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The restoration of mangroves has gained prominence in recent decades. Hydrological rehabilitation has been undertaken to connect impaired mangroves with the sea, lagoons or estuaries. Because mangrove hydrodynamics occurs on the surface and interstitial spaces in the sediment, we propose to determine the hydrological flow paths to restore the hydrological regimes of the impaired mangroves. The hydrological flow paths were determined through a micro basin analysis based on microtopographic data to generate a digital elevation model. Applying this methodological approach, the hydrology of an impaired area on a barrier island in the Gulf of Mexico was restored by excavating, desilting or clearing the channels on the identified hydrological flow paths. This area was compared to one in which impaired mangroves were reconnected to the marine lagoon but disregarding the flow paths. Data collected in both areas were evaluated by flood level analysis, using two methods: (i) a simple linear regression model (SLRM) and (ii) spectral analysis (SA), also known as dominant frequency analysis. The results suggest that restoration based on the hydrological flow paths was more effective than the direct opening to the nearest main water body without accounting for the microtopography. In both areas, soil salinity and sulfides decreased after hydrological reconnection. However, a greater efficiency in the investment of time and human resources was achieved when preferential flow paths were identified and taken into account. The methodological procedures described in this study are of universal application to other mangrove restoration programs.

More frequent and stronger flood hazards in the last two decades have caused considerable environmental and socio‐economic losses in many regions of the Amazon basin. It is therefore critical to advance predictions for flood levels, with adequate lead times, to provide more effective and earlier warnings to safeguard lives and livelihoods. Water‐level variations in large, low‐lying, free‐flowing river systems in the Amazon basin, such as the Negro River, follow large‐scale precipitation anomalies. This offers an opportunity to predict maximum water levels using observed antecedent rainfall. This study aims to investigate possible improvements in the performance and extension of the lead time of existing operational statistical forecasts for annual maximum water level of the Negro River at Manaus, occurring between May and July. We develop forecast models using multiple linear regression methods, to produce forecasts that can be issued in March, February and January. Potential predictors include antecedent catchment rainfall and water levels, large‐scale modes of climate variability and the long‐term linear trend in water levels. Our statistical models gain one month of lead time against existing models for same skill level, but are only moderately better than existing models at similar lead times. All models lose performance at longer lead times, as expected. However, our forecast models can issue skilful operational forecasts in March or earlier. We show the forecasts for the Negro River maximum water level at Manaus for 2020 and 2021. Water‐level variations in free‐flowing river systems in the Amazon basin, such as the Negro River, follow large‐scale precipitation anomalies, which offers an opportunity to predict water levels using observed antecedent rainfall. We develop statistical forecasts, that can be issued in March, February and January, for annual maximum water level of the Negro River at Manaus, occurring between May and July. Our statistical models gain one month of lead time against existing operational forecasts for the same skill level.

This research analyses beach vulnerability to erosion along the coast of Valencia province, Spain. The Coastal Vulnerability Index (CVI) is used to assess vulnerability, considering the following variables: beach width, beach erosion/accretion rate, dune width, wave height, relative coastal flood level, submerged vegetation, upper depth limit of submerged vegetation, and percentage of vegetated dune. The results show that vulnerability varies significantly along the coast. The vulnerability assessment revealed that 26.9% of the coastal sections were classified as having very low susceptibility to erosion, 34.5% as low, 22.3% as moderate, 12% as high, and 4.3% as very high. Urbanized areas with reduced dunes are more vulnerable than natural areas with wide beaches and well-developed dunes. The study highlights and discusses limitations of the CVI method and suggests using the mean instead of the square root to calculate the overall vulnerability index due to the influence of one single variable in this formula. It is concluded that natural areas characterized by the presence of dunes exhibit a diminished vulnerability to erosion when compared to highly urbanized regions devoid of dunes and marine vegetation.

