Suspended Sediment Load Research Articles

Quantifying Pleistocene loess provenance in midcontinental North America using a mixing model: Implications for glacial lobe evolution along the southern Laurentide ice sheet

Quaternary glaciations have significantly impacted the midcontinental North American landscape, leaving behind a proglacial sediment record of glacial lake deposits, glacio-fluvial sand and gravel, and windblown loess. This paper aims to expand upon a previous study using detrital zircon provenance analysis to investigate midcontinental North American loess provenance in relation to the glacial history of the southern Laurentide ice sheet during the middle to late Pleistocene. The study incorporates previously published detrital zircon data from last glacial tills (representing the Huron-Erie Lobe, Lake Michigan Lobe, Green Bay Lobe, Superior Lobe, Des Moines Lobe, and James Lobe), middle to late Pleistocene loess from several sites along the Illinois and Mississippi River valleys, and suspended sediment load inputs from the Missouri River and Arkansas River. A statistical mixing model (DzMix version 2.2) was used to estimate the relative proportions of glacial and nonglacial sources to Wisconsin Episode, Illinois Episode, and pre–Illinois Episode age loess. We show that mixing models that include the modern suspended sediment loads of the Missouri and Arkansas Rivers significantly improve (up to 23% increase in cross-correlation value) the source characterization of Wisconsin Episode (last glacial) and Illinois Episode (penultimate glacial) loess deposits within the Mississippi River drainage basin. These river sources are dominant inputs for certain loess sites (as much as 51%), but their relative contributions are not static across time and space, which has implications for temporal and spatial differences in relative sediment source estimates and glacial and fluvial sediment transport evolution. Specifically, differences in relative sediment proportion estimates among Wisconsin, Illinois, and pre–Illinois Episode loess support previous evidence for the persistence of a Quebec-Labrador ice dome source through multiple glacial cycles, even with the inclusion of river sources in updated mixing models. Illinois and pre–Illinois Episode loess in the southern portion of our study area received ~20% detrital zircon input from sources similar to the present-day Missouri and Arkansas Rivers, suggesting that these river systems were contributing a significant amount of detritus to the lower Mississippi River valley region during the middle Pleistocene.

Kernel-Based Versus Tree-Based Data-Driven Models: On Applying Suspended Sediment Load Estimation

River sediment load estimation poses a critical challenge for water engineers due to its complex and nonlinear hydrological processes. This study assessed the amount of suspended sediment at the Bagh-e-Kalayeh hydrometric station on the Alamut River in the Qazvin province of Iran using two hydrological and meteorological variables, including discharge and rainfall, by considering three scenarios (discharge, discharge + monthly rainfall, and discharge + monthly rainfall + daily rainfall). For modeling, kernel-based data-driven methods, including Gaussian process regression (GPR) and support vector regression (SVR), and tree models, including the M5 tree, random forest (RF), random tree (RT), extra trees, reduced error pruning tree (REPT), and multi-search methods, were used. The results showed that the best performance was achieved by the SVR, with r = 0.948, Wilmot index = 0.965, and RMSE = 0.011 in the first scenario (only discharge). Discharge had the most significant impact on sediment estimation compared to rainfall. It was determined that the suspended sediment load in the Alamut River can be successfully estimated by the SVR method, where only the discharge was used as the input parameter. Additionally, the results indicated that given its characteristics and inherent features, the multi-search method can be used as a complementary approach in sediment modeling, especially in situations where the data volume is not extensive.

Regional analysis of specific suspended sediment loads in northern Iran using multivariate statistical techniques

ABSTRACT Predicting suspended sediment loads in areas without detailed measurements, or with only short-term records, is crucial for the sustainable management of water resources. This study aimed to establish the relationships between specific suspended sediment loads and the characteristics of 23 sub-basins within the Haraz-Neka River basin, in Iran, to create regional models to estimate the sediment loads. To achieve this, several analytical methods were used, including cluster analysis, principal component analysis, principal component and classification analysis, and general linear modelling. Among these, the principal component analysis regression model was the most effective for estimating suspended sediment loads in the clusters. The principal component and classification analysis revealed that the best predictor was the first principal component, which strongly correlated with the minimum and mean elevation of the sub-basins. The general linear model regression showed the best overall performance for estimating regional suspended sediment loads in the study area.

