Search Results (385)

The article presents a long-term assessment of the surface water quality of six small rivers in the East Kazakhstan region (Breksa, Tikhaya, Ulba, Glubochanka, Krasnoyarka, and Oba) based on hydrochemical monitoring data from the Kazhydromet State Enterprise for the period 2017–2024. A unified water quality classification system was applied, along with statistical methods, including multiple linear regression. The Glubochanka and Krasnoyarka rivers were identified as the most polluted (reaching classes 4–5), with multiple exceedances of Zn (up to 2.96 mg/dm³), Cd (up to 0.8 mg/dm³), and Cu (up to 0.051 mg/dm³). The most stable and highest water quality was recorded in the Oba River, where from 2021 to 2024, water consistently corresponded to Class 2. Regression models of water quality class as a function of time and annual precipitation were constructed to assess the influence of climatic factors. Statistical analysis revealed no consistent linear correlation between average annual precipitation and water quality (correlation coefficients ranging from −0.49 to +0.37), indicating a complex interplay between climatic and anthropogenic factors. Significant relationships were found for the Breksa (R² = 0.903), Glubochanka (R² = 0.602), and Tikhaya (R² = 0.555) rivers, suggesting an influence of temporal and climatic factors on water quality. In contrast, the Oba (R² = 0.130), Ulba (R² = 0.100), and Krasnoyarka (R² = 0.018) rivers exhibited low coefficients, indicating the predominance of other, likely local, sources of pollution. It was found that summer periods are characterized by the highest pollution due to low water flow, while episodes of acid runoff occur in spring. A decrease in pH below 7.0 was first recorded in 2023–2024 in the Ulba and Tikhaya rivers. Forecasts to 2030 suggest relative stability in water quality under current climatic conditions; however, by 2050, the risk of water quality deterioration is expected to rise due to increased precipitation and extreme weather events. This study presents, for the first time, a systematic long-term analysis of small rivers in the East Kazakhstan region, offering deeper insight into the dynamics of surface water quality and providing a scientific foundation for developing adaptive strategies for the protection and sustainable use of water resources under climate change and anthropogenic pressure. The results emphasize the importance of prioritizing rivers with high variability in water quality for regular monitoring and the development of adaptive conservation measures. The research holds strong applied significance for shaping a sustainable water use strategy in the region. Full article

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

This study explores the application of a Long Short-Term Memory (LSTM) model for river water level forecasting, emphasizing the critical role of hyper-parameters optimization. Similar to physical and numerical rainfall-runoff models, LSTM relies on parameters to drive its data-driven modeling process. The performance of such models is highly sensitive to the chosen hyper-parameters, making their optimization essential. To address this, three algorithms—Grid Search, Random Search, and Bayesian Search—were applied to identify the most effective hyper-parameter combinations. Cross-correlation analysis revealed that average rainfall had a stronger influence on river water levels than upstream point rainfall, leading to its selection as the model input. The optimization focused on five key hyper-parameters: neuron units, learning rate, dropout rate, number of epochs, and batch size. Results showed that, while Grid Search required the most computational time, both Random and Bayesian Search were more efficient. Notably, Bayesian Search yielded the best predictive performance with minimal time cost, making it the preferred optimization method. Additionally, reproducible LSTM simulations were conducted to ensure the consistency and practical applicability of the forecasting in real-world scenarios. Overall, Bayesian Search is recommended for optimizing LSTM models due to its balance of accuracy and computational efficiency in hydrological forecasting. Full article

