#spatiotemporal — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2026-09-02 · 07:33 UTC

The Radiative Effects Of Water Vapour From Terrestrial Evapotranspiration
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https://doi.org/10.1088/1748-9326/adde72 <-- shared paper/letter
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https://zenodo.org/records/15413219 | https://zenodo.org/records/15416936 <-- shared open data, for “Model information and output for "The radiative effects…” ”
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https://doi.org/10.1007/s11269-025-04191-w <-- shared paper
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H/T @jan Umsonst | Earth System Nerd
“Water vapour accounts for roughly 50% of the modern greenhouse effect. Over continental regions, evapotranspiration (ET) is often limited by water availability. In this study, [the authors] spatially quantify how much of the total atmospheric water vapour evaporated most recently from land and calculate the relative contribution of that water vapour to the atmospheric radiative budget. Using a combination of tracer-enabled Earth system model simulations and radiative transfer calculations, [they were] able to explicitly quantify the 3D distribution of terrestrial vs. oceanic water vapour, and the spatial contribution of each to the surface and top of atmosphere radiative budgets. [They found] that over many continental regions, more than half of the total column-integrated water vapour originates from land ET, and that this vapour contributes up to 30 W/m² of longwave radiation into the surface in the annual mean (about 10% of the total). Understanding how terrestrial ET impacts the base-state of water vapour distribution and the water vapour greenhouse effect is critical to understanding how and where changes in terrestrial ET, driven by climate change, land use, etc, will modify the radiative properties of the atmosphere and thus the climate system…”
#water #hydrology #greehouseeffect #highperformancecomputing #HPC #evapotranspiration #Radiative #WaterVapour #spatial #spatialanalysis #spatiotemporal #atmosphere #model #modeling #earthsystemmodelling #terrestrial #oceanic #vapour #climatechange #landuse #changes #climatesystem

#water #hydrology #greehouseeffect #highperformancecomputing #hpc #evapotranspiration

💧🌏 Greg Cocks @[email protected] · 2026-08-25 · 23:19 UTC

Anthropogenic River Flows
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https://doi.org/10.1088/3033-4942/ae94a0 <-- shared technical perspective
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H/T @Homero Paltán | Water, Energy, Land & Infrastructure Systemic Risks | Lecturer
“Are global river flows no longer natural? Can we really talk about natural rivers? [The authors] started asking ourselves these questions a few years ago over wonderful discussion sessions.
[They] argue that global river systems have entered a new phase, one of Anthropogenic River Flows, where flow regimes arise from the persistent interaction of:
• the physical alteration of river systems,
• the institutional and socio-technical organisation of water use and allocation
• broader global and systemic processes operating across scales.
In the Anthropocene, these domains do not operate independently because a change in one alters how the other two function. Global water risks and river flow properties then become an emergent property of these interactions.
As a result, [the authors] call for a rethinking of how we study, understand, and manage river flows to better reflect this new reality…”
#water #hydrology #Anthropogenic #River #Flow #spatial #mapping #spatialanalysis #spatiotemporal #flowregimes #risk #waterrisk #global #climatechange #watersecurity #baseline #humanimpacts #magnitude, #seasonality #variability #waterquality #sociohydrology #hydroclimate #regulation #waterresources #engineering #governance #pumping #alteration

#water #hydrology #anthropogenic #river #flow #spatial

💧🌏 Greg Cocks @[email protected] · 2026-08-24 · 21:36 UTC

Modeling Climate Change Impacts On Blue And Green Water In The Ethiopian Upper Blue Nile Basin
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https://doi.org/10.1016/j.ejrh.2026.103871 <-- shared paper
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H/T @Dessalegn worku Ayalew
“The present study assesses the impacts of climate change on blue and green water in the Kessie Watershed of the Ethiopian Upper Blue Nile Basin using the SWAT+ model.
[The authors] set up [a] SWAT+ model using quality-controlled and homogenized observational climate time series… and calibrated it using a multisite calibration approach. [They] selected CMIP6 climate models for future simulations and bias-correction methods through a comprehensive performance assessment... Building on these previous studies, the present research further evaluates the reliability of combining robust climate-model selection with optimal bias-correction methods to improve the reliability of hydrological simulations. [They] then used the best-performing climate models, bias-corrected using the optimal methods, to project future changes in blue and green water in the study area…
KEY FINDINGS:
• SWAT+ effectively represented hydrological processes across multiple gauging stations in the Ethiopian Upper Blue Nile Basin.
• Ensembles of CMIP6 climate models improved the reliability of hydrological simulations compared with individual climate models.
• Optimized climate-model selection reduced biases in hydrological simulations more than bias correction alone.
• Arbitrary selection of climate models can degrade hydrological simulations, even when their outputs are bias-corrected using robust methods.
• Both blue and green water are projected to increase under future climate change in the Ethiopian Upper Blue Nile Basin.
• Blue water exhibits greater seasonality and climate sensitivity than green water flow and green water storage.
The study also provides sustainable water management options for adapting to the impacts of climate change, with implications for water resource planning and management in the Upper Blue Nile Basin…”
#Bluewater #Greenwater #CMIP6 #GCMs #Modelensemble #SSPscenarios #SWAT #Ethopia #UpperNile #Nile #NileBasin #gaging #gauging #Africa #climatechange #impacts #water #hydrology #KessieWatershed #EthiopianUpperBlueNileBasin #model #modeling #spatialanalysis #spatiotemporal #CMIP6 #waterresources #watermanagement #ecosystem #habitat #environment

