Upstream Stations Research Articles

Research on sedimentation characteristics of the end bend water extraction project of cascade reservoir backwater

Abstract The operation mode of upstream and downstream cascade power stations significantly impacts the river bed in the water intake section, leading to complex sediment deposition characteristics in the curved river section at the reservoir’s end. In this research, a standard physical model for bed load and suspended load at a scale of λl=λh=100 was conducted, combined with the operation mode of upstream and downstream power stations, to investigate the sediment deposition patterns in the intake section and to study the sediment deposition characteristics in the intake area. The experimental results reveal that bed load sediment predominantly accumulates on the right bank (convex bank) of the river, with no bed load sediment accumulation at the mushroom head layout of the water intake project on the left bank (concave bank). When the operation of upstream and downstream power stations is uncoordinated, suspended sediment settles in the water intake section, with no sediment accumulation in the local area of the mushroom head of the intake due to the disturbance effect. Safety sensitivity analysis indicates that the safety of the water intake project can be ensured.

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Unseen riverine risk: Spatio-temporal shifts of microplastic pollution and its bioavailability in freshwater fish within the Ikopa River urban system.

Growing concern over microplastic pollution, driven by their widespread accumulation in the environment, stresses the need for comprehensive assessments. This study investigates the spatial and temporal distribution of microplastics in the Ikopa River (Antananarivo - Madagascar), which flows through a densely populated area, and examines their correlation with contamination levels in local fish species. By analyzing upstream and downstream stations across wet and dry seasons, only a notable increase in microplastic concentration downstream during the wet season was observed, ranging from 138.6 ± 9.0 to 222.0 ± 24.5 particles m-3, with polyethylene-co-vinyl acetate being the predominant polymer at 62.3 ± 5.13% of the total sampled polymers. This distribution underlines the impact of urban activities on pollution levels. Fish species, gambusia and Nile tilapia, were assessed for microplastic occurrence in gills and gastrointestinal tracts. Higher contamination rates were found in gambusia, enlightening the influence of feeding behaviour and fish habitat on microplastics contamination. Ingestion of microplastics directly from the water column was evident in both species, with the detection of high-density plastics such as polytetrafluoroethylene and polyvinyl chloride suggesting likely sediment contamination. This research highlights the widespread contamination of aquatic environments and its direct impact on local wildlife, pointing to a clear requirement for effective pollution management strategies.

Study on the regularities of flow‐sediment transport and sedimentation in the lower Yellow River

AbstractThe training effectiveness of the lower Yellow River (LYR) depends on the understanding of the regularity of flow‐sediment transport and riverbed sedimentation. The measured data of daily flow discharge and sediment transport rate at the five hydrological stations (Xiaolangdi [Xld], Huayuankou [Hyk], Gaocun [Gc], Aishan [As], and Lijin [Lj]) in the LYR during the period from 1960 to 2017 are used to investigate the regularity of flow‐sediment transport and sedimentation in the LYR. The Xld station is used as the inlet control station, and the LYR is divided into four segments using four other stations, and the whole year is divided into three periods, namely, the dry season, the flood period, and the nonflood period of the wet season. On this basis, the relationships between the sediment transport rates at the four stations (Hyk, Gc, As, and Lj) and the rates at their respective closest upstream stations are analyzed in each of the three periods. According to the incoming sediment coefficient of the Xld station, the flow and sediment processes in the three periods are classified, and the refined equations for the relationship between the sediment transport rates at the downstream station and its upstream station are established. The results show that the calculated amount and process of erosion and deposition in each period and each segment of the LYR using the equations are in good agreement with the measured values. The relationship equations established in this study can conveniently predict the amount of erosion and deposition in different periods and different segments of the LYR in the future, which is of great significance to the rapid decision of the impact of the construction and operation of hydraulic projects in the upper and middle reaches of the Yellow River on the sedimentation in the LYR.

Nonlinear and abnormal relationship between turbidity and suspended solids concentration in mountainous rivers: A case study of the Lai Chi Wo river in Hong Kong, China

Suspended solids concentration (SSC) in a river is closely relevant to river water turbidity. Investigation of their relationship in this study is accompanied by observed turbidity and SSC values, which were obtained from the testing results of water samples and monitored conditions in streamflow. The water samples were collected from two observation stations with a broad range of sediment concentrations in the Lai Chi Wo catchment in Hong Kong, China. We classified the target rainfall events into single-peak event type and dual-peak event type for a distinguished discussion of the relationship between SSC and turbidity in this study. At a finer classification, each event is separated into defined processes for the analysis, where two main processes refer to the periods that SSC rises from a normal state to a peak state first and the followed periods that SSC recesses to ordinary status gradually. It is advised by the analysis results that the estimation of SSC through turbidity values should be based on the same rainfall types for the upstream station. However, the results show that the classification of rainfall types does not need to take downstream areas into consideration. Furthermore, current research implies that the individual established connections between SSC and turbidity value at different stages (particularly referring to the rising period and recessing period) could be applied to estimate SSC at the same station via continuous turbidity values for both this and other ungauged stations with similar topographical features in the future. Meanwhile, this research approach provides new insight exploring various behaviors of sediments at different stages during an integral rainfall event. A comparison of distinguished performances of sediment during corresponding stages in a rainfall event makes contributions to diverse relationship between SSC and turbidity in the mountainous river.

