新疆艾比湖流域地表水丰水期和枯水期水质分异特征及污染源解析

采用2015年艾比湖流域54个采样点的10个地表水水质指标数据,首先利用水质指数模型(WQI)和地统计学方法对流域水质污染情况进行全局评价,然后利用层次聚类法、判别分析法和因子分析法分析艾比湖流域地表水丰水期和枯水期水质分异特征.在水质时空分异特征研究的基础上,利用主成分回归分析法对艾比湖流域水质进行污染源解析.结果表明:艾比湖流域丰水期WQI值介于38~70之间,枯水期WQI值介于31~71之间,艾比湖流域丰水期的地表水水质污染情况比枯水期严重,而艾比湖、博尔塔拉河和精河靠近艾比湖湖区的河道污染程度均比其他河道严重.由聚类分析和判别分析得出艾比湖流域丰水期和枯水期的水质采样点在空间上均被分成A、B两组,A组包括艾比湖湖区西部、奎屯河、古尔图河和四棵树河,B组包括艾比湖湖区东部、精河和博尔塔拉河.艾比湖流域丰水期和枯水期的水体主要受到化学需氧量、溶解氧、氨氮和悬浮物浓度等指标的影响,B组水质污染指标的值相比于A组的值偏高,B组区域内存在高污染企业,艾比湖流域水环境治理工作需主要集中在B组所包括的艾比湖湖区、博尔塔拉河和精河.(4)艾比湖湖区、精河和博尔塔拉河地表水体的污染主要来自于有机物污染和营养物质污染,其次为工矿业污染;而奎屯河、古尔图河、四棵树河地表水体的污染主要来自于有机物污染,其次为营养物质污染,生物污染的影响较为微弱.该研究结果可为艾比湖流域地表水水环境改善和治理提供一定参考.     

A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index

In this paper, a new method for canopy water content (FMC) estimation for highly vegetated surfaces- shortwave infrared perpendicular water stress index (SPSI) is developed using NIR, SWIR wavelengths of Enhanced Thematic Mapper Plus (ETM ) on the basis of spectral features and distribution of surface targets with different water conditions in NIR-SWIR spectral space. The developed method is further explored with radiative transfer simulations using PROSPECT, Lillesaeter, SailH and 6S. It is evident from the results of validation derived from satellite synchronous field measurements that SPSI is highly correlated with FMC, coefficient of determination (R squared) and root mean square error are 0.79 and 26.41%. The paper concludes that SPSI has a potential in vegetation water content estimation in terms of FMC.     

Effects of Dietary Protein Level on Growth and Utilization of Protein and Energy by Juvenile Mangrove Red Snapper (Lutjanus argentimaculatus )

1Introduction The mangrove red snapper,Lutjanus argentimacu-latus(Forsskal,1775)is a carnivorous,warm-watereuryhaline fish that is considerably cultured in South-east Asia,Southern China and the Middle East(Le-ung et al.,1999;Estudillo et at.,2000;Ng et al.,2000;Catacutan et al.,2001).In Pakistan,it isknown for its good quality meat and also for its highconsumption rate.Although it fetches a premiumprice at local markets(Anonymous,2002),the in-creasing demand has generated interest towar…     

Disintegration of uncertainties associated with real-time multi-satellite precipitation products in diverse topographic and climatic area in Pakistan

Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km²)and overestimate in large systems(>2000 km²).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.     

