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Accurate streamflow prediction can mitigate flood losses, optimize power generation, and reduce drought impacts. Streamflow time series has many inherent natural frequencies driven by both climate cycles and watershed characteristics. Decomposition methods have been used to isolate underlying fluctuations related to influencing variables such as climate oscillations from a single streamflow time series. Variational mode decomposition (VMD) was recently developed to improve upon common decomposition methods. These methods are known to suffer from various limitations, including the time–frequency trade-off, boundary effect (not significant in hindcasting), and predefined bases functions. Extreme gradient descent boosting (XGBoost) is an increasingly popular ML approach that has shown promise in many fields but has not been thoroughly applied to streamflow forecasting. This study develops a VMD-XGBoost model for daily streamflow forecasting. Since XGBoost allows for a customizable loss function, various loss functions are implemented in model training. Specifically, a seldom-recognized forecasting performance measure, horizontal error (HE), is used to improve model susceptibility to imitation error. The VMD-XGBoost model is compared to a standalone XGBoost model. It highlights that VMD significantly improves forecasting, by reducing HE from 0.94 to 0.41 while improving NSE from 0.82 to 0.84, and bias from 1.20 m3/s to 0.20 m3/s. [ABSTRACT FROM AUTHOR]

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood forecasting accuracy by integrating geo-spatiotemporal analyses, cascading dimensionality reduction, and SageFormer-based multi-step-ahead predictions. The framework efficiently processes satellite-derived data, addressing the curse of dimensionality and focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- and intra-dependencies within a compressed feature space, making it particularly effective for long-term forecasting. Performance evaluations against LSTM, Transformer, and Informer across three data fusion scenarios reveal substantial improvements in forecasting accuracy, especially in data-scarce basins. The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. The proposed framework combines cascading dimensionality reduction with advanced deep learning, enhancing both interpretability and precision in capturing complex dependencies. By offering a straightforward and reliable approach, this study advances remote sensing applications in hydrological modeling, providing a robust tool for mitigating the impacts of hydroclimatic extremes. [ABSTRACT FROM AUTHOR]

Hydrological models and quantitative precipitation estimation (QPE) are critical elements of flood forecasting systems. Both are subject to considerable uncertainties. Quantifying their relative contribution to the forecasted streamflow and flood uncertainty has remained challenging. Past work documented in the literature focused on one of these elements separately from the other. With this in mind, we present a systematic approach to assess the impact of QPE uncertainty in streamflow forecasting. Our approach explores the operational Iowa Flood Center (IFC) hydrological model performance after altering two radar-based QPE products. We ran the Hillslope Link Model (HLM) for Iowa between 2015 and 2020, altering the Multi-Radar/Multi-Sensor (MRMS) system and the specific attenuation-based (IFCA) IFC radar-derived product with a multiplicative error term. We assessed the forecasting system performance at 112 USGS streamflow gauges using the altered QPE products. Our results suggest that addressing rainfall uncertainty has the potential for much-improved flood forecasting spatially and seasonally. We identified spatial patterns linking prediction improvements to the radar's location and the magnitude of rainfall. Also, we observed seasonal trends suggesting underestimations during the cold season (October–April). The patterns for different radar products are generally similar but also show some differences, implying that the QPE algorithm plays a role. This study's results are a step toward separating modeling and QPE uncertainties. Future work involving larger areas and different hydrological and error models is essential to improve our understanding of the impact of QPE uncertainty. Significance Statement: This study investigates the impact of radar rainfall on flood forecasting uncertainty. Previous research focused on rainfall–runoff models, ignoring the errors in rainfall estimation. We used a systematic approach to adjust two radar-rainfall products, forcing a simple hydrological model. Results show the potential improvement in streamflow prediction by correcting basinwide bias in rainfall. The optimal correction varies with basin size, location, season, and rainfall amount. [ABSTRACT FROM AUTHOR]

