#precipitation — Public Fediverse posts

Weather: Rain arrives later Saturday in the Baltimore area https://www.rawchili.com/mlb/803417/ #afternoon #alerts #Atlantic #Ava #Baltimore #BaltimoreOrioles #BaltimoreOrioles #Baseball #chances #changing #forecast #Local #marie #maryland #meteorologist #mid #MLB #news #Orioles #outdoor #plans #precipitation #rain #saturday #showers #storm #timing #update #video #WBAL #weather #weekend

Weather: Rain arrives later Saturday in the Baltimore area https://www.rawchili.com/mlb/803417/ #afternoon #alerts #Atlantic #Ava #Baltimore #BaltimoreOrioles #BaltimoreOrioles #Baseball #chances #changing #forecast #Local #marie #maryland #meteorologist #mid #MLB #news #Orioles #outdoor #plans #precipitation #rain #saturday #showers #storm #timing #update #video #WBAL #weather #weekend

Here's today's forecast map and today's high temperature map. Check out the NWS site for more National Forecast Maps www.weather.gov/forecastmaps
#weather #wx #Forecast #precipitation #Temperature #TheOutPost

Here's today's forecast map and today's high temperature map. Check out the NWS site for more National Forecast Maps www.weather.gov/forecastmaps
#weather #wx #Forecast #precipitation #Temperature #TheOutPost

Here's today's forecast map and today's high temperature map. Check out the NWS site for more National Forecast Maps www.weather.gov/forecastmaps
#weather #wx #Forecast #precipitation #Temperature #TheOutPost

Here's today's forecast map and today's high temperature map. Check out the NWS site for more National Forecast Maps www.weather.gov/forecastmaps
#weather #wx #Forecast #precipitation #Temperature #TheOutPost

Hydro-Climatic Extremes And Water Conflicts In The Kamala River Basin Of Nepal
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https://doi.org/10.1016/j.crm.2026.100871 <-- shared paper
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https://jvs.org.np/news/%22kamala-river-basin-20-year-water-management-strategy <-- shared 2024 technical article on planning and policy (from JVS, a Nepalese non-profit driving water resilience)
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[My sincere thoughts to those on those the Nepal/Tibet border so very effected by the recent GLOF-style flooding; the loss of your family and friends – and ‘things’ – must be so very hard!
May the missing individuals return safely. 🙏]
H/T @ Prakriti Niraula | Researcher at Kathmandu University School of Arts | Urban FutureScape Pilot Project | Urban Governance | Climate and Disaster Resilience | Development Researcher
“What happens when communities face 'too much water' during the monsoon and 'too little water' during the dry season? [Their] research in Dudhauli and Siraha municipalities [Nepal] shows that floods and droughts are not merely environmental challenges; rather, they interact with existing inequalities and vulnerabilities, intensifying conflicts over water and land resources.
From upstream-downstream tensions following flood damage and changing water flows to competition over wells and handpumps during periods of scarcity, the findings show that hydro-climatic extremes often act as amplifiers of existing social, economic, and political vulnerabilities.
The study underscores the importance of moving beyond managing climatic hazards alone towards conflict-sensitive and equitable water governance that addresses structural inequalities, strengthens local adaptive capacities, and supports fair water allocation and conflict resolution…”
#climatechange #flood #flooding #drought #monsoon #precipitation #rainfall #snowmelt #dryseason #socialconflict #KamalaRiver #basin #Dudhauli #Siraha #Nepal #hydroclimate #waterresources #risk #hazard #watersecurity #hydrosocial #exteremeweather #environment #inequality #vulnerable #socioeconomic #conflict #groundwater #water #hydrology #hydrography #hydroclimate #extremes #social #economic #political #watergovernance #watermanagement #localcommunities #conflictresolution

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

[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

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

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
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https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
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"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
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https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