Estimating future changes in streamflow and suspended sediment load under CMIP6 multi-model ensemble projections: a case study of Bitlis Creek, Turkey

Estimating future changes in streamflow and suspended sediment load under CMIP6 multi-model ensemble projections: a case study of Bitlis Creek, Turkey

Suspended Load Estimation in Data Scarce Rivers

AbstractSediment rating curves (SRCs) are tools of satisfactory reliability in the attempt to describe the sediment regime in catchments with limited or poor-quality records. The study valorised the most suitable SRC development method for the estimation of the coarse suspended sediment load at the outlet of nine Mediterranean sub-watersheds. Four established grouping techniques were assessed, to minimize the uncertainty of the results, namely simple rating curve, different ratings for the dry and wet season of the year, hydrographic classification, and broken line interpolation, at three major Greek rivers (Aliakmon, Acheloos – upper route, Arachthos). The methods’ performance was benchmarked against sediment discharge field records, utilizing statistical measures and graphical analyses. The necessary observations were conducted by the Greek Public Power Corporation. The results were site/station dependent, and no methodology emerged as universally accepted. The analysis designated that the simple rating curve performs best at the cross-sections Moni Ilarion, Moni Prodromou, and Arta bridge, the different ratings for the dry and wet season of the year at Grevena bridge and Gogo bridge, the hydrographic classification at Velventos and Plaka bridge, and the broken line interpolation at Avlaki dam and Tsimovo bridge. In this regard, the study advocates the use of multiple SRC methods. Despite its limitations, the method merits a rather simple and cost-effective generation of a (continuous, detailed, sufficiently accurate) synthetic suspended sediment discharge timeseries, with high interpolating, extrapolating and reproducibility potential. The success of the application could benefit, among others, water quality restoration and dam management operations.

Reconstructing the Spill Propagation of the Aznalcóllar Mine Disaster

AbstractThe mine pond failure of Los Frailes (Aznalcóllar, Spain) was one of the most catastrophic mining-related disasters worldwide. Despite having been analysed from different disciplines, there have been only two attempts to simulate the propagation of the spill. In both cases, the spill was reconstructed using poor or incorrect topographical data, assuming a spilled hydrograph at the breaking point, and considering the fluid as water. In this research, new pre-failure topographical data were obtained combining field data with remote sensing techniques. These data were used to estimate the spilled hydrograph at the breaking point utilising a two-dimensional hydrodynamic numerical tool. Finally, due to the nature of the spilled fluid, two different attempts of reconstructing the spill propagation process of the Aznalcóllar mine disaster were performed. First, the fluid was considered as water with a suspended sediment load (26–660 g/L), i.e. assuming Newtonian fluid flow. Then the fluid was assumed to be mud-like (non-Newtonian fluid flow). These new simulations revealed that using a Newtonian fluid model, such as water with or without sediment, produced the best results in matching observed and simulated data. The non-Newtonian approach (muds) performed poorly. This suggests the spill behaved more like a concentrated sediment-laden flow than a mud-like one, possibly due to changes in fluid behaviour caused by the mine tailings in the pond after the failure.

