Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine

In this paper, downscaling models are developed using a support vector machine (SVM) for obtaining projections of monthly mean maximum and minimum temperatures (T-max and T-min) to river-basin scale. The effectiveness of the model is demonstrated through application to downscale the predictands for the catchment of the Malaprabha reservoir in India, which is considered to be a climatically sensitive region. The probable predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1978-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. The predictor variables are classified into three groups, namely A, B and C. Large-scale atmospheric variables Such as air temperature, zonal and meridional wind velocities at 925 nib which are often used for downscaling temperature are considered as predictors in Group A. Surface flux variables such as latent heat (LH), sensible heat, shortwave radiation and longwave radiation fluxes, which control temperature of the Earth's surface are tried as plausible predictors in Group B. Group C comprises of all the predictor variables in both the Groups A and B. The scatter plots and cross-correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to Study the predictor-predictand relationships. The impact of trend in predictor variables on downscaled temperature was studied. The predictor, air temperature at 925 mb showed an increasing trend, while the rest of the predictors showed no trend. The performance of the SVM models that are developed, one for each combination of predictor group, predictand, calibration period and location-based stratification (land, land and ocean) of climate variables, was evaluated. In general, the models which use predictor variables pertaining to land surface improved the performance of SVM models for downscaling T-max and T-min

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha River Basin, India

A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely: Case 1: SR is measured, Case 2: SR is measured but sparse and Case 3: SR is not measured. In Case 1, future SR scenarios are derived using several downscaling methodologies that transfer the simulated large-scale information of global climate models to a local scale ( measurements). In Case 2, the SR was first estimated at the local scale for a longer time period using sparse measured records, and then future scenarios were derived using several downscaling methodologies. In Case 3: the SR was first estimated at a regional scale for a longer time period using complete or sparse measured records of SR from which SR at the local scale was estimated. Finally, the future scenarios were derived using several downscaling methodologies. The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies. Hence, this was further elaborated by applying the Case 3 methodology to a semi-arid Malaprabha reservoir catchment in southern India. A support vector machine was used in downscaling SR. Future monthly scenarios of SR were estimated from simulations of third-generation Canadian General Circulation Model (CGCM3) for various SRES emission scenarios (A1B, A2, B1, and COMMIT). Results indicated a projected decrease of 0.4 to 12.2 W m(-2) yr(-1) in SR during the period 2001-2100 across the 4 scenarios. SR was calculated using the modified Hargreaves method. The decreasing trends for the future were in agreement with the simulations of SR from the CGCM3 model directly obtained for the 4 scenarios.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

东北沼泽湿地的潜在分布对气候变化的响应

东北是我国沼泽分布最广泛、类型最多的地区，而该地区也是中国将来气温变化幅度较大的地区，气候趋于暖干化，这些都不利于沼泽的发育和存在。据CGCM3气候变化模型预测：到2100年，温室气体排放浓度增高（排放水平720ppm、大于720ppm和550ppm）的三种排放情景下，气温分别增高3.22℃、4.36℃和2.13℃，年降水量分别平均增长102mm、127mm和74mm，干燥度增大，变化的幅度和排放浓度极为一致。本文将Logistic模型结合CGCM3气候变化数据，以预测未来100年后沼泽湿地的潜在分布。 由于沼泽分布具有地带性和非地带性规律的特点，本文针对整个东北地区、东北山地和东北平原建立了三个Logistic模型，环境因子包括11种地形因子和7种气候因子。三个模型的ROC值分别为0.86、0.92 和0.76，这说明山地区模型的精度最高，平原区精度最低。概率阈值基于ROC曲线设定为0.23、0.24 和0.26。结合CGCM3，预测结果显示：100年后，沼泽分布都趋于减少，尤其在平原地区，沼泽可能会全部消失。在COMMIT模式下，虽然CO2浓度保持不变，但是气候变化造成的后果依然持续进行，平原地区沼泽大量消失，沼泽潜在分布面积将减少34.11%；在SRES B1情景下，沼泽潜在分布面积减少66.46%，南部平原和山地沼泽消失；SRES A1B情景下，沼泽潜在分布面积减少80.11%，松嫩平原、松辽平原、长白山、大兴安岭南部地区沼泽消失，三江平原和小兴安岭地区只有零星存在；SRES A2情景下，沼泽潜在分布面积减少了87.25%，只分布在大兴安岭北部和小兴安岭西部的沟谷地带，其它各地几乎全部消失。通过GIS手段计算沼泽潜在分布与环境因子的相关系数，在东北区域和山地区，影响最大的地形因子和气候因子分别是坡位和寒冷指数；在平原区，影响最大的地形因子和气候因子分别是与河流距离和温暖指数。 MODIS数据是近年来常用的一种适用于宏观区域的遥感数据源。本文利用Logistic模型，多时相数据配合地形辅助数据，对大兴安岭北部地区的沼泽进行提取，分类精度84.63%。利用该数据进行沼泽分布模拟，能取得更高的精度（ROC值为0.957）。模拟结果表明：CO2浓度增高的三种排放情景下，沼泽的潜在分布面积分别减少54.16%、59.62%和73.51%。沼泽分布由南向北、由两侧向中心萎缩，且分布趋于破碎化。

