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.     

Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble

Water managers are faced with a changing climate in the decision-making process while adaptation and mitigation strategies need to be developed. The climate change impact towards the end of the century, however, is highly uncertain and coping with this is a great challenge for decision makers. Over the recent years, combined efforts of hydrologists and climatologists have led to many climate change impact studies on water resources. However, most studies only use a limited ensemble size and/or focus on only one contributing source and hence possibly underestimate the total uncertainty.

For two Belgian catchments, we simulated daily flow with five different lumped conceptual hydrological models and ten different parameter sets each, forced by the output of 24 global climate models covering four different emission scenarios, combined with 9 different downscaling methods over reference (1961–1990) and future (2071–2100) periods, resulting in a large multi-model ensemble with 41,850 members. Results show that both low and peak flows would become more extreme in the future, and these changes are stronger with increased radiative forcing. The most important uncertainty sources in low-flow projections are the global climate models (explaining 27–36% of the total variance) and the hydrological model structure (34–42%). For peak flow projections, these are global climate models (32–39%) and statistical downscaling methods (21–26%). Also, interaction effects account for a significant part of the uncertainty (24–38%). The results of this study illustrate that one might end up with biased results and overly confident conclusions when only focusing on some of the uncertainty sources in multi-model ensembles.

     

A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall

Finer spatiotemporal resolution rainfall data is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing methods pay less attention to the inter-day correlation and diurnal cycle, which can strongly influence the hydrological cycle. To address this problem, we present a spatiotemporal downscaling method that is capable of reproducing the inter-day correlation, the diurnal cycle, and rainfall statistics on daily and hourly scales. The large-scale datasets, which we obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NNR) and general circulation model (GCM) outputs, and local rainfall data are analyzed to assess the impacts of climate change on rainfall. Our proposed method consists of two steps: spatial downscaling and temporal downscaling. We apply spatial downscaling first to obtain the relationship between large-scale datasets and daily rainfall at a site scale using a k-nearest neighbor method (KNN). Then, we conduct an hourly downscaling of daily rainfall in the second step using a genetic algorithm-based KNN (GAKNN) with the inter-day correlation and the diurnal cycle. Furthermore, we analyzed changes in rainfall statistics for the periods 2046–2065 and 2081–2100 under the A2, A1B, and B1 scenarios of the third generation Coupled Global Climate Model (CGCM3.1) and Bergen Climate Model version 2 (BCM2.0). An application of our proposed method to the Shihmen Reservoir basin (Taiwan) has shown that it could accurately reproduce local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal method is a powerful tool for downscaling hourly rainfall data from a large-scale dataset. The understanding of future changes of rainfall characteristics through our proposed method is also expected to assist the planning and management of water resources systems.     

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.

     

Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

Two screening methods aimed at selection of predictor variables for use in a statistical downscaling (SD) model developed for precipitation are proposed and evaluated in this study. The SD model developed in this study relies heavily on appropriate predictors chosen and accurate relationships between site-specific predictand (i.e. precipitation) and general circulation model (GCM)-scale predictors for providing future projections at different spatial and temporal scales. Methods to characterize these relationships via rigid and flexible functional forms of relationships using mixed integer nonlinear programming (MINLP) formulation with binary variables, and artificial neural network (ANN) methods respectively are developed and evaluated in this study. The proposed methods and three additional methods based on the correlations between predictors and predictand, stepwise regression (SWR) and principal component analysis (PCA) are evaluated in this study. The screening methods are evaluated by employing them in conjunction with an SD model at 22 rain gauge locations in south Florida, USA. The predictor variables that are selected by different predictor selection methods are used in a statistical downscaling model developed in this study to downscale precipitation at a monthly temporal scale. Results suggest that optimal selection of variables using MINLP and ANN provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Results from application and evaluations of screening methods indicate improved downscaling of precipitation is possible by SD models when an optimal set of predictors are used and the selection of the predictors is site-specific.     

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。     

基于多模式耦合的赣江流域设计暴雨估算

基于3种不同排放情景下LARS-WG天气发生器内嵌的6种大气环流模式结果,预估了21世纪3个时期赣江流域不同重现期下的设计暴雨值变化。研究表明,使用LARS-WG方法得到的暴雨系列,能得到可信的频率分析结果,基于贝叶斯模型平均方法的多模式耦合能降低预估结果的不确定性。赣江流域绝大多数站点未来设计暴雨值相对基准期减小,且变化幅度随重现期的大小而变化。设计暴雨增加或部分增加和减少的站点分别位于赣江流域上中游和中下游。在未来气候变化影响下,赣江流域按照我国现有规范计算的设计暴雨整体偏大。     

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.     

