#rainfall — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #rainfall, aggregated by home.social.
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https://www.alojapan.com/1524071/malaysians-in-japan-advised-to-avoid-unnecessary-travel-amid-chiba-flooding-2/ Malaysians in Japan advised to avoid unnecessary travel amid Chiba flooding #Chiba #Japan #JapanTourism #MalaysianEmbassy #rainfall #tourism TOKYO: Malaysians in Japan are advised to avoid unnecessary travel and remain vigilant as unprecedented rainfall has triggered severe flooding and major transport disruptions across Chiba Prefecture and surrounding areas of the Kanto region. The Malaysian Embassy in Tokyo said road and rail networks had
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Monsoon refuses to slow: Bay of Bengal could unleash rain systems till August-end https://english.mathrubhumi.com/news/india/bay-of-bengal-active-monsoon-august-2026-bp2villx?utm_source=dlvr.it&utm_medium=mastodon #Monsoon2026 #IndiaWeather #BayOfBengal #Rainfall #WeatherNews
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Farmers have also been asked to take precautions against waterlogging, strong winds and heavy rain. https://english.mathrubhumi.com/news/kerala/august-14-friday-kerala-districts-under-heavy-rainfall-warning-school-college-holiday-depends-on-local-conditions-g6lg6b86?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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A low-pressure area has formed over the North Bay of Bengal and is expected to intensify into a depression. https://english.mathrubhumi.com/news/kerala/5-kerala-districts-under-heavy-rainfall-warning-school-college-holiday-subject-to-local-conditions-s91vevrn?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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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 -
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 -
https://www.theguardian.com/environment/2026/aug/07/could-uk-ever-run-out-of-water. "In July, #England received 7% of the long-term average #rainfall, making it the #driest July since #records began almost 2 centuries ago."
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https://www.theguardian.com/environment/2026/aug/07/could-uk-ever-run-out-of-water. "In July, #England received 7% of the long-term average #rainfall, making it the #driest July since #records began almost 2 centuries ago."
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In 2022, a public interest litigation was filed in the Kerala High Court after a school holiday was announced nearly an hour after classes had begun. https://english.mathrubhumi.com/news/kerala/kerala-school-holidays-how-collectors-decide-when-heavy-rains-make-closure-necessary-n18pzr7y?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #Districtcollectors #heavyrains #schoolholidays
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The latest forecast indicates that weather conditions will remain active over the coming days. https://english.mathrubhumi.com/news/kerala/kerala-weather-update-orange-alert-for-8-districts-yellow-alert-for-several-others-today-m0s9mp0w?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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The powerful system is expected to make landfall in mainland China following Saturday. https://english.mathrubhumi.com/news/kerala/typhoon-dolphin-distant-influence-driving-kerala-rain-qxxnwed1?utm_source=dlvr.it&utm_medium=mastodon #TyphoonDolphin #Rainfall #KeralaNews
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Kerala is expected to receive light to moderate rainfall over the coming days, with isolated places likely to experience very heavy rain on August 6. https://english.mathrubhumi.com/news/kerala/kerala-weather-today-seven-districts-on-orange-alert-heavy-rain-strong-winds-likely-pg3m28s5?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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The IMD has warned of strong winds across isolated parts of Kerala from Wednesday (August 5) to Saturday (August 8), with wind speeds of 40-50 kmph. https://english.mathrubhumi.com/news/kerala/most-kerala-districts-under-heavy-rainfall-warning-school-college-holiday-decisions-depends-on-local-conditions-jolc099h?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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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 -
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 -
The districts under the red alert are Pathanamthitta, Kottayam, Idukki, Ernakulam, Kozhikode, Wayanad, Kannur and Kasaragod. https://english.mathrubhumi.com/news/kerala/14-kerala-districts-under-heavy-rainfall-warning-school-college-holiday-depends-on-local-conditions-rf10wt2z?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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[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
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https://app.geo.ca/en-ca/map-browser/record/a13a2575-5bda-4bfd-a9b1-5bd2dd583f09 <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
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https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc <-- shared data-portal link, Canada Flood Susceptibility Index
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https://doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
