Hydrological Stations Research Articles

Spatial‐Temporal Differentiation of Supra‐ and Sub‐Permafrost Groundwater Contributions to River Runoff in the Eurasian Arctic and Qinghai‐Tibet Plateau Permafrost Regions

AbstractSupra‐ and sub‐permafrost groundwater are the two main components of groundwater in permafrost regions. However, due to the lack of groundwater observational data, the spatial‐temporal differentiation of these groundwater components in permafrost basins remains unclear. Based on flow data from 17 hydrological stations in five permafrost rivers within the Eurasian Arctic and Qinghai‐Tibet Plateau permafrost regions, this study tries to determine the proportion of supra‐ and sub‐permafrost groundwater through the corresponding relationship between baseflow separation and baseflow index. The results showed that the annual average contribution of supra‐ and sub‐permafrost groundwater in river runoff to total streamflow in the Yangtze River source basin was 36.81% and 14.56%, respectively. Correspondingly, the Yellow River source basin was 36.58% and 24.46%, the Ob River basin was 37.05% and 26.83%, the Yenisei River basin was 28.80% and 36.56%, and the Lena River basin was 39.13% and 9.54%. Over the past 50–80 years, the ratio of sub‐permafrost groundwater discharge to river runoff and the flux of sub‐permafrost groundwater have shown an increasing trend in all study basins, which was significantly affected by air temperature and permafrost area. Relative contribution of supra‐permafrost groundwater exhibits a significant positive correlation with precipitation and permafrost area. Air temperature has both positive and negative effects on supra‐permafrost groundwater discharge, leading to a rising or falling trend of supra‐permafrost groundwater discharge. In the future, it is necessary to further explore the complex effects of groundwater discharge variations on streamflow in permafrost regions under climate warming.

Integrated watershed management strategies for sustainable resource utilization using the SWAT model: case study of the Kalte River watershed, Rift Valley Basin, Ethiopia

ABSTRACT Most people who live in rural areas are highly dependent on shared access to natural resources including land, water, and forests for their food requirements and livelihoods. However, land degradation remains one of the biggest environmental problems worldwide. Therefore, this study proposed to develop integrated watershed management strategies for sustainable resource utilization in the Kalte River watershed. To achieve this, the SWAT model was simulated for 31 years (1992–2022), calibrated and validated at Wajifo and Humbo hydrological stations to determine the sediment and runoff from the watershed, highly sediment erosion-vulnerable part of the watershed was identified from the result of the model and the best watershed management practice was suggested for the study watershed. In total, 87,920 tons/year of sediment are yielded to Lake Abaya from the Kalte River watershed. The sediment yield was reduced by terracing at 64%, strip cropping at 59.32%, grassed waterway at 54.06%, and contour planting at 47.93%. Therefore, the highest efficiency management method in the Kalte River watershed is terracing. The watershed managers and scientific community are beneficiaries of the output of this study. Watershed managers and decision-makers can make use of the information to help them choose appropriate watershed management strategies and ensure sustainable watershed management.

Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream

In the context of climate change and human influence, timely and reliable information about water level variations is crucial for downstream flood control, navigation, and water resource management, particularly for the Lancang-Mekong River, the largest transboundary river in Southeast Asia. However, accurate real-time water level forecasts remain challenging, especially in data-limited regions. This study focused on the Chiang Saen Station, the uppermost Mekong River hydrological station, comparing three methods to predict 48-hour water levels: the Variable Infiltration Capacity (VIC) and Hydrology-Hydraulic (HH) physics-based models, the Gated Recurrent Unit (GRU) model, and hybrid models (VIC-GRU and HH-GRU). Evaluations were conducted at the 1-h, 3-h, 6-h, 12-h, 24-h, and 48-h forecast lead times. Assessments at various lead times showed that hybrid models incorporating physics-based mechanisms significantly enhanced predictions. VIC-GRU demonstrated superior performance, with KGE values surpassing 0.94, NSE values exceeding 0.95, MAE below 0.17 m, and PBIAS within ± 1 % across all lead times. Compared to GRU, PBIAS decreased by 84 %, while MAE dropped by 76 % to 81 % relative to VIC and HH, respectively. Notably, seasonal variations affected hybrid model performance, especially in the dry season. Optimal parameter selections enhanced model accuracy by 48 % to 49 %. These results underscore the potential of combining physics-based and deep learning models for more accurate, high-temporal-resolution real-time water level forecasts in data-scarce regions. A comprehensive study of streamflow characteristics enhances the advantages of this integration. This research contributes valuable insights to water level prediction in data-scarce regions and informs flood risk reduction and management in the Lancang-Mekong mainstream.

