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Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

Maintaining the sustainable quantity and quality of water resources is crucial for fluvial systems, as well as for human life. This study describes the long-term annual river flow within the Brda River catchment of Poland, a fluvial system subjected to the strong hydrotechnical transformations of a cascade of dams. Our research was based on the following hydrological data (1951–2021), meteorological data (1971–2021), and climate scenarios (2022–50) to determine observed and simulated annual river flows. In this research, rising trends in the mean annual temperature and in the annual precipitation in the Brda River basin have been observed. In addition, significant research findings were the three complete river flow oscillations observed to date, and the further predicted river flow oscillations that have been projected by 2050. We modified the Turc model by linking the forecast of river flow patterns to the precipitation factor. Moreover, we predict a decrease in the river flow in the Brda River catchment of up to 10%. These studies, integrated with river flow scenarios, explicitly indicate that a river flow crisis will occur by 2050. However, it can be reduced through dam operation systems and good environmental practices in river basin management plans. This research contributes to the formulation of a sustainable management model for a cascade-dammed river that considers climate challenges.

River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study aims to compare different data-driven models in predicting monthly flow. Two river catchments located in the Guilan province in Iran, where rivers play an essential role in agricultural productions (mainly rice), are studied. The monthly river flow dataset was provided by Guilan Regional Water Authority during 1986–2015. The models are derived from two different numerical types of stochastic and machine learning (ML) models. The stochastic model is seasonal autoregressive integrated moving average (SARIMA), and the MLs are least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). The inputs were selected by autocorrelation and partial autocorrelation functions (ACF and PACF) from the flow rates of the previous months. The data was divided into 75% of training and 25% of testing phases, and then the mentioned models were implemented. Predictions were evaluated by the criteria of root mean square error (RMSE), normalized RMSE (NRMSE), and Nash Sutcliff (NS) coefficient. According to the calculated values of different criteria during the test phase, RMSE = 1.138 cms, NRMSE = 0.109, and NS = 0.826, it can be concluded that the SARIMA model was superior to its ML competitors. Among the ML models, GMDH had the best performance (by RMSE = 1.290 cms, NRMSE = 0.124, and NS = 0.777) because it has more optimization parameters and sample space for network make-up. The models were also evaluated in hydrological drought conditions of both rivers. It was resulted that the rivers’ flow can be well predicted in drought conditions by using these models, especially the SARIMA stochastic model. According to the NRMSE values (ranged between 0.1 and 0.2), the accuracy of predictions is evaluated in the appropriate range, and the present study shows promising results of the current approaches. Consequently, a comparison between the performance of linear stochastic models and complex black-box MLs, reveals that linear stochastic models are more suitable for the current region’s monthly river flow prediction.

In consideration of ongoing climate changes, it has been necessary to provide new tools capable of mitigating hydrogeological risks. These effects will be more marked in small catchments, where the geological and environmental contexts do not require long warning times to implement risk mitigation measures. In this context, deep learning models can be an effective tool for local authorities to have solid forecasts of outflows and to make correct choices during the alarm phase. In this study, we investigate the use of deep learning models able to forecast hydrometric height in very fast hydrographic basins. The errors of the models are very small and about a few centimetres, with several forecasting hours. The models allow a prediction of extreme events with also 4–6 h (RMSE of about 10–30 cm, with a forecasting time of 6 h) in hydrographic basins characterized by rapid changes in the river flow rates. However, to reduce the uncertainties of the predictions with the increase in forecasting time, the system performs better when using a machine learning model able to provide a confidence interval of the prediction based on the last observed river flow rate. By testing models based on different input datasets, the results indicate that a combination of models can provide a set of predictions allowing for a more comprehensive description of the possible future evolutions of river flows. Once the deep learning models have been trained, their application is purely objective and very rapid, permitting the development of simple software that can be used even by lower skilled individuals.

