Trend and persistence of precipitation under climate change scenarios for Kansabati basin,India     

Ashok K. Mishra  Mehmet Özger  Vijay P. Singh 《水文研究》2009,23(16):2345-2357

With increasing uncertainties associated with climate change, precipitation characteristics pattern are receiving much attention these days. This paper investigated the impact of climate change on precipitation in the Kansabati basin, India. Trend and persistence of projected precipitation based on annual, wet and dry periods were studied using global climate model (GCM) and scenario uncertainty. A downscaling method based on Bayesian neural network was applied to project precipitation generated from six GCMs using two scenarios (A2 and B2). The precipitation values for any of three time periods (dry, wet and annual) do not show significant increasing or decreasing trends during 2001–2050 time period. There is likely an increasing trend in precipitation for annual and wet periods during 2051–2100 based on A2 scenario and a decreasing trend in dry period precipitation based on B2 scenario. Persistence during dry period precipitation among stations varies drastically based on historical data with the highest persistence towards north‐west part of the basin. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

基于统计降尺度方法的夏季降水精细化预报     

黄惠镕  郭品文 《南京气象学院学报》2014,6(5):449-458

利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好.  相似文献   

A study on the impact of climate change on streamflow at the watershed scale in the humid tropics     

K. Y. Raneesh 《水文科学杂志》2013,58(6):946-965

Abstract

Climate change is recognized to be one of the most serious challenges facing mankind today. Driven by anthropogenic activities, it is known to be a direct threat to our food and water supplies and an indirect threat to world security. Increase in the concentration of carbon dioxide and other greenhouse gases in the atmosphere will certainly affect hydrological regimes. The consequent global warming is expected to have major implications on water resources management. The objective of this research is to present a general approach for evaluating the impacts of potential climate change on streamflow in a river basin in the humid tropical zone of India. Large-scale global climate models (GCMs) are the best available tools to provide estimates of the effect of rising greenhouse gases on rainfall and temperature. However the spatial resolution of these models (250 km?×?250 km) is not compatible with that of watershed hydrological models. Hence the outputs from GCMs have to be downscaled using regional climate models (RCMs), so as to project the output of a GCM to a finer resolution (50 km?×?50 km). In the present work, the projections of a GCM for two scenarios, A2 and B2 are downscaled by a RCM to project future climate in a watershed. Projections for two important climate variables, viz. rainfall and temperature are made. These are then used as inputs for a physically-based hydrological model, SWAT, in order to evaluate the effect of climate change on streamflow and vegetative growth in a humid tropical watershed.

Citation Raneesh, K. Y. & Santosh, G. T. (2011) A study on the impact of climate change on streamflow at the watershed scale in the humid tropics. Hydrol. Sci. J. 56(6), 946–965.  相似文献   

IPCCSRES情景下新疆未来气候变化格局分析     

李兰海  白磊  姚亚楠  杨青  赵鑫 《资源与生态学报(英文版)》2013,(1):27-35

全球气候模型（GCM）提供了有效的方法来评估全球气候变化的过程,并可预估包括人类活动因素驱动在内的未来气候变化情景。然而其较低的分辨率并不能捕捉到那些地表特性复杂区域的气候变化特性。因此,使用包括区域气候模型（RcM）、偏差校正法和统计方法等方法在内的降尺度方法来处理GCM的原始数据以达到评估区域的气候变化的目的。本研究应用使用偏差校正法中的delta方法将24个GCM在IPCC三种气候变化情景下的月尺度数据水平分辨率降尺度到0．5℃,进而用于分析新疆未来气候变化格局。基于降尺度后的计算结果与GCM模型原始数据比较表明：降尺度方法可以改善复杂地表和地形的区域气候变化预估特征,并降低GCM生成的气候数据在新疆地区的不确定性。结果表明：AIB、A2和B1三种情景模式下年均气温和年降水量在21世纪早期具有相似的空间格局与变化趋势,到21世纪中期会产生波动变化。年平均气温在A1B,A2和B1三种情景下到21世纪末将分别达到10℃,11．1℃和8．5℃;与此同时,年降水量将会有波动性的增加趋势。在2020—2070年间,AIB情景下区域年平均气温大于其他两个情景。A1B情景下的年降水量在2020-2040年间也大于其他两个情景。然而,在不同的情境下年平均气温与年降水存在很大的不确定性。不同情景下年平均气温的差异达6℃,而年平均降水差异大约200mm。在区域气候变化格局方面,到21世纪末,在天山中部、伊犁河流域、天山南部和塔里木河下游的年平均气温的增长要比准噶尔盆地、帕米尔高原和昆仑上北坡的小。年降水量在南疆西部呈现出轻微的下降趋势,但是在昌吉,吐鲁番,哈密和阿尔金山北部呈现出增长趋势。  相似文献   

A continuous simulation model for design-hydrograph estimation in small and ungauged watersheds     

