Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland

Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely “Shadegan” in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987–2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.     

Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation

Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.     

Statistical downscaling in the arid central Andes: uncertainty analysis of multi-model simulated temperature and precipitation

Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann?CWhitney and Brown?CForsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area.     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.     

Identifying future climate change and drought detection using CanESM2 in the upper Siem Reap River,Cambodia

Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site).     

Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs) have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3). The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed. The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general, all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios development. Authors’ addresses: Dr. Yonas B. Dibike, NSERC Research Fellow, OURANOS Consortium, 550 Sherbrooke Street West, 19^th Floor, Montreal (QC) H3A 1B9, Canada; Philippe Gachon, Adaptation and Impact Research Division (AIRD), Atmospheric Science and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada; André St-Hilaire and Taha B. M. J. Ouarda, Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada.     

Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India

The study evaluates statistical downscaling model (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum temperature, and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled climate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipitation, respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quantitative procedure. During validation, average R ² values by the annual sub-model (SDSM-A)—followed by bias correction using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R ² values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of the 2020s, 2050s, and 2080s in this basin. Both sub-models show a mean annual increase in max temperature, min temperature, and precipitation. Under H3A2, and according to both sub-models, changes in max temperature, min temperature, and precipitation are projected as 0.91–3.15 °C, 0.93–2.63 °C, and 6–12 %, and under H3B2, the values of change are 0.69–1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s. These results show that the climate of the basin will be warmer and wetter relative to the baseline period. SDSM-A, most of the time, projects higher changes in climate than SDSM-M. It can also be concluded that although SDSM-A performed well in predicting mean annual values, it cannot be used with regard to monthly and seasonal variations, especially in the case of precipitation unless correction is applied.     

基于统计降尺度模型的江淮流域极端气候的模拟与预估

利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method

The authors have applied an automated regression-based statistical method, namely, the automated statistical downscaling (ASD) model, to downscale and project the precipitation climatology in an equatorial climate region (Peninsular Malaysia). Five precipitation indices are, principally, downscaled and projected: mean monthly values of precipitation (Mean), standard deviation (STD), 90th percentile of rain day amount, percentage of wet days (Wet-day), and maximum number of consecutive dry days (CDD). The predictors, National Centers for Environmental Prediction (NCEP) products, are taken from the daily series reanalysis data, while the global climate model (GCM) outputs are from the Hadley Centre Coupled Model, version 3 (HadCM3) in A2/B2 emission scenarios and Third-Generation Coupled Global Climate Model (CGCM3) in A2 emission scenario. Meanwhile, the predictand data are taken from the arithmetically averaged rain gauge information and used as a baseline data for the evaluation. The results reveal, from the calibration and validation periods spanning a period of 40 years (1961–2000), the ASD model is capable to downscale the precipitation with reasonable accuracy. Overall, during the validation period, the model simulations with the NCEP predictors produce mean monthly precipitation of 6.18–6.20 mm/day (root mean squared error 0.78 and 0.82 mm/day), interpolated, respectively, on HadCM3 and CGCM3 grids, in contrast to 6.00 mm/day as observation. Nevertheless, the model suffers to perform reasonably well at the time of extreme precipitation and summer time, more specifically to generate the CDD and STD indices. The future projections of precipitation (2011–2099) exhibit that there would be an increase in the precipitation amount and frequency in most of the months. Taking the 1961–2000 timeline as the base period, overall, the annual mean precipitation would indicate a surplus projection by nearly 14~18 % under both GCM output cases (HadCM3 A2/B2 scenarios and CGCM3 A2 scenario). According to the model simulation, the September–November periods might be the more significant months projecting the increment of the precipitation amount around over 50 %, while the precipitation deficit would be seen in March–May periods.     

Conditional stochastic simulation model for spatial downscaling for assessing the effects of climate change on hydro-meteorological variables

The current study examines the recently proposed “bias correction and stochastic analogues” (BCSA) statistical spatial downscaling technique and attempts to improve it by conditioning coarse resolution data when generating replicates. While the BCSA method reproduces the statistical features of the observed fine data, this existing model does not replicate the observed coarse spatial pattern, and subsequently, the cross-correlation between the observed coarse data and downscaled fine data with the model cannot be preserved. To address the dissimilarity between the BCSA downscaled data and observed fine data, a new statistical spatial downscaling method, “conditional stochastic simulation with bias correction” (BCCS), which employs the conditional multivariate distribution and principal component analysis, is proposed. Gridded observed climate data of mean daily precipitation (mm/day) covering a month at 1/8° for a fine resolution and at 1° for a coarse resolution over Florida for the current and future periods were used to verify and cross-validate the proposed technique. The observed coarse and fine data cover the 50-year period from 1950 to1999, and the future RCP4.5 and RCP8.5 climate scenarios cover the 100-year period from 2000 to 2099. The verification and cross-validation results show that the proposed BCCS downscaling method serves as an effective alternative means of downscaling monthly precipitation levels to assess climate change effects on hydrological variables. The RCP4.5 and RCP8.5 GCM scenarios are successfully downscaled.     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Backcasting long-term climate data: evaluation of hypothesis

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.     