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https://doi.org/10.1002/2017WR020917 <-- shared (2017) paper
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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 -
[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
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https://app.geo.ca/en-ca/map-browser/record/a13a2575-5bda-4bfd-a9b1-5bd2dd583f09 <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
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https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc <-- shared data-portal link, Canada Flood Susceptibility Index
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https://doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
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https://doi.org/10.1002/2017WR020917 <-- shared (2017) paper
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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 -
Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
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https://doi.org/10.1371/journal.pwat.0000416 <-- shared paper
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H/T @Guillaume Wright | Executive Editor, PLOS
“💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
[The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
#GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater -
Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
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https://doi.org/10.1371/journal.pwat.0000416 <-- shared paper
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H/T @Guillaume Wright | Executive Editor, PLOS
“💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
[The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
#GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater -
NORTHLAND FORECAST FOR AUGUST 4, 2026:
A #storm system will bring #showers and #thunderstorms through the #Northland Monday night. Some #storms could have #gusty #winds and heavy #rainfall. It will be #breezy with low #temperatures in the 50s and low 60s.
Tuesday will see #clearing skies, lower #dewpoints, and #cooler highs in the upper 60s and low 70s.
VIDEO/DISCUSSION: fox21online.com/weather
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NORTHLAND FORECAST FOR AUGUST 4, 2026:
A #storm system will bring #showers and #thunderstorms through the #Northland Monday night. Some #storms could have #gusty #winds and heavy #rainfall. It will be #breezy with low #temperatures in the 50s and low 60s.
Tuesday will see #clearing skies, lower #dewpoints, and #cooler highs in the upper 60s and low 70s.
VIDEO/DISCUSSION: fox21online.com/weather
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Greenville gardeners look ahead to fall after summer heat takes toll https://www.allforgardening.com/1912724/greenville-gardeners-look-ahead-to-fall-after-summer-heat-takes-toll/ #autumn #CarolinaSeasonsNursery #drought #fall #FallSeason #garden #gardener #gardeners #gardening #Greenville #Heat #nc #NorthCarolina #nurseries #nursery #plant #planting #plants #rain #rainfall #season #summer #SummerHeat #toll #weather! #witn
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Greenville gardeners look ahead to fall after summer heat takes toll https://www.allforgardening.com/1912724/greenville-gardeners-look-ahead-to-fall-after-summer-heat-takes-toll/ #autumn #CarolinaSeasonsNursery #drought #fall #FallSeason #garden #gardener #gardeners #gardening #Greenville #Heat #nc #NorthCarolina #nurseries #nursery #plant #planting #plants #rain #rainfall #season #summer #SummerHeat #toll #weather! #witn
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Wind speeds are expected to range between 40 kmph and 50 kmph, with gusts reaching 60 kmph at times. https://english.mathrubhumi.com/news/kerala/kerala-rain-alert-orange-alert-in-12-districts-school-college-holiday-decisions-only-after-review-p9iozsyz?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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Strong winds of 40–50 kmph are also expected on Sunday (August 2) and Monday (August 3). https://english.mathrubhumi.com/news/kerala/14-kerala-districts-under-heavy-rainfall-warning-school-college-holiday-depends-on-local-conditions-jhfdglzt?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #yellowalert #weather #heavyrains
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The IMD has cautioned that the expected spell of extremely heavy rainfall could trigger hazardous conditions across parts of Kerala. https://english.mathrubhumi.com/news/kerala/very-heavy-rainfall-kerala-weather-red-alert-august-01-2026-saturday-u9bbxxeq?utm_source=dlvr.it&utm_medium=mastodon #Kerala #rainfall #weather #heavyrains
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Unraveling The Drivers Of Water Shortage Across Spatial Scales And Sectors In Colorado's West Slope River Basins
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https://doi.org/10.1029/2026EF008137 <-- shared paper
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['sorry' about your Kentucky Bluegrass, almonds, etc... /s]
H/T Sai Veena Sunkara | Postdoctoral Associate
“…Colorado’s West Slope basins provide nearly 70% of the inflows to Lake Powell and are also essential to communities, agriculture, industry, hydropower, and downstream Colorado River users.
