Rabiei, Ehsan
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Measurements and observations in the XXI century (MOXXI). Innovation and multi-disciplinarity to sense the hydrological cycle
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.
Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios
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.
Areal rainfall estimation using moving cars. Computer experiments including hydrological modeling
The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rain rate. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e., RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance but also for use in hydrological modeling. Considering measurement errors derived from laboratory experiments, the result shows that the RCs provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Moreover, by testing larger uncertainties for RCs, they observed to be useful up to a certain level for areal rainfall estimation and discharge simulation.
Applying bias correction for merging rain gauge and radar data
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.