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

Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
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https://doi.org/10.3389/fenvs.2026.1764771 <-- shared paper
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https://doi.org/10.1038/s43247-026-03661-2 <-- shared paper
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https://doi.org/10.1002/joc.6443 <-- shared paper
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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
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https://youtu.be/Ngbm0gsmYAw?si=haqV7t15pGkEmJB8 <-- shared overview video
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#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

Air well (condenser) (Hydrology 💧)

An air well or aerial well is a structure or device that collects water by promoting the condensation of moisture from air. Designs for air wells are many and varied, but the simplest designs are completely passive, require no external energy source and have few, if any, moving parts. Three principal designs are used for...

https://en.wikipedia.org/wiki/Air_well_(condenser)

#AirWell #Hydrology #WaterSupply #DrinkingWater #Precipitation #AppropriateTechnology

4 P.M. #RADAR UPDATE:

Scattered #showers and #thunderstorms continue across the #Northland. A few may have small #hail. Some are not moving much, bringing a decent #deluge.

The #precipitation threat will continue through the evening hours.

#wxtooter #weather #wx #MNwx #WIwx #UPwx

Why The Media Keep Quoting The Same Climate Scientist
(Daniel Swain has a knack for breaking down the complexities of climate and weather into precise but accessible ideas.)
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https://www.theatlantic.com/science/2026/06/climate-weather-scientist-daniel-swain/687457/?gift=ttHQV0PZWqrQUD2tsgA7Pt9wX8z1gPJGiANsir671zQ <-- shared media article
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https://doi.org/10.1038/s43017-024-00624-z <-- shared paper example, “Hydroclimate Volatility On A Warming Earth”
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https://www.youtube.com/@weatherwest <-- Shared YouTube channel: Weather West
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H/T @DanielSwain
"The success of the climatologist Daniel Swain rests on a simple foundation: His specialty has long been how global climate change messes with local weather. Many climatologists focus on subjects that seem arcane: mean global temperatures registered in Celsius, radiative forcing, the reflectivity of clouds. Swain, in contrast, talks in plain English—constantly, really, in interviews with CBS, NBC, the Weather Channel, and The Washington Post, as well as on his own blog and YouTube channel, Weather West—about the wind and the rain and the temperature outside, and how they are influenced by the larger forces of the atmosphere.
“He uses language that is both precise and deep but very accessible, and that’s why you see him quoted everywhere,” Mark Hertsgaard, a longtime climate journalist who is the executive director of Covering Climate Now, told [the Atlantic]. According to Swain’s own tally, he does more than 200 media interviews a year; he is, in other words, about as omnipresent as a weather guy can be in people’s lives. A climate scientist at the University of California Agriculture and Natural Resources’ research unit, Swain is not exactly a “weather influencer,” that breed of streamer who delivers breathless updates about the next big storm. But he has become one of the country’s most influential explainers of the weather’s relationship to the climate; you’ve almost certainly heard from him if you consume just a scintilla of climate-related news…”
#global #climatechange #extremeweather #climatescientist #climate #scientist #media #hydroclimate #whiplash #WeatherWest #weather #localweather #metrology #connection #relationship #education #publicscience #extremeweather #climatology #wind #rainfall #precipitation #temperature #atmosphere
@TheAtlantic

The gardener in me loves a heavy, soaking rain. The arthritis in me does not. Life is often a mixed bag. #rain #rainfall #heavyrain #garden #gardening #gardener #maple #maples #mapletree #mapletrees #precipitation #heavyprecipitation #pouring #raining #itsraining #itspouring #arthritis #arthritisfoundation #achesandpains #senior #seniors #seniorcomplaints #seniorcomplaint #seniorcitizen #seniorcitizengrump #seniorcitizengrumps #grumps #grumpy #achy

The gardener in me loves a heavy, soaking rain. The arthritis in me does not. Life is often a mixed bag. #rain #rainfall #heavyrain #garden #gardening #gardener #maple #maples #mapletree #mapletrees #precipitation #heavyprecipitation #pouring #raining #itsraining #itspouring #arthritis #arthritisfoundation #achesandpains #senior #seniors #seniorcomplaints #seniorcomplaint #seniorcitizen #seniorcitizengrump #seniorcitizengrumps #grumps #grumpy #achy

