Waterloo friends: if you’re interested in watershed hydrologic function and what current deep learning models still miss, join me today at the University of Waterloo for this WaterTalks public lecture.

Water flow in prairie watersheds is increasingly unpredictable — but AI could help In the Prairie Pothole Region, millions of wetlands fill, spill and connect in ways that defy prediction. Combining hydrology with AI offers a path forward.

Check out my piece in the Conversation on mechanisms of flow generation in wetland-dominated landscapes and how physics-informed AI can help to understand and generalize these mechanisms.

#wetland #AI

theconversation.com/water-flow-i...

Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in the Prairie Pothole Region Physics-informed deep learning outperforms LSTM in regionalizing hydrologic behavior and pothole storage across PPR (un)gauged catchments PPR-scale model captures inter(a)-annual variability of h...

Check out our new paper in WRR: in the Prairie Pothole Region, millions of wetlands fill, spill, and connect, making runoff hard to predict. We show how physics + AI can improve prediction of streamflow and wetland storage in ungauged watersheds. #Wetlands #NewPaper #AGUPubs #PhysicsInformedML

The wetland puzzle that stumped hydrology for decades—how physics and AI joined forces to predict unmeasured regions For years, the Prairie Pothole Region has bothered me in a very specific way. On a map, it looks like a normal landscape: fields, gentle slopes, small streams. But hydrologically, it behaves like some...

Check out my piece in
@sciencex.bsky.social
on how physics and #AI join forces to enable prediction in ungauged basins, particularly in one of hydrology’s most challenging environments—wetland-dominated watersheds.

phys.org/news/2026-03...

#AGUPubs #wetland

New in WRR Time Variance in Snowmelt Partitioning: A Mechanistic Modeling Approach to Explore the Role of Catchment Structure and Pre-Snow Rainfall By Mahbod Taherian, Ali A. Ameli

Not all watersheds respond equally to climate variability—and new research reveals that the difference lies underground.💧

🔗 Learn more in Water Resources Research: buff.ly/vvnKfkv

#Snow #Hydrology #Climate #Groundwater #STEM @ali-ameli.bsky.social

The hidden physics of watersheds: Why some are more sensitive to climate variability than others Water is everywhere, from the snowpack in the mountains to the tap in our kitchens. But while we often think about rainfall and snow as the main drivers of our water supply, it turns out that somethin...

Thanks for sharing my team's work. Also, please have a look at the piece below that I wrote about this paper

phys.org/news/2026-01...

The hidden physics of watersheds: Why some are more sensitive to climate variability than others Water is everywhere, from the snowpack in the mountains to the tap in our kitchens. But while we often think about rainfall and snow as the main drivers of our water supply, it turns out that something we rarely see has just as much influence: the underground structure of the landscape itself.

Subsurface structure plays a key role in how watersheds respond to climate variability, influencing water storage, flood risk, and water quality beyond what surface features alone can explain. doi.org/hbmjc3

Proud of my grad students—4 papers accepted in Water Resources Research journal in 2025!
Check out their work: www.eoas.ubc.ca/people/aliam...

Interested in joining this UBC team as a postdoc?
Email me ASAP with your CV to explore nomination for new Canada’s PDF opportunities.

#Hydrology #Postdoc

Time Variance in Snowmelt Partitioning: A Mechanistic Modeling Approach to Explore the Role of Catchment Structure and Pre‐Snow Rainfall Pre-snow rainfall variability alters snowmelt partitioning pattern (SPP) into storage versus runoff, with the magnitude of impact mediated by the extent of hydraulic conductivity's lateral heterog...

Time-variance in catchment hydrologic function: why do some catchments show it and others don't?

Our paper shows that subsurface structure mediates climate-induced year-to-year variability of catchment hydrologic function

agupubs.onlinelibrary.wiley.com/doi/10.1029/...

#agupubs #ClimateChange

Climate Science Digest: January 9, 2026 • Muser Press Explore the latest insights from top science journals in the Muser Press roundup (January 9, 2026), featuring impactful research on climate change challenges.

AI river forecasts may be accurate – but based on flawed logic

#AGUPubs #MachineLearning

www.muser.press/2026/01/09/c...

Deep Learning predicts streamflow beautifully. But does it "think" like a hydrologist?
Our WRR paper shows the answer is no, with implications for hydrologic forecasts.
Good news: we can diagnose this. Our new XAI framework opens the black box using hydrologic principles.

#AGUPubs #MachineLearning

If you are at #AGU25 this year, check out my group's oral presentations on catchment hydrologic function from local to global scales

H13D-03 (Monday, 14:35 - 14:45 in 228)
H22J-02 (Tuesday 10:45 - 10:55 in 342)
H41E-04 (Thursday 09:17 - 09:31 in 231)
H52G-03 (Friday, 10:57 - 11:09 in 228)

Young Scientist Award CGU Young Scientist Award The CGU Young Scientist Award recognizes outstanding research contributions by a young scientist who is a CGU member. Both the quality and impact of research are considere…

Great honor to receive the
@CGU_UGC 2025 #YoungScientistAward
cgu-ugc.ca/awards/young...
Thanks to my nominators. Read more about it here
www.eoas.ubc.ca/news/1749850...

Tackling water table depth modeling via machine learning: From proxy observations to verifiability Spatial patterns of water table depth (WTD) play a crucial role in shaping ecological resilience, hydrological connectivity, and human-centric systems…

New Open-Access Simulation of North American Groundwater Table Depth

Read the article here: www.sciencedirect.com/science/arti...

Explore and download the simulations here: hgs4wm.eoas.ubc.ca/products/map

Please share this announcement with others who may benefit from these simulations

PhD Position: Hydroclimatology and Deep Learning at Polytechnique Montreal or UBC-Vancouver

Keywords: Hydrology, Climate, Deep Learning Emulator, Convection-Permitting Models, Extreme Events, Physical Consistency, Spatio-Temporal Downscaling

Read more here: hgs4wm.eoas.ubc.ca/jobs

Please repost

Searching for Functional Simplicity of Stormflow Generation Dormant seasons' simple stormflow functionality is prevalent, occurring mostly along steep catchments with wet/out-of-phase climate Simple catchments transfer precipitation pulses into the stream...

Do all catchments exhibit nonlinear and non-stationary behavior?
A large-sample analysis of stormflow events reveals that the simple dynamics of Linear Time-Invariant systems are surprisingly common along wet/steep out-of-phase catchments.

Read here: agupubs.onlinelibrary.wiley.com/doi/10.1029/...