#camels — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #camels, aggregated by home.social.
-
Camels led through Budapest as activists highlight Hungary’s drought crisis
TWICE reflect on a decade as a group: ‘There’s so much more we can do’ | AP interview…
#Hungary #HU #Europe #Europa #EU #activism #agriculture #camels #Climate #climatechange #Droughts #generalnews #hír #hungary #Hungarygovernment #Magyarország #MediaAPIvideo #Watershortages
https://www.europesays.com/3149776/ -
#Camels R famously known as ships of #desert, but even these hardy animals arent being spared from extremes of rising #temperatures in #Africa. Afar region of north-eastern #Ethiopia is one of hottest driest places on Earth, where nomads often lead camel caravans carrying salt through desert. "I can see my camels suffering from extreme heat," camel herder from Semera in Afar - blisters on their feet, their eyes become watery, hot sand burns their skin, eats up their hair when they are sitting
-
#Camels R famously known as ships of #desert, but even these hardy animals arent being spared from extremes of rising #temperatures in #Africa. Afar region of north-eastern #Ethiopia is one of hottest driest places on Earth, where nomads often lead camel caravans carrying salt through desert. "I can see my camels suffering from extreme heat," camel herder from Semera in Afar - blisters on their feet, their eyes become watery, hot sand burns their skin, eats up their hair when they are sitting
-
#Camels R famously known as ships of #desert, but even these hardy animals arent being spared from extremes of rising #temperatures in #Africa. Afar region of north-eastern #Ethiopia is one of hottest driest places on Earth, where nomads often lead camel caravans carrying salt through desert. "I can see my camels suffering from extreme heat," camel herder from Semera in Afar - blisters on their feet, their eyes become watery, hot sand burns their skin, eats up their hair when they are sitting
-
#Camels R famously known as ships of #desert, but even these hardy animals arent being spared from extremes of rising #temperatures in #Africa. Afar region of north-eastern #Ethiopia is one of hottest driest places on Earth, where nomads often lead camel caravans carrying salt through desert. "I can see my camels suffering from extreme heat," camel herder from Semera in Afar - blisters on their feet, their eyes become watery, hot sand burns their skin, eats up their hair when they are sitting
-
#Camels R famously known as ships of #desert, but even these hardy animals arent being spared from extremes of rising #temperatures in #Africa. Afar region of north-eastern #Ethiopia is one of hottest driest places on Earth, where nomads often lead camel caravans carrying salt through desert. "I can see my camels suffering from extreme heat," camel herder from Semera in Afar - blisters on their feet, their eyes become watery, hot sand burns their skin, eats up their hair when they are sitting
-
It gets even more terrifying if you consider the affected nature. Not many animals or plants are heatresistant above a certain "used" bandwidth of temperature. Not even #camels
The ecosystems of oceans are already reacting, not only bc decreasing oxygene level in seawater. They are more fragile than ecosystems on land. #corals #krill ...
If that base of foodchains starts to collapse ...
...as we can already notice following certain (science) reports.😱 -
It gets even more terrifying if you consider the affected nature. Not many animals or plants are heatresistant above a certain "used" bandwidth of temperature. Not even #camels
The ecosystems of oceans are already reacting, not only bc decreasing oxygene level in seawater. They are more fragile than ecosystems on land. #corals #krill ...
If that base of foodchains starts to collapse ...
...as we can already notice following certain (science) reports.😱 -
Too hot for camels.
Not a sentence I ever thought I would have constructed.
-
Too hot for camels.
Not a sentence I ever thought I would have constructed.
-
Tuesday #TimeLapse - Desert Drive
Driving into the #Sahara #desert (though a herd of #camels) in southern #Tunisia.
-
Tuesday #TimeLapse - Desert Drive
Driving into the #Sahara #desert (though a herd of #camels) in southern #Tunisia.