Severe flooding induced by heavy rainfall presents a significant challenge in metropolitan areas worldwide. Despite the imperative for effective preflood emergency management, existing early warning systems rely primarily on meteorological or hydrological factors. These factors are often mapped with coarse spatial-temporal resolution and lack direct correlations to the immediate consequences of hazards, thus hindering fine-grained estimation of evacuation demand to support proactive preflood evacuation planning. To address this gap, our study makes a twofold contribution: (1) introduction of a flood-specific, evacuation-oriented, and multilayered building functionality metric (BFM-Flooding); this metric assesses building functionality states under flooding by aggregating building damage conditions at three distinct layers of granularity while accounting for disruptions in supporting utilities. (2) Establishment of a comprehensive building functionality state forecasting framework (BFS-Forecasting); the framework delves into the temporal transition dynamics between different functionality states for different building archetypes; it predicts the spatiotemporal progression of functionality states for building portfolios and the resulting evacuation demand within a community during flood evolution. Finally, an illustration using Suzhou Industrial Park (SIP) in Jiangsu Province, China, under severe rainstorm conditions demonstrates that the twofold solution can provide valuable support to policymakers in making well-informed preflood evacuation decisions at a fine spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

The article "Enhancing weather predictions: Initial condition sensitivity and error dynamics in data assimilation" published in SCIENCE CHINA Earth Sciences discusses the importance of Data Assimilation (DA) in improving weather forecasts by incorporating real-time observational data. The study emphasizes the significance of accurate initial conditions in numerical weather prediction and explores how errors in initial conditions can lead to forecast inaccuracies. The research by Wang et al. (2024) delves into the impact of different components of DA on initial conditions and error growth, highlighting the need for continuous DA to correct discrepancies and maintain forecast reliability. The article also mentions the broader implications of advancements in DA techniques on fields like hydrology and ocean circulation modeling. [Extracted from the article]

Copyright of Nonferrous Metals (Extractive Metallurgy) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A reliable and accurate flood forecasting procedure is crucial due to the hazardous nature of such disasters. Despite the growing interest in machine learning models over traditional methods for enhanced accuracy, there is a notable gap in guidelines for selecting the best-suited learning algorithm for flood forecasting. Furthermore, there is a lack of deep learning–based flood simulation studies in the South Asian Tropical Zone. This research addresses these gaps by investigating the viability of artificial neural network (ANN), long short-term memory (LSTM), bidirectional LSTM (BLSTM), two-dimensional (2D) convolutional LSTM (ConvLSTM2D), and transformer models for multiple-day-ahead flood simulation. A forecasting window of 3 days was selected for the task, focusing on the lower reaches of the Mahaweli catchment in Sri Lanka, which is heavily affected by the Northeast Monsoon. Observed rainfall data from three nearby rain gauges and historical discharges from the target river gauge serve as input features for the models. Unlike previous studies that used limited data sets, this study utilizes daily data spanning 28 years in order to examine the behavior of the transformer handling an extensive hydrological data set. The study finds that the ANN model performed the worst, with a mean Nash-Sutcliffe efficiency (NSE) of 0.67, while the transformer model showed superior performance, especially in multiday forecasts, with a mean NSE of 0.72 and a mean root-mean square error of 32.52, showcasing the effectiveness of handling this extensive data set. Practical Applications: Artificial intelligence (AI) is the most important invention of this era. Deep learning (DL) is a subset of AI, which tries to imitate the way the human brain works. Using these powerful DL techniques, Machines are trained to identify patterns in data and use the learned patterns to forecast future values. In this paper, we have investigated the performances of five DL techniques in flood forecasting. Predicting future flood conditions is an important subject area that helps to save lives and property in flood-prone regions. Accurate and timely flood forecasts allow disaster management agencies, and other relevant authorities for early evacuation of people, ultimately lowering the loss of life. Our results indicate that transformer, the latest DL architecture, has the highest accuracy among the models. Transformer architecture was first proposed in 2019 and started a revolution in AI. Large language models such as Open AI's ChatGPT and Google's Gemini are based on this architecture. In our opinion, transformer-based models perform well as they capture the underlying patterns of the data in a comprehensive manner compared to other traditional DL models. [ABSTRACT FROM AUTHOR]

