Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes

Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice.Hence,the limiting velocity should be determined to keep the channel bottom clean from sediment deposits.Recently,sediment transport modeling using various artificial intelligence(AI) techniques has attracted the interest of many researchers.The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems.A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport.Utilizing one to seven dimensionless parameters,127 models are developed in the current study.In order to evaluate the different parameter co mbinations and select the training and te sting data,four strategies are considered.Considering the densimetric Froude number(Fr) as the dependent parameter,a model with independent parameters of volumetric sediment concentration(C_V) and relative particle size(d/R) gave the best results with a mean absolute relative error(MARE) of 0.1 and a root means square error(RMSE) of 0.67.Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods.The percentage of the observed sample data bracketed by95% predicted uncertainty bound(95 PPU) is computed to assess the uncertainty of the best models.     

Comparing effects of land reclamation techniques on water pollution and fishery loss for a large-scale offshore airport island in Jinzhou Bay,Bohai Sea,China

Plans are being made to construct Dalian Offshore Airport in Jinzhou Bay with a reclamation area of 21 km². The large-scale reclamation can be expected to have negative effects on the marine environment, and these effects vary depending on the reclamation techniques used. Water quality mathematical models were developed and biology resource investigations were conducted to compare effects of an underwater explosion sediment removal and rock dumping technique and a silt dredging and rock dumping technique on water pollution and fishery loss. The findings show that creation of the artificial island with the underwater explosion sediment removal technique would greatly impact the marine environment. However, the impact for the silt dredging technique would be less. The conclusions from this study provide an important foundation for the planning of Dalian Offshore Airport and can be used as a reference for similar coastal reclamation and marine environment protection.     

Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River,India

The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models.     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.     

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash–Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.     

Application of Bayesian model and discriminant function analysis to the estimation of sediment source contributions

Bayesian and discriminant function analysis (DFA) models have recently been used as tools to estimate sediment source contributions. Unlike existing multivariate mixing models, the accuracy of these two models remains unclear. In the current study, four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models. These models were tested against the Walling-Collins model, a credible model used in estimation of sediment source contributions estimation, as a reference. The artificial mixtures were divided into five groups, with each group consisting of five samples with known source percentages. The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models. The mean absolute error (MAE) and standard error of (SE) MAE were used to test the accuracy of each model and the robustness of the optimized solutions. For the individual sediment samples, the calculated source contributions obtained with the Bayesian (MAE = 7.4%, SE = 0.6%) and Walling-Collins (MAE = 7.5%, SE = 0.7%) models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures. The DFA model produced the worst estimates (MAE = 18.4%, SE = 1.4%). For the grouped sediment samples, the Walling-Collins model (MAE = 5.4%) was the best predictor, closely followed by the Bayesian model (MAE = 5.9%). The results obtained with the DFA model were similar to the values for the individual sediment samples, with the accuracy of the source contribution value being the poorest obtained with any of the models (MAE = 18.5%). An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models, but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples. Generally, the accuracy of the Walling-Collins and Bayesian models was similar (p > 0.01), while there were significant differences (p < 0.01) between the DFA model and the other models. This study demonstrated that the Bayesian model could provide a credible estimation of sediment source contributions and has great practical potential, while the accuracy of the DFA model still requires considerable improvement.     

The changing dynamics of rill erosion on sloping farmland during the different growth stages of a maize crop

Rill erosion is a serious concern in the hilly region of China with purple soil, and maize is extensively cultivated in this region. Evaluations of the dynamic mechanisms of rill erosion in sloping farmland areas are particularly important during the maize growing season to determine whether rill erosion can occur. A new ridge tillage (RT) system was designed using local agricultural methods in China. Twelve artificial rainfall experiments were conducted in three 1 × 2 m experimental plots with a slope of 15°, which is a typical slope in the study area. The rainfall intensities were designated as 1.0, 1.5, and 2.0 mm min^?1. The rainfall experiments were performed in the field to determine the characteristics of run‐off and sediment transport related to rill erosion processes during different stages of maize growth and to analyse how hydraulic parameters and the sediment yield of the rill erosion process are related. The results showed that rill flow patterns were mainly classified as subcritical transition flow during all the growth stages of maize. The effects of hydrodynamic parameters on the sediment yield were ordered as follows: Reynolds number > stream power > Froude number > shear stress. The total sediment yield varied by stage as follows: seedling stage > jointing stage > mature stage > tasseling stage. The sediment yield and run‐off rate exhibited a linear relationship that was well described at the hillslope scale. To initiate soil loss in sloping farmland areas with purple soil during the maize growing season, the critical hydrodynamic shear stress and stream power must be at least 46.505 Pa and 1.541 N m^?1 s^?1, respectively.     

