GregCocks, 1 month ago to lunar The Moon Has A Curious Effect On The Sounds Emanating From Coral Reefs https://www.sciencealert.com/the-moon-has-a-curious-effect-on-the-sounds-emanating-from-coral-reefs <-- shared technical article https://doi.org/10.1371/journal.pone.0299916 <-- shared paper #GIS #spatial #mapping #coralreef #astrobiology #moon #lunar #lunarcycles #fish #noise #vocalisations #oceangraphy #ecosystem #activity #soundscape #pulsetrains #moonlight #hydrophones #sonic #monitoring #model #modeling #numericmodeling #Hawaii #acoustics #reefhealth #ecosystemhealth #spatiotemporal #spatialanalysis photo - cuttlefish at night charts - changes in ambient noise during moonlight hours charts / sonagraphs - high frequency pulse trains from fish
#GIS #spatial #mapping #coralreef #astrobiology #moon #lunar #lunarcycles #fish #noise #vocalisations #oceangraphy #ecosystem #activity #soundscape #pulsetrains #moonlight #hydrophones #sonic #monitoring #model #modeling #numericmodeling #Hawaii #acoustics #reefhealth #ecosystemhealth #spatiotemporal #spatialanalysis
photo - cuttlefish at night charts - changes in ambient noise during moonlight hours charts / sonagraphs - high frequency pulse trains from fish
GregCocks, 1 month ago to worldwithoutus How Melting Arctic Ice Leads To European Drought And Heatwaves https://insideclimatenews.org/news/01032024/links-between-melting-arctic-ice-and-summertime-extreme-weather-in-europe/ <-- shared technical article https://doi.org/10.5194/wcd-5-109-2024 <-- shared paper #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arctic #climate #climatechange #europe #ice #meltingice #drought #heatwaves #Greenland #northatlantic #model #modeling #numericmodeling #interaction #inflow #freshwater #weather #research #water #hydrology #marine #oceangraphy #climatology #extremeweather maps - The Combined Drought Indicator—used to identify areas affected by agricultural drought, and areas with the potential to be affected—estimated for the first 10 days of each month from April to September 2022. Credit: European Commission, Joint Research Centre photo - The Wamme river is seen at a low level during the European heatwave on Aug 10, 2022 in Rochefort, Belgium maps - Climatological mean (a) SST, (d) meridional winds at 700 hPa, (g) 2 m air temperature, and (j) precipitation minus evaporation in summer (May through to August). Regressions of (b, c) the SST (colour shading) and 700 hPa winds (arrows), (e, f) the meridional winds at 700 hPa, (h, i) the 2 m air temperature, and (k, l) the accumulated precipitation minus evaporation on FE in (b, e, g, k) the first and (c, f, h, l) the second summer (May through to August) after the freshwater anomalies (indicated by the “+1” and “+2” in the titles). We removed large-scale trends from the air temperature to reduce the direct warming effect of greenhouse gases (Sect. 2), and we excluded the anomaly in 2016 since it exhibited a different spatial SST distribution from the other anomalies (Fig. A1). Thick contours encompass regions that are significant at the 95 % confidence level, and the red and blue dotted lines in (b) and (c) delineate the regions in which the SST anomalies exceed 2 ∘C and fall below −2 ∘C.
#GIS #spatial #mapping #spatialanalysis #spatiotemporal #arctic #climate #climatechange #europe #ice #meltingice #drought #heatwaves #Greenland #northatlantic #model #modeling #numericmodeling #interaction #inflow #freshwater #weather #research #water #hydrology #marine #oceangraphy #climatology #extremeweather
maps - The Combined Drought Indicator—used to identify areas affected by agricultural drought, and areas with the potential to be affected—estimated for the first 10 days of each month from April to September 2022. Credit: European Commission, Joint Research Centre photo - The Wamme river is seen at a low level during the European heatwave on Aug 10, 2022 in Rochefort, Belgium maps - Climatological mean (a) SST, (d) meridional winds at 700 hPa, (g) 2 m air temperature, and (j) precipitation minus evaporation in summer (May through to August). Regressions of (b, c) the SST (colour shading) and 700 hPa winds (arrows), (e, f) the meridional winds at 700 hPa, (h, i) the 2 m air temperature, and (k, l) the accumulated precipitation minus evaporation on FE in (b, e, g, k) the first and (c, f, h, l) the second summer (May through to August) after the freshwater anomalies (indicated by the “+1” and “+2” in the titles). We removed large-scale trends from the air temperature to reduce the direct warming effect of greenhouse gases (Sect. 2), and we excluded the anomaly in 2016 since it exhibited a different spatial SST distribution from the other anomalies (Fig. A1). Thick contours encompass regions that are significant at the 95 % confidence level, and the red and blue dotted lines in (b) and (c) delineate the regions in which the SST anomalies exceed 2 ∘C and fall below −2 ∘C.
