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Urban flooding is a perennial problem, especially in developing countries with relatively weak infrastructure under ever‐increasing stress due to climate change and human activities. We simulate the temporally variable flood‐water depth and inundation area under four designed rainfall patterns in the typical tropical rainforest city of Hue, Vietnam. The four rainfall types are R1 (peak at fifth hour), R2 (peak at 20th hour), R3 (peak at first hour), and R4 (peak at 13th hour). Results show that temporal rainfall pattern R4 with peak rainfall in the middle of the total period yielded the maximum water depth of 1.88 m. R3, with peak rainfall in the first hour, yields the shallowest maximum water depth and the largest inundation extent. When the water depth for R3 is 0.1–0.2 m, the inundated area caused by R3 is 3–4 times that of the other three patterns. Analysis of urban flood inundation in Hue provides a management tool to facilitate flood risk management in the context of extreme rainfall.

This study attempted to examine the complex impact of dynamic inundation process of extreme events on flood hazard assessment (FHA) for the affected urban settings around a local river in New York, USA. Using HEC-RAS 2D, LIDAR DEM, distributed values of surface roughness, and hourly discharges at both ends of the selected reach, we simulated the full inundation process of a 500-year storm event, constructed in terms of the existing largest storm event. We presented flooding status at three flooding moments and quantitatively described the temporal changes of inundation area, depth, and the associated stream power over the entire flood period. Then, we analyzed differences of inundated areas in four classes defined using traditional classification (TC) and process-based classification (PBC). The (static) former was based on the maximum inundation map, while the (dynamic) latter accounted for both inundation depth and duration. We showed that inundated areas in higher classes based on TC were much greater than those in similar classes based on PBC, indicating the significant impact of inundation duration on classification of flood hazard. Next, we investigated the impact of different land use/cover on the difference of inundated areas between the two types of classifications and found that it was complex and displayed no consistent trend from areas surrounding individual buildings (local scale) to large inundated areas (global scale). We emphasize the importance of considering the overall impact of the entire flood processes of an event on future FHA.

Wetlands have been extensively modified by human activities worldwide. We provide a global-scale portrait of the threats and protection status of the world’s inland wetlands by combining a global map of inundation extent derived from satellite images with data on threats from human influence and on protected areas. Currently, seasonal inland wetlands represent approximately 6% of the world’s land surface, and about 89% of these are unprotected (as defined by protected areas IUCN I–VI and Ramsar sites). Wetland protection ranges from 20% in Central and 18% in South America to only 8% in Asia. Particularly high human influence was found in Asia, which contains the largest wetland area of the world. High human influence on wetlands even within protected areas underscores the urgent need for more effective conservation measures. The information provided here is important for wetland conservation planning and reveals that the current paradigm of wetland protection may be inadequate.

Wetland methane (CH₄) emissions are the largest natural source in the global CH₄ budget, contributing to roughly one third of total natural and anthropogenic emissions. As the second most important anthropogenic greenhouse gas in the atmosphere after CO₂, CH₄ is strongly associated with climate feedbacks. However, due to the paucity of data, wetland CH₄ feedbacks were not fully assessed in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The degree towhich future expansion of wetlands and CH₄ emissions will evolve and consequently drive climate feedbacks is thus a question of major concern. Here we present an ensemble estimate of wetland CH₄ emissions driven by 38 general circulation models for the 21st century. We find that climate change-induced increases in boreal wetland extent and temperature-driven increases in tropical CH₄ emissions will dominate anthropogenic CH₄ emissions by 38 to 56% toward the end of the 21st century under the Representative Concentration Pathway (RCP2.6). Depending on scenarios, wetland CH₄ feedbacks translate to an increase in additional global mean radiative forcing of 0.04 W·m−2 to 0.19 W·m−2 by the end of the 21st century. Under the “worst-case” RCP8.5 scenario, with no climate mitigation, boreal CH₄ emissions are enhanced by 18.05 Tg to 41.69 Tg, due to thawing of inundated areas during the cold season (December to May) and rising temperature, while tropical CH₄ emissions accelerate with a total increment of 48.36 Tg to 87.37 Tg by 2099. Our results suggest that climate mitigation policies must consider mitigation of wetland CH₄ feedbacks to maintain average global warming below 2 °C.

Remote Sensing‐derived Flood Inundation Maps (RS‐FIM) are an attractive and commonly used source of evaluation benchmarks. In this paper, we investigate several sources of bias in RS‐FIM benchmarking and their effect on model‐predicted FIM (M‐FIM) evaluation results. We do so by comparing M‐FIM evaluation results using a high‐confidence benchmark against degraded benchmarks. The evaluation results show considerable differences in M‐FIM accuracy assessment when using lower‐quality benchmarks. An RS‐FIM enhancement (gap‐filling) procedure is presented, and its effect on FIM evaluation results is analyzed. The results show that the enhancement can significantly improve the robustness of the evaluation, but can also degrade the benchmark when a considerable number of false‐positive grid cells are present in the RS‐FIM. 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, 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 considerably improve the robustness of the evaluation. 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.