The Chushandian Reservoir, a key control project on the Huaihe River, is vital for flood control, water allocation, and maintaining ecological baseflow. This study analyzes runoff evolution and provides predictive insights for sustainable water management. Methods employed include Extremum Symmetric Mode Decomposition (ESMD) for decomposing complex signals, a mutation detection algorithm to identify significant changes in time-series data, and cross-wavelet transform to examine correlations and phase relationships between time series across frequencies. Additionally, the hybrid models GM-BP and CNN-LSTM were used for runoff forecasting. Results show cyclical fluctuations in annual runoff every 2.3, 5.3, and 14.5 years, with a significant decrease observed in 2010. Among climate factors, the Atlantic Multidecadal Oscillation (AMO) had the strongest correlation with runoff variability, while ENSO and PDO showed more localized impacts. Model evaluations indicated strong predictive performance, with Nash–Sutcliffe Efficiency (NSE) scores of 0.884 for GM-BP and 0.909 for CNN-LSTM. These findings clarify the climatic drivers of runoff variability and provide valuable tools for water resource management at the Chushandian Reservoir under future climate uncertainties. Full article

Urbanization and climate change have disrupted natural water circulation by increasing impervious surfaces and altering rainfall patterns, leading to reduced groundwater infiltration, deteriorating water quality, and heightened flood risks. This study investigates the application of Low Impact Development (LID) and flood control facilities as structural measures to address these challenges in the upper watershed of the Fucha River in Bogotá, Colombia. The methodology involved analyzing watershed characteristics, defining circulation problems, setting hydrological targets, selecting facility types and locations, evaluating performance, and conducting an economic analysis. To manage the target rainfall of $26.5$$\mathrm{m}$$\mathrm{m}$ under normal conditions, LID facilities such as vegetated swales, rain gardens, infiltration channels, and porous pavements were applied, managing approximately 2362 ${\mathrm{m}}^3$ of runoff. For flood control, five detention tanks were proposed, resulting in a 31.8% reduction in peak flow and a 7.3% decrease in total runoff volume. The flooded area downstream was reduced by $46.8$$\mathrm{h}\mathrm{a}$, and the benefit–cost ratio was calculated at 1.02. These findings confirm that strategic application of LID and detention facilities can contribute to effective urban water cycle management and disaster risk reduction. While the current disaster management approach in Bogotá primarily focuses on post-event response, this study highlights the necessity of transitioning toward proactive disaster preparedness. In particular, the introduction and expansion of flood forecasting and warning systems are recommended as non-structural measures, especially in urban areas with complex infrastructure and climate-sensitive hydrology. Full article

Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets. Full article

Flash flood disasters occur frequently under the influence of climate change and human activities, with the characteristics of strong suddenness, a wide range of hazards, and difficult prediction. Obtaining high-spatial- and high-temporal-resolution and high-precision rainfall monitoring and forecasting data is of great significance for accurate early warnings for flash flood disasters. In order to evaluate the advantages of X-band radar inverted rainfall in flash flood simulations, two typical flood events (3 July 2024 and 13 July 2024) in the Guangrun River Basin were studied. A comparative study between X-band radar inversion-based rainfall and rainfall measured at rainfall stations in terms of the flooding process and inundation extent was carried out using the China Flash Flood Hydrological Model (CNFF) and the two-dimensional hydrodynamic model (FASFLOOD). The results indicated that the temporal and spatial distribution characteristics of rainfall inversion by X-band radar were highly consistent with the measured rainfall at rainfall stations; in terms of simulating flood processes, rainfall based on X-band radar inversion performed better in key indicators such as the relative error of runoff depth, relative error of peak flow, error in time of peak occurrence, and Nash–Sutcliffe efficiency coefficient (NSE). In terms of simulating flood inundation, the simulation results based on X-band radar inversion and the measured rainfall from rainfall stations were consistent in the trend of rising and falling water processes and inundation range changes, and X-band radar could more accurately capture the spatial heterogeneity of rainfall. This study can provide technical support for disaster prevention and reductions in mountain floods in small watersheds. Full article

