Omar Wani is an assistant professor in the Department of Civil and Urban Engineering at New York University, where he leads the Wani Research Group. The group combines statistical and computational methods to study the dynamics of hydroclimatic risk in various built and natural environments, which are often interconnected and coupled systems.
Before joining NYU, Dr. Wani conducted his postdoctoral work at the University of California Berkeley's Environmental Systems Dynamics Lab and the California Institute of Technology’s Division of Geological and Planetary Sciences. He has a Ph.D. in Environmental Engineering from ETH Zurich, Switzerland, a joint master's degree from the Delft Institute for Water Education, the Netherlands and the Technical University of Dresden, Germany, and a bachelor’s degree in Civil Engineering from the Indian Institute of Technology Roorkee, India.
Research News
NYU Tandon researcher advocates for uncertainty-aware water risk models to improve flood and drought preparedness
Researchers are calling for a more reliable approach to understanding water-related hazards by explicitly accounting for uncertainty in their predictions, arguing this could improve how communities prepare for the risk of floods, droughts, and river-related erosion.
Omar Wani, a hydrologist at NYU Tandon School of Engineering, and co-authors argue in a recent opinion piece published in PLOS Water that many current hydroclimatic hazard assessments have a major flaw: they only give one answer. These models might predict, for example, that a river will flood to 15 feet, but they don't say how confident scientists are in that prediction or what other outcomes are possible.
Wani, who joined NYU Tandon as an Assistant Professor in the Civil and Urban Engineering Department in 2023, leads the Hydrologic Systems Group, which combines statistical and computational methods to study water dynamics in built and natural environments. His group focuses on understanding hydroclimatic risk and enabling more reliable decision-making under uncertainty.
This uncertainty-focused approach is central to Wani and his PLOS co-authors' argument for models that work more like weather forecasts, giving a range of possibilities with probabilities attached. Rather than saying "the water level in the river will reach 15 feet," these models might say "there's an 80% chance of the water level exceeding 15 feet, a 30% chance of it exceeding 18 feet, and a 10% chance of it breaching the 20 feet mark."
The approach has real-world urgency. Approximately 75% of flood-related fatalities occur when people drive into or walk through floodwaters, while climate change is expected to cause additional capacity deficits in stormwater infrastructure, leading to enormous financial losses. Overwhelmed and damaged drainage structures under roads can cost millions to replace.
In research published in Earth Surface Dynamics, Wani and collaborators demonstrated practical applications of this approach, showing how probabilistic models can generate "geomorphic risk maps" that display the probability of riverbank erosion at different locations over time.
Using satellite data from the rapidly-migrating Ucayali River in Peru's Amazon basin, the researchers showed their novel probabilistic approach consistently outperformed traditional predictions. The method combines mathematical models based on river shape and curves with computer simulations that run thousands of different scenarios to explore possible future outcomes.
Apart from the scientific value of this research in improving our understanding of the river systems, such "risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards," said Wani. Their results showed that probabilistic forecasts assign appropriate probabilities to regions that might erode, avoiding the overconfident binary classifications of traditional approaches.
The implications extend beyond academic research. Behavioral science research shows that people can exhibit loss aversion and risk aversion when making decisions under uncertainty. However, these psychological preferences can only be utilized when the requisite uncertainty information is available.
"To allow for individuals to use these preferences and risk attitudes during hydroclimatic warning or design decisions, people would need to be aware of the uncertainties in quantitative analysis and forecasts," Wani explained.
His group's current work spans from improving the reliability of flood early warning systems for distributed stormwater infrastructure to testing advanced probabilistic algorithms for satellite-based flood damage classification.
The framework represents a shift from seeking the single "most likely" outcome to embracing the full range of possibilities.
The research has immediate practical applications for infrastructure planning, emergency management, and community resilience. As climate change introduces additional uncertainties into the behavior of streams and rivers globally, the researchers argue that probabilistic approaches become increasingly important. The work reflects growing recognition that uncertainty is not a limitation to overcome, but rather crucial information that enables better decision-making.
In addition to Wani, the PLOS opinion piece's authors are Mason Majszak, who is currently working on a Swiss National Science Foundation project as a Postdoctoral Fellow at NYU Tandon and in the NYU Department of Philosophy, Victor Hertel from the German Aerospace Center, and Christian Geiß from the German Aerospace Center and University of Bonn. Funding for the work was provided by the Swiss National Science Foundation.
The Earth Surface Dynamics paper's authors are, in addition to Wani, Brayden Noh from Caltech, Kieran B. J. Dunne from Caltech and Delft University of Technology, and Michael P. Lamb from Caltech. Funding for the research was provided by the Swiss National Science Foundation and the Resnick Sustainability Institute at Caltech under National Science Foundation awards.
