查看更多>>摘要：We are pleased to present this special issue of the Journal of Hydroinformatics, featuring selected papers from the 14th International Conference on Hydroinformatics (HIC 2022), held on 4-8 July 2022, in Bucharest, Romania. Organized by the Technical University of Civil Engineering Bucharest and the University 'Politehnica' of Bucharest, HIC 2022 marked a significant milestone for the hydroinformatics community, particularly as it was one of the first large scientific conferences to resume in-person meetings post-COVID. It was a unique opportunity for experts from around the world to reconnect, share ideas, and discuss the future of hydroinformatics in a rapidly evolving field.
P. L. Iglesias-ReyF. Martinez-AlzamoraF. J. Martinez- Solano
ⅲ-ⅴ页
查看更多>>摘要：The collection of articles in this Special Issue provides an overview of the advances and challenges in digitalizing water systems, addressing topics ranging from predictive modeling and network optimization to advanced monitoring and emerging technologies. Each of these areas tackles fundamental aspects of the transition toward Smart Water and the management of water resources in the context of increasing climatic and demographic pressure. The WDSA-CCWI 2022 conference emphasized the importance of these innovations, and the articles presented here illustrate how digital technologies are transforming the water sector into a smarter, more resilient, and sustainable future.
P. L. Iglesias-ReyF. Martinez-AlzamoraF. J. Martinez- Solano
ⅲ-ⅴ页
查看更多>>摘要：The collection of articles in this Special Issue provides an overview of the advances and challenges in digitalizing water systems, addressing topics ranging from predictive modeling and network optimization to advanced monitoring and emerging technologies. Each of these areas tackles fundamental aspects of the transition toward Smart Water and the management of water resources in the context of increasing climatic and demographic pressure. The WDSA-CCWI 2022 conference emphasized the importance of these innovations, and the articles presented here illustrate how digital technologies are transforming the water sector into a smarter, more resilient, and sustainable future.
查看更多>>摘要：Urban water bodies are often subject to high runoff temperatures from heat exchange between rainfall and urban surfaces; however, this process can be difficult to define due to the complexity of spatially heterogeneous urban areas. This research seeks to improve our understanding of heat exchange in urban stormwater runoff by integrating in situ measurements of runoff temperature with land surface temperature data captured in high spatial resolutions by a drone. To do so, this study monitored four urban catchments in Milwaukee, Wl that are dominated by different land surfaces (concrete parking lot, asphalt road, black bitumen roof, and grass). Results indicate that land surface temperature was variable among common land surface types (1.34-2.24 ℃), with higher variations in surfaces subject to foot and vehicular traffic. In addition, the temperature of runoff from impervious surfaces responded differently between buildings and those with a ground subsurface, with higher event mean temperatures from concrete (21.4 ℃) and asphalt (21.9 ℃) ground surfaces as compared with the bitumen roof (19.8 ℃), despite similar initial surface temperatures. Ultimately, these outcomes demonstrate how drone remote sensing of land surface temperature and in situ monitoring can be integrated to understand heat exchange processes in urban stormwater runoff.
查看更多>>摘要：The southern plain of Nepal recognized as the 'granary of Nepal', confronts recurrent monsoon-induced flooding, posing a substantial threat to its pivotal role as a major agricultural contributor to the national economy. As an analysis, this study employs advanced satellite imagery to delineate historical floods in nine flood-prone transboundary basins and compares the rainfall-induced model-based inundation in the West Rapti Basin (WRB) to validate the result. The extent of flooding was mapped between 2015 and 2022 using Sentinel-1 Synthetic Aperture Radar data processed on Google Earth Engine. Hydrodynamic modelling centred on the WRB, incorporated daily measured precipitation data with varying return periods over a 10 m resolution digital elevation model generated through an in situ survey. The model was calibrated for the August 2017 flood event with Nash-Sutcliffe efficiency greater than 70% and validation reasonably with satellite-derived flood maps with Cohen's Kappa value of 0.58 and an overall accuracy metric of 0.84. This synergic approach integrates climatology, remote sensing data, and hydraulics to monitor transboundary river floods in Nepal where precise hydro-meteorological data are limited, thus, offering continuous all-weather monitoring.
查看更多>>摘要：Accurate river discharge forecasts for short to intermediate time intervals are crucial for decision-making related to flood mitigation, the seamless operation of inland waterways management, and optimal dredging. River routing models that are physics based, such as RAPID ('routing application for parallel computation of discharge') or its variants, are used to forecast river discharge. These physics-based models make numerous assumptions, including linear process modeling, accounting for only adjacent river inflows, and requiring brute force calibration of hydrological input parameters. As a consequence of these assumptions and the missing information that describes the complex dynamics of rivers and their interaction with hydrology and topography, RAPID leads to noisy forecasts that may, at times, substantially deviate from the true gauged values. In this article, we propose hybrid river discharge forecast models that integrate physics-based RAPID simulation model with advanced data-driven machine learning (ML) models. They leverage runoff data of the watershed in the entire basin, consider the physics-based RAPID model, take into account the variability in predictions made by the physics-based model relative to the true gauged discharge values, and are built on state-of-the-art ML models with different complexities. We deploy two different algorithms to build these hybrid models, namely, delta learning and data augmentation. The results of a case study indicate that a hybrid model for discharge predictions outperforms RAPID in terms of overall performance. The prediction accuracy for various rivers in the case study can be improved by a factor of four to seven.