GregCocks, 2 months ago to 3dmodeling Future Changes In Global Atmospheric Rivers Projected By CMIP6 Models https://doi.org/10.1029/2023JD039359 <-- shared paper #GIS #spatial #mapping #atmosphericriver #risk #hazard #3dmodeling #spatialanalysis #spatiotemporal #climate #climatechange #extreme #model #modeling #numericmodeling #precipitation #rain #rainfall #global #atmosphere #CMIP6 #climatology #temperature #surfacetemperature #heavyrain #hydrology #water #hydrological #hydrologicalcycle #globalwarming #thermaleffects #naturalhazard #naturaldisaster #disaster maps and charts - (a) Atmospheric river (AR) characteristics, including duration, interval, area, and intensity during the DJF globally. The central plot displays the increase in AR frequency between the far-future (SSP585; 2070–2099) and the historical (1980–2009) periods, with the climatological AR frequency in the historical period represented by the contours. Black dots indicate unanimous frequency changes among CMIP6 models. The red rectangles are the boundaries of the target regions and the black hatches are the selected land regions. The surrounding plots show 2D Kernel Density Estimation (KDE) maps for AR characteristics over the historical period and far-future under different scenarios displayed in the bottom right corner for eight regions. The univariate distributions of the characteristics are shown on the outer axes of each subplot. (b) The comparison of the KDE maps for three selected regions with ARs detected with GuanWaliser_v2 and Mundhenk_v3 AR detection tools (ARDTs). Note that only the SSP585 scenario is available for the far-future period in these two data sets. The legend is the same as in Panel (a). Note that the figures of PanLu ARDT are presented based on daily scale data sets, while the figures of GuanWaliser_v2 and Mundhenk_v3 ARDTs are presented on 6-hourly scale data sets due to data accessibility. graphic - what is the science behind atmospheric rivers maps and charts - Projected increase in panel (a) atmospheric river (AR) frequency and (b) AR-induced precipitation for DJF and JJA in Northern Hemisphere and Southern Hemisphere under the SSP585 scenarios. The shading represents the differences between the far-future (2070–2099) and historical (1980–2009) periods, with contours delineating the historical climatology. ARs detected with the PanLu, GuanWaliser_v2 and Mundhenk_v3 method are all shown. Note that the figures of PanLu ARDT are presented based on daily scale data sets, while the figures of GuanWaliser_v2 and Mundhenk_v3 ARDTs are presented on 6-hourly scale data sets due to data accessibility.
#GIS #spatial #mapping #atmosphericriver #risk #hazard #3dmodeling #spatialanalysis #spatiotemporal #climate #climatechange #extreme #model #modeling #numericmodeling #precipitation #rain #rainfall #global #atmosphere #CMIP6 #climatology #temperature #surfacetemperature #heavyrain #hydrology #water #hydrological #hydrologicalcycle #globalwarming #thermaleffects #naturalhazard #naturaldisaster #disaster
maps and charts - (a) Atmospheric river (AR) characteristics, including duration, interval, area, and intensity during the DJF globally. The central plot displays the increase in AR frequency between the far-future (SSP585; 2070–2099) and the historical (1980–2009) periods, with the climatological AR frequency in the historical period represented by the contours. Black dots indicate unanimous frequency changes among CMIP6 models. The red rectangles are the boundaries of the target regions and the black hatches are the selected land regions. The surrounding plots show 2D Kernel Density Estimation (KDE) maps for AR characteristics over the historical period and far-future under different scenarios displayed in the bottom right corner for eight regions. The univariate distributions of the characteristics are shown on the outer axes of each subplot. (b) The comparison of the KDE maps for three selected regions with ARs detected with GuanWaliser_v2 and Mundhenk_v3 AR detection tools (ARDTs). Note that only the SSP585 scenario is available for the far-future period in these two data sets. The legend is the same as in Panel (a). Note that the figures of PanLu ARDT are presented based on daily scale data sets, while the figures of GuanWaliser_v2 and Mundhenk_v3 ARDTs are presented on 6-hourly scale data sets due to data accessibility. graphic - what is the science behind atmospheric rivers maps and charts - Projected increase in panel (a) atmospheric river (AR) frequency and (b) AR-induced precipitation for DJF and JJA in Northern Hemisphere and Southern Hemisphere under the SSP585 scenarios. The shading represents the differences between the far-future (2070–2099) and historical (1980–2009) periods, with contours delineating the historical climatology. ARs detected with the PanLu, GuanWaliser_v2 and Mundhenk_v3 method are all shown. Note that the figures of PanLu ARDT are presented based on daily scale data sets, while the figures of GuanWaliser_v2 and Mundhenk_v3 ARDTs are presented on 6-hourly scale data sets due to data accessibility.
GregCocks, 6 months ago to ai Scientists Map Loss Of Groundwater Storage Around The World https://phys.org/news/2023-11-scientists-loss-groundwater-storage-world.html <-- shared technical article https://doi.org/10.1038/s41467-023-41933-z <-- shared paper #GIS #spatial #mapping #remotesensing #insar #radar #groundwater #pumping #waterresources #AI #machinelearning #subsidence #landsubsidence #engineeringgeology #risk #hazard #water #hydrology #global #agriculture #urban #extraction #impacts #humanimpacts #opendata #model #modeling #numericmodeling #watersecurity #storage #storageloss #aquifer #groundwaterstress #aquiferdepletion #geomorphometry global map - Groundwater withdrawal induced global land subsidence predicted by the random forest model photo - subsidence - mexico city street & buildings map - subsidence rates - chinese examples
#GIS #spatial #mapping #remotesensing #insar #radar #groundwater #pumping #waterresources #AI #machinelearning #subsidence #landsubsidence #engineeringgeology #risk #hazard #water #hydrology #global #agriculture #urban #extraction #impacts #humanimpacts #opendata #model #modeling #numericmodeling #watersecurity #storage #storageloss #aquifer #groundwaterstress #aquiferdepletion #geomorphometry
global map - Groundwater withdrawal induced global land subsidence predicted by the random forest model photo - subsidence - mexico city street & buildings map - subsidence rates - chinese examples