Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool (SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case‐dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Influence of cover thickness in structural frames exposed to fire and service loads

Environmental Science and Pollution Research - This paper deals with the effect of cover thickness in a reinforced concrete columns of 3-D (three dimensional) frame structures under progressive...     

Assessment of source water contamination by estrogenic disrupting compounds in China

Detection of estrogenic disrupting compounds (EDCs) in drinking waters around China has led to rising concerns about health risks associated with these compounds. There is, however, a paucity of studies on the occurrence and identification of the main compounds responsible for this pollution in the source waters. To fill this void, we screened estrogenic activities of 23 source water samples from six main river systems in China, using a recombinant two-hybrid yeast assay. All sample extracts induced significant estrogenic activity, with E2 equivalents (EEQ) of raw water ranging from 0.08 to 2.40 ng/L. Additionally, 16 samples were selected for chemical analysis by gas chromatography-mass spectrometry. The EDCs of most concern, including estrone (E1), 17βup-estradiol (E2), 17αup-ethinylestradiol (EE2), estriol (E3), diethylstilbestrol (DES), estradiol valerate (EV), 4-t-octylphenol (4-t-OP), 4-nonylphenols (4-NP) and bisphenol A (BPA), were determined at concentrations of up to 2.98, 1.07, 2.67, 4.37, 2.52, 1.96, 89.52, 280.19 and 710.65 ng/L, respectively. Causality analysis, involving comparison of EEQ values from yeast assay and chemical analysis identified E2, EE2 and 4-NP as the main responsible compounds, accounting for the whole estrogenic activities (39.74% to 96.68%). The proposed approach using both chemical analysis and yeast assay could be used for the identification and evaluation of EDCs in source waters of China.     

Beyond the “General Public”: Implications of Audience Characteristics for Promoting Species Conservation in the Western Ghats Hotspot,India

Understanding how different audience groups perceive wildlife is crucial for the promotion of biodiversity conservation, especially given the key role of flagship species in conservation campaigns. Although the heterogeneity in preferences reinforces the need for campaigns tailored to specific target audiences, many conservation education and awareness campaigns still claim to target the “general public”. Audiences can be segmented according to social, economic, and cultural criteria across which species perceptions are known to vary. Different studies have investigated the preferences of different groups towards certain wildlife species, but these are largely confined to a single conservation stakeholder group, such as tourists, local communities, or potential donors in western countries. In this study, we seek to determine from a multi-stakeholder perspective, audience characteristics that influence perceptions towards wildlife at Valparai, a fragmented plateau in the Western Ghats region of the Western Ghats-Sri Lanka Hotspot. We found that stakeholder group membership was the most important characteristic followed by gender. While some characteristics had a wide-scale effect others were restricted to a few species. Our results emphasize the need to design conservation campaigns with specific audiences in mind, instead of the very often referred to “general public”.     

Assessing BMP effectiveness: multiprocedure analysis of observed water quality data

Observed water quality data obtained from eight stream monitoring locations within Richland–Chambers Watershed in north central Texas were analyzed for trends using box-and-whisker plots, exceedance probability plots, and linear and Mann–Kendall statistical methods. Total suspended solids decreased at seven out of eight stations, and at two of these stations, the decrease was significant. Mixed results were obtained for nitrogen across the stations. A nonsignificant and significant increase in nitrite plus nitrate nitrogen (nitrite + nitrate N) was noticed in two stations each, whereas at the other four stations showed nonsignificant decrease. The results of organic nitrogen (Org N) was similar to nitrite + nitrate N except that the two stations that showed significant increase in nitrite + nitrate N showed nonsignificant decrease in Org N. Mixed results were also noticed for orthophosphorus (Ortho P) including nonsignificant decrease at two stations, significant decrease and increase at one station each, and nonsignificant increase in four stations. In general, total phosphorus (TP) decreased at all stations, significantly at some, except one station where it increased significantly. Decreasing trends in sediment, Org N, Ortho P, and TP were likely related to implementation of best management practices (BMPs). Increasing trends in dissolved constituents including Ortho P and nitrite + nitrate N were likely due to increased surface residue as a result of some BMPs such as conservation tillage.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

Laser tempering based turning process for efficient machining of hardened AISI 52100 steel

Cost-effective machining of hardened steel components such as a large wind turbine bearing has traditionally posed a significant challenge. This paper presents an approach to machine hardened steel parts efficiently at higher material removal rates and lower tooling cost. The approach involves a two-step process consisting of laser tempering of the hardened workpiece surface followed by conventional machining at higher material removal rates with lower cost ceramic tools to efficiently remove the tempered material. The laser scanning parameters that yield the highest depth of tempered layer are obtained from a kinetic phase change model. Machining experiments are performed to demonstrate the possibility of higher material removal rates and improved tool wear behavior compared to the conventional hard turning process. Tool wear performance, cutting forces, and surface finish of Cubic Boron Nitride (CBN) tools as well as low cost ceramic tools are compared in machining of hardened AISI 52100 steel (~63 HRC). In addition, cutting forces and surface finish are compared for the laser tempering based turning and conventional hard turning processes. Experimental results show the potential benefits of the laser tempering based turning process over the conventional hard turning process.