Across High Asia, the amount, timing, and spatial patterns of snow and ice melt play key roles in providing water for downstream irrigation, hydropower generation, and general consumption. The goal of this paper is to distinguish the specific contribution of seasonal snow versus glacier ice melt in the major basins of High Mountain Asia: Ganges, Brahmaputra, Indus, Amu Darya, and Syr Darya. Our methodology involves the application of MODIS-derived remote sensing products to separately calculate daily melt outputs from snow and glacier ice. Using an automated partitioning method, we generate daily maps of (1) snow over glacier ice, (2) exposed glacier ice, and (3) snow over land. These are inputs to a temperature index model that yields melt water volumes contributing to river flow. Results for the five major High Mountain Asia basins show that the western regions are heavily reliant on snow and ice melt sources for summer dry season flow when demand is at a peak, whereas monsoon rainfall dominates runoff during the summer period in the east. While uncertainty remains in the temperature index model applied here, our approach to partitioning melt from seasonal snow and glacier ice is both innovative and systematic and more constrained than previous efforts with similar goals.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature ( T a ), river flow discharge ( Q ), and the components of the Gregorian calendar ( CGC ) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors ( T a , Q , and the CGC ) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only T a is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.

The objective of this study is to investigate the changes in the river flow regime under the influence of dam construction and climatic variability and disaggregate their individual effects. Daily discharge, temperature, and precipitation data were collected from Qaranqo River basin in East Azerbaijan (Iran) for the period of 1971–2017. To disaggregate the effects of climatic variability and dam construction, daily river discharge data after dam construction were simulated using an Artificial Neural Network (ANN). Then, with the use of Indicators of Hydrologic Alteration (IHA) and annual indexes, river flow regime changes were examined. Results showed that monthly flows in all months except for July, August, and September, decreased. Also, annual maximum flows (except for a maximum of 1 day) decreased and minimum flows (except for a minimum of 1 day) increased. The Julian dates of minimum and maximum flows had also preceded, in which both dam construction and climatic variability were influential. Both dam construction and climatic variability were equally effective for monthly flows, extreme flows, and Julian dates of extreme flows. However, dam construction played a greater role in the changes of high and low pulses, and fall and rise of flow hydrograph. The two indexes of relative and absolute ranges of variation of intra-annual parameters had decreased, while the non-uniformity coefficient, concentration degree, complete accommodation coefficient, and Richards-Baker index had increased, which were mainly affected by climatic variability. In the Environmental Flow Components (EFC) in the group of low monthly flows in most months, there was little difference between observed data and simulated data, which indicated the role of climatic variability in the river flow regime. By contrast, there was a significant difference in peak, duration, and time of large and small floods, which indicated the role of the dam construction in these components. Climate variability had the most role in changes of monthly low flows, extreme low flows, and high flows of river flow regime, while in small and large floods, the role of dam construction was significant. It can be concluded that the effect of climatic variability on flow regime in the Qaranqo River basin, was greater than the effect of dam construction.

Accurately estimating river water flow during floods is crucial to water resource management, dam reservoir operation, and flood mitigation strategies. Although hydrological models for flood prediction have improved, they still face constraints and make inaccurate forecasts. Hydraulic models face uncertainties related to riverbed Manning roughness coefficient and energy slope. This study employs a novel Friction-Slope (α parameter) method to estimate flood discharge. Investigation focuses on three alluvial rivers in Golestan, Iran. The computation method uses the Manning formula and accounts for river energy slope and riverbed Manning roughness coefficient. The α parameter is calculated using easy-to-access river cross-section variables: flow depth, area, and hydraulic radius. SVR-PSO, SVR-GWO, and SVR-RSM hybrid methods are used to achieve this. Calculated river flow discharges are compared to measured data. Statistical evaluation criteria like R2, MAE, RMSE, and conformity index determined the hybrid models' optimal structures. The SVR-RSM model had the highest accuracy during testing, with an R2 value of 0.97, MAE of 0.22, RMSE of 1.66, and d of 0.99. Once the α parameter was determined using the RSM-SVR model, river flow discharges were calculated and compared to observed values. The testing phase produced the most accurate results, with R2 = 0.88, MAE = 0.15, RMSE = 0.41, and d = 0.98.