Search Results (4)

Flood simulation in small- and medium-sized catchments is hindered by data scarcity and strong hydroclimatic heterogeneity. Distributed models with pedotransfer functions offer new opportunities, yet their parameter sensitivity and regional applicability remain insufficiently understood. In this study, the wflow_sbm model was applied to two catchments: the humid Tunxi basin and the semi-humid Chenhe basin, China. Model seamless parameters, defined as spatially continuous fields derived directly from global datasets using pedotransfer functions without local calibration, were generated using the HydroMT system. The parameter sensitivity, applicability of pedotransfer function derived parameters, and model performance were systematically evaluated and benchmarked against the well-established Xin’anjiang (XAJ) model, which is a conceptual lumped hydrological model widely used for flood simulation in humid and semi-humid regions of China. Sensitivity analysis identified KsatHorFrac and InfiltCapSoil as dominant in Tunxi, and KsatHorFrac and SoilThickness in Chenhe. SoilThickness derived by HydroMT underestimated flood volumes in the Chenhe basin but was substantially improved after applying a uniform scaling factor of 0.1, resulting in an effective SoilThickness of approximately 0.2 m. The wflow_sbm model achieved performance comparable to the XAJ model. Optimal calibration achieved N_SE = 0.85 in Tunxi with good performance at internal sub-catchments (Yuetan and Chengcun, N_SE > 0.70), and generally above 0.7 in Chenhe. These findings highlight the region-dependent validity of parameterization and provide guidance for distributed flood modeling in data-scarce basins. Full article

Plastic pollution is an increasing global concern, with estuaries being especially vulnerable as transition zones between freshwater and marine systems. These ecosystems often accumulate large amounts of waste, affecting wildlife and water quality. This study focuses on analysing the circulation patterns of the Ave Estuary, a small, shallow system on Portugal’s north-western coast, and their influence on litter transport and distribution. This site was selected for installing an aquatic litter removal technology under the EU-funded MAELSTROM project. A 2DH hydrodynamic model using Delft3D FM, coupled with the Wflow hydrological model, was implemented and validated. Various scenarios were simulated to assess estuarine dynamics and pinpoint zones prone to litter accumulation and flood risk. The results show that tidal action and river discharge mainly drive the estuary’s behaviour. Under low discharge, floating litter should be mostly transported toward the ocean, while high discharge conditions should result in litter movement at all depths due to stronger currents. High water levels and flooding occur mainly upstream and in specific low-lying areas near the mouth. Low-velocity zones, which can favour litter accumulation, were found around the main channel and on the western margin near the estuary’s mouth, even during high flows. These findings highlight persistent accumulation zones, even under extreme event conditions. Full article

Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and daily actual evapotranspiration (DAE) by integrating climate data and geophysical insights, with a focus on mountainous areas such as the Adige catchment. The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. Model accuracy is assessed by comparing the similarity of Fuzzy C-Means (FCM) clusters before and after fusion, providing a metric for feature reduction. A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). Furthermore, Kernel density estimation is used to ensure the reproducibility of the RFC-GBR process across large-scale applications. A comparative analysis is conducted across multiple SDL architectures, including LSTM, GRU, TCN, and ConvLSTM, over 50 epochs to better evaluate the beneficial effect of the transformed parameters on model performance and accuracy. Results indicate that adjusted parameter calibration improves model performance in all cases, with better alignment to Wflow ground truth during both wet and dry periods. The proposed model increases the accuracy by 20% to 42% when using simpler SDL models like LSTM and GRU, even with fewer epochs. Full article

Distributed attenuation in flood management relies on small and low-impact runoff attenuating features variously distributed within a catchment. Distributed systems of reservoirs, natural flood management, and green infrastructure are practical examples of distributed attenuation. The effectiveness of attenuating features lies in their ability to work in concert, by reducing and slowing runoff in strategic parts of the catchment, and desynchronizing flows. The spatial distribution of attenuating features plays an essential role in the process. This article proposes a framework to place features in a hydrologic network, group them into spatially distributed systems, and analyze their flood attenuation effects. The framework is applied to study distributed systems of reservoirs in a rural watershed in Iowa, USA. The results show that distributed attenuation can be an effective alternative to a single centralized flood mitigation approach. The different flow peak attenuation of considered distributed systems suggest that the spatial distribution of features significantly influences flood magnitude at the catchment scale. The proposed framework can be applied to examine the effectiveness of distributed attenuation, and its viability as a widespread flood attenuation strategy in different landscapes and at multiple scales. Full article