Search Results (148)

Accurate forecasting of river siltation is essential for ensuring inland waterway navigability and guiding sustainable sediment management. This study investigates the downstream reach of the Shihutang navigation power hub along the Ganjiang River in Jiangxi Province, China, an area characterized by pronounced seasonal sedimentation and hydrological variability. To enable fine-scale prediction, we developed a data-driven framework using a random forest regression model that integrates high-resolution bathymetric surveys with hydrological and meteorological observations. Based on the field data from April to July 2024, the model was trained to forecast monthly siltation volumes at a 30 m grid scale over a six-month horizon (July–December 2024). The results revealed a marked increase in siltation from July to September, followed by a decline during the winter months. The accumulation of sediment, combined with falling water levels, was found to significantly reduce the channel depth and width, particularly in the upstream sections, posing a potential risk to navigation safety. This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. The proposed framework provides a practical tool for early warning, targeted dredging, and adaptive channel management. Full article

Accurate precipitation forecasting remains a critical challenge due to the nonlinear and multifactorial nature of rainfall dynamics. This is particularly important in arid regions like Tamanghasset, where precipitation is the primary driver of agricultural viability and water resource management. This study evaluates the performance of several time series models for monthly rainfall prediction, including the autoregressive integrated moving average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal and Trend decomposition using Loess with ETS (STL-ETS), Trigonometric Box–Cox transform with ARMA errors, Trend and Seasonal components (TBATS), and neural network autoregressive (NNAR) models. Historical monthly precipitation data from 1953 to 2020 were used to train and test the models, with lagged observations serving as input features. Among the approaches considered, the NNAR model exhibited superior performance, as indicated by uncorrelated residuals and enhanced forecast accuracy. This suggests that NNAR effectively captures the nonlinear temporal patterns inherent in the precipitation series. Based on the best-performing model, rainfall was projected for the year 2021, providing actionable insights for regional hydrological and agricultural planning. The results highlight the relevance of neural network-based time series models for climate forecasting in data-scarce, climate-sensitive regions. 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

In recent years, both anthropogenic and climate changes have caused the depletion of surface water resources, shifts in rainfall and accelerations in temperature, which indicates the importance of their examination to flood forecasting analyses. This paper studies the importance of synchronised water management strategies, considering upstream and downstream dynamics using field data from 1986 to 2018. Seasonal and decadal variations show the need for adaptive management strategies to address potential climate change impacts on discharge, precipitation and temperature patterns in the Zarrinehrud River, Iran. The regression analysis was considered via R² values, and the statistical analysis was regarded by p-values. The regression analysis of monthly river peak discharge indicates strong correlations between the discharge of specific months (September–October upstream, December–January downstream). By the 2000s and 2020s, both stations showed a shift in peak precipitation to the spring months (April–May for upstream and May–June for downstream). This confirms a synchronisation of rainfall trends, which are influenced by climate changes or regional hydrological patterns. This temporal offset between stations confirms the spatial and seasonal variation in rainfall distribution across the basin. Higher temperatures during the dominant months, particularly late summer to early autumn, accelerate snowmelt from upstream catchments. This aligns with the river discharge peaks observed in the hydrograph. The statistical analysis of river peak discharge indicated that the Weibull (p-value = 0.0901) and the Lognormal (p-value = 0.1736) distributions are the best fitted distributions for the upstream and downstream stations, respectively. Full article

Wind and humidity are two very vital climate variables that have received little attention by researchers regarding Uganda. This study sought to close this knowledge gap by exposing the dynamics and relationship of windspeed and humidity in Uganda from 1980 to 2023 as well as predicting the future trends from 2025 to 2040. Using high-resolution gridded windspeed and relative humidity (RH) data for the past and seven downscaled and bias-adjusted global climate models within the coupled model intercomparison project phase 6 framework under two shared socioeconomic pathways (SSPs), SPP245 and SSP585, we employed variability, trend, and correlational analyses to expose the wind–humidity nexus at a monthly scale. The results showed a domination of winds of the calm to gentle breeze category across the country, with a maximum magnitude of 6 knots centered over eastern Lake Victoria and eastern Uganda over the historical period. RH was characterized by high to very high magnitudes, except the northern tips of the country, where RH was low for the historical period. Seasonally, both windspeed and RH demonstrated modest variations, with June–July–August (JJA) and September–October–November (SON) having the highest magnitudes, respectively. Similarly, both variables are forecasted to have significant distribution and magnitude changes. For example, windspeeds will be dominated by decreasing trends, while RH will be dominated by increasing trends. Finally, the correlation analysis revealed a strong negative correlation between windspeeds and RH for both the past and future periods, except for the March–April–May (MAM) and September–October–November (SON) seasons, where positive correlations were observed. These findings have practical applications in agriculture, hydrology, thermal comfort, disaster management, and forecasting, especially in the northern, eastern, and Lake Victoria basin regions. The study recommends further finer-scale research at various atmospheric levels and for prolonged future periods and scenarios. Full article

