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1.
汉江流域未来降水径流预测分析研究 总被引：7，自引：0，他引：7
本文应用统计降尺度法将全球气候模式和VIC分布式水文模型进行耦合,研究未来A2气候情景下汉江流域降水径流变化情况.首先应用基于光滑支持向量机的统计降尺度法在全球气候模式CGCM2和HadCM3的A2气候情景下,分别预测未来汉江流域日降水、气温过程,然后将预测降水过程作为VIC模型的输入,模拟预测未来汉江流域径流过程.研究结果表明,在CGCM2气候模式下,2020s(2011～2040年)时期汉江流域径流小于基准年,2050s(2041～2070年)时期与基准年基本相当,2080s(2071～2100年)时期大于基准年;在HadCM3气候模式下,2020s时期汉江流域径流小于基准年,2050s和2080s时期均比基准年增加;降水、气温预测结果与径流基本一致. 相似文献
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基于Budyko水热耦合平衡假设,推导了年径流变化的计算公式,分析了长江流域多年平均潜在蒸发量、降水量、干旱指数和敏感性参数的空间变化规律。选用BCC-CSM1-1全球气候模式和RCP4.5排放情景,把未来气候要素预估值与LS-SVM统计降尺度方法相耦合,预测长江流域未来的气温、降水和径流变化情况。采用乌江和汉江流域的长期径流观测资料,分析验证了基于Budyko公式计算年径流变化的可靠性。结果表明:降水量变化是影响径流量变化的主导因素;长江各子流域未来径流相对变化增减不一,最大变幅10%左右;在未来2020s(2010—2039年)、2050s(2040—2069年)和2080s(2070—2099年)3个时期内,长江南北两岸流域的径流将出现"南减北增"现象,北岸径流变化增幅逐渐升高,南岸径流变化减幅逐渐降低。 相似文献
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基于长江上游流域82个气象站点的实测数据和国际耦合模式比较计划第5阶段(Coupled Model Intercomparison Project phase 5,CMIP5)的2种排放情景下的8个气候模式1961～2099年的降水、气温数据,通过等距离累积分布函数法(Equidistance Cumulative Distribution Function Method,EDCDFm)进行气候模式的统计降尺度。在此基础上,构建0.5°×0.5°网格空间分辨率的可变下渗容量水文模型(Variable Infiltration Capacity,VIC),对历史流量进行模拟,并进一步模拟分析长江上游流域2006～2099年径流量、蒸散发的时空演变趋势。结果表明:VIC水文模型能够较好地模拟研究区的水文过程,从长江上游流域未来时期(2006～2099)主要水文过程变化趋势的预测来看,径流量变化趋势不明显、蒸散发呈增加趋势。此研究对于合理规划配置长江流域水资源及为气候影响评价和决策系统提供科技支撑具有重要意义。 相似文献
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利用政府间气候变化专门委员会第四次评估报告的22个新一代全球气候模式基准期(1961～1990年)模拟结果,从时空尺度分别讨论了与观测过程的差异,评估了模式对长江流域气温和降水的模拟性能。结果表明22个气候模式对长江流域具有一定的模拟能力,地面气温的模拟值都偏低,部分降水的模拟值局部偏高。不同的气候模式的模拟能力差异显著,大部分模式对长江流域的模拟精度有待进一步改进,只有少数几个模式（降水有6个模式,气温有5个模式）的年变化趋势与实况基本一致。综合比较,UKMO_HadCM3和NCAR_PCM两个模式基本能再现长江流域降水和气温的年变化特征。长江流域降水和气温未来情景预估表明各个模式和情景结果虽然存在差异,但对未来90年气候变化的模拟趋势基本一致,将持续增温、降水出现区域性增加,并着重讨论了UKMO_HadCM3模式在2020s（2010～2039年）、2050s（2040～2069年）和2080s（2070～2099年）3个时段的降水和气温时空变化特征,研究结果表明3个时段气温和降水在不同情景下都是逐渐增加的,A2情景下未来降水增幅最显著,B1情景增幅最小。 相似文献
5.
