Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

Increasing probability of concurrent drought between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project,China

Journal of Geographical Sciences - Water diversion projects are an effective measure to mitigate water shortages in water-limited areas. Understanding the risk of such projects increasing...     

湘江流域设计洪水地区组成方法研究

采用传统的同频率地区组成方法、基于copula函数的条件期望地区组成方法和最可能地区组成方法,对湘江流域洪水地区组成拟定多种设计洪水地区组成方案进行研究,经过各方案成果对比分析,推荐采用条件期望地区组成方案,为湘江流域水利水电工程和防洪系统工程的建设提供设计依据。     

基于Copula函数的太湖流域汛期洪涝灾害危险性分析

太湖流域位于长江入海口,地处中国沿海经济带和长江沿线内陆经济带的交汇处,是中国高度城镇化地区之一.流域汛期降水受到多重天气系统的影响,不同的天气系统带来时空分布各异的降水,给该地区城镇防洪排涝工作造成了巨大的挑战.本文基于Copula理论对太湖流域汛期洪涝风险进行研究,考虑了因降水主导因素不同所造成的流域洪涝风险的时空差异性.在时间角度,采用降水主导因素发生时间的概率分布,将汛期划分为梅汛期和台汛期;在空间角度,通过Copula函数,对研究区进行聚类划分;在此基础上,根据太湖流域防洪规划,对流域梅汛期和台汛期的洪涝风险进行分析.研究结果表明:①太湖流域的汛期划分为:6月24日7月21日为梅汛期,7月22日9月22日为台汛期;②根据各分区降水和太湖水位的联合分布函数拟合效果的优劣,在梅汛期,太湖流域被划分为P-Ⅰ区、P-Ⅱ区和P-Ⅲ区;在台汛期,整个流域的降水作为一个整体,不分区;③到2025年,太湖流域在梅汛期和台汛期出现排涝不利情境的风险概率分别为2.4%和1.1%.本文的研究方法可以为太湖流域设计暴雨的调整、洪水资源的利用以及防洪排涝实时调度的决策提供科学参考.     

Copula函数在水文水资源中的研究进展与述评

Copula函数是一种灵活构造多变量联合分布和处理多变量问题的有效工具,在水文水资源领域应用广泛;随着研究的广度和深度不断推进,显示出其良好的适用性和不可替代的优越性。重点综述了近10年来Copula函数在水文事件多变量频率分析、水文事件遭遇组合分析、水文随机模拟、水文模型与预报以及其他问题中的最新应用研究进展与不足。从应用条件、参数估计、不对称性、拟合优选、尾部特性、多变量重现期选取、成果抽样误差等方面阐述了Copula函数应用需要注意的问题。展望了未来Copula函数在水文水资源领域应用中的研究重点和发展方向,为今后的研究提供借鉴和参考。     

基于Copula函数的广东北江流域极端降水指标概率分布特征

选用9种概率分布函数对广东北江流域18测站近50 a极端降水指标R1d(年内最大1日降水量)和R5d(年内最大连续5日降水量)进行拟合,采用线性矩法估计参数,根据拟合优度检验,确定出最佳的概率分布函数,并在此基础上对50 a、100 a一遇设计值进行空间分析。通过择优选取GH Copula作为联接函数,对流域R1d-R5d联合概率分布进行研究。结果表明:1)翁源、清远地区出现50 a、100 a一遇的R1d或R5d概率较大;中北部地区50 a、100 a一遇的R1d和R5d同时出现的概率较大。2)特定R1d设计频率条件下,随着R5d设计频率的减小,二者遭遇概率也随之迅速减小;反之,特定R5d设计频率随R1d条件频率的减小,二者遭遇概率随之明显增大。