A novel Joint Probability Density Difference Approach for assessing the alteration of hydrologic regime

AbstractThe construction and operation of upstream reservoirs have significantly altered downstream hydrologic regime. Appropriate and quantifiable assessment method for the alteration of hydrologic regime is considerably vital and emergent for ecological protection and restoration. The Range of Variability Approach (RVA) and modified RVA methods have been widely used in practice to assess the hydrological alteration. However, these methods have failed to concurrently describe the distribution of indicator and morphological features in detail, which might inevitably lead to the misjudgment of alteration. This paper proposes a Joint Probability Density Difference Approach (JPDDA) method to address the major drawbacks of these previous methods with the introduction of Gaussian Kernel Density Estimation (KDE) and copula function. The annual average flow is selected as the reference variable to construct a proper joint probability density function between itself and other hydrological alteration indicators. The JPDDA method could describe the marginal distribution in detail through Gaussian KDE and also link the morphological features with copula function. Along with pervious methods, the hydrological alterations at Yichang hydrological station, Yangtze River are estimated based on the measured flow from 1949 to 2022. It is shown that the hydrologic regime has suffered from a moderate or even heavy alteration under the influence of massive upstream cascade reservoirs, and the JPDDA outperforms the other methods in terms of rationality and stability for practical assessments. Thus, the proposed JPDDA method is strongly advised to handle the hydrological alteration and could provide a reasonable reference for ecological operation.

Trends in Concentration and Flux of Total Suspended Matter in the Irrawaddy River

Large rivers without hydrological data from remote sensing observations have recently become a hot research topic. The Irrawaddy River is among the major tropical rivers worldwide; however, published hydrological data on this river have rarely been obtained in recent years. In this paper, based on the existing measured the total suspended matter flux (FTSM) and discharge data for the Irrawaddy River, an inversion model of the total suspended matter concentration (CTSM) is constructed for the Irrawaddy River, and the CTSM and FTSM from 1990 to 2020 are estimated using the L1 products of Landsat-8 OLI/TIRS and Landsat-5 TM. The results show that over the last 30 years, the FTSM of the Irrawaddy River decreased at a rate of 3.9 Mt/yr, which is significant at the 99% confidence interval. An increase in the vegetation density of the Irrawaddy Delta has increased the land conservation capacity of the region and reduced the inflow of land-based total suspended matter (TSM). The FTSM of the Irrawaddy River was estimated by fusing satellite data and data measured at hydrological stations. The research method employed in this paper provides a new supplement to the existing hydrological data for large rivers.

A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction

River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location.

Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance.

Integrated Water Resource Management of Panjshir River Basin

In recent years, integrated water resource management has attracted the attention of policymakers and experts in the water sector. This method, which is very effective, faces many challenges in developing countries such as Afghanistan. The following research is based on the field method, which has been used for information from books, journals, and reliable scientific sites, and to collect basic information from a questionnaire that has been interviewed with 103 residents of Panjshir province, as well as from the analyzed data. The National Water Resources Authority has also been used. The findings of the research show that in the Panjshir river basin, only a foundation has been laid in the form of policies, strategies, and laws, but there are deficiencies in the fields of regular institutional framework and tools needed to perform tasks in this river basin. One of the departments of integrated water resource management has not been formed, and there are still few hydrological and hydro meteorological stations in this field. On the other hand, according to the respondents to the questionnaire, the role of women as a huge group of society in decision-making is also weak. Therefore, we conclude that the effectiveness of applying the principles of integrated water resource management in the Panjshir river basin is low due to the above reasons.

Flood prediction based on feature selection and a hybrid deep learning network

Abstract As climates change globally, water-related disasters increase, causing substantial economic losses and safety risks. During floods, river water levels show unpredictable fluctuations, introducing substantial noise that complicates accurate prediction. A hybrid model that uses eight-dimensional input data from hydrological and meteorological stations is proposed to address these challenges. Initially, the Variational Mode Decomposition preprocesses and denoises water level data, resulting in decomposed Intrinsic Mode Functions (IMFs). Then, the Pearson correlation coefficient between each IMF and input characteristics is computed, and the fluctuation factor for each IMF is defined. IMFs are categorized based on a threshold, leading to a hybrid prediction model. This model integrates convolutional neural networks (CNNs) for spatial information and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism for learning from past and future data points. Comparative evaluations of mean absolute percentage error, root mean square error, mean absolute error, and goodness of fit (R2) show that the proposed model outperforms existing LSTM and CNN–BiLSTM frameworks, reducing RMSE by at least 20% and increasing R2 by approximately 10% on average. The model's practical significance lies in improving the accuracy and efficiency of meteorological forecasting and flood warning systems, contributing substantially to global disaster preparedness and response strategies.