#bluewater #greenwater #cmip6 #gcms #modelensemble #sspscenarios

💧🌏 Greg Cocks @[email protected] · 2026-08-21 · 04:02 UTC

Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
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https://doi.org/10.3390/rs18142282 <-- shared paper
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https://www.usgs.gov/publications/comparing-desis-hyperspectral-and-landsat-10-simulated-superspectral-data-crop-type <-- shared USGs publication page
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H/T @USGS
“How can we get better at classifying crops from space? 🛰️🌽
Some types of satellite imagery are better at distinguishing crops than others. A USGS study compared two approaches and found one produced more accurate crop maps.
Here's what the researchers found:
• Hyperspectral imagery achieved 86% accuracy, compared to 75% for simulated superspectral imagery.
• Using just 14 carefully selected hyperspectral bands produced nearly the same results as using all 60 DLR Earth Sensing Imaging Spectrometer (DESIS) bands, showing those wavelengths contain much of the information needed to distinguish crop types.
• Using superspectral imagery on the upcoming Landsat 10 mission will allow for routine tracking of crops and enhance finer crop mapping.
• The findings help scientists identify which wavelengths provide the most useful information for crop mapping and future remote sensing applications.
Better crop maps will help governments and scientists track global agriculture, monitor the current crop season, and study agricultural trends…”
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“HIGHLIGHTS:
• What are the main findings?
- The 14 DESIS hyperspectral narrowbands (10 nm) aligned with the Landsat 10 (formerly Landsat Next) spectral dataset produced similar accuracy results to the full 60-band DESIS hyperspectral dataset for classifying crop types. These 14 DESIS narrowbands resulted in higher accuracy than the 14 simulated Landsat 10 superspectral broadbands.
- When using DESIS narrowbands, Support Vector Machine (SVM) resulted in higher accuracy than Random Forest (RF).
• What are the implications of the main findings?
- A carefully selected set of 14 DESIS hyperspectral narrowbands (10 nm) can achieve classification accuracy comparable to those obtained using all 60 DESIS narrowbands across the 400–1000 nm range. These 14 strategically positioned narrowbands classified crop types with higher classification accuracy than the corresponding 14 Landsat 10 superspectral broadbands within the same spectral range.
- This study underscores the importance of multi-temporal imagery across the full crop-growing season for achieving more detailed and accurate crop type classifications. Such temporal coverage is more feasible with the planned Landsat 10 routine acquisition of broadband imagery than with task-based hyperspectral collections…”
#hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture #crops #croptype #classifaction #croplands #California #CentralValley #GIS #spatial #mapping #remotesensing #earthobservation #imagery #DESIS #Landsat #Landsat10 #satellite #spatialanalysis #spatiotemporal #global #AI #machinelearning #model #modeling #SupportVectorMachine #SVM #RandomForest #RF #GoogleEarthEngine
@USGS

#hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture

💧🌏 Greg Cocks @[email protected] · 2026-08-19 · 04:50 UTC

WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
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https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
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https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
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https://deepmind.google/science/weatherlab/ <-- shared data
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https://github.com/google-deepmind/weathernext <-- shared GitHub repository
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H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext

#google #deepmind #googleresearch #ai #ensembles #functionalgenerativenetworks

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 03:21 UTC

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

#massmovement #landslide #engineeringgeology #italy #piedmont #northernitaly

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 03:01 UTC

Widespread Landslide Activity in an Extreme Wet Season and Implications for Regional Sediment Management, Eastern San Francisco Bay Area, California
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https://doi.org/10.1029/2026EA005227 <-- shared paper
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H/T @amy East, Ph.D., P.G. | Researcher integrating geoscience and climate-change preparedness
“[This paper (link above) is] a collaboration with [the H/T’s] colleagues from [the] USGS Landslide Hazards Program, who mapped over 8,900 landslides in the eastern San Francisco Bay Area during an extreme wet winter.
How much sediment does such an extreme winter produce, from landslides or in stream discharge? How does that compare with long-term sediment production and landscape denudation rates?
[They] f[o]nd that landslide sediment mobilization is comparable to long-term denudation rates, emphasizing the role of extreme events in long-term sediment production. However, one extreme wet year has a negligible effect toward counteracting ongoing problems of sediment deficit in San Francisco Bay: to keep pace with sea-level rise, extreme wet conditions would need to occur in 50 out of the next 75 years…”
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"PLAIN LANGUAGE SUMMARY: Watersheds will likely produce more sediment in a warmer future with more extreme rain, primarily through landslides in steep terrain. This study examines how an extremely wet season affected sediment production and transport in the eastern San Francisco Bay area, California. By mapping and measuring 8,928 landslides, [they] found that rare, extreme rain conditions are likely responsible for the vast majority of long-term hillslope erosion rates in this region. However, due to long residence times for sediment on hillslopes and in stream channels, a maximum of 1%–2% of that newly mobilized landslide material could have potentially contributed to sediment carried by streams into the Bay that year. Even extremely wet years cannot provide enough sediment for Bay wetlands and shorelines to keep pace with rising sea levels. To meet the demand for sediment in the Bay, such extreme rain and sediment production would need to occur in most years, which is not realistic. To restore wetlands and protect shorelines, managers likely will need to supplement the coastal system with repurposed dredged material…”
#massmovement #soil #water #hydrology #hydrography #geology #soils #geomorphometry #hydrogeomorphology #geomorphology #landslide #masswasting #climatechange #extremeweather #precipitation #rainfall #weather #climate #mapping #engineeringgeology #mapping #SanFrancisco #BayArea #USA #California #fedscience #fedservice #oublicgood #sediment #stream #discharge #extremewinter #sealevelrise #SLR #hillslope #erosion #sedimentation #tidal #wetlands #coast #coastline #shoreline #GIS #spatial #spatialanalysis #spatiotemporal #watershed
#USGS | #USGSLandslideHazardsProgram