为评估IPCC第四次评估报告中的15个全球气候模式对碧流河水库流域气温和降水的模拟效果,通过LARS-WG降尺度方法,选取了HADCM3等3种气候模式,分析其在A2、A1B和B1三种排放情景下未来期(2011~2040年)碧流河水库流域气温和降水的变化,进而结合ABCD月尺度水文模型,预估未来气候变化下碧流河水库流域的径流变化特征,为流域水资源规划和管理提供依据。结果表明:CNCM3、HADCM3和IPCM4三个模式对碧流河水库流域模拟效果较好;与基准期相比,未来期多年平均降水变幅为-6.4%~3.7%,多年平均温度升高0.8℃~1.2℃,实际蒸发增幅为2.4%~4.4%;多年平均年径流量变化范围为4.8~6.2(108m3),三种排放情景下各模式平均径流量均呈减少趋势,较基准期减幅为-4.7%~-27.1%,未来水资源利用将会面临更大挑战。 相似文献
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预估喀斯特生态脆弱区的未来气候变化对于区域资源的合理开发利用及生态环境保护具有重要参考价值,而目前应用降尺度方法模拟喀斯特地区的未来气候情景仍存在较大的探讨空间。本文依据珠江流域红柳江区13个气象站1961-2001年的实测日气温、日降水量资料和全球大气NCEP再分析资料,采用SDSM模型预测流域在HadCM3模式SRES A2和B2两种排放情景下未来年份气温和降水的变化趋势。结果表明:（1）SDSM模型可以较为准确地模拟研究区的气温和降水变化,确定性系数分别可达99%和65%左右;（2）A2、B2两种情景下,21世纪气温和降水均表现出明显的上升趋势,且随时间推移增幅逐渐增大。截至21世纪末,A2、B2两种情景下的年平均气温变化分别为+3.39 ℃和+2.49 ℃,日均降水将分别增加117.30 %和80.90 %;（3）未来的气温上升以秋季和春季变化最为明显,降水则表现为夏季降水增幅最大。分析成果可为喀斯特区的气候变化影响评价与应对决策提供数据基础和理论依据。 相似文献
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选择长江中下游单季中稻为研究对象,结合45个气象站1961~2010年逐日气象资料,基于统计降尺度模型(SDSM),生成HadCM3气候模式A2和B2两种情景下各站点参考作物腾发量和降水数据。基于联合国粮食及农业组织(FAO)推荐的作物系数法,并考虑有效性降雨和不同地区深层渗漏量,分析历史和未来的水稻灌溉需水时空变化特征。结果表明:过去50年,除了太湖流域以外的长江中下游大部分区域的参考作物腾发量和水稻需水量都呈显著下降趋势,而显著下降的水稻灌溉需水量主要位于鄱阳湖流域;未来两种情景下,参考作物腾发量、水稻需水量和水稻灌溉需水量均值都呈下降趋势,但水稻灌溉需水量降幅最小;水稻需水量和水稻灌溉需水量在长江中下游地区的变化趋势具有明显的空间异质性,水稻需水量大幅减少的区域由太湖流域向汉江和洞庭湖流域扩展。未来水稻灌溉需水量减少的区域主要分布在太湖流域、汉江流域东部和洞庭湖流域北部,并随时间推移呈扩大趋势。 相似文献
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借助于全球气候模式(德国MPI ECHAM5.0)输出信息和流域最近40年的气象观测资料,建立青海湖流域统计降尺度模式(QH-SDM),从而得到流域尺度未来30年(2010-2030年)气候变化情景,并由此驱动水文模型SWAT及湖泊水量平衡模型模拟了青海湖近几十年水位变化过程,预估了未来30年青海湖湖泊水文变化情景。结果表明,青海湖水位的未来变化将经历缓慢下降、逐渐回升、稳步升高3个阶段,到2030年,湖泊水位将达到3195.4 m左右,高出目前水位约2.2 m,面积接近4500 km2,蓄水量达到813亿m3,湖泊恢复到了20世纪70年代初的水平,预计这一结果将会缓解目前青海湖流域水资源紧缺的格局,并有利于植被恢复,减少土地沙化面积,对流域生态环境的改善和国民经济的发展将十分有益。 相似文献
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受全球气候变化影响,澜沧江-湄公河流域气象水文干旱发生了较大变化,预测未来流域干旱的时空变化与传播特征是应对气候变化、开展澜湄水资源合作的基础。利用SWAT模型通过气陆耦合方式模拟了澜沧江-湄公河流域历史（1960—2005年）和未来时期（2022—2050年,2051—2080年）的水文过程,采用标准化降水指数和标准化径流指数预估并分析了流域未来气象水文干旱时空变化趋势。结果表明:①澜沧江-湄公河流域未来降水呈增长趋势,气象干旱将有所缓解,但降水年内分配不均与流域蒸发的增加,将导致水文干旱更为严峻,干旱从气象到水文的传播过程加剧;②水文干旱具有明显的空间异质性,允景洪和清盛站的水文干旱最为严重,琅勃拉邦、穆达汉和巴色站次之,万象站最弱;③未来流域水文干旱事件发生频次略有减少,但其中重旱、特旱事件占比增加,极端干旱将趋多趋强,且空间变化更加显著。 相似文献
11.
