Researcher in hydroclimatic extremes and water resources engineering
The University of Memphis · Memphis, Tennessee
I am a water resources engineer and researcher specializing in stochastic hydrology, meteorology, and engineering applications. My research improves modeling of short-duration extreme rainfall for urban infrastructure design, climate resilience, and flood risk management.
Advisor: Dr. Claudio Meier, University of Memphis
Current focus
Extreme precipitation, uncertainty, and resilient water infrastructure
Open-source work
nsEVDx, a Python library for extreme value modeling
Professional background
Water resources engineering and public infrastructure in Nepal
"Always curious. Always computing."
The University of Memphis, Memphis, Tennessee
Dissertation: “Rain-gauge Network Effects on the Uncertainty and Trends in Short-Duration Extreme Precipitation”
Advisor: Prof. Claudio I. Meier
The University of Memphis, Memphis, Tennessee
GPA: 3.95
Tribhuvan University, IOE Central Campus, Pulchowk, Nepal
Equivalent GPA: 3.92
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A selected record of research, software, reports, and scholarly presentations.
Kafle, N., Dell’Aira, F., Chadwick, C., & Meier, C. I. (2026). Robustness of Regionally Derived, Short-Duration Rainfall Depth-Duration-Frequency Estimates to the Choice of Minimum Interevent Time: Evidence Across Climates, Rain-gauge Densities, and Regionalization Approaches. Journal of Hydrologic Engineering. https://doi.org/10.1061/JHYEFF.HEENG-6729
Kafle, N. & Meier, C.I. (2026). nsEVDx: A Python Library for Stationary and Non-stationary Extreme Value Modeling. Journal of Open Source Software, 11(125),11187, https://doi.org/10.21105/joss.11187 (open-source package with 5,000+ downloads)
Kafle, N. & Meier, C. I. (2025). A Simple Method for Enhancing the Spatial Independence of Regional Partial Duration Series in Extreme, Short-Duration Rainfall Frequency Analyses. Authorea Preprints. https://doi.org/10.22541/essoar.176281288.81066358/v1
Accepted by Stochastic Environmental Research & Risk Assessment
Kafle, N., Dell’Aira, F., Burnette, D.J., & Meier, C.I. (2026). Space and Time Resolution of a Rain-gauge Network: An Underreported Source of Uncertainty in Extreme Precipitation Estimates. https://doi.org/10.22541/essoar.15008793/v1
Under Review in Water Resources Research
Kafle, N., Peleg, N., Chavez-Demoulin, V., Meier, C.I. (2026). Using a Neighborhood-Based Approach to Overcome the Limitations of Point-Based Trend Analyses in Extreme Precipitation Events.
Meier, C.I., Dell’Aira, F., Kafle, N., and Burnette, D.J. (2024). Updating Equations for Peak Flow Estimation in Urban Creeks and Streams of Tennessee (Part 1). Research final report from the University of Memphis, sponsored by the Tennessee Department of Transportation Long Range Planning Research Office & Federal Highway Administration
Kafle, N. (2025). Uncertainties and Trends in Short-Duration Extreme Precipitation: Implications for Urban Pluvial Flooding. Invited Presentation at the AMS–NWA Memphis Chapter, November 18, 2025, Agricenter International, Memphis, TN
Kafle, N., Peleg, N., & Meier, C. I. (2026). Detecting Trends in Short-Duration Extreme Rainfall: A Neighborhood-Based Framework Overcomes Limitations of Point-Based Analyses. ASCE-EWRI 2026
Dell'Aira, F., Kafle, N., Hathaway, J., & Meier, C. I. (2026). Connectivity-Informed Siting of Urban Stormwater Control Measures: The HydroDisconnect Approach. ASCE-EWRI 2026.
Kafle, N., Peleg, N., & Meier, C. I. (2025, December). Detecting Spatially Consistent Trends in Sub-Hourly Extreme Rainfall Using a Neighborhood-Based Method. In AGU Fall Meeting Abstracts (Vol. 2025, pp. H13G-07). https://ui.adsabs.harvard.edu/abs/2025AGUFMH13G...07K/abstract
Dell'Aira, F., Kafle, N., Hathaway, J., & Meier, C. I. (2025, December). Connectivity-Informed Siting of Urban Stormwater Control Measures: The HydroDisconnect Approach. AGU Fall Meeting Abstracts (Vol. 2025, pp. IN14A-05). https://ui.adsabs.harvard.edu/abs/2025AGUFMIN14A..05D/abstract
Kafle, N. & Meier, C.I. (2025, May). Ensuring spatial independence of rainfall maxima in partial-duration-based regional frequency analyses*. ASCE-EWRI 2025
Kafle, N. & Meier, C.I. (2025, May). Evaluating Methodologies for Detecting Trends in Short-Duration Extreme Rainfall in the Southeastern United States*. ASCE-EWRI 2025
Kafle, N. & Meier, C. I. (2024, December). A novel method for identifying spatially independent rainfall maxima in regional frequency analyses using partial duration series. AGU Fall Meeting Abstracts, 2024, A51L-1836. https://ui.adsabs.harvard.edu/abs/2024AGUFMA51L.1836K
Meier, C. I., & Kafle, N. (2024, December). Does the choice of minimum interevent time affect regionally derived depth-duration-frequency values? AGU Fall Meeting Abstracts, 2024, H23I-1117. https://ui.adsabs.harvard.edu/abs/2024AGUFMH23I.1117M
Kafle, N., Dell'Aira, F., Burnette, D. J., & Meier, C. I. (2024, December). How does the low-resolution of the US rain-gauge network affect the uncertainty of regionally derived depth-duration-frequency estimates? AGU Fall Meeting Abstracts, 2024, H23I-1115. https://ui.adsabs.harvard.edu/abs/2024AGUFMH23I.1115K
Kafle, N., Dell'Aira, F., Burnette, D.J., & Meier, C. I. (2024, April). Extreme Precipitation in the Southeastern United States: Are We Underestimating Depth-Duration-Frequency Values because of Low Rain Gauge Density and Temporal Resolution? TN AWRA 33rd Tennessee Water Resources Symposium (1B Flooding)
Meier, C., Muñoz-Proboste, P., Marasini, A., Kafle, N., and Dell'Aira, F.: Attributing the Variability of Hershfield Rainfall Sampling Adjustment Factors at Sub-Hourly Durations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13420, https://doi.org/10.5194/egusphere-egu24-13420, 2024
Kafle, N., Dell'Aira, F., Burnette, D.J., & Meier, C. I. (2022, December). Hershfield Factors for Extreme Precipitation: Variability and a Proposal for a Unified Definition. SEDHYD-2023 (8A Frequency and Design Storm Analysis III)
Kafle, N., Dell'Aira, F., Cancelliere, A., & Meier, C. I. (2022, December). A shift in paradigm from regional to global flood frequency analysis. AGU Fall Meeting Abstracts, 2022, H52I-0567. https://ui.adsabs.harvard.edu/abs/2022AGUFM.H52I0567K
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