Ali Sarhadi
University of Waterloo
28 Papers
77 Citations
Ali Sarhadi is an academic researcher from University of Waterloo. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 16, co-authored 19 publications. Previous affiliations of Ali Sarhadi include Stanford University & Isfahan University of Technology.
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Papers
Climate change is increasing the likelihood of extreme autumn wildfire conditions across California
Michael Goss,Daniel L. Swain,Daniel L. Swain,Daniel L. Swain,John T. Abatzoglou,John T. Abatzoglou,Ali Sarhadi,Crystal A. Kolden,Crystal A. Kolden,A. Park Williams,Noah S. Diffenbaugh +10 more
TL;DR: In this article, the authors quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change.
Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions.
TL;DR: It is found that ambitious emissions mitigation, such as that in the United Nations Paris Agreement, substantially curbs increases in the probability that extremely hot years co-occur with low precipitation simultaneously in multiple regions.
213
Rainfall trends analysis of Iran in the last half of the twentieth century
Reza Modarres,Ali Sarhadi +1 more
TL;DR: In this article, the authors performed the spatial and temporal trend analysis of the annual and 24-hr maximum rainfall of a set of 145 precipitation gauging stations of Iran and found that the negative trends of annual rainfall are mostly observed in northern and northwestern regions, whereas positive trends of 24-hour maximum rainfall were mostly located in arid and semi-arid regions of Iran.
Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis
TL;DR: In this paper, the authors presented an exhaustive methodology of floodplain mapping at ungauged rivers using regional flood frequency analysis using the L-moments approach and related criteria, a homogeneous region was formed and the 3-parameter Log normal distribution was identified as the robust regional frequency distribution.
188
Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate
TL;DR: In this article, a fully time varying risk framework using Bayesian Markov chain Monte Carlo (BMMC) techniques was proposed to incorporate the impact of different complex nonstationary conditions on the occurrence of extreme precipitation in the Great Lakes area.
142