Navid Jadidoleslam's Personal Website
Navid Jadidoleslam is a PhD student at the Iowa Flood Center, University of Iowa.

He has worked on multiple projects. He is currently involved in a research that improves flood forecasting performance by using satellite remote sensing data. Part of his research addresses the soil moisture variability in space. He incorporates different data from multiple satellite platforms in hydrologic modeling and predictions. He works on real-time flood forecasting where he implements new techniques for assimilating satellite remote sensing data in hydrologic models.

Navid is also interested in web technologies and web development. He develops tools and web applications that helps hydrologists visualize and analyze spatio-temporal data such as streamflow and soil moisture. Navid has also worked on ocean wave energy resource assessments during his Master's studies. He has used hydrodynamic modeling for calculating the currents, sediment transport in esturies and canals.


Satellite-based Soil Moisture

  • SMAP
  • SMOS
  • Soil Moisture
  • L-band Microwave

We estimate the soil moisture variability for basin and SMAP sub-grid using radar rainfall and soil properties. We drive the statistical information for dry-down and wetting of soils and we use this information to estimate the spatial and temporal variability of soil moisture at the basin and satellite sub-grid. Our methods and results is useful where higher resolution soil moisture data is needed. On the other hand, we show that the variance (and standard deviation) of a bounded variable should be bounded. However, our methodology leads to an estimation of variability and skewness of soil moisture. As there is precipitation event over the basin, the soil moisture tends to increase and the mean value increases. At the same time, standard deviation of soil moisture decreases for wetter conditions. This phase is more rapid than dry-downs and changes the state of the soils faster. I have created these a video for a demonstration of how the soil moisture distribution changes over time as the wetting and dry-down events occur. Our study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications.


  • Numerical
  • Hindcast
  • Wave
  • Modeling

In this study 10 different wave buoys were used to validate the wave model for data time span of 1 year. The wave power potential assessment takes place for 15 year time span. Spatial and temporal analyses are included in this study.

Navid's M.Sc. thesis was about wave modeling of the Aegean Sea for 1999-2013 years. The outcome of this research is an Elsevier article in Renewable Energy and an International Conference on Earth System Science.

remote Sensing Application

Application of Remote sensing in Hydrologic Predictions

My main research focus is on satellite-based soil moisture application in hydrologic modeling. Particularly, I work with SMAP and SMOS satellite soil moisture data to improve flood prediction using data assimilation and data-driven approaches.
rainfall-runoff processes

Rainfall Runoff Processes

I try to use satellite remote sensing data for understanding the rainfall-runoff processes in relevant scales to measurements.
Wave Modeling

Numerical Hydrodynamic and Wave Modeling

I have worked on numerical hydrodynamic and spectral wave modeling with different tools (eg. MIKE21SW, SWAN, DELFT3D). I have used these tools in different environments such as canals, estuaries, and ocean.


Iowa Institute for Hydraulic Research (IIHR)
S Riverside Drive
300 S Riverside Dr
Iowa City
Iowa City, IA


Monday - Friday
Monday - Friday
08:00 - 17:00 (UTC -06:00)
08:00 - 17:00 (UTC -06:00)