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 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...

[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...

Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical ...

Satellite precipitation products (SPPs) are critical data sources for hydrological prediction and extreme event monitoring, especially for ungauged basins. This study conducted a comprehensive hydrological evaluation of six mainstream SPPs (i.e., TMPA 3B42RT, CMORPH-RT, PERSIANN-RT, TMPA 3B42V7, CMORPH-CRT, and PERSIANN-CDR) over humid Xixian basin in central eastern China for a period of 14 ye...

In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5 ̋ spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Pr...

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mappin...