Gauge stations have uneven lengths of discharge records owing to the historical hydrologic data collection efforts. For watersheds with limited water data length, the flood frequency model, such as the Log-Pearson Type III, will have large uncertainties. To improve the flood frequency prediction for these watersheds, we propose a Bayesian Log-Pearson Type III model with spatial priors (BLP3-SP), which uses a spatial regression model to estimate the prior distribution of the parameters from nearby stations with longer data records and environmental factors. A Markov chain Monte Carlo (MCMC) algorithm is used to estimate the posterior distribution and associated flood quantiles. The method is validated using a case study watershed with 15 streamflow gauge stations located in the San Jacinto River Basin in Texas, US. The result shows that the BLP3-SP outperforms other choices of the priors for the Bayesian Log-Pearson Type III model by significantly reducing the uncertainty in the flood frequency estimation for the station with short data length. The results have confirmed that the spatial prior knowledge can improve the Bayesian inference of the Log-Pearson Type III flood frequency model for watersheds with short gauge period.

Remote sensing (RS)-derived Flood Inundation Maps (RS-FIM) are, in principle, a desirable source of observed data for the development, calibration, and validation of (model) predicted FIM. Advantages of using RS-FIM for evaluating predicted FIM include its spatial continuity (compared to point observations), and the representation of real flooding events (compared to synthetic events (e.g. 100-yr) or other models). Disadvantages may include low/mismatched spatial resolution, insufficient classification accuracy, lack of water depth information, and gaps in coverage (due to dense vegetation, buildings, clouds, etc.). Gaps in inundation coverage are very common in RS-FIM. While these may not be a major issue for some RS-FIM applications, they are a major, yet unacknowledged, issue for fair and robust evaluation of predicted FIM. This is because the evaluated model may correctly predict flooding in these gaps while the (RS-FIM) benchmark data is classified as non-flooded (leading to inaccurate identification of 'False-positives'). Techniques for 'closing the gaps' in RS-FIM using hydraulic models or terrain analysis can yield improved FIM but, depending on the scale of the 'gap-filling', can result in an RS-model hybrid which undermines the observational nature of RS-FIM. Here we will demonstrate and discuss the challenges in using RS-FIM for the evaluation of predicted FIM and present tools and analysis demonstrating a new framework for fair and robust evaluation of FIM predictions using RS-FIM.

Remote Sensing-derived Flood Inundation Maps (RS-FIM) are an attractive and commonly used source of evaluation benchmarks. Errors in model-predicted FIM (M-FIM) evaluation results due to biases in RS-FIM benchmarking are quantified by introducing a high-confidence benchmark FIM, which was manually delineated from ultra-resolution imagery, as Ground Truth. The evaluation results show considerable differences in M-FIM accuracy assessment when using lower-quality benchmarks. A RS-FIM enhancement (gap-filling) procedure is presented and its effect on FIM evaluation results is analyzed. The results show that the enhancement is insufficient for significantly improving the robustness of the evaluation. The impact of including/excluding Permanent Water Bodies (PWB) on FIM evaluation results is analyzed. The results show that including PWB in FIM evaluation can significantly inflate the model accuracy. A novel evaluation strategy is proposed and analyzed. The proposed evaluation strategy is based on excluding low-confidence grid cells and PWB from the M-FIM evaluation analysis. Low-confidence grid cells are those that were estimated to be flooded by the gap-filling procedure, but were not classified as such by the remote sensing analysis. The results show that the proposed evaluation strategy can dramatically improve the robustness of the evaluation, except when a considerable number of false positives exist in the RS-FIM. The analyses showcase the many challenges in FIM evaluation. We provide an in-depth discussion about the need for standards, user-centric evaluation, the use of secondary sources, and qualitative evaluation.

Optical Satellite imagery commonly suffers from the presence of cloud cover during flood events; Radar Satellites are disadvantaged from water look-alike conditions where the ground surface interacts with the incoming radar signal as if it were water; Regardless of modality of satellite, more importantly, satellite overpasses during a flood are chance occurrences where the capture of the maximum extent is a fortuitous incident. Low-altitude aerial remote sensing, on the other hand, can be used to survey the extent of flooding at the peak or soon after it has occurred, with a good measure of reliability. Opportune scheduling of these reconnaissance flights not only capture floods at ultra-high resolution, but also allows for seamless geographical coverage unhindered by cloud cover.The National Oceanic and Atmospheric Administration (NOAA) Emergency Response Imagery is very high resolution (50 cm Ground Sampling Distance between pixels) airborne imagery acquired by the Remote Sensing Division of the National Geodetic Survey (NGS) during major flood events in the United States to support NOAA’s homeland security and emergency response requirements.In this work, we evaluated the performance of four different machine learning models (Gradient Boosting, Random Forest, Support Vetor Machine, Convolutional Neural Network) on the ability to classify floods from raw aerial imagery. The classifier with the highest classification accuracy metrics - depending on geographical and hydrological setting – was used to produce flood inundation extent maps for 30 major flood events.We demonstrate the utility of these high-fidelity flood maps via two use-cases: both synergistic studies to this work. 1) As benchmarks for validation of hydrodynamic model results: Historic flooding occurred on the Neuse River in North Carolina in the United States triggered by Hurricane Matthew in 2016. Several hydrodynamic models were deployed to simulate flood dynamics with an end goal of understanding flood susceptibility in the Neuse basin under changing climate conditions. The aerial imagery-based flood maps were used as benchmarks for model validation. 2) Enhancing the versatility of FIMPEF: Flood Inundation Mapping Predictions Evaluation Framework (FIMPEF) is an open-source, cloud-based geospatial venture by the University of Alabama that calculates accuracy statistics between benchmark and modeled flood extents. Integration of aerial imagery, in addition to the satellite-based benchmarks that FIMPEF was ingesting so far, has vastly enhanced its robustness and user-demand. Free access (no account/login credentials needed) to these high-quality flood maps is granted through the United Sates Flood Inundation Map Repository (USFIMR), an online geospatial warehouse that provides high-resolution inundation extent maps of past U.S. flood events.