#hydrology — Public Fediverse posts

“Every river seems to come with a purpose”*…

A simple scaling law brings order to the chaos of flowing water, rock, and sediment. As Natalie Wolchover reports, new findings have extended the law even further…

A river has my heart. It’s not the austere, black Thames winding through London, where I was born, but a lazy green one 5,000 miles away, where I spent my adolescence: the Blanco River in Texas. My maternal ancestors have dipped into its waters for generations, as I have on countless summer days.

The Blanco is a tributary of the San Marcos, which flows into the Guadalupe, and on into the Gulf of Mexico. You can probably picture how this looks on a map because all river networks look similar, creeping through the landscape, merging into ever wider and longer channels, downhill to the sea. The pattern resembles twigs on branches that connect to trunks of trees (and the branching of their root systems, too), and it likewise resembles the veins of plant leaves, our own systems of blood vessels, and train and highway networks that feed into cities.

There’s something appealing about this ubiquitous pattern, so appealing to me personally that I have it tattooed on my forearm: the silhouette of a tree, with leafless branches reaching upward and roots burrowing downward, almost in mirror image. “The shapes of rivers and leaf vasculature and so on — branching networks — you can just about grasp the pattern, but it’s still chaotic, so there’s something fascinating with that,” said Chris Paola, a river scientist at the University of Minnesota.

Systems that branch in this way are “transport networks”: They transport some fluid substance (water, blood, traffic) from every place to a single place (the sea, a heart, a city center). Of the various examples, rivers are especially revealing, I think, since they arise from neither biological evolution nor urban planning, but rather chaotic Earth processes. Yet they obey simple, universal laws…

…

… In 1957, a U.S. Geological Survey scientist named John Hack discovered the most important law of river networks. In rivers and streams in Virginia and Maryland, Hack measured the length of each stream and the area of the land that slopes toward that stream and therefore drains into it, called its basin or drainage area. What he discovered is now known as Hack’s law: Any stream, from the littlest brook to the mightiest river, has a length that’s proportional to its drainage area raised to the power of 0.6. (In symbolic form: L ~ A0.6.) There’s a bit of variance around that 0.6 value — Earth is, after all, a complicated place — but “the general regularity of the relation is nevertheless remarkable,” Hack wrote. “Stream lengths tend to increase proportionally to the 0.6 power of the drainage area, regardless of the geological or structural characteristics of the area.”

As more and better data has accrued, especially from satellite imagery, Hack’s law has held worldwide. Why this is the case is the essential mystery geomorphologists have grappled with ever since. “Hack’s law is still the big question,” said Hansjörg Seybold, a geomorphologist at the Institute for Interdisciplinary Mountain Research at the Austrian Academy of Sciences.

It’s not so surprising that the bigger the land area of the basin, the longer the stream that drains it. But in a purely mathematical sense, one might expect that stream length would follow a slightly different power law. Imagine a square patch of land. You might guess that regardless of slope or size, in idealized form, the land would drain into a stream that’s the length of one of its sides — a vertical line down the middle, for example. That length is the square root of the area — or A to the power of 0.5.

Under that circumstance, big river basins would have the same proportions as the small river basins that feed the tributaries within them. Their structure would be the same, regardless of size. But that’s not what Hack’s law reveals.

Instead, as a drainage areas get larger, the length of their streams increases faster. “A nice way to phrase it would be that small basins are short and squat, and large basins are long and thin,” said Daniel Rothman, a geophysicist at the Massachusetts Institute of Technology. We unknowingly pick up on this pattern when we look at a network of tributaries on a map; a perfectly self-similar, fractal river network wouldn’t look quite right. Basins and streams become elongated at larger scales, so that river networks have an inherent directionality that stretches toward the sea. One result of that elongation is that neighboring river networks must lie closer together than they would with a 0.5 power law…

…

… Rivers do shift their layouts all the time. In the 1990s, in parallel with the work on optimal channel networks, geomorphologists developed powerful landscape evolution models to capture this constant adjustment and show the mechanism by which Hack’s law etches itself on the landscape. These computer simulations start with water flowing downhill, eroding rock as it goes. Tiny, random irregularities in the topography cause some channels to capture more runoff than others. Those channels in turn erode faster and deepen, which causes them to attract still more water. One streambed might grow toward its neighbor, and thereby intercept some of its runoff. The victorious stream grows longer and carries more water, while the losing stream shrinks or disappears. These sorts of local adjustments like these route water along ever more efficient paths. As the entire drainage network gradually reorganizes over thousands of years or more, it attains and then continues to tweak a configuration that transports water downhill with minimal energy dissipation.

