A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.     

Processing of an East Mediterranean phosphate ore sample by an integrated attrition scrubbing/classification scheme (part one)

ABSTRACT

In this study, an Egyptian siliceous/calcareous sedimentary phosphorite was subjected to beneficiation by unsophisticated economic means. The mineralogical and chemical examination showed that the ore sample is a typical example for the East Mediterranean phosphorites with 21.80% P₂O₅, 10.93% acid insoluble and 16.61% loss on ignition. The primary crushed sample was classified into three size fractions without any milling and they were separately subjected to an integrated attrition scrubbing study. Screening and classification on the 2.30 mm and 0.071 mm sieves were sequentially introduced with the attrition process. The effect of solid%, attrition speed and time were studied and optimized for each size fraction. Results showed that three products were produced and could be categorized according to their P₂O₅ content into the coarse tailing (+2.30 mm), the attrition concentrate (?2.30 + 0.071 mm) and the phosphate fines (?0.071 mm). The P₂O₅ contents of these products reached 12.24%, 28.72% and 18.77%, with P₂O₅ distribution reached 6.74%, 51.75% and 41.51%, respectively. A global P₂O₅ of 28.72% was obtained with a recovery of about 52%. A tentative flow-sheet for the whole process was postulated.     

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.     

一种基于区域分割的图像融合方法

提出了一种基于区域分割的图像融合方法.首先,通过对原图像进行小波分解得到其近似子图和细节子图,对细节子图运用分水岭分割法获得每一层的分割子图用于引导融合过程.然后在分割区域中对原图像小波系数的活动水平及匹配度进行度量,并在组合细节子图系数和近似子图系数的过程中分别采用最大值法则和加权平均法则.最后,通过逆小波变换得到最...     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

Segmentation and classification of hyperspectral images using watershed transformation

Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral-spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral-spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures.     

Redefining the Water Poverty Index

Abstract

The Water Poverty Index (WPI) of arid and semiarid climatic zones is redefined by introducing the contribution of rainfed agriculture into WPI calculations and by making the WPI equation account for diversities in climatic conditions within arid and semiarid zones and for recycled household water used in irrigation.     

分频相位分析技术在断层解释中的应用

相干技术通常是识别断层的常用方法,但在实际勘探过程中．常规的相干分析技术在识别复杂地质条件下的断裂系统有一定的不适应性。对此,本文开发了分频相位技术来解决常规相干的不适应性．利用分频突出断层响应的最优势频带．和相位对地层产状变化敏感的特点．克服了传统相干对垂向断距小的断层识别能力不足的问题。并将该技术应用在上海平湖地区及辽东地区,证实了分频相位技术针刈具体的目标研究区其优势频率的断层分布特征具有其独特的作用。     

南水北调东线工程输水系统运行风险研究

南水北调东线工程输水系统涉及堤防漫堤、渗透和失稳三种失事模式。分析了输水系统的风险机制,识别出系统的风险来自外来荷载、内部结构、运行管理等三方面,参考我国大坝安全评价和堤防工程设计规范等,建立了风险评价基准。采用层次-模糊分析法计算了输水河道运行综合风险概率;以河段长度为参数,通过聚类分析,得出了输水河道失事后果等级。在此基础上,根据风险评价基准,算出了各输水河道的风险等级。结果表明:高水河风险为低,中运河、胶东输水干线风险为中等,里运河等8段输水河道风险为较低。