Risk assessment of cigarette butts and microplastic pollution in a drinking and irrigation water basin of West Anatolia, Türkiye.

Cigarette butts (CBs) and Microplastics (MPs) have serious harmful effects on the environment and living organisms despite their small size. This research aims to investigate the abundance and pollution status of CBs and MPs in Tahtalı Dam Basin (West Anatolia, Türkiye) which is the most important drinking and irrigation water resources. Clean Environment Index (CEI) and Cigarette Butt Pollution Index (CBPI) were used to determine pollution degree of the basin. The total number of CBs were 1.478 items, the total number of MPs were 477 items/m2 in the basin. As a result of this study, MP particles weren't found in Balaban Stream. Highest number of MP particles observed in 100-250μm (45%) size class. The most abundant MP type and colour were, fragment (54%) and white (42%), respectively. Polyethylene terephthalate (50%) was the most abundant type of polymer according to the ATR- FTIR analysis. As a result of the CEI and CBPI, the upstream stations of the stream were classified as "clean" status, while downstream sampling points of the stream and Balaban Lake coasts were classified as "extremely dirty" status. The calculated volumes of MP particulates from mining facility, agricultural and recreational activities indicate that anthropogenic factors are the most important MP source in the Tahtalı Dam Basin. This study is the first study about MP and CB pollution of the freshwater ecosystems in the region.

Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

River dust-induced air pollution in a changing climate: A study of Taiwan's Choshui and Kaoping Rivers

This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors (temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.

Прогнозирование характеристик половодья 2024 года на реках Ишим, Тобол и Урал

The Hydrometeorological Center of Russia has developed a system of methods for forecasting spring flood characteristics, which was implemented for eight stations on the Ishim, Tobol, and Ural rivers in 2024. Specific features of the methods are the simplicity of their obtaining and implementation for a particular river reach and a possibility of the fast correction of forecasts as the current information is available. The peak water level at the station was predicted based on its dependence on the peak water level at the upstream station. The date of the flood peak was predicted with account of the data of the peak at the upstream station and probable values of the travel time from the upstream gauge to the predicted downstream one. The date of the water level drop to the level of a severe event was forecasted taking into account the height and date of the flood peak using the calculated recession curve obtained by the statistical analysis of flood recessions in the previous years. The date of the water level drop to the level of an adverse event was predicted by extrapolating the flood recession observed during 10–15 days after the flood peak. The mean forecast lead time is 9 days. The forecasts were issued in a deterministic and probabilistic form and gave quite satisfactory results, which were used in the organization and implementation of measures to protect the population and economic facilities from the floods observed in 2024. The efficiency of the developed methods allows recommending them for use in predicting flood characteristics in various regions of Russia.

Spatial and Temporal Variation of C, N, and S Stable Isotopes and Seagrass Coverage Related to Eutrophication Stress in Zostera marina

Zostera marina is an ecologically valuable species that has been declining due to anthropogenic environmental stressors. In this study, spatial and temporal indicators of eelgrass stress, such as coverage and biomass, were compared with the isotopic composition of C, N, and S to understand the mechanism(s) of plant stress. Eelgrass samples were collected in June, July, and August of 2020 at five stations along an estuary spatial gradient in the southern Gulf of St. Lawrence to measure above- and below-ground biomass and tissue isotopes in eelgrass leaves and roots/rhizomes. Eelgrass biomass was lowest at the innermost sampling station, which coincided with eutrophication-induced hypoxia relative to outer sampling stations. δ13C levels at the upstream station were depleted compared to downstream stations. Comparatively, δ15N and δ34S findings were not correlated with plant biomass. Thus, sulfide intrusion was not a major stressor for eelgrass in this estuary. Between the years 2014 and 2020, eelgrass coverage was found to have increased, which coincided with high and low recorded external nutrient loads from the Wheatley River, respectively. Ultimately, these findings indicate that isotopic composition and biomass can be useful in assessing the health of eelgrass in temperate estuaries.

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model.

