喀斯特土地利用变化及其区域生态环境效应--以广西都安瑶族自治县为例

从土地利用转移类型、空间格局变化定量分析、动态变化预测模型等方面分析近20年都安土地利用变化,进而对其生态环境质量变化进行定量分析,得出结论:都安各乡(镇)生态环境质量改善与恶化在很大程度取决于裸岩地扩展与收缩,土地利用时空变化与区域生态环境质量的区域差异有很大相关性。     

A real-time inertial-aided cycle slip detection method based on ARIMA-GARCH model for inaccurate lever arm conditions

GPS Solutions - The GPS satellite transmitter antenna phase center offsets (PCOs) can be estimated in a global adjustment by constraining the ground station coordinates to the current International...     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

表面多次波衰减技术在渤海海峡应用效果

浅水地震勘探对于了解近海底地质构造具有重要意义,目前已被广泛用于油气开发、近岸工程等领域。表面多次波的存在干扰了有效波信息,影响了资料品质,造成解释假象,如何有效地压制多次波,已成为浅水地震勘探中的关键问题。表面多次波衰减方法(SRME)是一种去除海面相关多次波效果较好的技术方法,但是,一般认为SRME技术并不适合于浅水区域。分析了SRME去除多次波的基本原理,并将其应用于海洋高分辨率浅水区域多次波的去除。实际处理效果表明,使用SRME技术处理后的叠加剖面多次波去除效果明显,剖面基底清楚,断面清晰。通过对SRME技术应用于浅水区域的探讨,证实了SRME技术在海洋高分辨率地震勘探浅水区域应用效果较好。     

Target setting and allocation of carbon emissions abatement based on DEA and closest target: an application to 20 APEC economies

Regarded as an effective method for treating the global warming problem, carbon emissions abatement (CEA) allocation has become a hot research topic and has drawn great attention recently. However, the traditional CEA allocation methods generally set efficient targets for the decision-making units (DMUs) using the farthest targets, which neglects the DMUs’ unwillingness to maximize (minimize) some of their inputs (outputs). In addition, the total CEA level is usually subjectively determined without any consideration of the current carbon emission situations of the DMUs. To surmount these deficiencies, we incorporate data envelopment analysis and its closest target technique into the CEA allocation problem. Firstly, a two-stage approach is proposed for setting the optimal total CEA level for the DMUs. Then, another two-stage approach is given for allocating the identified optimal total CEA among the DMUs. Our approach provides more flexibility when setting new input and output targets for the DMUs in CEA allocation. Finally, the proposed approaches are applied for CEA target setting and allocation for 20 Asia-Pacific Economic Cooperation economies.

     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatial and temporal variations of albedo on nine glaciers in western China from 2000 to 2011

This research demonstrates the spatiotemporal variations of albedo on nine glaciers in western China during 2000–2011, by the albedo derived from two types of datasets: Landsat TM/ETM + images and MOD10A1 product. Then, the influence factors of glacier albedo and its relationship with glacier mass balance are also analyzed by the correlation approach, which is frequently used in geostatistics. The paper finds that there are different spatiotemporal variations over the glaciers in western China: (1) For a single glacier, the albedo varies gently with altitude on its tongue and increases fast in the middle part, while in the accumulation zones, the albedo value appears in the form of fluctuation. This could provide a quantitative method to retrieve the snowline by determining the threshold albedo value of snowpack and bare ice. (2) For the glaciers in western China, the albedo decreases with distance to the center of Tibetan Plateau (TP). This may relate to the elevation of glacier, for the speed of glacier retreat highly depends on air temperature. (3) In the summer period, albedo on most glaciers declines over the last 12 years, and it decreases much faster in southeastern TP than other regions, for which air temperature overwhelms the black carbon concentration. In addition, the trend of glacier albedo in summer is greatly correlated with that of measured glacier mass balance, which implies that the long‐term albedo datasets by remote sensing technology could be used to monitor and predict the change of glacier mass balance in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Response of Plant Community Carbon and Nitrogen Stoichiometry to Experimental Warming on the Qinghai-Tibet Plateau

Low temperature is an important limiting factor for alpine ecosystems on the Tibetan Plateau. This study is based on data from on-site experimental warming platforms (open top chambers, OTC) at three elevations (4300 m, 4500 m, 4700 m) on the Qinghai-Tibet Plateau. The carbon and nitrogen stoichiometry characteristics of plant communities, both above-ground and below-ground, were observed in three alpine meadow ecosystems in August and September of 2011 and August of 2012. Experimental warming significantly increased above-ground nitrogen content by 21.4% in September 2011 at 4500 m, and reduced above-ground carbon content by 3.9% in August 2012 at 4300 m. Experimental warming significantly increased below-ground carbon content by 5.5% in August 2011 at 4500 m, and the below-ground ratio of carbon to nitrogen by 28.0% in September 2011 at 4300 m, but reduced below-ground nitrogen content by 15.7% in September 2011 at 4700 m, below-ground carbon content by 34.3% in August 2012 at 4700 m, and the below-ground ratio of carbon to nitrogen by 37.9% in August 2012 at 4700 m. Experimental warming had no significant effect on the characteristics of community carbon and nitrogen stoichiometry under other conditions. Therefore, experimental warming had inconsistent effects on the carbon and nitrogen stoichiometry of plant communities at different elevations and during different months. Soil ammonium nitrogen and nitrate nitrogen content were the main factors affecting plant community carbon and nitrogen stoichiometry.