The gardener in me loves a heavy, soaking rain. The arthritis in me does not. Life is often a mixed bag. #rain #rainfall #heavyrain #garden #gardening #gardener #maple #maples #mapletree #mapletrees #precipitation #heavyprecipitation #pouring #raining #itsraining #itspouring #arthritis #arthritisfoundation #achesandpains #senior #seniors #seniorcomplaints #seniorcomplaint #seniorcitizen #seniorcitizengrump #seniorcitizengrumps #grumps #grumpy #achy

The gardener in me loves a heavy, soaking rain. The arthritis in me does not. Life is often a mixed bag. #rain #rainfall #heavyrain #garden #gardening #gardener #maple #maples #mapletree #mapletrees #precipitation #heavyprecipitation #pouring #raining #itsraining #itspouring #arthritis #arthritisfoundation #achesandpains #senior #seniors #seniorcomplaints #seniorcomplaint #seniorcitizen #seniorcitizengrump #seniorcitizengrumps #grumps #grumpy #achy

The gardener in me loves a heavy, soaking rain. The arthritis in me does not. Life is often a mixed bag. #rain #rainfall #heavyrain #garden #gardening #gardener #maple #maples #mapletree #mapletrees #precipitation #heavyprecipitation #pouring #raining #itsraining #itspouring #arthritis #arthritisfoundation #achesandpains #senior #seniors #seniorcomplaints #seniorcomplaint #seniorcitizen #seniorcitizengrump #seniorcitizengrumps #grumps #grumpy #achy

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
--
https://doi.org/10.1088/2752-5295/ae4f14 <-- shared paper
--
“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

On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
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https://doi.org/10.1016/j.enggeo.2026.108860 <-- shared paper
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#rainfallinduced #landslide #Functionalregression #Japan #Earlywarningsystem #Spacetimeprediction #spatialanalysis #spatiotemporal #massmovement #engineeringgeology #geomorphology #hydrogeomorphology #geomorphometry #remotesensing #geostatistics #model #modeling #mechanics #rainfall #threshold #precipitation #water #hydrology #risk #hazard #hazardassessment #terrain #landscape #landform #landuse #geology #soil #lithology #functionalGeneralizedAdditiveModel #FGAM #prediction #casestudy #AI #LLM

Water Velocity And Discharge From Tidal Freshwater Creeks In Forested And Herbaceous Wetlands
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https://doi.org/10.1007/s12237-026-01690-w <-- shared paper
--
#water #hydrology #tide #tidal #ecosystem #wildlife #fish #fisheries #nursery #nutrients #trophicenergy #discharge #ColumbiaRiver #Oregon #Washington #USA #fluvial #tidalfreshwater #TFW #habitat #restoration #flowregime # herbaceous #forested #wetland #marsh #aquatic #sediment #sedimentation #estuary #inundation #monitoring #measurement #velocity #discharge #volume #spatialanalysis #spatiotemporal #river #riverine #hydraulic #pluvial #precipitation #upstream #subtidal #wavelet #tidalcreek #AcousticDopplerCurrentProfiler #instrumentation #elevation #spatial #mapping

Adapting Dams To Climate Extremes - Lessons From Global Reservoir Operations
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https://www.waterpowermagazine.com/analysis/adapting-dams-to-climate-extremes-lessons-from-global-reservoir-operations/ <-- shared technical article
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https://doi.org/10.1596/43980 <-- shared World Bank Report, ‘Enhancing the Safety and Resilience of Dams in the Context of Climate Change and Extreme Hydrological Events’ (technical note)
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#dams #waterresources #infrastructure #climatechange #risk #hazard #extremeweather #GLOF #global #flood #flooding #management #adaptative #reservoir #operations #planning #damsafety #forecasting #spatialanalysis #spatiotemporal #streamflow #rainfall #precipitation #hydrology #events #water #hydrography #impactassessment #riskmanagement
@world Bank @GWSP

