Inferring air–water temperature relationships from river and catchment properties

Water temperature (Tw) is a key determinant of freshwater ecosystem status and cause for concern under a changing climate. Hence, there is growing interest in the feasibility of moderating rising Tw through management of riparian shade. The Loughborough University Temperature Network (LUTEN) is an array of 36 water and air temperature (Ta) monitoring sites in the English Peak District set‐up to explore the predictability of local Tw, given Ta, river reach, and catchment properties. Year 1 of monitoring shows that 84%–94% of variance in daily Tw is explained by Ta. However, site‐specific logistic regression parameters exhibit marked variation and dependency on upstream riparian shade. Perennial spring flows in the lower River Dove also affect regression model parameters and strongly buffer daily and seasonal mean Tw. The asymptote of the models (i.e. maximum expected Tw) is particularly sensitive to groundwater inputs. We conclude that reaches with spring flows potentially offer important thermal refuges for aquatic organisms against expected long‐term warming of rivers and should be afforded special protection. Copyright © 2013 John Wiley & Sons, Ltd.     

基于温度植被干旱指数的电网输电线路走廊干旱分析

以湖北省输电线路走廊地区作为研究区,利用2013年1~9月MODIS卫星影像数据,处理得到月尺度的归一化植被指数(Normalized Differential Vegetation Index,NDVI)与地表温度(Land Surface Temperature,LST)数据,构建NDVI-Ts特征空间,计算得到温度植被干旱指数(Temperature Vegetation Dryness Index,TVDI),用TVDI监测结果分析湖北省输电线路走廊区域2013年干旱时空分布情况。结果表明,湖北省输电线路走廊地区TVDI和土壤含水量之间存在显著的负相关,相关系数达到0.525(p0.05),由MODIS卫星影像计算得到TVDI影像可以有效表明湖北省输电线路走廊地区的土壤含水情况。     

中国北方地区秋季植被变化及对气候变化的响应研究

基于1982~2006年的AVHRR GIMMS NDVI数据,使用一元线性回归和分段线性回归等方法,通过对中国北方地区植被变化及其与气候因子的关系研究,揭示该地区近25年来在不同时段的植被变化趋势及对气候变化的响应规律,从而为该地区的生态环境变化研究提供理论依据。研究结果表明:1)中国北方地区秋季植被在25年时间内整体呈上升趋势。秋季NDVI在秋季温度断点之前以上升趋势为主,秋季NDVI在秋季温度断点之后仍以上升趋势为主,但上升趋势有所放缓。2)通过分段线性回归方法和相关分析研究得出中国北方地区秋季温度是秋季NDVI变化的主要驱动力。在秋季温度断点之后,秋季温度仍呈上升趋势而降水呈显著减少的面积增多,从而在温度和降水双重影响下的干旱胁迫导致植被下降;当秋季温度下降而秋季降水增多时干旱发生概率变小,从而使秋季NDVI呈上升趋势。     

PMODTRAN: a parallel implementation based on MODTRAN for massive remote sensing data processing

MODerate resolution atmospheric TRANsmission (MODTRAN) is a commercial remote sensing (RS) software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth's atmosphere and the radiation observed by a remote sensor. However, when very large RS datasets must be processed in simulation applications at a global scale, it is extremely time-consuming to operate MODTRAN on a modern workstation. Under this circumstance, the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task. This paper presents PMODTRAN, an implementation of a parallel task-scheduling algorithm based on MODTRAN. PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster. In addition, PMODTRAN can distribute tasks with different levels of granularity and has some extra features, such as dynamic load balancing and parameter checking.     