One effective method for analyzing complexity involves applying information measures to time series derived from observational data. Permutation entropy (PE) is one such measure designed to quantify the degree of disorder or complexity within a time series by examining the order relations among its values. PE is distinguished by its simplicity, robustness, and exceptionally low computational cost, making it a benchmark tool for complexity analysis. This text reviews the advantages and limitations of PE while exploring its diverse applications in hydrology from 2002 to 2025. Specifically, it categorizes the uses of PE across various subfields, including runoff prediction, streamflow analysis, water level forecasting, assessment of hydrological changes, and evaluating the impact of infrastructure on hydrological systems. By leveraging PE’s ability to capture the intricate dynamics of hydrological processes, researchers can enhance predictive models and improve our understanding of water-related phenomena. Full article

This study combines artificial intelligence (AI) with mathematical modeling to improve the forecasting of the water cycle mechanism. The proposed model simulates the development of global temperature, precipitation, and water availability, integrating key climate parameters that control these dynamics. Using a system of fractional-order differential equations in the fractal–fractional sense of derivatives, the model captures interactions between solar radiation, the greenhouse effect, evaporation, and runoff. The deep learning framework is trained on extensive climate datasets, allowing it to refine predictions and identify complex patterns within the water cycle. By applying AI techniques alongside mathematical modeling, this procedure provides valuable insights into climate change and water resource administration. The model’s predictions can contribute to assessing future climate states, optimizing environmental policies, and designing sustainable water management strategies. Furthermore, the hybrid methodology improves decision-making by offering data-driven solutions for climate adaptation. The findings illustrate the effectiveness of AI-driven models in addressing global climate challenges with improved precision. Full article

Continuous use of agricultural chemicals and fertilizers, sporadic sewer overflow events, and an increase in urbanization have led to significant nutrient/pollutant loadings into the semi-arid Arroyo Colorado River basin, which is located in South Texas, U.S. Priority nutrients that require reduction include phosphorus and nitrogen and to mitigate issues of low dissolved oxygen, in some of its river segments. Consequently, the river’s potential to support aquatic life has been significantly reduced, thus highlighting the need for restoration. To achieve this restoration, a watershed protection plan was developed, comprising several preventive mitigation measures, including installing green infrastructure (GI) practices. However, for effective reduction of excessive nutrient loadings, there is a need to study the effects of different combinations of GI practices under current and future land use scenarios to guide decisions in implementing the cost-effective infrastructure while considering factors such as the existing drainage system, topography, land use, and streamflow. Therefore, this study coupled the Soil and Water Assessment Tool (SWAT) model with the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model to determine the effects of different combinations of GI practices on the reduction of nitrogen and phosphorus under changing land use conditions in three selected Arroyo Colorado subwatersheds. Two land use maps from the U.S. Geological Survey (USGS) Forecasting Scenarios of land use (FORE-SCE) model for 2050, namely, A1B and B1, were implemented in the coupled SWAT-SUSTAIN model in this study, where the urban area is projected to increase by 6% and 4%, respectively, with respect to the 2018 land use scenario. As expected, runoff, phosphorus, and nitrogen slightly increased with imperviousness. The modeling results showed that implementing either vegetated swales or wet ponds reduces flow and nutrients to meet the Total Maximum Daily Loads (TMDLs) targets, which cost about USD 1.5 million under current land use (2018). Under the 2050 future projected land use changes (A1B scenario), the cost-effective GI practice was implemented in vegetated swales at USD 1.5 million. In contrast, bioretention cells occupied the least land area to achieve the TMDL targets at USD 2 million. Under the B1 scenario of 2050 projected land use, porous pavements were most cost effective at USD 1.5 million to meet the TMDL requirements. This research emphasizes the need for collaboration between stakeholders at the watershed and farm levels to achieve TMDL targets. This study informs decision-makers, city planners, watershed managers, and other stakeholders involved in restoration efforts in the Arroyo Colorado basin. Full article