CO2 flooding plays a crucial role in enhancing oil recovery and achieving carbon reduction targets, particularly in unconventional reservoirs with complex pore structures. The phase behavior of CO2 and hydrocarbons at different scales significantly affects oil recovery efficiency, yet its underlying mechanisms remain insufficiently understood. This study improves existing thermodynamic models by introducing Helmholtz free energy as a convergence criterion and incorporating adsorption effects in micro- and nano-scale pores. This study refines existing thermodynamic models by incorporating Helmholtz free energy as a convergence criterion, offering a more accurate representation of confined phase behavior. Unlike conventional Gibbs free energy-based models, this approach effectively accounts for confinement-induced deviations in phase equilibrium, ensuring improved predictive accuracy for nanoscale reservoirs. Additionally, adsorption effects in micro- and nano-scale pores are explicitly integrated to enhance model reliability. A multi-scale thermodynamic model for CO2-hydrocarbon systems is developed and validated through physical simulations. Key findings indicate that as the scale decreases from bulk to 10 nm, the bubble point pressure shows a deviation of 5% to 23%, while the density of confined fluids increases by approximately 2%. The results also reveal that smaller pores restrict gas expansion, leading to an enhanced CO2 solubility effect and stronger phase mixing behavior. Through phase diagram analysis, density expansion, multi-stage contact, and differential separation simulations, we further clarify how confinement influences CO2 injection efficiency. These findings provide new insights into phase behavior changes in confined porous media, improving the accuracy of CO2 flooding predictions. The proposed model offers a more precise framework for evaluating phase transitions in unconventional reservoirs, aiding in the optimization of CO2-based enhanced oil recovery strategies. [ABSTRACT FROM AUTHOR]

To improve flood forecasting accuracy, the dynamic system response curve correction method was employed to invert and establish an error time series of areal rainfall in the Shouxi River Basin in Sichuan Province and the Qingyangcha Basin in Shaanxi Province. The areal rainfall in the watershed was corrected using the obtained error time series. The corrected areal rainfall was then used as input for flood forecasting using the excess storage and excess infiltration simultaneously model. Additionally, a hierarchical optimization method and LSTM error output correction method were applied to calibrate the three sources of errors. The results showed that the accuracy of flood peak discharge improved after the correction of areal rainfall. Specifically, in the validation set of the Shouxi River Basin, the absolute error of flood peak discharge decreased by 0.56% to 6.3% for 12 out of 15 flood events. The Nash–Sutcliffe Efficiency (NSE) of flood discharge increased by 0.002 to 0.015 for 13 flood events, and the time lag of two flood peaks shortened by 1 h. In the validation set of the Qingyangcha Basin, the absolute error of flood peak discharge decreased by 0.23% to 5.49% for 5 out of 6 flood events. The NSE of flood discharge increased by 0.01 to 0.071 for 5 flood events, and the time lag of two flood peaks shortened by 1 h. Overall, the results demonstrate that this method can reduce the forecast error and improve the accuracy of flood forecasting in the watershed. [ABSTRACT FROM AUTHOR]

The scenarios when heavy rainfall and high tides occur in succession or simultaneously can lead to compound flooding. Compound floods exhibit greater destructiveness than floods caused by one driver in coastal cities. Prediction for compound floods with real-time and high accuracy can contribute to mitigating the losses caused by floods. However, existing rapid forecasting studies neglect the compound impact of rainfall and tides in coastal floods. In this study, the information on rainfall and tides is utilized as input features to capture the drivers of compound flooding. To reduce the risk of overfitting, the light gradient boosting machine (LightGBM) is employed for feature selection. The one-dimensional convolutional neural network (CNN) is then trained on the reduced-dimensionality data. Hence, we construct LightGBM-CNN to predict flood distribution in coastal cities. The model is applied on Haidian Island, Hainan Province, China. The results indicate that incorporating rainfall and tides as input features significantly reduced the mean absolute error (MAE) from 0.179 to 0.044 and the root mean square error (RMSE) from 0.223 to 0.101, compared to using rainfall as input features. Compared to the CNN without feature selection using LightGBM, the performance of LightGBM-CNN has shown a significant improvement. The results suggest that the LightGBM-CNN offers a foundational reference for compound flood forecasting in coastal cities. [ABSTRACT FROM AUTHOR]

Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The asynchronous ensemble Kalman filter (AEnKF), an adaptation of the ensemble Kalman filter (EnKF), is capable of utilizing observations from both the assimilation moment and the preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both the observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. Tests involving both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF. [ABSTRACT FROM AUTHOR]