Karst Flash Floods - An Example From The Dinaric Karst (Croatia)
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https://doi.org/10.5194/nhess-6-195-2006 <-- shared (older) paper
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#flood #flooding #pluvial #fluvial #subsurface #risk #hazard #naturalhazard #disaster #flashflood #infrastructure #damage #building #destabilisation #foundations #karstburst #Marina #Dinaric #Croatia #extremeweather #climatechange #intense #prolonged #rainfall #precipitation #speed #fast #caves #conduits #fractures #aquifers #water #hydrology #groundwater #hydrogeology #waterpressure #sinkhole #subsidence #massmovement #landslide #monitoring #mapping #geology #engineeringgeology #hydroseismic #humanimpacts #lossoflife #death #karstterrain #springs #debris #sediment #cavities #riskassessment #Grazalema #Andalusia #Cadíz #Spain #StormLeonard #emergencymanagement #planning

Detection And Spatial Modelling Of Trends In UK Rainfall Frequency
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https://doi.org/10.1080/02626667.2026.2622458 <-- shared paper
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#GIS #spatial #mapping #extremeweather #rainfall #pluvial #frequency #trends #nonstationarity #extremevalue #model #modeling #monitoring #UnitedKingdom #UK #Britian #England #Scotland #Wales #precipitation #spatialanalysis #spatiotemporal #drainage #monitoring #planning #resilience #naturalhazards #flooding #flood #water #hydrology # #sewerage #sewage #wastewater #waterways #pollution #spills #releases #stormwater #erosion #massmovement #landslides #engineeringgeology #risk #hazard #climatic #covariates #raingauges #maximums #duration #winter #NorthernEngland #NorthAtlanticOscillation #NAO #index #metrics #annualmaximum #AMAX #seasonalmaximum #SMAX #peaksoverthreshold #POT #statistics #geostatistics #MetOffice #weather #climate

Comparing Flood Inundation Map Features and Diagnosing Decision Support Design Challenges
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https://doi.org/10.1002/hyp.70362 <-- shared paper
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#GIS #spatial #mapping #climatechange #extremeweather #pluvial #fluvial #water #surfacewater #precipitation #flood #flooding #floodinnundation #innundation #NOAA #FIM #floodinnundationmaps #model #modeling #spatialanalysis #spatiotemporal #floodresponse #response #naturalhazard #monitoring #forecasting #forecast #visualisation #FIMAN #remotesensing #Copernicus #impactassessment #research #emergencymanagement #humanimpact #safety #publicsafety #risk #hazard #infrastructure #FloodMapper #floodwatch #flashflood
@FEMA | @NOAA | @USGS |

Evaluating The Functional Realism Of Deep Learning Rainfall-Runoff Models Using Catchment Hydrology Principles
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https://doi.org/10.1029/2025WR040076 <-- shared paper
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#water #hydrology #surfacewater #pluvial #fluvial #rainfall #snow #snowmelt #runoff #precipitation #model #blackbox #robustness #functionalrealism #screening #parameters #accuracy #hydrologic #principles #trustworthy #modeling #spatialanalysis #spatial #mapping #GIS #spatiotemporal #USA #CONUS #AI #ExplainableAI #celerity #machinelearning #artificialintelligence #LSTM #deeplearning #evapotranspiration #waterresources #extremeweather #flood #flooding #risk #hazard #monitoring #prediction #catchments #streamflow #geomorphometry #network #flow #calibration

9.3% Of Earth’s Land Flagged In New Outbreak Risk Map
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https://www.earth.com/news/93-of-earths-land-flagged-in-new-outbreak-risk-map-pr25/ <-- shared technical article
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https://doi.org/10.1126/sciadv.adw6363 <-- shared paper
--
#GIS #spatial #mapping #global #outbreak #risk #hazard #publichealth #publicsafety #death #AI #machinelearning #remotesensing #earthobservation #epidemic #pandemic #disease #environment #cost #highrisk #mediumrisk #zoonotic #zoonoticdisease #climate #climatechange #temperature #extremeweather #pluvial #rainfall #precipitation #water #hydrology #drought #landuse #changes #deforestation #population #demographics #livestock #biodiversity #mitigation #planning #response #monitoring
@EUCommission | @WHO