Accurate runoff forecasting is crucial for disaster prevention and mitigation, as well as water resource allocation planning. However, the accuracy of runoff forecasting in high mountain watersheds is limited by the complexity of terrain and the scarcity of observation data. In recent years, machine learning models have been widely used for runoff prediction. In order to explore the application effect of the Long Short-Term Memory (LSTM) network in high mountain watersheds, this paper takes the Nyang River Basin (NRB) in a typical watershed on the Qinghai–Tibet Plateau (QTP) as the research object, and uses LSTM models to study the impact of different input variable combinations on runoff prediction under multiple prediction periods. The results indicate that with the extension of the forecast period, the impact of historical runoff on runoff prediction accuracy gradually decreases, while the impact of precipitation and temperature on runoff prediction accuracy gradually increases. When the forecast period exceeds 13 days, the contribution of precipitation increases more significantly. The use of historical runoff and forecasting that includes historical runoff and precipitation yields the most robust results, with good forecasting performance within 25 days of the forecast period. Moreover, the larger the watershed area, the better the runoff forecasting effect. Full article

Runoff prediction is a complex hydrological, nonlinear time-series problem. Many machine learning methods have been put forth in recent years to predict runoff. A sliding window method is typically used to preprocess the data in order to rebuild the time series of runoff data into a standard machine learning dataset. The size of the window is a variable parameter that is commonly referred to as the time step. With developments in computer and AI technology, data-driven models have demonstrated tremendous potential for runoff prediction. And AI technology has opened up a new avenue for weather prediction, with Pangu-Weather demonstrating considerable improvements in both accuracy and processing efficiency. This study creates two novel prediction models, LSTM-Pangu and GRU-Pangu, by combining Pangu with Long Short-Term Memory (LSTM) and the Gate Recurrent Unit (GRU). We concentrated on the Beipanjiang River Basin in China, using Guizhou Qianyuan Power Company Limited’s daily runoff data and meteorological satellite data from the Climate Data Store platform to forecast daily runoff. These models were used to anticipate runoff on various future days (known as the lead time). The results show that regardless of time step, both LSTM-Pangu and GRU-Pangu outperform the LSTM and GRU models. Furthermore, this advantage is more evident as the advance time increases. When the time step is 7 and the lead time is 5, the Nash–Sutcliffe Efficiency (NSE) of the LSTM-Pangu model improves by 8.1% compared to the LSTM model, while the NSE of the GRU-Pangu model improves by 11.7% compared to the GRU model. Furthermore, LSTM-Pangu and GRU-Pangu outperform LSTM and GRU models in terms of the predictive accuracy under high-flow conditions, highlighting their significant advantages in flood forecasting. This integration strategy displays great transferability and may be expanded to other typical data-driven models. Full article

Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (TDSHM) and data-driven deep learning models (LSTM and CNN) for runoff forecasting under variable rainfall conditions. Using 214 rainfall–runoff events (2013–2023) from the Qiaotou watershed in Nanjing, China, the TDSHM integrates rainfall momentum, wind effects, and hydrodynamic principles to resolve spatiotemporal flow dynamics, while LSTM and CNN models leverage seven hydrological features for data-driven predictions. Results demonstrate that the TDSHM achieved superior accuracy, with a mean relative error of 10.77%, Nash–Sutcliffe Efficiency (NSE) of 0.801, and Mean Absolute Error (MAE) of 3.17 mm, outperforming LSTM (24.38% error, NSE = 0.751, MAE = 4.61 mm) and CNN (28.10% error, NSE = 0.506, MAE = 6.82 mm). The TDSHM’s explicit physical interpretability enabled precise simulation of vegetation-modulated runoff processes, validated against field observations (92% predictions within ±15% error). While LSTM captured temporal dependencies effectively, CNN exhibited limitations in sequential data processing. This study highlights the TDSHM’s robustness for scenarios requiring mechanistic insights and the complementary role of LSTM in data-rich environments. The findings provide critical guidance for flood risk management, soil conservation, and model selection trade-offs between physical fidelity and computational efficiency. 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

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