Gravity and friction are the driving forces of this process. Gravity supplies potential energy to flowing water. Friction, the cause of erosion, dissipates that energy. A channel configuration that wastes energy by forcing water along inefficient routes tends to erode rapidly and change. A configuration that routes water more effectively is stabler and therefore more persistent. The network becomes optimal through this dynamic evolution, eventually arriving at a form that adheres to Hack’s law.

That explanation of river network geometry hangs together for me, though geomorphologists still have many questions. Some study rivers that deviate from Hack’s law. Others organize transport networks that follow Hack’s law into one class of optimal transport networks, among a whole family of them. Trees, which branch in three dimensions instead of two, would be in a different class from rivers and follow different optimal scaling laws, for instance.

Now, geomorphologists have a new finding to explain. In April 2026, Tian Dong of the University of Texas, Rio Grande Valley and co-authors made the cover of Science for discovering that Hack’s law holds not only for rivers’ tributary networks, but also for their deltas, the fanlike structures that form where a river meets the sea.

Rivers essentially hit a brick wall when they reach the (nonflowing) ocean. The sudden deceleration of the water causes it to drop the sediments it carries. These pile up to form new land. In the process, the river’s water splits into a different kind of network of channels, which shift locations constantly as sediments build up and wash away.

Scientists told me that they’ve long wondered about the organization of channels in river deltas, but they are hard to study. Unlike the upstream river network, where slope and elevation differences make it easy to calculate the area of land that drains into any given tributary, deltas are flat and especially dynamic. But through a sophisticated analysis of satellite data that allowed them to distinguish land from water, Dong and his collaborators determined that the length of a channel in a river delta scales with the size of its nourishment area — the area that it supplies with sediments — raised to the power of 0.6. Rivers’ tributary networks and distributary networks are opposites — sediments are transported away from one end and deposited at the other — yet they abide by the same math. Geomorphologists are now considering why Hack’s law should apply in this inverse context.

Reflecting on my own question, I think it’s the coexistence of simplicity and determinism with chaos and randomness that makes the optimal structure of rivers so captivating. Natural efficiency is, perhaps, innately appealing to us…

The order in seeming chaos: “Why Are Rivers So Mathematical?” from @nattyover.bsky.social in @quantamagazine.org.

* Haruki Murakami, Kafka on the Shore

###

As we go with the flow, we might send carefully-calculated birthday greetings to Moritz Cantor; he was born on this date in 1829. A historian of mathematics, he is best remembered for the four volume work Vorlesungen über Geschichte der Mathematik (“Lectures on the History of Mathematics”) which traces the history of mathematics up to 1799, the year of Gauss‘s doctoral thesis. Modern historians credit Moritz with introducing a new discipline to a field, the history of mathematics, that had hitherto lacked the sound, conscientious, and critical methods of other fields of history.

source

For flash-drought early warning, which single indicator has served you best? VPD, soil moisture, ET anomaly, an index? My hunch is VPD, but I want to be argued with.
#Drought #RemoteSensing #Hydrology #ClimateChange

Where The Water Once Flowed - Rivers And Lakes Dry Up As Droughts Take Toll
(Surreal images of scorched landscapes where water once flowed, as severe droughts lead to dwindling water supplies around the world.)
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https://www.reuters.com/pictures/where-water-once-flowed-rivers-lakes-dry-up-droughts-take-toll-2026-08-14/ <-- shared media article
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[damn!]
#water #hydrology #hydrography #waterresources #rivers #lakes #photojournalism #drought #climatechange #extremeweather #heatwave #temperature #watershortage #global #risk #hazard #infrastructure #watermanagement #global #impacts #watersecurity #humanimpacts #publichealth

🎉We did it!
Thanks to the amazing support from the #QGIS community, both the initial €6000 goal and the extended €7000 goal have been achieved.
Native #hydrology tools and the new flow direction & wind barb renderers will land in QGIS 4.4.