多GCM模型集合下开都河流域未来气候变化预估

基于逐步聚类分析的统计降尺度模型(SCADS模型),在多GCM模型集合的9个大尺度气象变量与开都河流域6个气象变量之间,建立统计降尺度关系,并进行开都河流域未来气候变化的预估。结果表明,SCADS模型生成的开都河流域各气象变量的模拟值与实测值拟合较好。各气象变量在率定期(1961年-1990年)和验证期(1991年-1999年)的NSE系数均大于0.55,精度较高。此外,利用SCADS模型进行开都河流域各气象变量的预估。发现在三个不同时期内(2011年-2040年,2041年-2070年和2071年-2100年),月均气温升高,月均蒸发量、降水量、日照时数增加,月均相对湿度升高。     

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.     

统计降尺度方法对黄河上游流域气象要素模拟分析

将CMIP5模式的输出作为降尺度的输入来预估区域性气候的研究较少,本文使用CMIP5中精度较高的Can ESM2模式下的RCP4.5情景(中等温室气体排放)对黄河上游流域未来气象要素进行预估。利用黄河上游流域(上诠站以上)14个气象站点1967-2010年的逐月降水、气温和NCEP再分析资料,选取拟合度、均值相对误差、标准差相对误差作为评价指标,利用逐步回归算法筛选22个预报因子,建立了月资料序列的统计降尺度模型,并将模型应用于CMIP5中Can ESM2模式下RCP4.5情景,产生了未来气候要素的变化情景。结果表明:该模型对降水的模拟效果好于对气温的模拟。     

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.     

日雨量随机解集模式研究

全球气候模式(GCMs)预测的气候变化情景，必须经解集模式得出小尺度上未来气候变化时空分布资料，才能满足评估气候变化对资源、环境和社会经济等影响的需要。本文提出由随机天气生成器和统计参数尺度转换关系组成的随机解集模式，应用17个站32年实测日降雨资料，对随机解集模式进行了分析和验证。首先利用随机天气生成器，通过对站点和GCM尺度面平均降雨系列的模拟，确定模型参数，验证模型模拟历史降雨过程的可靠性。然后，建立模型参数从大尺度向站点转换的关系，并从历史降雨系列中抽出某一日雨量系列，假设为未来气候变化情形，对降雨系列在不同尺度间的转换关系进行了验证。在此基础上，对GCMs预测结果的时空解集方法进行了探讨。     

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。     

Assessing Adaptability of Cyclic and Non-Cyclic Approach to Conjunctive use of Groundwater and Surface water for Sustainable Management Plans under Climate Change

Groundwater overdraft in many regions throughout the world has been threatening the sustainability of this valuable resource. It has been argued that climate change may contribute to the severity of the issue; hence “impact assessment” is being replaced by “adaptation,” which explores more adapting scenarios and approaches. This study explores the adaptability of the proposed cyclic and non-cyclic conjunctive use of groundwater and surface water resources in increasing groundwater sustainability while increasing the sustainability of water allocation to the agricultural sector under possible climate change scenarios. To simulate climate change in the study area, precipitation and temperature variables are extracted from the results of three global atmospheric circulation models (Ensemble, CMCC-CMS, MRI-CGCM3) under RCP2.6 and RCP8.5 greenhouse gas emission scenarios in the period of 2021–2031. Spatial downscaling is performed using the M5 decision tree algorithm. The Wavelet-M5 hybrid model is used to predict runoff values as a rainfall-runoff model. Also, the Kharrufa method is applied to calculate evaporation in the future seasons. The system's adaptability to climate change is examined using the multi-objective cyclic and non-cyclic conjunctive use of surface and groundwater models. The study reveals that cyclic operation strategy improves the conjunctive use system adaptability compared to the optimal operation strategy that employs the non-cyclic approach. In this study's case study, the improvement in groundwater sustainability index exceeds 27 percent over the non-cyclic conjunctive use strategy.

     

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.     

The Correlation Between Statistically Downscaled Precipitation Data and Groundwater Level Records in North-Western Turkey

Downscaling of atmospheric climate parameters is a sophisticated tool to develop statistical relationships between large-scale atmospheric variables and local-scale meteorological variables. In this study, the variables selected from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data set were used as predictors for the downscaling of monthly precipitation in a watershed located in north-western Turkey where station records terminated two decades ago. An Artificial Neural Network (ANN) based approach was used to downscale global climate predictors that are positively correlated to the existing time frame of precipitation data in the basin. The downscaled precipitation information were used to extend the non-existing data from the meteorological station, which were later correlated with groundwater level data obtained from automatic pressure transducers that continuously record depth to groundwater. The results of the study showed that, among a large set of NCEP/NCAR parameters, surface precipitation data recorded at the meteorological station was strongly correlated with precipitation rate, air temperature and relative humidity at surface and air temperature at 850, 500, and 200 hPa pressure levels, and geopotential heights at 850 and 200 hPa pressure levels. The gaps in station data were then filled with the correlations obtained from NCEP/NCAR parameters and a complete precipitation data set was obtained that extended to current time line. This extended precipitation time series was later correlated with the existing groundwater level data from an alluvial plain in order to develop a general relationship that can be used in basin-wide water budget estimations. The proposed methodology is believed to serve the needs of engineers and basin planners who try to create a link between related hydrological variables under data-limited conditions.     

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.