The general unit hydrograph (GUH), recently established by Guo, represents the most advanced hydrograph model today, but how to implement it with hydrologic data is another story. In this work, an effective initial value-based method for estimating the parameters in the GUH model is proposed and applied to the analysis of flood processes. In contrast to the flood-rainfall united fitting method, which heavily depends on the flood records and has a broad range of parameter variations, which makes it practically intractable, the initial value-based method enables the calculation of model parameters directly from the measured rainstorm data and greatly enriches the discharge dataset so that more accurate prediction of flood processes becomes achievable. From the data collected from several watersheds, we find that smaller-shape parameters usually indicate a multi-peak flood process, and the rainfall patterns have a significant impact on flood peaks. These results provide a reliable approach for the prediction of floods in streams with scarce discharge data. Additionally, it is observed that the peak time lags have a notable increase from the southwest to the northeast of Zhejiang. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of Generalised Additive Models (GAMs) and log-log linear models for estimating the parameters of the generalised extreme value (GEV) distribution, which are then used to estimate flood quantiles in ungauged catchments. This is known as the parameter regression technique (PRT). Using data from 88 gauged catchments in New South Wales, Australia, flood quantiles were estimated for various annual exceedance probabilities (AEPs) of 50%, 20%, 10%, 5%, 2%, and 1%, corresponding to return periods of 2, 5, 10, 20, 50, and 100 years, denoted by Q2, Q5, Q10, Q20, Q50, and Q100, respectively. These flood quantiles were then used as dependent variables, while several catchment characteristics served as independent variables in the regression. GAMs were employed to capture non-linearities in flood generation processes. This study evaluates different GAMs and log-log linear models, identifying the best ones based on significant predictors and various statistical metrics using a leave-one-out (LOO) validation approach. The results indicate that GAMs provide more accurate and reliable predictions of flood quantiles compared to the log-log linear models, demonstrating better performance in capturing observed values across different quantiles. The absolute median relative error percentage (REr%) ranges from 33% to 39% for the GAMs and from 36% to 45% for the log-log models. GAMs demonstrate better performance compared to the log-log linear models for quantiles Q2, Q5, Q10, Q20, and Q50; however, their performances appear to be similar for Q100. [ABSTRACT FROM AUTHOR]

Pakistan's geographic location makes it an important land hub between Central Asia, Middle East-North Africa, and China. However, the railways, roads, farmland, riverways, and residential quarters in the Piedmont plains of Baluchistan province in northwestern Pakistan are under serious threat of flooding in the summer of 2022. The urgency and severity of climate change's impact on humanity are underscored by the significant threats posed to human life and property in Piedmont Plains environments through extreme flood events, which has garnered widespread concerns. In flood scenarios, accurately predicting the extent of flooding is crucial for disaster assessment, emergency response, and the efficient allocation of resources. Previous research has primarily predicted flooding likelihood based on topographical factors or integrated annual rainfall data, failing to account for the extent of flooding from short-term rainfall before and after an event. Flood disasters are not caused by a single factor but are influenced by a variety of elements, including terrain and climate. Therefore, current research still lacks a comprehensive consideration of these influencing factors to accurately predict both the range and severity of flood impacts. In this paper, in response to the inability to accurately predict the flood damage in the pre-hill plains region in previous studies, combined with the current Pakistan mega-flood disaster, will couple the impacts of various flood-inducing factors on flooding, construct a prediction model for the degree of inundation of the Pakistani pre-hill plains flood disaster, and combined with the distribution of regional bearers, analyze the risk-resistant capacity of different types of bearers, and draw a comprehensive risk map piece under the flooding disaster. This paper bridges the gap of not integrating various factors in previous studies. Our research results provide strong evidence for flood prediction in Pakistan and similar regions, which is of great significance in reducing the loss of life and property of people around the world. [ABSTRACT FROM AUTHOR]

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and for mitigating the adverse impacts of flood events. While deep learning methods have demonstrated exceptional performance in time series prediction through advanced feature extraction and pattern recognition, they encounter significant limitations when applied to scenarios with sparse data, especially in flood forecasting. The scarcity of historical data can severely hinder the generalization capabilities of traditional deep learning models, presenting a notable challenge in practical flood prediction applications. To address this issue, we introduce MetaTrans-FSTSF, a pioneering meta-learning framework that redefines few-shot time series forecasting. By innovatively integrating MAML and Transformer architectures, our framework provides a specialized solution tailored for the unique challenges of flood prediction, including data scarcity and complex temporal patterns. This framework goes beyond standard implementations, delivering significant improvements in predictive accuracy and adaptability. Our approach leverages Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to new forecasting tasks with minimal historical data. Our inner architecture is a Transformer-based meta-predictor capable of capturing intricate temporal dependencies inherent in flood time series data. Our framework was evaluated using diverse datasets, including a real-world hydrological dataset from a small catchment area in Wuyuan, China, and other benchmark time series datasets. These datasets were preprocessed to align with the meta-learning approach, ensuring their suitability for tasks with limited data availability. Through extensive evaluation, we demonstrate that MetaTrans-FSTSF substantially improves predictive accuracy, achieving a reduction of up to 16%, 19%, and 8% in MAE compared to state-of-the-art methods. This study highlights the efficacy of meta-learning techniques in overcoming the limitations posed by data scarcity and enhancing flood forecasting accuracy where historical data are limited. [ABSTRACT FROM AUTHOR]

