Search Results (116)

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article

This paper presents a Systematic Mapping Study (SMS) on data-driven approaches in flood forecasting from 2019 to 2024, a period marked by transformative developments in Deep Learning (DL) technologies. Analysing 363 selected studies, this paper provides an overview of the technological evolution in this field, methodological approaches, evaluation practices and geographical distribution of studies. The study revealed that meteorological and hydrological factors constitute approximately 76% of input variables, with rainfall/precipitation and water level measurements forming the core predictive basis. Long Short-Term Memory (LSTM) networks emerged as the dominant algorithm (21% of implementations), whilst hybrid and ensemble approaches showed the most dramatic growth (from 2% in 2019 to 10% in 2024). The study also revealed a threefold increase in publications during this period, with significant geographical concentration in East and Southeast Asia (56% of studies), particularly China (36%). Several research gaps were identified, including limited exploration of graph-based approaches for modelling spatial relationships, underutilisation of transfer learning for data-scarce regions, and insufficient uncertainty quantification. This SMS provides researchers and practitioners with actionable insights into current trends, methodological practices, and future directions in data-driven flood forecasting, thereby advancing this critical field for disaster management. Full article

In Taiwan, hydro-meteorological data are fragmented across multiple agencies, limiting the effectiveness of coordinated flood response. To address this challenge and the increasing uncertainty associated with extreme rainfall, a real-time disaster prevention platform has been developed. This system integrates multi-source data and geospatial information through a cluster-based architecture to enhance pluvial flood management. Built on a Service-Oriented Architecture (SOA) and incorporating Internet of Things (IoT) technologies, AI-based convolutional neural networks (CNNs), and 3D drone mapping, the platform enables automated alerts by linking sensor thresholds with real-time environmental data, facilitating synchronized operational responses. Deployed in New Taipei City over the past three years, the system has demonstrably reduced flood risk during severe rainfall events. Region-specific action thresholds and adaptive strategies are continually refined through feedback mechanisms, while integrated spatial and hydrological trend analyses extend the lead time available for emergency response. Full article

Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in situ rainfall data limit drought assessment in developing countries. Recently developed satellite-based rainfall products, available at different temporal and spatial resolutions, offer a valuable alternative in data-poor regions like Sri Lanka, where rain gauge networks are sparse and maintenance issues are prevalent. This study evaluates the accuracy of satellite-based rainfall estimates compared to in situ observations for drought assessment within the Mi Oya River Basin, Sri Lanka. We assessed the performance of various satellite-based rainfall products, including IMERG, GSMaP, CHIRPS, PERSIANN, and PERSIANN-CDR, by comparing them with ground-based observations over 20 years, from 2003 to 2022. Our methodology involved checking detection accuracy using the False Alarm Ratio (FAR), Probability of Detection (POD), and Critical Success Index (CSI), and assessing accuracy through metrics such as Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC), Percentage Bias (PBias), and Nash–Sutcliffe Efficiency (NSE). The two best-performing satellite-based rainfall products were used for meteorological and hydrological drought assessment. In the accuracy detection metrics, the results indicate that while products like IMERG and GSMaP generally provide reliable rainfall estimates, others like PERSIANN and PERSIANN-CDR tend to overestimate rainfall. For instance, IMERG shows a CSI range of 0.04–0.25 for moderate and heavy rainfall and 0.10–0.30 for light rainfall. On a monthly scale, IMERG and CHIRPS showed the highest performance, with CC (NSE) values of 0.81–0.94 (0.53–0.83) and 0.79–0.86 (0.54–0.74), respectively. However, GSMaP showed the lowest bias, with a range of −17.1–13.2%. Recorded drought periods over 1981–2022 (1998–2022) were reasonably well captured by CHIRPS (IMERG) products in the Mi Oya River Basin. Our results highlighted uncertainties and discrepancies in the capability of different rainfall products to assess drought conditions. This research provides valuable insights for optimizing the use of satellite rainfall products in hydrological modeling and disaster preparedness in the Mi Oya River Basin. Full article

Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with the three typical hydrological stations (Wangjiaba, Runheji, and Lutaizi sections) along the mainstem of the Huai River exceeded their guaranteed water levels within 11 h and synchronously reached peak flood levels within a 9-h window, whereas the inter-station lag times during the 2003 and 2007 floods ranged from 24 to 48 h, causing a critical emergency in the flood defense. By integrating operational hydrological data, meteorological reports, and empirical rainfall-runoff model schemes for the Meiyu periods of 2003, 2007, and 2020, this research systematically dissects the 2020 flood’s spatial composition patterns. Comparative analyses across spatiotemporal rainfall distribution, intensity metrics, and flood peak response dynamics reveal distinct characteristics of southward-shifted torrential rain and flood variability. The findings provide critical technical guidance for defending against extreme weather events and unprecedented hydrological disasters, directly supporting revisions to flood control planning in the Huai River Ecological and Economic Zone. Full article

This study introduces a novel framework with the use of extended reality (XR) systems in hydrology, particularly focusing on immersive visualization of hydrologic data for enhanced environmental planning and decision making. The study details the shift from traditional 2D data visualization methods in hydrology to more advanced XR technologies, including virtual and augmented reality. Unlike static 2D maps or charts that require cross-referencing disparate data sources, this system consolidates real-time, multivariate datasets, such as streamflow, precipitation, and terrain, into a single interactive, spatially contextualized 3D environment. Immersive information systems facilitate dynamic interaction with real-time hydrological and meteorological datasets for various stakeholders and use cases, and pave the way for metaverse and digital twin systems. This system, accessible via web browsers and XR devices, allows users to navigate a 3D representation of the continental United States. The paper addresses the current limitations in hydrological visualization, methodology, and system architecture while discussing the challenges, limitations, and future directions to extend its applicability to a wider range of environmental management and disaster response scenarios. Future application potential includes climate resilience planning, immersive disaster preparedness training, and public education, where stakeholders can explore scenario-based outcomes within a virtual space to support real-time or anticipatory decision making. Full article

This study fosters tropical hydroclimatology research by implementing a computational modeling framework based on artificial neural networks and machine learning techniques. We evaluated two models, Multilayer Perceptron (MLP) and Support Vector Machine (SVM), in their ability to simulate 20-year monthly time series (2001–2021) of minimum and maximum river stage in the Itacaiúnas River Basin (BHRI), located in the eastern Brazilian Amazon. The models were configured using explanatory variables spanning meteorological, climatological, and environmental dimensions, ensuring representation of key local and regional hydrological drivers. Both models exhibited robust performance in capturing fluviometric variability, with a comprehensive multimetric statistical evaluation indicating MLP’s superior accuracy over SVM. Notably, the MLP model reproduced the maximum river level during a sequence of extreme hydrological events linked to natural disasters (floods) across BHRI municipalities. These findings underscore the computational model’s potential for refining hydrometeorological products, thus supporting water resource management and decision-making processes in the Amazon region. Full article

Accurate and reliable precipitation prediction remains a significant challenge due to an incomplete understanding of regional meteorological dynamics and limitations in forecasting routine weather events. To overcome these challenges, we propose a novel model, DC-CNN-BiLSTM, which integrates a dilation causal convolutional neural network (DC-CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network. The DC-CNN component, by fusing causal and dilated convolutions, extracts multi-scale spatial features from time series data. In parallel, the BiLSTM module leverages bidirectional memory cells to capture long-term temporal dependencies. This integrated approach effectively links localized meteorological inputs with broader hydrological responses. Experimental evaluation demonstrates that the DC-CNN-BiLSTM model significantly outperforms traditional models. Specifically, the model improves the Root Mean Square Error (RMSE) by 9.05% compared to ConvLSTM and by 32.3% compared to ConvGRU, particularly in forecasting medium- to long-term precipitation. In conclusion, our results validate the benefits of incorporating advanced spatio-temporal feature extraction techniques for precipitation forecasting, ultimately improving disaster preparedness and resource management. Full article

