporting Managed Aquifer Recharge
    --
    doi.org/10.1029/2025WR041955 <-- shared paper
    --
    latimes.com/environment/story/ <-- shared media article
    --
    #water #hydrology #groundwater #watersecurity #waterresources #watersupply #watermanagement #USA #CONUS #aquifer #ManagedAquiferRecharge #MAR #FloodMAR #depletion #overpumping #flood #flooding #recharge #planning #policy #streamflow #extremes #hydrography #highflowvolumes #HFV #model #modeling #uncertainly #siting #screening #usecase #spatialanalysis #geostatics #spatiotemporal #mapping #hydrogeomorphology

    #water #hydrology #groundwater #watersecurity #waterresources #watersupply
  39. 💧🌏 Greg Cocks @GregCocks ·

    Statistical Characterization Of High Flow Volumes Across The Conterminous United States Supporting Managed Aquifer Recharge
    --
    doi.org/10.1029/2025WR041955 <-- shared paper
    --
    latimes.com/environment/story/ <-- shared media article
    --

    #water #hydrology #groundwater #watersecurity #waterresources #watersupply
  40. 💧🌏 Greg Cocks @[email protected] ·

    Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
    --
    doi.org/10.3389/fenvs.2026.176 <-- shared paper
    --
    doi.org/10.1038/s43247-026-036 <-- shared paper
    --
    doi.org/10.1002/joc.6443 <-- shared paper
    --
    pik-potsdam.de/en/news/latest- | publications.iom.int/books/eva <-- shared 2021 Peru hydroclimate technical article | report
    --
    youtu.be/Ngbm0gsmYAw?si=haqV7t <-- shared overview video
    --
    #water #GIS #spatial #mappping #spatialanalysis #spatiotemporal #remotesensing #earthobservation #Hydrology #Hydroclimatology #ClimateChange #extremeweather #WaterResources #WaterSecurity uncertainity #SouthAmerica #ClimateData #PeerReview #OpenScience #Hydrometeorology #opendata #datasets #rainfall #precipitation #fluvial #heatwave #temperature #changing #consistency #flood #flooding #drought #riskmanagement #risk #hazard #earthsystems #resilience #waterquality #waterpollution #model #modeling #monitoring #records #hydroclimate #hydrogeomorphology #review #SouthAmerica #planning #policy #sustainability #evapotranspiration #runoff #waterstorage #SAHCD

    #water #gis #spatial #mappping #spatialanalysis #spatiotemporal
  41. 💧🌏 Greg Cocks @GregCocks ·

    Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
    --
    doi.org/10.3389/fenvs.2026.176 <-- shared paper
    --
    doi.org/10.1038/s43247-026-036 <-- shared paper
    --
    doi.org/10.1002/joc.6443 <-- shared paper
    --
    pik-potsdam.de/en/news/latest- | publications.iom.int/books/eva <-- shared 2021 Peru hydroclimate technical article | report
    --
    youtu.be/Ngbm0gsmYAw?si=haqV7t <-- shared overview video
    --
    uncertainity

    #water #gis #spatial #mappping #spatialanalysis #spatiotemporal
  42. 💧🌏 Greg Cocks @[email protected] ·

    Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
    --
    doi.org/10.3389/fenvs.2026.176 <-- shared paper
    --
    doi.org/10.1038/s43247-026-036 <-- shared paper
    --
    doi.org/10.1002/joc.6443 <-- shared paper
    --
    pik-potsdam.de/en/news/latest- | publications.iom.int/books/eva <-- shared 2021 Peru hydroclimate technical article | report
    --
    youtu.be/Ngbm0gsmYAw?si=haqV7t <-- shared overview video
    --
    #water #GIS #spatial #mappping #spatialanalysis #spatiotemporal #remotesensing #earthobservation #Hydrology #Hydroclimatology #ClimateChange #extremeweather #WaterResources #WaterSecurity uncertainity #SouthAmerica #ClimateData #PeerReview #OpenScience #Hydrometeorology #opendata #datasets #rainfall #precipitation #fluvial #heatwave #temperature #changing #consistency #flood #flooding #drought #riskmanagement #risk #hazard #earthsystems #resilience #waterquality #waterpollution #model #modeling #monitoring #records #hydroclimate #hydrogeomorphology #review #SouthAmerica #planning #policy #sustainability #evapotranspiration #runoff #waterstorage #SAHCD

    #water #gis #spatial #mappping #spatialanalysis #spatiotemporal
  43. 💧🌏 Greg Cocks @[email protected] ·

    Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
    --
    doi.org/10.3389/fenvs.2026.176 <-- shared paper
    --
    doi.org/10.1038/s43247-026-036 <-- shared paper
    --
    doi.org/10.1002/joc.6443 <-- shared paper
    --
    pik-potsdam.de/en/news/latest- | publications.iom.int/books/eva <-- shared 2021 Peru hydroclimate technical article | report
    --
    youtu.be/Ngbm0gsmYAw?si=haqV7t <-- shared overview video
    --
    #water #GIS #spatial #mappping #spatialanalysis #spatiotemporal #remotesensing #earthobservation #Hydrology #Hydroclimatology #ClimateChange #extremeweather #WaterResources #WaterSecurity uncertainity #SouthAmerica #ClimateData #PeerReview #OpenScience #Hydrometeorology #opendata #datasets #rainfall #precipitation #fluvial #heatwave #temperature #changing #consistency #flood #flooding #drought #riskmanagement #risk #hazard #earthsystems #resilience #waterquality #waterpollution #model #modeling #monitoring #records #hydroclimate #hydrogeomorphology #review #SouthAmerica #planning #policy #sustainability #evapotranspiration #runoff #waterstorage #SAHCD

    #water #gis #spatial #mappping #spatialanalysis #spatiotemporal
  44. 💧🌏 Greg Cocks @[email protected] ·

    Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
    --
    doi.org/10.3389/fenvs.2026.176 <-- shared paper
    --
    doi.org/10.1038/s43247-026-036 <-- shared paper
    --
    doi.org/10.1002/joc.6443 <-- shared paper
    --
    pik-potsdam.de/en/news/latest- | publications.iom.int/books/eva <-- shared 2021 Peru hydroclimate technical article | report
    --
    youtu.be/Ngbm0gsmYAw?si=haqV7t <-- shared overview video
    --
    #water #GIS #spatial #mappping #spatialanalysis #spatiotemporal #remotesensing #earthobservation #Hydrology #Hydroclimatology #ClimateChange #extremeweather #WaterResources #WaterSecurity uncertainity #SouthAmerica #ClimateData #PeerReview #OpenScience #Hydrometeorology #opendata #datasets #rainfall #precipitation #fluvial #heatwave #temperature #changing #consistency #flood #flooding #drought #riskmanagement #risk #hazard #earthsystems #resilience #waterquality #waterpollution #model #modeling #monitoring #records #hydroclimate #hydrogeomorphology #review #SouthAmerica #planning #policy #sustainability #evapotranspiration #runoff #waterstorage #SAHCD

    #sahcd #waterstorage #runoff #evapotranspiration #sustainability #policy
  45. 💧🌏 Greg Cocks @[email protected] ·

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
    #Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

    #sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement
  46. 💧🌏 Greg Cocks @GregCocks ·

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”

    #sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement
  47. 💧🌏 Greg Cocks @[email protected] ·

    Advancing Detailed Flood Hazard Identification in Alberta, Canada - Insights from Two Recent Flood Studies
    --
    doi.org/10.3390/w18131592 <-- shared paper
    --
    “The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelised prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains…”
    #FloodMapping #FloodRisk #Hydrology #HydraulicModeling #HECRAS #WaterResources #Alberta #Resilience #RiverSurvey #spatialanlaysis #spatiotemporal #floodhazardmapping #HECRAS #model #modeling #remotesensing #LiDAR #bathymetry #floodfrequencyanalysis #unsteadysimulation #FHIMP #FHIP #WoldCreek #Lacombe #SwanRiver #Kinuso #Alberta #Canada #localscale #provincialfloodstudy # prairie #stream #river #flood #flooding #water #hydrology #risk #hazard #watershed #publicsafety #cost #damage #economics #infrastructure #use #practicedriven #floodhazard #survey #hydraulic #terrainmodels #hydrogeomorphology #topography #elevation #floodplain
    @Alberta Environment and Protected Areas | @Government of Alberta | @Barr Engineering

    #floodmapping #floodrisk #hydrology #hydraulicmodeling #hecras #waterresources
  48. 💧🌏 Greg Cocks @GregCocks ·

    Advancing Detailed Flood Hazard Identification in Alberta, Canada - Insights from Two Recent Flood Studies
    --
    doi.org/10.3390/w18131592 <-- shared paper
    --
    “The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelised prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains…”
    # prairie
    @Alberta Environment and Protected Areas | @Government of Alberta | @Barr Engineering

    #floodmapping #floodrisk #hydrology #hydraulicmodeling #hecras #waterresources
  49. 💧🌏 Greg Cocks @[email protected] ·