东北多年冻土对气候变化的响应

近地表面多年冻土对寒区生态系统的植被覆盖、水文条件、土地利用和工程建设具有重要影响，随着气候变化研究的广泛开展，区域冻土环境的变化也成为学者关心的重要议题。中国东北的多年冻土处于欧亚大陆多年冻土带的南缘，多年冻土不如以北地区发育，是十分脆弱的多年冻土。然而，多年冻土在东北寒区生态系统中却起着重要的作用。若东北多年冻土发生退缩，则有可能加速落叶松北移和湿地退缩的过程，也会对C的释放产生重要影响。因而探明现实气候条件下东北区域多年冻土的影响因子和发育状况以及未来气候条件下多年冻土的退缩趋势，将有助于促进东北寒区生态系统的冻土和其它学科研究，同时也可为寒区开发建设提供有意义的参考。 本研究从分析东北多年冻土的主要影响因子——气候、地形和土壤条件等入手，准确地掌握了多年冻土的发育状况，并以此为根据进行了景观尺度上多年冻土分布信息的提取和融深信息的研究。同时，在区域尺度上对多年冻土的现实分布和未来气候条件下多年冻土的可能分布状况进行了探讨。最终得到以下重要结论： （1）冻结数对东北多年冻土分布具有重要的指示作用 冻结数模型具有明确的物理意义，可以指示多年冻土的发生状况。研究中，利用地形、纬度等因子，结合气温和降水数据模拟了现实气候条件下东北地区的冻结数值；并依据冻结数模型的区划标准对东北多年冻土进行分区。结果表明，冻结数在指示多年冻土分布时具有重要作用。 （2）土壤含水量、地形坡度和群落因子对多年冻土具有重要影响 以大兴安岭呼中国家级自然保护区为例，调查了该区多年冻土活动层厚度，并利用多重对比分析和相关分析的统计方法，对多年冻土活动层的影响因子进行了分析。结果表明，多年冻土活动层厚度与多个环境因子之间存在着复杂的关系。其中，土壤表层含水量与活动层厚度具有极显著的负相关关系（P<0.001），其相关系数在0.90以上，说明含水量越高，活动层厚度越浅。地形坡度和活动层厚度的相关性也达到显著水平（相关系数为0.321，P=0.006），表明坡度越陡，活动层厚度越大。几乎每个样带的海拔与活动层厚度都有显著的相关性，但在整体研究区域内海拔与活动层厚度不存在相关性。这说明活动层厚度的变异仅在本研究的样带尺度上具有规律性，而在稍大尺度上这种规律性就消失了。对于不同的群落活动层厚度的多重对比分析表明，群落的差异对活动层厚度也有明显的影响，其中狭叶杜香-泥炭藓群落（Larix gmelini-Ledum palustre var. anqustum-sphagnum magellanicum）更有利于多年冻土的保存。 （3）景观尺度上的多年冻土分布状况 在景观尺度上，以呼中国家级自然保护区为研究区，应用神经网络方法，同时以土地覆盖、等效纬度、坡向和土壤湿度多种影响因子为数据源，对多年冻土分布信息进行提取。结果表明，考虑土地覆盖、等效纬度和土壤湿度的数据源组合可以获得高精度最高的多年冻土分布信息，分类精度可以达到89.0%，多年冻土面积占研究区面积百分比达到46.71%，为780.1 km2。 （4）景观尺度上多年冻土的融深状况 研究考虑了包括植被和等效纬度两个影响活动层厚度的重要因子，并将Stefan公式进行变形，简化为包含热量条件的等效纬度因子和植被条件的C因子的函数关系。