To materialize the inherent opportunities of incoming sediment load, various interventions are being practiced for sediment management in rivers and delta systems. Broadly, the practiced methods can be divided into two categories: (i) management for accelerating siltation to enhance land reclamation and counterbalancing bank erosion (ii) management for de-siltation in the channels to maintain required navigational flow depth. The prerequisites for achieving the above targets at the same time are quite contradictory, as land reclamation requires more sediment, while maintaining navigational depth requires less sediment load. To address the aforementioned constraints, Bandal-like Structures (BLS), an indigenous nature-based solution, has proposed for redistributing the local flow-sediment regime to create a sediment deficit zone within the river side and a sediment surplus zone along the bankside, which can eventually be useful for the maintenance of navigational channels as well as bank stabilization. Based on research conducted over the last 20 years, this article discusses BLS's experiences in achieving both functions simultaneously. A well-documented case study of its application along a reach of the braided Jamuna River is one of many implemented BLS at various scales in the Ganges-Jamuna-Brahmaputra (GBM) system. Based on the performance of the implemented case study and existing indigenous knowledge, a hybrid approach integrating conventional and community sciences is proposed as a nature-based solution for sustainable sediment management in Bangladesh's river systems.

Motivated by the observation that vortex flow structure was evident in the energy loss at the surcharged junction manhole due to changes of hydraulic and geometrical parameters, a physical model was used to calculate energy loss coefficients and investigate the relationship between flow structure and energy loss at the surcharged three-way junction manhole. The effects of the flow discharge ratio, the connected angle between two inflow pipes, the manhole geometry, and the downstream water depth on the energy loss were analyzed based on the quantified energy loss coefficients and the identified flow structure. Moreover, two empirical formulae for head loss coefficients were validated by the experimental data. Results indicate that the effect of flow discharge ratio and connected angle are significant, while the effect of downstream water depth is not obvious. With the increase of the lateral inflow discharge, the flow velocity distribution and vortex structure are both enhanced. It is also found that a circular manhole can reduce local energy loss when compared to a square manhole. In addition, the tested empirical formulae can reproduce the trend of total head loss coefficient.

Streamflow gauging records remain sparse in many regions, limiting hydrological predictions. Transfer Learning (TL), which uses knowledge from data‐rich sources to improve predictions in data‐scarce targets, has emerged as a promising solution. Most TL applications in hydrology pre‐train deep learning (DL) models on large meteo‐hydrological data sets to enhance generalization across diverse catchments. However, this strategy faces two key limitations: (a) TL performance for a specific target may be hindered by irrelevant source inputs and suboptimal fusion of source information; and (b) TL data requirements are demanding. Here, we propose a Multi‐Source Adaptive‐Fusion Transfer Learning (MSAF‐TL) network to address these limitations. MSAF‐TL first extracts independent temporal features from each highly related source catchment and then weights them based on the similarity of their static attributes to those of the target. These weighted features are adaptively fused using a cross‐attention mechanism, strengthening the final target prediction. We test this framework using a large‐sample hydrological data set for Great Britain (CAMELS‐GB). Results show that small sets of highly similar source catchments can match the transferability of large data sets. For a target catchment with only 1 year of training data, MSAF‐TL achieves an NSE of 0.85 using only the top two similar sources, compared with 0.78 when using all 668 CAMELS‐GB catchments. With limited sources, MSAF‐TL consistently outperforms conventional multi‐source TL (MSTL) across varying source quantities and target conditions. This performance gap widens under greater data scarcity, with average NSE improvements over MSTL reaching 22% when only 6 months of target training data are available. Overall, this study demonstrates that MSAF‐TL can achieve more accurate and stable TL performance in data‐scarce regions, and that data requirements can be reduced without compromising performance through source pre‐selection.

Against the backdrop of extreme weather, flooding has become one of Taiwan’s main disaster risks, highlighting the importance of promoting self-reliant disaster prevention communities and strengthening residents’ autonomous response capabilities. This study aims to examine community residents’ perceptions of flood risk and their disaster preparedness attitudes, while analyzing the influence of participation in disaster prevention education and demographic background variables. In the results of an online questionnaire survey and inferential statistical analysis, residents who participated in disaster prevention education demonstrated significantly higher risk perceptions and disaster preparedness attitudes than non-participants. The participants showed a significantly increased willingness to use the mobile water situation app (a government-provided real-time flood information application), strongly proving the effectiveness of localized educational interventions in promoting the application of digital disaster prevention tools. The analysis results of demographic variables revealed that age and years of community residence significantly influenced disaster preparedness attitudes and participation willingness. Overall, this study confirms that localized disaster prevention education effectively enhances community resilience, providing an empirical foundation for advancing self-reliant disaster prevention communities and refining disaster prevention and mitigation policies.