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.     

A comparison study on high-temperature water–gas shift reaction over Fe/Al/Cu and Fe/Al/Ni catalysts using simulated waste-derived synthesis gas

A comparative study on Fe/Al, Fe/Al/Cu, and Fe/Al/Ni catalysts in high-temperature water–gas shift reaction (HT–WGS) using simulated waste-derived synthesis gas has been carried out. The metal oxide (Cu and Ni) and aluminum incorporated Fe catalysts were designed to get highly active HT–WGS catalysts. Despite the high CO concentration in the simulated waste-derived synthesis gas, Fe/Al/Cu catalyst exhibited the highest CO conversion (84 %) and 100 % selectivity to CO₂ at a very high gas hourly space velocity (GHSV) of 40,057 h^?1. The outstanding catalytic performance is mainly due to easier reducibility, the synergy effect of Cu and Al, and the stability of the magnetite.     

A comparative study on per capita waste generation according to a waste collecting system in Korea

As cities are becoming increasingly aware of problems related to conventional mobile collection systems, automated pipeline-based vacuum collection (AVAC) systems have been introduced in some densely populated urban areas. The reasons are that in addition to cost savings, AVAC systems can be efficient, hygienic, and environmentally friendly. Despite difficulties in making direct comparisons of municipal waste between a conventional mobile collection system and an AVAC system, it is meaningful to measure the quantities in each of these collection methods either in total or on a per capita generation of waste (PCGW, g/(day*capita)) basis. Thus, the aim of this study was to assess the difference in per capita generation of household waste according to the different waste collection methods in Korea. Observations on household waste show that there were considerable differences according to waste collection methods. The value of per capita generation of food waste (PCGF) indicates that a person in a city using AVAC produces 60 % of PCGF (109.58 g/(day*capita)), on average, compared with that of a truck system (173.10 g/(day*capita)) as well as 23 %p less moisture component than that with trucks. The value of per capita generation of general waste (PCGG) in a city with an AVAC system showed 147.73 g/(day*capita), which is 20 % less than that with trucks delivered (185 g/(day*capita)). However, general waste sampled from AVAC showed a 35 %p increased moisture content versus truck delivery.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Characterization of the monthly variation in (1 → 3)-β-d-glucan concentrations in university laboratories

We characterize the monthly variation in (1 → 3)-β-d-glucan concentration measured over the course of 1 year, and we evaluate the characteristics of size selection using a two-stage cyclone sampler. The (1 → 3)-β-d-glucan concentrations were measured in four bio-related laboratories. A total of 156 samples were collected using a new two-stage cyclone sampler. Analysis of (1 → 3)-β-d-glucan was performed using the kinetic Limulus amebocyte lysate assay. The study showed that airborne (1 → 3)-β-d-glucan concentrations were significantly higher in laboratory D (mean ± SD 1,105?±?1,893 pg/m³) and in the spring (5,458 pg/m³). The highest concentration of (1 → 3)-β-d-glucan occurred in the spring, particularly in May.     

Abundance and sources of hydrophilic and hydrophobic water-soluble organic carbon at an urban site in Korea in summer

In this study, the characteristics of total water-soluble organic carbon (WSOC) and isolated WSOC fractions were examined to gain a better understanding of the pathway of organic aerosol production. 24 h PM(2.5) samples were collected during the summer (July 28-August 28, 2009) at an urban site in Korea. A glass column filled with XAD7HP resin was used to separate the filtered extracts into hydrophilic (WSOC(HPI)) and hydrophobic (WSOC(HPO)) fractions. The origins of air mass pathways arriving at the sampling site were mostly classified into three types, those originating over the East Sea of Korea that passed over the eastern inland urban and industrial regions (type I); those from the marine (western/southwestern/southern marine) and passed over the national industrial complex regions (type II); and those from northeastern China that passed through North Korea and metropolitan areas of South Korea (type III). Measurements showed an increase in the average WSOC fraction of total OC from the type II to III air mass (53 to 64%) periods. Also, higher SO(4)(2-)/SO(x) (=SO(2) + SO(4)(2-)) was observed in the type III air mass (0.70) than those in the types I (0.49) and II (0.43). According to the average values of WSOC/OC and SO(4)(2-)/SO(x), measurements suggest that the aerosols collected during the type III air mass period were more aged or photo-chemically processed than those during the types I and II air mass periods. The relationship between the SO(4)(2-)/SO(x) and WSOC/OC (R(2) = 0.64) suggests that a significant fraction of the observed WSOC at the site could be formed by an oxidation process similar to SO(4)(2-) aerosols, probably the oxidation process using OH radicals, or in-cloud processing. The photochemical production of WSOC(HPO) was also observed to significantly contribute to the total OC.     

Numerical simulation on multi-scale diffusion of CO2 injected in the deep ocean in a practical scenario

The direct injection of CO₂ into the deep ocean is one of the ways for the mitigation of the global warming. There is, however, a concern about its environmental impact near the injection point. To minimize its biological impact, it is necessary to make CO₂ disperse as fast as possible and it is thought that injection with pipes towed by moving-ships is effective for this purpose. Because the injection ships are planned to move in the site, the order of magnitude of which is 10² km, a mesoscale model is required to predict CO₂ fate in seawater. At the same time, it is required to predict the concentration precisely near the injection points, which move with the ships in the mesoscale domain. In this study, a multi-scale ocean model was developed to analyze the dispersion of CO₂ in the deep ocean: the model consists of a fixed mesoscale domain and 5 small-scale domains nesting in the mesoscale domain. Each small-scale domain involves 6 pipes and moves along with the trajectories of the injection ships. From the results of the present numerical simulation, the developed technique demonstrated its applicability as a tool to optimise the system to dilute CO₂ below some criterion of biological impact.