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| 1 | 变化环境下降雨集中度的变异与驱动力探究显示文摘降雨的时空分布过于集中会诱发洪旱灾害。研究降雨集中度的变异及其驱动力有助于全面掌握降雨对变化环境的响应特征,为区域水资源的综合利用与灾害预警提供依据。以汉江流域为研究对象,选取月降雨集中度指数(CIM)和日降雨集中度指数(CID)表征降雨集中度,并采用Mann-Kendall趋势检验法与启发式分割算法对降雨集中度进行变异分析,利用交叉小波变换探究太阳黑子与大气环流异常因子对降雨集中度变化的影响。结果表明:①汉江流域CIM北大南小,呈不显著的下降趋势;CID东大西小,在6个站呈显著上升趋势;②流域部分站点CID发生突变,而CIM序列较为平稳,表明CID相比CIM对变化环境的响应更为敏感;③太阳黑子和大气环流异常因子对降雨集中度的变化有较强的影响,其中太阳黑子的影响最大,它通过影响大气环流异常因子间接影响汉江流域的降雨集中度。 | 黄生志 杜梦 李沛 郭怿 王璐 | 2019 | 水科学进展2019，30，4： | 14 |
| 2 | 西北地区降雨集中度时空演变及其影响因素显示文摘在全球气候变化背景下,深入研究中国西北地区降雨集中度时空演变规律具有重要的现实意义。该研究基于1960—2017年逐月栅格降雨数据,利用Mann-Kendall趋势检验方法、Mann-Kendall突变点检验方法、Morlet小波方法和冷热点分析方法分析了西北地区降雨集中度的时空变化特征,并通过交叉小波变换探讨大气环流因子变化与降雨集中度的关系,同时讨论地貌分布对降雨集中度的影响。结果表明:1)在1960—2017年,西北地区及其3个子区域的降雨集中度平均值呈现减少趋势,且存在显著突变点(P<0.05),降雨量年内分配不均,存在明显的季节性变化,部分区域降雨量年内异常集中且降雨集中度未来将会持续减少;2)整个研究区及其3个子区域在1960—2017年平均降雨集中度变化均存在一个40 a左右的主周期和一个24 a左右的次周期,各个子区域降雨集中度的变化与研究区整体的周期变化保持一致性;3)在1960—2017年,降雨集中度空间分布存在冷热点,冷点区域降雨集中度呈现显著减少趋势(P<0.05)。热点区域降雨集中度呈现不显著减少趋势,年际变化幅度高于冷点区域;4)北大西洋涛动和太平洋十年涛动指数等大气环流因子变化对降雨集中度的变化具有较强的影响,不同的大气环流因子对降雨集中度的影响存在差异。研究成果将有助于进一步深化对西北地区降雨年内分配变化的认识,为西北地区生态环境保护和水资源规划制定提供一定的科学依据。 | 贾路 于坤霞 邓铭江 李鹏 李占斌 时鹏 徐国策 | 2021 | 农业工程学报2021，37，16： | 12 |
| 3 | 近58年天山降雪/降水量比率变化特征及未来趋势显示文摘降雪/降水量比率(S/P)能够反映不同形态降水特征,对气候变化十分敏感。该文基于天山及周边49个气象台站观测数据和IPCC-CMIP5气候情景数据,分析了近58 a来中国天山山区冷季(10-4月)降雪量、降水量和S/P时空变化特征,并预估在RCP4.5排放情景下各指标的未来变化趋势。结果表明:天山山区冷季S/P受地形影响,呈山区大于盆地,北坡大于南坡的分布格局,与海拔显著正相关。1961—2018年天山山区平均冷季降雪量、降水量均显著增加,S/P变化不大,在0.35~0.67之间波动,以-0.016%/10a的速率呈微弱减少趋势;平均气温变化是引起S/P变化的重要因素。在RCP4.5气候情景下,天山山区未来冷季降雪量缓慢减少,降水量显著增加,S/P显著减少。相比基准期(1986—2005年),到2050s冷季降雪量平均减少8.9%,降水量增加10.1%,S/P减少14.7%。该研究对科学认识全球变暖背景下天山地区水文响应以及区域水资源调控具有重要意义。 | 秦艳 赵求东 孟杰 丁建丽 | 2020 | 农业工程学报2020，36，4： | 8 |
| 4 | Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model显示文摘In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology. | REN Rui LI Xue-mei LI Zhen LI Lan-hai HUANG Yi-yu | 2022 | Journal of Mountain Science2022，19，3： | 1 |
| 5 | An observational study of precipitation types in the Alaskan Arctic显示文摘The effects of various precipitation types,such as snow,rain,sleet,hail and freezing rain,on regional hydrology,ecology,snow and ice surfaces differ significantly.Due to limited observations,however,few studies into precipitation types have been conducted in the Arctic.Based on the high-resolution precipitation records from an OTT Parsivel^(2) disdrometer in Utqiaġvik,Alaska,this study analysed variations in precipitation types in the Alaskan Arctic from 15 May to 16 October,2019.Results show that rain and snow were the dominant precipitation types during the measurement period,accounting for 92%of the total precipitation.In addition,freezing rain,sleet,and hail were also observed(2,4 and 11 times,respectively),accounting for the rest part of the total precipitation.The records from a neighbouring U.S.Climate Reference Network(USCRN)station equipped with T-200B rain gauges support the results of disdrometer.Further analysis revealed that Global Precipitation Measurement(GPM)satellite data could well characterise the observed precipitation changes in Utqiaġvik.Combined with satellite data and station observations,the spatiotemporal variations in precipitation were verified in various reanalysis datasets,and the results indicated that ECMWF Reanalysis v5(ERA5)could better describe the observed precipitation time series in Utqiaġvik and the spatial distribution of data in the Alaskan Arctic.Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)overestimated the amount and frequency of precipitation.Japanese 55-year Reanalysis(JRA-55)could better simulate heavy precipitation events and the spatial distribution of the precipitation phase,but it overestimated summer snowfall. | YUE Handong DOU Tingfeng LI Shutong LI Chuanjin DING Minghu XIAO Cunde | 2021 | Advances in Polar Science2021，32，4： | 0 |
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