Weili Duan Bin He Kaoru Takara Pingping Luo Daniel Nover Maochuan Hu 《Environmental Earth Sciences》2017,76(14):490
Evidence for climate change impacts on the hydro-climatology of Japan is plentiful. The objective of the present study was to evaluate the impacts of possible future climate change scenarios on the hydro-climatology of the upper Ishikari River basin, Hokkaido, Japan. The Soil and Water Assessment Tool was set up, calibrated, and validated for the hydrological modeling of the study area. The Statistical DownScaling Model version 4.2 was used to downscale the large-scale Hadley Centre Climate Model 3 Global Circulation Model A2 and B2 scenarios data into finer scale resolution. After model calibration and testing of the downscaling procedure, the SDSM-downscaled climate outputs were used as an input to run the calibrated SWAT model for the three future periods: 2030s (2020–2039), 2060s (2050–2069), and 2090s (2080–2099). The period 1981–2000 was taken as the baseline period against which comparison was made. Results showed that the average annual maximum temperature might increase by 1.80 and 2.01, 3.41 and 3.12, and 5.69 and 3.76 °C, the average annual minimum temperature might increase by 1.41 and 1.49, 2.60 and 2.34, and 4.20 and 2.93 °C, and the average annual precipitation might decrease by 5.78 and 8.08, 10.18 and 12.89, and 17.92 and 11.23% in 2030s, 2060s, and 2090s for A2a and B2a emission scenarios, respectively. The annual mean streamflow may increase for the all three future periods except the 2090s under the A2a scenario. Among them, the largest increase is possibly observed in the 2030s for A2a scenario, up to approximately 7.56%. Uncertainties were found within the GCM, the downscaling method, and the hydrological model itself, which were probably enlarged because only one single GCM (HaDCM3) was used in this study. 相似文献
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Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011–2040, 2041–2070, and 2071–2099, at large scale. Rainfall erosivity (R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data – IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility (K) factor map of the watershed. Topographic factors, slope length (L) and steepness (S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985–2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers. 相似文献
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Simulating climate change impact on soil carbon sequestration in agro-ecosystem of mid-Himalayan landscape using CENTURY model 总被引：1，自引：0，他引：1
Soils play significant roles in global carbon cycle. The increase in atmospheric CO2 due to climate change may have a significant impact on both soil organic carbon storage and management practices to sequester organic carbon in agricultural areas. The aim of the study was to simulate climate change impact on soil carbon sequestration using CENTURY model. The statistical downscaling model (SDSM) was used to downscale the climate variables (temperature and rainfall) under two scenarios A2 and B2 for three periods: 2020 (2011–2040), 2050 (2041–2070) and 2080 (2071–2099). Downscaling was better in case of temperature than precipitation, which was evident from coefficient of correlation for temperature (r 2 = 0.91–0.99) and precipitation (r 2 = 0.71–0.80). Downscaling of climate data revealed that the temperature may increase for the years 2020, 2050 and 2080 periods, whereas precipitation may increase till 2020 and then it may reduce in 2050 and 2080 as compared to 2020 in the study area. For CENTURY model, the input parameters were obtained through soil sampling and interviewing the farmers as well, whereas the climatic variables (maximum temperature, minimum temperature and precipitation) were taken from the SDSM output. The historical data of soils were collected from the literature, and six agricultural sites were selected for estimating soil carbon sequestration. After soil sampling of the same sites, it was found that the organic carbon had increased two times than historical data might be due to the addition of high organic matter in the form of farm yard manure. Therefore, the model was calibrated, considering more organic carbon in the area, and was validated using random points in the study area. Determination coefficient (r 2 = 0.95) and RMSE (538 g c/m2) were computed to assess the accuracy of the model. The organic carbon was predicted from 2011 to 2099 and was compared with the 2011 predicted data. The study revealed that the amount of soil organic carbon in Bhaitan, Kanatal, Kotdwar, Malas, Pata and Thangdhar sites may reduce by 11.6, 15.8, 17.19, 13.54, 19.2 and 12.7%, respectively, for A2 scenario and by 9.62, 15.6, 15.72, 11.45, 16.96 and 13.36% for B2 scenario up to 2099. The study provides comprehensive possible future scenarios of soil carbon sequestration in the mid-Himalaya for scientists and policy makers. 相似文献