    Impact Of Floods On Surface Water Quality - A Systematic Review And Comprehensive Assessment
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    epa.gov/system/files/documents <-- shared paper
    --
    “Floods, as extreme flow events, are among the costliest and devastating natural hazards. Among the various domains impacted by flooding, environmental degradation, particularly the deterioration of water quality (WQ), is one of the most impacted yet often overlooked. Therefore, it is essential to understand the nature and source of water pollution associated with flooding. This study aims to evaluate and assess multiple studies conducted globally to determine the impact of floods on WQ. A literature review and assessment of 66 studies published between 2007 and 2026 was conducted using the total comprehensiveness score (TCS). To support the scoring process, studies that scored more than 70% of the maximum achievable TCS (15.4) are considered the most detailed and comprehensive in addressing the objectives of this review. 16 studies achieved a TCS above 15.4, indicating that a limited number of studies incorporate a broader set of factors in this domain. A higher number of studies were conducted post the year 2021, highlighting both scientific progress and a growing focus on WQ impacts from disasters such as floods, beyond the traditionally emphasized socio-economic loss. Among the shortlisted studies, fluvial floods are the most frequently examined, followed by pluvial floods and coastal floods. During fluvial floods, turbidity increased by up to two orders of magnitude, while nutrient concentrations (TN, TP) typically rose by ∼ 10–30%. In contrast, pluvial floods were characterised by dilution-driven decreases in EC and TDS, with DOX, BOD and COD showing variable responses across flood types. This review evaluates flood impacts on WQ, catchment characteristics, and sources of WQ modification. The findings of the research reveal that not all WQ parameters are responsible for WQ degradation during every flood event. Rather, it is a combination of certain parameters that leads to deteriorated WQ. WQ degradation depends on interacting factors such as flood duration, extent, depth, and flow dynamics. In overall, this study provides an overview of the multiple cascading impacts of floods on WQ, along with a detailed perspective on the set of criteria that should be considered in future research…”
    #water #hydrology #hydrography #flood #flooding #criteriaassessment #waterpollution #waterquality #parameters #extremeflow #waterresources #extremeweather #waterresources #watermanagement #global #literaturereview #morphology #source #type #watersecurity #research #papers #compilation #humanimpacts #PRISMA #spatiotemporal #fluvial #pluvial #coast #coastal #risk #hazard #riverine #climatechange #EnvironmentalScience #Research #ClimateResilience #floodtype #pollution #naturalhazard

    #water #hydrology #hydrography #flood #flooding #criteriaassessment
  50. 💧🌏 Greg Cocks @GregCocks ·

    Impact Of Floods On Surface Water Quality - A Systematic Review And Comprehensive Assessment
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    epa.gov/system/files/documents <-- shared paper
    --
    “Floods, as extreme flow events, are among the costliest and devastating natural hazards. Among the various domains impacted by flooding, environmental degradation, particularly the deterioration of water quality (WQ), is one of the most impacted yet often overlooked. Therefore, it is essential to understand the nature and source of water pollution associated with flooding. This study aims to evaluate and assess multiple studies conducted globally to determine the impact of floods on WQ. A literature review and assessment of 66 studies published between 2007 and 2026 was conducted using the total comprehensiveness score (TCS). To support the scoring process, studies that scored more than 70% of the maximum achievable TCS (15.4) are considered the most detailed and comprehensive in addressing the objectives of this review. 16 studies achieved a TCS above 15.4, indicating that a limited number of studies incorporate a broader set of factors in this domain. A higher number of studies were conducted post the year 2021, highlighting both scientific progress and a growing focus on WQ impacts from disasters such as floods, beyond the traditionally emphasized socio-economic loss. Among the shortlisted studies, fluvial floods are the most frequently examined, followed by pluvial floods and coastal floods. During fluvial floods, turbidity increased by up to two orders of magnitude, while nutrient concentrations (TN, TP) typically rose by ∼ 10–30%. In contrast, pluvial floods were characterised by dilution-driven decreases in EC and TDS, with DOX, BOD and COD showing variable responses across flood types. This review evaluates flood impacts on WQ, catchment characteristics, and sources of WQ modification. The findings of the research reveal that not all WQ parameters are responsible for WQ degradation during every flood event. Rather, it is a combination of certain parameters that leads to deteriorated WQ. WQ degradation depends on interacting factors such as flood duration, extent, depth, and flow dynamics. In overall, this study provides an overview of the multiple cascading impacts of floods on WQ, along with a detailed perspective on the set of criteria that should be considered in future research…”

    #water #hydrology #hydrography #flood #flooding #criteriaassessment