Uncertainty Modeling of Statistical Downscaling to Assess Climate Change Impacts on Temperature and Precipitation

Consideration of different Statistical Downscaling (SD) models and multi-sources global climate models’ (GCMs) data can provide a better range of uncertainty for climatic and statistical indices. In this study, results of two SD models, ASD (Automated Statistical Downscaling) and SDSM (Statistical Downscaling Model), were used for uncertainty analysis of temperature and precipitation prediction under climate change impacts for two meteorological stations in Iran. Uncertainty analysis was performed based on application of two GCMs and climate scenarios (A2, A1B, A2a and B2a) for 2011–2040, 2041–2070 and 2071–2100 future time slices. A new technique based on fuzzy logic was proposed and only used to describe uncertainties associated with downscaling methods in temperature and precipitation predictions. In this technique, different membership functions were defined to fuzzify results. Based on these functions width, precipitation had higher uncertainty in comparison with the temperature which could be attributed to the complexity of temporal and local distribution of rainfall. Moreover, little width of membership functions for temperatures in both stations indicated less uncertainty in cold months, whereas the results showed more uncertainty for summer. The results of this study highlight the significance of incorporating uncertainty associated with two downscaling approaches and outputs of GCMs (CGCM3 and HadCM3) under emission scenarios A2, A1B, A2a and B2a in hydrologic modeling and future predictions.     

Statistical Downscaling of River Runoff in a Semi Arid Catchment

Linear and non-linear statistical ‘downscaling’ study is applied to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in west Iran. This study aims to investigate and evaluate the more promising downscaling techniques, and provides a through inter comparison study using Karkheh catchment as an experimental site in a semi arid region for the years of 2040 to 2069. A hybrid conceptual hydrological model was used in conjunction with modeled outcomes from a General Circulation Model (GCM), HadCM3, along with two downscaling techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN), to determine how future streamflow may change in a semi arid catchment. The results show that the choice of a downscaling algorithm having a significant impact on the streamflow estimations for a semi-arid catchment, which are mainly, influenced, respectively, by atmospheric precipitation and temperature projections. According to the SDSM and ANN projections, daily temperature will increase up to +0.58 0C (+3.90 %) and +0.48 0C (+3.48 %), and daily precipitation will decrease up to ?0.1 mm (?2.56 %) and ?0.4 mm (?2.82 %) respectively. Moreover streamflow changes corresponding to downscaled future projections presented a reduction in mean annual flow of ?3.7 m^3/s and ?9.47 m^3/s using SDSM and ANN outputs respectively. The results suggest a significant reduction of streamflow in both downscaling projections, particularly in winter. The discussion considers the performance of each statistical method for downscaling future flow at catchment scale as well as the relationship between atmospheric processes and flow variability and changes.     

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.     

Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin,China

Reference evapotranspiration(ET_0) is often used to estimate actual evapotranspiration in water balance studies. In this study, the present and future spatial distributions and temporal trends of ET_0 in the Xiangjiang River Basin(XJRB) in China were analyzed. ET_0 during the period from1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith(FAO P-M) method, while ET_0 during the period from 2011 to 2100 was downscaled from the Coupled Model Intercomparison Project Phase 5(CMIP5) outputs under two emission scenarios, representative concentration pathway 4.5 and representative concentration pathway 8.5(RCP45 and RCP85), using the statistical downscaling model(SDSM). The spatial distribution and temporal trend of ET_0 were interpreted with the inverse distance weighted(IDW)method and Mann-Kendall test method, respectively. Results show that:(1) the mean annual ET_0 of the XJRB is 1 006.3 mm during the period from 1961 to 2010, and the lowest and highest values are found in the northeast and northwest parts due to the high latitude and spatial distribution of climatic factors, respectively;(2) the SDSM performs well in simulating the present ET_0 and can be used to predict the future ET_0 in the XJRB; and(3) CMIP5 predicts upward trends in annual ET_0 under the RCP45 and RCP85 scenarios during the period from 2011 to 2100.Compared with the reference period(1961e1990), ET_0 increases by 9.8%, 12.6%, and 15.6% under the RCP45 scenario and 10.2%, 19.1%, and27.3% under the RCP85 scenario during the periods from 2011 to 2040, from 2041 to 2070, and from 2071 to 2100, respectively. The predicted increasing ET_0 under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100.     

