Detection and forecast of climate change effect on siltation using copulas

Theoretical and Applied Climatology - Being a subject of a worldwide growing importance, climate change and its impact on future water-management-solutions have become crucial to the planet’s...     

基于GIS潍坊市暴雨洪涝灾害损失评估方法研究

利用潍坊市1984—2007年暴雨洪涝灾情数据,基于GIS技术结合模糊综合评价方法确定了灾害综合评价指数,根据灾损率指标与灾害综合评价指数得到潍坊市农业经济损失率评估模型。模型表明灾害综合评价指数与农业经济损失率具有较好的线性相关关系,相关系数达0.842。并对潍坊市一次暴雨洪涝灾害进行评估,验证该模型精度。与实际灾情数据对比,相对误差最小值为15.37%,最大值为21.29%,模拟结果与历史灾情数据基本一致。     

Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms

Theoretical and Applied Climatology - Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood...     

Spatial and temporal analysis of urban flood risk assessment

广东暴雨强度大、范围广、季节长,造成的灾害重、影响大。为合理、定量地评估广东暴雨洪涝过程强度及其损失,基于1994-2018年广东致灾暴雨过程和相应灾情资料,构建了广东暴雨过程综合强度评估模型和灾情指数模型,并采用百分位数法进行暴雨强度和灾情等级划分,以第60、第80、第90和第95百分位数为临界阈值,分别将致灾暴雨过程强度和灾情划分为弱（1级）、较弱（2级）、中等（3级）、较强（4级）、强（5级）和微灾、小灾、中灾、大灾、巨灾5个等级,进而分析了不同强度等级暴雨过程可能造成的人口、农作物、房屋和经济等承灾体损失。结果表明：（1）1994-2018年间,广东各等级致灾暴雨过程主要出现在4-9月的汛期,5-7月尤其多,要特别注意防御;（2）致灾暴雨过程强度等级与各类承灾体灾情指数存在显著正相关关系：随暴雨强度的增强,倒塌房屋数呈指数增长,受灾人口、死亡人数、农作物受灾面积和直接经济损失呈线性增长;（3）平均而言,当暴雨强度达到强（5级）等级时,受灾人口、死亡人数、农作物受灾面积、倒塌房屋数和直接经济损失标准分别约为187.19万人、22人、10.52×10⁴ hm²、1.12万间和13.07亿元。

     

基于遥感与GIS的浙江省洪涝灾害综合风险评估研究

暴雨洪涝是最为频发、多发的自然灾害之一,已成为我国实现可持续发展的严重障碍。开展暴雨洪涝灾害风险评估技术与应用研究,是当今防灾减灾中的一项迫切要求。本文基于应用场景时间顺序从灾前预评估、灾中跟踪评估、灾后评估三个方面系统回顾了前人的研究历史和当前研究所取得的成果,并在对研究现状进一步认识的基础上,提出了当今暴雨洪涝灾害风险评估研究中存在的主要问题,指出了有待进一步研究和发展的新方向。

     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

Probabilistic assessment of projected climatological drought characteristics over the Southeast USA

The study makes a probabilistic assessment of drought risks due to climate change over the southeast USA based on 15 Global Circulation Model (GCM) simulations and two emission scenarios. The effects of climate change on drought characteristics such as drought intensity, frequency, areal extent, and duration are investigated using the seasonal and continuous standard precipitation index (SPI) and the standard evapotranspiration index (SPEI). The GCM data are divided into four time periods namely Historical (1961–1990), Near (2010–2039), Mid (2040–2069), and Late (2070–2099), and significant differences between historical and future time periods are quantified using the mapping model agreement technique. Further, the kernel density estimation approach is used to derive a novel probability-based severity-area-frequency (PBS) curve for the study domain. Analysis suggests that future increases in temperature and evapotranspiration will outstrip increases in precipitation and significantly affect future droughts over the study domain. Seasonal drought analysis suggest that the summer season will be impacted the most based on SPI and SPEI. Projections based on SPI follow precipitation patterns and fewer GCMs agree on SPI and the direction of change compared to the SPEI. Long-term and extreme drought events are projected to be affected more than short-term and moderate ones. Based on an analysis of PBS curves, especially based on SPEI, droughts are projected to become more severe in the future. The development of PBS curves is a novel feature in this study and will provide policymakers with important tools for analyzing future drought risks, vulnerabilities and help build drought resilience. The PBS curves can be replicated for studies around the world for drought assessment under climate change.     

Probabilistic spatial risk assessment of heat impacts and adaptations for London

High temperatures and heatwaves can cause large societal impacts by increasing health risks, mortality rates, and personal discomfort. These impacts are exacerbated in cities because of the Urban Heat Island (UHI) effect, and the high and increasing concentrations of people, assets and economic activities. Risks from high temperatures are now widely recognised but motivation and implementation of proportionate policy responses is inhibited by inadequate quantification of the benefits of adaptation options, and associated uncertainties. This study utilises high spatial resolution probabilistic projections of urban temperatures along with projections of demographic change, to provide a probabilistic risk assessment of heat impacts on urban society. The study focuses on Greater London and the surrounding region, assessing mortality risk, thermal discomfort in residential buildings, and adaptation options within an integrated framework. Climate change is projected to increase future heat-related mortality and residential discomfort. However, adjusting the temperature response function by 1–2 °C, to simulate adaptation and acclimatisation, reduced annual heat related mortality by 32–69 % across the scenarios tested, relative to a no adaptation scenario. Similar benefits of adaptation were seen for residential discomfort. The study also highlights additional benefits in terms of reduced mortality and residential discomfort that mitigating the urban heat island, by reducing albedo and anthropogenic heat emissions, could have.     

Modeling flood event characteristics using D-vine structures

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure.     

改进的AHP在县域尺度暴雨洪涝风险评价的应用

以淮河流域为例,选取降水、土地利用、经济、人口等指标作为淮河流域暴雨洪涝灾害风险指标,利用信息熵改进的层次分析法确定淮河流域暴雨洪涝的风险评估指标权重,并应用于县域尺度淮河流域暴雨洪涝灾害风险评价。结果表明:(1)淮河流域暴雨洪涝灾害风险空间分布整体呈现南部高、北部低,东西高、中部次之的形态。(2)改进的层次分析法得到的高风险区比传统方法的面积减少,市县个数下降,而次高风险区、中风险区、次低以及低风险区面积比之传统方法均有增加。同时风险平均值升高,导致受灾程度可能加大。(3)改进方法得到的岳西县风险等级由高风险区降为次高风险区,低于金寨县风险等级。宿州市风险等级由次高风险区降为中风险区,较灵璧、泗县风险低,与实际情况更为相符,提高了淮河流域暴雨洪涝灾害风险评价精度。     

Probabilistic assessment of regional climate change in Southwest Germany by ensemble dressing

Since single-integration climate models only provide one possible realization of climate variability, ensembles are a promising way to estimate the uncertainty in climate modeling. A statistical model is presented that extracts information from an ensemble of regional climate simulations to estimate probability distributions of future temperature change in Southwest Germany in the following two decades. The method used here is related to kernel dressing which has been extended to a multivariate approach in order to estimate the temporal autocovariance in the ensemble system. It has been applied to annual and seasonal mean temperatures given by ensembles of the coupled general circulation model ECHAM5/MPI-OM as well as the regional climate simulations using the COSMO-CLM model. The results are interpreted in terms of the bivariate probability density of mean and trend within the period 2011–2030 with respect to 1961–1990. Throughout the study region one can observe an average increase in annual mean temperature of approximately +0.6K and a corresponding trend of +0.15K/20a. While the increase in 20-year mean temperature is virtually certain, the 20-year trend still shows a 20% chance for negative values. This indicates that the natural variability of the climate system, as far as it is reflected by the ensemble system, can produce negative trends even in the presence of longer-term warming. Winter temperatures are clearly more affected and for both quantities we observe a north-to-south pattern where the increase in the very southern part is less intense.     

Ensemble flood risk assessment in Europe under high end climate scenarios

At the current rate of global warming, the target of limiting it within 2 degrees by the end of the century seems more and more unrealistic. Policymakers, businesses and organizations leading international negotiations urge the scientific community to provide realistic and accurate assessments of the possible consequences of so called “high end” climate scenarios.This study illustrates a novel procedure to assess the future flood risk in Europe under high levels of warming. It combines ensemble projections of extreme streamflow for the current century based on EURO-CORDEX RCP 8.5 climate scenarios with recent advances in European flood hazard mapping. Further novelties include a threshold-based evaluation of extreme event magnitude and frequency, an alternative method to removing bias in climate projections, the latest pan-European exposure maps, and an improved flood vulnerability estimation.Estimates of population affected and direct flood damages indicate that by the end of the century the socio-economic impact of river floods in Europe is projected to increase by an average 220% due to climate change only. When coherent socio-economic development pathways are included in the assessment, central estimates of population annually affected by floods range between 500,000 and 640,000 in 2050, and between 540,000 and 950,000 in 2080, as compared to 216,000 in the current climate. A larger range is foreseen in the annual flood damage, currently of 5.3 B€, which is projected to rise at 20–40 B€ in 2050 and 30–100 B€ in 2080, depending on the future economic growth.     

济南市长清区暴雨洪涝灾害风险区划

基于自然灾害形成机理及风险评估原理,利用济南市长清区气象数据、自然地理和社会经济等数据,建立起致灾因子危险性、孕灾环境敏感性、承灾体易损性和防灾减灾能力4个评价指标,采用加权综合评价法和层次分析法,借助GIS空间分析技术,对暴雨灾害风险性进行评价和等级划分,并绘制出长清地区暴雨灾害综合风险区划图。结果显示:长清区暴雨灾害综合风险性分布空间性强,无明显的地域分布界限,东部高于其它地区。暴雨灾害高综合风险区分布面积较为分散且最小,占全区总面积的14.60%;中综合风险区主要分布在高综合风险区的外围,占全区总面积的30.31%;轻、低综合风险区分别占全区总面积的20.72%和34.37%。     

Risk assessment of flood disaster and forewarning model at different spatial-temporal scales

Aiming at reducing losses from flood disaster, risk assessment of flood disaster and forewarning model is studied. The model is built upon risk indices in flood disaster system, proceeding from the whole structure and its parts at different spatial-temporal scales. In this study, on the one hand, it mainly establishes the long-term forewarning model for the surface area with three levels of prediction, evaluation, and forewarning. The method of structure-adaptive back-propagation neural network on peak identification is used to simulate indices in prediction sub-model. Set pair analysis is employed to calculate the connection degrees of a single index, comprehensive index, and systematic risk through the multivariate connection number, and the comprehensive assessment is made by assessment matrixes in evaluation sub-model. The comparison judging method is adopted to divide warning degree of flood disaster on risk assessment comprehensive index with forewarning standards in forewarning sub-model and then the long-term local conditions for proposing planning schemes. On the other hand, it mainly sets up the real-time forewarning model for the spot, which introduces the real-time correction technique of Kalman filter based on hydrological model with forewarning index, and then the real-time local conditions for presenting an emergency plan. This study takes Tunxi area, Huangshan City of China, as an example. After risk assessment and forewarning model establishment and application for flood disaster at different spatial-temporal scales between the actual and simulated data from 1989 to 2008, forewarning results show that the development trend for flood disaster risk remains a decline on the whole from 2009 to 2013, despite the rise in 2011. At the macroscopic level, project and non-project measures are advanced, while at the microcosmic level, the time, place, and method are listed. It suggests that the proposed model is feasible with theory and application, thus offering a way for assessing and forewarning flood disaster risk.     

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

An assessment of the potential impact of climate change on flood risk in Mumbai

Managing risks from extreme events will be a crucial component of climate change adaptation. In this study, we demonstrate an approach to assess future risks and quantify the benefits of adaptation options at a city-scale, with application to flood risk in Mumbai. In 2005, Mumbai experienced unprecedented flooding, causing direct economic damages estimated at almost two billion USD and 500 fatalities. Our findings suggest that by the 2080s, in a SRES A2 scenario, an ??upper bound?? climate scenario could see the likelihood of a 2005-like event more than double. We estimate that total losses (direct plus indirect) associated with a 1-in-100 year event could triple compared with current situation (to $690?C$1,890 million USD), due to climate change alone. Continued rapid urbanisation could further increase the risk level. The analysis also demonstrates that adaptation could significantly reduce future losses; for example, estimates suggest that by improving the drainage system in Mumbai, losses associated with a 1-in-100 year flood event today could be reduced by as much as 70%.,We show that assessing the indirect costs of extreme events is an important component of an adaptation assessment, both in ensuring the analysis captures the full economic benefits of adaptation and also identifying options that can help to manage indirect risks of disasters. For example, we show that by extending insurance to 100% penetration, the indirect effects of flooding could be almost halved. We conclude that, while this study explores only the upper-bound climate scenario, the risk-assessment core demonstrated in this study could form an important quantitative tool in developing city-scale adaptation strategies. We provide a discussion of sources of uncertainty and risk-based tools could be linked with decision-making approaches to inform adaptation plans that are robust to climate change.     

Theory for Reconstruction of an Unknown Number of Contaminant Sources using Probabilistic Inference

We address the inverse problem of source reconstruction for the difficult case of multiple sources when the number of sources is unknown a priori. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to derive the posterior probability density function for the number of sources and for the parameters (e.g., location, emission rate, release time and duration) that characterize each source. A mapping (source–receptor relationship) that relates a multiple source distribution to the concentration measurements made by an array of detectors is formulated based on a forward-time Lagrangian stochastic model. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source–receptor relationship and realized in terms of a backward-time Lagrangian stochastic model, is described. An efficient computational algorithm based on a parallel tempered Metropolis-coupled reversible-jump Markov chain Monte Carlo (MCMC) method is formulated and implemented to draw samples from the posterior probability density function of the source parameters. This methodology allows the MCMC method to initiate jumps between the hypothesis spaces corresponding to different numbers of sources in the source distribution and, thereby, allows a sample from the full joint posterior distribution of the number of sources and the parameters for each source to be obtained. The proposed methodology for source reconstruction is tested using synthetic concentration data generated for cases involving two and three unknown sources.     

中小河流域暴雨洪涝灾害风险评估及效果检验

本文基于FloodArea水动力模型及WRF模式,运用气象资料、水文资料、地理信息资料、社会经济统计资料以及灾情调查资料等,以长江一级支流的秋浦河流域为研究区,开展中小河流域暴雨洪涝灾害风险评估及效果检验。结果表明:FloodArea模型对洪水的淹没范围、淹没水深以及淹没历时等具有较好的模拟效果,可用于中小河流域暴雨洪涝灾害风险评估与预警业务,并由此初步建立了包含模式降水预报→面雨量计算→洪涝淹没模拟→风险评估→预警发布→效果检验等环节的暴雨洪涝灾害风险评估及效果检验业务流程,实现从以往常规的强降水预报到暴雨洪涝灾害预报的业务延伸,可进行推广应用。     

WRF模式与Topmodel模型在洪水预报中的耦合预报试验研究

基于空间分辨率90 m×90 m的湖北荆门漳河水库数字高程模型（DEM）地形数据,并从2012-2015年选取了20场洪水过程（其中16场用于模拟,4场用于检验）,将华中区域数值天气预报业务模式WRF提供的三重嵌套空间分辨率3 km×3 km、9 km×9 km和27 km×27 km预报降雨与集总式新安江模型以及半分布式水文模型Topmodel耦合进行洪水预报试验。通过对比试验得到以下结论：当流域降雨的时、空分布比较均匀时,集总式新安江模型可以较准确地预报出洪峰流量和峰现时间,而当降雨时、空分布差异较大时,预报误差也会随之增大。基于DEM数据建立的Topmodel模型可以反映不同降雨时、空分布下洪水预报结果的差异,试验结果表明,3 km×3 km和9 km×9 km洪水预报的输出结果比较接近,且在确定性系数和洪峰相对误差上要优于27 km×27 km的洪水预报结果,而在峰现时差的预报上,则是27 km×27 km的洪水预报结果与实测较吻合。通过研究还发现,虽然当流域降雨的时、空分布存在一定差异时,3种空间分辨率的WRF预报降雨均无法预报出与实测一致的降雨分布,但是在某些情况下,当降雨的时间分布误差和空间分布误差相抵消时,仍然可以得到较为准确的洪水预报结果。因此,高时、空分辨率的模式预报降雨并不一定就能对洪水预报结果产生正贡献,需要通过反复尝试寻找水文模型和数值模式耦合的最佳时、空分辨率。