The mid- and long-term hydrological forecast is important for water resource management and disaster prevention. Moreover, mid- and long-term hydrological forecasts in the region with poorly observed field meteorological data are a great challenge for traditional hydrological models due to the complexity of hydrological processes. To address this challenge, a machine learning model, particularly the deep learning model (DL), provides a new tool for improving the accuracy of runoff prediction. In this study, we took the Irtysh River, one of the longest rivers in Central Asia and a well-known trans-boundary river basin with poor field meteorological observations, as an example to develop a deep learning model based on LSTM and combined with runoff decomposition by Maximal Overlap Discrete Wavelet Transform (MODWT) to process target variables for predicting monthly streamflow. We also proposed an XGBoost-SHAP (Extreme Gradient Boost-SHapley Additive Explanations) method for the identification of predictors from large-scale indices for the streamflow forecast. The results suggest that MODWT shows the robustness of runoff decomposition between the training and test period. The deep learning model combined with MODWT shows better performance than the benchmark deep learning model without MODWT illustrated by an increased NSE. The XGBoost-SHAP method well identified the nonlinear relationship between the predictors and streamflow, and the predictors determined by XGBoost-SHAP can be physically explained. Compared with the traditional mutual information method, the XGBoost-SHAP method improves the accuracy of the deep learning model for streamflow forecast. The results of this study illustrate the ability of a deep learning model for mid- and long-term streamflow forecast, and the methods we developed in this study provide an effective approach to improve the streamflow prediction in the scarcely observed catchments. Full article

Rapid climate warming and intensified human activities are causing profound alterations in terrestrial hydrological systems. Understanding shifts in hydrological regimes and the underlying mechanisms driving these changes is crucial for effective water resource management, watershed planning, and flood disaster mitigation. This study examines the hydrological regimes of the Heilongjiang-Amur River Basin, a transboundary river basin characterized by extensive permafrost distribution in northeastern Asia, by analyzing long-term daily meteorological (temperature, precipitation, evaporation) and hydrological data from the Komsomolsk, Khabarovsk, and Bogorodskoye stations. Missing daily runoff data were reconstructed using three machine learning methods: Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Convolutional Long Short-Term Memory Networks (CNN-LSTM). Trend analysis, abrupt change detection, and regression techniques revealed significant warming and increased actual evapotranspiration in the basin from 1950 to 2022, whereas precipitation and snow water equivalent showed no significant trends. Climate warming is significantly altering hydrological regimes by changing precipitation patterns and accelerating permafrost thaw. At the Komsomolsk station, an increase of 1 mm in annual precipitation resulted in a 0.48 mm rise in annual runoff depth, while a 1 °C rise in temperature led to an increase of 1.65 mm in annual runoff depth. Although annual runoff exhibited no significant long-term trend, low-flow runoff demonstrated substantial increases, primarily driven by temperature and precipitation. These findings provide critical insights into the hydrological responses of permafrost-dominated river basins to climate change, offering a scientific basis for sustainable water resource management and strategies to mitigate climate-induced hydrological risks. Full article

China, with its complex geology and diverse climate, is highly prone to landslides, endangering public safety and infrastructure. To address disaster prevention needs, this study comprehensively assesses national landslide susceptibility. We divided China into 37 geomorphic districts, diverging from traditional methods. By using a 2018–2022 surface deformation dataset, we introduced a rarely—considered dynamic aspect for more accurate mapping of landslide—prone areas. Nine key environmental factors were carefully considered, including terrain, geology, meteorology, hydrology, seismic activities, and engineering activities. Based on these innovative methods and data, we created a 40 m—resolution landslide susceptibility map (LSM) for the whole country. Our assessment showed high accuracy, with an AUC of 0.927, precision of 0.859, recall of 0.815, F1—score of 0.828 and Matthews correlation coefficient of 0.773. Seven high—risk regions, like the Tianshan Mountains and the southern Tibetan valleys, were analyzed. The study revealed regional differences in landslide occurrences and key influencing factors. The LSM and findings enrich landslide susceptibility theory and offer a valuable resource for engineering, disaster management, and mitigation in China, helping reduce potential landslide losses. Full article

Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies. Full article

Drought is one of the most widespread natural disasters globally, and its spatiotemporal distribution is profoundly influenced by the El Niño-Southern Oscillation (ENSO). As a typical humid coastal basin, the Jiaojiang River Basin in southeastern China frequently experiences hydrological extremes such as dry spells during flood seasons. This study focuses on the Jiaojiang River Basin, aiming to investigate the response mechanisms of drought evolution to ENSO in coastal regions. This study employs 10-day scale data from 1991 to 2020 to investigate the drought mechanisms driven by ENSO through a comprehensive framework that combines standardized indices with climate–drought correlation analysis. The results indicate that the Comprehensive Drought Index (CDI), integrating the advantages of the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), effectively reflects the basin’s combined meteorological and hydrological wet-dry characteristics. A strong response relationship exists between drought indices in the Jiaojiang River Basin and ENSO events. Drought characteristics in the basin vary significantly during different ENSO phases. The findings can provide theoretical support for the construction of resilient regional water resource systems, and the research framework holds reference value for sustainable development practices in similar coastal regions globally. Full article

The Hengduan Mountains region (HMR) is vulnerable to flash flood disasters, which account for the largest proportion of flood-related fatalities in China. Flash flood regionalization, which divides a region into homogeneous subdivisions based on flash flood-inducing factors, provides insights for the spatial distribution patterns of flash flood risk, especially in ungauged areas. However, existing methods for flash flood regionalization have not fully reflected the spatial topology structure of the inputted geographical data. To address this issue, this study proposed a novel framework combining a state-of-the-art unsupervised Graph Neural Network (GNN) method, Dink-Net, and Shapley Additive exPlanations (SHAP) for flash flood regionalization in the HMR. A comprehensive dataset of flash flood inducing factors was first established, covering geomorphology, climate, meteorology, hydrology, and surface conditions. The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. The SHAP model was then applied to quantify the impact of all the inducing factors on the regionalization results by Dink-Net. The newly developed framework captured the spatial interactions of the inducing factors and characterized the spatial distribution patterns of the factors. The unsupervised Dink-Net model allowed the framework to be independent from historical flash flood data, which would facilitate its application in ungauged mountainous areas. The impact analysis highlights the significant positive influence of extreme rainfall on flash floods across the entire HMR. The pronounced positive impact of soil moisture and saturated hydraulic conductivity in the areas with a concentration of historical flash flood events, together with the positive impact of topography (elevation) in the transition zone from the Qinghai–Tibet Plateau to the Sichuan Basin, have also been revealed. The results of this study provide technical support and a scientific basis for flood control and disaster reduction measures in mountain areas according to local inducing conditions. Full article

High spatial resolution snow depth (SD) is crucial for hydrological, ecological, and disaster research. However, passive microwave SD product (10/25 km) is increasingly insufficient to meet contemporary requirements due to its coarse spatial resolution, particularly in heterogeneous alpine areas. In this study, we develop a superior SD downscaling algorithm based on the FT-Transformer (Feature Tokenizer + Transformer) model, termed FTSD. This algorithm fuses the latest calibrated enhanced resolution brightness temperature (CETB) (3.125/6.25 km) with daily cloud-free optical snow data (500 m), including snow cover fraction (SCF) and snow cover days (SCD). Developed and evaluated using 42,692 ground measurements across China from 2000 to 2020, FTSD demonstrated notable improvements in accuracy and spatial resolution of SD retrieval. Specifically, the RMSE of temporal and spatiotemporal independent validation for FTSD is 7.64 cm and 9.74 cm, respectively, indicating reliable generalizability and stability. Compared with the long-term series of SD in China (25 km, RMSE = 10.77 cm), FTSD (500 m, RMSE = 7.67 cm) provides superior accuracy, especially improved by 48% for deep snow (> 40 cm). Moreover, with the higher spatial resolution, FTSD effectively captures the SD’s spatial heterogeneity in the mountainous regions of China. When compared with downscaling algorithms utilizing the raw TB data and the traditional random forest model, the CETB data and FT-Transformer model optimize the RMSE by 10.08% and 4.84%, respectively, which demonstrates the superiority of FTSD regarding data sources and regression methods. Collectively, these results demonstrate that the innovative FTSD algorithm exhibits reliable performance for SD downscaling and has the potential to provide a robust data foundation for meteorological and environmental research. Full article