Evaluating Future Joint Probability of Precipitation Extremes with a Copula-Based Assessing Approach in Climate Change

Changes in climate extremes may cause the variation of occurrence and intensity of floods and droughts. To investigate the future changes in joint probability behaviors of precipitation extremes for water resources management, an approach including three stages for analyzing the spatial variation of joint return periods of precipitation extremes is proposed in this paper. In the first stage, a weather generator model (WGM) was conducted with general circulation models (GCMs) under representative concentration pathway (RCP) scenarios to generate daily rainfall time series during 2021–2040 (S) and 2081–2100 (L) based on the statistics of the observed rainfall data. Four extreme precipitation indices are defined to represent extreme precipitation events. In the second stage, copula methods are adopted to establish the joint distribution of the precipitation extreme indices. The watershed-scale assessment of flood and drought applied in Shih-Men reservoir in northern Taiwan is conducted to demonstrate the possible change of joint return period. In the third stage, the change rates of joint return periods for bivariate extreme indices are demonstrated to present the occurrence possibility of floods or droughts in the future. The results indicate that floods and droughts might occur more frequently in the upstream region of the reservoir during the twenty-first century. The reservoir operations would be more important for water supply and flood mitigation. In conclusion, the possible changes of future joint probability of the precipitation extremes should be paid attention to for water resources management and draft plans to confront potential challenges in the future.     

Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods

In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972–2000), near future (2020–2040) and far future (2070–2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE)?=?0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period).

     

Analysis of Linear Scaling Method in Downscaling Precipitation and Temperature

Climate change is one of the greatest challenges in the 21^st century that may influence the long haul and the momentary changeability of water resources. The vacillations of precipitation and temperature will influence the runoff and water accessibility where it tends to be a major issue when the interest for consumable water will increase. Statistical downscaling model (SDSM) was utilized in the weather parameters forecasting process in every 30 years range (2011-2040, 2041-2070, and 2071-2100) by considering Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The Linear Scaling (LS) method was carried out to treat the gaps between ground/ observed data and raw/ simulated results after SDSM. After the LS method was executed to raw/ simulated data after SDSM, the error decrease reaches over 13% for rainfall data. The Concordance Correlation Coefficient (CCC) value clarifies the correlation of rainfall amount among observed and corrected data for all three (3) RCPs categories. There are very enormous contrasts in rainfall amount during the wet season where CCC-values recorded are 0.22 and beneath (low correlation). The findings demonstrated that the rainfall amount during the dry season will contrast for all RCPs with the CCC-values are between 0.44-0.53 (moderate correlation). RCP8.5 is the pathway with the the most elevated ozone-depleting substance emanations and demonstrated that the climate change impact is going on and turn out to be more awful step by step.

     

An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation

Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model.

     

多模式下泾河上游流域未来降水变化预估

利用站点实测资料、GCMs 月数据对 GCMs 进行秩评分评估排序, 从 21 种 GCMs 模式优选出的 6 种 GCM模式的日数据、6 种 GCM 集成的气候模式、站点实测资料和 NCEP 再分析资料构建统计降尺度模型 SDSM, 预估泾河上游流域的未来降水变化。结果表明: 构建的降尺度模型对降水模拟较为可靠, 率定期各模式决定系数 R2 为 0.228~ 0.324, 标准误差为 0.354~ 0.450, 率定期和验证期模拟月均降水与实测值年内分布相近。在降尺度性能评价中集成模式表现最好。在 RCP 4.5 情景下, 泾河上游流域未来降水大多数模式和集成模式呈增加趋势, 到 2030 年泾河上游流域降水量将增加 4.8% , 且当地的春季雨量会增加, 夏季雨量会减少。     

Climate change impact on water quality in the integrated Mahabad Dam watershed-reservoir system