Interactions between aquatic vegetation,hydraulics and fine sediment: A case study in the Halswell River,New Zealand

This paper contributes a field study of suspended sediment transport through aquatic vegetation. The study was run over a 3 month period which was selected to coincide with scheduled weed cutting activities. This provided the opportunity to obtain data points with no vegetation cover, as well as to investigate the effects of weed cutting on Suspended Sediment Concentrations (SSC), particle size distributions and river hydraulics. Aquatic vegetation cover was quantified through remote sensing with Unmanned Aerial Vehicles and biomass estimated from ground truth sampling. SSC was highly dependent on aquatic vegetation abundance, and the distance upstream that had been cleared of aquatic vegetation. The data indicates that fine sediment was being trapped and stored by aquatic vegetation, then likely remobilised after vegetation removal. Investigation of suspended sediment spatial dynamics illustrated changes in particle size distribution due to preferential settling of coarse particles within aquatic vegetation, for example D50 decreased from 36.08 μm to 15.64 μm after suspended sediment travelled 304.2 m downstream and passed ~3700 kg of aquatic vegetation biomass. Hydraulic resistance in the study reach (parameterized by Manning's n) dropped by over 70% following vegetation cutting. Prior to cutting hydraulic resistance was discharge dependent (likely due to vegetation pronating at higher flows), while post cutting hydraulic resistance was approximately invariant of discharge. Aerial surveying captured interesting changes in aquatic vegetation cover prior to vegetation cutting, where some very dense regions of aquatic vegetation were naturally removed (without any high flow events) leaving behind unvegetated riverbed and fine sediment. The weed cutting boat had a lower impact on SSC than was originally expected, which indicates that it may offer a less damaging solution to aquatic vegetation removal in rivers than some other approaches such as mechanical excavation. This paper contributes valuable field data (which are generally scarce) on the research topic of flow-vegetation-sediment interactions, to supplement laboratory and numerical studies.     

基于人工智能的胸部CT肺结节检出及良恶性诊断效能评估

目的:评估基于深度学习的人工智能（AI）软件在胸部CT肺结节检出及良恶性诊断的价值。方法:收集2018年6月至2020年4月本院经手术确诊的肺结节患者172例,共切除204枚结节。将172例术前高分辨胸部CT图像导入人工智能识别系统,分别采用人工智能和影像医师阅片检出肺结节及良恶性诊断,对比两种阅片方法的敏感度、阳性预测值及假阳性结节个数。以病理结果为诊断金标准,对比AI与影像医师在恶性肺结节诊断中的敏感度、特异度及受试者工作特征（ROC）曲线下面积。结果:172例胸部高分辨CT共检出796枚真结节;AI与影像医师检出结节的敏感度分别为90.5%和75.0%,阳性预测值分别为74.5%和99.7%,假阳性结节总数分别为247个和2个。204枚经手术切除的结节中,AI、影像医师及AI联合影像医师诊断恶性肺结节的敏感度分别为93.3%、78.5%和98.6%,特异度分别为34.8%、79.7%和79.7%;AI、影像医师及AI联合影像医师诊断恶性肺结节的ROC曲线下面积分别为0.641、0.791和0.819。结论:AI检测肺结节的敏感度明显高于影像医师,但AI假阳性率亦较高;AI联合影像医师诊断恶性肺结节效能高于AI或影像医师单独诊断;建议AI联合影像医师共同检出肺结节和良恶性诊断,可以降低漏诊率、提高诊断正确率。      