#massmovement #soil #water #hydrology #hydrography #geology

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 02:56 UTC

The Portrait of Flood Risk in Italy - Past, Present and Future, From 1870 to 2100
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https://doi.org/10.1029/2026GL122987 <-- shared paper
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“ABSTRACT: Among European countries, Italy ranks as one of the most susceptible to flood risk. While this figure is already substantial, climate change and rapid urbanization in flood-prone areas have been identified as the two main drivers expected to elevate the number of individuals at risk. This study offers a comprehensive assessment of these two drivers of flood risk in Italy over 230 years, from 1870 to 2100, focusing on how they interact to increase risk. Using the large-scale flood risk model RESCUE-FR, [the authors] analyze[d] the population at risk under the 200-year return period scenario to provide a targeted assessment of population risk, how it has evolved in the past, and its projection in the future. [Their] findings indicate that while historical flood risk in Italy has primarily been influenced by population growth and migration into at-risk areas, future projections suggest that climate change will become the dominant driver of flood risk.
PLAIN LANGUAGE SUMMARY: Italy is one of the European countries most at risk of flooding. This study examines the impact of two risk factors on flood risk in Italy over the long term, from 1870 to 2100: climate change and the evolution of population in areas prone to flooding. Using a large-scale flood risk model, [they] simulated different scenarios for different time periods, such as with and without climate change, to estimate how each factor contributes to the number of people exposed to floods in the past and future. [Their] results show that population growth and migration into flood-prone areas were the main reasons for the increased risk in the past. In the future, however, climate change is likely to become the dominant factor, putting more people at risk. Understanding how these factors interact can help communities to plan more effectively for floods and reduce the number of people affected…”
#flood #flooding #risk #hazard #Italy #Europe #national #history #historic #cost #damage #infrastructure #floodrisk #population #urbanisation #development #climatechange #extremeweather #dominantfactor #floodprone #national #regional #spatialanalysis #spatiotemporal #model #RESCUEFR #modeling #factors #parameters #drivers #publicsafety

#flood #flooding #risk #hazard #italy #europe

💧🌏 Greg Cocks @[email protected] · 2026-08-14 · 03:20 UTC

Geospatial Analysis of Carbon Offset Projects - A Broader Scientific Outlook
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https://www.eldhollow.com/blogs/geospatial-analysis-of-carbon-offset-projects <-- shared technical blog
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[this post should not be considered an endorsement of a particular organisation, rather scrutinising the spatial use case & technical approach]
H/T @kyle Arvisais | Forest Carbon Scientist
“Geospatial analysis is at the core of [the H/T’s company’s] project quality assessments, and [the author is] constantly finding ways to make the pipeline better and ways to use it. [They are] obviously not the only one who uses these types of tools, but to be perfectly honest, the quality of models [they have] seen over the years has been all over the place.
This blog makes a casual introduction to [their] pipeline while talking about the field at large…”
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“The world has committed to protecting and restoring nature at an unprecedented scale. Whether that commitment delivers what it promises comes down project execution on the ground. Local socioeconomics and forest ecology intertwine to create complex challenges for projects to overcome during implementation, and at the end of the day, projects boil all of these complexities down to one single unit: the carbon credit. So the question becomes: can we actually measure what is happening to a forest, accurately and honestly, and everywhere at once?
For a long time, the honest answer has been no. Historically, many forest carbon projects overstated their impact. Usually it was because the baseline was too generous, or because the measurements underneath were flawed. For anyone with a stake in nature markets, that uncertainty is one of the core risks.
Robust geospatial analysis can help mitigate that risk. If you treat a carbon credit as what it really is, a scientific claim, then we can hold it to that standard and assess it objectively. [Their] geospatial pipeline turns satellite data and ground truth data into models about how much forest is standing, how it is changing, and what might put it at risk in the future. The pipeline does this anywhere on Earth…”
#GIS #spatial #mapping #usecase #carbonoffset #spatialanalysis #spatiotemporal #qualityassessment #objectivity #projectpipeline #model #modeling #application #nature #environment #ecosystems #ecology #local #regional #factors #socioeconomics #forestecology #vegetation #forest #tree #carboncredit #climatechange #climatecrisis #forestcarbonprojects #global

