基于模式集成的松花江流域气候模拟预估

以松花江流域为研究对象,采用CMIP5已发布的9个气候模式,模拟1951年～2000年历史降水和气温的月数据,对比实测资料运用3项数理统计指标评估模式的模拟性能;各模式降尺度后应用到4种集成方法。模拟结果表明,Can ESM2和MPI-ESM-MR模式模拟效果较好,多元回归集成表现最佳。利用优选的2个模式方法预估流域下游佳木斯站在RCP4. 5气候情景下未来逐月的降水和气温,并以预估的降水气温为因子构建线性回归模型计算流域未来径流的月变化过程。预估径流结果可为水资源管理及洪旱防治提供数据参考。     

日雨量随机解集模式研究

全球气候模式(GCMs)预测的气候变化情景，必须经解集模式得出小尺度上未来气候变化时空分布资料，才能满足评估气候变化对资源、环境和社会经济等影响的需要。本文提出由随机天气生成器和统计参数尺度转换关系组成的随机解集模式，应用17个站32年实测日降雨资料，对随机解集模式进行了分析和验证。首先利用随机天气生成器，通过对站点和GCM尺度面平均降雨系列的模拟，确定模型参数，验证模型模拟历史降雨过程的可靠性。然后，建立模型参数从大尺度向站点转换的关系，并从历史降雨系列中抽出某一日雨量系列，假设为未来气候变化情形，对降雨系列在不同尺度间的转换关系进行了验证。在此基础上，对GCMs预测结果的时空解集方法进行了探讨。     

气候变化条件下流域径流演变趋势分析

为了预测流域未来径流演变趋势,通过主分量分析、降尺度模型和SWAT模型,预测分析了流域在大气环流模型(GCMs)A2/B2气候情景下2010—2099年的日最高最低气温、日降水和月径流量。主分量分析提取大尺度下气候预测因子的主成分,降尺度模型利用提取的主成分预测站点的最高最低气温和降水,SWAT模型利用预测的站点数据计算未来径流量。结果表明,A2/B2两种气候情景下流域未来气温呈波动上升趋势,降水、径流均呈波动下降趋势,其中B2情景变化幅度大于A2情景。     

应用统计学降尺度方法预测汉江流域降水变化

统计降尺度方法是目前国内外研究气候变化的一个新途径.以汉江流域为例,选择全球气候观测NCEP再分析数据(1960～2000年)来率定和检验模型,利用主成分分析方法和多元线性回归模型建立大尺度GCMs模型的日降水统计降尺度方法,并应用全球气候模型CGCM2的A2气候情景来预测和分析汉江流域未来降水变化.相对于1961～2000年实测降水量均值,上游2001～2020、2121～2040年和中游2001～2020年的年降水量分别减少3.97%、4.85%和1.5%,其余统计时间年降水量大于实测值.     

TRMM卫星降水数据在黄河源区的适用性研究

具有较高时空分辨率的卫星降水数据为研究缺乏地面实测站点流域的水文循环过程提供了有力的支撑。以黄河源区为研究对象,在对比分析2002—2015年TRMM 3B42卫星降水数据与站点实测降水数据的基础上,利用VIC分布式水文模型验证了TRMM降水数据在我国资料稀缺的高寒流域径流模拟的适用性。结果表明,TRMM卫星测雨数据与站点实测降水数据在时空分布及变化趋势上有较好的一致性,TRMM数据多年平均相对误差为-7.1%,相关系数达到0.86。根据VIC模型模拟结果可知,TRMM数据与站点实测数据的月尺度模拟效果较好,日尺度模拟效果不佳,丰水年TRMM数据模拟精度较枯水年高。     

基于SDSM-SWAT的气候变化下东江流域径流预测模拟

通过耦合SDSM统计降尺度模型和SWAT水文模型,探讨气候变化下东江流域的未来气候及其径流响应。首先基于SDSM模型,将2011—2099年HadCM3模式下A2和B2两种情景数据降尺度到东江流域各站点,生成未来气候要素(气温和降水);然后建立适用于东江流域的SWAT模型,并模拟该流域未来气候变化下的径流响应。结果表明:未来东江流域的气温、降水量和径流量均呈增加趋势;且A2情景下各气候水文要素的增加速度比B2情景下更快。研究结果可为东江流域的流域综合管理和水资源的可持续利用提供一定的科学依据。     

