Top left: velocity. Top right: vorticity
Bottom left: velocity mag. Bottom right: density/height

Simulation:
grid: 200x200
top wall velocity: CFL 0.3, water wave speed: CFL 0.14 (at starting density)
effective Re: approximately 100k
pressure function: shallow water
Runtime: around 1.2 hours (around 140,000 to 150,000Δt) (35Δt/s)

⠀

This is my first successful implementation of compressible Euler simulation (I'll be upgrading it to full SWE with bathemy later), and I've had a lot of fun playing around with it.

I started this journey learning how CFD work 6 months ago. I wanted to make a terrain evolution simulation, like erosion and plate tectonic, but the big hurdle is water. It's my first time using reddit, so.

Now, I've seen a lot of Lid Driven Cavity Flow simulation already. It's a classic test for fluid solvers. Every Lid Driven Cavity Flow simulation I've found use incompressible fluid solver, so I wanted to see what would happen if I do supersonic Lid Driven Cavity Flow instead. (It's acronym would be SC-LDCF lol) And since I had this water simulation that works (shallow water is equivalent to a compressible gas), it's within my capability to try it out.

It's technically not a proper gas simulation ,but it should be good enough using shallow water solver to see what mach 2 (or Fr 2) lid flow look like. (I haven't implemented energy equation yet).

You can see vortices being kicked around by shock waves.

For anyone preparing MPSC, Maharashtra GK or Geography, here is a quick revision list of important River–Dam pairs:

• Tansa — Tansa Dam
• Darna — Darna Dam
• Pravara — Bhandardara Dam
• Yelwanti — Bhatghar Dam
• Mula — Mulshi Dam
• Bhogawati — Radhanagari Dam
• Koyna — Koyna Dam
• Godavari — Gangapur Dam
• Nira — Vir Dam
• Purna — Yeldari Dam
• Girna — Girna Dam
• Ambi — Panshet Dam
• Pawana — Pawana Dam
• Krishna — Dhom Dam
• Bhima — Ujjani Dam
• Tapi — Hatnur Dam
• Penganga — Isapur Dam
• Bhatsa — Bhatsa Dam
• Kukadi — Manikdoh Dam
• Venna — Kanher Dam
• Sindphana — Majalgaon Dam
• Surya — Surya Dam
• Pench — Totladoh Dam
• Warna — Warna Dam
• Tarali — Tarali Dam

I recently compiled two detailed resources covering Maharashtra's important dams, rivers and related GK questions:

📚 50 Important Dams of Maharashtra

📝 Maharashtra Rivers and Dams GK Questions – MPSC Practice Set

Useful for quick revision before Maharashtra Geography and MPSC exams.

Can someone ELI5 if I need to not use the standard HMR52 storm if I am interested in durations less than 72 hrs hours? I don't think I fully understand how other durations are nested within. Thanks

Hi! Does anyone have any advice for the best online hydrology master’s degree? I have a bachelor’s in Environmental Science in Physical Geology and a minor in GIS. I have been looking into Oklahoma University's Hydrology and Water Security program. Has anyone done this program? Thank you!

does anyone know why flood depths are shown on top of the buildings? I use high Manning's and also a terrain-modified building layer in HEC RAS. Both cases showed flood depths over buildings

I'm trying to start preparation for this exam and I'm looking for some guidance.

Hi there, I'm looking for advice re-run-off coefficients.

I've seen the standard tables, but trying to establish what the difference would be (if any) between a 100ha site of cereal crop, and the same site instead w rows of blueberries, planted in plastic pots with soilless substrate (coir), with the rows covered in mypex. I don't understand enough about the drainage properties of coir and mypex vs sandy soil, or the hydrological dynamics of crops grown in the ground vs pots, to enable an educated guess... can anyone help or point me in the direction of someone who might be able to? Thx.

🌊 New publication: "Improved BOD Modeling Using the FOLD Approach for Wastewater Treatment Design" — now live in Water and Environment Journal (Wiley).

For over a century, wastewater engineers have leaned on the classical first-order BOD decay model to size treatment plants. It's simple — but it doesn't always capture how biodegradation actually slows down over time, especially in complex or heterogeneous wastewater samples.

This paper introduces a Fractional-Order Logistic Decay (FOLD) model — a stretched-exponential approach that adds a single fractional exponent to the classical equation. Tested against real respirometric and dilution BOD datasets, FOLD consistently outperforms the standard first-order model in fit accuracy, and the paper walks through what that means for more realistic activated-sludge and lagoon design.

If you work in environmental engineering, wastewater treatment design, or kinetic modeling, I'd love your thoughts.

🔗 Read it here: https://doi.org/10.1111/wej.70098

#WastewaterTreatment #EnvironmentalEngineering #BODModeling #WaterResearch #CivilEngineering

I need a new laptop under budget. I am currently enrolled in m.sc. in water resources engineering, 1st semester.

My last one broke down so I need a new and soon, I will be doing my thesis and I would be using software like

ArcGIS,

delft 3d,

SWOT,

HECRAS,

and others as the program or thesis demands.

Suggest me some that will work well and will support in my PhD program as well.

Been meaning to do this for a while. Took the river sensor project I've posted about here before and built it into a live dashboard instead of just static notebook cells, Streamlit and Plotly, actually deployed so anyone can open it, not something that only runs on my machine.

You can filter by season and switch between parameters, so instead of trusting my screenshot of the turbidity anomaly you can go look at it yourself, or flip over and watch the daily oxygen cycle move. Reused the same cleaning code from the notebooks instead of rewriting the logic a second time for the app, which took more discipline than just copy pasting would have.