Estimation of suspended sediment load to the Volta Lake under changing climate using empirical discharge-sediment equations

ABSTRACT Reliable estimates of sediment fluxes into rivers provide valuable information for current and future water resource development and management. In this study, empirical equations relating discharge to suspended sediment loads were developed for the three major tributaries (Black Volta, White Volta, and Oti) of the Volta Lake. The empirical equations were fed with projected future river discharges for the three tributaries, to estimate the suspended sediment fluxes into the Volta Lake for the future (2051–2080) relative to a baseline period (2014–2016). The Soil and Water Assessment Tool (SWAT) was used to model the projected discharges. The SWAT model was driven by downscaled climate projections from two regional climate models (RCA4 and RACMO22 T) forced by two emission scenarios (RCP 4.5 and RCP 8.5) from the IPCC Fifth Assessment. Compared to the baseline, the estimated future sediment fluxes show average increments of about 19% and 28%, respectively under RCP 4.5 and RCP 8.5. This has negative consequences as less water is stored for hydropower generation and the lake water becomes more turbid, which inhibits zooplankton and fishery production. The result underscores the need to improve the management of the Lake's watershed by ensuring proper landcover/use planning, afforestation and enforcing regulations on land degradation and general environmental conservation.

Modeling stage‐discharge and sediment‐discharge relationships in data‐scarce Himalayan River Basin Dhauliganga, Central Himalaya, using neural networks

AbstractThis study focuses on the hydro‐sedimentological characterization and modeling of the Dhauliganga River in Uttarakhand, India. Field data collected from 2018–2020, including stage, velocity, and suspended sediment concentration (SSC), showed notable variations influenced by melting snow, glaciers, and precipitation. Challenges in accurately modeling rivers with a topography and sparse gauging stations were addressed using artificial neural networks (ANN). The calibrated models precisely predicted stage‐discharge and sediment‐discharge relationships, demonstrating the effectiveness of machine learning, particularly ANN‐based modeling, in such challenging terrains. The model's performance was assessed using coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). During the calibration phase, the model exhibited notable performance with R2 values of 0.96 for discharge and 0.63 for SSC, accompanied by low RMSE values of 5.29 cu m s–1 for discharge and 0.61 g for SSC. Subsequently, in the prediction phase, the model maintained its robustness, achieving R2 values of 0.97 for discharge and 0.63 for SSC, along with RMSE values of 5.67 cu m s–1 for discharge and 0.68 g for SSC. The study also found a strong agreement between water flow estimates derived from traditional methods, ANN, and actual measurements. The suspended sediment load, influenced by both water flow and SSC, varied annually, potentially modifying aquatic habitats through sediment deposition, and altering aquatic communities. These findings offer crucial insights into the hydro‐sedimentological dynamics of the studied river, providing valuable applications for sustainable water‐resource management in challenging terrains and addressing environmental concerns related to sedimentation, water quality, and aquatic ecosystem.

Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm

This study aimed to develop an accurate and reliable model for predicting suspended sediment load (SL) in river systems, which is crucial for water resource management and environmental protection. While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). To this end, streamflow (Q) and sediment concentration (SC) values as well as their lag times (1 to 3 month lag times) were fed as input variables – under 9 scenarios – into ML models. A time series of datasets from March 1973 to December 2011 and January 2012 to March 2023 were used for training and testing of ML models, respectively. The superiority of the proposed model (XGB-MPA) compared to two other hybrid models, including XGB-PSO (Particle Swarm Optimization) and XGB-GWO (Grey Wolf Optimization) was also investigated. According to the results, the simultaneous application of Q and SC lag time values as inputs has led to the best SL estimates by XGB-MPA, with XGB-MPA9 (RMSE = 103.7 ton/day; NSE = 0.96) exhibiting the lowest error rates. In addition, XGB-MPA has performed better than XGB in all scenarios, with the lowest and highest reduction in RMSE being 19.3% (scenario 5) and 97.4% (scenario 1), respectively. When comparing the performance of hybrid models, the proposed XGB-MPA model has performed best with MAE, RMSE and NSE of 40.94, 103.7 and 0.96, respectively, in comparison with 816.02, 1063.74 and −2.94 for XGB-PSO and 693.16, 981.68 and −2.37 for XGB-GWO. Further research can include the use of time series of efficient variables extracted from satellite images (e.g. land cover, river morphology, etc.) as model inputs. Highlights Improvement of SL estimation by coupling MPA with XGB model Using delayed combinations of streamflow and sediment concentration as model inputs Superiority of MPA compare to PSO and GWO in SL estimation Greater variations in SHAP caused by sediment concentration compared to streamflow Further studies required on the effects of hydrological and topographical features

