#populationpressure — Public Fediverse posts

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

New Zealand’s Flightless Birds Are Retreating To Extinct Moa Graveyards [spatial analysis and fossil record analysis]
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https://www.iflscience.com/new-zealands-flightless-birds-are-retreating-to-extinct-moa-graveyards-75303 <-- shared technical article
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https://doi.org/10.1038/s41559-024-02449-x <-- shared paper
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https://doi.org/10.1073/pnas.1314972111 <-- shared paper
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[facetious maybe, who can resist a 50lb drumstick?]
#GIS #spatial #mapping #NewZealand #weta #kiwi #takahe #moa #polynesia #maori #colonialisation #extinction #habitats #humanimpacts #hunting #habitatloss #ecology #spatialanalysis #spatiotemporal #remote #elevation #flightless #birds #endangeredspecies #endangered #RuahineRange #MountAspiring #model #modeling #fossil #fossilrecord #palaeontology #paleoclimate #demographics #populationpressure #spatialdynamics