最后应用该函数关系模拟了呼中自然保护区活动层厚度空间分布，模拟结果的精度为87.25%。在模拟结果中，面积和所占比例最大的活动层厚度为70-80 cm间的活动层厚度，所占面积达到341.4 km2 ，占整个研究区面积的20.43%。而面积最小的活动层厚度为30-40 cm间的活动层厚度，面积为0.02 km2 。通过群落与活动层厚度的空间分布对比发现，呼中自然保护区占最大比例的活动层（70-80 cm）所对应的植物群落主要为落叶松-丛桦-笃斯-藓类群落（Larix gmelini-Betula ovalifolia-Vaccini uliginosum-moss）。说明呼中自然保护区冻土湿地植被主要以该群落类型为主，演替处于中间阶段。 （5）区域尺度上多年冻土的分布状况 利用证据权重法，以可能影响多年冻土分布的气候、地形和土壤等因子作为数据源，对研究区在现实气候条件下的多年冻土分布进行预测，获得了多年冻土在现实气候条件下的分布概率等信息。结果表明，当分布概率大于0.17时，划分出的多年冻土的精度最高，为78.71 %。此时，多年冻土面积为2.03×104 km2 ，约占研究区总面积的1.76%。 （6）东北多年冻土分布对气候变化的响应 利用空间代时间的方法和Kappa指数，对证据权重法在预测未来气候变化条件下多年冻土分布的准确性进行了验证，结果表明，证据权重法预测气候变化条件下多年冻土的分布状况是可行的。 在CGCM3模拟的三种气候模式下，多年冻土在2050年和2100年都将发生明显的退缩。2050年，SRES A1、SRES A2和SRES B1三种气候情景下多年冻土的面积分别为786.38 km2，705.94 km2和1 028.81 km2。与现实气候下多年冻土的面积2.03×104 km2相比，多年冻土分别退缩了96.13%，96.53%和94.94%。而2100年的模拟结果表明，三种气候情景模式下，多年冻土已经全部退化。 （7）气候变化条件下东北多年冻土的分区变化 研究将冻结数等值图与2000年中国东北冻土分区图进行叠加，计算了不同多年冻土亚区的边界对应的冻结数值，建立了利用冻结数进行中国东北多年冻土分区的标准。根据冻结数指标确定的新的中国东北冻土分区与原中国东北冻土分区进行Kappa指数认证。结果表明，冻结数分区标准更适用于中国东北多年冻土的区划。 利用新的冻结数分区标准对CGCM3模拟的三种气候情景模式下的气候变化数据进行区划表明，三种气候模式下东北多年冻土区在21世纪都会有非常明显的退缩。2050年时冻土区缩减了37.7%-42.6%，2100年时缩减了62.5%-74.0%。同时，研究结果显示，东北多年冻土区域的退缩不仅发生在多年冻土区的南界，同时多年冻土的中心退缩也较为明显，即大片连续多年冻土亚区和大片连续—岛状多年冻土亚区的退缩最为剧烈。2050年时，三种气候情景下，大片连续多年冻土亚区将退缩88.8%以上；2100年时，SRES A2模式下，大片连续多年冻土亚区将完全消失。