The role of catchment population size, data normalization, and chronology of public health interventions on wastewater-based COVID-19 viral trends

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic presented the most challenging global crisis in recent times. A pandemic caused by a novel pathogen such as SARS-CoV-2 necessitated the development of innovative techniques for the monitoring and surveillance of COVID-19 infections within communities. Wastewater surveillance (WWS) is recognized as a non-invasive, cost-effective, and valuable epidemiological tool to monitor the prevalence of COVID-19 infections in communities. Seven municipal wastewater sampling sites representing distinct sewershed communities were selected for the surveillance of the SARS-CoV-2 virus in Durham Region, Ontario, Canada over 8 months from March 2021 to October 2021. Viral RNA fragments of SARS-CoV-2 and the normalization target pepper mild mottle virus (PMMoV) were concentrated from wastewater influent using the PEG/NaCl superspeed centrifugation method and quantified using RT-qPCR. Strong significant correlations (Spearman's rs = 0.749 to 0.862, P < 0.001) were observed between SARS-CoV-2 gene copies/mL of wastewater and clinical cases reported in each delineated sewershed by onset date. Although raw wastewater offered higher correlation coefficients with clinical cases by onset date compared to PMMoV normalized data, only one site had a statistically significantly higher Spearman's correlation coefficient value for raw data than normalized data. Implementation of community stay-at-home orders and vaccinations over the course of the study period in 2021 were found to strongly correspond to decreasing SARS-CoV-2 wastewater trends in the wastewater treatment plants and upstream pumping stations.

Trends in Water Quality in a Tropical Kenyan River-estuary System: Responses to Anthropogenic Activities

Aims: To determine the spatial variation in physico-chemical water quality attributes in estuarine ecosystems of South Coast Kenya to inform its management. Study Design: We employed diagnostic research design where such factors as anthropogenic activities, hydrology, and accessibility were considered in choosing 12 sampling stations. A mixed sampling design (probability and non-probability) was used to sample. Place and Duration of Study: Four discharging rivers into estuarine system with a reference point were identified. 12 sampling points were earmarked and sampled for 12 months. Methodology: Nine selected water quality parameters were collected in triplicates monthly for 12 months. Temperature, dissolved oxygen, pH, conductivity, salinity and TDS were collected in situ using YSI Multiparameter meter (Professional plus) while nutrients were analyzed in the laboratory calorimetrically. Data was tested for normality and homogeneity of variances using Levene's and Shapiro-Wilk tests. Two-way analysis of variance (ANOVA), and Principal Component Analysis (PCA) were done using the 64-bit R Software version 4.3.0 to test for significant differences and correlate the parameters among the sampling sites respectively. Observed differences were considered statistically significant at P &lt; 0.05. Results: The measured environmental variables differed significantly among the sampling stations (P &lt; 0.05). temperature and salinity exhibited moderate to strong effect on water quality, while pH and nutrients (phosphates, nitrates, and ammonia) exhibited mixed effects with both negative and positive loadings on the Principal Components. It is demonstrated that the south coast estuary, Kenya is affected by two gradients: mineralization from the sea and the organic matter gradient at the upstream stations dominated by the fluvial inflows from the respective rivers. Conclusion: The south coast of Kenya estuary is impacted by natural processes and anthropogenic activities. we recommend for continued regular water quality assessment and monitoring to acquire sufficient data to shape policy frameworks towards its sustainable management.

Utilizing sequential modeling in collaborative method for flood forecasting

Flood forecasting has been a major challenge in hydrology for decades. A variety of approaches have been developed, including numerical models and data-driven models, such as Deep Learning (DL), for disaster early-warning and monitoring systems. This study proposed two combination models, exploring different feature separation techniques to improve the 12-hour multi-step discharge prediction. Specifically, two different approaches were implemented, namely the separation of wet and dry periods (Combination Model No. 1; CM-1) and the separation of flood and non-flood events (Combination Model No. 2; CM-2) for comparison with the baseline model trained on an entire dataset. Multivariate time series discharge data were used, measured at upstream and downstream stations within the same river basins. Experiments were conducted in three case study areas in Thailand: the Lower Loei River Basin, the Upper Nan River Basin, and the Upper Chi River Basin.In the proposed model combination approach, three deep-learning models were investigated: vanilla recurrent neural networks (RNNs) and their variants, long short-term memory networks (LSTMs), and gated recurrent units (GRUs). In general, CM-2 exhibited superior performance compared to the other models in most of the experimented neural networks, while the CM-1 approach outperformed the baseline model. This emphasized the advantages of our proposed feature separation framework, particularly in improving model performance, with respect to flood events. However, some variations were observed in the Upper Chi River Basin because the more complex models, such as LSTMs and GRUs, possibly overfitted the training set with less fluctuating data. Our study highlighted the significant contribution of combining two models through feature separation, tailored to specific periods, thereby enhancing the accuracy of flood predictions.

