Estimation Of Sediment Load Research Articles

Suspended sediment yield estimation using geomorphologic instantaneous unit sedimentgraph: a case study from the Southern Caspian Sea, Iran

ABSTRACT This study develops and validates a Geomorphological-based Instantaneous Unit Sediment Graph (GIUS) model to estimate the event-based sediment yield of the Talar catchment, a significant sediment source in the southern Caspian Sea. Delivering approximately 11,190 tons of sediment annually, which directly impacts coastal dynamics and port operations, necessitates accurate sediment load estimation to the Caspian Sea. The catchment was divided into three sub-catchments, where the GIUS model was applied. Model calibration and verification utilized paired data of daily rainfall, streamflow, and suspended sediment concentration for 475 events. After separating direct runoff from streamflow, the Soil Conservation Service Curve Number (SCS-CN) model was employed to estimate the excess rainfall, serving as the primary input for the GIUS model. The erosion rate, a critical parameter in the GIUS model, was calibrated as a power function of excess rainfall depth. The results indicate that the sediment delivery ratio for the channel networks ranges from 28% to 44%. The strong correlation (CE = 0.64−0.85, SE = 0.75−1.96, R 2 = 0.85–0.92) between observed and simulated sediment yield values underscores the reliability of the developed model. This research presents a repeatable approach for event scale sediment yield modeling, essential for sustainable water resources management and soil erosion control in the region.

Watershed Grouping Based on Suspended Sediment Yield Using Morphological Criteria: A Comparison of Hierarchical and Nonhierarchical Techniques

ABSTRACTRegional sediment analysis methods have traditionally been used to reliably estimate sediment yield in ungauged watersheds with adequate hydrometric stations. Watershed‐grouping techniques are a suitable approach for estimating sediment load, which can be used to assess erosion processes, and surface water quality, and plan soil and water conservation measures. Additionally, the relationship between sediment yield and watershed characteristics can be used to identify different stages of erosion evolution and prioritize watersheds for conservation efforts. To this end, this study aimed to assess the applicability of various analytical hierarchy process (AHP) methods, including Ward's, Single linkage, and β‐flexible methods, as well as the nonhierarchical fuzzy C‐means (FCM) method in the Gorganroud and Qareh‐Sou watersheds of northern Iran based on hydrological, geological, climatic, physiographical, and land‐use characteristics. Furthermore, the study evaluated clustering methods to determine the optimal number of subwatershed clusters. The AHP methods identified two, three, or four clusters, while the FCM method identified three clusters as the best number of clusters. The results indicated that the FCM method had a well‐regionalized estimation in the study area with the maximum amount of validation indices (The highest Dunn coefficient 0.31–0.65, the lowest TESS 16.62–36.8, and acceptable Pseudo‐F coefficient 8.1–10.4). Notably, the fuzzy clustering methods associated each input sample with two or more clusters, which reduced the uncertainty of sediment yield estimation. The recommended clustering method can be used for regional sediment analysis in ungauged watersheds and evaluated against other hydrological variables in the study area.

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.

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.

Sediment load assessments under climate change scenarios and a lack of integration between climatologists and environmental modelers

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

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.

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

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.

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

Estimating stream sediment loads to assess management options for a Southern Appalachian mountain lake

Enka Lake is a 25-ha reservoir in the Southern Appalachian mountains surrounded by the Biltmore Lake residential neighborhood. The aesthetic and recreational value of Enka Lake can be negatively affected by turbid water and sediment buildup, resulting from stormwater originating upstream in its 15.38 km2 watershed. In this study, we monitored streamflow and sediment loads for nearly 2 years to better understand sedimentation dynamics and evaluate management options. We collected nearly 500 water samples throughout the watershed, focusing on two tributaries that provide the majority of streamflow into Enka Lake. During baseflow conditions, those tributaries are very clear (turbidity ranging from 1 to 20 NTU), but sediment-laden stormflow (turbidity > 1000 NTU) from dirt roads, gravel driveways, and poorly vegetated yards is common and, during extreme storm events, causes lake discoloration. Discharge-sediment data pairs were used to construct sediment rating curves and estimate sediment fluxes through these tributaries. The predicted sediment fluxes were tested with high-frequency sampling during and after three storms in different seasons; predicted values underestimated the peak sediment fluxes (0–400%) but generally matched total measured sediment loads. Estimates of annual sediment load from the two tributaries (540–900 tonnes/year) suggest that a dredging effort commissioned by the neighborhood association in 2016 may have removed only about 1–3 years’ worth of sediment. Installing sediment forebays or other retention structures is likely the preferred sediment management strategy moving forward (over a regular dredging schedule) since they can be maintained more easily without affecting lake levels. Designing these retention structures will benefit from the estimates of stormflow and sediment fluxes, and the study design presented here—including the help of community-based volunteer samplers—provides a model that could be used at other sites in the Southern Appalachians and elsewhere.