AI在双源CT不同管电压下对肺结节的检测效能

目的:探讨人工智能辅助诊断系统在双源CT不同管电压下对肺结节的检测效能。方法:回顾性的搜集行双源CT的门诊患者200例,经排除最终筛选得到198例符合标准的图像,将图像进行后处理,得到100kVp,融合120kVp和140kVp下的胸部CT图像;根据结节大小、密度及位置分组,比较在不同管电压下人工智能检测肺结节的假阳性与假阴性个数。结果:AI在双源CT 100kVp下对于磨玻璃结节具有较好的分辨能力;在双源CT融合120kVp图像中,对肺结节的误诊率最高,但具有较低的漏诊率;然而,在双源CT 140kVp下对肺结节自动检出效能最差。结论:人工智能在融合120kVp下对肺结节的检测的假阴性率较低,可以降低医师诊断肺结节的漏诊率。      

The settling of resuspended lake sediment related to physicochemical properties of particles of different sizes: Implication for environmental remediation

Sediment resuspension is an important way for shallow lake internal pollution to interact with the overlying water column,and the pollution risks are reasonably related to the retention of resuspended sediment particles in overlying water.In the current study,the settling of resuspended sediment particles was comprehensively investigated under different disturbances using five urban lake sediments.The results show that the particle size distributions of resuspended sediment from different lakes exhibited similar variations during settling with disturbance,although varied settling times were observed under static conditions.During settling with and without disturbance,sediment particle sizes were mainly within 8-63μm at the initial stage,and were<8μm in the later stages of settling.Based on these settling characteristics,the sediment particle size was divided into sand(>63μm),silt(8-63μm),and very fine silt and clay(<8μm)fractions.Kinetic analysis suggested that sediment settling for different particle sizes could be well described by the first-and second-order kinetic equations,especially when settling was disturbed(r²=0.727-0.999).The retention of resuspended sediment could be enhanced as particle sizes decreased and disturbance intensities increased.Furthermore,a water elutriation method was successfully optimized,with separation efficiencies of 56.1%-83%,to separate sediment particles into the defined three particle size fractions.The chemical compositions of sediment were found to change with different particle sizes.Typically,calcium tended to form large-size sediment,while the total contents of aluminum,iron,magnesium,and manganese showed significantly negative correlations with sediment particle sizes(p<0.01)and tended to distribute in small-size particles(e.g.,<8μm).Overall,the sediment particle size related settling dynamics and physicochemical properties suggested the necessity on determining the pollution of resuspended sediment at different particle sizes for restoration of shallow lakes.     

探讨CT扫描剂量对人工智能检测肺结节效能的影响

目的:探讨影响人工智能检测肺结节效能的因素,力求为不同性质的结节提供个性化的扫描剂量及人工智能系统,同时为各人工智能系统适宜的扫描条件提供参考。方法:标准成人男子胸部X线/CT影像模型,内部随机分布15个不同密度和大小的模拟肺结节,采用不同的管电压和管电流对模型进行扫描,共扫描50次。应用不同公司的人工智能系统进行肺结节检测,采用Pearson χ2检验或Fisher确切概率法比较各组检出率和假阴性率;采用Kruskal-Wallis H检验比较假阳性率。结果:①不同管电压条件下,公司A和公司C对不同性质肺结节的检出率无统计学差异;公司B对+100HU结节的检出率,70kV（100%）组高于120kV（80%）和140kV（80%）组;公司B对3mm结节的检出率,70kV组（33.33%）高于120kV（0%）和140kV（0%）组,差异有统计学意义。②各管电压组内不同管电流间及各管电压组间,检出率、假阴性率的差异无统计学意义。各管电压组间假阳性率的差异具有统计学意义。③公司A在70kV组检出率（64.44%）低于公司B（80.00%）、假阴性率（35.56%）高于公司B（20.00%）;公司A的假阳性率高于公司B和公司C;公司B和公司C间检出率、假阴性率、假阳性率无统计学差异。结论:人工智能辅助肺结节检测的灵敏度与CT扫描剂量无关,与结节性质及AI系统性能有关。本研究中公司B和公司C整体性能高于公司A,最佳扫描管电压分别是70kV、70kV和100kV。      

Benthic ecosystem development in an artificial tidal flat constructed from dredged spoil

