The Ganges‐Brahmaputra‐Meghna delta faces growing risks from subsidence, sediment depletion, and sea‐level rise. Sustaining delta elevation requires continued sediment supply, yet the spatial and seasonal dynamics of sediment retention across the fluvial system remain poorly resolved. This study employed a process‐based two‐dimensional model (Delft3D FM) to simulate suspended and bedload sediment transport across the Ganges, Brahmaputra and Padma Rivers in Bangladesh under contrasting monsoon conditions. Model results showed that over 35%–40% of annual suspended load was retained within the fluvial system, particularly in the upstream braided (Brahmaputra) and meandering (Ganges) reaches and their adjacent floodplains. The Padma reach also contributed notable retention, particularly under reduced Brahmaputra flow dominance. Seasonal variability governs sediment transport, with 80%–95% of annual suspended sediment delivered during the monsoon. Backwater effects in the Padma reach modulate sediment transport by altering flow gradients near fluvial‐tidal transition zone. However, sediment transport and retention within and upstream of this zone remain spatially variable, shaped by discharge intensity and channel morphology. Bedload transport remains active in the Brahmaputra reach but becomes increasingly variable downstream, especially in the Ganges‐Padma corridor. These findings clarify spatial contrasts in fluvial sediment routing and deposition, providing a system‐scale basis for mass balance assessments and sediment management. Identifying key zones of sediment retention and bypass is essential for maintaining sediment connectivity and supporting delta resilience under increasing environmental and anthropogenic stress.

Study Region -This study is conducted for a cluster of watersheds within the Matagorda Basin along the south Texas coast in the United States. Study Focus - Retrospective streamflow simulations of two hydrologic models of contrasting formulations and complexity, namely the TxRR, a simple analog model, and the National Water Model (NWM), a land surface-hydrologic model that explicitly accounts for surface energy balance in calculating water budget, and whose output. The comparison was motivated by a) the need for improving the modeling of runoff dynamics for watersheds along central Texas coast through the introduction of NWM to inform freshwater inflow monitoring and flood planning, and b) the need to better understand the potential ability of the NWM to address shortcomings of TxRR related to the latter’s mechanistic deficiencies in a region known for hydroclimatic extremes. The study focuses on the temporal scale and climate dependence in the relative performance of the two models, the behaviors of models during extreme floods and droughts, and the relative efficacy of parameter transfer schemes. It further seeks to relate the performance differentials to model physics and calibration approaches. New Hydrologic Insights – NWM, with sophisticated representations of energe closure, is advantageous to TxRR in capturing low-frequency (interannual) variations in streamflow modulated by the surface energy balance, as evidenced by the superior correlation of its streamflow simulations at the interannual range. Yet, its overall performance trails behind TxRR at daily scale, and it tends to underpredict runoff volumes during two major floods. These features underscore the tendency of the model to suppress soil moisture at the onset of these events. On the other hand, TxRR outperforms in reproducing the volumes for the flooding events, but it overpredicts runoff during the extreme drought episode of 2011, likely an outcome of inadequate representation of the impacts of root zone soil moisture in regulating runoff. TxRR’s parameteralization scheme proves more effective for adaptation across watersheds due to the presence of steep gradation in soil properties .

Prediction and reanalysis of storm surge rely on wind and pressure fields from either parametric tropical cyclone wind models or numerical weather model reanalysis, and both are subject to large errors during landfall. This study assesses two sets of wind/pressure fields for Hurricane Florence that made landfall along the Carolinas in September 2018, and appraises the impacts of differential structural errors in the two suites of modeled wind fields on the predictive accuracy of storm surge driven thereby. The first set was produced using Holland 2010 (H10), and the second set is the Hurricane Weather Research and Forecasting (HWRF) reanalysis created by the NWS National Centers for Environmental Prediction (NCEP). Each is validated using a large surface data set collected at public and commercial platforms, and then is used as input forcing to a 2-D coastal hydrodynamic model (Delft3D Flexible Mesh) to produce storm surge along the Carolina coasts and major sounds. Major findings include the following. First, wind fields from HWRF are overall more accurate than those based on H10 for the periphery of the storm, though they exhibit limitations in resolving high wind speeds near the center. Second, applying H10 to the best track data for Florence yields an erroneously spike in wind speed on September 15^th when the storm reduced to a tropical depression. Third, HWRF wind fields exhibit a progressively negative bias after landfall, likely due to deficiencies of the model in representing boundary layer processes, and to the lack of assimilation of surface product after landfall for compensating for these deficiencies. Fourth, using HWRF reanalysis as the forcings to Delft3D yields more accurate peak surges simulations, though there is severe underestimation of surge along the shoreline close to the track center. The peak surge simulations by Delft3D are biased low when driven by H10, even though over several locations the H10 model clearly overpredicts surface wind speeds. This contrast highlights the importance of resolving wind fields further away from the center in order to accurately reproduce storm surge and associated coastal flooding.

In this review, I embark on a critical examination of the manuscript "Forecasting by Analogy: A Parallel between the Trend of Confirmed COVID-19 Deaths in the Winters of 2022/2023 and 2023/2024 in Italy." The objective is to scrutinize the methodolog...