Multi-Disciplinary Reconstruction Of Debris Flow Events And Dynamics In The Northern Apennines, Italy - A Multiscale Approach Linking Ground Evidence With Climatic Triggers
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https://doi.org/10.1016/j.catena.2025.109708 <-- shared paper
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#GIS #spatial #mapping #engineeringgeology #massmovement #geology #landslide #debrisflow #multidisciplinary #geomorphology #pluvial #precipitation #dendrogeomorphological #treerings #dendrochronology #dating #chronology #reconstruction #geomorphmetry #processmodel #flowchart #spatialanalysis #spatiotemporal #mountains #orthophotos #historicphotos #geopedology #soil #hydraulic #alpedisucciso #trees #vegatation #downslope #risk #hazard #assessment #remotesensing #water #hydrology #rainfall #records #NorthernApennines #susceptibility #Italy

Master Gardener: The good and bad of snow in your garden | Lifestyles https://www.allforgardening.com/1546639/master-gardener-the-good-and-bad-of-snow-in-your-garden-lifestyles/ #AppliedAndInterdisciplinaryPhysics #clouds #EarthPhenomena #EarthSciences #FogAndPrecipitation #FormsOfWater #frost #garden #gardener #gardening #hail #ice #IceCrystal #MeteorologicalPhenomena #meteorology #nature #PhysicalGeography #precipitation #snow #snowflake #tree #WaterIce #WilsonBentley #WinterPhenomena

The Rise Of Community-Led Landslide Monitoring
Could an innovative natural-disaster monitoring system in Alaska be a model for the rest of the country?
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https://seaklandslideworkinggroup.substack.com/p/article-in-sierra-about-southeast <-- shared technical #Substack
--
#communitybased #landslidemonitoring #NationalLandslidePreparednessAct #massmovement #engineeringgeology #risk #hazard #landslide #publicsafety #monitoring #Sitka #Alaska #debrisflow #risk #hazard #water #hydrology #pluvial #fluvial #rainfall #precipitation #AK #extremeweather #climatechange #atmosphericriver #coast #coastal #slope #steepness #Haines #Ketchikan #Wrangell #Juneau #propertydamage #cost #economics #soilmositure #sensors #raingauge #Skagway #emergencymanagement #forecasting #tool #online #dashboard #situationalawareness #NSF #grant #infrastructure #threshold #historic #analysis #geomorphology #geomorphometry #communication #buddysystem #risktolerance #SitkaTribeofAlaska
#NationalScienceFoundation #USFS

Four [US] States Leading the Way on Flood Resilience
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From Iowa's pioneering flood-monitoring network to North Carolina's comprehensive resilience blueprint, states are demonstrating what's possible when local leaders take ownership of their climate futures.
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https://www.epicenterinsights.com/four-states-leading-the-way-on-flood-resilience/ <-- shared technical media article
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https://floodplainsbydesign.org/ <-- shared Floodplains By Design, Washington State
--
#water #hydrology #floodplainsbydesign #FBD #flood #flooding #floodplains #natural #risk #hazard #naturalhazard #publicsafety #cost #economics #infrastructure #mitigation #resilience #extremeweather #rainfall #surge #precipitation #hydrospatial #Iowa #NorthCarolina #Wisconsin, #Washington #WashingtonState #policy #planning #spatialanalysis #floodresilience #program # readiness #public #private #regulations #USStates #USA #monitoring #blueprint

