Search Results (68)

Reliable forecasts of dam releases are essential to anticipate downstream hydrological responses and to improve the operation of fluvial early warning systems. This study integrates an explicit release prediction module into a digital forecasting framework using the Lindoso–Touvedo hydropower cascade in northern Portugal as a case study. A data-driven approach couples short-term electricity price forecasts, obtained with a gated recurrent unit (GRU) neural network, with dam release forecasts generated by a Random Forest model and an LSTM model. The models (GRU and LSTM) were trained and validated on hourly data from November 2024 to April 2025 using a rolling 80/20 split. The GRU achieved R² = 0.93 and RMSE = 3.7 EUR/MWh for price prediction, while the resulting performance metrics confirm the high short-term skill of the LSTM model, with MAE = 4.23 m³ s⁻¹, RMSE = 9.96 m³ s⁻¹, and R² = 0.98. The surrogate Random Forest model reached R² = 0.91 and RMSE = 47 m³/s for 1 h discharge forecasts. Comparison tests confirmed the statistical advantage of the AI approach over empirical rules. Integrating the release forecasts into the Delft FEWS environment demonstrated the potential for real-time coupling between energy market information and hydrological forecasting. By improving forecast reliability and linking hydrological and energy domains, the framework supports safer communities, more efficient hydropower operation, and balanced river basin management, advancing the environmental, social, and economic pillars of sustainability and contributing to SDGs 7, 11, and 13. Full article

Global warming is recognized as a climate crisis that extends beyond a mere increase in the Earth’s temperature, triggering rapid and widespread climatic changes worldwide. In particular, the frequency and intensity of extreme rainfall events have increased in Korea and the Association of Southeast Asian Nations (ASEAN) region, leading to a significant increase in flood damage. The growing number of large-scale hydrological disasters underscores the urgent need for accurate and rapid flood-forecasting systems that can support disaster preparedness and mitigation. Compared with conventional physics-based forecasting systems, artificial intelligence (AI) models can provide faster predictions using limited observational data. In this study, a river water-level prediction model was constructed using real-time observation data and a long short-term memory (LSTM) algorithm, which is a recurrent neural network-based deep learning approach suitable for hydrological time-series forecasting. A repeated k-fold cross-validation technique was applied to enhance model generalization and prevent overfitting. In addition, water-level differencing was employed to convert nonstationary water-level data into stationary time-series inputs, thereby improving the prediction stability. Water-level observation stations in the Philippines, Indonesia, and the Republic of Korea were selected as study sites, and the model performance was evaluated at each location. The differenced LSTM model achieved a root mean square error of 0.13 m, coefficient of determination (R²) of 0.866, Nash–Sutcliffe efficiency (NSE) of 0.844, and Kling–Gupta efficiency of 0.893, thus outperforming the non-differenced baseline by approximately 17%. The repeated k-fold validation approach was particularly effective when the training data period was short or the number of input variables was limited. These results confirm that ensuring temporal stationarity and applying repeated cross-validation can significantly enhance the predictive accuracy of real-time flood forecasting. The proposed framework exhibits strong potential for implementation in regional early warning systems across data-limited flood-prone areas in the ASEAN region. Ongoing studies that apply and verify this approach in diverse hydrological contexts are expected to further improve and expand AI-based flood prediction models. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article

Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental factors, and frequent missing data in multi-source observations. To address these issues, we propose DAHG, a novel long-term precipitation forecasting framework based on dynamic augmented heterogeneous graphs with reinforced graph generation, contrastive representation learning, and long short-term memory (LSTM) networks. Specifically, DAHG constructs a temporal heterogeneous graph to model the complex interactions among multiple meteorological variables (e.g., precipitation, humidity, wind) and remote sensing indicators (e.g., NDVI). The forecasting task is formulated as a dynamic spatiotemporal regression problem, where predicting future precipitation values corresponds to inferring attributes of target nodes in the evolving graph sequence. To handle missing data, we present a reinforced dynamic graph generation module that leverages reinforcement learning to complete incomplete graph sequences, enhancing the consistency of long-range forecasting. Additionally, a self-supervised contrastive learning strategy is employed to extract robust representations of multi-view graph snapshots (i.e., temporally adjacent frames and stochastically augmented graph views). Finally, DAHG integrates temporal dependency through long short-term memory (LSTM) networks to capture the evolving precipitation patterns and outputs future precipitation estimations. Experimental evaluations on multiple real-world meteorological datasets show that DAHG reduces MAE by $\stackrel{\sim }{3}$% and improves R² by $\stackrel{\sim }{0}$.02 over state-of-the-art baselines (p < 0.01), confirming significant gains in accuracy and robustness, particularly in scenarios with partially missing observations (e.g., due to sensor outages or cloud-covered satellite readings). Full article

