#geostatistics — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2026-09-10 · 04:17 UTC

A Coastal Exposure Index For Ireland - Relative Hazard Exposure And The Protective Role Of Coastal Habitats
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https://doi.org/10.1007/s11069-026-08361-w <-- shared paper
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http://alturl.com/4e83i <-- shared webmap / data portal
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https://www.independent.ie/irish-news/revealed-researchers-pinpoint-county-facing-greatest-risk-of-coastal-erosion-as-sea-levels-rise/a/161113880.html <-- shared media article
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[“The index complements site-specific flood and erosion modelling.”]
H/T @kevin Walsh | PhD Researcher at University College Cork
“In this paper [1st link above], [the authors] seek to answer the question “what is the relative distribution of exposure to coastal hazards around the Irish coast, and where can coastal habitats potentially reduce exposure?”
The method [they] chose to achieve this was the Coastal Vulnerability Model (CVM), within the InVEST software suite. This involves combining the key physical and climatic variables which can show us where along the coast might be most adversely affected by extreme winds, powerful waves and surges, and sea level rise.
It also reveals the potential protection provided by coastal habitats, such as dunes and saltmarsh, as well as populations and sites of cultural heritage located within zones deemed “Very High Exposure”.
[They] tested variations to the model, including seeing how the results change when certain variables are left out, what happens if we add in other variables, and how ranking choices impact exposure distribution. The results show that these choices, especially the ranking methods, can have large impacts on hazard distribution and the overall interpretation of the results. However, [they] found that the CVM quintile based approach was most suitable for Ireland, as it reveals high exposure sites on the east coast, which tends to be overlooked when different methods are applied.
[The H/T] also prepared an interactive map, where you can view the results in detail [2nd link above]
There’s a lot of work ongoing in this space around Ireland, [they] hope this will be a valuable contribution towards sustainable management of our coastline…”
#spatialanalysis #exposure #coastal #hazards #mitigation #risk #Ireland #Irish #coast #CoastalVulnerabilityModel #CVM #InVEST #model #modeling #spatiotemporal #geostatistics #statistics #spatial #mapping #geomorphology #geology #climate #wind #wave #tidal #erosion #stormsurge #storms #ocean #sealevelrise #climatechange #extremeweather #dunes #saltmarshes #machair #seagrass #habitat #protection #vulnerability #parametersensitivity #exposure #sustainable #populationpressure #CoastalExposureIndex #culturalheritage #coastalzonemanagement #planning #policy #evidencebased #adaption #strategies

#spatialanalysis #exposure #coastal #hazards #mitigation #risk

💧🌏 Greg Cocks @[email protected] · 2026-08-27 · 14:34 UTC

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
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https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
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H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
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“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

#gis #spatial #mapping #modis #trmm #riverdischarge

💧🌏 Greg Cocks @[email protected] · 2026-08-07 · 04:46 UTC

Coastal Flooding At Predictable Hours
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https://doi.org/10.1038/s41467-026-75710-5 <-- shared paper
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H/T @bruna Alves | Nature Communications | Editor
“When do coastal floods happen? ⏱️🌊
A new study [link above] shows that, in many tide-dominated coastal regions, flooding tends to occur at specific and recurring times of day. Rather than being completely random, flood events often cluster around predictable hours driven by local tidal patterns.
By analysing coastal flood observations from the UK and the US, [the authors] show[ed] that the timing of flood events can be highly structured, with some locations experiencing floods disproportionately during certain parts of the day.
This adds a temporal dimension to coastal flood risk. We often focus on how frequently floods occur, how severe they are, and where they happen. This study highlights that when they occur may also matter, particularly as rising sea levels increase the frequency of coastal flooding.
The findings provide a useful perspective for coastal adaptation, risk communication, and emergency planning…”
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“Flooding is typically perceived as a sudden and unpredictable hazard. Here, [they] show that recurrent flooding can occur at highly predictable times in tidally dominated coastal systems. This predictability stems from phase-locking of tidal constituents and constituent pairs with the solar day, causing peak tides to recur at consistent local times set by regional tidal propagation. Using tide-gauge records from the United States and the United Kingdom, [they] quantif[ied] the intraday timing of coastal flood events and show strong clustering at specific hours, particularly where semidiurnal or mixed tides dominate. For example, floods in Boston cluster around noon and midnight, whereas in southern California they occur in the morning. Sites with stronger non-tidal variability show weaker clustering. This temporal predictability extends beyond nuisance flooding to larger consequential events involving inundation, road closures and infrastructural damage, highlighting opportunities for anticipatory risk communication, emergency planning and time-sensitive coastal adaptation…”
#coast #coastal #flood #flooding #spatialanalysis #spatiotemporal #time #statistics #geostatistics #tide #tidal #timing #temporal #floodrisk #risk #hazard #sealevel #sealevelrise #climatechange #emergency #planning #tideguage #UK #USA #innundation #infrastructure #transportation #riskcommunication

#coast #coastal #flood #flooding #spatialanalysis #spatiotemporal

💧🌏 Greg Cocks @[email protected] · 2026-08-04 · 04:50 UTC

Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
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https://openscholar.uga.edu/record/26998/files/Zhang_uga_0077E_16145.pdf <-- shared technical publication / dissertation
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https://www.aag.org/award-grant/william-l-garrison-award-for-best-dissertation-in-computational-geography/ <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
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[again, way outside any expertise I might have, but fascinating spatial analysis use case…]
H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
“[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
#explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling