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Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters. [ABSTRACT FROM AUTHOR]

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson’s correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.

We investigate the correlation between Disturbance Storm Time (Dst) characteristics and solar wind conditions for the main phase of geomagnetic storms, seeking possible factors that distinguish extreme storms (minimum Dst

The suspended particulate organic matter (SPOM) in transitional waters such as the Maryland Coastal Bays (MCBs) is derived from allochthonous and autochthonous sources. Little is known, however, about the contribution of terrestrially derived organic matter to SPOM in the MCBs. The sources of SPOM in the MCBs were evaluated using stable isotope ratios of nitrogen (δ15N) and carbon (δ13C), and C/N molar ratios. The values of SPOM δ15N, δ13C and C/N ratios from samples collected seasonally (July 2014 to October 2017) at 13 sites ranged from −0.58 to 10.51‰, −26.85 to −20.33‰, and 1.67 to 11.36, respectively, indicating a mixture of terrestrial SPOM transported by tributaries, marine organic matter from phytoplankton, and sewage. SPOM δ13C levels less than −24‰, suggesting the dominance of terrestrially derived carbon, occurred mainly at sites close to the mouths of tributaries, and were less depleted at sites near the ocean. The mean value of SPOM δ13C was higher in October 2014 (−22.76‰) than in October 2015 (−24.65‰) and 2016 (−24.57‰) likely due to differences in river discharge. Much lower values (15N observed in February 2016 coincided with a high freshwater inflow that accompanied a major storm, indicating a strong influence of untreated sewage. Results from a two end-member mixing model suggest that on average, the SPOM in the MCBs is composed of 44% terrestrial materials with the highest percent contributions in October 2015 and 2016 (61%), and lowest (28%) in July 2015. The contribution of terrestrial materials to the SPOM was highest (58%) near the mouth of St. Martin River and lowest (25%) near the Ocean City inlet. SPOM composition and distribution in MCBs are, therefore, a function of land use, freshwater inflow, and water circulation that influence in situ phytoplankton production, and the transport and distribution of terrestrially derived materials.

The suspended particulate organic matter (SPOM) in transitional waters such as the Maryland Coastal Bays (MCBs) is derived from allochthonous and autochthonous sources. Little is known, however, about the contribution of terrestrially derived organic matter to SPOM in the MCBs. The sources of SPOM in the MCBs were evaluated using stable isotope ratios of nitrogen (δ15N) and carbon (δ13C), and C/N molar ratios. The values of SPOM δ15N, δ13C and C/N ratios from samples collected seasonally (July 2014 to October 2017) at 13 sites ranged from −0.58 to 10.51‰, −26.85 to −20.33‰, and 1.67 to 11.36, respectively, indicating a mixture of terrestrial SPOM transported by tributaries, marine organic matter from phytoplankton, and sewage. SPOM δ13C levels less than −24‰, suggesting the dominance of terrestrially derived carbon, occurred mainly at sites close to the mouths of tributaries, and were less depleted at sites near the ocean. The mean value of SPOM δ13C was higher in October 2014 (−22.76‰) than in October 2015 (−24.65‰) and 2016 (−24.57‰) likely due to differences in river discharge. Much lower values (<4‰) of δ15N observed in February 2016 coincided with a high freshwater inflow that accompanied a major storm, indicating a strong influence of untreated sewage. Results from a two end-member mixing model suggest that on average, the SPOM in the MCBs is composed of 44% terrestrial materials with the highest percent contributions in October 2015 and 2016 (61%), and lowest (28%) in July 2015. The contribution of terrestrial materials to the SPOM was highest (58%) near the mouth of St. Martin River and lowest (25%) near the Ocean City inlet. SPOM composition and distribution in MCBs are, therefore, a function of land use, freshwater inflow, and water circulation that influence in situ phytoplankton production, and the transport and distribution of terrestrially derived materials. [ABSTRACT FROM AUTHOR]