To examine the wide range of possible futures, [they] simulated 2.1 million years, defining 20,000 plausible scenarios applying changes to streamflow, snowmelt timing, drought persistence, and agricultural, municipal, and industrial water demand.
A key finding is that there is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗮𝘁𝗲𝗿 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲𝘀. The most influential drivers vary by basin, sector, and water user. In some areas, shortages are driven primarily by persistent low-flow conditions or changing snowmelt timing. In others, increasing municipal, industrial, or irrigation demand plays a larger role. This suggests that adaptation strategies must be tailored to specific basins and users rather than relying on a single, system-wide solution. Other major findings are
• West Slope deliveries to Lake Powell could fall more than 50% below the current median baseline
• Storage in major West Slope reservoirs could decline 40–55% below historical medians
These results underscore the need for water-planning approaches that account for deep uncertainty, persistent drought, shifting snowmelt patterns, and sector-specific demand…”
#Colorado #waterallocation #StateMod #USWest #USA #WesternSlope #waterresources #watersecurity #watershortage #drought #snowmelt #rainfall #precipitation #riverbasin #water #hydrography #hydrology #reasons #agriculture #industry #hydropower #streamflow #surfacewater #municipal #irrigation #adaptationstrategies #planning #policy #mitigation #ColoradoRiver #basins #climatechange #extremeweather #populationpressure #waterdemand #waterrights #model #modeling #HiddenMarkovModel #stochastic #projecteddemand #ColoradoRiverBasin #wateruse #spatial #mapping #spatialanalysis #spatiotemporal #strategy -
Unraveling The Drivers Of Water Shortage Across Spatial Scales And Sectors In Colorado's West Slope River Basins
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https://doi.org/10.1029/2026EF008137 <-- shared paper
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['sorry' about your Kentucky Bluegrass, almonds, etc... /s]
H/T Sai Veena Sunkara | Postdoctoral Associate
“…Colorado’s West Slope basins provide nearly 70% of the inflows to Lake Powell and are also essential to communities, agriculture, industry, hydropower, and downstream Colorado River users.
To examine the wide range of possible futures, [they] simulated 2.1 million years, defining 20,000 plausible scenarios applying changes to streamflow, snowmelt timing, drought persistence, and agricultural, municipal, and industrial water demand.
A key finding is that there is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗮𝘁𝗲𝗿 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲𝘀. The most influential drivers vary by basin, sector, and water user. In some areas, shortages are driven primarily by persistent low-flow conditions or changing snowmelt timing. In others, increasing municipal, industrial, or irrigation demand plays a larger role. This suggests that adaptation strategies must be tailored to specific basins and users rather than relying on a single, system-wide solution. Other major findings are
• West Slope deliveries to Lake Powell could fall more than 50% below the current median baseline
• Storage in major West Slope reservoirs could decline 40–55% below historical medians
These results underscore the need for water-planning approaches that account for deep uncertainty, persistent drought, shifting snowmelt patterns, and sector-specific demand…”
#Colorado #waterallocation #StateMod #USWest #USA #WesternSlope #waterresources #watersecurity #watershortage #drought #snowmelt #rainfall #precipitation #riverbasin #water #hydrography #hydrology #reasons #agriculture #industry #hydropower #streamflow #surfacewater #municipal #irrigation #adaptationstrategies #planning #policy #mitigation #ColoradoRiver #basins #climatechange #extremeweather #populationpressure #waterdemand #waterrights #model #modeling #HiddenMarkovModel #stochastic #projecteddemand #ColoradoRiverBasin #wateruse #spatial #mapping #spatialanalysis #spatiotemporal #strategy -
NORTHLAND FORECAST FOR JULY 31, 2026:
A #cold front passes through the #Northland Thursday night and Friday, bringing scattered #showers and #thunderstorms. Some of the #storms may contain strong #winds, #hail, and heavy #rainfall.
