Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach

Unlike parametric alternatives for time series generation, non-parametric approaches generate new values by conditionally resampling past observations using a probability rationale. Observations lying ‘close’ to the conditioning vector are resampled with higher probability, ‘closeness’ is defined using a Euclidean or Mahalanobis distance formulation. A common problem with these approaches is the difficulty in distinguishing the importance of each predictor in the estimation of the distance. As a consequence, the conditional probability and hence the resampled series, can offer a biased representation of the true population it aims to simulate. This paper presents a variation of the K-nearest neighbour resampler designed for use with multiple predictor variables. In the modification proposed, an influence weight is assigned to each predictor in the conditioning set with the aim of identifying nearest neighbours that represent the conditional dependence in an improved manner. The workability of the proposed modification is tested using synthetic data from known linear and non-linear models and its applicability is illustrated through an example where daily rainfall is downscaled over 15 stations near Sydney, Australia using a predictor set consisting of selected large-scale atmospheric circulation variables.     

Area-to-point Kriging with inequality-type data

In practical applications of area-to-point spatial interpolation, inequality constraints, such as non-negativity or more general constraints on the maximum and/or minimum attribute value, should be taken into account. The geostatistical framework proposed in this paper deals with the spatial interpolation problem of downscaling areal data under such constraints, while: (1) explicitly accounting for support differences between sample data and unknown values, (2) guaranteeing coherent (mass-preserving) predictions, and (3) providing a measure of reliability (uncertainty) for the resulting predictions. The formal equivalence between Kriging and spline interpolation allows solving constrained area-to-point interpolation problems via quadratic programming (QP) algorithms, after accounting for the support differences between various constraints involved in the problem formulation. In addition, if inequality constraints are enforced on the entire set of points discretizing the study domain, the numerical algorithms for QP problems are applied only to selected locations where the corresponding predictions violate such constraints. The application of the proposed method of area-to-point spatial interpolation with inequality constraints in one and two dimension is demonstrated using realistically simulated data.     

降尺度方法在中国不同区域夏季降水预测中的应用

在中国降水气候分区的基础上,利用降尺度方法进行区域夏季降水预测(RSPP),预测模型建立的基础是寻找影响区域气候的关键因子。降尺度预测模型中使用的资料有国家气候中心海-气耦合模式(CGCM/NCC)回报资料、NCEP/NCAR再分析资料和台站观测资料。为了避免年代际变化特征对季节尺度降水预测的影响,首先对CGCM/NCC模式输出资料、NCEP/NCAR再分析资料、区域平均降水资料去除年代际线性变化趋势,即去除所有预报因子场和预报对象场的长期变化趋势。然后分别计算预报对象和模式资料的预报因子场以及再分析资料的预报因子场的相关系数,把相关系数值同时达到0.05显著性检验水平的区域平均环流特征作为预测因子,保证挑选出的预测因子既能反映实际大气中预测因子与预报对象的关系,同时又是海-气耦合模式预测的高技巧信息。利用最优子集回归作为转换函数的降尺度方法建立区域夏季降水预测模型。交叉检验和独立样本检验结果表明,文中设计的区域夏季降水预测模型对中国大部分地区的夏季降水趋势预测的准确率较高且比较稳定,其预测效果远高于CGCM/NCC直接输出降水结果。进一步对具有较高预测技巧的代表性区域的可预报性来源分析发现,物理意义明确且独立性强的预测因子有助于提高预测准确率。     

基于海气耦合模式的山西省夏季降水统计降尺度预测研究

基于中国气象局国国家气候中心海气耦合模式(CGCM/NCC)预测产品和山西省50站夏季降水资料,利用典型因子回归的方法(CCA),建立了山西省夏季降水的统计降尺度预测模型。该预测模型选取了CGCM/NCC模式夏季500 h Pa高度场和海平面气压作为预测因子,分别选取了长江中下游地区和热带中东太平洋作为预报关键区。统计降尺度模型对2007~2014年山西省夏季降水的回算较模式原始结果有显著提高,除2008年外,空间距平相似系数(ACC)均通过了0.01的显著性检验,时间相关系数(TCC)在山西省大部分地区都有显著提高,最大可达0.6,降水预测(PS)评分在70分以上。检验结果显示,基于CCA降尺度方法建立的预测模型对山西省夏季降水模态预测的准确率较高且比较稳定,其预测效果远高于CGCM/NCC直接输出降水结果。     