-
Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
--
https://doi.org/10.5194/hess-30-3945-2026 <-- shared paper
--
H/T @cyril THEBAULT | Postdoctoral Fellow chez Earth Sciences New Zealand
“[They] compared different multi-model approaches for streamflow simulation using 78 hydrological models across 559 catchments in the United States. [Their] results show that while multi-model combinations can slightly improve accuracy and reduce uncertainty, no single approach performs best everywhere.
One interesting takeaway is that a carefully selected single model can perform as well as more complex multi-model approaches when it is chosen based on a comparative evaluation rather than simply inherited from legacy operational systems... 👀”
--
“The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, [they] assess[ed] the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on [their] selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment…”
#water #hydrology #streamflow #USA #CONUS #multimodel #simulation #hydrologic #model #modeling #catchments #watermanagement #waterresources #planning #watersecurity #flood #flooding #prediction #FUSE #CAMELS #spatialanalysis #spatiotemporal -
Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
--
https://doi.org/10.5194/hess-30-3945-2026 <-- shared paper
--
H/T @cyril THEBAULT | Postdoctoral Fellow chez Earth Sciences New Zealand
“[They] compared different multi-model approaches for streamflow simulation using 78 hydrological models across 559 catchments in the United States. [Their] results show that while multi-model combinations can slightly improve accuracy and reduce uncertainty, no single approach performs best everywhere.
One interesting takeaway is that a carefully selected single model can perform as well as more complex multi-model approaches when it is chosen based on a comparative evaluation rather than simply inherited from legacy operational systems... 👀”
--
“The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, [they] assess[ed] the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on [their] selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment…”
#water #hydrology #streamflow #USA #CONUS #multimodel #simulation #hydrologic #model #modeling #catchments #watermanagement #waterresources #planning #watersecurity #flood #flooding #prediction #FUSE #CAMELS #spatialanalysis #spatiotemporal -
Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
--
https://doi.org/10.5194/hess-30-3945-2026 <-- shared paper
--
H/T @cyril THEBAULT | Postdoctoral Fellow chez Earth Sciences New Zealand
“[They] compared different multi-model approaches for streamflow simulation using 78 hydrological models across 559 catchments in the United States. [Their] results show that while multi-model combinations can slightly improve accuracy and reduce uncertainty, no single approach performs best everywhere.
One interesting takeaway is that a carefully selected single model can perform as well as more complex multi-model approaches when it is chosen based on a comparative evaluation rather than simply inherited from legacy operational systems... 👀”
--
“The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, [they] assess[ed] the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on [their] selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment…”
#water #hydrology #streamflow #USA #CONUS #multimodel #simulation #hydrologic #model #modeling #catchments #watermanagement #waterresources #planning #watersecurity #flood #flooding #prediction #FUSE #CAMELS #spatialanalysis #spatiotemporal -
Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
--
https://doi.org/10.5194/hess-30-3945-2026 <-- shared paper
--
H/T @cyril THEBAULT | Postdoctoral Fellow chez Earth Sciences New Zealand
“[They] compared different multi-model approaches for streamflow simulation using 78 hydrological models across 559 catchments in the United States. [Their] results show that while multi-model combinations can slightly improve accuracy and reduce uncertainty, no single approach performs best everywhere.
One interesting takeaway is that a carefully selected single model can perform as well as more complex multi-model approaches when it is chosen based on a comparative evaluation rather than simply inherited from legacy operational systems... 👀”
--
“The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, [they] assess[ed] the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on [their] selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment…”
#water #hydrology #streamflow #USA #CONUS #multimodel #simulation #hydrologic #model #modeling #catchments #watermanagement #waterresources #planning #watersecurity #flood #flooding #prediction #FUSE #CAMELS #spatialanalysis #spatiotemporal -
Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
--
https://doi.org/10.5194/hess-30-3945-2026 <-- shared paper
--
H/T @cyril THEBAULT | Postdoctoral Fellow chez Earth Sciences New Zealand
“[They] compared different multi-model approaches for streamflow simulation using 78 hydrological models across 559 catchments in the United States. [Their] results show that while multi-model combinations can slightly improve accuracy and reduce uncertainty, no single approach performs best everywhere.