More info: www.qwast-gis.com/l/crowdfundi...

🎉We did it!
Thanks to the amazing support from the #QGIS community, both the initial €6000 goal and the extended €7000 goal have been achieved.
Native #hydrology tools and the new flow direction & wind barb renderers will land in QGIS 4.4.
Thank you to everyone who contributed! 💚

More info about the features that will be implemented by @northroadgeo and @lutraconsulting: https://www.qwast-gis.com/l/crowdfunding-native-qgis-tools-for-hydrology/

#GIS #OpenSource #FOSS4G #OSGeo

Eventually the riverbed stabilizes into a new landscape and the intern builds houses and plants trees along the banks.
#science #hydrology #erosion #SciComm

The stream table is an old favorite at the county Fair. All the employees love it because they get to play like little kids. And little kids love it because sandbox!
(Technically it's not sand, it's micro plastics. Hey at least it's not in the soil I guess. The different colored particles have different weight/density.)
#science #erosion #hydrology #SciComm

Bankfull discharge is the maximum amount of water a river channel can hold before it spills onto its floodplain, a critical threshold for accurate flood modeling.
#Hydrology #Climatology #EnvironmentalEngineering #sflorg
https://www.sflorg.com/2026/08/eng08182601.html

Spatiotemporal Distribution, Climatic Factors, And Seasonal Precipitation Patterns Characterizing 66 Years Of Widespread Shallow Landslide Events In Piedmont (Northwestern Italy)
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https://doi.org/10.3389/feart.2026.1884300 <-- shared paper
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“Widespread shallow landslide events constitute one of the primary drivers of recurrent societal and economic losses in mountain regions. Although the influence of climate variability on landslide frequency and magnitude has been widely recognized, long-term, event-level analyses linking spatiotemporal landslide patterns to precipitation climatology at the regional scale remain scarce. This study presents a statistical analysis of 128 widespread shallow landslide events recorded in Piedmont, northwestern Italy, over the 66-year period 1960–2025, cross-referenced against the regional precipitation climatology. Events were characterized by season, primary physiographic unit, provincial coverage, and an ordinal magnitude index (scale 1–7) encoding combined spatial extent and estimated landslide count. Results indicate that no statistically significant monotonic trend in event frequency was detected over the study period (mean rate: 1.97 events yr⁻1); however, a moderate positive correlation was established between event magnitude and the number of provinces affected (R2 = 0.51, p < 0.00), validating the magnitude index as a proxy for spatial footprint. Mean event magnitude reached its highest value in the 2010s (3.71), while all five events of magnitude ≥ 6 occurred in autumn. The summer fraction of the catalogue increased markedly, from 6% in the 1960s to 31% in the 2000s and 25% in the 2020s, concurrent with stable or declining summer mean precipitation totals, consistent with Clausius–Clapeyron amplification of convective intensity under documented regional warming. Cross-analysis with regional records identifies three tiers of rainfall–landslide coupling: a stationary direct seasonal coupling; a non-stationary, strengthening intensity-mediated summer coupling; and a structural susceptibility-mediated spatial decoupling whereby the driest provinces generate the highest landslide occurrence frequencies due to the lower triggering thresholds characteristic of Tertiary Piedmont Basin sedimentary environments. A post-hoc assessment of the triggering thresholds used for regional shallow landslide early warning system demonstrates superior detection performance for high-magnitude autumn events (hit rate up to 89% for the 2000–2025 sub-period) and identifies sub-daily convective accumulation windows in summer as the primary domain requiring threshold recalibration…”
#massmovement #landslide #engineeringgeology #Italy #Piedmont #NorthernItaly #weather #rainfall #precipitation #climate #risk #hazard #corrleation #relationship #earlywarningsystems #damage #loss #community #infrastructure #mountain #spatiotemporal #mapping #spatialanalysis #statistics #geostatistics #climatology #regional #scale #weatherpatterns #physiography #geomorphology #water #hydrology #hydrogeomorphology #geology #soils