Torrential and long-lasting rainfall often causes long-duration floods in flat and lowland areas in data-scarce Nyaungdon Area of Myanmar, imposing large threats to local people and their livelihoods. As historical hydrological observations and surveys on the impact of floods are very limited, flood hazard assessment and mapping are still lacked in this region, making it hard to design and implement effective flood protection measures. This study mainly focuses on evaluating the predicative capability of a 2D coupled hydrology-inundation model, namely the Rainfall-Runoff-Inundation (RRI) model, using ground observations and satellite remote sensing, and applying the RRI model to produce a flood hazard map for hazard assessment in Nyaungdon Area. Topography, land cover, and precipitation are used to drive the RRI model to simulate the spatial extent of flooding. Satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Phased Array type L-band Synthetic Aperture Radar-2 onboard Advanced Land Observing Satellite-2 (ALOS-2 ALOS-2/PALSAR-2) are used to validate the modeled potential inundation areas. Model validation through comparisons with the streamflow observations and satellite inundation images shows that the RRI model can realistically capture the flow processes (R2 ≥ 0.87; NSE ≥ 0.60) and associated inundated areas (success index ≥ 0.66) of the historical extreme events. The resultant flood hazard map clearly highlights the areas with high levels of risks and provides a valuable tool for the design and implementation of future flood control and mitigation measures.

The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.

Flooding is an important factor influencing the biomass production of vegetation in natural wetland ecosystems. However, how biomass production is linked to flooding patterns in wetland areas remains unclear. We utilized gauging station data, a digital elevation model, vegetation survey data, and a Landsat 8 image to study the effects of average inundation depth (AID) and inundation duration (IDU) of flooding on end-of-season biomass of vegetation in Poyang Lake wetland, in particular, after operation of Three Gorges Dam. The end-of-season biomass of wetland vegetation showed Gaussian distributions along both the AID and IDU gradients. The most favorable flooding conditions for biomass production of vegetation in the wetland had an AID ranging from 3.9 to 4.0 m and an IDU ranging from 39% to 41%. For sites with a lower AID (<3.9 m; IDU < 39%), the end-of-season biomass values were positively related, whereas for sites with a higher AID (4.0 m; IDU > 41%), the end-of-season biomass values were negatively related. After the operation of the Three Gorges Dam, flooding patterns characterized by AID and IDU of the Poyang Lake wetland were significantly alleviated, resulting in a mixed changing trend of vegetation biomass across the wetland. Compared with 1980–2002, the increase of end-of-season biomass in lower surfaces caused by the alleviated flooding pattern far exceeded the decrease of end-of-season biomass in higher surfaces, resulting in an end-of-season biomass increase of 1.0%–6.7% since 2003. These results improved our understanding of the production trends of vegetation in the wetland and provided additional scientific guidance for vegetation restoration and wetland management in similar wetlands.

Storm surges are an important coastal hazard component and it is unknown how they will evolve along Europe’s coastline in view of climate change. In the present contribution, the hydrodynamic model Delft3D-Flow was forced by surface wind and atmospheric pressure fields from a 8-member climate model ensemble in order to evaluate dynamics in storm surge levels (SSL) along the European coastline (1) for the baseline period 1970–2000; and (2) during this century under the Representative Concentration Pathways RCP4.5 and RCP8.5. Validation simulations, spanning from 2008 to 2014 and driven by ERA-Interim atmospheric forcing, indicated good predictive skill (0.06 m < RMSE < 0.29 m and 10 % < RMSE < 29 % for 110 tidal gauge stations across Europe). Peak-over-threshold extreme value analysis was applied to estimate SSL values for different return periods, and changes of future SSL were obtained from all models to obtain the final ensemble. Values for most scenarios and return periods indicate a projected increase in SSL at several locations along the North European coastline, which is more prominent for RCP8.5 and shows an increasing tendency towards the end of the century for both RCP4.5 and RCP8.5. Projected SSL changes along the European coastal areas south of 50°N show minimal change or even a small decrease, with the exception of RCP8.5 under which a moderate increase is projected towards the end of the century. The present findings indicate that the anticipated increase in extreme total water levels due to relative sea level rise (RSLR), can be further enforced by an increase of the extreme SSL, which can exceed 30 % of the RSLR, especially for the high return periods and pathway RCP8.5. This implies that the combined effect could increase even further anticipated impacts of climate change for certain European areas and highlights the necessity for timely coastal adaptation and protection measures. The dataset is publicly available under this link: http://data.jrc.ec.europa.eu/collection/LISCOAST .

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.