#gis #spatial #mapping #usecase #carbonoffset #spatialanalysis

💧🌏 Greg Cocks @[email protected] · 2026-08-11 · 03:35 UTC

Classification And Conceptualization Of Karst Recharge Processes Through Spectral And Change Point Analysis Of Drip Water Dynamics
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https://doi.org/10.1029/2025WR042816 <-- shared paper
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H/T @ Danyang Sun | UNSW-PhD student
“… [The authors] analysed one year of drip water monitoring data from 46 monitoring sites across six karst regions in southeastern Australia. By integrating fast Fourier analysis, cross-wavelet transform and change point analysis, [they] identified five characteristic recharge behaviours and developed a conceptual framework linking temporal drip dynamics with recharge mechanisms. [They] hope this framework will contribute to a better understanding of recharge heterogeneity in karst systems and support future groundwater research under a changing climate…”
#karst #Australia #water #hydrology #underground #subsurface #recharge #dynamics #spectral #changepoint #cave #dripwater #analysis #spatiotemporal #groundwater #research #climatechange #extremeweather #flow #storage #vadose #epikarst #watertable #aquifer #percolation #rainfall #precipitation #climate #lithology #geology #spatialanalysis

#karst #australia #water #hydrology #underground #subsurface

💧🌏 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] · 2026-08-05 · 05:02 UTC

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
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https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
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H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

#gis #spatial #mapping #googleearthengine #machinelearning #remotesensing

💧🌏 Greg Cocks @[email protected] · 2026-08-05 · 04:54 UTC

Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
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https://doi.org/10.3390/geosciences15030110 <-- shared paper
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H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
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“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement

#floodsusceptibility #floodrisk #machinelearning #gis #naturalhazards #disastermanagement

💧🌏 Greg Cocks @[email protected] · 2026-08-04 · 04:55 UTC

[Open] Data Related To Flood Mapping [Canada]
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https://natural-resources.canada.ca/science-data/science-research/natural-hazards/flood-mapping/data-related-flood-mapping <-- shared link to technical details
--
https://app.geo.ca/en-ca/map-browser/record/a13a2575-5bda-4bfd-a9b1-5bd2dd583f09 <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
--
https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc <-- shared data-portal link, Canada Flood Susceptibility Index
--
https://doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
--
https://doi.org/10.1002/2017WR020917 <-- shared (2017) paper
--
H/T @Michael DePue | VP & AtkinsRéalis Fellow for Water Resources Engineering | PE, PMP, CFM
“At the Canadian Water Resources Association National Conference in Winnipeg, colleagues shared insights from Canada's Flood Hazard Identification and Mapping Program. This initiative has seen over 400 flood mapping projects and more than 1,000 flood hazard maps produced, supported by a substantial investment of $164.2 million from 2024 to 2028.
Two key datasets:
• The Canada Flood Map Inventory, which records the locations of flood hazard maps and provides information on how to access them.
• The national Flood Susceptibility Index, a machine-learning assessment of flood-prone areas, including regions that have not been mapped in detail.
When these two layers are combined on a single screen, it becomes clear where future mapping efforts should be directed — specifically, areas with high susceptibility that currently lack detailed maps…”
#water #hydrography #flood #flooding #risk #hazard #model #modeling #fedscience #publicsafety #humaninpacts #opendata #Canada #GIS #spatial #mapping #damage #infrastructure #floodmapping #prediction #spatialanalysis #spatiotemporal #historic #current #future #preduction #extremeweather #metrology #rainfall #precipitation #atmosphericriver #FloodMapInventory #CFM #floodhazard #FloodSusceptibilityIndex #floodprone #research #susceptibility
@NRCAN

#water #hydrography #flood #flooding #risk #hazard

💧🌏 Greg Cocks @[email protected] · 2026-07-30 · 04:47 UTC

Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
--
https://doi.org/10.1016/j.jhydrol.2026.135999 <-- shared paper
--
https://youtu.be/VHzYLvSYR7k?si=5oGGPeH6T0dFusfY <-- recent overview video created about the research
--
https://doi.org/10.1016/j.wroa.2025.100396 <-- share (earlier) paper
--
H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
“… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
[The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”
#publichealth #risk #hazard #watersecurity #Floodrisk #Humanhealthrisk #Urbanflooding #Hydrodynamicmodelling #waterquality #model #modeling #SupportVectorRegression #flood #flooding #urban #city #sewage #pathogens #contaminant #disease #streets #community #quantification #remotesensing #GIS #spatial #mapping #earthobservation #spatialanalysis #water #hydrology #climatechange #extremeweather #spatiotemporal #AI #machineleraning #fateandtransport #hydrodynamic #microbial #rainfall #drainage #streamflow #topography #hydrogeomorphology #Delhi #India #floodplain