基于SDSM的赣江流域未来降水与气温的时空变化分析

利用赣江流域6个气象站数据(1961年～2005年)和NCEP再分析资料,建立了气候要素的SDSM降尺度模型,并将模型应用于Can ESM2模式的RCP4. 5情景,得到了流域未来气温与降水的变化趋势。即SDSM降尺度模型对赣江流域气温的模拟效果较好,降水略差;赣江流域未来降水均呈增加的趋势,降水空间分布基本呈南低北高趋势;未来气温均呈增加的趋势,各时期最高气温稍大于基准期;各时期最低气温稍大于基准期;赣江流域未来不同季节的平均气温均大于基准期;赣江流域未来气温空间分布呈现南高北低,西高东低的趋势。     

RF-SVR降尺度模型在滦河流域的适用性分析

探讨了RF-SVR统计降尺度模型用于汛期极端降雨模拟的可能性。该统计降尺度模型由降雨状态分类和降雨量预测回归两部分构成,降雨状态分类过程中采用了随机森林（RF）方法,降雨量预测回归过程采用了支持向量机回归（SVR）法。选用1961-2000年的NCEP/NCAR再分析资料及滦河流域10个雨量站点的降雨观测数据进行模型率定,并用2001-2012年数据进行了验证。将RF-SVR统计降尺度模型与SVR模型降尺度效果做了对比。结果表明：RF-SVR模型模拟的滦河流域日降雨量偏差显著减小,并可以改善流域极端降雨的模拟预测效果。     

基于SDSM的抚河流域未来极端气温模拟与预估

SDSM(Statistical Downscaling Model)统计降尺度模型是解决空间尺度问题的一种有效工具。基于统计降尺度技术和GCM输出数据,结合站点实测数据,将SDSM模型应用在抚河流域,分析了抚河流域未来最高气温与最低气温的变化趋势。使用1961—1990年和1991—2005年2个时段的实测数据和NCEP再分析数据,选取合适的NCEP大气环流因子作为预报因子,建立最高和最低气温预报量和预报因子之间的经验统计关系;并以CanESM2输出的未来数据(包括RCP2.6、RCP4.5和RCP8.5 3种情景)为输入,模拟未来3个时期的极端气温变化,即A(2006—2035年)、B(2036—2065年)、C(2066—2100年)。结果表明:流域未来最高气温和最低气温都呈现明显上升趋势,最高气温和最低气温平均增加1.69℃、2.44℃,最低气温上升幅度高于最高气温;在2种气温各个情景下平均增温约2.07℃,说明未来抚河流域有出现极端高温天气的风险。分析结果对抚河流域开展气候变化的水文响应研究、水资源合理利用及生态环境保护具有重要意义。     

统计降尺度方法研究进展综述

统计降尺度方法是将大气环流模式GCMs输出的低分辨率的气象资料转换为流域尺度的主要方法之一,现已发展成为气候学中较为完善的领域。简要介绍了统计降尺度方法的基本原理,包括基本假设条件及主要步骤和关键点;重点介绍统计降尺度方法,大致分为转换函数法、天气分析技术和天气发生器这三类,并对几种方法的国内外应用进展做了阐述;对统计降尺度方法的不确定性研究做了简要介绍。指出未来研究应重点研究统计降尺度模型的适用条件及范围、提高降水模拟的精度;统计降尺度与动力降尺度两种降尺度结合的方法将是降尺度主要发展方向之一。     

Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.     

Uncertainty Modeling of Statistical Downscaling to Assess Climate Change Impacts on Temperature and Precipitation

Consideration of different Statistical Downscaling (SD) models and multi-sources global climate models’ (GCMs) data can provide a better range of uncertainty for climatic and statistical indices. In this study, results of two SD models, ASD (Automated Statistical Downscaling) and SDSM (Statistical Downscaling Model), were used for uncertainty analysis of temperature and precipitation prediction under climate change impacts for two meteorological stations in Iran. Uncertainty analysis was performed based on application of two GCMs and climate scenarios (A2, A1B, A2a and B2a) for 2011–2040, 2041–2070 and 2071–2100 future time slices. A new technique based on fuzzy logic was proposed and only used to describe uncertainties associated with downscaling methods in temperature and precipitation predictions. In this technique, different membership functions were defined to fuzzify results. Based on these functions width, precipitation had higher uncertainty in comparison with the temperature which could be attributed to the complexity of temporal and local distribution of rainfall. Moreover, little width of membership functions for temperatures in both stations indicated less uncertainty in cold months, whereas the results showed more uncertainty for summer. The results of this study highlight the significance of incorporating uncertainty associated with two downscaling approaches and outputs of GCMs (CGCM3 and HadCM3) under emission scenarios A2, A1B, A2a and B2a in hydrologic modeling and future predictions.     

