Design and implementation of a general software library for using NSGA-II with SWAT for multi-objective model calibration

Calibrating watershed-scale hydrologic models remains a critical but challenging step in the modeling process. The Soil and Water Assessment Tool (SWAT) is one example of a widely used watershed-scale hydrologic model that requires calibration. The calibration algorithms currently available to SWAT modelers through freely available and open source software, however, are limited and do not include many multi-objective genetic algorithms (MOGAs). The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been shown to be an effective and efficient MOGA calibration algorithm for a wide variety of applications including for SWAT model calibration. Therefore, the objective of this study was to create an open source software library for multi-objective calibration of SWAT models using NSGA-II. The design and implementation of the library are presented, followed by a demonstration of the library through a test case for the Upper Neuse Watershed in North Carolina, USA using six objective functions in the model calibration.     

Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis – A study on effect of image magnification

The scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, non-reproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.     

Size distribution of suspended particulates in different areas and seasons as a function of their sulphate and ammonium content

The size distribution of particles as a function of sulphate, ammonium and total suspended particulates was investigated in the air of two urban and one industrial area in winter and in summer. An Andersen cascade impactor was used for sampling. Regardless of the character of the area and irrespective of season the size weight distributions of sulphate and ammonium-containing particles were comparable in all separation stages.A relatively high value of equivalent ammonium ion/sulphate ion ratio for separation stages IV and V for the urban areas as well as a good correlation between sulphate and ammonium ion concentrations were determined. The results support the assumption of ammonium ion being a dominant cation which binds to sulphates in urban atmospheres.     

Multiscale analysis of the urbanization pattern of the Phoenix metropolitan landscape of USA: Time,space and thematic resolution

Investigating the ecological consequences of urbanization require knowledge of land-cover dynamics. Quantification of land-use/land-cover change in Phoenix, Arizona during the period of 1985–2005 using landscape metrics computed from Landsat-derived maps revealed temporal patterns of landscape composition and configuration. With accelerated urbanization the landscape as a whole became more fragmented ecologically and more complex compositionally and geometrically. However, the majority of individual patches became more compact in shape. Urban land covers, especially xeric residential, increased substantially and the desert decreased by 20%. Spatial and thematic resolution of data was shown to have large effects on the analysis of land-cover pattern. Our results, while agreeing in general with previously reported scaling relations with respect to changing spatial grain and extent, showed that scaling relations are also robust and consistent across thematic resolutions and time periods. Some metrics behaved unpredictably and some exhibited scale-free behavior. Compositional metrics, such as patch density, diversity, evenness, and largest patch index, were well correlated with vegetative cover, its spatial variation, and population density. Many of these correlations exhibited hump-shaped patterns with respect to increasing grain size, indicating a characteristic scale at approximately 500–1000 m. By simultaneously manipulating spatial and thematic resolutions, the importance of the Modifiable Area Unit Problem in relating landscape patterns to vegetation and socio-economic variables was also demonstrated. Additionally, highly variable desert vegetation due to precipitation variability poses a challenge for accurately quantifying urbanization pattern in arid environments. Choosing appropriate spatial, temporal and thematic resolutions is essential in meeting this challenge.     

考虑弥散尺度效应与不动水体的反应性溶质运移动力学模型及半解析解

将弥散度概化为运移距离的渐进函数,并考虑土壤孔隙中存在的不动水体以及溶质的吸附和降解,建立了考虑弥散尺度效应的溶质运移两区模型(TRMS,Two-Region Model with Scale-dependent Dispersion),通过Laplace变换和de Hoog数值反演方法求得了模型的半解析解,并运用混合拉普拉斯变换有限差分法验证了半解析解的准确性。通过TRMS与弥散度为常数的两区模型(TRMC,Two-Region Model with Constant Dispersion)之间的比较,分析了弥散尺度效应对溶质运移过程的影响,并利用算术平均方法计算了TRMS的等效弥散度,最后应用TRMS和TRMC模拟了长度为1250cm的一维非均质土柱中的溶质运移过程。结果表明:TRMS的等效弥散度反映了弥散尺度效应的影响,可以近似作为区域弥散度的平均值,采用等效弥散度时,TRMC描述的穿透曲线与TRMS的模拟结果基本一致;TRMC的模拟结果与非均质长土柱的浓度实测值存在较大偏差,而TRMS的模拟精度有了较大程度的提高,能够更好地模拟非均质长土柱中溶质的不规则运移过程,说明本文建立的TRMS能够较好地模拟非均质介质中溶质在较大尺度上的运移过程。     

An enhanced dust index for Asian dust detection with MODIS images

An enhanced dust index (EDI) for Moderate Resolution Imaging Spectroradiometer (MODIS) solar reflectance bands is proposed that provides a means to detect the dust status of the atmosphere. The EDI utilizes only solar reflectance channels and may therefore be applied consistently to the entire MODIS time series records (1999 to present) for daytime dust observation, producing a higher spatial resolution (500 m) dust result than that from thermal-infrared records (1000 m), which were developed previously and are currently being used. The index introduces dust optical density (α), which can be simply estimated by spectral unmixing, into the normalized difference between reflectance at near-infrared (2.13 μm) and blue (0.469 μm). Dust severity can thus be rated from weak to severe within a standard range of –1 to 1. The index was applied to 11 typical dust events during 2000–2010 in East Asia, where it showed good coherence with meteorological station-observed visibility (R ² = 0.7909, p < 0.05) and standardized visibility (R ² = 0.7128, p < 0.05). Further comparison with the commonly used normalized difference dust index (NDDI) and brightness temperature difference (BTD) between MODIS bands 31 and 32 also indicated a better performance of the EDI in identifying the spatial and density distributions of dust. Previously applied satellite-based dust indices, particularly for the visible and near-infrared, can therefore be improved for a better quantification of dust aerosols.     

Spatial variations of rain-use efficiency along a climate gradient on the Tibetan Plateau: A satellite-based analysis

Understanding the spatial relationship between rain-use efficiency (RUE) and climate conditions is crucial for predicting the steady-state responses of an ecosystem to climate variations. We clarified the spatial variations of RUE in different ecosystems along both mean annual precipitation (MAP) and mean annual temperature (MAT) gradients in the Three-river Headwaters Region (THR) of China. RUE displayed a unimodal pattern along a MAP gradient across different ecosystems, with a higher value in forest, shrub, and less-arid alpine meadow than in alpine steppe, alpine talus vegetation, and more-arid alpine meadow. RUE followed an increasing trend along the MAT gradient both in a given ecosystem and across various ecosystems. The varying maximum RUE (RUE_max) with temperature suggested that no common spatial RUE_max existed across different ecosystems in the driest pixels. With the mutual interference between precipitation and temperature excluded, RUE increased both with precipitation at a given temperature level and with temperature at a given precipitation level in cold and dry ecosystems, while RUE probably decreased both with precipitation at a given temperature level and with temperature at a given precipitation level in warm and humid ecosystems. Our study illuminates the response of RUE to climate variations in alpine areas based on the spatial model, aiming to improve our understanding of the interactions between vegetation and climate conditions and the potential trade-offs between the ecosystem’s carbon and water.