A Comparative Analysis of Sediment Concentration Using Artificial Intelligence and Empirical Equations

Morphological changes in canals are greatly influenced by sediment load dynamics, whose estimation is a challenging task because of the non-linear behavior of the sediment concentration variables. This study aims to compare different techniques including Artificial Intelligence Models (AIM) and empirical equations for estimating sediment load in Upper Chenab Canal based on 10 years of sediment data from 2012 to 2022. The methodology involves utilization of a newly developed empirical equation, the Ackers and White formula and AIM including 20 neural networks with 10 training functions for both Double and Triple Layers, two Artificial Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization, and Ensemble Learning Random Forest models. Sensitivity analysis of sediment concentration variables has also been performed using various scenarios of input combinations in AIM. A state-of-the-art optimization technique has been used to identify the parameters of the empirical equation, and its performance is tested against AIM and the Ackers and White equation. To compare the performance of various models, four types of errors—correlation coefficient (R), T-Test, Analysis of Variance (ANOVA), and Taylor’s Diagram—have been used. The results of the study show successful application of Artificial Intelligence (AI) and empirical equations to capture the non-linear behavior of sediment concentration variables and indicate that, among all models, the ANFIS outperformed in simulating the total sediment load with a high R-value of 0.958. The performance of various models in simulating sediment concentration was assessed, with notable accuracy achieved by models AIM11 and AIM21. Moreover, the newly developed equation performed better (R = 0.92) compared to the Ackers and White formula (R = 0.88). In conclusion, the study provides valuable insights into sediment concentration dynamics in canals, highlighting the effectiveness of AI models and optimization techniques. It is suggested to incorporate other AI techniques and use multiple canals data in modeling for the future.

Suspended Sediment Source and Transport Mechanisms in a Himalayan River

The process of estimating sediment load has been a daunting issue in hydraulics and the water resource field. Several methods exist for predicting the sediment load in a catchment or river, but the majority of these methods are empirical and depend on the specific location where they are used. Understanding the underlying mechanism of sediment generation and its transport in connection with precipitation, topography, and subsurface conditions to characterize its process is helpful for determining the sediment load in a river. For this purpose, we analyzed the daily suspended sediment data measured for 8 years at the headworks of the Kabeli A hydropower project in the Kabeli River, which originates from the Himalayan region. The analyses show that the suspended sediment concentration (SSC) varies in an orderly manner over time and asynchronously between seasons with respect to the river discharge. Clockwise hysteresis is observed in the yearly plots between the SSC and river discharge. The hysteresis becomes narrower when compared with the direct runoff obtained from a digital filtering algorithm and, even more so with the direct runoff from the hydrological model SWAT. The analysis shows that the sediment concentration is controlled not only by the total discharge in the river but also by the contribution of ground water to the river discharge, indicating that the total discharge alone cannot reflect the seasonal variation in SSC. It is inferred that the river is supply-limited and the hillslope is transport-limited with respect to sediment sources. The SWAT model suggests that the base flow contribution to the total river discharge is 78%. Here, we present a method for constructing the suspended sediment rating curve by comparing the direct runoff with the sediment concentration. The deduced sediment rating curve captures 84.51% of the total sediment load over the study period in the Kabeli River. This method may potentially be used in similar catchments with supply-limited rivers and transport-limited hillslopes.