气候变化对大兴安岭北部沼泽景观格局的影响

大兴安岭北部地区由于在高纬、高海拔双重因素作用下,形成了独特的山地沼泽类型。首先使用Logistic回归模型分析了沼泽湿地与18种环境因子(11种地形因子和7种气候因子)的关系,结合CGCM3未来气候模型(加拿大气候模拟和分析中心推出的第三代全球气候耦合模型)预测未来分布,然后利用Fragstats软件计算景观格局的变化。所建模型具有很高的预测精度(ROC为0.96),预测结果表明:到2100年,SRESB1情景下,大兴安岭北部沼泽的潜在分布面积减少54.16%,南部相对平坦的丘陵和山间平原的沼泽大量消失;SRESA1B情景下,面积减少59.62%,南部林区的沼泽几乎全部消失;SRESA2情景下,面积减少73.51%,沼泽几乎完全退化到北部海拔较高处。另外,沼泽景观格局随气候变暖,平均斑块面积变小,形状指数减小,聚集度减少但幅度不大,这说明沼泽分布趋于破碎化,斑块的形状趋向于简单化,沼泽分布由边缘向中心收缩。

Impacts potentiels d’un changement climatique sur le pergélisol dans le nord canadien

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IPCC global coupled model simulations of the South America monsoon system

This study examines the variability of the South America monsoon system (SAMS) over tropical South America (SA). The onset, end, and total rainfall during the summer monsoon are investigated using precipitation pentad estimates from the global precipitation climatology project (GPCP) 1979-2006. Likewise, the variability of SAMS characteristics is examined in ten Intergovernmental Panel on Climate Change (IPCC) global coupled climate models in the twentieth century (1981-2000) and in a future scenario of global change (A1B) (2081-2100). It is shown that most IPCC models misrepresent the intertropical convergence zone and therefore do not capture the actual annual cycle of precipitation over the Amazon and northwest SA. Most models can correctly represent the spatiotemporal variability of the annual cycle of precipitation in central and eastern Brazil such as the correct phase of dry and wet seasons, onset dates, duration of rainy season and total accumulated precipitation during the summer monsoon for the twentieth century runs. Nevertheless, poor representation of the total monsoonal precipitation over the Amazon and northeast Brazil is observed in a large majority of the models. Overall, MI-ROC3.2-hires, MIROC3.2-medres and MRI-CGCM3.2.3 show the most realistic representation of SAMS`s characteristics such as onset, duration, total monsoonal precipitation, and its interannual variability. On the other hand, ECHAM5, GFDL-CM2.0 and GFDL-CM2.1 have the least realistic representation of the same characteristics. For the A1B scenario the most coherent feature observed in the IPCC models is a reduction in precipitation over central-eastern Brazil during the summer monsoon, comparatively with the present climate. The IPCC models do not indicate statistically significant changes in SAMS onset and demise dates for the same scenario.

A Spatial-Temporal Downscaling Approach To Construction Of Rainfall Intensity-Duration-Frequency Relations In The Context Of Climate Change

This paper proposes a spatial-temporal downscaling approach to construction of the intensity-duration-frequency (IDF) relations at a local site in the context of climate change and variability. More specifically, the proposed approach is based on a combination of a spatial downscaling method to link large-scale climate variables given by General Circulation Model (GCM) simulations with daily extreme precipitations at a site and a temporal downscaling procedure to describe the relationships between daily and sub-daily extreme precipitations based on the scaling General Extreme Value (GEV) distribution. The feasibility and accuracy of the suggested method were assessed using rainfall data available at eight stations in Quebec (Canada) for the 1961-2000 period and climate simulations under four different climate change scenarios provided by the Canadian (CGCM3) and UK (HadCM3) GCM models. Results of this application have indicated that it is feasible to link sub-daily extreme rainfalls at a local site with large-scale GCM-based daily climate predictors for the construction of the IDF relations for present (1961-1990) and future (2020s, 2050s, and 2080s) periods at a given site under different climate change scenarios. In addition, it was found that annual maximum rainfalls downscaled from the HadCM3 displayed a smaller change in the future, while those values estimated from the CGCM3 indicated a large increasing trend for future periods. This result has demonstrated the presence of high uncertainty in climate simulations provided by different GCMs. In summary, the proposed spatial-temporal downscaling method provided an essential tool for the estimation of extreme rainfalls that are required for various climate-related impact assessment studies for a given region.