In dam operations, sudden discharges during extreme rainfall events can pose severe flood risks to downstream communities. This study developed a dam discharge-based river water level forecasting model using a data-driven deep learning approach, long short-term memory (LSTM). To enhance predictive performance, physics-based HEC-RAS simulation outputs, including extreme events, were incorporated as additional inputs. The Seomjin River Basin in South Korea, which recently experienced severe flooding, was selected as the study area. Hydrological data from 2010 to 2023 were utilized, with 2023 reserved for model testing. Forecasts were generated for four lead times (3, 6, 12, and 24 h), consistent with the operational flood forecasting system of the Ministry of Environment, South Korea. Using only observed data, the model achieved high accuracy at upstream sites, such as Imsil-gun (Iljung-ri, R² = 0.92, RMSE = 0.27 m) and Gokseong (Geumgok Bridge, R² = 0.91, RMSE = 0.35 m), for a 6-h lead time. However, performance was lower at Gurye-gun (Songjeong-ri, R² = 0.72, RMSE = 1.48 m) due to the complex influence of two dams. Incorporating enhanced inputs significantly improved predictions at Gurye-gun (R² = 0.91, RMSE = 1.17 m at 3 h). Overall, models using only observed data performed better at upstream sites, while enhanced inputs were more effective in downstream or multi-dam regions. The 6-h lead time yielded the highest overall accuracy, highlighting the potential of this approach to improve real-time dam operations and flood risk management. Full article

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

Flood hazards have increasingly threatened the East Kazakhstan region in recent decades due to climate change and growing anthropogenic pressures, leading to more frequent and severe flooding events. This article considers an approach to modeling and forecasting river runoff using the example of the small Kurchum River in the East Kazakhstan region. The main objective of this study was to evaluate the numerical performance of the flood hazard model by comparing simulated flood extents with observed flood data. Two types of data were used as initial data: topographic data (digital elevation models and topographic maps) and hydrological data, including streamflow time series from stream gauges (hourly time steps) and lateral inflows along the river course. Spatially distributed rainfall forcing was not applied. To build the model, we used the software packages of HEC-RAS version 5.0.5 and MIKE version 11. Using retrospective data for 3 years (2019–2021), modeling was performed, the calculated boundaries of possible flooding were obtained, and the highest risk zones were identified. A dynamic map of depth changes in the river system is presented, showing the process of flood wave propagation, the dynamics of depth changes, and the expansion of the flood zone. Temporal flood inundation mapping and performance metrics were evaluated for each individual flood event (2019, 2020, and 2021). The simulation outcomes closely correlate with actual flood events. The assessment showed that the model data coincide with the real ones by 91.89% (2019), 89.09% (2020), and 95.91% (2021). The obtained results allow for a clarification of potential flood zones and can be used in planning measures to reduce flood risks. This study demonstrates the importance of an integrated approach to modeling, combining various software packages and data sources. Full article

The continuous release of global precipitation products offers a stable data source for flood forecasting in areas without rainfall gauges. However, due to constraints of forecast timeliness, only no/short-lag precipitation products can be utilised for flood forecasting, but these products are prone to significant errors. Therefore, the keys of flood forecasting in areas lacking rainfall gauges are selecting appropriate precipitation products, improving the accuracy of precipitation products, and reducing the errors of precipitation products by combination with hydrology models. This paper first presents the current no/short-lag precipitation products that are continuously updated online and for which the download of long series historical data is supported. Based on this, this paper reviews the utilisation methods of multi-source precipitation products for flood forecasting in areas with insufficient rainfall gauges from three perspectives: methods for precipitation product performance evaluation, multi-source precipitation fusion methods, and methods for coupling precipitation products with hydrological models. Finally, future research priorities are summarized: (i) to construct a quantitative evaluation system that can take into account both the accuracy and complementarity of precipitation products; (ii) to focus on the improvement of the areal precipitation fields interpolated by gauge-based precipitation in multi-source precipitation fusion; (iii) to couple real-time correction of flood forecasts and multi-source precipitation; and (iv) to enhance global sharing and utilization of rain gauge–radar data for improving the accuracy of satellite-based precipitation products. Full article