Landsliding Follows Signatures Of Wildfire History And Vegetation Regrowth In A Steep Coastal Shrubland
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https://www.linkedin.com/posts/tmatthew_the-geoscience-community-is-finding-more-activity-7365791741355950080-VzES <-- shared LinkedIn post
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https://pubs.geoscienceworld.org/gsa/geosphere/article/doi/10.1130/GES02856.1/660392/Landsliding-follows-signatures-of-wildfire-history <-- shared paper
---
#GIS #spatial #mapping #massmovement #debrisflow #debrisslide #California #USA #climatechange #extremeweather #fire #engineeringgeology #risk #hazard #geoscience #water #hydrology #rainfall #precipitation #wildfire #postwildfire #landslide #vegetation #cover #type #causality #coast #coastal #shrubland #model #modeling #atmosphericriver #storm #geomorphology #geomorphometry #SantaYnezMountains #imagery #remotesensing #delineation #downslope #sediment #damage #cost #economics #infrastructure #geology #regrowth #postfire
U.S. Geological Survey (USGS) USDA Forest Service California Geological Survey

California valley fever cases hit record highs again in 2025

The latest California numbers suggest 2025 will be another record-smashing year for valley fever, the illness linked to…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Health #aridsoil #californiavalleyfevercase #centralcoast #coccidioides #Disease #Drought #ericapan #fungus #lastyear #morepeople #northerncentralvalley #pneumonia-likesymptom #precipitation #recordhigh #statereport
https://www.newsbeep.com/us/85194/

Environmental & Anthropogenic Influences On Fire Patterns In Tropical Dry Deciduous Forests
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https://doi.org/10.1038/s41598-025-98051-7 <-- shared paper
--
#GIS #spatial #mapping #wildfire #fire #busgfire #nvironmental #anthropogenic #influences #humanimpacts #deciduous #forests #firepatterns #spatialanalysis #tropical #ecosystems #management #tree #vegetation #spatiotemporal #hotspots #india #Satpura #tiger #reserve #ecogeography #geography #factors #parameters #drivers #temperature #precipitation #rainfall #model #modeling #bayesian #framework #slope #water #hydrology #infrastructure #mitigation #monitoring #preparedness #forestfires

NOAA/USGS Water Map – National Water Model
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https://water.noaa.gov/map <-- shared web map
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https://water.noaa.gov/about/nwm <-- shared NWM ‘About’ page
--
[ the post’s author did the spatial analysis to get the first round of elevation values of the majority of the 10s of millions offshore points, etc - http://alturl.com/3nrbm ]
#GIS #spatial #mapping #model #modeling #NationalWaterModel #NOAA #NWM #FIM #water #hydrology #framework #streamflow #realtime #observed #watercycle #rainfall #precipitation #snowmelt #overlandflow #flood #flooding #floodinnundationmodel #FIM #infiltration #bigdata #supercomputer #risk #hazard #mitigation #warning #warningsystems #threat #guidance #NationalWeatherService #forecast #forecasting #meteorology #river
@NOAA @USGS @nws

Atop The Oregon Cascades, [Research] Team Finds A Huge Buried Aquifer
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https://www.eurekalert.org/news-releases/1069774 <-- shared technical article
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https://doi.org/10.1073/pnas.2415155122 <-- shared paper
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https://www.usgs.gov/centers/oregon-water-science-center/science/mckenzie-river-source-water-study <-- shared USGS technical article, 2011, ‘McKenzie River Source Water Study’
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https://youtu.be/6R8e5HwGMwU?si=sniW4bvvzzviTYie <-- shared media video overview
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#GIS #spatial #mapping #model #modeling #water #hydrology #Oregon #Cascades #CascadeMountains #PNW #PacificNorthwest #volcanic #rocks #aquifer #waterresources #watermanagement #climatechange #snowpack #drought #spatialanalysis #CriticalZone #geohazards #geology #engineeringgeology #landscapes #weathering #bedrock #holocene #groundwater #structuralgeology #orogeny #remotesensing #geophysics #sampling #drilling #porosity #permability #tomography #lava #spring #seep #breccia #discharge #streams #runoff #precipitation #climate #rainfall #recharge