One interesting takeaway is that a carefully selected single model can perform as well as more complex multi-model approaches when it is chosen based on a comparative evaluation rather than simply inherited from legacy operational systems... 👀”
--
“The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, [they] assess[ed] the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on [their] selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment…”
#water #hydrology #streamflow #USA #CONUS #multimodel #simulation #hydrologic #model #modeling #catchments #watermanagement #waterresources #planning #watersecurity #flood #flooding #prediction #FUSE #CAMELS #spatialanalysis #spatiotemporal -
People along the Nile River, Egypt, ~1900?
-
People along the Nile River, Egypt, ~1900?
-
People along the Nile River, Egypt, ~1900?
https://piefed.social/c/historyphotos/p/2123205/people-along-the-nile-river-egypt-1900
-
People along the Nile River, Egypt, ~1900?
-
People along the Nile River, Egypt, ~1900?
-
Camels carrying grain, Morocco, 1917
-
Camels carrying grain, Morocco, 1917
-
Jordan describing a 'compound word' that was a pop cultural 'moment that inspired songs, music, poems, musical sketches and high fashion'
immediately, me, thinking she was being a little tongue-in-cheek:
To find out what she was *actually* talking about, check out our most recent #podcast on #Camelopardalis / #giraffes in #PopCulture here: https://starrytimepodcast.podbean.com/e/camelopardalis-pop-culture-superstar/
-
Jordan describing a 'compound word' that was a pop cultural 'moment that inspired songs, music, poems, musical sketches and high fashion'
immediately, me, thinking she was being a little tongue-in-cheek:
To find out what she was *actually* talking about, check out our most recent #podcast on #Camelopardalis / #giraffes in #PopCulture here: https://starrytimepodcast.podbean.com/e/camelopardalis-pop-culture-superstar/
-
A camel visiting Le Faouët, France
https://piefed.social/c/historyphotos/p/2081699/a-camel-visiting-le-faouet-france
-
A camel visiting Le Faouët, France
-
Passing traffic in Lebanon, ~1939
https://piefed.social/c/historyphotos/p/2046969/passing-traffic-in-lebanon-1939
-
Camel with solar cells, Djibouti
Didn’t know where else to put this, but I felt the urge to share it when I saw it, and thought you slrpnk folks might get a kick out of it!
-
Camel with solar cells, Djibouti
Didn’t know where else to put this, but I felt the urge to share it when I saw it, and thought you slrpnk folks might get a kick out of it!
-
Kalmyk soldier of Tsarist Russia riding a Bactrian Camel, Ukraine, WW1, 1915
-
Kalmyk soldier of Tsarist Russia riding a Bactrian Camel, Ukraine, WW1, 1915
-
Kalmyk soldier of Tsarist Russia riding a Bactrian Camel, Ukraine, WW1, 1915
-
Kalmyk soldier of Tsarist Russia riding a Bactrian Camel, Ukraine, WW1, 1915
-
Arab troops during the time of the Prophet Muhammad
https://piefed.social/c/historyart/p/2010747/arab-troops-during-the-time-of-the-prophet-muhammad
-
Arab troops during the time of the Prophet Muhammad
https://piefed.social/c/historyart/p/2010747/arab-troops-during-the-time-of-the-prophet-muhammad
-
Arab troops during the time of the Prophet Muhammad
https://piefed.social/c/historyart/p/2010747/arab-troops-during-the-time-of-the-prophet-muhammad
-
SAS - Middle East Travel Poster by Otto Neilsen 1955
.
#SAS #Mideast #MiddleEast #TravelPoster #Travel #Aviation #Avgeek #Artwork #Camels -
SAS - Middle East Travel Poster by Otto Neilsen 1955
.
#SAS #Mideast #MiddleEast #TravelPoster #Travel #Aviation #Avgeek #Artwork #Camels -
Camel decorated for a wedding celebration in Laft, Iran
-
Camel decorated for a wedding celebration in Laft, Iran
-
Fatimid Caliphate troops in North Africa, ~10th century AD