River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

River flow monitoring serves as a crucial data source in hydrological stations, employing the widely adopted space-time image velocimetry (STIV) method both domestically and internationally. However, current methods yield space-time (ST) image trajectories with limited usable information, particularly due to the interference of complex flow characteristics and harsh outdoor environments in natural rivers. This interference leads to errors in detecting the main orientation of texture, consequently reducing measurement accuracy. To overcome these challenges, this paper proposes an river video flow measurement algorithm with ST image fusion of object tracking and statistical characteristics. The Lucas–Kanade tracking algorithm is employed to track velocity points and generate tracking trajectories. These trajectories’ coordinates are then fitted into straight lines to automatically arrange velocity-measuring lines in natural rivers. The algorithm leverages multi-scale detail boosting and Gaussian directional stretch filtering to enhance the quality and texture trajectories of ST images. Furthermore, background noise filtering and stripe noise filtering are applied to denoise the ST images, effectively mitigating the impact of noise caused by the natural river environment and camera equipment. The proposed algorithm incorporates projection statistical characteristics algorithms to accurately detect the dominant texture direction, enabling precise calculation of mean velocity and cross-sectional discharge of the river. Experimental validation was conducted in both natural and regular river channels, demonstrating the superior performance of the proposed solution compared to widely used large-scale particle image velocimetry, STIV, MobileNet-STIV and FD-DIS-G algorithms. The algorithm exhibited higher accuracy, stability, and applicability, with errors at each velocity-measuring point below 10%, and relative errors of the calculated mean velocity and cross-sectional discharge below 3%. This affirms the algorithm’s superior accuracy, stability, and applicability.

Development of Experimental Low-Cost Rain Gauges and their Evaluation During a High Intensity Storm Event

The measurement of rainfall via ground sensors is fundamental in a variety of hydrological applications, including rainfall-runoff simulations, basin water balance and flood forecasting. The tipping bucket rain gauge (TBR) constitutes the most common type of automatic gauge for the measurement of rainfall intensity. The objective of this work is the development of low-cost and reliable rain gauges, including their data logger, which could be installed at remote, rural areas, in order to supplement with rainfall data the limited or non-existing network of hydrological stations. To achieve this target, two experimental TBRs with diameters of 20 cm (RG20) and 28 cm (RG28) were developed. Electronic boards Arduino UNO and Raspberry Pi were used for their data logger. The measurements of RG20 and RG28 were compared with those of a high quality rain gauge ARG100 and a daily non-recording rain gauge. The cyclone Daniel on 06-09-2023 caused an intermittent storm event in the city of Athens, Greece, which was measured by all three TBRs for purposes of evaluation. The results showed that the variations between ARG100 and RG28 were lower than 6%, while the variations between ARG100 and RG20 were about 10% during a few time intervals of high rainfall intensity. The return period of the storm event were estimated at 43, 59 and 45 years for rainfall durations of 10, 20 and 30 min, respectively.

Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

AbstractThe flow duration curve (FDC) is the cumulative distribution function, which represents the relationship between the frequency and magnitude of streamflow, and the precipitation duration curves (PDC) follows the same principle. Nowadays, the correlation between the shape of PDC and FDCs, their respective physical control factors including human activities, and their fitting conditions in unmeasured catchments across China have not been fully understood. In this paper, daily precipitation and streamflow data with 30 years records from 224 hydrological stations in the middle and lower Yangtze River basin were chosen to fit PDC and FDCs through gamma distribution. Framework was proposed for modelling FDCs to analyse the relationship, similarity, regional patterns and response mechanism of fitting parameters between PDC and FDCs, dividing the streamflow time series into fast and base flow and stations into three categories in consideration of human activities to attribute the shapes of PDC and FDCs to catchment meteorological and geographical characteristics and physical processes under natural conditions. Results indicate that the parameters of PDC and certain FDCs (TFDC, FFDC, SFDC) share similar spatial patterns but vary for the different duration and interactions of the processes. The climate and catchment characteristics such as extreme properties of precipitation, base flow index (BFI), Pmax*αp and concentration ratio index (CIM) will influence the shape of PDC and FDCs. The relationship between BFI and SFDC/TFDC can be better reflected in “Regulated watersheds”, while CIM/Pmax*αp and PDC/FFDC in “watersheds in the mainstream” are more related. This paper provides a deeper understanding and more accurate way of the application of these parameters in describing and predicting hydrological processes and estimation of PDC and FDCs in unmeasured catchments, and can be applied to more future research about processes based on catchment rainfall‐runoff responses and physical controls.