Scale-specific controls of monthly suspended sediment load in a typical inland river

Sediment transport is a multi-scale and non-linear process controlled by multiple variables. Given the intricate nature of sediment transport mechanisms and the overlap of different factors’ effects on it at various time scales, unraveling the relationship between sediment load and its influencing factors is challenging. This study aimed to elucidate the multi-timescale effects of factors on monthly suspended sediment load in a seasonal inland river. In this study, monthly sediment loads measured by the depth-integrating method were obtained from the Tabu River Basin in northern China from 2007 to 2021, alongside data on four controlling factors: runoff, precipitation, water surface evaporation, and the normalized difference vegetation index (NDVI). The results showed that sediment load, runoff, and precipitation showed strong variability during 2007–2021, while water surface evaporation and NDVI exhibited moderate variability. Multivariate empirical mode decomposition (MEMD) decomposed the temporal patterns of sediment load and associated variables into five intrinsic mode functions (IMF) and a residue. IMF1 (4.7 months), IMF2 (9.0 months) and IMF3 (14.2 months) collectively explained 115.88 %, 91.73 %, 91.28 %, 107.69 %, and 106.09 % of the total variability in sediment load, runoff, precipitation, water surface evaporation, and NDVI, respectively. Subsequently, the time-dependent intrinsic correlation (TDIC) was utilized to illustrate the inherent relationships between sediment load and its controlling factors across various time scales. The associations between sediment load and the controlling factors changed significantly with the scale. Runoff was the dominant factor controlling sediment load at IMF1 (4.7 months), IMF2 (9.0 months), IMF3 (14.2 months), and IMF4 (25.1 months). Water surface evaporation controlled the sediment transport process at IMF5 (63.6 months). The prediction of sediment load on the measurement scale, using scale-specific controls analyzed with MEMD (R2 = 0.993), demonstrated superior performance compared to traditional multiple linear regression prediction (R2 = 0.748). This study will provide a theoretical basis for effective prevention and control of soil erosion and watershed management in inland river basins.

Toward coupling of nonlinear support vector regression and crowd intelligence optimization algorithms in estimation of suspended sediment load

Sediment phenomenon is very important in hydraulic and water resources issues. The existence of this phenomenon causes many problems in water storage. Sediment simulation in rivers helps in controlling sediment as well as reducing damages. In this study, an attempt was made to estimate the suspended sediment load using the corresponding river flow rate in the Zohreh River, Iran using the newest intelligent simulation methods. This study seeks to couple the nonlinear support vector regression (SVR) with crowd intelligence optimization algorithms. For this purpose, support vector regression was optimized using four new crowd optimization algorithms including the ant colony optimizer (ACO), the ant lion optimizer (ALO), the dragonfly algorithm (DA), and the salp swarm algorithm (SSA). Simulation was done in the two phases of train and test. Due to the integration of the nonlinear support vector regression with the optimization algorithms, the model train phase requires more time than usual situations. Therefore, in the current study, taking into account the number of different iterations including 25, 50, 100 and 200 iterations to perform the optimization of the model and tried to find the best optimizer by considering the calculated error and the run time. It was generally found that the SVR model is accurate in estimating the suspended sediment load. Finally, according to the calculated error as well as the run time, the support vector regression model optimized with the salp swarm algorithm with 25 iterations was chosen as the best model. Also, the values of R2, NSE, and RMSE for the best model in the test phase were calculated as 1, 1, and 10.2 tons per day, respectively, and the algorithm run time was 252 s.