#publichealth #risk #hazard #watersecurity #floodrisk #humanhealthrisk

💧🌏 Greg Cocks @[email protected] · 2026-07-28 · 05:07 UTC

Busy Beavers - The Turbidity Signature Of Ecosystem Engineers At Work
--
https://doi.org/10.1002/hyp.70661 <-- shared paper
--
H/T @alan Puttock
“Beavers are the quintessential ecosystem engineers. In slow-flowing streams, they create complex wetlands with ponds by building dams and canals that can positively impact biodiversity, hydrology and water quality. These activities can interchangeably capture or release sediment along the watercourse. To date this has not been quantified at the resolution of rainfall events or beaver activity. This study used 15-min frequency, sustained monitoring upstream and downstream of a newly establishing beaver wetland to measure episodic changes in water turbidity at an event resolution. Monitoring showed no significant differences between upstream and downstream turbidity over 160 days when the first pair of beavers, known not to be building dams or canals, were resident. Shortly after introduction of another beaver pair, however, dam building, burrows and canal excavations were quickly observed, resulting in the creation of a complex beaver wetland between 2021 and 2024. Monitoring over 375 days during this period showed significant differences. Downstream turbidity was significantly higher overall than upstream: 13.1 Nephelometric Turbidity Units (NTU) compared to 4.2 NTU. Stochastic spikes in downstream turbidity during the study period not recorded upstream were associated with dam building and burrowing. Overall, there was no significant difference in turbidity loads, which was at least partially explained by a reduction in discharge downstream, particularly in higher flows, during the dam building period. This demonstrates a complex system with the trapping of influent sediment, the storing of water and the periodic release of beaver wetland sediment leading to net balance in loads. These results help provide context for other studies which have used temporally discrete sampling campaigns rather than continuous high-frequency monitoring. They provide a unique insight into the downstream impacts of a rapidly developing beaver wetland over its first three and a half years in a landscape that hasn't had beavers for over 400 years…"
#hydromorphic #water #hydrology #dam #beaverdam #waterquality #biodiversity #ecology #benefits #NatureBasedSolutions #Wetlands #Ecology #Biodiversity #EnvironmentalScience #Wildlife #Ecosystem #bioviversity #conservation #restoration #landscaperecovery #floodmanagement #FloodMitigation #flooding #energy #floodrisk #sustainability #wetlands #hydrography #dams #impoundment #deadwood #waterresources #landscapeengineer #benefits #vegetation #ecology #ecosystem #riversystemsstabilisation #naturalwaterregulation #resilience #valleysreborn #fisheries #invertebrates #extremeweather #floodflows #sediment #baseflow #drought #landmanagement #naturalsystems #landuse #monitoring #spatialanalysis #spatiotemporal

#hydromorphic #water #hydrology #dam #beaverdam #waterquality

💧🌏 Greg Cocks @[email protected] · 2026-07-23 · 04:30 UTC

Global Performance of #RemoteSensing Based and Reanalysis-Driven Models to Estimate Open Water Evaporation
--
https://doi.org/10.1029/2025WR042363
--
“ABSTRACT: Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, [they] analyze[d] the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. [They] compare[d] three remote sensing-based models, one reanalysis-driven model and one ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. [Their] results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm/day, respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.
PLAIN LANGUAGE SUMMARY: Water loss through evaporation in lakes and reservoirs directly affects water availability, which highlights the need to monitor these losses. However, measuring evaporation in situ is challenging and expensive. An alternative is to estimate evaporation using remote-sensing models and compare these estimates with in-situ data to verify their accuracy. Here, [they] evaluated four models and their ensemble (the models' mean value) using measurements from 27 lakes and reservoirs worldwide. [They] found that the ensemble presented higher accuracy and consistency than any individual model because it benefits from the strengths of each model. This approach can guide future improvements in estimating open-water evaporation, which is essential for large-scale water-resource management…”
#global #mapping #earthobservation #GIS #spatial #spatialanalysis #spatiotemporal #model #modeling #water #hydrology #surfacewater #waterbody #lake #reservoir #evaporation #evapotranspiration #watercycle #weather #meteorology #usecase #waterresources #watermanagement #waterloss #regional #estimate #policy #planning #instrumentation #comparasion

#remotesensing #global #mapping #earthobservation #gis #spatial

💧🌏 Greg Cocks @[email protected] · 2026-07-21 · 03:13 UTC

Impact of River Morphology on River–Groundwater Exchange in Braided River Systems
--
https://doi.org/10.1111/gwat.70092 <-- shared paper
--
H/T @thomas Wöhling | Professor at TU Dresden
“Do you like braided rivers? We think they are soooo beautiful. And they are interesting to study as well. Particularly how these complex and transient systems interact with regional aquifers…”
--
“Coupled models of two braided rivers with real, pre- and postflood event morphologies are studied for river–groundwater exchange changes…”
--
“Braided river systems are an important source for groundwater recharge, but their complex morphology makes river–groundwater exchange fluxes difficult to estimate. Their river channel morphology changes frequently after floods, which has effects on recharge rates that have rarely been studied in the past. This work aims to isolate the effects of changes in braided river morphology on groundwater recharge for two sections of the Wairau River and Waikirikiri River in New Zealand. For each study site, two different river morphology variants of a fully coupled surface water–groundwater model utilizing high-resolution DEMs of river bathymetry before and after a major flood event were set up while keeping parameterization and boundary conditions the same. The models demonstrate that flood-induced morphology changes in braided river systems alter groundwater recharge. [They] identif[ied] features, both simulated and observed, that explain the direction of change. Features that increase groundwater recharge are a larger braidplain aquifer extent and volume, larger wetted area and, specifically, an increase of areas with high exchange rates in locations of larger gradients between braidplain aquifer and regional aquifer. These factors influence groundwater recharge independent of connection (Wairau River) or disconnection (Waikirikiri River) of the system to the regional aquifer, albeit with different magnitudes. An extension of [their] research to other braided rivers is needed to more broadly generalize [their] findings...”
#NewZealand #river #morphology #braided #Waikirikiri #Wairau #water #hydrology #hydrography #model #modeling #groundwater #aquifer #waterresources #recharge #infiltration #flood #flow #flooding #hydrogeomorphology #exchangefluxes #surfacewater #remotesensing #DEM #elevation #spatialanalysis #GIS #spatial #mapping #change #dynamic #spatiotemporal #bathymetry

