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In this research work, an analysis is conducted concerning the impact on rainfall-runoff simulations of utilizing rain gauge precipitation measurements against weather radar quantitative precipitation estimates. The study area is the Sarantapotamos river basin, a peri-urban basin located in the greater area of Athens, and measurements from a newly installed X-Band weather radar system, referred to as rainscanner, along with ground rain gauge stations were used. Rainscanner, in contrast to rain gauges, is able to provide with higher resolution surface precipitation datasets, but due to signal errors, uncertainty is involved, and thus proper calibration and evaluation of these estimates must be first performed. In this context, this research work evaluates the impact of adopting different precipitation datasets and interpolation methods for generating runoff, through the use of a lumped based rainfall-runoff model. Initially, the analysis focuses on the correlation between the rain gauge and the rainscanner estimations for each station, as well as for the calculated mean areal precipitation. The results of the rainfall-runoff simulations show that even though a different spatial and temporal variability of the rainfall field is calculated through the two datasets, in a lumped-based scheme, the most important factor that dictates the runoff generation is the amount of total precipitation.

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In this research work, the development and application of a distributed rainfall – runoff model, to be used in flood related simulations was performed. The model utilizes the time–area diagram theory in order to calculate and route the runoff of each grid to the basin’s outlet. The selected study area is the upper part of the Alfeios river basin, the Karitaina basin, located in southern Greece, while historic rainfall data from regional rain gauges were used, which were interpolated through GIS tools into spatially gridded rainfall fields, with a one-hour temporal scale. The performance of the distributed model was evaluated through its comparison with two lumped models, one based on GIS techniques and the other one based on the unit hydrograph derived from historical rainfall-runoff events. Finally, the abovementioned models were also compared and evaluated with the observed hydrograph of the studied event. The results showed that the distributed model performed well considering that no calibration has been carried out regarding the hydrological losses.

This research work focuses on the investigation of the Z-R relationship impact on weather radar rainfall estimates, through the correlation of a X-Band weather radar estimates and rain gauge measurements in the region of Attica, Greece. The methodology followed in this work is performed into two sections, where first, a framework is applied in order to access the raw rainscanner datasets and convert them into a compatible form for comparison, since the radar measurements consist of gridded reflectivity values of 2-min temporal scale, whereas the rain gauge measurements pertain to 10-min point precipitation measurements. Secondly, a statistical analysis is performed, regarding the spatial variability of the Z-R relationship for a single event. Specifically, four Z-R relationships are used and compared based on the correlation with the actual rain gauge measurements, for each station, and the results are then shown upon the study area. The results provide with important findings about the spatial distribution of the optimal Z-R relationship based upon the proximity of the stations to the weather radar location, the coastline and station elevation.

In weather radar applications, the Z-R relationship is considered one of the most crucial factors for providing quality quantitative precipitation estimates. However, the relationship’s parameters vary in time and space, making the derivation of an optimal relationship for a specific weather radar system challenging. This research focused on the analysis of the spatiotemporal variability of the parameters for a newly installed X-Band weather radar in Athens, Greece, by performing correlation and optimization analyses between high temporal resolution weather radar and rain gauge datasets. The correlation analysis was performed to assess the available datasets and provide the base of quality control. Multiple Z-R relationships were then derived for the following three optimization procedures; event-based relationships, station-based relationships, and a single area-based relationship. The results highlighted the region’s spatial variability regarding the Z-R relationship and the correlation between the station location and its parameter values. Moreover, it was found that stations near the coast and the front end of precipitation systems featured parameter values typical of convective type events. Finally, a single Z-R relationship was determined under a calibration and validation scheme, Z = 321R1.53,, which was validated with good agreement.