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

Rivers are never still. 🌊 They carve, flood, and rebuild the land grain by grain, and from orbit, satellites watch it happen over years. What looks permanent on a map is really a slow-motion river of change. 🛰️

#RemoteSensing #Hydrology #Rivers #Geography #Satellites #EarthObservation

North Dakota's surface water, fire, and vegetation are coupled — change one and the others answer. I built an Earth Engine explorer over 2000–2024 to watch it happen.
#EarthEngine #RemoteSensing #Fire #Hydrology

Socio-Hydrology Modeling Captures How Inequalities Impact Community Flood Resilience
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https://doi.org/10.1029/2025WR041393 <-- shared paper
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https://www.americanprogress.org/article/how-fema-can-build-rural-resilience-through-disaster-preparedness/ <-- shared technical/opinion article
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https://www.carbonbrief.org/us-flooding-increase-will-disproportionately-impact-black-and-low-income-groups <-- shared technical/opinion article
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https://headwaterseconomics.org/natural-hazards/unequal-impacts-of-flooding/ <-- shared technical/opinion article
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https://youtu.be/8jVRsD8wgMM?si=qBHchWZWkYRGbTdE <-- shared opinion video
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https://fedcommunities.org/lower-income-neighborhoods-face-greater-flood-risk-tougher-recovery/ <-- shared technical/opinion article
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H/T @matthew Preisser | PhD Civil Engineering, Natural Hazard Modeler and Socio-Hydrologists
“How can we better understand the dynamic feedbacks between the environment and society?
Socio-hydrology models are often built around the assumption that cities act as homogeneous entities, without capturing the variable capacity of communities with different underlying socioeconomic characteristics to respond to and recover from disasters.
In this study, [the authors] developed a disaggregated approach to model community-specific adaptive capacity, allowing [them] to examine how inequalities influence flood recovery and resilience. This framework provide[d] a basis for exploring hypotheses about the relationships between growth, inequality, and community resilience in the face of flood hazards.
[Their] results highlighted] the importance of considering community-level dynamics when developing flood mitigation and disaster response strategies that balance economic growth with equity. Many challenges remain in applying socio-hydrology models to real-world settings, but this work takes a step toward incorporating more realistic representations of socioeconomic inequality into human–water systems while preserving the generality and flexibility that make conceptual models useful…”
#risk #hazard #water #hydrology #flood #society #flooding #USA #SocioHydrology #naturalhazard #socioeconomic #population #demographics #infrastructure #damage #disaster #disaggregated #model #modeling #community #adaptiveresponse #growth #inequality #communityresilience #floodhazard #floodmitigation #disasterresponse #equity #city #town #rural #urban #economy #cost

🎉We've just passed 50% of the crowdfunding goal for bringing native #hydrology tools in #qgis.
Thanks for all donations so far🙏🏼

If you rely on hydrological workflows in QGIS please consider supporting this effort.

👉 Full details & donation: www.qwast-gis.com/l/crowdfundi...

#gis #OSGeo #FOSS4G

🎉We've just passed 50% of the crowdfunding goal for bringing native #hydrology tools in #qgis. Thanks for all donations so far🙏🏼

If you rely on hydrological workflows in QGIS or simply want to strengthen #OpenSource #gis, please consider supporting the effort so these tools land in QGIS 4.4.