Applying a transfer function model to improve the sediment rating curve

ABSTRACT Information relating to suspended sediment load is of particular importance in terms of water management and environmental protection schemes. The sediment rating curve (SRC) is used to express the common relation between suspended sediment load and flow discharge in a basin. SRC, however, does not consider the effect of time lag in flow discharge; instead, it makes a kind of bias in its estimation, toward overestimation or underestimation of a sediment load. In this direction, the current research aims to improve and extend the SRC model by using a transfer function method. The proposed method was applied to a specific basin to better capture the effect of time lag on suspended sediment load estimation. This function estimated the current sediment load in current time. Three case studies were conducted in different climate conditions of Iran to evaluate the application of transfer function with SRC in assessing the relation between flow and suspended sediment discharge. The results revealed that the transfer function method demonstrated high precision in forecasting suspended sediment load, as evidenced by its mean absolute difference (MADIF) and bias criterion calculations. The findings indicated that the proposed method could enhance the accuracy of sediment load estimations in basins with similar characteristics. This approach was based on an unbiased future forecast and provided a high level of confidence. The results suggested that this method could be successfully applied to other basins to improve the accuracy of sediment load estimations.

Assessment the impacts of land cover and climate changes on rainwater harvesting systems using remote sensing and runoff model in some Wadis of West Matrouh – Egypt

The aim of this study is to evaluate the potential changes in land cover and their impacts on rainwater harvesting systems, with a focus on Egypt's northwestern coastal region. To accomplish this objective, we utilized the kinematic runoff and erosion model (KINEROS2) to estimate the effects of projected land use changes on runoff and sediment load. The results demonstrated satisfactory performance of the model, with average Nash-Sutcliffe Efficiency values of 0.75 for calibration and 0.77 for validation, indicating successful simulations. The analysis revealed that land use changes had minimal impact in the base scenario (S1), resulting in moderate fluctuations in estimated sediment load. Conversely, the supplementary scenarios (S2 and S3) exhibited more pronounced alterations in land use, resulting in significant deviations in model estimations, notably indicating a substantial reduction in soil erosion. The integration of land use change analysis with KINEROS2 modeling emphasizes the potential for implementing effective management strategies.

Comparison of Artificial Intelligence (AI) Based Models for Sediment Transport Prediction Using SWOT and Statistical Analyses

The dynamics involved in sediment scour are complicated to create a general empirical optimization algorithm to offer reliable sediment load estimation. The existing study was conducted to analyse the architectures of assorted artificial intelligence (AI) based model to forecast suspended sediment load in fluvial system. An in-depth study on traditional approach including Artificial Neural Network (ANN), Adaptive NeuroFuzzy Inference System (ANFIS), and Genetic Programming (GP) was carried out. The goal of this study is to evaluate the performance of AI-based models from various research using SWOT and statistical analyses. Three statistical measures of model prediction accuracy including coefficient of correlation (R), root mean square error (RMSE), and mean absolute error (MAE) were used. The results revealed that the SVM and ANFIS models outperformed the other soft computing and conventional models. It is concluded that the SVM and ANFIS models are preferred and may be successfully used to estimate the suspended sediment concentration for the research area.

Estimating sediment yield from a small urban catchment of a heterogeneous structure

AbstractSmall urban areas, due to their specificity (high anthroposphere, multiple forms of land use, variable drainage conditions), are often overlooked in studies on soil erosion and sediment transport that predominantly focus on a large scale. The sediment yield from such small urban areas may greatly vary depending, for example, on the state of the land development. Therefore, its proper estimation is crucial for water resources management, improving urban runoff quality or designing and maintaining local retention ponds. In this study, two different approaches were applied to estimate sediment yield from a small urban catchment of Służew Creek, located in Warsaw, Poland. While the first was based on direct hydrological measurements and field samples, the second (indirect) used various empirical formulas to predict bed load transport (local formula of Skibiński) in the stream channel and the suspended sediment delivery from the catchment (USLE equation, Build‐up/Wash‐off model). The sediment load established according to the direct approach equals 714 Mg/year, of which 83 Mg/year results from the bed load transport and 631 Mg/year comes from the suspended sediment transport. The total sediment load calculated according to the indirect approach equals 579 Mg/year. The relative difference in loads calculated with these two methods is about 20%. This paper combines various established methods into a novel composite analysis of sediment yield. The methodology presented in this study may support the estimation of sediment load in small urban catchments with a heterogenous land use structure. Hence, it may support urban planning and water resources management in such small catchments.