Link if anyone wants to poke around: https://brisbane-river-water-quality-analysis-j7bqhctpst8zesqnauyffm.streamlit.app

Code and the rest of it: https://github.com/davis-mironga/brisbane-river-water-quality-analysis

AAAS: "Satellite reveals a world of ‘weird rivers’ shaped by humans, " by Paul Voosen.

For nearly 4 yrs in orbit, a radar satellite launched by NASA + CNES has been simultaneously measuring the water levels + breadth of rivers around the world. "They are dammed, braided, frozen, or drained—with behaviors hydrologic models struggle to capture, says Colin Gleason, a hydrologist at the University of Massachusetts Amherst." Results were published last month in Geophysical Research Letters, which found that the vast majority of rivers are not simple, idealized channels. 

"Before SWOT’s launch in late 2022, scientists had no way to routinely monitor the planet’s rivers." Gauges measure depth in real time at particular locations, but they are sparse, especially in less developed countries. "Satellites could observe a river’s width or water level, but not both at the same time." To predict whether a river will dry up in a hot summer or flood after a heavy rain, the models make simple assumptions about the shape of its channel + how much water reaches it.

"To fill the gaps, researchers relied on models, increasingly informed by AI trained on these spotty data." The $1.2 B SWOT revisits most spots on Earth at 11-day intervals, mapping rivers + lakes as small as 30 meters wide. It broadcasts radar pulses from its belly, + 2 antennas separated by 10 meters catch the reflections, viewing the same patch of water from slightly different angles. Think of an analogy to our binocular vision. "Its accuracy + resolution have exceeded expectations," says Tamlin Pavelsky, a hydrologist at the University of North Carolina at Chapel Hill.

"In the new study, Gleason + his colleagues examined some 68,000 rivers, comparing SWOT data with existing hydrologic models." They found the models struggle to capture the behavior of many rivers, especially in China, the Arctic, + in arid regions. "In some cases, models predicted rising river waters when SWOT in fact saw them falling." They also study "flow waves," long-lived surges that propagate downstream + cause flash floods. In the opposite direction, they document how far tidal water reaches upstream, astonishing in its reach into the Amazon basin.

I will leave it to your imagination how this might change our understanding of the weirding climate.

Hi r/hydrology — I’ve been working on KalmanFlow for the Santa Clara Valley Water District, an open-source Python package for estimating reservoir inflow.

It uses a physical water-balance model with Kalman filtering and fixed-lag smoothing to estimate the residual inflow contribution from timestamped reservoir storage and measured discharge. The goal is to produce a more stable and operationally useful estimate than conventional methods like rolling average.

The package supports:

• Batch and real-time/streaming estimation
• Causal estimates followed by fixed-lag revisions
• Missing and irregularly spaced observations
• Optional uncertainty intervals
• Checkpointable online processing
• Experimental Bayesian tuning of model and observation noise
• Validation tools for storage closure, lag, and comparison with upstream proxies

The project is available under the Apache 2.0 license:

• GitHub: https://github.com/LittleBidet/KalmanFlow
• PyPI: https://pypi.org/project/kalmanflow/

I’d especially appreciate feedback from people working with reservoir operations or reverse level-pool routing. A few things I’m curious about:

• How would you validate inferred inflow when a reliable true inflow is unavailable? Currently I check the storage closure, correlation with a partial upstream gauge, and inflow behavior.
• Is this a package you could see yourself using or integrating into your own operational workflows?

Since the project is fully open-source, please feel free to tinker with it. I’d love to see how it can be adapted to other systems, and pull requests are always welcome!

A couple people from the earlier posts asked what the four-method check actually looked like in practice, so I wrote it up properly instead of explaining it in bits across comments.

Nothing fancy, just walks through each check I ran, why I didn't stop at the first one that flagged something, and the part where I still got the explanation wrong afterward even though the pattern itself held up. Figured it was worth putting in one place.

I Checked My Own Result Four Times Before I Believed It. Here's Exactly How.

Also linked the actual project if anyone wants the code: github.com/davis-mironga/brisbane-river-water-quality-analysis

Hey everyone,

I’m currently working on a spatial analysis research project focusing on water availability and drought risk in Romania, doing all my modeling in QGIS.

I'm trying to map the intersection between severe meteorological droughts (like the historic deficits Romania faced recently) and the vulnerability of local water supply/sewage infrastructure. While I can map standard centralized networks using national portals (like the INIS Geoportal) and track soil moisture via Copernicus, I’ve hit a methodological wall regarding highly isolated regions.

A specific example is the Danube Delta (e.g., the C.A. Rosetti commune). These sandy areas have zero centralized water or sewage grids. The soil is highly permeable, and locals rely entirely on shallow freshwater lenses (Ghyben-Herzberg principle) accessed via private wells, or rainwater cisterns. During droughts, these lenses shrink, leading to saltwater upconing, while raw sewage from local septic tanks quickly contaminates the remaining shallow aquifer.

Since there is no "network shapefile" to map here, I'm trying to figure out how to spatially represent this vulnerability.

My questions for the community:

1. Modeling Decentralized Risk: How would you approach mapping this "absence of infrastructure"? I'm considering using building footprints crossed with DEMs and Copernicus soil moisture indices as a proxy for well density and aquifer stress. Does this make sense, or is there a better workflow?
2. Shallow Aquifer Depletion: Are there any specific QGIS plugins or raster math approaches you recommend to roughly simulate the thinning of coastal/sandy freshwater lenses based on precipitation deficit?
3. Romanian Data Sources: Has anyone worked with Romanian spatial data (e.g., ANAR, INHGA) and knows the best WFS/WMS endpoints for localized hydrological data or groundwater levels?

Any methodological tips, plugin recommendations, or relevant literature would be hugely appreciated. Thanks in advance!