Tuesday, 15 October 2019 13:50

Satellite remote sensing has brought an unprecedented opportunity to hydrologic community. The availability of satellite data helps us understand some of the main controls on the runoff production in any region in the world. Yes! That is the power of satellite remote sensing. 


In this new study, we have investigated the role of antecedent soil moisture, event-scale rainfall and vegetation on the runoff generation. Our study domain is the state of Iowa where we have used SMAP satellite data, MODIS vegetation index and radar rainfall to find the main factors that control the runoff production. 

Main finding is that although there are issues with the sensitivity of L-band microwave to vegetation, still we have found a strong relationship soil moisture estimated from SMAP (Soil Moisture Active Passive) satellite data. Interestingly, we have shown that we should expect higher runoff ratios for the rainfall events with larger total rainfall depth.  We combine soil moisture and rainfall into one measure so called "Soil Moisture Deficit Normalized Rainfall". We defined it as the ratio of event-scale rainfall to available space in the soil prior to intiation of the rainfall. 

Well, lets say you have a bucket and you want to fill it with water. if you fill it more than the available space, then it will overflow. On the other hand, if you fill it less than the available space, then you will be able to carry it easier. Similarly, if rainfall is more than available space in the top soils, then the water will runoff. "Deficit-normalized rainfall" can help us find the total runoff. However, we save this for another day!

We finally study the annual cycles of runoff ratio and vegetation by using 17 years of data from MODIS (Moderate Resolution Imaging Spectroradiometer). We found that vegetation plays a  major role in decreaseing the runoff ratio.  The findings of our study can be used for hydrologic assessment of watershed management over long time periods. Also, the impact of vegetation on runoff is significant that we need to think and incorporate these dynamics in the hydrologic modeling frameworks so as to account for the dynamics of the runoff. 


Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

Tuesday, 26 April 2016 18:19

This is very simple and the most straightforward flow modeling using HEC-RAS 5.0 which is capable of hydraulic modeling in 2-dimensional space. The input files are not that much accurate and you may double check it again from the technical aspect. There are different parameters that should be defined and given as input to HEC-RAS 5.0.

The screen recording was done by the Webinaria program which is a free and open source program.

The background image of the desktop is Berlin, Germany(Source ESA : http://www.esa.int/spaceinimages/content/search?SearchText=IOW&img=1?collection=&mission=&keyword=IOW&idf=+--%3E+ID&Ic=on&Vc=on&Ac=on&subm3=GO)

Friday, 11 September 2015 17:10

I have published a new article in Renewable Energy Journal of Elsevier which is an outcome of my Master's Thesis. You could refer to my article and ask any questions regarding to the wave modeling with MIKE 21 SW.


In this study, wave power atlas is generated for Aegean Sea for years from 1999 to 2013 using a third-generation spectral wave model MIKE 21 SW. Wind data was obtained from ECMWF(ERA-Interim) with 0.125◦ spatial resolution and 6 hourly temporal resolution which was then interpolated to 10 minutes interval data. Distribution plots and statistical parameters were used to evaluate the performance of the model.

points for spatial analysis of Aegean SeaModel calibration for Athos Station from HCMR

Calibration results with 9 observational buoy data show high accuracy of the model. Analyses of the proposed model are done for offshore in this study. Analysis in time domain is divided into 15-year, seasonal and monthly mean wave height and wave powers. For analysis in space domain, wave characteristics are calculated on 4 bands with different widths parallel to Turkish coast. Moreover, 10 separate points were selected in different locations of the domain to derive wave roses and scatter diagrams. Maximum wave power values occur in Winter in northern part of the Aegean Sea with more than 8 kW/m. Maximum mean wave power occurrence location changes from north to middle southern part of the study region between Crete and Kasos islands in the period of Winter to Spring season.


 Mean wave height, wave power and procedure of calculating wave power potential in 4 different bands are shown in this picture.

Abstract of this research is as below: For further information on this research you could reach to its full-text:

N. Jadidoleslam, et al., Wave power potential assessment of Aegean Sea with an integrated 15-year data, Renewable Energy (2015), http://dx.doi.org/10.1016/j.renene.2015.09.022

Wave power potential assessment of Aegean Sea with an integrated 15-year data