👉 Full details & donation: https://www.qwast-gis.com/l/crowdfunding-native-qgis-tools-for-hydrology/

#OSGeo #FOSS4G @northroadgeo

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

Hydrologic unit system (United States) (Hydrology 💧)

For the use of hydrologists, ecologists, and water-resource managers in the study of surface water flows in the United States, the United States Geological Survey created a hierarchical system of hydrologic units. Originally a four-tier system divided into regions, sub-regio...

https://en.wikipedia.org/wiki/Hydrologic_unit_system_(United_States)

#HydrologicUnitSystem #Geocodes #Hydrology #Limnology #SourceAttribution #WaterAndTheEnvironment

Three new papers using caves as hydrological observatories

A short post, highlighting three papers published in recent weeks that I have had the fortune of contributing to. All use networks of drip loggers in caves to record water movement from the surface to the water table.

First, led by Akhilesh Kumar at UNSW Sydney, is a satellite remote sensing study. Can we use satellite-derived products, such as rainfall, vegetation characteristics and soil moisture, to understand when recharge events might occur? Using a network of cave drip loggers in SE Australia, the paper ‘Investigating correlative & causal drivers of precipitation-driven groundwater recharge occurrences in southeastern Australia’ gives the answer. Find out more here (Open Access) in the Journal of Hydrology: Regional Studies.

Second, led by Kashif Mahmud and Rowann Remie at Midwestern State University in the USA, uses another network of cave drip loggers, this time distributed throughout Natural Bridge Caves in Texas. The paper, ‘Heterogeneity of karst water movement revealed through cave drip hydrology’, tackles the question of the controls on water movement in karstified limestone. What is more important control on the variability of water movement – depth below surface, geology, or something else? You can read more here (Open Access) in Frontiers in Water.

Third, led by Danyang Sun at UNSW Sydney, uses time series analyses to conceptualise water movement in karstified limestones using a network of drip loggers in SE Australia. Her paper, ‘Classification and Conceptualization of Karst Recharge Processes Through Spectral and Change Point Analysis of Drip Water Dynamics’ is just published in the journal Water Resources Research, and you can read it here (Open Access).

The references:

Sun, D., Baker, A., Andersen, M.S., McDonough, L.K., Shanafield, M.S and Kumar, A., 2026. Classification and Conceptualization of Karst Recharge Processes Through Spectral and Change Point Analysis of Drip Water Dynamics. Water Resources Research, 62, e2025WR042816.

Mahmud, K., Remie, R,, Gary, M., Price, J.D., Katumwehe, A., Vauter, B. and Baker, A., 2026. Heterogeneity of Karst Water Movement Revealed Through Cave Drip Hydrology. Frontiers in Water, 8, 1892078.

Kumar, A., Baker, A., Andersen, M.S., Timms, W., Fisher, A.G. and Sun, D., 2026. Investigating correlative & causal drivers of precipitation-driven groundwater recharge occurrences in southeastern Australia. Journal of Hydrology: Regional Studies 66, 103547

USFS in California is hiring GS 7-9 Hydrologists. Open to the public. Locations include Upper Lake and Willows, offices that take care of the Mendocino National Forest. Closes August 10. #FediHire #Hydrologist #Hydrology #USFS https://www.usajobs.gov:443/job/878846600

📢 #crowdfunding to bring essential #hydrology tools directly into #QGIS.

If you use QGIS for #groundwater or surface #watermanagement, this will make your workflows faster and more reliable.

👉 More info: www.qwast-gis.com/l/crowdfundi...

#OSGeo #FOSS4G

Crowdfunding: Native QGIS Tool...

📢 #crowdfunding to bring essential #hydrology tools directly into #QGIS.

If you use QGIS for #groundwater or surface #watermanagement, this will make your workflows faster and more reliable.