S. Grimaldi 《水文科学杂志》2013,58(6):1035-1051

Abstract

A detailed investigation of the behaviour of various hydraulic parameters, using data from rivers in Greece, was conducted in order to explore the universality of features that many natural streams are believed to have in common. Analysis of vertical profiles of temporal mean of horizontal velocities (u) in the longitudinal (river flow) direction and of transverse profiles of depth-mean longitudinal velocities (U) estimated from these vertical profiles, measured at 232 cross-sections of several rivers in Greece, provided valuable information: on the distribution of local roughness coefficients (n_i ) along the wetted perimeter of the cross-sections examined; on the shape of u profiles; on the ratio of maximum to mean cross-sectional velocity, V_max/V_m , and its relation to a dimensionless entropy parameter, M; on the shape of U profiles; and on the normalized intensity, r, of the spatial departure of u velocities from V_m . The similarities among the quantities (u, U, n, V_max/V_m , M, r) analysed in this study and in pertinent literature reveal that the rivers examined exhibit many of the basic features, of rather universal character, shown by other rivers (all over the world) having different geometric and/or other characteristics (aspect ratios, bottom roughness, flow kinematics, etc.). Corresponding differences are also described and explained.  相似文献   

水文集合预报试验及其研究进展     

陆桂华  吴娟  吴志勇 《水科学进展》2012,23(5):728-734

水文集合预报是一种既可以给出确定性预报值,又能提供预报值的不确定性信息的概率预报方法。简述了水文集合预报试验(Hydrologic Ensemble Prediction Experiment,HEPEX)国际计划的主要研究内容,回顾了HEPEX研究进展,分析了对水文预报发展有重要意义的3个HEPEX前沿研究:降尺度研究、集合预报系统研究以及不确定性研究。研究表明,动力-统计降尺度法和高分辨率"单一"模式及低分辨率集合相结合是HEPEX未来研究的方向。  相似文献   

THE DOWNSCALING FORECASTING OF SEASONAL PRECIPITATION IN GUANGDONG BASED ON CLIMATE FORECAST SYSTEMS PRODUCTS     

李春晖  林爱兰  谷德军  王婷  潘蔚娟  郑彬 《热带气象学报(英文版)》2014,20(2):143-153

The Climate Forecast Systems （CFS） datasets provided by National Centers for Environmental Prediction （NCEP）, which cover the time from 1981 to 2008, can be used to forecast atmospheric circulation nine months ahead. Compared with the NCEP datasets, CFS datasets successfully simulate many major features of the Asian monsoon circulation systems and exhibit reasonably high skill in simulating and predicting ENSO events. Based on the CFS forecasting results, a downscaling method of Optimal Subset Regression （OSR） and mean generational function model of multiple variables are used to forecast seasonal precipitation in Guangdong. After statistical analysis tests, sea level pressure, wind and geopotential height field are made predictors. Although the results are unstable in some individual seasons, both the OSR and multivariate mean generational function model can provide good forecasting as operational tests score more than sixty points. CFS datasets are available and updated in real time, as compared with the NCEP dataset. The downscaling forecast method based on the CFS datasets can predict three seasons of seasonal precipitation in Guangdong, enriching traditional statistical methods. However, its forecasting stability needs to be improved.  相似文献   

An Approach for Improving Short-Term Prediction of Summer Rainfall over North China by Decomposing Interannual and Decadal Variability简   总被引：1，自引：0，他引：1  

HAN Leqiong  LI Shuanglin  and LIU Na 《大气科学进展》2014,31(2):435-448

A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs.Both predictands and predictors were first decomposed into interannual and decadal components.Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation.For the interannual timescale,850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models＇ hindcasts and SSTs from observational datasets were used to construct predictors.For the decadal timescale,two well-known basin-scale SST decadal oscillation （the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation） indices were used as predictors.Then,the downscaled predictands were combined to represent the predicted/hindcasted total rainfall.The prediction was compared with the models＇ raw hindcasts and those from a similar approach but without timescale decomposition.In comparison to hindcasts from individual models or their multi-model ensemble mean,the skill of the present scheme was found to be significantly higher,with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1.The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall,with the coefficients ranging from-0.1 to 0.87,obviously higher than the models＇ raw hindcasted rainfall results.Thus,the present approach exhibits a great advantage and may be appropriate for use in operational predictions.  相似文献   

Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors   总被引：1，自引：0，他引：1  

Mohammad Sajjad Khan  Paulin Coulibaly  Yonas Dibike 《水文研究》2006,20(14):3085-3104

Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

统计降尺度法对未来区域气候变化情景预估的研究进展   总被引：65，自引：5，他引：65  

范丽军  符淙斌  陈德亮 《地球科学进展》2005,20(3):320-329

由于迄今为止大部分的海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了3种常用的降尺度法：动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括：统计降尺度法的基本假设,统计降尺度法的优缺点，以及常用的3种统计降尺度法；还论述了用统计降尺度法预估未来气候情景的一般步骤，以及方差放大技术在统计降尺度中的应用；同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性；最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。  相似文献