Modeling and analysis of rainfall processes in the context of climate change for Mekong,Chi, and Mun River Basins (Thailand)

This paper provides a detailed characterization of the observed daily rainfall series available for the Mekong, Chi, and Mun River Basins in the context of climate change; and describes the linkage between climate simulations given by Global Circulation Models (GCMs) and the local rainfall characteristics using the popular Statistical Downscaling Model (SDSM). Observed daily rainfall records at 11 stations in the study area for the 1961–2007 period were considered. Results of characterizing the available rainfall data for the 1961–1990 and 1991–2007 periods show different trends of rainfall characteristics for different locations in the study area. However, a consistent increase in the annual maximum number of consecutive dry days (CDD) was observed in the Chi catchment area, the eastern part of the Mun watershed, and the western portion of the Mekong River Basin. In addition, decrease in the annual maximum daily rainfall (AMDR) was found in most locations of the study area, except for the central part of the Chi and Mun River Basins. Moreover, it has been shown in this paper that the SDSM could adequately describe the basic statistical and physical characteristics of the observed rainfall processes for the calibration (1961–1975) and validation (1976–1990) periods. This statistical downscaling method was then used to project future rainfall characteristics for the 1961–2099 period using the climate simulations given by the UK HadCM3 (HadCM3) model under A2 and B2 scenarios (HadCM3A2 and HadCM3B2), and by the Canadian GCM3 (CGCM3) model under A2 and A1B scenarios (CGCM3A2 and CGCM3A1B). In general, the projected trends of rainfall characteristics by both HadCM3 and CGCM3 were found to be consistent with the observed historical trends. However, there was a large difference in the projection results given by these two models. This would indicate the presence of high uncertainty in climate simulations provided by different GCMs. In addition, the climate change impacts on the flood and drought problems in the study area were shown using the CDD and AMDR indices of 100-year return period.     

RCP情景下都柳江上游气候变化及径流响应分析

建立都柳江上游三水源新安江月水文模型,根据1968-2004年历史降雨、蒸发及径流数据运用遗传算法来率定和优选敏感参数,使用降尺度GCM数据驱动模型得到不同RCP情景下的流域未来月平均径流,并通过线性拟合方法分析都柳江上游未来气象要素及模拟径流过程。结果表明:2020-2099年都柳江年平均降雨基本处于稳定状态,年平均气温和蒸发呈波动增长,局部短时段下降。流域径流流量总体低于历史平均值,年际间波动降低,年内分布不均,主要集中在5-10月份。     

多GCM模型集合下开都河流域未来气候变化预估

基于逐步聚类分析的统计降尺度模型(SCADS模型),在多GCM模型集合的9个大尺度气象变量与开都河流域6个气象变量之间,建立统计降尺度关系,并进行开都河流域未来气候变化的预估。结果表明,SCADS模型生成的开都河流域各气象变量的模拟值与实测值拟合较好。各气象变量在率定期(1961年-1990年)和验证期(1991年-1999年)的NSE系数均大于0.55,精度较高。此外,利用SCADS模型进行开都河流域各气象变量的预估。发现在三个不同时期内(2011年-2040年,2041年-2070年和2071年-2100年),月均气温升高,月均蒸发量、降水量、日照时数增加,月均相对湿度升高。     

气候变化影响下黄河上游梯级水库群未来发电量预测

为分析气候变化影响下黄河上游大型水库入库来水过程及梯级发电量的时程变化规律,以黄河上游龙羊峡刘家峡梯级水库群为例,采用Mann-Kendall 突变检验方法对唐乃亥和小川水文序列进行突变识别,在此基础上构建了考虑融雪过程的HBV 水文模型,利用统计降尺度方法对CanESM2 和GFDL_ESM2G 两种气候模式3 种气候变化情景(RCP2. 6、RCP4. 5 和RCP8. 5) 下的降水、气温数据进行空间降尺度处理,并将其驱动水文模型预测未来入库来水过程,构建黄河上游梯级联合发电调度模型分析气候变化对未来发电调度过程的影响。结果表明:黄河上游径流序列突变年份集中于20 世纪80 年代,且2000 年之后径流量显著减少;气候变化将导致未来(2021—2050年)汛期6—9 月径流增加,非汛期径流显著减少;随着时间推移,不同气候变化情景下,龙羊峡和刘家峡两库的梯级发电量变化规律不同,RCP8.5 气候变化情景下,气候模式不确定性对其影响最大。     

