Research
Our research lies at the intersection of hydrology, hydrometeorology, and remote sensing and includes topics that deal with hydroclimatic risk, modeling and monitoring of hydrologic extremes, and climate change impact on water resources, among others.
Our research is funded by federal (NASA, NSF, NOAA) and state agencies (FOIR) as well as the industry.
A list of the main thematic areas of our research activities is provided below. For more details about our research projects, see here.
Estimation of hydroclimatic extremes
Estimation of the magnitude, duration and frequency of extremes (precipitation, floods, droughts) across various temporal and spatial scales is of paramount importance for accurate risk assessments and for developing effective mitigation strategies. We integrate state-of-art hydroclimatic observations with novel statistical procedures to provide robust estimates of past and future changes of extremes.
Selected Publications:
Araujo, D. S., Marra, F., Merow, C., and Nikolopoulos, E. I. (2022). Today’s 100 year droughts in Australia may become the norm by the end of the century. Environmental Research Letters, 17(4), 044034. [link]
Emmanouil, S., Langousis, A., Nikolopoulos, E.I. and Anagnostou, E.N., (2020). Quantitative assessment of annual maxima, peaks-over-threshold and multifractal parametric approaches in estimating intensity-duration-frequency curves from short rainfall records. Journal of Hydrology, p.125151. [link]
McGraw, D., Nikolopoulos, E.I., Marra, F. and Anagnostou, E.N., (2019). Precipitation frequency analyses based on radar estimates: An evaluation over the contiguous United States. Journal of Hydrology, 573, pp.299-310. [link]
Hydrologic and Hydraulic Modeling
Simulation of hydrologic processes at high spatiotemporal scales is important for advancing our understanding of hydrologic response of catchments and predicting hazards such as floods. We use hydrologic (conceptual and physics-based) and hydraulic 1D/2D models to simulate hydrologic variables (soil moisture, runoff, snowmelt) and flood inundation at high space/time resolutions and at scales ranging from catchment to regions.
Selected Publications:
Rasheed, Z., Aravamudan, A., Zhang, X., Anagnostopoulos, G.C. and Nikolopoulos, E.I., (2024). Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate. Advances in Water Resources, 192, p.104781.[link]
Khanam, M., Sofia, G., Koukoula, M., Lazin, R., Nikolopoulos, E.I., Shen, X. and Anagnostou, E.N., (2021). Impact of compound flood event on coastal critical infrastructures considering current and future climate. Natural Hazards and Earth System Sciences, 21(2), pp.587-605. [link]
Nikolopoulos, E.I., Anagnostou, E.N., Borga, M., Vivoni, E.R. and Papadopoulos, A., (2011). Sensitivity of a mountain basin flash flood to initial wetness condition and rainfall variability. Journal of Hydrology, 402(3-4), pp.165-178. [link]
Precipitation Estimation and Uncertainty Quantification
Reliable precipitation information from remote sensors is a crucial requirement for meaningful hydrologic assessment. We are using statistical methods to optimally combine in-situ observations and atmospheric reanalysis with satellite-based precipitation estimates. Quantification of precipitation uncertainty and its propagation in simulated hydrologic variables provides important information for several fields in water resources and environmental sciences, such as water resources management, agriculture and irrigation practices, and hydrologic hazard prediction.
Selected Publications:
Bhuiyan, M.A.E., Nikolopoulos, E.I., Anagnostou, E.N., Polcher, J., Albergel, C., Dutra, E., Fink, G., Martinez-De La Torre, A. and Munier, S., (2019). Assessment of precipitation error propagation in multi-model global water resource reanalysis. Hydrology and Earth System Sciences, 23(4), pp.1973- 1994. [link]
Nikolopoulos, E.I., Crema, S., Marchi, L., Marra, F., Guzzetti, F. and Borga, M., (2014). Impact of uncertainty in rainfall estimation on the identification of rainfall thresholds for debris flow occurrence. Geomorphology, 221, pp.286-297. [link]
Nikolopoulos, E.I., Anagnostou, E.N., Hossain, F., Gebremichael, M. and Borga, M., (2010). Understanding the scale relationships of uncertainty propagation of satellite rainfall through a distributed hydrologic model. Journal of Hydrometeorology, 11(2), pp.520-532. [link]
Prediction of hydrogeomorhic hazards
Accurate and timely prediction of hydrologic and hydrogeomorphic hazards such as flash floods, debris flows and landslides are mandatory for improving preparedness and response during imminent disasters. Our research focuses on developing predictive models that integrate with remote sensing observations and weather forecasts to improve early warning procedures and advance resilience of our communities.
Selected Publications:
Rasheed, Z., Aravamudan, A., Sefidmazgi, A. G., Anagnostopoulos, G. C., and Nikolopoulos, E. I. (2022). Advancing flood warning procedures in ungauged basins with machine learning. Journal of Hydrology, 609, 127736. [link]
Nikolopoulos, E.I., Destro, E., Bhuiyan, M.A.E., Borga, M. and Anagnostou, E.N., (2018). Evaluation of predictive models for post-fire debris flow occurrence in the western United States. Natural Hazards and Earth System Sciences, 18(9), pp.2331-2343. [link]
Destro, E., Amponsah, W., Nikolopoulos, E.I., Marchi, L., Marra, F., Zoccatelli, D. and Borga, M., (2018). Coupled prediction of flash flood response and debris flow occurrence: Application on an alpine extreme flood event. Journal of Hydrology, 558, pp.225-237. [link]
Nikolopoulos, E.I., Anagnostou, E.N. and Borga, M., (2013). Using high-resolution satellite rainfall products to simulate a major flash flood event in northern Italy. Journal of Hydrometeorology, 14(1), pp.171-185. [link]