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A dynamical downscaling approach using a regional climate model WRF (Weather Research and Forecasting Model Vision 3.5) driven by a global climate model CCSM4 (The Community Climate System Model Version 4) was adopted, and the downscaling results for the historical period (1982-2005) were evaluated for annual mean precipitation rate and evaporation rate over the Tibetan Plateau (TP). Furthermore, the spatial distribution and seasonal variation characteristics of Precipitation Recycling Ratio (PRR) simulated by CCSM4 and WRF were analyzed with the QIBT (Quasi-isentropic Back-trajectory method). The results show that the historical spatial distributions of annual mean precipitation rate and evaporation rate over the TP were found to better reproduce in the dynamical downscaling modeling compared to its coarse-resolution forcing. The PRR of the TP is 32% simulated by WRF, with a higher PRR in the wet season and a lower PRR in the dry season for the river basins in the northern TP, but the opposite seasonal variation was found for the river basins in the southern TP. In addition, the different land covers over the TP are more precisely represented in the WRF model, the PRR of grassland, shrubland and sparsely vegetation is higher than that of other land cover types. 相似文献
15.
A.V. Karmalkar M.A. Taylor J. Campbell T. Stephenson M. New A. Centella A. Benzanilla J. Charlery 《Atmósfera》2013,26(2):283-309
Observed and projected changes in climate have serious socio-economic implications for the Caribbean islands. This article attempts to present basic climate change information—based on previous studies, available observations and climate model simulations—at spatial scales relevant for islands in the Caribbean. We use the General Circulation Model (GCM) data included in the Coupled Model Intercomparison Project phase 3 (CMIP3) and the UK Hadley Centre regional climate model (RCM) data to provide both present-day and scenario-based future information on precipitation and temperature for individual island states. Gridded station observations and satellite data are used to study 20th century climate and to assess the performance of climate models. With main focus on precipitation, we also discuss factors such as sea surface temperature, sea level pressure and winds that affect seasonal variations in precipitation. The CMIP3 ensemble mean and the RCM successfully capture the large-scale atmospheric circulation features in the region, but show difficulty in capturing the characteristic bimodal seasonal cycle of precipitation. Future drying during the wet season in this region under climate change scenarios has been noted in previous studies, but the magnitude of change is highly uncertain in both GCM and RCM simulations. The projected decrease is more prominent in the early wet season erasing the mid-summer drought feature in the western Caribbean. The RCM simulations show improvements over the GCM mainly due to better representation of landmass, but its performance is critically dependent on the driving GCM. This study highlights the need for high-resolution observations and ensemble of climate model simulations to fully understand climate change and its impacts on small islands in the Caribbean. 相似文献
16.
Assessing climate change impacts on water balance in the Mount Makiling forest, Philippines 总被引：1，自引：0，他引：1
A statistical downscaling known for producing station-scale climate information from GCM output was preferred to evaluate the impacts of climate change within the Mount Makiling forest watershed, Philippines. The lumped hydrologic BROOK90 model was utilized for the water balance assessment of climate change impacts based on two scenarios (A1B and A2) from CGCM3 experiment. The annual precipitation change was estimated to be 0.1–9.3% increase for A1B scenario, and ?3.3 to 3.3% decrease/increase for the A2 scenario. Difference in the mean temperature between the present and the 2080s were predicted to be 0.6–2.2°C and 0.6–3.0°C under A1B and A2 scenarios, respectively. The water balance showed that 42% of precipitation is converted into evaporation, 48% into streamflow, and 10% into deep seepage loss. The impacts of climate change on water balance reflected dramatic fluctuations in hydrologic events leading to high evaporation losses, and decrease in streamflow, while groundwater flow appeared unaffected. A study on the changes in monthly water balance provided insights into the hydrologic changes within the forest watershed system which can be used in mitigating the effects of climate change. 相似文献
17.
Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century. 相似文献
18.
Hassan Khosravi Ali Azareh Hadi Eskandari Dameneh Elham Rafiei Sardoii Hamed Eskandari Dameneh 《Arabian Journal of Geosciences》2017,10(4):93
The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively. 相似文献