Time-varying microplastic contributions of a large urban and industrial area to river sediments

The quantification of microplastic (MP) pollution in rivers is often constrained by a lack of historical data on a multi-decadal scale, which hinders the evaluation of public policies. In this study, MP contents and trends were analyzed in dated sediment cores sampled upstream and downstream of a large metropolis, in environmental deposits that exhibited consistent sedimentation patterns from the 1980s to 2021. After a thorough sedimentological analysis, MPs were quantified in samples by micro Fourier Transform InfraRed spectroscopy (μFTIR imaging) and a density separation and organic matter digestion procedure. Microplastics recorded in the upstream core are relatively ubiquitous all along the dated sequence. The results also confirmed a sever increase of microplastics levels in the downstream core, by one order of magnitude, and an increase of polymer types. Polypropylene, polyethylene, and polystyrene represent ubiquitous contamination and were predominant at the two stations, whereas polyvinyl chloride and polytetrafluoroethylene were suspected to be abundant at the downstream station, but were not detected at the upstream station. Their presence could be linked to local contamination from specific industrial sources that manufactured and utilized these polymers. Surprisingly, in the downstream station sediment has recorded a relative improvement in polymers associated with industrial sources since the 2000s and, to a lesser extent, for ubiquitous ones since the 2010s. This trend of mitigation diverges from that of global assessments, that assume uncontrolled MP pollution, and suggest that European Union wastewater policy and regulation on industrial discharges have positively influenced water quality, and certainly also on MPs. However, the accumulation of microplastics remains high in recent deposits and raises the emerging concern of the long-term management of these reservoirs.

A comprehensive multivariate investigation of the water quality of Kallada River in Kerala, India

The Kallada River is a remarkable water resource in Kerala and reviving several villages through its glorifying journey. An investigation was directed to evaluate the spatial and temporal status of water quality in Kallada River, Kerala using multivariate statistical tools from February 2019 to January 2021. The status of fifteen environmental variables was detected by using standard methodology. The increased levels of conductivity, total dissolved solids, biochemical oxygen demand, hardness, alkalinity, calcium, and magnesium were detected during the post-monsoon (PoM) season and those parameters were tend to increase towards the downstream stations. Some other variables such as turbidity, dissolved oxygen, NO3−, SiO2, and PO43− were higher during the monsoon (MoN) season. Temperature, pH, and chemical oxygen demand were higher during the pre-monsoon (PrM) season. Furthermore, multivariate statistical tools like principal component analysis (PCA), agglomerative cluster analysis (AHC), and pearsons correlation were used to assess spatio-temporal changes in water quality to reveal pollution. Contrarily, the midstream stations such as K6, K10, and the entire downstream area (K11 to K15) of the Kallada River is under the threats of many anthropogenic activities such as sewage effluents, sand mining and agricultural runoff when compared to the entire upstream stations (K1 to K5), and three midstream stations (K7 to K9). The presence of various organic and inorganic pollutants begins from K6 and drastically rises at K11. Hence, efforts are required to prevent the contamination in the Kallada River.

Analysis of Heavy Metals and Gene Expression of African Arowana Fish (Heterotis niloticus) obtained from Igbalegbe River, Ughelli, Delta State, Nigeria

Heavy metal pollution in aquatic ecosystems can be assessed with suitable biomarkers in fish. This study was carried out to determine the concentration of heavy metals and gene expression of African Arowana Fish (Heterotis niloticus) obtained from Igbalegbe River, Ughelli, Delta State, Nigeria using Atomic Absorption Spectrophotometer (Varian 220 Fast sequential) for the metals and Polymerase chain reaction (PCR) for the gene expression study. Results of the study showed that fishes in the downstream sectionof the river and the effluent discharge point recorded higher metal pollution Index (MPI) compared to the upstream station. The concentration of heavy metals in the fish tissue were generally within the limits of Food and Agricultural Organisation (FAO)/World health Organisation (WHO) except for that of Cadmium and Lead in the discharge station. The selected genes investigated were biomarkers for general stress (HSP70), xenobiotic metabolism (CYPA1), antioxidative defence (SOD), Insulin growth factor-1 (IGF-1) and hypoxia inducible factor (HIF) respectively. The expression of the genes revealed significant variations (P&lt;0.05) in the fishes obtained from the different stations of the river. Some of the genes were down-regulated and suppressed while others were upregulated to enable the fish cope with stress while adapting to environmental pollution. Changes in biomarkers can therefore be considered as early signals of stress in the selected fish species.

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.

The Impact of Satellite Soil Moisture Data Assimilation on the Hydrological Modeling of SWAT in a Highly Disturbed Catchment

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km2) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at &gt;70% and &gt; 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement.

Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting.