Publish Date
30 Nov -0001
<![CDATA[Urban areas in Germany face increasing hydrological challenges due to climate change, extreme precipitation events, and the expansion of impervious surfaces. Traditional rainfall monitoring systems, such as rain gauges and radar networks, often fall short in capturing the spatiotemporal variability of urban precipitation, particularly in regions with complex topography or during high-intensity events. Satellite precipitation datasets such as IMERG, TRMM, CMORPH, GSMaP, and PERSIANN have emerged as essential tools for enhancing urban flood risk management. This paper evaluates the performance, limitations, and future prospects of these datasets in the context of Germany's urban environments. Key limitations include resolution constraints, latency, and accuracy issues related to orographic and winter precipitation. Nonetheless, recent advancements in machine learning, data fusion with ground-based systems like RADOLAN, IoT sensor integration, and downscaling techniques show significant promise in overcoming these challenges. The study highlights the potential of multi-sensor satellite systems and real-time data assimilation to improve predictive accuracy in urban hydrology. The findings emphasize the need for continued technological innovation and inter-operable data infrastructures to support climate-resilient urban water management strategies.]]>
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