Future changes in rainfall,temperature and reference evapotranspiration in the central India by least square support vector machine

Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotranspiration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ET₀ is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ET₀ have been shown by downscaling the HadCM3 (Hadley Centre Coupled Model version 3) model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ET₀ and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine (LS-SVM) model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ET₀. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081–2091 and 2091–2099 in maximum temperature and 2091–2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ET₀ in some decades. Two peaks of the increase are observed in ET₀ in the April–May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.     

Simulated changes due to global warming in the variability of precipitation, and their interpretation using a gamma-distributed stochastic model

The interannual variability of monthly mean January and July precipitation and its possible change due to global warming are assessed using a five-member ensemble of climate for the period 1871–2100, simulated by the CSIRO Mark 2 global coupled atmosphere–ocean model. In the 1961–1990 climate, for much of the middle to high latitudes the standard deviation of precipitation for both months is roughly proportional to the mean, with the coefficient of variation (C) typically 0.3–0.5. The variability there is shown to be largely consistent with that from a first-order Markov chain model of the daily rainfall occurrence, with the distribution of wet-day amounts approximated by a gamma distribution. Global distributions of Mark 2-based parameters of this stochastic model, commonly used in weather generators, are presented. In low latitudes, however, the variability from the coupled model is typically double that anticipated by the stochastic model, as quantified by an ‘overdispersion ratio’. C often exceeds one at subtropical locations, where rain is less frequent, but sometimes relatively heavy.The standard deviation of monthly mean precipitation S generally increases as the global model warms, with the global mean S in 2071–2100 in January (July) being 9.0% (11.5%) larger than in 1961–1990. Decreases in some subtropical locations occur, particularly where mean precipitation decreases. The global pattern of overdispersion is largely unchanged, however, and the changes in S can be related to those in the stochastic model parameters. Much of the increase in S is associated with increases in the scale parameter of the gamma distribution of wet-day amounts. Changes in C, which is unaffected by this parameter, are generally small. Increases in C in several subtropical bands and over northern midlatitude land in July are related to a decreased frequency of precipitation, and (to a lesser degree) changes in the gamma shape parameter. Some potential applications of the results to downscaling are discussed, and illustrated using observed rainfall from southeast Australia.     

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.     

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.     

Study on Precipitation Image Downscaling Based on the Method of Ill-posed Problems Solving

Downscaling of remote sensing precipitation products and the forecasting of circulation model are always the intense interests in hydrology and meteorology. The essence of downscaling is primarily to enhance resolution of observation or simulated rainfall field, and to appropriately increase its details or high frequency characteristics. Precipitation, as the main driving factors of the earth’s hydrologic cycle, not only affects the moisture and heat condition of a certain river basin, but also affects the global water and heat circulation. Based on the properties of rainfall self-similarity structure, the mathematically ill-posed precipitation problem solving method was used in low resolution downscaling precipitation for high resolution reconstruction. When solving the downscaling ill-posed problem, the greedy method of orthogonal matching pursuit was introduced so as to get the best high-resolution estimation in an optimal sense. It is hard to imagine that we might be able to find very similar (in mathematical norms) precipitation patterns over relatively large storm-scales. However, finding similar features over sufficiently small sub-storm scales seems more feasible. Based on the characteristics that small scale organized precipitation features tend to recur across different storm environments, the precipitation of both high and low resolution was obtained by training, which could be used to reconstruct the desired high-resolution precipitation field. Multi-source merged precipitation products were used in this experiment. Given the consideration of incompleteness of merged precipitation dataset, it was firstly interpolated based on the method of Fields of Experts (FoEs), which could solve the problem that common interpolation method could hardly work on the interpolation for dataset where consecutive missing data exists. Secondly, ideal experiments of precipitation products downscaling were carried out, where smooth coupling sampling and resampling operator were adopted respectively. Assessment based on the metrics of fidelity and spatial structural similarity demonstrates that the method used in this paper is feasible.     

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.     

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 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     

Stochastic downscaling method: application to wind refinement

In this article, we propose a new stochastic downscaling method: provided a numerical prediction of wind at large scale, we aim to improve the approximation at small scales thanks to a local stochastic model. We first recall the framework of a Lagrangian stochastic model borrowed from Pope. Then, we adapt it to our meteorological framework, both from the theoretical and numerical viewpoints. Finally, we present some promising numerical results corresponding to the simulation of wind over the Mediterranean Sea.