Most existing inundation inventories are based on surveys, news, or passive remote sensing imagery. Affected by spatiotemporal resolution or weather conditions, these inventories are limited in spatial details or coverage. Satellite Synthetic Aperture Radar (SAR) data has recently enabled flood mapping at unprecedented spatiotemporal resolution. However, the bottleneck in producing SAR-based flood maps is the requirement of expert manual processing to maintain acceptable accuracy by most SAR-driven mapping techniques. To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system. RAPID uses U.S. Geological Survey (USGS) water watch system and accumulated precipitation to identify SAR images that potentially overlap a flood event. The dataset includes inundation events with the temporal scale from several days to months. Concluded from all 559 overlapping images in the period from 2016 to the first half of 2019, the comparison of the proposed dataset against the USGS Dynamic Surface Water Extent (DSWE) product yields an overall, user, producer agreements, and critical success index of 99.06%, 87.63%, 91.76%, and 81.23%, respectively, demonstrating the high accuracy of the proposed dataset and the robustness of the automated system. We anticipate this archive to facilitate many applications, including large-scale flood loss and risk assessment, and inundation model calibration and validation.
The changing environment enhances the hydrological cycle and increases the frequency of extreme floods. In this paper, the impacts of climate variability on flood season segmentation are determined and the scientific basis for determining corresponding flood limiting water levels (FLWLs) is provided. Climate variation was determined and then the flood season was divided into several sub-seasons using the results of the set pair analysis method (SPAM) and four indices; peak floods crossing the transitional periods were sampled to obtain a design flood hydrograph; and, finally, seasonal FLWLs were determined for reservoir operation. The performance of this reservoir staging operation was evaluated for a case study in the Chengbihe Reservoir, China.
Frequency analysis of precipitation extremes is significant for the selection of design rainfalls, which are essential inputs for the design of water infrastructure projects, especially when the climate has changed. Therefore, the objective of this study was to propose a framework for more reasonably analysing the frequency of extreme rainfalls. The proposed framework consists of a maximum likelihood estimate (MLE) method for analysing the parameter trends, a hydrological variation diagnosis system to determine abrupt change times, generalized extreme value (GEV) and generalized Pareto distribution (GPD) models for frequency analysis of precipitation extremes, and an ensemble‐methods approach for choosing the most appropriate distributions. The methodology was successfully implemented using a 52‐year time series (1963–2014) of rainfall data recorded by eight rain gauges in Chengbi River basin (south China). The results show that the rainfall series mutated in 1993 and that the entire data set could be divided into two slices (1963–1992 and 1993–2014). Climate change was found to have some impacts on the precipitation extremes: the extreme rainfall value and the parameters of GEV and GPD were variable in the context of climate change. Furthermore, the GPD distribution model outperformed the GEV distribution model.
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