Climate change, besides global warming, is expected to intensify the hydrological cycle, which can impact watershed nutrient yields and affect water quality in the receiving water bodies. The Mahabad Dam Reservoir in northwest Iran is a eutrophic reservoir due to excessive watershed nutrient input, which could be exacerbated due to climate change. In this regard, a holistic approach was employed by linking a climate model (CanESM2), watershed-scale model (SWAT), and reservoir water quality model (CE-QUAL-W2). The triple model investigates the cumulative climate change effects on hydrological parameters, watershed yields, and the reservoir’s water quality. The SDSM model downscaled the output of the climate model under moderate (RCP4.5) and extreme (RCP8.5) scenarios for the periods of 2021–2040 and 2041–2060. The impact of future climate conditions was investigated on the watershed runoff and total phosphorus (TP) load, and consequently, water quality status in the dam’s reservoir. The results of comparing future conditions (2021–2060) with observed present values under moderate to extreme climate scenarios showed a 4–7% temperature increase and a 6–11% precipitation decrease. Moreover, the SWAT model showed a 9–16% decline in streamflow and a 12–18% decline in the watershed TP load for the same comparative period. Finally, CE-QUAL-W2 model results showed a 3–8% increase in the reservoir water temperature and a 10–16% increase in TP concentration. It indicates that climate change would intensify the thermal stratification and eutrophication level in the reservoir, especially during the year’s warm months. This finding specifies an alarming condition that demands serious preventive and corrective measures.     

Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.     

New climate change scenarios for the Netherlands.

A new set of climate change scenarios for 2050 for the Netherlands was produced recently. The scenarios span a wide range of possible future climate conditions, and include climate variables that are of interest to a broad user community. The scenario values are constructed by combining output from an ensemble of recent General Climate Model (GCM) simulations, Regional Climate Model (RCM) output, meteorological observations and a touch of expert judgment. For temperature, precipitation, potential evaporation and wind four scenarios are constructed, encompassing ranges of both global mean temperature rise in 2050 and the strength of the response of the dominant atmospheric circulation in the area of interest to global warming. For this particular area, wintertime precipitation is seen to increase between 3.5 and 7% per degree global warming, but mean summertime precipitation shows opposite signs depending on the assumed response of the circulation regime. Annual maximum daily mean wind speed shows small changes compared to the observed (natural) variability of this variable. Sea level rise in the North Sea in 2100 ranges between 35 and 85 cm. Preliminary assessment of the impact of the new scenarios on water management and coastal defence policies indicate that particularly dry summer scenarios and increased intensity of extreme daily precipitation deserves additional attention in the near future.     

Downscaling Monsoon Rainfall over River Godavari Basin under Different Climate-Change Scenarios

Evaluating the impact of climate change at river basin level has become essential for proper management of the water resources. In the present study, Godavari River basin in India is taken as study area to project the monthly monsoon precipitation using statistical downscaling. The downscaling method used is a regression based downscaling termed as fuzzy clustering with multiple regression. Among the atmospheric variables simulated by global circulation/climate model (GCM) mean sea level pressure, specific humidity and 500 hPa geopotential height are used as predictors. 1^o × 1^o gridded rainfall data over Godavari river basin are collected from India Meteorological Department (IMD). A statistical relationship is established between the predictors and predictand (monsoon rainfall) to project the monsoon rainfall for the future using the Canadian Earth System Model (CanESM2) over IMD grid points under the Representative Concentration Pathways 2.6, 4.5 and 8.5 (RCP 2.6, 4.5, 8.5) scenarios of Fifth Coupled Model Inter-Comparison Project (CMIP 5). Downscaling procedure is applied to all 25 IMD grid points over the basin to find out the spatial distribution of monsoon rainfall for the future scenarios. For 2.6 and 4.5 scenarios results show an increasing trend. For scenario 8.5 rainfall showed a mixed trend with rainfall decreasing in the first thirty years of prediction and then increasing gradually over the next sixty years.     

