2024-04-19, Rabiei, Ehsan, Hoppe, Holger, Lebrenz, Henning

The need for precipitation data for calibrating hydrodynamic sewer network models is often compromised by using the nearest available rain gauges to study area. Due to the scarcity and irregular locations of the rain gauges, this way of satisfying the need for precipitation data can lead to incorrect conclusions with respect to the temporal and spatial patterns of precipitation, depending on the location of the rain gauges in the study area. Recent developments in the field of precipitation measurement by means of weather radar data open up new possibilities for the use of such data sources in hydrodynamic sewer network models. Even though weather radar provides precipitation information with a high temporal and spatial resolution, the raw radar data contains several sources of error and is inaccurate. The radar data are therefore often corrected and merged with ground measurements. The main objective of this study is to investigate the resolution of precipitation data required to obtain robust results in a hydrodynamic channel network model. The study area is a small catchment close to Munich in Bavaria, Germany. Data from the Isen weather radar station of the German Weather Service (DWD), which is located around 33 km from the study area, was used. Following the objectives of this study, various weather radar data products were processed in order to be used as input for a hydrodynamic sewer network model. The data with a temporal resolution of 5 minutes to 1h and a spatial resolution of 250 m x 250 m up to 1.000 m x 1.000 m form the basis for creation of datasets to be investigated. It has been observed that the use of high-resolution precipitation data leads to better model results, especially when the data is merged with rain gauges. However, it should be noted that the quality of the model results does not decrease linearly when the resolution of the precipitation data is reduced.

2013-10-26, Berndt, Christian, Rabiei, Ehsan, Haberlandt, Uwe

This study investigates the performance of merging radar and rain gauge data for different high temporal resolutions and rain gauge network densities. Three different geostatistical interpolation techniques: Kriging with external drift, indicator kriging with external drift and conditional merging were compared and evaluated by cross validation. Ordinary kriging was considered as the reference method without using radar data. The study area is located in Lower Saxony, Germany, and covers the measuring range of the radar station Hanover. The data used in this study comprise continuous time series from 90 rain gauges and the weather radar that is located near Hanover over the period from 2008 until 2010. Seven different temporal resolutions from 10 min to 6 h and five different rain gauge network density scenarios were investigated regarding the interpolation performance of each method. Additionally, the influence of several temporal and spatial smoothing-techniques on radar data was evaluated and the effect of radar data quality on the interpolation performance was analyzed for each method. It was observed that smoothing of the gridded radar data improves the performance in merging rain gauge and radar data significantly. Conditional merging outperformed kriging with an external drift and indicator kriging with an external drift for all combinations of station density and temporal resolution, whereas kriging with an external drift performed similarly well for low station densities and rather coarse temporal resolutions. The results of indicator kriging with an external drift almost reached those of conditional merging for very high temporal resolutions. Kriging with an external drift appeared to be more sensitive in regard to radar data quality than the other two methods. Even for 10 min temporal resolutions, conditional merging performed better than ordinary kriging without radar information. This illustrates the benefit of merging rain gauge and radar data even for very high temporal resolutions.

2018-01-18, Tauro, Flavia, Selker, John, Giesen, Nick van de, Abrate, Tommaso, Uijlenhoet, Remko, Porfiri, Maurizio, Manfreda, Salvatore, Caylor, Kelly, Moramarco, Tommaso, Benveniste, Jerome, Ciraolo, Giuseppe, Estes, Lyndon, Domeneghetti, Alessio, Perks, Matthew T., Corbari, Chiara, Rabiei, Ehsan, Ravazzani, Giovanni, Bogena, Heye, Harfouche, Antoine, Brocca, Luca, Maltese, Antonino, Wickert, Andy, Tarpanelli, Angelica, Good, Stephen, Lopez Alcala, Jose Manuel, Petroselli, Andrea, Cudennec, Christophe, Blume, Theresa, Hut, Rolf, Grimaldi, Salvatore

To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery.

2015-01-13, Rabiei, Ehsan, Haberlandt, Uwe

Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.