基于贝叶斯模型的中国未来气温变化预估及不确定性分析

利用第5次耦合模式比较计划(CMIP5)中35个全球气候模式历史模拟与RCP4.5预估结果,通过贝叶斯模型平均(Bayesian Model Averaging,BMA)对中国气温进行多模式集合研究,给出了中国未来气温变化预估及其不确定性的时空分布。结果表明,中国21世纪冬夏将持续升温,且升温具有冬季高于夏季,北方高于南方的特点。初期(2016—2035年)北方有很大可能(80%)升温超过0.7℃,南方升温相同幅度的概率则超过50%;中期(2046—2065年)北方和南方升温超过1.5℃的概率分别为80%和50%;末期(2081—2100年),北方(南方)有80%(50%)的可能的升温超过2℃。气温预估的不确定性研究发现,无论冬夏,21世纪不同时期升温相对较弱的塔里木盆地、青藏高原南侧和中国东南地区为不确定性低值区,基本低于0.6℃,对应可信度较高,如21世纪初期信噪比超过4;而不确定性的高值区则主要分布在新疆北部、东北平原北部和青藏高原东南侧等升温相对较大的地区,普遍高于1℃,对应可信度较低,如初期信噪比低于2.5。此外,基于信噪比对比发现除青藏高原东部外,其他区域夏季预估的可信度均高于冬季,21世纪末期高于初期,且空间分布特征一致。     

Searching for Added Value in Simulating Climate Extremes with a High-Resolution Regional Climate Model over Western Canada

We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10?km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX?>?25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45?km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.     

基于均一化资料的中国大陆极端温度的长期趋势

近百年来,全球气候变暖。这与暖日和暖夜增加,冷日和冷夜减少相关联。文章研究结果进一步证实了这一发现。本文基于1960-2012年中国大陆542个台站均一化气温资料,通过将中国大陆划分为8个次区域,利用百分位定义法计算了极端温度指数序列,同时,运用时间趋势分析法,对中国大陆各区域极端温度和极端温度指数的时空分布及变化趋势特征进行了分析。结果表明:在全球变暖的背景下,从地理分布而言,中国大陆在过去53年除西南地区外,大部分地区最低和最高温度有显著的升高趋势,其中,东北温度升高最为明显;从季节而言,冬季极端温度升高最为明显,夏季升高最少;最低温度明显升高,最高温度也有所升高,但是最低温度的升高幅度更大。冷夜和冷日出现频率呈减少趋势,暖夜和暖日出现频率呈增加趋势,其中以冷夜指数变化最为突出,均呈现一种区域差异的现象。本文利用更新的资料验证了前人的工作,也进一步分区分析,结果可为更多地区评估以及进一步的相关研究提供参考。     

Multi-model assessment of global hydropower and cooling water discharge potential under climate change

Worldwide, 98% of total electricity is currently produced by thermoelectric power and hydropower. Climate change is expected to directly impact electricity supply, in terms of both water availability for hydropower generation and cooling water usage for thermoelectric power. Improved understanding of how climate change may impact the availability and temperature of water resources is therefore of major importance. Here we use a multi-model ensemble to show the potential impacts of climate change on global hydropower and cooling water discharge potential. For the first time, combined projections of streamflow and water temperature were produced with three global hydrological models (GHMs) to account for uncertainties in the structure and parametrization of these GHMs in both water availability and water temperature. The GHMs were forced with bias-corrected output of five general circulation models (GCMs) for both the lowest and highest representative concentration pathways (RCP2.6 and RCP8.5). The ensemble projections of streamflow and water temperature were then used to quantify impacts on gross hydropower potential and cooling water discharge capacity of rivers worldwide. We show that global gross hydropower potential is expected to increase between +2.4% (GCM-GHM ensemble mean for RCP 2.6) and +6.3% (RCP 8.5) for the 2080s compared to 1971–2000. The strongest increases in hydropower potential are expected for Central Africa, India, central Asia and the northern high-latitudes, with 18–33% of the world population living in these areas by the 2080s. Global mean cooling water discharge capacity is projected to decrease by 4.5-15% (2080s). The largest reductions are found for the United States, Europe, eastern Asia, and southern parts of South America, Africa and Australia, where strong water temperature increases are projected combined with reductions in mean annual streamflow. These regions are expected to affect 11–14% (for RCP2.6 and the shared socio-economic pathway (SSP)1, SSP2, SSP4) and 41–51% (RCP8.5–SSP3, SSP5) of the world population by the 2080s.