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the sensitivity and complexity of flood generation attributes. This study explores the application of Recurrent Neural Networks (RNNs)—specifically Vanilla Recurrent Neural Networks (VRNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in flood prediction and assessment. By integrating catchment-specific hydrological and meteorological variables, the RNN models leverage sequential data processing to capture the temporal dynamics and seasonal patterns characteristic of flooding. These models were employed across diverse terrains, including mountainous watersheds in the state of South Carolina, USA, to examine their robustness and adaptability. To identify significant hydrological events for flash flood analysis, a discharge frequency analysis was conducted using the Pearson Type III distribution. The 1-year and 2-year return period flows were estimated based on this analysis, and the 1-year return flow was selected as a conservative threshold for flash flood event identification to ensure a sufficient number of training instances. Comparative benchmarking with the National Water Model (NWM v3.0) revealed that the RNN-based approaches offer notable enhancements in capturing the intensity and timing of flood events, particularly for short-duration and high-magnitude floods (flash floods). Comparison of predicted disharges with the discharge recorded at the gauges revealed that GRU had the best performance as it achieved the highest mean NSE values and exhibited low variability across diverse watersheds. LSTM results were slightly less consistent compared to the GRU albeit achieving satisfactory performance, proving its value in hydrological forecasting. In contrast, VRNN had the highest variability and the lowest NSE values among the three. The NWM model trailed the machine learning-based models. The study highlights the efficacy of the RNN models in advancing hydrological predictions. Full article

It is well understood that climate change is a major cause of the environmental shifts that are significantly impacting human lives. The floods caused by climate change are not only occurring more frequently each year, but they also bring up the problem of access to clean water for drinking and other daily usage for the affected communities. The Swat district of the Khyber Pakhtunkhwa province in Pakistan is one of the impacted regions and the growing concern for clean water access is yet to be resolved. This study aims to propose a sustainable solution to water access during the emergencies, particularly in flood and drought situations. While the roof water harvesting system (RWHS) is well established and functional in many developed regions, its potential remains underexplored in Pakistan. This research study analyzed the climate change projection data for the Saidu Sharif region of Swat. The regional climate data are gathered from the Shared Socio-economic Pathways (SSPs) for the period from 2015 to 2045. Five general circulation models (GCMs) were selected based on their performance in South Asian climate simulations. Analysis of the regional forecasted climate data indicates that almost all of the five climate models have predicted the periods of excessive rainfall to occur in the months of July, August, and September, while prolonged dry seasons may last between 271 and 325 days annually. Hydrological modeling was used to estimate RWHS performance, which incorporated the key parameters such as catchment area, runoff coefficient, and rainfall intensity. The findings suggest that the proposed RWHS could meet basic drinking water needs during the floods and even during the drought periods near around 100% satisfaction of water demand under certain conditions. For example, for an average drought period of 273 days, a household of seven people with a per capita daily water demand of 17 L requires a storage capacity of 33 m³. On the other hand, for a maximum drought duration of 325 days, the required storage volume increases to 39 m³. Demand satisfaction calculations are also used to evaluate the effectiveness of the proposed model. This research contributes to addressing the growing water scarcity challenge posed by climate change in the Swat region and offers a sustainable and practical solution. Full article

Streamflow prediction is vital for effective water resource management, enabling a better understanding of hydrological variability and its response to environmental factors. This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. Using 30 years of monthly streamflow data from 20 monitoring stations, the STGNN predicted streamflow over a 36-month horizon and was evaluated against traditional models, including random forest regression (RFR), LSTM, gated recurrent units (GRU), and seasonal auto-regressive integrated moving average (SARIMA). The STGNN outperformed these models across multiple metrics, achieving an R² of 0.78, an RMSE of 0.81 mm/month, and a KGE of 0.79 at critical locations like Lees Ferry. A sequential analysis of input–output configurations identified the (36, 36) setup as optimal for balancing historical context and forecasting accuracy. Additionally, the STGNN showed strong generalizability when applied to other locations within the UCRB. These results underscore the importance of integrating spatial dependencies and temporal dynamics in hydrological forecasting, offering a scalable and adaptable framework to improve predictive accuracy and support adaptive water resource management in river basins. Full article

This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using a pseudo-global warming approach. Climate perturbations for the future climate are given by the CMIP5 ensemble-mean global climate models under the high-end emission scenario. The study analyzes the projected changes in spatial patterns of seasonal precipitation and snowpack, with particular emphasis on the effects of elevation on orographic precipitation and snowpack changes in four key mountain ranges: the Montana Rockies, Greater Yellowstone area, Wasatch Range, and Colorado Rockies. The IWUS simulations reveal an increase in annual precipitation across the majority of the IWUS in this warmer climate, driven by more frequent heavy to extreme precipitation events. Winter precipitation is projected to increase across the domain, while summer precipitation is expected to decrease, particularly in the High Plains. Snow-to-precipitation ratios and snow water equivalent are expected to decrease, especially at lower elevations, while snowpack melt is projected to occur earlier by up to 26 days in the ~2050 climate, highlighting significant impacts on regional water resources and hydrological management. Full article

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. Full article

Streamflow forecasting is of great importance in water resource management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time-series data on river flows, weather patterns, and other relevant factors, machine learning models can learn patterns and relationships to present predictions about future river flows. In this study, an autoregressive integrated moving average (ARIMA) model was constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time-series data were used for model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt–Winters’ method. The model’s forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resource management and flood warnings. Full article

Amidst changing climatic conditions, accurately predicting reservoir inflows in an extreme event is challenging and inevitable for reservoir management. This study proposed an innovative strategy under such circumstances through rigorous experimentation and investigations using 18 years of monthly data collected from the Huai Nam Sai reservoir in the southern region of Thailand. The study employed a two-step approach: (1) isolating extreme and normal events using quantile regression (QR) at the 75th, 80th, and 90th quantiles and (2) comparing the forecasting performance of individual machine learning models and their combinations, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Multiple Linear Regression (MLR). Forecasting accuracy was assessed at four lead times—3, 6, 9, and 12 months—using ten-fold cross-validation, resulting in 16 model configurations for each forecast period. The results show that combining quantile regression (QR) to distinguish between extreme and normal events with hybrid models significantly improves the accuracy of monthly reservoir inflow forecasting, except for the 9-month lead time, where the XG model continues to deliver the best performance. The top-performing models, based on normalized scores for 3-, 6-, 9-, and 12-month-ahead forecasts, are XG-MLR-75, RF-XG-80, XG-75, and XG-RF-75, respectively. Another crucial finding of this research is the uneven decline in prediction accuracy as lead time increases. Notably, the model performed best at t + 9, followed by t + 3, t + 12, and t + 6, respectively. This pattern is influenced by model characteristics, error propagation, temporal variability, data dynamics, and seasonal effects. Improving the accuracy and efficiency of hybrid model forecasting can greatly enhance hydrological operational planning and management. Full article

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies. Full article

Seasonal precipitation forecasting (SPF) is critical for effective water resource management and risk mitigation. Large-scale climate drivers significantly influence regional climatic patterns and forecast accuracy. This study establishes relationships between key climate drivers—El Niño–Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Real-time Multivariate Madden–Julian Oscillation (MJO), and Multivariate ENSO Index (MEI)—and seasonal precipitation anomalies (rainy, summer, and winter) in Eastern Thailand, utilizing Pearson’s correlation coefficient. Following the establishment of these correlations, the most influential drivers were incorporated into the forecasting models. This study proposed an advanced SPF methodology for Eastern Thailand through a Seasonal WaveNet-LSTM model, which integrates Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) with Wavelet Transformation (WT). By integrating large-scale climate drivers alongside key meteorological variables, the model achieves superior predictive accuracy compared to traditional LSTM models across all seasons. During the rainy season, the WaveNet-LSTM model (SPF-3) achieved a coefficient of determination (R²) of 0.91, a normalized root mean square error (NRMSE) of 8.68%, a false alarm rate (FAR) of 0.03, and a critical success index (CSI) of 0.97, indicating minimal error and exceptional event detection capabilities. In contrast, traditional LSTM models yielded an R² of 0.85, an NRMSE of 10.28%, a FAR of 0.20, and a CSI of 0.80. For the summer season, the WaveNet-LSTM model (SPF-1) outperformed the traditional model with an R² of 0.87 (compared to 0.50 for the traditional model), an NRMSE of 12.01% (versus 25.37%), a FAR of 0.09 (versus 0.30), and a CSI of 0.83 (versus 0.60). In the winter season, the WaveNet-LSTM model demonstrated similar improvements, achieving an R² of 0.79 and an NRMSE of 13.69%, with a FAR of 0.23, compared to the traditional LSTM’s R² of 0.20 and NRMSE of 41.46%. These results highlight the superior reliability and accuracy of the WaveNet-LSTM model for operational seasonal precipitation forecasting (SPF). The integration of large-scale climate drivers and wavelet-decomposed features significantly enhances forecasting performance, underscoring the importance of selecting appropriate predictors for climatological and hydrological studies. Full article