Dredged spoil (DS) was used as a silt and clay additive in the construction of artificial tidal flats from mountain sand (MS). As the ratio of DS in the sediment media increased, the number of emerging macrobenthos increased. The composition of the macrobenthic community was also affected by the addition of DS, and the changes might be dependent on the ratio of DS to MS. In addition, the macrobenthos in the artificial tidal flats was more abundant than that in the control tidal flat, which was constructed with natural tidal flat sediment. With a silt and clay content of 25%, polychaetes Ceratonereis erythraeensis and Capitella sp. and the gastropod Batillaria cumingii were dominant, whereas no bivalves were present. With less silt and clay (5% and 10%), the bivalves Ruditapes philippinarum and Musculista senhousia were observed in the artificial flats, while their numbers in the control tidal flat were lower.     

Estimation of suspended sediment concentration and yield using linear models,random forests and quantile regression forests

For sediment yield estimation, intermittent measurements of suspended sediment concentration (SSC) have to be interpolated to derive a continuous sedigraph. Traditionally, sediment rating curves (SRCs) based on univariate linear regression of discharge and SSC (or the logarithms thereof) are used but alternative approaches (e.g. fuzzy logic, artificial neural networks, etc.) exist. This paper presents a comparison of the applicability of traditional SRCs, generalized linear models (GLMs) and non‐parametric regression using Random Forests (RF) and Quantile Regression Forests (QRF) applied to a dataset of SSC obtained for four subcatchments (0·08, 41, 145 and 445 km²) in the Central Spanish Pyrenees. The observed SSCs are highly variable and range over six orders of magnitude. For these data, traditional SRCs performed inadequately due to the over‐simplification of relating SSC solely to discharge. Instead, the multitude of acting processes required more flexibility to model these nonlinear relationships. Thus, alternative advanced machine learning techniques that have been successfully applied in other disciplines were tested. GLMs provide the option of including other relevant process variables (e.g. rainfall intensities and temporal information) but require the selection of the most appropriate predictors. For the given datasets, the investigated variable selection methods produced inconsistent results. All proposed GLMs showed an inferior performance, whereas RF and QRF proved to be very robust and performed favourably for reproducing sediment dynamics. QRF additionally provides estimates on the accuracy of the predictions and thus allows the assessment of uncertainties in the estimated sediment yield that is not commonly found in other methods. The capabilities of RF and QRF concerning the interpretation of predictor effects are also outlined. Copyright © 2008 John Wiley & Sons, Ltd.     

PHENOMENA AND CONTROLLING FACTORS IN DRAWDOWN FLUSHING PROCESSES

IINTRODUCTIONByutilizingthenatUralpowerofflow,hydraulicflushingisaneconomicaltechniquetoremovethedepositedsedimentineitherasettlingbasinorareservoir.Inmanyreservoirsaroundtheworld,hydraulicflushinghasbeenprovenasaneffectivemethodtosustainthereservoirstoragecapacity.Hydraulicflushingprocessesmayinvolveboththeprocessesofscouringpreviousdepositsandpassingincomingsediment-ladenflow,suchasgravitycurrent,throughareservoirtobereleased.Inthisarticle,onlytheformercaseisdiscussed.Thekeythinginthef…     

Evaluation of efficiency of different estimation methods for missing climatological data

Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001–2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.     

Flow fields around tandem and staggered piers on a mobile bed

An experimental investigation on flow fields within the scour holes upstream and downstream of circular piers positioned in tandem and staggered arrangements is reported and compared with isolated piers on mobile beds with uniform sediment. The instantaneous bed elevations and instantaneous three dimensional (3D) velocities were measured using a 5 MHz Ultrasonic Ranging system and 16 MHz micro down looking acoustic Doppler velocimeter, respectively. The velocity and flow depth were measured at different locations under near equilibrium bed scour conditions. The measured 3D velocities were processed for the computation of flow parameters, such as velocity fields, streamline patterns, vorticity fields, and circulation. Furthermore, turbulence intensities, turbulent kinetic energy, Reynolds shear stresses, and bed shear stresses around the piers for all three pier configurations were computed from the detrended velocity signals to identify significant differences in the flow parameters and turbulence in the tandem and staggered pier arrangements as compared to those for an isolated pier. A recirculation zone was found near the bed in front of the rear pier in the tandem case from the streamline patterns. The vortices in the bi-vortex system were observed to be opposite to each other in the gap between the three piers in the staggered case. A strong secondary vortex also was observed apart from the primary vortex at the foot of the pier (θ = 0°) in all the three configurations. The strength of the horseshoe vortex (combination of primary and secondary vortices) was found to be higher at the front piers of the staggered arrangement as compared to those of the tandem piers, followed by the isolated pier. The bed shear stresses were found to be higher for the staggered piers than for the tandem piers in the direction of flow (θ = 0°). However, a 50% reduction in the bed shear stresses was observed behind the tandem piers at θ = 180°. The study reported in this paper provides the foundation for further investigation of countermeasures against local scour around tandem and staggered bridge piers on a mobile bed with non-uniform sediment.     