Light Rare Earth Elements in Freshly Deposited River Sediments of Ganga Alluvial Plain, Northern India: Geogenic Variability and Anthropogenic Influences

ABSTRACT Rare Earth Elements (REE) are the industrial “Vitamins” because of the high-technology based modern uses and their anthropogenic inputs in natural environment, this study mainly focused on Geogenic variability and anthropogenic imprints of Light Rare Earth Elements (LREE), in the recently deposited sediments of the Ganga Alluvial Plain. All the Sediment samples were analyzed by using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). LREE comprises about ~91% of total REE in the Gomati River Sediments and Cerium (300 µg/g) was the most abundant LREE in the bedload sediments. Average LREE concentrations in bedload (170 µg/g) and suspended load (184 µg/g) sediments of the Gomati River were observed higher than the Average Sediments and the World Major Rivers Suspended Sediments and therefore, characterizing the LREE contamination in sediments. This contamination level assessed on the basis of Potential Ecological Risk Assessment (PERI), Contamination Factors (CF), Degree of Contamination (CD) and Geo-accumulation Index (Igeo), the trend of element contamination showed that Sm>La>Pr>Ce>Nd and PERI shows that LREE pose moderate to strong ecological risk, mostly by Pr and Sm in the study area. The spatial and temporal enrichment of LREE in sediments were observed in the Basin since last two decades. This paper mainly highlights the effects of socio-economic development linked modern high-tech processes on LREE contamination in the river sediments and cycling by the sub-tropical fluvial environment of southern Asia; where rivers supply ~30% of the global sediment input to the world’s ocean.

Quantifying the impacts of climate change and human activities on runoff and suspended sediment load in the Lhasa River Basin, Tibetan Plateau

AbstractQuantifying the attribution of climate change and human activities on runoff and suspended sediment load is crucial for formulating future watershed management measures, especially in the ecologically fragile alpine region, which is more susceptible to climate change and human activities. In this study, the temporal changes in runoff‐suspended sediment load and the potential impact factors (i.e., precipitation, potential evapotranspiration (PET), land use/land cover change (LULC) and reservoir operation) were investigated in the Lhasa River Basin (LRB) from 1956 to 2020. In addition, the contributions of those factors to the changes in runoff and suspended sediment load were quantitatively evaluated based on physically‐based Soil and Water Assessment Tools (SWAT). The results indicated that annual runoff and suspended sediment load showed an increasing but insignificant trend during 1956–2020, while annual precipitation and PET showed a significant increasing trend at a rate of 0.94 and 1.07 mm/yr, respectively. LULC mainly presented an increase in forestland area followed by a decrease in grassland and bare land area due to the implementation of ecological projects. Runoff and suspended sediment load changed abruptly at approximately 1995 and 2005 based on three abrupt change identification methods, thus, the study period was further divided into three substages: baseline period (P0, 1956–1994), dramatically increased period (P1, 1995–2004) and slightly decreased period (P2, 2005–2020). The attribution analysis showed that climate change was the dominant contributor to runoff and suspended sediment load increments during P1. LULC caused the decline in runoff and suspended sediment load in both P1 and P2, with their contribution increasing significantly from 0.40% and 4.73% in P1 to 50.24% and 51.79% in P2, respectively. Reservoir operation was the second key factor in runoff and suspended sediment load reduction in P2, contributing 42.07% and 43.72%, respectively. These findings provide the scientific foundation for reasonably allocating water resources and statistical support for the benefit evaluation of implementing ecological projects in the basin.

Confluence-based suspended sediment fingerprinting: an exploratory approach to determining source to sink sediment contributions in the Tsitsa River catchment, Eastern Cape, South Africa

ABSTRACT In the Tsitsa River catchment of the Eastern Cape Province, South Africa, concerns regarding the sedimentation of a proposed large, multipurpose, in-channel water storage scheme prompted investigations of sub-catchment suspended sediment loads and yields. Confluence-based fingerprinting and source apportionment was undertaken to explore whether this simple approach could identify relative sub-catchment contributions to downstream sediment loads, supplementing modelling and direct sampling. In November 2018, fine sediment sequestered in bed substrates was sampled above and below confluences, using a resuspension technique. The < 63 µm sample fraction underwent analysis of mineral magnetic properties, gamma emitting radionuclide activities and geochemistry to identify suitable tracers. Conservatism and range tests were used to select different combinations of composite fingerprints at seven confluences. Bivariate source apportionment using an unmixing model showed that the approach could be used as a preliminary means of determining relative sub-catchment sediment contributions. Catchment characteristics such as geology and land cover, as well as land tenure and management, were considered as explanatory factors for the difference in sediment contributions. The results for most sub-catchments were broadly similar to those from other studies. Shortcomings of this approach included the reliance on a single sampling effort, and uncertainty associated with sub-catchment sediment transport and residence times.

