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Giuseppe Mascaro

Statistical Characterization and Modeling of Precipitation 

We are working on the statistical characterization of precipitation across multiple scales using gage records and weather radars, placing a particular focus on extremes. We are using this information to improve space-time stochastic precipitation models, as well as stationary and non-stationary spatial models of extreme precipitation. This work has been funded by the NSF Environmental Sustainability, NSF Smart & Connected Communities, and the NISF-NSF Disaster Resilience Research Grants programs.

Recent publications: Huang et al. (2022), Farris et al. (2021), Hjelmstad et al. (2021), Morbidelli et al. (2020), Mascaro (2020).

Food-Energy-Water Nexus

Our research lab is part of an interdisciplinary team that is developing a basic scientific understanding of food, energy, and water (FEW) system dynamics to inform an integrated modeling, visualization, and decision support infrastructure for comprehensive systems. Our lab has developed a multi-resolution integrated modeling framework that explicitly captures the feedback among the FEW sectors at the metropolitan scale. This work has been funded by the NSF INFEWS program.

PublicationsMounir et al. (2021)Guan et al. (2020)Opejin et al. (2020)Mounir et al. (2019)White et al. (2017)



FloodAware, NSF Smart and Connected Communities program (award 1831475).

Goal: assess the effectiveness of several real-time flood detection, reporting, and communication technologies for cities and local communities. See the project website for additional information.

Graduate students: Annika Hjelmstad and Nehal Ansh Srivastava


Huang, J., S. Fatichi, G. Mascaro, G. Manoli, and N. Peleg, 2022. Intensification of sub-daily rainfall extremes in a low-rise urban area. Urban Climate, 42, 101124.

Shrestha, A., G. Mascaro, and M. Garcia, 2022. Effect of stormwater infrastructure data completeness and model resolution on urban flood modeling, Journal of Hydrology, 127498,

Hjelmstad, A., A. Shrestha, M. Garcia, and G. Mascaro, 2021. Propagation of radar rainfall uncertainties into urban pluvial flood modeling during the North American monsoon. Hydrological Sciences Journal, DOI: 10.1080/02626667.2021.1980216

Mascaro, G. 2020. Comparison of local, regional, and scaling models for rainfall intensity-duration-frequency analysis. Journal of Applied Meteorology and Climatology, 59(9), 1519-1536.


Averting Drought Shortages in the Colorado River, NASA’s Earth Science Division

Goal: provide long-range scenarios for water management for the Colorado River Basin. This project is in partnership with the Central Arizona Project. Our lab is improving the reliability of the Variable Infiltration Capacity large-scale hydrologic model in the Colorado River Basin using a suite of NASA remotely-sensed products.

Post-doctoral research assistant: Mu Xiao



Ensemble Generation of Downscaled Soil Moisture from Satellite Observations, NASA Terrestrial Hydrology program (completed).

Goal: design and calibrate a statistical downscaling model for generating high-resolution soil moisture fields from coarse satellite data using aircraft-based data collected during intensive field campaigns, as well as high-resolution (10 to 100 m) soil moisture fields generated by a distributed hydrologic model applied to a set of study basins.

Graduate student: Ara Ko


Mascaro, G., A. Ko, and E. R. Vivoni, 2019. Closing the loop of satellite soil moisture estimation via scale invariance of hydrologic simulations, Scientific Reports, 9, 16123.

Ko, A, G. Mascaro, and E. R. Vivoni, 2019. Strategies to improve and evaluate physics-based hyperresolution hydrologic simulations at regional basin scales. Water Resources Research, 55.

Mascaro, G., and E.R. Vivoni, 2016. On the observed hysteresis in field-scale soil moisture variability and its physical controls. Environmental Research Letters, 11(8), 084008.

Ko, A., G. Mascaro, and E. R. Vivoni, 2016. Irrigation impacts on scaling properties of soil moisture and the calibration of a multifractal downscaling model. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3128-3142, doi: 10.1109/TGRS.2015.2511628.