#newzealand #river #morphology #braided #waikirikiri #wairau

💧🌏 Greg Cocks @[email protected] · 2026-07-14 · 03:14 UTC

Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
--
https://doi.org/10.1038/s43247-026-03515-x <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-14 · 02:51 UTC

Watching A #NOAA #Webinar on Flash Droughts
--
https://noaaresearch.webex.com/wbxmjs/joinservice/sites/noaaresearch/meeting/download/9b3e684d45ca47fc9469070eabd9a142?MTID=m2fa4a8af7bd8647fc48619af5eeecb5a <-- shared NOAA Summer Science Series individual webinar
--
https://www.drought.gov/what-is-drought/flash-drought <-- shared NOAA overview technical article
--
https://www.star.nesdis.noaa.gov/star/NOAAScienceSeminars.php <-- subscribe to the NOAA Summer Science Series
--
https://doi.org/10.1038/s41612-024-00618-0 <-- shared paper
--
https://communities.springernature.com/posts/the-prevalent-life-cycle-of-agricultural-flash-droughts <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-10 · 22:36 UTC

Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
--
https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
--
https://doi.org/10.1007/s11600-022-00943-z <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-10 · 04:44 UTC

A Global Systematic Review Of The Effects Of Hydromorphological Floodplain Restoration On Biodiversity
--
https://doi.org/10.1111/1365-2664.70485 <-- shared paper
--
https://zenodo.org/records/20561534 <-- shared open data
--
https://doi.org/10.1038/s43247-026-03428-9 <-- shared paper
--
https://adriadapt.eu/adaptation-options/rehabilitation-and-restoration-of-rivers/ <-- shared technical article
--
https://www.americanrivers.org/threats-solutions/restoring-damaged-rivers/benefits-of-restoring-floodplains/ <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-09 · 05:39 UTC

A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
--
https://doi.org/10.5194/nhess-26-3077-2026 <-- shared paper / brief communication
--
https://doi.org/10.5194/nhess-15-2313-2015 <-- shared 2015 paper that this communication updates/adds-to
--
https://doi.org/10.1007/s12665-023-10844-z <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-09 · 05:27 UTC

Statistical Characterization Of High Flow Volumes Across The Conterminous United States Supporting Managed Aquifer Recharge
--
https://doi.org/10.1029/2025WR041955 <-- shared paper
--
https://www.latimes.com/environment/story/2025-06-24/california-2024-groundwater-report <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-07 · 19:33 UTC

Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
--
https://doi.org/10.3389/fenvs.2026.1764771 <-- shared paper
--
https://doi.org/10.1038/s43247-026-03661-2 <-- shared paper
--
https://doi.org/10.1002/joc.6443 <-- shared paper
--
https://www.pik-potsdam.de/en/news/latest-news/from-droughts-to-floods-climate-change-and-migration-in-peru | https://publications.iom.int/books/evaluacion-de-la-evidencia-cambio-climatico-y-migracion-en-el-peru <-- shared 2021 Peru hydroclimate technical article | report
--
https://youtu.be/Ngbm0gsmYAw?si=haqV7t15pGkEmJB8 <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-07-02 · 01:32 UTC

Advancing Detailed Flood Hazard Identification in Alberta, Canada - Insights from Two Recent Flood Studies
--
https://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

💧🌏 Greg Cocks @[email protected] · 2026-06-30 · 02:55 UTC

Mountain Lions Have Major Ecological Impact Even In Small Preserves
--
https://phys.org/news/2026-06-mountain-lions-major-ecological-impact.html <-- shared technical article
--
https://doi.org/10.1002/ece3.73775 <-- shared paper
--
https://youtu.be/jy-ngOhoNPU?si=WTSMWz9XuaqT8Z4O <-- shared Standford overview video
--
https://youtu.be/CzSCu2FOj0Q?si=bb15-e03DLuWhQ4Y <-- shared Stanford overview video
--
[not my usual fare to post, but fascinating…]
“#Bigcats have a big impact. A long-term study showed that when mountain lions began regular visits to a small suburban preserve about 45 miles (72 kilometers) south of San Francisco, they changed the behavior of many other animals.
Mountain lions (Puma concolor) started appearing with increasing frequency on trail cameras at Stanford's Jasper Ridge Biological Preserve ('Ootchamin 'Ooyakma) from 2015 to 2020. Researchers documented a corresponding drop in deer activity compared with the prior years of lower or absent puma activity. Vegetation surveys also showed that many woody plants deer like to eat or tend to trample, including young oak trees, began to thrive.
These types of multilevel effects, called trophic cascades, have been studied primarily in large wilderness areas, particularly cascades caused by #apexpredators such as #wolves reintroduced into Yellowstone National Park...”
#mountainlion #cougar #puma #trophiccascade #JasperRidgeBiologicalPreserve #OotchaminOoyakma #monitoring #spatiotemporal #spatialanalysis #trailcamera #deer #rabbit #coyote #bobcat #fox #vegetation #survey #oak #tree #young #sapling #plant #predator #preyabundance #herbivore #health #ecosystem #balance #habitat #mesopredator #crossmapping #nocturnal #GIS #spatial #mapping #ecology #conservation #wilderness
#StanfordUniversity

