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.     

Evaluation of Reanalysis Precipitation Data and Potential Bias Correction Methods for Use in Data-Scarce Areas

Water Resources Management - Data availability and accessibility often present challenges to resolving regional water management issues. One primary input essential to models and other tools used...     

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.     

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.

     

Examination of Various Feature Selection Approaches for Daily Precipitation Downscaling in Different Climates

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate.

     

Evaluation of Simple Statistical Downscaling Methods for Monthly Regional Climate Model Simulations with Respect to the Estimated Changes in Runoff in the Czech Republic

An ensemble of fifteen regional climate model (RCM) simulations has been used to estimate the climate change impacts on runoff and several drought characteristics for 250 basins in the Czech Republic. The scenario series of precipitation and temperature have been derived with four simple statistical downscaling methods (SDMs): the delta change and bias correction method, both in two alternatives considering the changes/biases in the mean, and in the mean and variance. Bootstrap resampling has been used to assess the effect of sampling variability and the differences in the estimated changes in runoff obtained by different SDMs were evaluated. Further simplification of the SDMs (spatial-average changes/biases and ensemble-average changes in precipitation and temperature) have been considered. It was shown, that given the spread between the projections of individual RCM simulations and the sampling variability, the differences in the estimated changes in mean runoff between the SDMs are not very large. The same partly holds also for the effect of spatial averaging. In general, the SDMs accounting for variability have led to smaller decrease (or larger increase) in runoff and the decrease was also smaller for bias correction methods than in the case of delta change methods. In contrast to changes in mean runoff, significant differences between the estimates based on different SDMs were found for the drought characteristics. In addition, the averaging of the changes in precipitation and temperature over the RCMs resulted in much stronger decrease in runoff than indicated by ensemble average changes in runoff.     

不同空间插值方法在辽西降水空间插值的精度对比分析

本文结合区域降水站点实测降水数据,对比分析了反距离加权插值和克里金插值方法在辽西降水空间插值的精度。研究结果表明:反距离加权插值法由于考虑插值点之间的权重,在辽宁西部降水空间插值的精度高于克里金插值方法,反距离加权插值方法下降水插值和实测降水之间年和月尺度的误差分别为6.2%和20.7%,年相关系数达到0.79,克里金插值方法下降水插值和实测降水之间年和月尺度误差分别为10.9%和24.6%,年相关系数为0.51,反距离插值方法更适用于辽西地区的降水空间插值计算。研究成果可为辽宁西部无资料地区降水空间插值计算提供方法参考。     

边坡稳定性分析的三种极限平衡法对比研究

极限平衡法在边坡稳定性分析中应用十分广泛,对多余变量假定的不同构成了不同的极限平衡条分法。采用瑞典条分法、毕肖普法、M-P法三种传统条分法,结合某土石坝下游坝坡滑面受力情况,对比分析了考虑渗流与不考虑渗流作用下三种方法计算的滑动面各条块底部的法向力和切向力的分布,以及各条块安全系数和整体安全系数。分析表明:毕肖普法和M-P法计算的条块底部法向力和切向力分布规律基本一致,条块底部法向合力和切向合力均较接近,且计算的各条块安全系数的平均值及整体安全系数也较接近;而瑞典条分法忽略条块间作用力,导致其计算的条块底部受力分布与毕肖普法和M-P法存在一定差异,且条块底部法向合力和切向合力略小,计算所得各条块安全系数的平均值和整体安全系数均略小。     

河道实测冲淤量不同计算方法结果比较分析

在生产实践中,采用不同计算方法得出的冲淤量结果往往差别很大,部分河段、部分时段甚至存在冲淤性质相反情况。采用大量的实测水沙、地形资料,运用断面地形法、网格地形法和输沙量平衡法3种方法计算了三峡水库蓄水后三峡坝下游宜昌至监利河段的实测河床冲淤量,对不同计算方法的计算结果和影响计算精度的因素进行了分析,运用非线性分形理论对河床起伏程度与断面代表性关系进行了研究。研究结果表明输沙量平衡法需对悬沙测验成果进行改正,并考虑泥沙测验定线误差、水量不平衡、河道采砂和分流洪道冲淤;断面地形法需在弯道、汊道、河道急剧放宽和束窄的局部河段布置相对较密的断面;网格地形法需选择适宜的插值方法,且网格尺寸不宜大于施测断面点间距。     

多普勒天气雷达不同仰角数据定量估测降水对比分析

多普勒天气雷达能定量估测大范围的降雨,是高时空分辨率降雨数据的重要来源,日益成为应用、研究的热门科学问题.针对雷达站能够同时产生多层不同仰角数据的特点,该文利用不同仰角雷达数据分别估算出降水量,对比分析了不同仰角多普勒雷达估测的降雨数据和自动雨量站雨量数据.研究结果表明,多普勒雷达低三层仰角的估测结果与自动雨量站点实测...     