CARACTERIZAÇÃO QUÍMICA E MINERALÓGICA DOS SUSPENSATOS DOS RIOS PURUS E JURUÁ NO ESTADO DO ACRE

The state of Acre, located in the southwest of the Brazilian Amazon, is drained by two large river basins, the Purus and Juruá rivers, which have their sources on the sub-Andean slopes of Peru and cross the state in a path almost parallel to each other and a southwest-northeast direction. They are meandering rivers, with a large amount of suspended sediment (suspended solids or load materials), which gives them their white or muddy color, in addition to transporting a lot of silty-sandy sediments by traction, forming significant beach bodies (pointed bars). The geological terrains drained by these rivers are made up of rocks from the Solimões Formation and sediments from the Alluvial Terraces. The present work sought to characterize the granulometry, mineralogy and chemical composition of suspensions from rivers in the State of Acre. These were subjected to granulometric analysis, mineral identification using X-ray diffraction (XRD) and scanning electron microscopy (SEM/SED) and total chemical analysis of trace elements. The highest concentration of suspended solids was found in the central part of the State both during the flood and dry periods, while the lowest are in the Moa and Paraná dos Mouras rivers. They are predominantly made up of silt with little fine sand and clay, averaging 12 microns. Mineralogically, they are made up of quartz, mica, smectite, illite, kaolinite, as well as albite and K-feldspar. These are sediments rich in SiO2 (55.69% +/- 3.89%), Al2O3 (18.68% +/- 1.76%), Fe2O3 (5.89% +/- 0.41%), with significant K2O values ​​(1, 93% +/- 0.07%), MgO (1.17% +/- 0.22%), Na2O (0.41% +/- 0.06%), in addition to TiO2 (0.85% +/- 0.10%). The values ​​of P2O5 (0.10% +/- 0.014%), CaO (0.14% +/- 0.17%) and MnO (0.10% +/- 0.021%) are at crustal levels. In terms of trace elements Rb, Sr, Cs, Ba, Th, U, Zr, Hf, Y, Nb, Cr, Co, Ni, Cu, Sc and V, are the elements that presented the highest concentrations. The average concentrations of these elements in the suspended materials when compared with beach sediments show enrichment in Al2O3, Fe2O3, MnO, MgO, Rb, Cs, Sc, and V. On the other hand, with the upper earth’s crust they show enrichment in Cs, Y, Cr, Co, Ni, Cu, Sc and V and with post-Archaean Australian shales only slightly enriched in Y. The presence of clay minerals (smectite, illite and kaolinite) and feldspars (albite and K-feldspars) are responsible for the high concentrations not only Al2O3 and Fe2O3, but also K2O, MgO and Na2O. The mineralogical and chemical composition shown by these suspensions, in addition to their granulometry, contribute to fertilizing both the floodplains (“varzeas”) as well as the beaches exposed by the rivers during the dry period, which are intensely cultivated with corn (Zea-mays) and beans (Vigna unguiculata (L) Walp) by riverside dwellers.

