Floods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. This research applied the high-resolution coupled hydrologic–hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. HiResFlood-UC...

Robust validation of the space–time structure of remotely sensed precipitation estimates is critical to improving their quality and confident application in water cycle–related research. In this work, the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) precipitation product is evaluated agai...

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of is on distinctive geographical features each location, they remain at a considerable distance from station-based data. This paper examines effectiveness convolutional autoencoder (CAE) architecture p...

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation c...

The acquisition of the precise spatial distribution precipitation is great importance and necessity in many fields, such as hydrology, meteorology ecological environments. However, arid semiarid regions Northwest China, especially over mountainous areas Heihe River basin (HRB), scarcity uneven rainfall stations have created certain challenges gathering information that accurately describes for ...

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, remote sensing. Each has its own sets of uncertainties limitations. Therefore, evaluating these datasets is critical in assessing their applicability various climatic regions. We used ten datasets,...

[1] Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) is a satellite infrared-based algorithm that produces global estimates of rainfall at resolutions of 0.25 0.25 and a half-hour. In this study the model parameters of PERSIANN are routinely adjusted using coincident rainfall derived from the Tropical Rainfall Measurement Mission Microwave Im...