Simulation of extreme precipitation indices in the Yangtze River basin by using statistical downscaling method (SDSM)

In this study, the applicability of the statistical downscaling model (SDSM) in modeling five extreme precipitation indices including R10 (no. of days with precipitation ≥10?mm?day^?1), SDI (simple daily intensity), CDD (maximum number of consecutive dry days), R1d (maximum 1-day precipitation total) and R5d (maximum 5-day precipitation total) in the Yangtze River basin, China was investigated. The investigation mainly includes the calibration and validation of SDSM model on downscaling daily precipitation, the validation of modeling extreme precipitation indices using independent period of the NCEP reanalysis data, and the projection of future regional scenarios of extreme precipitation indices. The results showed that: (1) there existed good relationship between the observed and simulated extreme precipitation indices during validation period of 1991–2000, the amount and the change pattern of extreme precipitation indices could be reasonably simulated by SDSM. (2) Under both scenarios A2 and B2, during the projection period of 2010–2099, the changes of annual mean extreme precipitation indices in the Yangtze River basin would be not obvious in 2020s; while slightly increase in the 2050s; and significant increase in the 2080s as compared to the mean values of the base period. The summer might be the more distinct season with more projected increase of each extreme precipitation indices than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean extreme precipitation indices in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.     

Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator

This study provides a multi-site hybrid statistical downscaling procedure combining regression-based and stochastic weather generation approaches for multisite simulation of daily precipitation. In the hybrid model, the multivariate multiple linear regression (MMLR) is employed for simultaneous downscaling of deterministic series of daily precipitation occurrence and amount using large-scale reanalysis predictors over nine different observed stations in southern Québec (Canada). The multivariate normal distribution, the first-order Markov chain model, and the probability distribution mapping technique are employed for reproducing temporal variability and spatial dependency on the multisite observations of precipitation series. The regression-based MMLR model explained 16?%?~?22?% of total variance in daily precipitation occurrence series and 13?%?~?25?% of total variance in daily precipitation amount series of the nine observation sites. Moreover, it constantly over-represented the spatial dependency of daily precipitation occurrence and amount. In generating daily precipitation, the hybrid model showed good temporal reproduction ability for number of wet days, cross-site correlation, and probabilities of consecutive wet days, and maximum 3-days precipitation total amount for all observation sites. However, the reproducing ability of the hybrid model for spatio-temporal variations can be improved, i.e. to further increase the explained variance of the observed precipitation series, as for example by using regional-scale predictors in the MMLR model. However, in all downscaling precipitation results, the hybrid model benefits from the stochastic weather generator procedure with respect to the single use of deterministic component in the MMLR model.     

自组织映射神经网络(SOM)降尺度方法对江淮流域逐日降水量的模拟评估

利用1961~2002年ERA-40逐日再分析资料和江淮流域56个台站逐日观测降水量资料,引入基于自组织映射神经网络(Self-Organizing Maps,简称SOM)的统计降尺度方法,对江淮流域夏季(6~8月)逐日降水量进行统计建模与验证,以考察SOM对中国东部季风降水和极端降水的统计降尺度模拟能力。结果表明,SOM通过建立主要天气型与局地降水的条件转换关系,能够再现与观测一致的日降水量概率分布特征,所有台站基于概率分布函数的Brier评分(Brier Score)均近似为0,显著性评分(Significance Score)全部在0.8以上;模拟的多年平均降水日数、中雨日数、夏季总降水量、日降水强度、极端降水阈值和极端降水贡献率区域平均的偏差都低于11%;并且能够在一定程度上模拟出江淮流域夏季降水的时间变率。进一步将SOM降尺度模型应用到BCCCSM1.1(m)模式当前气候情景下,评估其对耦合模式模拟结果的改善能力。发现降尺度显著改善了模式对极端降水模拟偏弱的缺陷,对不同降水指数的模拟较BCC-CSM1.1(m)模式显著提高,降尺度后所有台站6个降水指数的相对误差百分率基本在20%以内,偏差比降尺度前减小了40%~60%;降尺度后6个降水指数气候场的空间相关系数提高到0.9,相对标准差均接近1.0,并且均方根误差在0.5以下。表明SOM降尺度方法显著提高日降水概率分布,特别是概率分布曲线尾部特征的模拟能力,极大改善了模式对极端降水场的模拟能力,为提高未来预估能力提供了基础。     

Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961–1990) and for two future emission scenarios (2071–2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.