人工智能体积密度法判断肺亚实性结节的浸润性研究

目的:探讨人工智能（AI）体积密度法判断肺亚实性结节（SSNs）浸润性的价值。方法:回顾性分析106例患者的108枚SSNs的CT和病理结果,将结节分为腺体前驱病变组和腺癌组。通过肺结节AI软件测量并比较两组的最大CT值、最小CT值、平均CT值、峰度、偏度、Perc.25％、Perc.50％、Perc.75％、Perc.95％、结节体积、结节平均径等CT定量参数。使用Medcalc软件得出受试者工作特征曲线（ROC）,评价诊断SSNs浸润性的敏感度、特异度、阳性预测值及阴性预测值,用逻辑回归分析评估他们的诊断性能。结果:SSNs的多数CT定量参数差异存在统计学意义,其中,诊断效能最高的是Perc.25％,AUC达0.797;其次为Perc.50％ 和平均CT值,AUC均为0.787。Logistic回归分析显示,将诊断效能最高的Perc.25％ 分别与Perc.50％ 和平均CT值两两建立联合诊断模型1,其中Perc.25％ 与平均CT值的模型诊断效能最高,且联合诊断模型诊断效能高于Perc.25％ 与平均CT值单独的诊断效能。Medcalc软件分析显示,Perc.25％≥-578 HU和平均CT值≥-468 HU的SSNs病理表现为腺癌的可能性大。将Perc.25％ 与结节平均径结合,可获得对判断SSNs浸润性非常有价值的联合诊断模型2。结论:AI体积密度法对SSNs的浸润性有较高的诊断价值,联合使用Perc.25％ 与平均CT值比单独使用更能准确地判断浸润性。      

Hillslope sediment fence catch efficiencies and particle sorting for post-fire rain storms

Sediment fences are often used to monitor hillslope erosion, but these can underestimate sediment yields due to overtopping of runoff and associated sediment. We modified four sediment fences to collect and measure the runoff and sediment that overtopped the fence in addition to the sediment deposited behind the fence. Specific objectives were to: (1) determine the catch efficiency of sediment fences measuring post-fire hillslope erosion; (2) assess particle sorting of sand, silt/clay, and organic matter from each hillslope through the sediment fence and subsequent runoff collection barrels; (3) evaluate how catch efficiency and particle size sorting relate to site and rainfall-runoff event characteristics; and (4) use runoff simulations to estimate sediment fence volumes for future post-fire monitoring. Catch efficiency ranged from 28 to 100% for events and 38 to 94% per site for the entire sampling season, indicating a relatively large underestimation of sediment yields by sediment fences. Most of the eroded sediment had similar proportions of sand and silt/clay as the hillslope soils, but the sediment behind the fence was significantly enriched in sand while the sediment that overtopped the fence was more strongly enriched in silt/clay. The sediment fences had capacities of 3 m³ for hillslopes of 0.19–0.43 ha, but simulations of runoff for 2- to 100-year storms indicate that the sediment fences would need a capacity of up to 240 m³ to store all of the runoff and associated sediment. More accurate measurements of sediment yields with sediment fences require either increasing the storage capacity of the sediment fence(s) to accommodate the expected volume of runoff and sediment, reducing the size of the contributing area, or directly measuring the runoff and sediment that overtop the fence. © 2020 John Wiley & Sons, Ltd.