About Me

Welcome! I am a PhD Candidate at Arizona State University, working at the intersection of data-driven hydrology, earth observation, and machine learning.

My work is deeply rooted in my experiences growing up in Nepal. During my undergraduate studies in Civil Engineering, I came face-to-face with the real-world impact of natural hazards. I saw how floods, droughts, and landslides could disrupt lives and entire communities. These events are not just statistics. They are powerful reminders that our ability to anticipate and adapt to our changing planet is still very limited.

This realization became my driving force. I knew I didn’t want to just build tools that watch our planet struggle. I wanted to engineer solutions that could actively help us build a more sustainable and resilient future. My research is my attempt to do just that. I focus on bridging the gap between complex scientific knowledge and the actionable, accessible tools that people need. To do this, I’ve built my work on three core pillars.

1. Hydrometeorology and Hazard Science

My first focus is on understanding the fundamental science. This means digging into hydrometeorology. It’s the intricate dance between water and the atmosphere that creates everything from life-giving rain to destructive floods. I am especially focused on what we call compound and cascading disasters. This is where a chain of simple events can combine to create a catastrophe. A single heavy rainfall might not be a disaster on its own. But when it triggers a landslide that blocks a river, which then causes a flood upstream, the impact multiplies.

We need to understand these complex interactions to get ahead of them. A lot of my work involves building frameworks to model and predict these events. We can build better models to understand these cascading hazards on mountain terrain. We can also map out which areas are most susceptible to rainfall-triggered landslides and identify the critical infrastructure at risk. This allows for smarter spatial decision-making before a disaster happens. This kind of proactive work is essential. It is part of a larger vision for a more Integrated, Coordinated, Open, and Networked (ICON) approach to science that can truly serve society.

2. Harnessing Earth Observation

We live in an era of an unbelievable amount of data. Space agencies like NASA and ECMWF hold hundreds of petabytes of information about our planet. This is a treasure trove. Satellites are constantly watching our forests, rivers, and cities. But raw data is not the same as knowledge. What are we actually doing with all of it?

My second pillar is about transforming this flood of data into actionable intelligence. The goal is to develop scalable and generalizable methods that make sense of it all. We can use this information to create immense value. For example, we can use it to map things like groundwater potential in data-scarce regions. We can also monitor and understand the long-term impacts of land use and land cover change on agriculture and natural resources. This is not just about making maps. It is about providing decision-makers with a clear, current picture of our changing world so they can act on it.

3. Bridging the Gap with Artificial Intelligence

If Earth Observation data is the fuel, then Artificial Intelligence is the engine that puts it to work. AI and Machine Learning are the critical bridge. They connect the vast datasets we have to the complex, real-world problems we need to solve. These tools can see patterns and relationships that are simply too complex for traditional models to capture.

My work involves engineering a suite of intelligent software and models. These tools are designed for scalability and transferability. A major part of my PhD is dedicated to this. I am building next-generation streamflow forecasting systems, particularly for arid regions like Arizona. Many current systems are based on older, empirical knowledge. They struggle to adapt when the environment changes, like when a city expands and creates more concrete surfaces. My approach is different. I am developing methods that can assimilate satellite information directly into hydrological models, aiming for more robust and accurate forecasts. This includes exploring techniques like Bayesian model averaging to improve reliability. The key is to create systems that learn and adapt, because our world is constantly changing.

To summarize, I want to contribute to a future where scientific innovation directly supports sustainable development and community resilience. I believe in creating tools that are not just scientifically novel, but genuinely useful. I am committed to this journey of turning data into decisions and research into real-world impact.

I am currently seeking a faculty position where I can start my own research group and continue advancing this vision. If you wish to connect or collaborate, please reach out at kdahal3@asu.edu.

News & Updates

2025

July 29: 🎓 Officially became a PhD Candidate after successfully passing my comprehensive exam.

2024

October 21: 🎤 Presented a conference poster, ‘A Framework to Improve Hydrological Forecasting with Deep Learning’, at ASU Flow 2024.
October 21: 🏆 Received the Outstanding Poster Award for my work on ‘A Framework to Improve Hydrological Forecasting with Deep Learning’ at the ASU Flow 2024 symposium.
October 02: 🎤 Presented a conference talk, ‘Operational Streamflow Forecasting Tool for Arizona Streams’, at CMWR 2024.
May 15: ✨ Selected as a Community Science Fellow by the American Geophysical Union’s Thriving Earth Exchange.
March 13: 🎤 Presented a invited webinar, ‘Explainable Machine Learning in Groundwater Potential Mapping’, at UNESCO GWYN.
February 13: 🎤 Presented a conference talk, ‘Mapping wetland potential in arid environments: A machine learning approach with geospatial interpretability’, at AGU Chapman Conference on Remote Sensing of the Water Cycle.

2023

December 11: 🎤 Presented a conference poster, ‘Advances in Hyperspectral Remote Sensing for Water Resources’, at AGU Fall Meeting 2023.
September 19: 🎤 Presented a invited talk, ‘Discussion Facilitator at Session 1 –Development of core use cases in environmental sciences’, at 5th NOAA Workshop on Leveraging AI in Environmental Sciences.
June 12: 🏆 Won 1st place in the SpaceHack for Sustainability Hackathon at Arizona State University.
May 22: 🎤 Presented a workshop, ‘Remote Sensing, Big Data Analytics, and Cloud Computing: Application to Water Quality Modeling’, at Environmental & Water Resources Institute (EWRI) Congress 2023, ASCE.
May 21: 🎤 Presented a conference talk, ‘Explainable Artificial Intelligence to visualize the unseen’, at EWRI Congress 2023.

2022

December 01: ✨ Started my PhD journey at Arizona State University.
October 12: 🎤 Presented a conference talk, ‘Spatial decision making with landslide susceptibility and critical infrastructure’, at DRI Technical Conference 2022.
June 21: 🎤 Presented a invited lecture, ‘Landslide susceptibility and monsoon preparedness in Nepal: An engineering perspective’, at Khwopa College of Engineering, Tribhuvan University.
April 07: 🎤 Presented a invited discussion, ‘Introduction to Google Earth Engine for cloud computing’, at S4W Nepal.
April 06: 🎤 Presented a invited lecture, ‘Google Earth Engine and cloud computing’, at Central Department of Geography, Tribhuvan University.

2021

December 13: 🎤 Presented a conference poster, ‘National landslides database and susceptibility assessment of Nepal’, at AGU Fall Meeting 2021.
December 13: 🎤 Presented a conference poster, ‘Framework for multi-hazards susceptibility assessment in Google Earth Engine’, at AGU Fall Meeting 2021.
September 17: 🏆 Won 1st place in the Hackathon Competition at the 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
September 13: 🎤 Presented a conference talk, ‘Spatial downscaling of coarse resolution satellite-based precipitation estimates (SPEs) to 1 km using Machine Learning’, at 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
September 13: 🎤 Presented a conference talk, ‘Machine Learning to Estimate Precipitation with Satellite-based and Gauged Observations’, at 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
August 28: 🎤 Moderated the session, ‘Chocolate Talk on DRR #3: Artificial intelligence (AI) for disaster risk reduction’, at U-INSPIRE Alliance.
July 30: 🎤 Presented a invited talk, ‘DRR talk #1: The future of disaster risk governance in 2045’, at Disaster Risk Reduction and Tsunami Information, UNESCO Office, Jakarta.

2020

October 20: 🎤 Presented a conference talk, ‘Landslide Susceptibility Mapping in Nepal using Google Earth Engine’, at Geo for Good 2020.

Kshitij Dahal