#apexpredators #wolves #mountainlion #cougar #puma #trophiccascade

💧🌏 Greg Cocks @[email protected] · 2026-06-26 · 21:30 UTC

How Space Weather Could Bust The AI Boom
--
https://spacenews.com/how-space-weather-could-bust-the-ai-boom/ <-- shared technical article
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https://futurism.com/artificial-intelligence/ai-data-centers-electric-grid-meltdown <-- shared technical article, “AI Data Centers Pushing Electric Grid Into Meltdown”
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https://doi.org/10.1146/annurev-earth-032524-012356 <-- shared 2026 paper, “Magnetic Storms and Geoelectric Hazards”
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#AI #datcenters #infrastructure #impacts #solarstorms #spaceweather #risk #hazards #overloading #electricity #energy #powersupply #energygrid #vulnerable #transmission #energy #demand #consumers #geoelectrical #geomagnetism #blackout #damage #cost #economics #equipment #transformers #carringtonevent #NERC #grid #reliability #electricaldemand #utilities #magneticstorm #electromagneticinduction #extremeevent #historicalevent #hazardanalysis #spaceweather #history #Carrington #geoelectric #humanimpacts #risk #hazard #monitoring #network #geology #geomagnetism #impedance #rock #soil #utilities #electricaltransmission #powerlines #magnetotelluric #sensor #blackout #brownout #energy #geoelectrichazard #geoelectric #GIS #spatial #mapping #spatialanalysis #spatiotemporal #model #modeling #geomagnetism #geomagneticstorm #telecommunication #electronics #hardened #geography #mitigation #preparedness #geomorphology #geomorphometry #surfacegeology #cost #economics #disaster #impacts #technology #InternetOfThings #internet #USA #review #CONUS #numericalmodeling #realtimemonitoring #AIBoom #Bust
@North American Electric Reliability Corporation (NERC)

#ai #datcenters #infrastructure #impacts #solarstorms #spaceweather

💧🌏 Greg Cocks @[email protected] · 2026-06-25 · 03:43 UTC

Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
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https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
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https://doi.org/10.5194/esurf-13-1281-2025 <-- shared paper
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https://doi.org/10.1007/s11069-025-07766-3 <-- shared paper
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[I recognise that the photo is instead for the floods, etc in Lubra, Nepal - but felt it better showed the hydrogeomorphical setting (sic) for the 'casual' post viewer...]
H/T @Kuldeep Dutta
“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…”
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“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/) 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 #Flooding #alluvial #fluvial #water #hydrology #hydrography #flood #flooding #spatialanalysis #spatiotemporal #mountain #plain #hydrometeorological #hydrogeomorphology #ArunachalPradesh #Assam #India #hillslope #floodplain #rainfall #precipitation #extremeweather #engineeringgeology #massmovement #landslide #debrisflow #risk #hazard #monitoring #GIS #spatial #mapping #remotesensing #satellite #Sentinel #sedimentation #humanimpacts #infrastructure #damage #cost #economics #public #safety #model #modeling #downstream

#earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports

💧🌏 Greg Cocks @[email protected] · 2026-06-18 · 03:09 UTC

Hydroclimate Volatility On A Warming Earth
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https://doi.org/10.1038/s43017-024-00624-z <-- shared 2025 paper
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https://newsroom.ucla.edu/releases/floods-droughts-fires-hydroclimate-whiplash-speeding-up-globally <-- shared UCLA article, “Floods, Droughts, Then Fires: Hydroclimate Whiplash Is Speeding Up Globally “
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H/T @Daniel Swain
“Hydroclimate volatility refers to sudden, large and/or frequent transitions between very dry and very wet conditions. In this Review, we examine how hydroclimate volatility is anticipated to evolve with anthropogenic warming. Using a metric of ‘hydroclimate whiplash’ based on the Standardized Precipitation Evapotranspiration Index, global-averaged subseasonal (3-month) and interannual (12-month) whiplash have increased by 31–66% and 8–31%, respectively, since the mid-twentieth century. Further increases are anticipated with ongoing warming, including subseasonal increases of 113% and interannual increases of 52% over land areas with 3 °C of warming; these changes are largest at high latitudes and from northern Africa eastward into South Asia. Extensive evidence links these increases primarily to thermodynamics, namely the rising water-vapour-holding capacity and potential evaporative demand of the atmosphere. Increases in hydroclimate volatility will amplify hazards associated with rapid swings between wet and dry states (including flash floods, wildfires, landslides and disease outbreaks), and could accelerate a water management shift towards co-management of drought and flood risks. A clearer understanding of plausible future trajectories of hydroclimate volatility requires expanded focus on the response of atmospheric circulation to regional and global forcings, as well as land–ocean–atmosphere feedbacks, using large ensemble climate model simulations, storm-resolving high-resolution models and emerging machine learning methods…
#water #hydrology #hydroclimate #whiplash #global #spatialanalysis #spatiotemporal #weatherwhiplash #ecogeomorphology #sustainability #ecology# ###
#water #hydrology #hydroclimate #volatility #dry #wet #drought #flood #flooding #wildfire #landslide #massmovement #whiplash #global #spatialanalysis #spatiotemporal #weatherwhiplash #ecogeomorphology #sustainability #ecology #hydrogeomorphology #climatechange #extremeweather #anthropogenicwarming #climate #weather #connection #StandardizedPrecipitationEvapotranspiration #precipitation #rainfall #research #evapotranspiration #risk #hazard #riskassessment #disease #pandemic #publichealth #publicsafety #waterquality #watersecurity #watermanagement #hydrography #atmospheric #regional #global #forcing #climatemodel #model #modeling #AI #machinelearning