👉 More info: https://www.qwast-gis.com/l/crowdfunding-native-qgis-tools-for-hydrology

@northroadgeo #OSGeo #FOSS4G

From Fragmentation To Integration - A Review Of Data–Model Integration In Land Subsidence Research
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https://doi.org/10.1016/j.ancene.2026.100567 <-- shared paper
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H/T @manonzero current drought conditions, increasing pressure on ecosystems, and ongoing climate adaptation challenges, land subsidence is receiving growing attention worldwide.
For anyone wanting to learn more about land subsidence, or get a refresher, [the authors] provide an overview of the processes involved, the ways it can be measured or estimated, the models used to simulate it, and how observations and models can be combined, [their] new [#openaccess] review paper [link above] may be of interest!
A central message of the paper is that understanding and managing land subsidence requires bringing these different sources of information together…”
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“HIGHLIGHTS:
• Presents a comprehensive synthesis that unifies all major elements of integral land-subsidence research.
• Defines the methodological steps needed for full integration and positions them within the broader challenges posed by subsidence.
• Brings together and distills the key components of data, modelling, and integration into a coherent framework.
ABSTRACT: Land subsidence, the sinking of the Earth’s surface, is a multi-faceted hazard driven by both natural and anthropogenic factors, and poses significant risk to environments, ecosystems, and society. Despite decades of growing research output, substantial gaps persist between investigations on the diversity of causes, reflected in data and model insufficiency. These gaps hinder the understanding of the issue and impede the effectiveness of mitigation measures. Many studies have urged to include all identified subsidence processes acting in a single area in an integral framework, for which a complex analysis has not systematically been outlined before. Therefore, [they] focus here on bridging the gaps between various technical research disciplines involved. [They] stress the urgency for an integral approach that combines observations with subsurface information of all known subsidence processes in an area, and [they] appeal for utilizing them in physics-guided data integrations. Only then can all subsidence drivers be understood, and effective mitigation measures designed. [They] outline the elements for an integral approach in categorical tables and schematized figures, and [they] discuss the main opportunities and challenges in a stepwise workflow. Leveraging these opportunities naturally leads to more robust, scalable, and policy-relevant solutions and fosters a more sustainable future…”
#Land #subsidence #processes #Verticallandmotion #SLR #sealevel #sealevelrise #relativesealevelrise #modeling #data #model #InSAR #elevation #holistic #GIS #spatial #mapping #climate #drought #extremeweather #climatechange #climateadaption #ecosystems #measurement #monitoring #research #review #framework #overview #risk #hazard #anthropogenic #mitigation #engineering #water #hydrology #policy #planning #design #remotesensing #spatialanalysis #spatiotemporal

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

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

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

Climate inertia (Glaciology 🗻)

Climate inertia or climate change inertia is the phenomenon by which a planet's climate system shows a resistance or slowness to deviate away from a given dynamic state. It can accompany stability and other effects of feedback within complex systems, and includes the inertia exhibited by physical movements of matter and exchanges of energy. The t...

https://en.wikipedia.org/wiki/Climate_inertia

#ClimateInertia #Hydrology #Glaciology #Oceanography #ClimateChange

Remarkable. I don’t understand why the currents in this patch of sea seem to move icebergs in opposite directions. I must look into why that happens.

A huge iceberg basically falls apart in 2.5 hours in this 10-minute timelapse video from earlier this week.

Massive Iceberg Collapses and Flips Over in Ilulissat, Greenland | Full Event in 4K (July 25, 2026)

10-minute video: https://www.youtube.com/watch?v=UufMqwyO7pY&feature=youtu.be

#Icebergs #Greenland #Geology #Hydrology

At today’s #QGIS Open Day, I launched a crowdfunding for native #hydrology tools for #QGIS.

Want to support the effort?
👉️ Full details on the blog: www.qwast-gis.com/l/crowdfundi...

⏯️ Link to QGIS Open Day session: www.youtube.com/live/fkX0mZj...

#GIS #hydrology #OpenSource

Crowdfunding: Native QGIS Tool...

At today’s QGIS Open Day, I launched a crowdfunding to develop native #hydrology tools for #QGIS. These tools will make stream extraction, basin delineation and #groundwater workflows faster, stable and fully cross‑platform. The tools will be implemented by @northroadgeo.

Want to support the effort?
👉 Full details on the blog: https://www.qwast-gis.com/l/crowdfunding-native-qgis-tools-for-hydrology

⏯️ Link to QGIS Open Day session: https://www.youtube.com/live/fkX0mZjdyb4

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