R语言在降水极值统计分析中的应用

利用1951—2010年河南省5个气象站年最大日降水量序列,运用R语言进行编程,对其统计特征进行分析。运用冯诺依曼检验法对其进行独立性检验,采用Mann-Kendall检验法对其进行趋势性检验,通过KS检验法找出降水量序列服从的概率分布。结果表明:1951—2010年河南省5个站最大日降水量独立性明显,且极端降水事件的发生频率在增高;选用广义极值分布拟合最大日降水量序列效果相对较好,其变化趋势的空间分布具有很好的一致性;安阳、驻马店和信阳百年一遇的降水量很大,在进行防汛规划时应按高标准进行;郑州虽然降水量处于中等水平,但是人口密度大,也应按高标准进行防汛规划。     

不同框格形式密肋复合墙体抗震性能对比分析

密肋复合墙体结构是近年来出现的一种建筑结构新体系,具有多方面的优点。结合课题组前期的3块不同框格形式(三肋柱复合墙体、标准复合墙体、五肋柱复合墙体)1/2比例的密肋复合墙体低周反复试验结果,通过对其承载力、延性、耗能能力、滞回曲线、骨架曲线及破坏模式等抗震性能进行了对比分析。结果表明:由于五肋柱复合墙体发生弯曲型破坏,外框柱过早破坏退出工作,而三肋柱复合墙体与标准复合墙体均发生剪切型破坏,砌块、墙板、外框依次发生破坏,两者之间相比较,标准复合墙体抗震性综合比较要好于三肋柱复合墙体。试验结果可为实际工程中墙板设计及制作提供理论依据。     

沙坪站年降水量统计分析

对沙坪站年降水量进行统计分析,对P-Ⅲ线型(Г分布线型)和指数Г分布线型与经验频率点据拟合结果进行比较,并尝试采用指数Г分布线型拟合沙坪站年降水量资料系列进行频率特性分析,取得了较为合理的分析研究成果。结果表明,指数Г分布线型优于P-Ⅲ分布线型,从而验证了沙坪站年降水量频率分布线型为指数Г分布线型。     

Analysis of Orographic Precipitation on Jeju-Island Using Regional Frequency Analysis and Regression

The orographic effect is a common phenomenon in mountainous regions. Our goal is to analyze the orographic effect with quantile by regional frequency analysis and multiple regression. Multiple regression was used to develop models to estimate the amount and the spatial distribution of orographic precipitation in mountainous terrain using elevation, latitude, longitude, duration, and return period. Multiple linear regression analysis indicated that the model using the three parameters of elevation, latitude, and longitude, produces better results than four- or five-parameter models. Therefore, multiple nonlinear forms, the combination of the intensity-duration-frequency (IDF) relationship and the general linear regression form of orographic statistics were proposed to improve the accuracy of models. The models in this study showed an increase in accuracy of 18.31~86.27%. Moreover, these models produced good results in GIS analysis and were able to represent all cases examined in this study using only a few equations, in contrast to multiple linear models.     

Assessment of Extreme Precipitation in Future through Time-Invariant and Time-Varying Downscaling Approaches