#Temperatures remain #warm for lows overnight in the 60s. They will be #seasonal for highs Friday in the 70s and low 80s as #dewpoint levels drop throughout the day.
VIDEO/DISCUSSION: fox21online.com/weather
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NORTHLAND FORECAST FOR JULY 31, 2026:
A #cold front passes through the #Northland Thursday night and Friday, bringing scattered #showers and #thunderstorms. Some of the #storms may contain strong #winds, #hail, and heavy #rainfall.
#Temperatures remain #warm for lows overnight in the 60s. They will be #seasonal for highs Friday in the 70s and low 80s as #dewpoint levels drop throughout the day.
VIDEO/DISCUSSION: fox21online.com/weather
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The newest #Severe #Thunderstorm #Warning has been issued for the southern portion of this line.
This is the only area of the #Northland that is seeing #showers and thunderstorms at this time.
#Wind gusts up to 60 mph and heavy #rainfall are the greatest threat.
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The potential #severe #thunderstorm in #Koochiching County in the #Northland continues to slowly move east.
The #warning has extended to include the eastern half of the county, along with far northwest #SaintLouis County near Kabetogama.
Strong #winds and heavy #rainfall remain the greatest threats, along with potential #hail.
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SEVERE THUNDERSTORM WARNING:
A #Severe #Thunderstorm #Warning covers most of #Koochiching County through 5:30 p.m. Potential threats are #wind gusts up to 60 mph, and #hail up to 1-inch in diameter.
This #storm is not moving very fast, so expect plenty of #rainfall as well. Minor #flooding is also a threat.
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SEVERE THUNDERSTORM WARNING:
A #Severe #Thunderstorm #Warning covers most of #Koochiching County through 5:30 p.m. Potential threats are #wind gusts up to 60 mph, and #hail up to 1-inch in diameter.
This #storm is not moving very fast, so expect plenty of #rainfall as well. Minor #flooding is also a threat.
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Happy #Thursday afternoon from the #Northland!
It is another #hot and #muggy day for the region, even with the smoky #haze high in the sky.
We are watching for #showers and #thunderstorms to pass through. Strong #winds and heavy #rainfall are the greatest threats.
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Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
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https://doi.org/10.1016/j.jhydrol.2026.135999 <-- shared paper
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https://youtu.be/VHzYLvSYR7k?si=5oGGPeH6T0dFusfY <-- recent overview video created about the research
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https://doi.org/10.1016/j.wroa.2025.100396 <-- share (earlier) paper
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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 -
NORTHLAND FORECAST FOR JULY 30, 2026:
The #Northland will be very #warm and very #humid Wednesday night with low #temperatures in the upper 60s and low 70s under partly #cloudy skies.
A #storm system brings scattered #showers and #thunderstorms through the region Thursday. A few may contain strong #winds, #hail, and heavy #rainfall. It remains #muggy with highs in the 80s.
VIDEO/DISCUSSION: fox21online.com/weather
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The opposite of wildfires in Europe are heavy non-stop rainfalls from Afghanistan to Taiwan with dozens dead and houses and infrastructure destroyed:
#wildfires #ClimateBreakdown #rainfall
https://www.theguardian.com/environment/2026/jul/28/extreme-rainfall-asia-flash-floods-typhoons-climate-change -
Climate change makes extreme West Africa rainfall five times likelier, study finds https://phys.org/news/2026-07-climate-extreme-west-africa-rainfall.html
#Africa #environment #rain #rainfall #climate #ClimateChange #ClimateCrisis
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Climate Change and urban pressures intensify flood risk on the Gulf of Guinea coast
Between 20–22 June, the coastal regions of the countries bordering the #GulfOfGuinea, particularly Côte d’Ivoire, #Ghana, #Togo, and #Nigeria, experienced exceptionally widespread and severe #flooding following intense and persistent #rainfall. In several locations, more than 140 mm of rain fell within less than 24 hours, overwhelming natural drainage systems and triggering extensive #FlashFloods.