新疆艾比湖主要入湖河流精河与博尔塔拉河三维荧光光谱特性及其与水质的关系

以艾比湖主要入湖河流——精河与博尔塔拉河为研究对象,分别分析了精河与博尔塔拉河的水体溶解性有机质(DOM)的组成结构及水质参数与荧光指数的关系.利用平行因子分析法对三维荧光光谱(EEM)分析发现,精河与博尔塔拉河均含有C1(260/420 nm)腐殖酸等有机质、C2(240,240/490 nm)UVC类腐殖质、C3(220/280,300/450 nm)蛋白质类有机质和C4(260,270/530 nm)类腐殖质,且不同荧光组分结构具有一定差异性.为了进一步了解DOM组分特征,采用三维荧光区域积分法分析各区域标准体积百分比,结果表明精河与博尔塔拉河EEM的区域Ⅰ与区域Ⅱ蛋白质有机质含量最高,区域Ⅲ富里酸含量最低.相关性分析表明,主要入湖河流的水质参数与荧光指数中,自生源指数(BIX)与总氮(TN)浓度以及腐殖化指数(HIX)与铵态氮(NH_4~+-N)浓度的相关性较强,相关系数分别为0.831和0.684,且具有显著性;HIX与TN浓度的相关系数为0.604,达到显著性水平.进而对相关性较强的水质参数与荧光指数进行3次拟合,其中HIX与NH_4~+-N浓度的拟合效果最好,相关性系数为0.908,其次是BIX与TN浓度,相关性系数为0.844.总之,通过分析精河与博尔塔拉河三维荧光特征,以及探讨荧光指数与水质参数的关系,可为治理干旱区水环境问题提供理论依据和参考.     

Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

The Soil and Water Assessment Tool(SWAT) was implemented in a small forested watershed of the Soan River Basin in northern Pakistan through application of the sequential uncertainty fitting(SUFI-2) method to investigate the associated uncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period(1991–2000) with an initial 4-year warm-up period(1987–1990), and was validated for the subsequent 10-year period(2001–2010). The model evaluation indices R~2(the coefficient of determination), NS(the Nash-Sutcliffe efficiency), and PBIAS(percent bias) for stream flows simulation indicated that there was a good agreement between the measured and simulated flows. To assess the uncertainty in the model outputs, p-factor(a 95% prediction uncertainty, 95PPU) and r-factors(average wideness width of the 95 PPU band divided by the standard deviation of the observed values) were taken into account. The 95 PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factor was 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients(R~2 and NS) for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78 and 0.74, respectively. Meanwhile, the 95 PPU covered more than 60% of the observed sediment data during calibration and validation. Moreover, improved model prediction and parameter estimation were observed with the increased number of iterations. However, the model performance became worse after the fourth iterations due to an unreasonable parameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertainty during the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessment of water balance components, climate change studies, and land use management practices.     

新疆植被覆盖度趋势演变实验性分析

基于MODIS-NDVI数据,提取新疆2005-2015年植被覆盖度（FVC）。通过依据海拔和植被覆盖度的指标划分出山地、绿洲、平原、荒漠等11个子系统。通过斜率、变异系数、线性回归模型等方法来对全疆和不同生态分区的现状和未来发展趋势进行分析,并用BP人工神经网络来预测新疆2016-2020年的植被覆盖度的时空变化和分析2005-2020年时空动态变化趋势。主要结论为：① 新疆植被覆盖度总体为上升趋势,从西北向东南逐渐下降;山地呈逐年上升趋势,荒漠呈不显著退化趋势。植被覆盖度的变化主要是由降水量的变化引起;② 在整个新疆的荒漠和绿洲边缘构成了一个“绿洲—荒漠改善过渡带”,绿洲呈明显的改善趋势;③ 2009年是研究期内多数分区植被覆盖度的历史最低点;④ 在山脉的冰川积雪、湖泊周围的变异性很大,范围在150%~316%之间,这主要是由于气候变化、冰川消融和湖泊水位的波动变化所致;⑤ 北疆生态明显好于东疆与南疆,其绿洲区域呈现明显的改善趋势。伊犁地区的植被覆盖度相比于其他3个分区的变幅很大,山地区域呈明显的逐年退化趋势。伊犁地区植被覆盖度的局部最低点是在2008年,比其他分区的2009年提前了一年,相应的存在“实时”（伊犁）和“滞后”（东疆、南疆和北疆）的效应,主要是由于降水量和气温的变化所致。