Flow and sediment measurement for the Three Gorges Project

AbstractFlow and sediment problem is one of the key factors which affect the dispatching operation and life of the Three Gorges Project (TGP). Many approaches have been employed to research the flow and sediment problems of the TGP during its demonstration, planning, design, construction and operation, and many important results have been obtained. To understand the progress of flow and sediment measurement in China's representative projects and the experience of sediment observation in super large reservoirs, the flow and sediment measurement of the TGP is mainly introduced in this paper. It includes the general situation of the TGP, the distribution of the hydrological station network, the measurement factors, the new measurement technology, and the sediment changes in the reservoir and downstream after the impoundment of the TGP. The sediment measurement results show that the basic situation of sediment problems is good, and these sediment problems may probably accumulate, develop, and transform over time, so they should be paid continuous attention.

Historical and projected future runoff over the Mekong River basin

Abstract. The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m3s-1a-1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m3s-1a-1 (13 ± 10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

Human Management Decreased Suspended Particle Size in the Loess Plateau Rivers during the 1980s to the 2010s

The study of river sediment is a broad and complex field. One of the very important parameters is suspended particle size (SPS), which is indispensable for understanding water–sediment dynamics. As one of the most serious soil erosion areas in the world, the Loess Plateau delivers a large amount of sediment to the Yellow River and its numerous tributaries. Studies on riverine SPS in the Loess Plateau have received extensive attention. In this study, we investigate the spatiotemporal variations of SPS in the Loess Plateau rivers and analyze the driving factors along with their relative importance. Through the analysis of SPS data from 62 hydrological stations, the results indicated the spatial distribution of SPS was similar in the 1980s and 2010s, with both coarser particles mainly distributed in the northern rivers and finer particles mainly distributed in the southern rivers. During the 1980s to the 2010s, the mean SPS on the Loess Plateau decreased from 33 μm to 20 μm, with mean reductions of 42.0%, 29.4%, 46.3%, and 36.8% in the northern, western, southwestern, and southeastern basins, respectively. The most significant changes in SPS were observed in the Kuye, Wuding and Jalu River basins in the northern region, with decreases ranging from 27 to 73 μm. In the 1980s, topography (slope) and human management, followed by precipitation, were the key factors affecting SPS variability, contributing 25.7%, 25.9% and 24.0%, respectively. In the 2010s, the explanatory power of topographic slope on SPS variability declined by 16.6%, and other natural factors no longer significantly influenced SPS variability. The results of this study can serve as a reference for integrated basin management and sustainable ecosystem development in river catchments around the world.

Evaluation of the toxicological effects of uranium on human health in Chandrapur, Maharashtra, with reference to the water quality index

Groundwater is an important resource and approximate 80% of the world’s population depends solely on it to satisfy their needs. In the present investigation groundwater quality was assessed using WQI. For the same parameters viz, temperature, pH, conductivity, DO, alkalinity, TDS, sulphate, fluoride, bicarbonate, chloride, uranium, total hardness, calcium and magnesium hardness were assessed during post monsoon period in November 2021 in the Chandrapur region. For conducting this research total 116 groundwater samples were collected from different hydrological stations for the assessment of WQI. WQI is an important tool to assess quality of water for drinking and is classified as excellent to unsafe, i.e., 0-100 score. In this investigation WQI was found to be in the range between 27.63-674.56 exceeding both minimum and maximum score. The results of the present investigation showed that only 5.17% of water is safe for drinking,18% of water indicated poor water quality, 15% of water has very poor water quality and 77% of water is unsuitable for drinking purpose and proper treatment is required before use. During this investigation uranium in groundwater was also assessed considering its chemotoxical and radiological effects on human health. The chemotoxical and radiological effects were analyzed adopting standard equations given by USEPA. The radiological risk of mortality and morbidity was found to be 1.37E-05-1.47E-05, respectively. Thus, presence of uranium was noted and radiological risk was found to be below the permissible limit of AERB standard. The chemical toxicity of average value of Lifetime Average Daily Dose (LADD) and Hazard Quotient (HQ) was observed 0.315 and 0.00571, respectively and chemical toxicity of LADD was found to be in 8.62% sample population and above the permissible limits which can affect human health and can have kidney toxicity, bone and lung toxicity.

Simulation and Evaluation of Runoff in Tributary of Weihe River Basin in Western China

Model simulation plays a significant role in the water resources cycle, and the simulation accuracy of models is the key to predicting regional water resources. In this research, the Qianhe tributary at the Weihe River basin in Western China was selected as the study area. The tributary was divided into 29 sub-basins and 308 hydrological response units according to the spatial raster data and attribute data of the hydrology, meteorology, topography, land use, and soil types. On this basis, a soil and water assessment tool (SWAT) model for runoff simulation and evaluation of this region was established. A sensitivity test and parameter calibration were then executed on 15 parameters involved with surface runoff, soil flow, and shallow underground runoff. The simulation results demonstrate a calibration and verification error of 3.06–10.08%, with very small uncertainties throughout the simulation, whereas they exhibit relatively large errors in the simulation of the dry period (winter) but, in contrast, quite small errors in the rainy period (summer). In addition, the simulated runoff with a low value is overestimated. When the annual, monthly, and daily runoff are 4–13.5 m3/s, 4–69.8 m3/s, and 40–189.3 m3/s, respectively, the relative error is smaller, and the simulation results are more accurate. The sensitive parameters predominantly affecting the runoff simulation of the basin include soil evaporation compensation, runoff curve coefficient, vegetation transpiration compensation, and saturated hydraulic conductivity in this region. In the case of hypothetical land use change scenarios, we observe a great reduction in simulated runoff in arable land, woodland, and grassland, while we observe an increment in construction and residential land and wasteland. The annual and monthly runoff are increased by above 54.5%. With the increase in cultivated land and forestland, the annual and monthly runoff decrease by 24.6% and 6.8%, respectively. In the case of hypothetical scenarios under 24 climate combinations, if the precipitation remains unchanged, the increase and decrease in temperature by 1 °C leads to a decline and increment of runoff by −0.72% and 5.91%, respectively. With regard to the simulation for the future under the RCP2.6 and RCP8.5 climate scenarios, downscaling was employed to predict the runoff trend of the future. In short, this study provides a method for runoff inversion and water resources prediction in small mountainous watersheds lacking hydrological and meteorological observation stations.

Monitoring Suspended Sediment Transport in the Lower Yellow River using Landsat Observations

The spatiotemporal variations in suspended sediment concentration (SSC) in the lower reaches of the Yellow River exhibit significant variability and are influenced by reservoir operations. Understanding the spatiotemporal distribution characteristics of SSC in water holds crucial implications for environmental protection and reservoir operation management. Based on daily-scale SSC monitoring data from four hydrological stations in the lower Yellow River, this study established an SSC remote sensing model applicable to Landsat series satellite data. The independent variable of the model, Rrs(NIR)/(Rrs(G) + Rrs(R) + Rrs(SWIR)), demonstrated sensitivity to water bodies with different SSC values. Distinctive spatiotemporal characteristics in sediment transport were observed across the lower Yellow River. Spatially, the SSC values in the Sanmenxia and Xiaolangdi reservoirs were notably lower than those in other river sections, averaging 1008.42 ± 602.83 mg/L and 1177.89 ± 627.95 mg/L, respectively. Over time, the majority of the river sections (96%) exhibited decreasing trends in SSC during 1984–2022, particularly in the downstream Xiaolangdi reservoir, with average SSC values of 4265.58 ± 1101.77 mg/L in the 1980s and 1840.80 ± 2255.15 mg/L in the 2020s. Seasonal variations in SSC were prominent, with higher summer concentrations, averaging 5536.43 ± 2188.77 mg/L (2020s summer) and 814.11 ± 158.27 mg/L (2020s winter). Reductions in SSC during 1984–2022 primarily occurred in summer, weakening its seasonal variability in the lower Yellow River. Water discharge emerged as a critical factor influencing suspended sediment transport, with SSC increasing in high-water-flow months. Following the construction of the Xiaolangdi reservoir, the relationship between SSC and water discharge at different stations underwent notable alterations. This study enhances our understanding of the spatiotemporal dynamics of suspended sediment transport in the lower Yellow River, providing valuable insights for utilizing long-term Landsat series data in the dynamic monitoring of river sediment transport.