Developing ensemble models for estimating sediment loads for different times scales

Developing ensemble models for estimating sediment loads for different times scales

Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model

Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R2), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R2, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively.

Sequence stratigraphy and sedimentary processes since the last glacial maximum in Nha Phu Bay and adjacent shelf, central Vietnam

Three sedimentary systems tracts (LST, TST, and HST) have been identified in Nha Phu Bay and the adjacent shelf, Khanh Hoa Province, Vietnam. These have been deposited since the Last Glacial Maximum (LGM) and interpreted in this study via the analysis of high-resolution acoustic data and sediment cores. The lowstand systems tract (LST) comprises sandy sediments exposed at seabed on the outer shelf as sand ridges, and underlying the transgressive systems tract (TST) on the middle shelf. The LST developed under shallow-marine settings during the sea-level lowstand and early sea-level rise before 14.6 cal kyr BP. The TST comprises primarily fine-grained sediments deposited during the transgression between 14.6 and 8.0 cal kyr BP in an incised trough of the outer shelf, on the middle shelf, and in Nha Phu Bay. Erosion of the seaward portion of the TST on the middle shelf might occur during the late transgression (9.0–8.0 kyr BP). The highstand systems tract (HST) comprises sediments deposited during the sea-level highstand between 8.0 and 0.0 cal kyr BP and is distributed widely in the bay, on the inner-middle shelf, and partially on the outer shelf. A paleo-coast zone, located in 100–120 m present-day water depth, is recognized through the appearance of sand ridges and sandy sediments. The seafloor sediment distribution is mapped via four major acoustic echo types, combined with seabed sediment samples. The coarse-grained sediment distribution, subaqueous outcrops, and thin deglacial sediment thickness on the inner shelf indicate erosion-dominated processes under vigorous hydrodynamic conditions. Nha Phu Bay and the middle shelf are interpreted as the depocentres. The estimated sediment load deposited in Nha Phu Bay is 0.18 Mt./yr, accounting for 15–35% of the annual sediment discharge from the Dinh River. The seabed morphology, muddy sediment distribution, and deglacial sediment thickness reveal a dominant sediment-transport pathway, across the inner shelf to the middle shelf, near the northern shoreline of Nha Phu Bay under the influence of the southwest monsoon.

Sediment load estimation using a novel regionalization sediment-response similarity method for ungauged catchments

The development and application of reliable hydrological models are challenging in ungauged or poorly gauged catchments. Although several regionalization methods have been developed for predicting hydrological variables in ungauged catchments, a large uncertainty usually exists in the prediction because previous methods typically depended on several static catchment attributes or long-term mean climatic descriptors, thereby neglecting the spatiotemporal variability of the climate. In this study, we propose a new regionalization method called sediment-response similarity (SRS) using the soil and water assessment tool model and self-organizing maps to estimate sediment load in ungauged catchments, considering spatiotemporal variability and the sediment load relation to rainfall characteristics of individual catchments. The performance of SRS was evaluated by comparing it against the conventional regionalization method (i.e., physical similarity) and calibration model results, in four gauged catchments of the lower Mekong River basin. The SRS method showed improved performance over the conventional approach in estimating sediment loads, with an average Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) of 0.75 and 0.76, respectively, which were close to the respective values of NSE and R2 = 0.77, of the calibration model. Additionally, the SRS approach showed an error reduction of up to 7 %, compared with that of the physical similarity method, indicating the potential of the proposed regionalization method in estimating catchment-wide sediment load in ungauged catchments. The findings indicate that the SRS method ideally determines the similarity of sediment loads between gauged and ungauged catchments. The SRS approach effectively tackled the main challenge of selecting catchment attributes in the conventional regionalization method and minimizing the uncertainty of sediment prediction in ungauged catchments. This novel SRS method is a promising method for estimating, not only sediment load, but also other hydrological variables and rainfall-driven phenomena in the ungauged catchments globally.