To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to the compounded errors inherent in hydrological modeling frameworks. In this study, a Bayesian ensemble learning-based correction (BELC) scheme is proposed which integrates hydrological modeling with multiple machine learning methods to enhance real-time error correction for flood forecasting. The Xin’anjiang (XAJ) model is selected as the hydrological model for this study, given its proven effectiveness in flood forecasting across humid and semi-humid regions, combining structural simplicity with demonstrated predictive accuracy. The BELC scheme straightforwardly post-processes the output of the XAJ model under the Bayesian ensemble learning framework. Four machine learning methods are implemented as base learners: long short-term memory (LSTM) networks, a light gradient-boosting machine (LGBM), temporal convolutional networks (TCN), and random forest (RF). Optimal weights for all base learners are determined by the K-means clustering technique and Bayesian optimization in the BELC scheme. Four baseline schemes constructed by base learners and three ensemble learning-based schemes are also built for comparison purposes. The performance of the BELC scheme is systematically evaluated in the Hengshan Reservoir watershed (Fenghua City, China). Results indicate the following: (1) The BELC scheme achieves better performance in both accuracy and robustness compared to the four baseline schemes and three ensemble learning-based schemes. The average performance metrics for 1–3 h lead times are 0.95 (NSE), 0.92 (KGE), 24.25 ${\mathrm{m}}^3/\mathrm{s}$ (RMSE), and 8.71% (RPE), with a PTE consistently below 1 h in advance. (2) The K-means clustering technique proves particularly effective with the ensemble learning framework for high flow ranges, where the correction performance exhibits an increment of 62%, 100%, and 100% for 1 h, 2 h, and 3 h lead hours, respectively. Overall, the BELC scheme demonstrates the potential of a Bayesian ensemble learning framework in improving real-time error correction of flood forecasting systems. Full article

The southern Baltic Sea is characterized by highly variable weather conditions, particularly in autumn and winter, when storms, strong westerly winds, and temporary sea ice formation disrupt maritime operations. This study presents a climatographic overview and evaluates key hydrometeorological factors that influence the safe and efficient use of liquefied natural gas (LNG) as bunkering fuel in the region. The analysis draws on long-term meteorological and hydrological datasets (1971–2020), including satellite observations and in situ measurements. It identifies operational constraints, such as wind speed, wave height, visibility, and ice cover, and assesses their impact on LNG logistics and terminal functionality. Thresholds for safe operations are evaluated in accordance with IMO and ISO safety standards. An ice severity forecast for 2011–2030 was developed using the ECHAM5 global climate model under the A1B emission scenario, indicating potential seasonal risks to LNG operations. While baseline safety criteria are generally met, environmental variability in the region may still cause temporary disruptions. Findings underscore the need for resilient port infrastructure, including anti-icing systems, heated transfer equipment, and real-time environmental monitoring, to ensure operational continuity. Integrating weather forecasting into LNG logistics supports uninterrupted deliveries and contributes to EU goals for energy diversification and emissions reduction. The study concludes that strategic investments in LNG infrastructure—tailored to regional climatic conditions—can enhance energy security in the southern Baltic, provided environmental risks are systematically accounted for in operational planning. Full article

Flash floods pose serious risks to human life and infrastructure, leading to significant economic losses. While traditional conceptual models have long been used for runoff estimation, recent advancements in artificial intelligence have introduced machine learning and deep learning models for more accurate predictions. This study presents a deep learning framework that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Neural Ordinary Differential Equations (Neural ODEs) to enhance precipitation-induced runoff forecasting. A six-year dataset (2016–2022) from Al Ain, United Arab Emirates (UAE), was employed for model training, with validation conducted using data from a severe April 2024 flash flood. The proposed framework was compared against standalone CNN, RNN, and Neural ODE models to evaluate its predictive performance. Results show that the combination of the CNN’s feature extraction, the RNN’s temporal analysis, and the Neural ODE’s continuous-time modeling achieves superior accuracy, with an R² value of 0.98, RMSE = 2.87 × 10⁶, MAE = 1.13 × 10⁶, and PBIAS of −8.38. These findings highlight the model’s ability to effectively capture complex hydrological dynamics. The framework provides a valuable tool for improving flash-flood forecasting and water resource management, especially in arid regions like the UAE. Future work may explore its application in different climates and integration with real-time monitoring systems. Full article

