Flash flooding is one of the most damaging weather types, yet it remains challenging to quantify its severity. We propose a novel development – the Flashiness-Intensity-Duration-Frequency (F-IDF) curve – to quantify and spatially analyze flash flood intensity based on the frequency and duration of the event. As a proof-of-concept, we mapped Contiguous US (CONUS)-wide F-IDF values at 3,722 stream gage locations and explored their relations with 59 basin attributes. It is found that (1) Climatological precipitation amounts exhibit the most positive correlation with flashiness while a basin’s drainage area is the most negatively correlated; (2) Correlation of flashiness with basin attributes decreases with increasing F-IDF return periods and shorter event durations. Both aspects are attributable to the rainfall signal overwhelming the underlying basin attributes as the intensities become more extreme. This new term can have implications for hydrology, especially for hydrologic modelers, decision-makers, and emergency responders.

Precipitation is an essential climate and forcing variable for modeling the global water cycle. Particularly, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product retrospectively provides unprecedented two-decades of high-resolution satellite precipitation estimates (0.1-deg, 30-min) globally. The primary goal of this study is to examine the similarities and differences between the two latest and also arguably most popular GPM IMERG Early and Final Run (ER and FR) products systematically over the globe. The results reveal that: (1) ER systematically estimates 13.0% higher annual rainfall than FR, particularly over land (13.8%); (2) ER and FR show less difference with instantaneous rates (Root Mean Squared Difference: RMSD=2.38 mm/h and normalized RMSD: RMSD_norm=1.10), especially in Europe (RMSD=2.16 mm/h) and cold areas (RMSD_norm=0.87); and (3) with similar detectability of extreme events and timely data delivery, ER is favored for use in hydrometeorological applications, especially in early warning of flooding. Throughout this study, large discrepancies between ER and FR are found in inland water bodies, (semi) arid regions, and complex terrains, possibly owing to morphing differences and gauge corrections while magnified by surface emissivity and precipitation dynamics. The exploration of their similarities and differences provides a first-order global assessment of various hydrological utilities: FR is designed to be more suitable for retrospective hydroclimatology and water resource management, while the earliest available ER product, though not bias-corrected by ground gauges, shows suitable applicability in operational modeling setting for early rainfall-triggered hazard alerts.

The historical record of rainfall observation rarely provides sufficient record length and resolution required in many applications. The work described here presents a stochastic framework for long-term simulations of non-tropical storms at high spatial and temporal resolution. The framework adopts optimal estimation for spatio-temporal modeling of rain fields. A non-parametric approach featuring K-Nearest Neighbor Resampling (KNNR) plus the Genetic Algorithm (GA) mixing process is utilized for generating parameters in the long-term simulation. A case study is conducted in Dallas-Fort-Worth metroplex as the simulation domain. Ensemble parameters are generated using the KNNR+GA method from adjacent homogeneous areas and 10 years of radar rainfall observation. One hundred most rainy days in the 10 years are simulated at the resolutions of 4 × 4 km and 1 hour for 50 ensemble members. The simulated rainfall is thoroughly evaluated against the observed radar rainfall with respects to statistical moments, spatio-temporal structure, and frequency distribution of rainfall at both near-point scale and domain scale. The results indicate that ensemble simulations successfully reproduce key statistical properties of the observed rainfall. In addition, the approach is also effective and flexible in capturing heavy rainfall values, which is important for many hydrologic/hydraulic practices. As essentially a downscaling tool, this stochastic rainfall generator can have many applications where rainfall needs to be represented at finer spatiotemporal resolution.

Coupled Hydrologic & Hydraulic (H&H) models have been widely applied to simulate both discharge and flood inundation due to their complementary advantages, yet the H&H models oftentimes suffer from one-way and weak coupling and particularly disregarded run-on infiltration or re-infiltration. This could compromise the model accuracy, such as under-prediction (over-prediction) of subsurface water contents (surface runoff). In this study, we examine the H&H model performance differences between the scenarios with and without re-infiltration process in extreme events¬ – 100-year design rainfall and 500-year Hurricane Harvey event – from the perspective of flood depth, inundation extent, and timing. Results from both events underline that re-infiltration manifests discernable impacts and non-negligible differences for better predicting flood depth and extents, flood wave timings, and inundation durations. Saturated hydraulic conductivity and antecedent soil moisture are found to be the prime contributors to such differences. For the Hurricane Harvey event, the model performance is verified against stream gauges and high water marks, from which the re-infiltration scheme increases the Nash Sutcliffe Efficiency score by 140% on average and reduces maximum depth differences by 17%. This study highlights that the re-infiltration process should not be disregarded even in extreme flood simulations. Meanwhile, the new version of the H&H model – the Coupled Routing and Excess STorage inundation MApping and Prediction (CREST-iMAP) Version 1.1, which incorporates such two-way coupling and re-infiltration scheme, is released for public access.