Weather radar plays a critical role in flash flood forecasting, providing an effective and comprehensive guide for the identification of possible flood-prone areas. However, the utilization of radar precipitation data remains limited in current research and applications, particularly in addressing flash flood hazards in complex environments such as in Athens, Greece. To address this gap, this study introduces the Gridded Flash Flood Guidance (GFFG) method, a short-term flash flood forecasting and warning technology based on radar precipitation and hydrological model coupling, and implements it in the region of Athens, Greece. The GFFG system improves upon the traditional flash flood guidance (FFG) concept by better integrating the weather radar dataset's spatial and temporal flexibility, leading to increased resolution results. Results from six flood events underscore its ability to identify high-risk areas dynamically, with urban regions frequently flagged for flooding unless initial soil moisture conditions are low. Moreover, the sensitivity analysis of the system showed that the most crucial parameter apart from rainfall input is the soil moisture conditions, which define the amount of effective rainfall. This study highlights the significance of incorporating radar precipitation and real-time soil moisture assessments to improve flood prediction accuracy and provide valuable flood risk assessments. [ABSTRACT FROM AUTHOR]

Flooding is a natural event often associated with floodplain areas, characterised by large, sudden and significant rises in river water levels that drastically alters the surrounding landscape. The research employs ArcGIS tools, multi-criteria evaluation techniques and the Maximum Entropy (MaxEnt) model to assess flood hazard zones. The key physical elements of slope, elevation, rainfall, drainage density, land use, and soil types have been integrated to identify areas vulnerable to flooding. Overlay analysis has been used to construct zones specifically designated for flood hazards. Additionally, pairwise comparison using Saaty's scale was employed to calculate the Eigenvector weights for each physical factor. A comparison of AUC values is estimated to find the most effective method for delineating flood hazard zones. The MaxEnt model achieved an Area Under Curve (AUC) of 0.978, outperforming the Analytical hierarchy Process (AHP) model with an AUC of 0.967. The higher AUC indicates that the MaxEnt model is better at distinguishing between positive and negative occurrences. This could lead to more reliable predictions of the flood hazard zones. Overall, the higher AUC of the MaxEnt model suggests greater reliability and robustness. [ABSTRACT FROM AUTHOR]

In recent years, many researchers have used machine learning approaches to bridge the relationship between big data and physics in the practical engineering field. However, the widely used machine learning models are highly dependent on the quality and quantity of data. These long-term monitoring data usually are expensive to obtain in water system. This paper presents a novel neural network structure, the physics-informed neural network (PINN), which can implement the shallow-water equations (SWEs) directly so that the training stage is based fully on physical laws. Similar to numerical models, our PINN model requires the same data as the numerical method, e.g., boundary conditions, the digital elevation of the terrain, and so forth. Because the SWEs are solved directly in our framework, this framework can be understood as a data-free method. The PINN was tested using two case studies: a flow spike in a hypothetical trapezoidal channel, and a historical scenario of downstream Cypress Creek, Houston. The results indicated great agreement with the widely used numerical solver, HEC-RAS. [ABSTRACT FROM AUTHOR]

Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future. This study assesses the projected changes in flood characteristics based on eight downscaled and bias-corrected General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6. The analysis considers two emission scenarios, including SSP2-4.5 and SSP5-8.5 for far-future (2070–2100), mid-term future (2040–2070), and historical (1982–2012) periods. Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. Then, a trivariate copula-based framework assesses flood events in terms of duration, volume, and flood peak in the Kan River basin, Iran. These analyses are carried out using the hierarchical Archimedean copula in three structures, and their accuracy in estimating the flood frequencies is ultimately compared. The results show that a heterogeneous asymmetric copula offers more flexibility to capture varying degrees of asymmetry across different parts of the distribution, leading to more accurate modeling results compared to homogeneous asymmetric and symmetric copulas. Also it has been found that climate change can influence the trivariate joint return periods, particularly in the far future. In other words, flood frequency may increase by approximately 50% in some cases in the far future compared to the mid-term future and historical period. This demonstrates that flood characteristics are expected to show nonstationary behavior in the future as a result of climate change. The results provide insightful information for managing and accessing flood risk in a dynamic environment. [ABSTRACT FROM AUTHOR]

This work presents the approach used to estimate coastal flood impact, developed within the EU H2020 European Coastal Flood Awareness System (ECFAS) project, for assessing flood direct impacts on population, buildings, and roads along European coasts. The methodology integrates object-based and probabilistic evaluations to provide uncertainty estimates for damage assessment. The approach underwent a user-driven co-evaluation process. It was applied to 16 test cases across Europe and validated against reported impact data in three major reference cases: Xynthia in La Faute-sur-Mer (France) in 2010, Xaver in Norfolk (UK) in 2013, and Emma in Cádiz (Spain) in 2018. A comparison with grid-based damage evaluation methods was also conducted. The findings demonstrate that the ECFAS impact approach offers valuable estimates for affected populations, reliable damage assessments for buildings and roads, and improved accuracy compared to traditional grid-based approaches. The methodology also provides information for prevention and preparedness activities, and it facilitates further evaluations of risk scenarios and cost–benefit analysis of disaster risk reduction strategies. The approach is a tool suitable for large-scale coastal flood impact assessments, offering improved accuracy and operational capability for coastal flood forecasts. It represents a potential advancement of the existing European-scale impact method used by the European Flood Awareness System (EFAS) for riverine flood warnings. The integration of object-based and probabilistic evaluations, along with uncertainty estimation, enhances the understanding and management of flood impacts along European coasts. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately modeling and predicting flood flows across multiple sites within a watershed presents significant challenges due to potential issues of insufficient accuracy and excessive computational demands in existing methodologies. In response to these challenges, this study introduces a novel approach centered around the use of vine copula models, termed RDV-Copula (reduced-dimension vine copula construction approach). The core of this methodology lies in its ability to integrate and extract complex data before constructing the copula function, thus preserving the intricate spatial–temporal connections among multiple sites while substantially reducing the vine copula's complexity. This study performs a synchronization frequency analysis using the devised copula models, offering valuable insights into flood encounter probabilities. Additionally, the innovative approach undergoes validation by comparison with three benchmark models which vary in dimensions and nature of variable interactions. Furthermore, the study conducts stochastic simulations, exploring both unconditional and conditional scenarios across different vine copula models. Applied in the Shifeng Creek watershed, China, the findings reveal that vine copula models are superior in capturing complex variable relationships, demonstrating significant spatial interconnectivity crucial for flood risk prediction in heavy-rainfall events. Interestingly, the study observes that expanding the model's dimensions does not inherently enhance simulation precision. The RDV-Copula method not only captures comprehensive information effectively but also simplifies the vine copula model by reducing its dimensionality and complexity. This study contributes to the field of hydrology by offering a refined method for analyzing and simulating multi-site flood flows. [ABSTRACT FROM AUTHOR]

The structures and parameters of physically-based distributed hydrological models (PBDHMs) can now be established and derived from remote-sensing data with relative ease. When engineers apply PBDHMs for flood forecasting in mesoscale catchments, they encounter varying rain gauge infrastructure conditions. Understanding model performance expectations under varying rain gauge density conditions is crucial for wide PDBHM construction. This study presents a case study of a PBDHM called the Liuxihe Model and examines six rain gauge density scenarios designed based on real-world data to assess the impact of rain gauge density on model flood forecasting performance. The study focuses on a mesoscale catchment in Jiangxi Province, China, covering an area of 2364 km2 with 62 rain gauges. The results indicate that models optimized under an adequate rain gauge density condition are less affected by gauge density changes, maintaining accuracy within a range of change. Compared to Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE), the indicators absolute peak time error (APTE) and peak relative error (PRE) are less sensitive to variation in rain gauge density. The study further discusses how rain gauge density changes related to the interpolated rainfall surfaces and parameter optimization, hoping to facilitate the broader application of PBDHMs and offer insights for future practices. [ABSTRACT FROM AUTHOR]

This study explored how dam operation and rainfall patterns interact to influence flooding in the Niger Delta, Nigeria. This study utilizes Sentinel-1 Synthetic Aperture Radar (SAR) data to analyze the flood events from 2018 to 2022, focusing on key locations such as Obiafu (downstream), Kainji/Jebba, and Lagdo dams (upstream). Analyzed Sentinel-1 satellite data [European Space Agency (ESA), 2018–2022], shuttle Radar Topography Mission, alongside rainfall data [Center for Hydrometeorology and Remote Sensing (CHRS), 2018–2022] and surface water level to assess flood variations between 2018 and 2022. The flood extent was differentiated from that of permanent water bodies using Sentinel-1 images with an RGB band (Snap 7.0), and the rainfall patterns were examined via inverse distance weighting (IDW) interpolation (ArcGIS 10.5). The findings reveal that the intensity and timing of flooding vary significantly across these regions. Obiafu experienced high flooding in October 2020 and 2022, while Kainji/Jebba and Lagdo dams had contrasting flood severities during these periods. Additionally, a comparative analysis of hydrograph data from these locations shows a delayed downstream response, highlighting the need for coordinated water release strategies to mitigate flood risks. The results also indicate that upstream dam operations, particularly water releases, contribute to downstream flooding, although local rainfall patterns remain a crucial factor. By aligning SAR data with real-time water levels, this study emphasizes the importance of integrating satellite-based monitoring with hydrological models to improve flood prediction accuracy and minimize socio-economic impacts in the Niger Delta. The study concludes that enhanced flood management strategies, incorporating both rainfall forecasting and dam operation schedules, are critical for reducing flood-related vulnerabilities in the region. Highlights: Sentinel-1 SAR effectively mapped flooding, with peak levels in October 2022 in the Niger Delta. September and October floods link rainfall peaks and dam releases, heightening flood risks. Coordinated dam water release could reduce downstream flood severity in low-lying areas. [ABSTRACT FROM AUTHOR]

Forecasting flood characteristics (e.g., water levels and velocity) is a growing concern due to climate change. It is therefore necessary to consider the stability conditions of earthen levees used to mitigate floods during a flood risk assessment. This technical note presents a method to assess probabilistic flood hazard that takes into account levee failures, for a levee located along Etobicoke Creek in Toronto, Canada. We compute flood scenario probabilities resulting from multiple flood scenarios that accounts for both the levee failures across the length of the levee, and different levee-failure mechanisms (e.g., backward erosion and overtopping). Then, for each location of the flooded area, we compute a cumulative flood exceedance probability curve for flood depth and velocity. This method provides a flood-hazard map (depth and velocity) for a given probability and probabilistic maps for given values of depth or velocity. [ABSTRACT FROM AUTHOR]

Real-time flood forecasting is crucial for early flood warnings. It relies on real-time hydrological and meteorological data. Satellite Precipitation Products offer real-time global precipitation estimates and have emerged as a suitable option for rainfall input in flood forecasting models. This study first compared the daily Satellite Precipitation Products of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) with observed rainfall data of India Meteorological Department from the year 2001 to 2009 using contingency tests. Rainfall data of IMERG are used to build four Real-time flood forecasting models based on machine learning: feedforward neural network (FFNN), extreme learning machine (ELM), wavelet-based feedforward neural network, and wavelet-based extreme learning machine. The models consider the IMERG gridded data at 1 h resolution as input to predict water level at Hayaghat gauging station of Bagmati River with lead times from 1 h to 10 days. These models have been trained and tested with the observed water level data. The model performance was also evaluated using various statistical criteria. Results showed good correlation between IMERG and observed data with a probability of detection of 85.42%. Overall, wavelet-based models outperformed their singular counterparts. Among the singular models, the FFNN model performed better than ELM with satisfactory predictions up to 5 days of lead time. For a 7 days lead time, only wavelet-based-FFNN performs well, whereas none of the models produced satisfactory results for 10 days lead time. [ABSTRACT FROM AUTHOR]

Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70–80% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER's versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern. [ABSTRACT FROM AUTHOR]

