Nonparametric Estimates of the Sea State Bias for the Jason-1 Radar Altimeter

The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved.     

State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment

A four-dimensional variational data assimilation system has been applied to an experiment to describe the dynamic state of the North Pacific Ocean. A synthesis of available observational records and a sophisticated ocean general circulation model produces a dynamically consistent dataset, which, in contrast to the nudging approach, provides realistic features of the seasonally-varying ocean circulation with no artificial sources/sinks for temperature and salinity fields. This new dataset enables us to estimate heat and water mass transports in addition to the qualification of water mass formation and movement processes. A sensitivity experiment on our assimilation system reveals that the origin of the North Pacific Intermediate Water can be traced back to the Sea of Okhotsk and the Bering Sea in the subarctic region and to the subtropical Kuroshio region further south. These results demonstrate that our data assimilation system is a very powerful tool for the identification and characterization of ocean variabilities and for our understanding of the dynamic state of ocean circulation. This revised version was published online in July 2006 with corrections to the Cover Date.     

ASM1中污水水质参数与化学计量系数的估测

简要介绍了活性污泥1号模型中14个污水水质参数与5个化学计量系数的估测方法和典型取值。     

长江和长江口高含量无机氮的主要控制因素

根据1998－1998年长江和长江口河水和雨水的现场调查、历史资料以及相关文献，定量分析长江流域无机氮的主要来源和输送调查。估算表明，降水无机氮、农业非点源氮（化肥和土壤流失的氮）和点源污水氮的输入分别占长江口无机氮输出通量的62．3％、18．5％和14．4％。氮的降水输入是长江口高含量无机氮的主要来源，进入长江的降水氮仅仅大约占长江流域全部降水氮的36．8％。降水米要受控于化肥气态损失、化石燃料及动植物过程中释放的物质等。实际上，化肥N的气态损失和农业非点源流失大约占长江流域年化肥N使用量的60％，这是控制长江口高含量无机氮的关键因素。     

厄尔尼诺现象和浙江近海鲐鲹鱼渔获量变化关系──长江口ENSO渔场学问题之二

本文研究浙江近海1970年到1992年鲐鱼渔获量变化与Elnino现象的关系，结合捕捞强度，长江中下游汛期（4—9月）降水量距平年际变化综合分析，探讨浙江近海鲐鱼渔获量变动规律，为合理开发渔业资源提供科学依据。     

经典选权迭代法研究与两步抗差估计的提出

随着权函数的不同出现了不同的抗差估计方法,相应不同的抗差估计方法在一定程度上存在着不足或者缺陷。详细地论述了几种经典的选权迭代抗差估计方法的不足,并从理论出发,提出了两步抗差方案。     

CPⅢ测量数据处理系统开发若干关键技术研究

本文首先介绍了无砟轨道的相关内容,指出CPⅢ测量数据处理是整个建设中的关键,提出系统开发的总体目标与要求且设计了系统总体结构,详细阐述了系统开发中涉及的关键技术与基本理论,文中最后结合具体工程实例验证系统开发成果。     

Applications of AnnAGNPS model for soil loss estimation and nutrient loading for Malaysian conditions

The study evaluated the performance and suitability of AnnAGNPS model in assessing runoff, sediment loading and nutrient loading under Malaysian conditions. The watershed of River Kuala Tasik in Malaysia, a combination of two sub-watersheds, was selected as the area of study. The data for the year 2004 was used to calibrate the model and the data for the year 2005 was used for validation purposes. Several input parameters were computed using methods suggested by other researchers and studies carried out in Malaysia. The study shows that runoff was predicted well with an overall R² value of 0.90 and E value of 0.70. Sediment loading was able to produce a moderate result of R² = 0.66 and E = 0.49, nitrogen loading predictions were slightly better with R² = 0.68 and E = 0.53, and phosphorus loading performance was slightly poor with an R² = 0.63 and E = 0.33. The erosion map developed was in agreement with the erosion risk map produced by the Department of Agriculture, Malaysia. Rubber estates and urban areas were found to be the main contributors to soil erosion. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for planning and management of watersheds under Malaysian conditions.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.