基于ATAK的MSWEP数据空间降尺度及对降水融合的影响研究

全球性降水数据为获取大范围降水空间分布提供了新途径,但其空间分辨率不高一直是制约其应用于流域或区域尺度上的重要因素之一,因此研究全球性降水数据的空间降尺度方法具有重要的理论和实用价值。本文采用从区域到区域的Kriging（Area to Area Kriging,ATAK）和反距离权重（Inverse Distance Weighted,IDW）两种方法,不考虑地面雨量资料及影响雨量的有关辅助信息,在汉江流域将全球性降水数据MSWEP的空间分辨率由0.1°×0.1°提高至0.02°×0.02°。结果发现ATAK降尺度得到的月雨量场虽然在统计精度上与IDW无明显差异,但提高了对月降水量局部空间变异特征的描述能力,在一定程度上克服了IDW的平滑效应。进一步以ATAK、IDW降尺度处理后的MSWEP数据以及不作空间降尺度处理的原始MSWEP数据为背景场,采用GWR方法分别与雨量站网降水数据融合,发现3种情况下得到的月降水融合数据在空间基本格局上相同,精度统计结果也较为接近,但雨量场的空间连续性及细节特征仍有一定差异。在地表雨量站网密度较高的情况下,背景场差异对MSWEP和站点降水融合结果的影响不能完全消除,甚至在局部可能放大。因此,对于MSWEP等全球性降水数据与站网降水资料的融合而言,选择适当的空间降尺度方法是必要的。本文的结论和认识为全球性降水数据的空间降尺度和雨量场精细化估计提供了重要参考。     

Assessment of Extreme Precipitation in Future through Time-Invariant and Time-Varying Downscaling Approaches

Skill of a time-varying downscaling approach, namely Time-Varying Downscaling Model (TVDM), against time-invariant Statistical Downscaling Model (SDSM) approach for the assessment of precipitation extremes in the future is explored. The downscaled precipitation is also compared with a Regional Climate Model (RCM) product obtained from Coordinated Regional Climate Downscaling Experiment (CORDEX). The potential of downscaling the extreme events is assessed considering Bhadra basin in India as the study area through different models (SDSM, TVDM and RCM) during historical period (calibration: 1951–2005, testing: 2006–2012). Next, the changes in precipitation extremes during future period (2006–2035) have been assessed with respect to the observed baseline period (1971–2000), for different Representative Concentration Pathway (RCP) scenarios. All the models indicate an increasing trend in the precipitation, for the monsoon months and maximum increase is noticed using RCP8.5. The annual precipitation during the future period (RCP8.5) is likely to increase by 7.6% (TVDM) and 4.2% (SDSM) in the study basin. An increase in magnitude and number of extreme events during the future period is also noticed. Such events are expected to be doubled in number in the first quarter of the year (January–March). Moreover, the time-invariant relationship (in SDSM) between causal-target variables is needed to be switched with time-varying (TVDM). This study proves that the time-varying property in TVDM is more beneficial since its performance is better than SDSM and RCM outputs in identifying the extreme events during model calibration and testing periods. Thus, the TVDM is a better tool for assessing the extreme events.

     

统计降尺度方法对黄河上游流域气象要素模拟分析

将CMIP5模式的输出作为降尺度的输入来预估区域性气候的研究较少,本文使用CMIP5中精度较高的Can ESM2模式下的RCP4.5情景(中等温室气体排放)对黄河上游流域未来气象要素进行预估。利用黄河上游流域(上诠站以上)14个气象站点1967-2010年的逐月降水、气温和NCEP再分析资料,选取拟合度、均值相对误差、标准差相对误差作为评价指标,利用逐步回归算法筛选22个预报因子,建立了月资料序列的统计降尺度模型,并将模型应用于CMIP5中Can ESM2模式下RCP4.5情景,产生了未来气候要素的变化情景。结果表明:该模型对降水的模拟效果好于对气温的模拟。     

统计降尺度方法及其评价指标比较研究

针对目前气候变化对水资源影响研究中关注的问题,以汉江白河上游为研究对象,比较研究统计降尺度方法及其评价指标。以美国环境预报中心/美国国家大气研究中心全球再分析资料、CGCM3和HadCM3的A2情景为大尺度气候背景资料,应用SSVM和SDSM统计降尺度方法对大尺度气候因子进行尺度降解,得到降水情景序列后作为水文模型的输入,通过模拟径流比较分析统计降尺度方法的优劣。研究结果表明,由不同统计降尺度方法得到的降水作为水文模型输入,模拟径流的结果相差很大;对广泛应用于统计降尺度方法的降水模拟评价指标和径流模拟结果进行比较,发现所采用的降水评价指标侧重于考虑降水的统计分布特征,不能完整地描述降水过程特性。分析认为,径流模拟结果应该作为气候变化对径流影响研究中统计降尺度方法评价的重要参考。