油膜波谱特征分析与遥感监测溢油

分析了海洋溢油波谱特征的测试结果，提出遥感监 煤油、轻柴油、润滑油、重柴油和原油等最佳波段。并利用ＮＯＡＡ－ＡＶＨＲＲ和ＴＭ资料，对几次海上溢油事故的油膜图象进行处理和解译，得到了较为清晰的溢油图象，并与事故现场调查结果吻合。     

徐州市三环西路硝基苯泄漏事故应急监测案例分析及研究

2006年4月徐州市三环西路发生一起硝基苯泄漏事故。通过对该环境应急监测案例的分析与研究,认识到我们在突发性污染事故环境监测与管理方面的不足,提出了加强与改进的措施,使环境应急监测系统更完善。     

Satellite Observations of Lava Lake Activity at Nyiragongo Volvano, Ex-Zaire, during the Rwandan Refugee Crisis

In June 1994 the summit crater of Nyiragongo volcano, located in the Great Lakes region of central Africa, began to fill with new lava, ending nearly 12 years of quiescence. An earlier eruption of the volcano in 1977 had culminated in the catastrophic draining of a lava lake through fissures in the crater wall, feeding highly mobile lava flows which reached the outskirts of Goma and killed more than 70 people. By July 1994, as many as 20,000 Hutu refugees were arriving in Goma every hour, only 18km south from the summit of Nyiragongo. The exodus brought more than one million people to the camps near the town raising fears of a repeat of the 1977 eruption. This paper examines the role that satellite remote sensing could have played in surveillance of the volcano during this time, and demonstrates the potential for monitoring this and other volcanoes in the future. Images recorded by the spaceborne Advanced Very High Resolution Radiometer (AVHRR) – freely available over the Internet – provide semi-quantitative information on the activity of the volcano. The aim of this paper is to promote the wider use of readily available technologies.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States1

Tobin, Kenneth J. and Marvin E. Bennett, 2012. Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States. Journal of the American Water Resources Association (JAWRA) 48(2): 221‐234. DOI: 10.1111/j.1752‐1688.2011.00604.x Abstract: The precipitation science community has expressed concern regarding the ability of satellite‐based precipitation products to accurately capture rainfall values over land. There has been some work that has focused on addressing the deficiencies of satellite precipitation products, particularly on the adjustment of bias. This article outlines a methodology that adjusts satellite products utilizing ground‐based precipitation data. The approach is not a simple bias adjustment, but is a three‐step process that transforms a satellite product based on a ground‐based precipitation product (NEXRAD‐derived Multisensor Precipitation Estimator [MPE] product or rain‐gauge data). The developed methodology was successfully applied to seven moderate‐to‐large sized watersheds from continental United States (CONUS) and northern Mexico over a spectrum of climatic regimes ranging from dry to humid settings. Methodology validation is based on comparison of observed and simulated streamflow generated with SWAT (Soil and Water Assessment Tool) model using unadjusted and adjusted precipitation products as input. Streamflow comparison is based on mass balance error and Nash‐Sutcliffe efficiency coefficient. Finally, the contribution of how adjustment to correct misses, false alarms, and bias impacts adjusted datasets and the potential impact that the adjustment methodology can have on hydrological applications such as water resource monitoring and flood prediction are explored.     

基于FY2C卫星数据的藏北高原降水估算研究

如何确切的掌握降水的时空分布,对区域气候、水文和生态应用等至关重要。以藏北高原典型区为研究区,在大量地面实况降水观测数据与对长时间序列FY 2C 影像光谱特征和云图特征分析的基础上,获取卫星降水模拟参数特征集以刻画云降水的发生与发展过程,选用最值归一化方法对不同量纲云图特征参数进行归一化处理。构建基于三层前向型反向传播神经网络的卫星降水估算模型,用于该地域降水估算,并采用多指标体系分析模型的降水模拟精度。结果表明：静止气象卫星红外波段能较精确地揭示云的降水机理,较高时间分辨率遥感图像可以监测云图的变化细节,并获取能够反映云图降水特征的降水模拟参数;人工神经网络能较好地刻画该地域卫星降水特征的非线性规律;三层前向型反向传播神经网络卫星降水估算模型的估算结果与雨量计实测值间的相关性可以达到0.57。模型估算结果系统性的低估偏小,预示着对该地域弱降水强度将有较好的指示性     

汛期连云港市重点断面污染来源调查研究

长期以来汛期江苏省部分重点断面水质下降明显,是当地水环境质量提升的难点之一.选取连云港市重点断面为研究对象,应用手工监测和卫星遥感监测方法,分析4个典型断面水质变化特点;通过三维荧光溯源分析方法,确定农业源和生活源是主要污染源;结合现场核查,发现秸秆还田沤水和雨污分流不到位是导致典型断面水质下降的主要原因.研究提出,手...     

The Importance of Mixing Height in Characterising Pollution Levels from Aerosol Optical Thickness Derived by Satellite

In the present study the horizontal distribution of columnar aerosol optical thicknessderived at high spatial resolution from Earth observation satellite data in the Lombardy area (Italy) was converted to the horizontal distribution of optically effective aerosols concentration at the ground level. This was achieved by incorporating information on atmosphere's mixing height, at which pollutants released at ground level are vertically dispersed by convection or mechanical turbulence. The resulted fields compared favourably to pollutant concentration measurements provided by the ground stations. These results show that it is possible to calculate mean concentration fields by using the spatial distribution of aerosol optical thickness (AOT) measured by satellite normalized by the atmospheric mixing height. The advantage of satellites in measuring AOT is that they can capture all actual emissions compared to the models, which are based on inventoried data.     

青藏高原东南部及其邻近地区FY-2E卫星晴空大气可降水量评估

为了解青藏高原东南部及邻近地区FY-2E卫星大气可降水量(FY-2ETPW)的可靠性,利用2010~2012年探空计算值(RS TPW)对其进行对比分析。结果表明:青藏高原地区月平均FY-2ETPW除7月偏小外,其余月份都偏大,相对偏差仅有6月和7月在25%以内,其余月份都在50%以上,尤其是1月高达780.45%,FY-2ETPW与RS TPW之间为负相关系数,FY-2ETPW在青藏高原地区不可靠;低海拔地区,6月和7月平均FY-2ETPW偏小,其余月份都偏大,5~8月相对偏差不足4%,但1月相对偏差却高达105.84%;低海拔地区4~10月,FY-2E TPW与RS TPW相关系数大于0.5,FY-2ETPW订正模型估计标准误差为6.57mm,个别站点误差较大,还有待进一步的改进。     

中国2013年1月PM2.5重污染过程卫星反演研究

利用第三代空气质量模型CMAQ(community multiscale air quality modelling system)模拟的PM2.5垂直分层数据和中尺度气象模型WRF(weather research and forcasting model)模拟的高分辨率湿度数据,分别对MODIS AOD(aerosol optical depth)资料进行垂直与湿度订正,建立了订正后的AOD数据与PM2.5地面监测数据之间的线性拟合模型,其线性相关系数r=0.77(n=57,P0.01).基于此线性拟合模型,首次反演了2013年1月全国10 km分辨率PM2.5月均浓度的空间分布特征,并分析了人口暴露水平.结果表明,2013年1月我国PM2.5月均浓度大于100μg·m-3、200μg·m-3的面积占国土面积的比例分别高达10.99%、1.34%,暴露人口占全国总人口的比例分别高达45.01%、6.31%.