Lake Ontario ice coverage: Past,present and future

Lake Ontario ice conditions are statistically linked to regional temperatures recorded in Toronto, during the most recent climate normal (1980/81–2009/10). A metric was developed to capture the net melting effect of average winter temperatures to characterize lake ice conditions, referred to as Net Melting-Degree Days (NMDD). This metric was able to account for 78% of lake ice interannual variability (R² = 0.783, P < 0.001). Based on NMDD parameters, current lake ice conditions were characterized in four ways: heavy, moderate, light and very light. Lake Ontario ice conditions were reconstructed to create a hindcast for the span of the instrumental temperature record (1840/41–1979/80). Based on a decadal analysis, heavy ice seasons decreased significantly (R² = 0.658, P < 0.001) from the 1840s to the 2000s, declining from an average of 6 heavy ice seasons per decade during the most distant climate normal (1840s to 1960s) to an average of only 1 heavy ice season per decade during the most recent climate normal (1980s to 2000s). Finally, lake ice conditions are projected to the end of the 21st century, using an optimal ensemble of Global Climate Model outputs for two different climate change scenarios (RCP4.5, RCP8.5). Heavy ice seasons no longer occur as early as the 2050s under both RCP4.5 and RCP8.5. Whereas, very light ice seasons go from being an extreme in the baseline period (10%), to the dominant characterization of Lake Ontario ice conditions by the 2080s, for both RCP4.5 (73%) and RCP8.5 (100%).     

多模式集合模拟未来气候变化对水稻需水量及水分利用效率的影响

在模拟水稻水分利用对未来气候变化响应的研究中,考虑气候模式以及降尺度方法的不确定性,有助于获取更加稳健的模拟结果。本文采用SDSM和BP神经网络两种统计降尺度模型分别对Had CM3和CGCM3两种大气环流模式的3种气候情景(A1B,A2和B2)进行统计降尺度模拟,再基于贝叶斯模型平均方法(BMA)集合降尺度结果,并由此驱动ORYZA2000水稻模型,模拟21世纪中后期(2050s和2080s)3种情景下水稻生长周期、产量、需水量及水分利用效率。结果表明:BMA相对于简单模型平均法(SA)可以更有效地减小气候模式的偏差;在未来的两个时期内,由于气温的不断升高以及辐射的下降,水稻产量显著下降,生长周期明显缩短;需水量随着辐射的降低而降低,但在2080s,气温的迅速上升带来了需水量的升高,但仍低于历史基准期水平,而需水量的下降并不能抵消产量下降对水分利用效率的负面影响。     

The change and prediction of temperature and precipitation in the Dawen River basin using the statistical downscaling model (SDSM)

In order to explore the climate change in the Dawen River basin,based on the data of six weather stations in the Dawen River basin from 1966 to 2017,Mann Kendall test and wavelet analysis were used to study the temperature and precipitation trends,mutations and cycles in the region.In addition,based on the three scenarios of RCP2.6,RCP4.5,and RCP8.5 under the CanESM2 model,SDSM was used to compare and analyze the future climate change of the Dawen River basin.The results revealed that:the annual mean temperature of the Dawen River basin had increased significantly since 1966 (p<0.01);in different scenarios,the spatial distribution of the projected maximum temperature,minimum temperature and precipitation will hardly change compared with that in history;the temperature and precipitation in the Dawen River basin will generally increase in the future.The rising trend of maximum and minimum temperature under the three scenarios is in the EP相似文献   

太湖流域气候变化检测与未来气候变化情景预估

基于1954—2006年太湖流域6个气象站点的降水、气温资料,探讨了1954年以来太湖流域的气候变化问题,并同时应用统计降尺度模型SDSM和动力降尺度模型PRECIS,对太湖流域的日降水量和日最高、最低气温进行降尺度处理,建立未来2021—2050年的气候变化情景。结果表明:20世纪90年代以来,太湖流域发生了突变式增温,冬、春季节尤为显著;太湖流域降水变化相对较复杂,Mann Kendall法检测到太湖流域年降水量呈振荡性周期变化,并在1980年和2003年发生突变,而Pettitt方法没有检测出太湖流域年降水量的突变。两种降尺度方法模拟的未来时期日最高、最低气温季节和年的变化情景增幅总体上基本一致,均呈显著增加趋势,与Mann Kendall趋势分析结果一致,高排放情景A2下模拟生成的情景增温幅度较低排放情景B2大,最高气温增加幅度比最低气温明显。降水变化情景差异较大,SDSM模拟的未来时期降水并无明显变化趋势,而PRECIS模拟结果与趋势检验结果较为一致,即未来降水增加趋势明显,增幅较大,总体上全流域年降水量呈增加趋势,并且在未来一段时间内仍将持续增加。     

Modeling Uncertainty Resulting from Multiple Downscaling Methods in Assessing Hydrological Impacts of Climate Change

Climate Change Impacts Assessment using Statistical Downscaling is observed to be characterized by uncertainties resulting from multiple downscaling methods, which may perform similar during training, but differs in projections when applied to GCM outputs of future scenarios. The common wisdom in statistical downscaling, for selection of downscaling algorithms, is to select the model with the best overall system performance measure for observed period (training and testing). However, this does not guarantee that such selection will work best for any rainfall states, viz., low rainfall, or extreme rainfall. In the present study, for Assam and Meghalaya meteorological subdivision, India, three downscaling methods, Linear Regression (LR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used for simulating rainfall with reanalysis data and similar training and testing performances are obtained for observed period. When the developed relationships are applied to GCM output for future (21st century), differences are observed in downscaled projections for extreme rainfalls. ANN shows decrease in extreme rainfall, SVM shows increase in extreme rainfall and LR shows first decrease and then increase in extreme rainfall. Such results motivate further investigation, which reveals that, although, the overall performances of training and testing for all the transfer functions are similar, there are significant differences, when the performance measures are computed separately for low, medium, and high rainfall states. To model such uncertainty resulting from multiple downscaling methods, different transfer functions (LR, ANN and SVM) are used for different rainfall states (viz., low, medium and high), where they perform best. The rainfall states are predicted from large scale climate variables using Classification and Regression Tree (CART). As muti-model averaging (with equal weights or performance based weights) is commonly used in climatic sciences, the resulting output are also compared with the average of multiple downscaling model output. Rainfall is projected for Assam and Meghalaya meteorological subdivision, using this logic, with multiple GCMs. GCM uncertainty, resulting from the use of multiple GCMs, is further modeled using reliability ensemble averaging. The resultant Cumulative Distribution Function (CDF) of projected rainfall shows an increasing trend of rainfall in Assam and Meghalaya meteorological subdivision.     

Modeling and analysis of rainfall processes in the context of climate change for Mekong,Chi, and Mun River Basins (Thailand)

This paper provides a detailed characterization of the observed daily rainfall series available for the Mekong, Chi, and Mun River Basins in the context of climate change; and describes the linkage between climate simulations given by Global Circulation Models (GCMs) and the local rainfall characteristics using the popular Statistical Downscaling Model (SDSM). Observed daily rainfall records at 11 stations in the study area for the 1961–2007 period were considered. Results of characterizing the available rainfall data for the 1961–1990 and 1991–2007 periods show different trends of rainfall characteristics for different locations in the study area. However, a consistent increase in the annual maximum number of consecutive dry days (CDD) was observed in the Chi catchment area, the eastern part of the Mun watershed, and the western portion of the Mekong River Basin. In addition, decrease in the annual maximum daily rainfall (AMDR) was found in most locations of the study area, except for the central part of the Chi and Mun River Basins. Moreover, it has been shown in this paper that the SDSM could adequately describe the basic statistical and physical characteristics of the observed rainfall processes for the calibration (1961–1975) and validation (1976–1990) periods. This statistical downscaling method was then used to project future rainfall characteristics for the 1961–2099 period using the climate simulations given by the UK HadCM3 (HadCM3) model under A2 and B2 scenarios (HadCM3A2 and HadCM3B2), and by the Canadian GCM3 (CGCM3) model under A2 and A1B scenarios (CGCM3A2 and CGCM3A1B). In general, the projected trends of rainfall characteristics by both HadCM3 and CGCM3 were found to be consistent with the observed historical trends. However, there was a large difference in the projection results given by these two models. This would indicate the presence of high uncertainty in climate simulations provided by different GCMs. In addition, the climate change impacts on the flood and drought problems in the study area were shown using the CDD and AMDR indices of 100-year return period.