The floods affected a very densely populated region where rapid urbanisation and the expansion of formal and informal settlements into floodplains and conversion of natural vegetation to croplands have reduced natural drainage capacity and increased exposure to #flooding. Population growth is projected to continue its upward trajectory in this region, further increasing exposure in the decades to come. -
Can Himalayan Crops Help Secure The Future Of Food?
(Exploring how genomic diversity, traditional crops, and regional cooperation can strengthen climate resilience and food security across the Hindu Kush Himalaya [HKH])
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https://substack.com/home/post/p-207443153 <-- shared technical post
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https://openlibrary.substack.com/ <-- shared Substack, “Open Library on Green Economy”
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https://adaptationwithoutborders.org/knowledge-base/adaptation-without-borders/shifting-cooperation-in-the-hindu-kush-himalaya/ <-- shared technical article, “Shifting cooperation in the [HKH]”
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https://www.icimod.org/who-we-are/the-hindu-kush-himalaya/ <-- background on the HKH, International Centre for Integrated Mountain Development (ICIMOD)
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https://doi.org/10.48130/cas-0026-0003 <-- shared paper
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[not at all my technical area - but fascinating, including the spatial component #alldataisspatial]
H/T @jeevan Labh
“As we navigate the intensifying impacts of the 2026 El Niño, the vulnerability of our global food systems require careful evaluation. With this year’s erratic weather patterns driving severe, prolonged droughts in some regions and unseasonal, devastating floods in others, the climate crisis is proving that it is not a distant threat; it is happening right now, in our fields and on our plates.
This crisis is more visible in the Hindu Kush Himalaya (HKH) region. Often called the “water tower of Asia,” this vast mountain range sustains 240 million people across eight countries and provides essential ecosystem services to nearly 2 billion people downstream. When glaciers melt at accelerated rates and monsoons become unpredictable, the narrative often focuses solely on the mountain communities. But the truth is much broader: a disrupted harvest in the high hills of Nepal or Bhutan creates a domino effect. It leads to displaced populations, reduced agricultural output flowing into the Indus, Ganges, and Brahmaputra basins, and ultimately, skyrocketing food prices for families living in the sprawling downstream plains.
The climate crisis respects no borders and recognizes no difference between altitude and sea level. Because this problem affects us all, the solution must protect us all. Fortunately, the means to overcome this challenge are already in our hands, locked within the ancient seeds of the mountains…”
## #GreenEconomy #economy #monoculture #agriculture #farming #crop #cropland #HinduKushHimalaya #himalaya #mountain #HKH #spatial #mapping #elevation #climatechange #foodsecurity #food #extremeweather #weather #ElNiño #drought #rainfall #precipitation #snowmelt #climatecrisis #mountainrange #water #hydrology #ecosystem #glacier #moonsoons #community #harvest #Nepal #Bhutan #watersheds #watersecurity #risk #hazard #waterresources #genes #genomicdiversity #cooperation -
Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
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https://doi.org/10.5194/nhess-26-3231-2026 <-- shared #openacess paper
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[part of my old stomping ground as an engineering geologist]
H/T @Kees Nederhoff
“Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
[They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”
#USGS #supercomputing #Hovenweep #HPC #coast #coastal #PNW #Seattle #PacificNorthwest #risk #hazard #riskmanagement #model #modeling #CFRM #deterministic #probabilistic #climatechange #extremeweather #fedscience #WA #KingCounty #PierceCounty #WashingtonState #USA #flood #flooding #compoundflooding #floodmaps #SFINCS #storm #weather #climate #climatechange #rainfall #precipitation #sealevel #sealevelrise #SLR #100yearflood #floodhazardmapping #returnperiods #pluvial #fluvial #spatialanalysis #spatiotemporal #remotesensing #streamgage #history #historicflooding #projections #predictions
#USGS