Floods, among the most destructive climate-induced natural disasters, necessitate effective prediction models for early warning systems. The proposed Multi-Attention Encoder-Decoder-based Temporal Convolutional Network (MA-TCN-ED) prediction model combines the strengths of the Temporal Convolutional Network (TCN), Multi-Attention (MA) mechanism, and Encoder-Decoder (ED) architecture along with filter-wrapper feature selection for optimal feature selection. This framework aims to improve flood prediction accuracy by effectively capturing temporal dependencies and intricate patterns in atmospheric and hydro-meteorological data. The proposed framework was pervasively assessed for predicting the real-world flood-related data of the river Meenachil, Kerala, and the results showed that MA-TCN-ED using a filter-wrapper feature selection approach achieved higher accuracy in flood prediction. Further the model was validated on the dataset of river Pamba, Kerala. The proposed model exhibits better performance with about 32% reduced MAE, 39% reduced RMSE, 12% increased NSE, 14% enhanced R2, and 17% enhanced accuracy relative to the average performance of all the compared baseline models. The proposed work holds promise for enhancing early warning systems and mitigating the impact of floods and contributes to the broader understanding of leveraging deep learning models for effective climate-related risk mitigation. [ABSTRACT FROM AUTHOR]

Large‐scale hydrologic modeling is important for understanding changes in water resources and flood hazard across a broad range of climatic and hydrologic conditions. Parsimonious models, although simple, allow for an efficient way to model river systems across multiple decades to even centuries. Therefore, this study aims to assess the ability of the distributed Hillslope Link Model (HLM) TETIS to simulate streamflow observations across the contiguous United States (CONUS) from 1981 to 2020. To obtain model parameters across this domain, we partition the study area into 234 HydroSHEDS level 5 basins and calibrate the model to a single representative location near the outlet of each basin using dynamical dimension search for 100 realizations. Performance is then assessed at 5046 US Geological Survey streamgages with respect to the Kling Gupta Efficiency (KGE) and bias. Our simulations result in a median KGE of 0.43, with 89% of the sites having a value above the reference of 1 − √2 (~ ‐0.41). Furthermore, there is a dependence of the model performance on climate regions, with the model performing better in basins in cold and temperate regions than in arid ones. While the parameters are estimated based on daily precipitation inputs, it is shown that the model performs well even when forced with hourly precipitation, highlighting the robustness of the selected parameters to different inputs. Finally, the soil related parameters show dependence on soil properties, providing a basis for future model improvement. Overall, this study highlights the model's flexibility in performing across a vast domain with different runoff generation mechanisms. [ABSTRACT FROM AUTHOR]

Disasters that occur in modern cities are considered complex disasters because of several urban factors. One type of disaster that exemplifies this complexity is urban flooding, which causes long-term consequences for urban facilities, is an example of such a complex disaster. Floods evolved into complex disasters because of various social and urban factors, resulting in extensive harm to urban facilities and resources. This paper proposes a crowdmovement-based geofence-construction method for collective-intelligence-based evacuation in flooding scenarios. The proposed method can be used to perform area construction based on prior flood predictions, capture crowd movements during disasters, and block roads in urban built environments. This paper presents a qualitative analysis of the area and road construction for four scenarios of agent evacuation strategies and a quantitative analysis of the effect of the proposed method. The results demonstrate that evacuation routes are more effective when the user movements are based on road networks. [ABSTRACT FROM AUTHOR]

The catastrophic situations of recent years—in June 2021 near Yalta and in January 2024 in Sevastopol—associated with abundant precipitation, water level rise in rivers, and the formation of mudflows—once again showed the need for advance forecasting of events with extreme precipitation in Crimean territory for prompt response and minimization of economic losses. The region of the mountain Crimea with its complicated relief and considerable slopes is especially susceptible to the formation of dangerous situations after heavy (often multi-day) rains. Daily data on precipitation from the Ai-Petri weather station were used to calculate and analyze the cases with total precipitation ≥40 mm within three consecutive days. Such conditions were used in the analysis as a threshold of extreme precipitation leading to channel erosion in mountainous Crimea rivers and the formation of debris flows. The catastrophic flood on the Chernaya River in January 2024, which was due to three days of extreme precipitation in the Sevastopol region, is considered. This situation was analyzed to determine the possibility to forecast it up to 3 months in advance with the use of the developed artificial neural network model. The obtained results showed that the quality of the developed neural network is satisfactory to forecast with a lead time of 3 months 2–3-day long extreme precipitation, which intensifies the erosion processes in the mountainous Crimea. [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)