Increasing environmental variability and operational complexity in reservoir systems necessitate advanced optimization frameworks for flood control. This study proposes the ε-constrained Improved Zebra Optimization Algorithm (ε-IZOA), a novel metaheuristic algorithm integrating an enhanced Zebra Optimization Algorithm (ZOA) with adaptive ε-constraint handling, to address multi-reservoir flood control optimization. Three strategic modifications advance the standard ZOA: (1) Bernoulli chaotic mapping for diversified population initialization; (2) adaptive weight balancing for exploration-exploitation trade-off mitigation; and (3) golden sinusoidal vectorization for global search refinement, collectively forming the Improved ZOA (IZOA). The ε-IZOA synergizes IZOA with ε-dominance criteria to dynamically resolve constrained optimization conflicts. Applied to the Yellow River Basin’s five-reservoir cascade, ε-IZOA achieves a 52.97% peak shaving rate at Huayuankou Station, reducing the maximum discharge to 18,745.02 m³/s—a performance surpassing benchmark methods. The algorithm’s success stems from its bio-inspired hybrid architecture, which embeds swarm intelligence principles into nonlinear constraint management. This work establishes ε-IZOA as a computationally robust tool for large-scale reservoir optimization, with implications for mitigating flood risks in climate-sensitive basins. Future research should prioritize its integration with real-time hydrological forecasting systems. Full article

Effective and sustainable water reserve management faces increasing challenges due to climate-induced variability, data fragmentation, and the limitations of traditional, static modeling systems. This study introduces a conceptual framework designed to address these challenges by integrating digital twins, IoT-driven real-time monitoring, game engine simulations, and AI-driven decision support systems (AI-DSS). The methodology involves constructing a digital twin ecosystem using IoT sensors, GIS layers, remote-sensing imagery, and game engines. This ecosystem simulates water dynamics and assesses policy interventions in real time. AI components, including machine-learning models and retrieval-augmented generation (RAG) chatbots, are embedded to synthesize real-time data into actionable insights. The framework enables the continuous assessment of hydrological dynamics, predictive risk analysis, and immersive, scenario-based decision-making to support long-term water sustainability. Simulated scenarios demonstrate accurate flood forecasting under variable rainfall intensities, early drought detection based on soil moisture and flow data, and real-time water-quality alerts. Digital elevation models from UAV photogrammetry enhance terrain realism, and AI models support dynamic predictions. Results show how the framework supports proactive mitigation planning, climate adaptation, and stakeholder communication in pursuit of resilient and sustainable water governance. By enabling early intervention, efficient resource allocation, and participatory decision-making, the proposed system fosters long-term, sustainable water security and environmental resilience. This conceptual framework suggests a pathway toward more transparent, data-informed, and resilient decision-making processes in water reserves management, particularly in regions facing climatic uncertainty and infrastructure limitations, aligning with global sustainability goals and adaptive water governance strategies. Full article

Hydrological runoff prediction plays a crucial role in water resource management and sustainable development. However, it is often constrained by the nonlinearity, strong stochasticity, and high non-stationarity of hydrological data, as well as the limited accuracy of traditional forecasting methods. Although Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) have been widely used for data augmentation to enhance predictive model training, their direct application as forecasting models remains limited. Additionally, the architectures of the generator and discriminator in WGAN-GP have not been fully optimized, and their potential in hydrological forecasting has not been thoroughly explored. Meanwhile, the strategy of jointly optimizing Variational Autoencoders (VAEs) with WGAN-GP is still in its infancy in this field. To address these challenges and promote more accurate and sustainable water resource planning, this study proposes a comprehensive forecasting model, VXWGAN-GP, which integrates Variational Autoencoders (VAEs), WGAN-GP, Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), Gated Recurrent Units (GRUs), and Attention mechanisms. The VAE enhances feature representation by learning the data distribution and generating new features, which are then combined with the original features to improve predictive performance. The generator integrates GRU, BiLSTM, and Attention mechanisms: GRU captures short-term dependencies, BiLSTM captures long-term dependencies, and Attention focuses on critical time steps to generate forecasting results. The discriminator, based on CNN, evaluates the differences between the generated and real data through adversarial training, thereby optimizing the generator’s forecasting ability and achieving high-precision runoff prediction. This study conducts daily runoff prediction experiments at the Yichang, Cuntan, and Pingshan hydrological stations in the Yangtze River Basin. The results demonstrate that VXWGAN-GP significantly improves the quality of input features and enhances runoff prediction accuracy, offering a reliable tool for sustainable hydrological forecasting and water resource management. By providing more precise and robust runoff predictions, this model contributes to long-term water sustainability and resilience in hydrological systems. Full article

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. Full article