Особенности формования и трансформации максимального стока реки Миус

Purpose: to study the features of the formation and transformation of the maximum river runoff based on empirical data. Materials and methods. The series of observations of water levels and flows in two existing gauging stations Kuibyshevo (hydrological station 1) and Matveev Kurgan (hydrological station 2) with a duration of 51 years, were used as source materials. Statistical and theoretical methods were used as information processing methods, including system analysis and hydrological and geographical generalizations. Results and discussions. A study consisting of four stages: analysis of hydrographic characteristics, construction of dependency graphs of maximum flow rates Q = f(H), analysis of hydrological series and calculated data, was carried out. As a result of the analysis of hydrographic characteristics, a significant difference in the average heights of the watershed area and the bed slope was revealed. When comparing the maximum parameters by year, it was revealed that the maximum level recorded at the gauging station does not correspond to the maximum flow rate: at the hydrological station 1, the maximum water level was observed in 1979 and corresponded to 2 % availability, while the same year discharge of 151 cubic m/s corresponded to 6 % availability, the highest discharge was recorded in 1970 and was higher by 30 cubic m/s, the water level corresponded to 4 % availability and was lower by 0.49 m; at hydrological station 2, the maximum water level was recorded in 1985 with a flow rate of 202 cubic m/s, corresponding to 6 % probability, the maximum flow rate was observed in 1964 at a water level 0.38 m below the maximum, which corresponds to 12 % availability. Conclusions. The materials presented in the article confirm that the use of the coefficients of variation and asymmetry of one gauging station for the long-term average values of another gauging station is not entirely correct, despite the fact that they are located on the same river at a fairly close distance.

Comparative Analysis of Nonlinear Methods for Multivariable Water Level Prediction: The Case Study of the Atrato River

This paper presents an innovative multivariate forecasting application for hydrological variables, using an online model based on backpropagation (BP) neural networks. The effectiveness of this approach is verified by using the Nonlinear Autoregressive with Exogenous Inputs (NARX) and the linear Autoregressive with Exogenous Inputs (ARX) methods. The proposed methodology is implemented in two hydrological stations in the department of Chocó, Colombia, located along the Atrato River. The BP neural network structure is applied to establish correlations between water level, flow, and precipitation. This structure is designed to handle the dynamic complexity of the hydrological variables and their interconnections. In addition, a short‐term water level prediction is developed for incorporation into a flood early warning system. Validation of the approach is performed by comparing various estimation errors using regression metrics such as root mean square error (RMSE), relative mean square error (MSRE), mean absolute error (MAE), relative mean absolute error (MARE), and Nash–Sutcliffe efficiency coefficient (NSE). The computation time and the accuracy of the estimated weights versus real data are also considered. The results show that the proposed structure is the most suitable for accurately predicting water levels. It is shown that the backpropagation neural network structure of the proposed model is more effective for water level prediction. This is observed when evaluating the real data compared to the estimated data, obtaining values corresponding to the regression metrics. In particular, for output 2, the RMSE is 0.0742. Compared to the NARX model (RMSE of 0.0985) and the ARX model (RMSE of 0.1265), it is observed that the backpropagation neural network model improves the estimation by 32.6 over the NARX model and by 70.4% over the ARX model. As for the NSE coefficient for output 2 of the model, the backpropagation neural network is the closest to 1 compared to the other two models. For output 2, the backpropagation model obtains a value of 0.9966, the NARX model 0.9958, and the ARX model 0.9914. Another evaluation metric applied to this model is the Kling–Gupta efficiency coefficient (KGE), which is used to evaluate the performance of hydrological models. For the output of the two hydrological stations located in the Atrato River, we observed that for the backpropagation model a value of 0.9992 was obtained, for the NARX model a value of 0.9988 was obtained, and for the linear ARX model with nonlinear structure a value of 0.9984 was obtained. This indicates that the backpropagation neural network model provides a better estimate, as its value is closer to 1. Furthermore, the computational time executed in the algorithm of the three proposed nonlinear models is calculated, where the backpropagation obtains a calculated value of 350.9 microseconds, for the NARX model the computational time is 5908.7 microseconds and for the ARX nonlinear model, the computational time is 5172.6 microseconds. The model put forward demonstrates its effectiveness in accurately predicting water levels at two key hydrological stations along Colombia’s Atrato River. By incorporating intricate interactions among hydraulic structures and dynamic hydrological conditions, it enhances prediction accuracy. This not only aids in disaster risk management but also streamlines planning processes, particularly within flood early warning systems.