Abstract. Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments.

This study highlights the features of vine copula for examining compound events involving underlying conditions that amply the compounding effects. To illustrate, we study compound floods in Texas (TX), USA. These compound floods consist of combinations of precipitation and surface runoff with the El Niño‐Southern Oscillation (ENSO) and rising temperatures as underlying conditions. Although the individual variable of precipitation and runoff may not itself be extreme, large exceedances can lead to flooding situations when combined. The presence of underlying conditions (e.g., El Niño and/or rising temperatures) can exacerbate the associated flood impacts. We use observational data during May–August for each climate division of TX. A three‐dimensional vine copula is used first to quantify the ENSO effect on precipitation and runoff through conditioning sets of vine copula. We further examine the interplay of a warming signal and El Niño to reveal their mutual effects on compound floods by placing these two factors as interrelated conditions in a four‐dimensional vine copula. Our results show that El Niño is much stronger than the other ENSO states in conditioning a high likelihood of TX compound floods by amplifying mean and extreme states of rainfall and runoff. Conditioned by both El Niño and global temperatures, a slight reduction occurs in TX compound floods under the warmer condition. This is consistent with the trend of precipitation and runoff composites under given conditions, while no appreciable changes are found to suggest a different joint effect of El Niño and rising temperatures on TX compound floods.

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+).Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash-Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point.The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage.Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning.