基于GAMLSS模型的水文系列非平稳性研究

针对水文气象时间序列的非平稳性现象,为了定量分析非平稳序列的时变特征,基于GAMLSS模型原理,根据水文时间序列参数模型和半参数模型模拟方案,采用美国Little Sugar Creek研究案例的83年洪峰流量时间系列,分析了GAMLSS模型研究水文系列非平稳性的方法和流程。基于GAMLSS模拟,该流域内2006年10年一遇洪峰流量,相当于1999年20年重现期洪峰,接近于1989年100年一遇洪峰量级。GAMLSS模拟结果揭示了近年来由于气候变化、城市化进程等影响,极端事件出现频率。研究成果表明GAMLSS模型可定量有效的分析变化环境对水文频率设计值和工程设计的影响。     

基于GAMLSS模型的大渡河流域极值降水非一致性分析

以GAMLSS模型为基础,分析大渡河流域降水频率的非一致性特征,并将时间和多项气候因素作为解释变量进行参数拟合。结果表明,大渡河流域年最大日降水序列均呈现不显著变化的趋势,与一致性模型相比,大渡河流域降水序列的时间非一致性的特征不明显,而气候指标可以更好地反映大渡河流域降水非一致性的特征,表现为所有气象站点GAIC值与一致性模型的值相比明显减少。GAMLSS模型可以模拟水文序列的方差、偏态系数等其他统计参数非线性变化趋势,能够反映水文序列的非一致性特征。     

基于GAMLSS的雅砻江流域极端降水时空特性研究

由于气候和环境变化,气象水文时间序列的非平稳性特征普遍存在,直接影响防洪兴利工程的设计和运行。研究基于MK趋势检验和GAMLSS非平稳性分析,以雅砻江流域为研究对象,选取流域内18个气象站自建站至2016年日降水值数据集,基于9个极端降水指标,分析降水极值的时空变化趋势,研究时间序列的非平稳性特征对频率设计值的影响。     

论变化环境下的地表水资源评价方法

由于受气候变化和人类频繁活动的影响,用于水资源评价计算的天然年径流序列失去了一致性。针对非一致序列的“还原”和“还现”计算方法不能反映不同时期环境变化的问题,从水文变异诊断系统、考虑土地利用和覆被变化的流域水文模型、适应变化环境的非一致性序列水文频率计算方法3方面论述了变化环境下的地表水资源评价方法,指出水文变异诊断系统可以识别非一致性年径流序列发生变异的形式和时间。采用考虑土地利用/覆被变化的流域水文模型或适应变化环境的水文频率计算方法,可以从成因途径或统计途径得到反映过去、现在和未来变化环境下地表水资源量的评价成果。     

基于基流退水过程的非一致性枯水频率分析

枯水流量频率研究在水资源管理中具有重要作用。在气候变化和人类活动的影响下,水文极值序列的频率分布随时间变化。近年来,非一致性水文系列频率分析作为一项新课题逐渐被水文学者重视和研究。非一致性序列的频率计算方法主要集中于洪水和年径流,而对枯水频率研究不足。本文探讨了流域基流退水和降雨特征对河道枯水流量的重要影响,并通过结合基流退水分析和广义回归模型,研究了基于基流退水过程的非一致性枯水频率分析方法。以渭河为实例,结果表明:以流域退水参数和降雨参数为协变量的非一致性模型更好地描述了渭河华县站枯水流量分布特征的变化;渭河华县站枯水流量总体呈减少趋势,主要原因是基流退水速率加快、日平均降雨深减少以及降雨事件平均时间间隔增加。研究成果对变化环境下水资源供水设计管理、河流生态系统保护以及旱灾风险管理等问题具有实践意义。     

非一致性条件下水文设计值估计方法探讨

非一致性水文频率分析中,为了刻画未来环境变化对水文极值分布函数的影响,常假定分布函数中的分布参数随时间或其它因子变化,这就导致了某一量级洪水在未来发生的可能性每年均不同,是随时间变化的,使得现行水文频率分析框架中熟于理解的重现期/设计值概念难于应用。为此,提出"等可靠度"概念,即假定在工程的设计使用寿命期内,非一致条件下的频率分析结果与平稳条件下的成果应具有相同的水文设计可靠度,由此可以继续采用现行水文频率分析框架中的重现期与可靠度的概念探讨非一致条件下频率分析中设计值的估计问题,并建立了一致/非一致性条件下计算方法的联系,保证了非一致性条件下水文设计成果与现行工程采用的成果之间的衔接与协调。     

非稳态条件下的中长期径流耦合预报方法

气候变化会导致水文序列的非稳态性,从而给水文预报带来新的挑战。以疏勒河上游为例,提出了一种适于非稳态条件下的新的中长期径流预报方法。根据疏勒河径流的补给来源及其受气候变化的影响,按照时间序列模型的思路,依次提取趋势项和周期项,对剩余的随机项采用基于水文-气象遥相关模型,构建了时间序列与水文-气象遥相关的耦合模型。对比分析时间序列法、水文-气象遥相关法和耦合预报法对昌马堡站径流预报的结果,发现耦合预报方法不仅精度最高、模型可信度最高,而且可以描述非稳态的趋势性变化。     

基于改进的AR-LSSVM模型的河道水位预测研究

文章引入正交小波变化函数对传统AR-LSSVM模型进行改进,解决传统AR-LSSVM模型在非平稳时间序列计算中方程组求解出现无最优解的局限,并将改进的AR-LSSVM模型用于辽宁中部某河道水位预测研究中。研究结果表明:改进的AR-LSSVM模型可以求解非平稳时间序列变量的最优解,在河道水位预测中具有较好的适用性,相比于传统AR-LSSVM模型,改进的AR-LSSVM模型年和月尺度河道水位预测值和实测水位值相关系数分别提高0.5和0.3,适用于河道非平稳时间序列的水位预测。研究成果对于河道水位预测以及其他非平稳序列的水文系列预测提供参考方法。     

气候变化下水文模拟不确定性若干问题讨论

在阐释水文不确定性定义的基础上,根据气候变化下水文模拟不确定性的分类,总结了气候变化情景、水文模型和评估过程方面不确定性研究的基本范式,概述了每种不确定性的来源及影响,综述了气候变化下水文模拟不确定性研究进展。指出了未来水文系统模拟不确定性研究的重点和方向：结合复杂网络,增强对极端气候事件预估的可靠性;科学处理数据时间窗问题和冗余性,为无资料地区径流预测提供支撑;揭示变化环境下非平稳异方差性水文序列的发生规律。     

基于极值同频率法的非一致性年径流过程设计方法--以跳跃变异为例

由于气候系统变化和高强度人类活动的影响,天然年径流序列失去了一致性。为了适应变化环境对工程水文分析计算的新要求,本文以跳跃变异为例提出了基于极值同频率法的非一致性年径流过程设计方法:首先,采用水文变异诊断系统对不同时间尺度径流序列进行年际和年内分配情势变异分析;其次,根据年际变异和年内分配情势变异的时间点,将径流序列分段;再次,基于径流年际变异诊断结果,采用变化环境下非一致性水文频率计算方法计算不同时间尺度径流量变异前后过去不同保证率下的设计值;最后,基于径流年内分配变异诊断结果,在径流年内分配变异前后利用极值同频率法进行年径流过程设计。以东江岭下站为例,进行了非一致性年径流过程设计,并与未考虑"非一致性"影响的年径流过程设计结果进行了比较,发现岭下站设计年径流过程受"非一致性"影响显著,该结果可为东江流域水利工程的规划设计提供参考和依据。     

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.     

Evidence of Climate Change in the Senegal River Basin

International development policy makers are recognizing climate change and desertification as fundamental obstacles to the social and economic development of the Third World. Sub-Saharan Africa, particularly the Sahel region, has been severely impacted by the compounding effects of drought, deforestation and desertification. The Senegal River Basin in the West Africa is a prime example of a region where development objectives are seriously undermined by the drought-induced desertification process. The basic hydrologic constraint on development is revealed in a time series decompositionof Senegal River annual flow volumes, which strongly suggests that water resources availability has been substantially curtailed since 1960. Two alternative time series mechanisms are hypothesized to account for the decreased flow volumes in recent decades. The first time series model suggests the presence of a long-term periodicity, while the second model hypothesizes an ARMA(1,1,) process. The second hypothesis provides a superior model fit. The stationary ARMA(1,1) model can be fitted successfully, however, only after explicitly removing a non-stationary component by linearly detrending after 1960. The implication of non-stationarity in Senegal River hydrology provides additional analytic evidence that the landscape degradation and desertification processes observed in Sahelian Africa can be in part attributed to climate change effects. Efforts to redress desertification should be at once conscious of complex socioeconomic forces exacerbating the desertification process and fundamental hydrologic constraints to river basin development.     

Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization

Seasonal inflow variability, climate non-stationarity and climate change are matters of concern for water system planning and management. This study presents optimization methods for long-term planning of water systems in the context of a non-stationary climate with two levels of inflow variability: seasonal and inter-annual. Deterministic and stochastic optimization models with either one time-step (intra-annual) or two time-steps (intra-annual and inter-annual) were compared by using three water system optimization models. The first model used one time-step sampling stochastic dynamic programming (SSDP). The other models with two time-steps are long-term deterministic dynamic programming (LT-DDP) and long-term sampling stochastic dynamic programming (LT-SSDP). The study area is the Manicouagan water system located in Quebec, Canada. The results show that there will be an increase of inflow to hydropower plants in the future climate with an increase of inflow uncertainty. The stochastic optimization with two time-steps was the most suitable for handling climate non-stationarity. The LT-DDP performed better in terms of reservoir storage, release and system efficiency but with high uncertainty. The SSDP had the lowest performance. The SSDP was not able to deal with the non-stationary climate and seasonal variability at the same time. The LT-SSDP generated operating policies with smaller uncertainty compared to LT-DDP, and it was therefore a more appropriate approach for water system planning and management in a non-stationary climate characterized by high inflow variability.     

Analysis of Impacts of Climate Change and Human Activities on Hydrological Drought: a Case Study in the Wei River Basin,China

Climate change and human activity are the two major drivers that can alter hydrological cycle processes and influence the characteristics of hydrological drought in river basins. The present study selects the Wei River Basin (WRB) as a case study region in which to assess the impacts of climate change and human activity on hydrological drought based on the Standardized Runoff Index (SRI) on different time scales. The Generalized Additive Models in Location, Scale and Shape (GAMLSS) are used to construct a time-dependent SRI (SRI_var) considering the non-stationarity of runoff series under changing environmental conditions. The results indicate that the SRI_var is more robust and reliable than the traditional SRI. We also determine that different driving factors can influence the hydrological drought evolution on different time scales. On shorter time scales, the effects of human activity on hydrological drought are stronger than those of climate change; on longer time scales, climate change is considered to be the dominant factor. The results presented in this study are beneficial for providing a reference for hydrological drought analysis by considering non-stationarity as well as investigating how hydrological drought responds to climate change and human activity on various time scales, thereby providing scientific information for drought forecasting and water resources management over different time scales under non-stationary conditions.     

The effect of climate change on urban drainage: an evaluation based on regional climate model simulation.

That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 x 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at timescales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.     

气候-水文变化对水力发电的影响预估综述

气候变化可能改变河川径流的均值、极值、过程及可利用量,从而影响流域水电工程的规划建设和运行调度。因此,预测评估未来气候-水文变化对水力发电的影响具有重要意义。本文在简要阐述气候-水文-水电三者纽带关系的基础上,总结了国内外相关文献中影响预估的技术流程、主要模型与方法,从区域与季节差异、不确定性、综合影响及适应性调控等三方面分析预估结果,探讨现有研究存在的不足,并结合中国气候变化与水电行业的实际特点,展望了未来研究重点。建议进一步加强水文极值事件对水力发电影响、高海拔地区水循环机理及演变趋势等基础研究,并在考虑电力需求与多能互补的前提下,开展气候变化影响下的综合风险预估和整体适应策略研究。     

珠江流域非平稳性降雨极值时空变化特征及其成因

受全球气候变化和人类活动影响,珠江流域极端降雨事件发生的频次和强度均发生变化,变化环境导致极端降雨样本存在非平稳性。本文以珠江流域43个站点1960—2012年的日降雨数据为基础资料,通过分析广义帕累托分布(GPD)的参数时变特性及其空间分布规律,探索珠江流域非平稳性极端降雨的时空变化特征及其成因。结果表明:珠江流域极端降雨序列的极值指数呈从东到西逐渐减小特点,表征强降雨之间的相关性由东向西减弱;极端降雨变化程度大的区域其变化程度呈减弱趋势,而变化程度小的区域其变化程度呈加强趋势;珠江三角洲和东江流域南部、柳江流域东北部地区50/100年一遇的日降雨量级较大,而南盘江西部地区则较小。7个影响因子中,厄尔尼诺指标(SMEI)对流域的极端降雨影响最明显。非平稳时变超定量(POT)模型与平稳POT模型的结果比较表明,本文提出的时变POT模型较好地处理了珠江流域部分区域降雨存在的非平稳性特征。     

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-Driven Characterization of Climate Change Effects on Drought Frequency Using Enhanced SPI

The Standardized Precipitation Index (SPI) is a well-established drought index that is based on transforming the interannual distribution of precipitation to a standard normal distribution. Because of its robust statistical basis, SPI is readily applicable to different regions making comparisons between locations and time windows possible. Nevertheless, the usability of SPI results is undermined by shortcomings that are partly resultant from data and model uncertainties. One such shortcoming is the inability of the existing SPI model to include change in variability of interannual precipitation from non-stationary normal – mostly caused by climate change. In addition, epistemic uncertainty in the form of incompleteness in station-wide precipitation records results in heterogeneity and inconsistency in SPI results. The effects of such epistemic uncertainty on the accuracy of estimations of long-term changes in drought frequency are mostly unknown. Given such deficiency, SPI’s procedure and subsequent results remain deterministic and inadequately informative. Here, we introduce modifications to the traditional SPI using Dempster-Shafer theory (DST) to enable modeling and propagation of variability and epistemic uncertainty with the regular SPI procedure. By generalizing the SPI model from a deterministic setting to an “uncertainty-driven setting” provided by DST, this work makes possible: (a) efficiently propagating data uncertainty in interpolation of station-wide precipitation and SPI, and (b) modeling the effects of shift in precipitation normals (due to e.g., climate change) on drought frequency. In addition, the significance of this shift may then be evaluated with respect to the epistemic uncertainty by measuring how much of the surrounding epistemic uncertainty this shift encloses (i.e., “probability of enclosing”). The latter is especially important due to large unknowns already associated with climate change modeling. We implement the model on summer extreme drought for the Okanagan Basin, BC, Canada. For a single general circulation model and scenario (CGCM3 A2) a maximum 7 % increase in summer extreme drought (for 2080s, as per current definition) is estimated with a maximum probability of enclosing of 36 %.