💧🌏 Greg CocksFeb 5
Climatic And Anthropogenic Controls On Late Holocene Sediment Transport To The Gulf Of Mexico By The Mississippi River
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https://doi.org/10.1016/j.earscirev.2026.105401 <-- shared paper
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#water #hydrology #hydrography #humanimpacts #human #settlement #anthropogenic #levees #engineering #spatialanalysis #spatialtemporal #erosion #weathering #Mississippi #River #Sediment #Provenance #Zircon #grainsize #alluvial #alluvium #hydrogeomorphology #headwater #coast #coastal #delta #geology #Holocene #geochemistry #Sr #Nd #isotope #Strontium #Neodymium #detrital #climate #GulfofMexico
💧🌏 Greg CocksFeb 24, 2025
Gap Between Water Supply And Demand Will Increase As Climate Shifts, Analysis Finds
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https://phys.org/news/2025-02-gap-demand-climate-shifts-analysis.html <-- shared technical article
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https://doi.org/10.1038/s41467-025-56517-2 <-- shared paper
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#GIS #spatial #mapping #spatialanalysis #spatialtemporal #watermanagement #waterresources #watersecurity #water #hydrology #watersupply #global #climatechange #warming #consumption #foodsecurity #agriculture #food #availability #economics #development #model #modeling #energy #scarcity #watergap #freshwater #groundwater #river #lake #aquifer #reserves #environment #stress #rainfall #precipitation #research #sustainable #planning #policy #gap #demand
Gap between water supply and demand will increase as climate shifts, analysis finds

Robust water-management strategies will be necessary to overcome discrepancies between water supply and demand in a warming world, according to a new analysis by Carnegie Science's Lorenzo Rosa and Matteo Sangiorgio of the Polytechnic University of Milan.

Phys.org
💧🌏 Greg CocksJul 16, 2024
Spectacular Auroras Are Caused By Head-On Blows To Earth’s Magnetic Field That Could Damage Critical Infrastructure [space weather]
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https://www.eurekalert.org/news-releases/1050418 <-- shared technical article / press release
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https://doi.org/10.3389/fspas.2024.1392697 <-- shared paper
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#GIS #spatial #mapping #spatialanalysis #spatialtemporal #spaceweather #risk #hazard #infrastructure #electricity #electricalsupply #model #modeling #sun #solar #aurora #powerlines #pipelines #geology #forecasting #warning #geomagnetism #currents #geomagneticstorm #Mäntsälä #Finland #solarwind #CME #electromagnetism #coronalmassejection
Spectacular auroras are caused by head-on blows to Earth’s magnetic field that could damage critical infrastructure

Auroras are caused by particles from the sun hitting the Earth’s magnetic field — but these impacts also cause geomagnetically induced currents at ground level, which can damage infrastructure that conducts electricity. Scientists studying these currents to protect critical infrastructure have carried out the first research which compares interplanetary shocks to real-time measurements of geomagnetically induced currents, showing that the angle of the shocks’ impact is key for forecasting possible damage to infrastructure: shocks that hit the magnetic field at an angle produce less powerful currents.

EurekAlert!
💧🌏 Greg CocksApr 9, 2024
Pywaterflood - Well Connectivity Analysis Through Capacitance-Resistance Modeling
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https://doi.org/10.21105/joss.06191 <-- shared paper
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https://github.com/frank1010111/pywaterflood?tab=readme-ov-file <-- GitHub page
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#GIS #spatial #mapping #oilandgas #geothermal #energy #aquifer #groundwater #spatialanalysis #spatialtemporal #well #connectivity #analysis #pywaterflood #CapacitanceResistanceModeling #python #opensource #CRM #pythonlibrary #reservoir #geology #wellconnectivity #production #injection #water #waterresources #waterindustry
Pywaterflood: Well connectivity analysis through capacitance-resistance modeling

Male, F., (2024). Pywaterflood: Well connectivity analysis through capacitance-resistance modeling. Journal of Open Source Software, 9(95), 6191, https://doi.org/10.21105/joss.06191

Journal of Open Source Software
WorldPopProjectDec 6, 2022
New #LearningModel using our #Population #OpenData - Dynamic population #mapping with #AutoGluon - in Urban Informatics #spatialtemporal #MachineLearning #Modelling #Prediction #GeoAI https://doi.org/10.1007/s44212-022-00017-x
Dynamic population mapping with AutoGluon - Urban Informatics

Timely and accurate population mapping plays an essential role in a wide range of critical applications. Benefiting from the emergence of multi-source geospatial datasets and the development of spatial statistics and machine learning, multi-scale population mapping with high temporal resolutions has been made possible. However, the over-complex models and the strict data requirement resulting from the constant quest for increased accuracy pose challenges to the repeatability of many population spatialization frameworks. Therefore, in this study, using limited publicly available datasets and an automatic ensemble learning model (AutoGluon), we presented an efficient framework to simplify the model training and prediction process. The proposed framework was applied to estimate county-level population density in China and received a good result with an r2 of 0.974 and an RMSD of 427.61, which is better than the performances of current mainstream population mapping frameworks in terms of estimation accuracy. Furthermore, the derived monthly population maps and the revealed spatial pattern of population dynamics in China are consistent with earlier studies, suggesting the robustness of the proposed framework in cross-time mapping. To our best knowledge, this study is the first work to apply AutoGluon in population mapping, and the framework’s efficient and automated modeling capabilities will contribute to larger-scale and finer spatial-temporal population spatialization studies.

SpringerLink