A Shorter, Sharper Rainy Season Amplifies California Wildfire Risk
--
https://doi.org/10.1029/2021GL092843 <-- shared paper
--
https://doi.org/10.1029/2020GL090350 <-- shared paper
--
[a paper from a few years ago, but very pertinent considering the horrific events in Los Angeles area in early 2025]

#GIS #spatial #mapping #spatiotemporal #spatialanalysis #precipitation #California #WestCoast #USA #LosAngeles #wildfire #LA #LosAngelesfires #losangelesfires2025 #climatechange #rainfall #precipitation #weather #wind #dry #water #hydrology #rainyseason #wildfirerisk #WildfireCrisis #risk #hazard #loss #damage #fire #house #community #publicsafety #autumn #fall #cost #economics #CaliforniaWildFires #vegetation #seasonality #model #modeling #climatemodel

Climate Change Key Driver Of Catastrophic Impacts Of Hurricane Helene That Devastated Both Coastal And Inland Communities [technical report]
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http://alturl.com/5n38k <-- shared technical report
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#GIS #spatial #mapping #spatialanalysis #hurricane #hurricanehelene #spatiotemporal #risk #hazard #naturaldisaster #impact #climatechange #precipitation #seasurfacetemperature #Appalachia #damage #loss #infrastructure #deaths #rainfall #precipitation #flood #flooding #engineeringgeology #erosion #massmovement #mudslides #landslides #debrisflows #disastermanagement #prediction #mitigation #climatemodels #model #modeling #spatiotemporal #IRIS #winds #extremeweather #weather #storm #humanimpacts #costs #economy #SST #warning #weatherprediction #dams #reservoirs #loss #lossoflife

Flood Susceptibility Mapping Of Kathmandu Metropolitan City Using GIS-Based Multi-Criteria Decision Analysis
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https://doi.org/10.1016/j.ecolind.2023.110653 <-- shared paper
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#GIS #spatial #mapping #analyticalhierarchyprocess #floodhazardcriteria #floodpotentialzone #localadministrativeunit #Nepal #spatialanalysis #spatiotemporal #model #modeling #processmodeling #water #hydrology #flood #flooding #Kathmandu #distance #proximity #river #climatechange #extremeweather #precipitation #rainfall #monsoon #builtenvironments #impervious #impervioussurfaces #concrete #stormwater #drainage #economics #environment #infrastructure #damage #ecology #floodinnundation #urbanplanning #criteria #slope #elevation #drainagedensity #rainfall #landuse #landcover #distancefromriver #distance #topography #elevation #floodsusceptibility #floodprone #risk #hazard #mitigation #budgets #costs #policymakers

Declining Groundwater Storage Expected To Amplify Mountain Streamflow Reductions In A Warmer World
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https://www.nature.com/articles/s44221-024-00239-0 <-- shared technical article
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#GIS #spatial #mapping #groundwater #Colorado #ColoradoRiver #river #streamflow #climatechange #groundwater #hydrology #water #model #modeling #snowmelt #recharge #waterresources #watersecurity #headwaters #mountain #mountainous #forest #vegetation #storage #storageloss #depletion #rainfall #precipitation #Gunnison #warming #spatialanalysis

Meteosat-9 🛰 at 45.5° E - Natural Colour RGB - MSG - Indian Ocean 2024-01-12 10:00 #EUMETSAT #satellites #satelliteimagery #imaging #europe #climatechange #weather #temperature #forecast #prognosis #precipitation #snowstorm #greece

As Groundwater Dwindles, Powerful Players Block Change
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https://www.nytimes.com/interactive/2023/11/24/climate/groundwater-levels.html <-- shared media article
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#GIS #spatial #mapping #USA #groundwater #hydrology #hydrogeology #aquifer #farming #mining #agriculture #crops #cropland #development #realestate #industry #heavyindustry #climatechange #evaporation #rainfall #precipitation #waterresources #watermanagement #watersecurity #waterconservation #watercrisis #overpumping #underregulation #regulations #groundwatermining