Mustafa AbedMonzur Alam lmteazYuk Feng HuangAli Najah Ahmed...
2538-2556页
查看更多>>摘要：In drought-prone regions like Australia, accurately assessing evaporation rates is essential for effectively managing and maximising the use of precious water resources and reservoirs. Current estimates show that evaporation reduces Australia's open water lake capacity by about 40% annually. With climate change, this water loss is expected to become an even greater concern. This study investigates a transformer-based neural network (TNN) to estimate monthly evaporation in three Australian locations. The models were trained and tested using monthly weather data spanning from 2009 to 2022. Input parameters were chosen based on Pearson's correlation coefficient values to identify the most impactful combinations. The developed TNN model was compared with two widely used empirical methods, namely Thornthwaite and Stephens and Stewart. The TNN model's impressive accuracy in evaporation prediction, attributed to its unique self-attention mechanism, suggests its promising potential for future use in evaporation forecasting. Additionally, the study revealed an intriguing result: Despite using the same input datasets, the TNN model surpassed traditional methods, achieving an average improvement of 18% in prediction accuracy. The TNN prediction model accurately predicts water loss (average R~2 = 0.970), supports irrigation management and agricultural planning and offers financial benefits to farming and related industries.
查看更多>>摘要：Stairs in subway stations are vulnerable to floods when rainstorm disasters occur in cities. The stairs, as a critical way for human evacuation, can affect the safe evacuation of people on flood-prone stairs. To evaluate the risk of people evacuating through different slopes and forms of stairs when floods invade subway stations, a numerical model for the water flow on stairs based on the volume of fluid model and the realizable k-e model was established. The water flow patterns on stairs at the subway station entrance under different slope conditions and with/ without rest platforms were simulated. The real-time water flow process on stairs at different inlet depths was obtained, and the escape control index F was used to evaluate the risk of people evacuating through stairs at different slopes and water depths. The results indicate that the presence of a rest platform can cause an increase in water velocity and depth on pedestrian stairs, and people should choose stairs without a rest platform for evacuation during the evacuation process. The research results hope to provide a reference for the people evacuation on stairs, and further improve the theory of safe evacuation of personnel on flood-prone stairs.
Saeed MemariMantha S. PhanikumarVishnu BoddetiNarendra N. Das...
2581-2600页
查看更多>>摘要：Machine learning models for water quality prediction often face challenges due to insufficient data and uneven spatial-temporal distributions. To address these issues, we introduce a framework combining machine learning, numerical modeling, and remote sensing imagery to predict coastal water turbidity, a key water quality proxy. This approach was tested in the Great Lakes region, specifically Cleveland Harbor, Lake Erie. We trained models using observed data and synthetic data from 3D numerical models and tested them against in situ and remote sensing data from PlanetLabs' Dove satellites. High-resolution (HR) data improved prediction accuracy, with RMSE values of 0.154 and 0.146 logiO(FNU) and R2 values of 0.92 and 0.93 for validation and test datasets, respectively. Our study highlights the importance of unified turbidity measures for data comparability. The machine learning model demonstrated skill in predicting turbidity through transfer learning, indicating applicability in diverse, data-scarce regions. This approach can enhance decision support systems for coastal environments by providing accurate, timely predictions of water quality variables. Our methodology offers robust strategies for turbidity and water quality monitoring and has potential for improving input data quality for numerical models and developing predictive models from remote sensing data.
查看更多>>摘要：in the present study, a new theoretical framework and analytical solutions to the problem of hysteresis loops due to hydraulic jump in power-law open channels under supercritical flows are introduced. This investigation primarily focuses on the flow dynamics encountered at a vertical sluice gate across channels of various shapes: rectangular, parabolic, and triangular. By the application of energy and momentum conservation principles, the dual flow configurations emerging under identical initial conditions are shown. An intensive analytical computational analysis led to the development of approximate theoretical models, facilitating the prediction of hysteresis loops for a wide range of Froude numbers and dimensionless channel geometry parameters. Several illustrative examples were treated and the results show a high degree of accuracy of the proposed models in predicting the hysteresis loops. The present research contributes to a novel methodology for enhancing predictions regarding the behavior of supercritical flows and the design of open channels for specific scenarios of the hysteresis phenomenon.
NETL
NSTL
Iwa Publishing