Both the river network and the regions outside the estuary mouths in the Pearl River Delta (PRD) of China experienced significant changes from 1999 to 2014. A validated hydrodynamic model across the entire PRD and adjacent regions outside the estuary mouths is employed to simulate both present (circa 2014) and past conditions (circa 1999). The total net water flux of the PRD decreased. The flow division of the West River is increasing, with values of 3.63% and 4.66% for the Makou and Denglongshan sections, respectively. The flood flow division of the North River is correspondingly decreasing. The value of the flood levels significantly decreased (more than 2 m) in the upper portion of the PRD, moderately decreased in the middle of the PRD (more than 1.1 m) and slightly decreased in the bottom part of the PRD (less than 0.22 m). In addition, the effects of morphodynamic evolution in different regions (i.e., the river network, coastline and bathymetry changes outside the estuary mouth) on floods are quantified. The results indicate that the decreased net water flux was caused by the increased channel’s capacity and the gentler water-level profile from the downcutting riverbed of the river network. The uneven morphodynamic evolution of the riverbed of the river network was primarily responsible for changes in the flood flow division in the PRD, and morphological evolution outside the estuary mouth was primarily responsible for reallocation within the outlets. The downcutting riverbed in the river network was primarily responsible for the lower flood levels in the upper and middle portion of the PRD. Reclamation seemed to have barely affected the flood level. The deepening bathymetry outside the estuary mouth was mainly responsible for the decrease in the flood level in the bottom portion of the PRD. The downcutting riverbed may decrease the stability of the riverbank, increasing the flood risk. The morphodynamic evolution of both the river network and the regions outside the estuary mouth should be considered to avoid unwanted side effects when designing local projects and flood mitigation strategies for the PRD.

The mountainous Yeongdong region of South Korea contains mountains over 1 km. Owing to this topographic blockage, the region has a low-density rain-gauge network, and there is a low-altitude (~1.5 km) observation gap with the nearest large S-band radar. The Korean government installed an X-band dual-polarization radar in 2019 to improve rainfall observations and to prevent hydrological disasters in the Yeongdong region. The present study analyzed rainfall estimates using the newly installed X-band radar to evaluate its hydrological applicability. The rainfall was estimated using a distributed specific differential phase-based technique for a high-resolution 75 m grid. Comparison of the rainfall estimates of the X-band radar and the existing rainfall information showed that the X-band radar was less likely to underestimate rainfall compared to the S-band radar. The accuracy was particularly high within a 10 km observation radius. To evaluate the hydrological applicability of X-band radar rainfall estimates, this study developed a rain-based flood forecasting method—the flow nomograph—for the Samcheok-osib stream, which is vulnerable to heavy rain and resultant floods. This graph represents the flood risk level determined by hydrological–hydraulic modeling with various rainfall scenarios. Rainfall information (X-band radar, S-band radar, ground rain gauge) was applied as input to the flow nomograph to predict the flood level of the stream. Only the X-band radar could accurately predict the actual high-risk increase in the water level for all studied rainfall events.

Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system’s operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.

The most crucial function of lowland-confined floodplains with low slopes is to support flood conveyance and fasten floods; however, obstacles can hinder it. The management of riparian vegetation is often neglected, though woody species increase the vegetation roughness of floodplains and increase flood levels. The aims are (1) to determine the branch density of various riparian vegetation types in the flood conveyance zone up to the level of artificial levees (up to 5 m), and (2) to assess the spatial distribution of densely vegetated patches. Applying a decision tree and machine learning, six vegetation types were identified with an accuracy of 83%. The vegetation density was determined within each type by applying the normalized relative point density (NRD) method. Besides, vegetation density was calculated in each submerged vegetation zone (1–2 m, 2–3 m, etc.). Thus, the obstacles for floods with various frequencies were mapped. In the study area, young poplar plantations offer the most favorable flood conveyance conditions, whereas invasive Amorpha thickets and the dense stands of native willow forests provide the worst conditions for flood conveyance. Dense and very dense vegetation patches are common in all submerged vegetation zones; thus, vegetation could heavily influence floods.

One of the most destructive natural hazards, tropical cyclone (TC)–induced coastal flooding, will worsen under climate change. Here we conduct climatology–hydrodynamic modeling to quantify the effects of sea level rise (SLR) and TC climatology change (under RCP 8.5) on late 21st century flood hazards at the county level along the US Atlantic and Gulf Coasts. We find that, under the compound effects of SLR and TC climatology change, the historical 100-year flood level would occur annually in New England and mid-Atlantic regions and every 1–30 years in southeast Atlantic and Gulf of Mexico regions in the late 21st century. The relative effect of TC climatology change increases continuously from New England, mid-Atlantic, southeast Atlantic, to the Gulf of Mexico, and the effect of TC climatology change is likely to be larger than the effect of SLR for over 40% of coastal counties in the Gulf of Mexico. Tropical cyclone-induced coastal flooding will increase under climate change. Here the authors estimate the effects of sea level rise and tropical cyclone climatology change on late–21st–century flood hazards along the US Atlantic and Gulf Coasts and find that the effect of tropical cyclone change could surpass the effect of sea level rise at some areas in the Gulf of Mexico.

Current global riverine flood risk studies assume a constant mean sea level boundary. In reality high sea levels can propagate up a river, impede high river discharge, thus leading to elevated water levels. Riverine flood risk in deltas may therefore be underestimated. This paper presents the first global scale assessment of the joint influence of riverine and coastal drivers of flooding in deltas. We show that if storm surge is ignored, flood depths are significantly underestimated for 9.3% of the expected annual population exposed to riverine flooding. The assessment is based on extreme water levels at 3433 river mouth locations as modeled by a state-of-the-art global river routing model, forced with a multi-model runoff ensemble and bounded by dynamic sea level conditions derived from a global tide and surge reanalysis. We first classified the drivers of riverine flooding at each location into four classes: surge-dominant, discharge-dominant, compound-dominant or insignificant. We then developed a model experiment to quantify the effect of surge on flood hazard and impacts. Drivers of riverine flooding are compound-dominant at 19.7% of the locations analyzed, discharge-dominant at 69.2%, and surge-dominant at 7.8%. Compared to locations with either surge- or discharge-dominant flood drivers, locations with compound-dominant flood drivers generally have larger surge extremes and are located in basins with faster discharge response and/or flat topography. Globally, surge exacerbates 1-in-10 years flood levels at 64.0% of the locations analyzed, with a mean increase of 11 cm. While this increase is generally larger at locations with compound- or surge-dominant flood drivers, flood levels also increase at locations with discharge-dominant flood drivers. This study underlines the importance of including dynamic downstream sea level boundaries in (global) riverine flood risk studies.

A novel stage projection method is used to estimate present-day flood levels at 12 sites on the Missouri River, using present-day rating curves and historical discharge estimates. These results are compared to several other flood estimates, including methods based only on historical stage data, and official “100-year” flood (base flood) levels determined from the statistics of discharge. Differences among these estimates vary with location, and their utility depends on river management style. At sites in the upper basin, channel configuration has changed little, but peak discharges have decreased slightly, due to tributary reservoirs and withdrawals. Little difference is seen between the various estimates of flood levels, and historical changes appear to be minimal. In contrast, flow behavior and channel character have been drastically modified along the middle Missouri River by a system of dams and huge reservoirs that were constructed and filled between 1933 and 1964; estimates of flood levels depend on location relative to these facilities. Further downstream, the lower Missouri River is lined with levees and has been transformed into a narrow navigational channel. Results are complex at and above Kansas City, because the channel at many sites has become incised due to decreased sediment loads. Below Kansas City, the water levels of significant floods are now higher than historical values, and official base flood levels are underestimated. Profound changes to the Missouri River have destabilized it in many complex ways, causing it to be less predictable, and many decades or centuries will be required for a new state of equilibrium to develop.