A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling

Climate maps have been widely used for the construction of species distribution models. These maps derive from interpolation of data collected by meteorological stations. The sparse distribution of stations generates maps with coarse spatial resolution that are unable to detect microclimates or areas that can serve as plant or animal refuges. This work proposes a method for downscaling temperature maps using the solar radiation falling upon hillsides as predictor for the influence of relief on local variability. Solar irradiance is estimated from a digital elevation model of the study area using a routine based on analytical hillshading. Some examples of downscaling from 1 km to 25 m spatial resolution are shown. The results are compared with the surface temperature maps from Landsat 8 satellite imagery.     

Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China

Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5.     

Downscaling of precipitation for climate change scenarios: A support vector machine approach

The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.     

Namaqualand's climate: Recent historical changes and future scenarios

A brief outline of some issues concerning global climate change research is presented before discussing local-scale changes in Namaqaland's rainfall. Using a gridded data set derived through interpolation of station records, trends in observed rainfall for the period 1950–1999 are discussed. To assess what changes may occur during the 21st century, the downscaled results of six different General Circulation Model projections are presented. The historical trends show some clear spatial patterns, which depict regions of wetting in the central coastal belt and the north-eastern part of the domain, and extensive drying along the escarpment. Reasonably good agreement is shown by the different downscaled projections. These suggest increased late summer convective precipitation in the north-east, but extensive drying along the coast in early and mid winter consistent with the poleward retreat of rain-bearing mid-latitude cyclones.     

A method for predicting the impact of climate change on slope stability

 A major effect of man-induced climate change could be a generally higher frequency and magnitude of extreme climatological events in Europe. Consequently, the frequency of rainfall-triggered landslides could increase. However, assessment of the impact of climate change on landsliding is difficult, because on a regional scale, climate change will vary strongly, and even the sign of change can be opposite. Furthermore, different types of landslides are triggered by different mechanisms. A potential method for predicting climate change impact on landsliding is to link slope models to climate scenarios obtained through downscaling General Circulation Models (GCM). Methodologies, possibilities and problems are discussed, as well as some tentative results for a test site in South-East France. Received: 25 October 1997 · Accepted: 25 June 1997     

MRI-CGCM模式气候预测回报试验在东亚夏季的检验和降尺度订正

应用1979—2010年MRI-CGCM模式回报、NCEP/NCAR再分析数据和中国东部降水观测资料检验了模式对东亚夏季风的模拟能力,并利用模式500 hPa高度场回报资料建立了中国东部夏季降水的奇异值分解（SVD）降尺度模型。模式较好地模拟了亚洲季风区夏季降水的气候态,但模拟的季风环流偏弱、偏南,导致降水偏弱。模拟降水的方差明显偏小,且模拟降水的外部、内部方差比值低,模拟降水受模式初值影响较大。模式对长江雨型的模拟能力最高,华南雨型次之,华北雨型最低。模式对东亚夏季风第1模态的模拟能力明显高于第2模态。对于东亚夏季风第1模态,模式模拟出了西太平洋异常反气旋,但强度偏弱,且未模拟出中高纬度的日本海气旋、鄂霍次克海反气旋,导致长江中下游至日本南部降水偏弱。各时次模拟环流均能反映但低估了ENSO衰减、印度洋偏暖对西太平洋反气旋的增强作用。对于东亚夏季风第2模态,模式对西太平洋的“气旋-反气旋”结构有一定的模拟能力,但未模拟出贝加尔湖异常反气旋和东亚沿海异常气旋,导致中国东部“北少南多”雨型在模拟中完全遗漏。仅超前时间小于4个月的模拟降水能够反映ENSO发展对降水分布的作用。通过交叉检验选取左场时间系数可以提高降尺度模型的预测技巧,SVD降尺度模型在华南、江南、淮河、华北4个区域平均距平相关系数分别为0.20、0.23、0.18、0.02,明显高于模式直接输出。