Skill of a time-varying downscaling approach, namely Time-Varying Downscaling Model (TVDM), against time-invariant Statistical Downscaling Model (SDSM) approach for the assessment of precipitation extremes in the future is explored. The downscaled precipitation is also compared with a Regional Climate Model (RCM) product obtained from Coordinated Regional Climate Downscaling Experiment (CORDEX). The potential of downscaling the extreme events is assessed considering Bhadra basin in India as the study area through different models (SDSM, TVDM and RCM) during historical period (calibration: 1951–2005, testing: 2006–2012). Next, the changes in precipitation extremes during future period (2006–2035) have been assessed with respect to the observed baseline period (1971–2000), for different Representative Concentration Pathway (RCP) scenarios. All the models indicate an increasing trend in the precipitation, for the monsoon months and maximum increase is noticed using RCP8.5. The annual precipitation during the future period (RCP8.5) is likely to increase by 7.6% (TVDM) and 4.2% (SDSM) in the study basin. An increase in magnitude and number of extreme events during the future period is also noticed. Such events are expected to be doubled in number in the first quarter of the year (January–March). Moreover, the time-invariant relationship (in SDSM) between causal-target variables is needed to be switched with time-varying (TVDM). This study proves that the time-varying property in TVDM is more beneficial since its performance is better than SDSM and RCM outputs in identifying the extreme events during model calibration and testing periods. Thus, the TVDM is a better tool for assessing the extreme events.

     

不同卫星遥感降水数据在辽宁东部区域的适用性和精度对比分析

本文采用M-K检验方法和相关分析方法 ,结合辽宁东部区域降水站点数据,对比TRMM和GSMaP两种卫星遥感降水数据在辽宁东部地区的适用性和精度。研究结果表明:在年尺度上,GSMaP和TRMM降水数据和实测降水之间相关系数分别为0.62和0.36;在季节尺度上,两种卫星遥感降水数据均在夏季和实测降水相关性最高,且M-K检验值最高。在月尺度上,GSMaP卫星遥感降水数据和实测降水之间的差距小于TRMM卫星遥感降水数据。研究成果对于卫星遥感降水数据的应用分析提供参考价值。     

2种不同方法计算坝址环境水活度的比较分析

对坝址区地下水中离子活度的研究,在探讨水一岩一坝相互作用方面起着重要的作用,由其计算得到的饱和指数是判断坝基帷幕体防渗时效性的一个重要参数.分别应用Debye-Hückel方程和Pitzer模型2种方法计算坝址环境水的离子活度,并对结果进行了比较分析.结果表明,对于坝址区普遍存在的高pH值地下水,Debye-Hückel方程求解过程中可能会出现迭代异常而出现无解的情况,且对于CO32-离子,计算结果也存在较大偏差;相比之下,Pitzer模型则具有较好的普遍性和计算精度.因而认为,在计算大坝等水工建筑物地区地下水的离子活度时,Pitzez模型可得到更广泛的应用.     

五华县近52年降水统计分析及最大降水量重现期的估算

对1957～2008年五华县降水量进行统计分析,得出降水的年月际分布、降水相对变率以及暴雨日数的演变特征,并利用指数分布法对最大日降水量及年降水量的重现期进行分析。结果表明:近52年来五华县年降水量可分为2个多雨期、3个少雨期和1个平稳变化期;降水主要集中在汛期(4～9月),占年降水量的76%;冬季降水相对变率最大;平均暴雨日数为4.7d,年降水量与暴雨日数之间相关性较好,暴雨雨量对年降水量的平均贡献率为21.9%;五华县50年一遇的最大日降水量及年降水量分别是221.8mm和2 573.3mm,100年一遇的则分别为250.1mm和2 822.1mm。     

基于多种趋势分析方法的京津冀地区极端降水趋势分析

变化环境下我国极端降水事件频发,导致洪涝灾害加剧,严重影响社会经济的可持续发展。分析京津冀地区极端降水趋势,预测未来极端降水,更合理地开展降水频率分析,对合理配置水资源有重要意义。基于京津冀及周边地区25个气象站1960—2016年逐日降水数据,分别采用普通Mann-Kendall检验、预置白Mann-Kendall检验、去趋势预置白Mann-Kendall检验和改进的Mann-Kendall检验方法,选取7个极端降水指标对京津冀地区极端降水进行趋势分析。结果表明:京津冀日极端降水95%分位数表现为西北地区下降、东部邻渤海地区和南部地区上升的趋势;日降水强度表现为上升趋势;边界区域年降水量表现为上升趋势,内部呈下降趋势;其他指标呈显著或不显著的下降趋势。对4种方法所得结果进行对比,除年降水量外,各极端降水指标的检验结果均有不同程度的差别,预置白Mann-Kendall方法最为严格。     

长春地区降水量代表性规律计算分析方法

分析了长春地区降水量的分布规律,分析成果将为该区开展水资源综合规划提供参考,为水资源的开发、利用、保护等决策提供科学依据。