#water #hydrology #hydroclimate #whiplash #global #spatialanalysis

💧🌏 Greg Cocks @[email protected] · 2026-06-16 · 03:41 UTC

🚨 FEMA’s Hazus v7.2 Is Here — A Major Upgrade For Disaster Risk Modeling
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https://www.fema.gov/flood-maps/products-tools/hazus <-- shared link to FEMA HAZUS download, documentation, use case, etc
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[I used to work some with Hazus back in the back, but my career changed path; I still appreciate its strength and unity of purpose (sic) #alldataisspatial]
H/T @Laban "L.J." Johnson | Founder, LJ Learn & Concordia Initiative | Crisis Support · Leadership Development · Community Resilience | Bridging worlds to help people rise
“FEMA’s Hazus GIS platform has been updated with a new ArcGIS Pro–based version, bringing faster, more powerful tools for estimating losses from floods, hurricanes, earthquakes, and other natural hazards.
Key updates in Hazus 7.2 include:
• Streamlined workflows for flood and hurricane modeling
• New Earthquake ShakeMap integration using USGS data
• Expanded and improved results exports and reporting (including geodatabase outputs)
• Stronger security with known vulnerabilities addressed
• Performance improvements and optimized installation process
• [Significantly enhanced and comprehensive summary reports for flood and earthquake are now available for download.]
• Full integration with ArcGIS Pro (3.4–3.6) for a modern GIS experience
This release represents a significant step forward in how hazard planners, emergency managers, and GIS professionals analyze and prepare for disaster impacts…”
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“FEMA’s Hazus program provides software, data, methods, and guidance for estimating risk from natural hazards. Hazus can estimate building damages, economic losses, displaced households, casualties, debris generation and more resulting from a natural hazard event and can be used in all phases of emergency management…”
#HAZUS #fedservice #fedscience #oublicgood #publicsafety #emergencyresponse #software #spatialdata #GIS #spatial #mapping #risk #hazard #riskassessment #naturalhazard #humanimpacts #earthquake #wildfire #spatialanalysis #spatiotemporal #flood #flooding #cost #damage #economic #publicsafety #publichealth #emergencymanagement #opensource #opendata #tsunami #tornado #hurricane #ShakeMap #infrastructure #planning #policy #preparedness #impacts #geology #engineeringgeology #remotesensing #earthobservation
@FEMA

#alldataisspatial #hazus #fedservice #fedscience #oublicgood #publicsafety

💧🌏 Greg Cocks @[email protected] · 2026-06-09 · 15:19 UTC

Study Highlights Growing Importance Of Multi-Day Storms In Future U.S. Flood Risk
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https://news.okstate.edu/articles/engineering-architecture-technology/2026/study-highlights-growing-importance-of-multi-day-storms-in-future-u.s.-flood-risk <-- shared technical article
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https://doi.org/10.1088/2752-5295/ae4f14 <-- shared paper
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“Extreme rainfall is projected to intensify as the climate warms, yet whether the greatest increases will occur in multi-day or single-day events remains uncertain. This knowledge gap is particularly pressing given recent catastrophic floods triggered by multi-day rainfall events, prompting the question of whether multi-day events could, in fact, intensify more than their daily counterparts, and by how much. This study addresses this question using an ensemble of 34 downscaled Earth System Models under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), focusing on changes in extreme rainfall by the end of the century across ten regions of the contiguous United States. [Their] statistical framework evaluates model agreement, ensemble-mean changes, and the significance of these changes for both daily and multi-day rainfall extremes. Results show that extreme rainfall amounts are expected to increase for most regions and durations. The degree of intensification, however, depends strongly on event rarity and regional climate characteristics. Notably, in the U.S. western Gulf Coast region, very rare multi-day events (e.g., 500 year return period) are projected to intensify more than their daily counterparts, a phenomenon that could be explained by increased stalling of tropical cyclones, which can prolong heavy rainfall over multiple days. These results challenge the assumption that daily extremes dominate future risk and highlight the need to consider event duration when updating flood-hazard maps, design standards, and adaptation planning…”
#Flooding #FloodRisk #FloodInsurance #FloodAwareness #Explore #FloodPreparedness #FlashFlooding #ClimateResilience #climatechange #extremeweather #DisasterPreparedness #StormwaterManagement #FloodSafety #CommunityResilience #risk #hazard #model #modeling #floodrisk #multiday #rainfall #precipitation #storm #water #hydrology #hydrography #planning #policy #regulations #climatemodel #CONUS #USA #publicsafety #cost #economics #damage #loss #infrastructure #spatiotemporal #spatialanalysis #earthsystemmodels #forecasting #meteorology #designstandards #floodmapping #mitigation #flood