Daniel Bachmann

@Bachmann_h2

Lehre, forsche und entwickle im Bereich Hoch- und Niedrigwasser! Möchte das Thema aber auch an die Öffentlichkeit bringen! Ab und an auch privat hier.

1 839Followers

423Following

2 057Posts total

396.8KViews on collected posts

Последние посты

Daniel Bachmann@Bachmann_h231 Aug

@Bachmann_h2 @hs_magdeburg Not a glacial collapse 👇 3.6K views · 24 likes · 1 reposts · 3 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Well, this changes the story considerably. The initial headlines called this a “glacial collapse.” Satellite analysis now indicates the major failure was a massive bedrock collapse beneath the glacier. That distinction matters. This was fundamentally a geologic mass-failure htt 232.5K views · 3.4K likes · 1.1K reposts · 148 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Does this mean we must incorporate such events into flood risk management in the near future? More info in https://t.co/YjIPJOAVsl /n 5K views · 66 likes · 5 reposts · 4 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Granted, the underlying concept of risk analysis is to capture all possible events. But what was the actual probability of such scenarios? They could easily have been categorized as a 'residual' risk. However, climate change could cause the probability of these events to rise./9 5K views · 52 likes · 2 reposts · 2 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

It demonstrates that such events could, at least approximately, be modeled in advance. But in all honesty: who would use an initial volume of 100 million m³ as an initial condition in a flood risk analysis? /8 5.1K views · 64 likes · 5 reposts · 2 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Grid based on AW3D30. Best calibration achieved with a 100M m³ reservoir at impact zone, implying a bulking factor of 3.0 (30M m³ pure glacier water). Runtime on an NVIDIA RTX 5090: only 25 minutes for 120 mins real-time. /7 https://t.co/cW1qbaRMlD 5.7K views · 44 likes · 2 reposts · 2 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Tech Setup: The 2D hydrodynamic model solves Saint-Venant equations on a GPU (~1.37M 10x10m elements, 80 km river section). To account for heavy sediment/mud loads, we applied a very high roughness (Manning's n = 0.1) upstream. /6 https://t.co/FMeo050WM6 5.9K views · 51 likes · 3 reposts · 1 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

At 9:46 AM, the wave hit Trishuli (62 km away) at 30m depth and 41 km/h.The simulation visualizes the exact, narrow escape window where a heroic teacher evacuated his students just minutes before the school was completely wiped out. /5 https://t.co/1bGMtVutqT 6.4K views · 64 likes · 3 reposts · 1 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Traveling further downstream: At 8:58 AM, the wave struck Syabrubesi (23 km from the impact zone).Water depths here still peaked at an astonishing 50 meters, with flow velocities reaching 12 m/s (43 km/h), causing total destruction. /4 https://t.co/K9ePdqrbC5 6.9K views · 54 likes · 3 reposts · 1 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

At 8:47 AM, the wave struck Timure (15 km away). Model depths exceeded 60m at 24 m/s (86 km/h). The inundation area matches reality: the entire village is wiped out, except for the red-roofed church on the upper edge. /3 https://t.co/JnNkuXtNW3 7.4K views · 60 likes · 4 reposts · 1 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

Impact: 8:37 AM. Just 7 mins later (8:44 AM), the wave hit the Rasuwagadhi border 13 km downstream. 🌊 Water depths: >70 METERS 🏃‍♂️ Flow velocity: 30 m/s (108 km/h) The model perfectly mirrors the actual BBC satellite destruction data (https://t.co/sZRrP3dhD3). /2 https://t.co/FV 16.1K views · 75 likes · 6 reposts · 3 replies Open on X →

Daniel Bachmann@Bachmann_h231 Aug

6 days ago, a glacier collapse triggered a catastrophic flash flood in Nepal/Tibet. Our FRM group at @hs_magdeburg did a 2D hydrodynamic hindcast via ProMaIDes. The modeled numbers are frightening. 👇 /1 #NepalFlood #Hydrology #Simulation https://t.co/sX9ZUPFBoh 97.1K views · 467 likes · 92 reposts · 18 replies Open on X →

На фоне аккаунтов своего размера

12 постов за последние 90 дней рядом с диапазоном under 10K подписчиков. показывается широко, но откликается мало кто.

Медианные просмотры6 162этот аккаунт4 175медиана для under 10K

Охват, %335.05%этот аккаунт250.82%медиана для under 10K

Вовлечённость, %0.90%этот аккаунт1.41%медиана для under 10K

Показатель	Этот аккаунт	Медиана для under 10K	Отношение
Медианные просмотры на пост	6 162	4 175	1.48×
Охват (просмотры ÷ подписчики)	3.4× audience	2.5× audience	1.34×
Вовлечённость	0.90%	1.41%	0.64×

Другие в этом диапазоне → Сравнить с другим аккаунтом → Как считаются эти ориентиры →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

97.1K31 Aug

16.1K

7.4K

6.9K

6.4K

5.9K

5.7K

5.1K

232.5K

3.6K

Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.61%31 Aug

0.53%

0.87%

0.85%

1.06%

0.93%

0.86%

1.39%

1.12%

1.50%

2.06%

0.77%

Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.

What the audience does

Likes70.4%4 452 in total

Reposts20.0%1 263 in total

Replies2.9%186 in total

Quotes1.5%95 in total

Bookmarks5.2%327 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Последние посты

На фоне аккаунтов своего размера

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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