The effect of seasonal variation, flow conditions and erosion forces on suspended matter fluxes from boreal gypsum-treated agricultural fields

Like many other bodies of water, the Baltic Sea is facing a severe eutrophication problem, the excessive nutrient load currently originating primarily from agriculture. Gypsum treatment has shown promising results in reducing phosphorus and suspended sediment loads, especially in fine-textured soils, but its efficacy has not been investigated on catchment scale to any wide extent. This study examines the functionality of the gypsum treatment in an agriculture-dominated boreal catchment in the Archipelago Sea basin. The relation between turbidity and hydro-climatological factors was analysed during different years and seasons and the effect of stream erosion during different flow stages was investigated by applying 2D hydraulic modeling. A special focus was given to deviating winter conditions and freeze-thaw cycles.The results highlight the dependency of stream water turbidity on the discharge and the potential negative impact of the climate change-altered winter conditions of the hemiboreal climate zone on gypsum treatment effectiveness. The turbidity increased even more during high winter discharges, compared to other seasons suggesting that the more frequent freeze-thaw cycles and precipitation coming as rain during the unvegetated period increase erosion challenging the gypsum efficacy. Modelling results revealed that the increased erosional power during very high discharges caused settled material resuspension or stream bed or bank erosion leading to a cumulative increase in turbidity at downstream, an issue needing further research. Demonstrating the real and long-term effects of nutrient reduction measures requires catchment scale investigations over years, considering climatological factors. Based on this study, local and short-term investigations can potentially lead to over-simplified interpretations.

Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network

Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Assessing the impacts of rice terraces and ponds on the sediment and phosphorus loads in a typical hilly watershed of three gorge reservoir, China

Phosphorus is the primary eutrophication cause of river, lake, reservoir and estuary and it may be one of the principal pollutants for the agricultural watershed with middle and small cities. The sediment and phosphorus loads, related migration paths and contribution of source regions to receiving waters at different sections of a river basin are not clear. The modified Soil and Water Assessment Tool (SWAT) model was utilized to investigate the phosphorus loads and the relationships among water resources, sediment, and phosphorus loads in one of the important tributaries of the Three Gorge Reservoir (TGR) of China, and to investigate the retention dynamics of phosphorus in the river continuum and the influencing factors. It showed that under the scenario of rice terraces and ponds, the annual sediment loss and total phosphorus load were 125t and 2.5t, being reduced by 40%–80% compared to the natural slope scenario; the daily peak value of suspended sediment load was reduced by 30%–75%, and the relationship between the rainfall and runoff was weakened slightly; the retention rate of phosphorus sources reached 40%–78%. The results confirm that the rice terraces and ponds have altered local hydrological conditions, enhanced a substantial water, sediment, and phosphorus retention capacity that is highly consistent over time and space scales, thereby altering the predominant forms of phosphorus and improving the environmental resilience in soil and nutrition loss for the hilly watershed of the TGR.

The role of ponds in pesticide dissipation at the catchment scale: The case of the Save agricultural catchment (Southwestern France)

Pesticides are a major source of pollution for ecosystems. In agricultural catchments, ponds serve as buffer areas for pesticide transfers and biogeochemical hotspots for pesticide dissipation. Some studies have highlighted the specific impact of ponds on the dynamics of pesticides, but knowledge of their cumulative effect at the watershed scale is scarce. Hence, using a modelling approach, we assessed the cumulative role of ponds in pesticide transfer in an agricultural basin (Southwest of France, 1110 km2). The Soil and Water Assessment Tool (SWAT) model was used to model the Save basin, including 197 ponds selected with a Multi-Criteria Decision Aiding Model based on their pesticide interception capacities. The daily discharge, the suspended sediment loads and two herbicide loads (i.e. S-metolachlor and aclonifen) in dissolved and particulate phases were accurately simulated from January 2002 to July 2014 at a daily time step. The presence of ponds resulted in a yearly mean reduction at the watershed outlet of respectively 61 % and 42 % of aclonifen and S-metolachlor fluxes compared to the simulations in the absence of ponds. Sediment-related processes were the most efficient for pesticide dissipation, leading to a mean dissipation efficiency by ponds of 51.0 % for aclonifen and 34.4 % for S-metolachlor. This study provides a first quantification of the cumulative role of ponds in pesticide transfer at the catchment scale in an intensive agricultural catchment.