#geostatistics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #geostatistics, aggregated by home.social.
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Spatiotemporal Distribution, Climatic Factors, And Seasonal Precipitation Patterns Characterizing 66 Years Of Widespread Shallow Landslide Events In Piedmont (Northwestern Italy)
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https://doi.org/10.3389/feart.2026.1884300 <-- shared paper
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“Widespread shallow landslide events constitute one of the primary drivers of recurrent societal and economic losses in mountain regions. Although the influence of climate variability on landslide frequency and magnitude has been widely recognized, long-term, event-level analyses linking spatiotemporal landslide patterns to precipitation climatology at the regional scale remain scarce. This study presents a statistical analysis of 128 widespread shallow landslide events recorded in Piedmont, northwestern Italy, over the 66-year period 1960–2025, cross-referenced against the regional precipitation climatology. Events were characterized by season, primary physiographic unit, provincial coverage, and an ordinal magnitude index (scale 1–7) encoding combined spatial extent and estimated landslide count. Results indicate that no statistically significant monotonic trend in event frequency was detected over the study period (mean rate: 1.97 events yr⁻1); however, a moderate positive correlation was established between event magnitude and the number of provinces affected (R2 = 0.51, p < 0.00), validating the magnitude index as a proxy for spatial footprint. Mean event magnitude reached its highest value in the 2010s (3.71), while all five events of magnitude ≥ 6 occurred in autumn. The summer fraction of the catalogue increased markedly, from 6% in the 1960s to 31% in the 2000s and 25% in the 2020s, concurrent with stable or declining summer mean precipitation totals, consistent with Clausius–Clapeyron amplification of convective intensity under documented regional warming. Cross-analysis with regional records identifies three tiers of rainfall–landslide coupling: a stationary direct seasonal coupling; a non-stationary, strengthening intensity-mediated summer coupling; and a structural susceptibility-mediated spatial decoupling whereby the driest provinces generate the highest landslide occurrence frequencies due to the lower triggering thresholds characteristic of Tertiary Piedmont Basin sedimentary environments. A post-hoc assessment of the triggering thresholds used for regional shallow landslide early warning system demonstrates superior detection performance for high-magnitude autumn events (hit rate up to 89% for the 2000–2025 sub-period) and identifies sub-daily convective accumulation windows in summer as the primary domain requiring threshold recalibration…”
#massmovement #landslide #engineeringgeology #Italy #Piedmont #NorthernItaly #weather #rainfall #precipitation #climate #risk #hazard #corrleation #relationship #earlywarningsystems #damage #loss #community #infrastructure #mountain #spatiotemporal #mapping #spatialanalysis #statistics #geostatistics #climatology #regional #scale #weatherpatterns #physiography #geomorphology #water #hydrology #hydrogeomorphology #geology #soils -
Изолинии как документ и кригинг, которому можно верить
Мы создаем софт для горно-геологических служб калийных рудников, и это третья статья цикла про геостатистику в QGIS: в первой был кригинг на чистом NumPy, во второй - выбор между пятью его видами. Сегодня две темы, которые остались за кадром, а в повседневной работе геолога решают едва ли не больше самой интерполяции. Первая - изолинии: не картинка поверх растра, а документ, который идёт в подсчёт запасов и на печатные планы, со строгими требованиями к геометрии. Вторая - всё, что живёт вокруг кригинга и превращает интерполяцию в решения: карта стандартной ошибки, кросс-валидация, внешний дрейф, вероятность превышения кондиции, гидравлика потока по Дарси и гауссова симуляция неопределённости.
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Какой кригинг выбрать: простой, ординарный, с трендом, блочный, индикаторный
Мы создаем софт для горно-геологических служб калийных рудников, и после первой статьи про [кригинг на чистом NumPy]( https://habr.com/ru/articles/1055612/ ) самый частый вопрос звучал одинаково: «Хорошо, а какой именно кригинг брать?» Вопрос правильный: под словом «кригинг» живёт целое семейство методов, и выбор между ними влияет на результат сильнее, чем тонкая настройка вариограммы. В плагине Isoliner их пять - простой, ординарный, с полиномиальным трендом, блочный и индикаторный, - и каждый существует не для галочки, а под конкретный класс геологических задач. Под катом - разбор всех пяти на живых примерах калийного месторождения: когда какой, какие параметры за что отвечают, как это выглядит в коде и на каких ошибках спотыкаются чаще всего. <cut/> ## Общая механика: что меняется, а что нет Напомню каркас из первой статьи: оценка в точке - взвешенная сумма соседних скважин, веса - решение системы уравнений с ковариациями из вариограммы: ```python s = np.linalg.solve(A, r) # A - ковариации соседей между собой, w = s[:na] # r - ковариации соседей с точкой оценки est = float( np.dot (w, vra)) ``` Все пять видов кригинга решают эту систему. Различаются они тем, что считается неизвестным про среднее поле и что именно оценивается - точка, блок или вероятность. Это и есть ключ к выбору. ## Простой кригинг (SK): среднее известно Простой кригинг предполагает, что среднее значение поля вам известно заранее и постоянно по площади. Тогда система решается без дополнительных условий, а недобор веса соседей компенсируется этим средним:
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Геостатистика в QGIS без SAGA: кригинг на чистом NumPy
Мы создаем софт для горно-геологических служб калийных рудников. Наши геологи и маркшейдеры каждый день превращают тысячи скважинных проб в карты: отметки кровли пласта, содержания KCl, мощности, газоопасность. Классический инструмент для этого - кригинг, и в QGIS он формально есть: SAGA, GRASS, Smart-Map, связки со SciPy. На практике же каждый вариант чем-то не устраивал, и год назад я начал писать свой плагин. Сейчас Isoliner - это 24 инструмента в официальном репозитории plugins.qgis.org : кригинг четырёх видов, вариограммный анализ, кросс-валидация с отчётами, изолинии с контурными полигонами, геологические разрезы и собственный 3D-просмотр. Вычислительное ядро - чистый NumPy, ни одной внешней зависимости. Под катом - зачем понадобился ещё один кригинг, как выглядит система кригинга в двадцати строках NumPy, что такое вариограмма на пальцах и почему абсолютные единицы силла — главные грабли всех, кто впервые открывает такие инструменты.
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Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
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https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
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https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared (earlier) paper
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H/T @abhinav Alangadan
“Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
[The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
[Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
#permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche -
On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
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https://doi.org/10.1016/j.enggeo.2026.108860 <-- shared paper
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#rainfallinduced #landslide #Functionalregression #Japan #Earlywarningsystem #Spacetimeprediction #spatialanalysis #spatiotemporal #massmovement #engineeringgeology #geomorphology #hydrogeomorphology #geomorphometry #remotesensing #geostatistics #model #modeling #mechanics #rainfall #threshold #precipitation #water #hydrology #risk #hazard #hazardassessment #terrain #landscape #landform #landuse #geology #soil #lithology #functionalGeneralizedAdditiveModel #FGAM #prediction #casestudy #AI #LLM -
Influence Of Modeling Assumptions On Pedestrian Evacuation Success For Non-Eruptive Lahar Hazards At Mount Rainier, Washington
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https://doi.org/10.1016/j.ijdrr.2026.106132 <-- shared paper
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https://www.sciencebase.gov/catalog/item/697ba3e5b66b0197c3043d2f <-- shared, related open data source
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[I still remember working on and being fascinated by lahars being an engineering geologist in Washington State (and from my time studying in New Zealand), although (of course) not to this level of detail/focus]
#volcano #lahar #evacuation #exposure #model #modeling #engineeringeology #risk #hazard #naturalhazard #MountRainer #Washington #USA #spatialanalysis #spatiotemporal #emergencymanagement #GIS #spatial #mapping #publicsafety #hazardzone #vulcanism #downstream #debrisflow #massmovement #monitoring #detection #geostatistics #demographics #atrisk #fedscience #publicgood #fedservice #opendata
@USGS -
Drivers Of Forest Disturbance In Southeast Asia [incl. spatial analysis]
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https://doi.org/10.1016/j.jag.2026.105220 <-- shared paper 🔗
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https://github.com/shijuanchen/SEA_forest_dis <-- shared GitHub ‘data repository 🔗
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#GIS #spatial #mapping #spatialanalysis #spatiotemporal #forest #vegetation #Forestdisturbance #Forestdegradation #Deforestation #remotesensing #SoutheastAsia #Asia #tropicalforests #timeseriesanalysis #geostatistics #disturbance #clearing #planting #agriculture #earthobservation #imagery #CCDC #changedetection #landcover #landuse #change #plantation #cultivation #slashandburn #planning #mitigation #catalogue #conservation #management #landmanagement #machinelearning #imageanalysis #AI -
Detection And Spatial Modelling Of Trends In UK Rainfall Frequency
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https://doi.org/10.1080/02626667.2026.2622458 <-- shared paper
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#GIS #spatial #mapping #extremeweather #rainfall #pluvial #frequency #trends #nonstationarity #extremevalue #model #modeling #monitoring #UnitedKingdom #UK #Britian #England #Scotland #Wales #precipitation #spatialanalysis #spatiotemporal #drainage #monitoring #planning #resilience #naturalhazards #flooding #flood #water #hydrology # #sewerage #sewage #wastewater #waterways #pollution #spills #releases #stormwater #erosion #massmovement #landslides #engineeringgeology #risk #hazard #climatic #covariates #raingauges #maximums #duration #winter #NorthernEngland #NorthAtlanticOscillation #NAO #index #metrics #annualmaximum #AMAX #seasonalmaximum #SMAX #peaksoverthreshold #POT #statistics #geostatistics #MetOffice #weather #climate -
Adaptive Thresholding (AT) For Snowmelt Detection With Calibrated Enhanced-Resolution Brightness Temperatures (CETB) - Timing And Regional Patterns For Case Study Of Alaska
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https://doi.org/10.1016/j.srs.2026.100390 <-- shared paper
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“HIGHLIGHTS:
• Data-driven Adaptive Thresholding (AT) for snowmelt detection using PMW CETB data.
• AT improves melt detection accuracy to ≤ 1.0 day MAE over legacy methods in Alaska.
• AT captures spatial and regional melt variability linked to snow class and terrain.
• AT is sensor-agnostic, scalable across frequencies and polarizations for broad use..."
#snowmelt #winter #snow #spatialanalysis #GIS #spatial #mapping #spatiotemporal #water #waterresources #hydrology #spatialdata #AdaptiveThresholding #Passivemicrowave #remotesensing #EnhancedResolutionData #Snowmeltdetection #DiurnalAmplitudeVariation #US #USA #Alaska #geostatistics #melt #melting #monitoring #regional -
Changes In Dominant Streamflow Drivers As Network-Scale Flow Regime Shifts From Intermittent To Ephemeral Across A Multi-Year Drought
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https://doi.org/10.1002/hyp.70413 <-- shared paper
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#GIS #spatial #mapping #water #hydrology #fluvial #pluvial #precipitation #extremeweather #California #USA #semiarid #watershed #headwaters #weather #metrology #spatialanalysis #spatiotemporal #model #modeling #geostatistics #monitoring #sensors #instrumentation #insitu #drought #streamflow #ephemeral #intermittent #catchments #waterresources #watermanagement #watersecurity #planning
#coast #coastal #hydrogeomorphic #geology #climate -
A Study Debunks [sic] Decades Of Scientific Research - Underwater Canyons Are NOT Created By Rivers… And The Explanation Is Brutal
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https://www.ecoticias.com/en/a-study-debunks-decades-of-scientific-research-underwater-canyons-are-not-created-by-rivers-and-the-explanation-is-brutal/24721/ <-- shared technical article
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https://doi.org/10.1126/sciadv.adv3942 <-- shared paper #GetNZOnTheMap
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https://blogs.egu.eu/divisions/nh/2018/02/05/twenty-or-more-leagues-under-the-sea-a-journey-to-understand-submarine-canyons/ <-- shared interview transcript, “A journey to understand submarine canyons [and their related geohazards]”, European Geosciences Union (EGU.)
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https://www.sustainableseaschallenge.co.nz/our-research/submarine-canyons-connecting-coastal-and-deep-sea-ecosystems <-- shared technical article, “Submarine Canyons - Connecting Coastal And Deep-Sea Ecosystems”
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#spatial #mapping #model #modeling #moprhology #spatialanalysis #tectonics #continentalmargins #submarinecanyons #formation #river #hydrology #massmovement #processes #global #oceanfloor #geology #slumps #currents #seafloor #steepness #debrisflow #geohazard #ecosystems #sediment #movement #infrastructure #damage #valley #marine #underwater #geomorphology #geomorphometry #deepocean # #geostatistics #earthquake #gravity #erosion #continentalslope #biogeochemical #coast #coastal -
Land Surface Heterogeneity And Tornado Occurrence - An Analysis Of Tornado Alley And Dixie Alley
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https://doi.org/10.1080/19475705.2019.1583292 <-- shared paper
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#GIS #spatial #mapping #NLCD #climatechange #severeweather #extremeweather #environment #slope #remotesensing #landcover #extremeweather #landcover #landuse #tornado #risk #hazard #damage #cost #economics #publicsafety #infrastructure #loss #USA #TornadoAlley #DixieAlley #landsurface #topography #vegetation #spatialanalysis #model #modeling #heterogeneity #variables #parameters #factors #geostatistics #statistics #EROS #fedscience -
Identification Of Geothermal Anomalies From Landsat Derived Land Surface Temperature, Mount Meager Volcanic Complex, British Columbia, Canada
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https://doi.org/10.1016/j.rse.2025.114649 <-- shared paper
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“Highlights:
• A novel method for detecting geothermal components from solar energy dominated LST.
• Using LST time series to eliminate temporal variant solar energy input.
• Uncertainty in anomaly identification quantified by probability measure.
• Capable of revealing LST anomalies caused by geothermal, anthropogenic and surface processes..."
#GIS #spatial #mapping #britishcolumbia #BC #solar #geothermal #remotesensing #earthobservation #LST #spatialanalysis #spatiotemporal #naturalresources #volcanic #geology #geostatistics #landsurfacetemperature #satellite #geothermalheatflux #GHF #energybalance #calculation #model #MountMeager #Landsat #landsat8 #hotspring #landslide #massmovement #engineeringgeology #spring #seep #anthropogenic #HEP #hydropower #monitoring #risk #hazard