大尺度水循环模拟系统不确定性研究进展(英文)

The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions.And the hydrological models play a very important role in simulating the complex system.However,there have not been effective methods for the model reliability and uncertainty analysis due to its complexity and difficulty.The uncertainties in hydrological modeling come from four important aspects:uncertainties in input data and parameters,uncertainties in model structure,uncertainties in analysis method and the initial and boundary conditions.This paper systematically reviewed the recent advances in the study of the uncertainty analysis approaches in the large-scale complex hydrological model on the basis of uncertainty sources.Also,the shortcomings and insufficiencies in the uncertainty analysis for complex hydrological models are pointed out.And then a new uncertainty quantification platform PSUADE and its uncertainty quantification methods were introduced,which will be a powerful tool and platform for uncertainty analysis of large-scale complex hydrological models.Finally,some future perspectives on uncertainty quantification are put forward.     

REGRESSION ON MULTIVARIATE IMAGES:PRINCIPAL COMPONENT REGRESSION FOR MODELING,PREDICTION AND VISUAL DIAGNOSTIC TOOLS

Regression between two blocks(usually called‘dependent’or Y and‘independent’or X)of data is a veryimportant scientific and data-analytical tool.Regression on multivariate images is possible and constitutesa meaningful addition to existing univariate and multivariate techniques of image analysis.The regressioncan be used as a modeling tool or for prediction.The form of the regression equation chosen is dependentupon problem specification and information at hand.This paper describes the use of principal componentregression(PCR).Both model building and prediction are presented for continuous Y-variables.The finalgoal is to supply new image material that can be used for visual inspection on a screen.Also,visual toolsfor diagnosis of model and prediction are provided,often based on derived image material.Examplesof modeling and prediction are given for six channels in a seven-channel satellite image     

1971-2000年青藏高原气候变化趋势

Trends of annual and monthly temperature, precipitation, potential evapotranspi- ration and aridity index were analyzed to understand climate change during the period 1971–2000 over the Tibetan Plateau which is one of the most special regions sensitive to global climate change. FAO56–Penmen–Monteith model was modified to calculate potential evapotranspiration which integrated many climatic elements including maximum and mini- mum temperatures, solar radiation, relative humidity and wind speed. Results indicate gen- erally warming trends of the annual averaged and monthly temperatures, increasing trends of precipitation except in April and September, decreasing trends of annual and monthly poten- tial evapotranspiration, and increasing aridity index except in September. It is not the isolated climatic elements that are important to moisture conditions, but their integrated and simulta- neous effect. Moreover, potential evapotranspiration often changes the effect of precipitation on moisture conditions. The climate trends suggest an important warm and humid tendency averaged over the southern plateau in annual period and in August. Moisture conditions would probably get drier at large area in the headwater region of the three rivers in annual average and months from April to November, and the northeast of the plateau from July to September. Complicated climatic trends over the Tibetan Plateau reveal that climatic factors have nonlinear relationships, and resulte in much uncertainty together with the scarcity of observation data. The results would enhance our understanding of the potential impact of climate change on environment in the Tibetan Plateau. Further research of the sensitivity and attribution of climate change to moisture conditions on the plateau is necessary.     

OPTIMIZATION IN ORGANIC SYNTHESIS.AN APPROACH TO OBTAINING KINETIC INFORMATION BY SEQUENTIAL RESPONSE SURFACE MODELLING.OUTLINE OF THE PRINCIPLES

A method is presented by which it is possible to estimate the initial rate of chemical reactions when theexperimental conditions are varied according to a response surface design.The method is intended as acomplementary method for analysing data obtained from experiments in synthetic chemistry when theobjective is to optimize the yield of the reaction.Data obtained by simulations have been used to develop the method.From the simulated reactions itis shown that sequential analysis of the chemical yield of the reaction makes it possible to estimate modelswhich describe how the parameters of the response surface of the yield vary over time.The derivativesof these time functions of the response surface parameters can be used to define a rate function whichdescribes how the variations in the experimental conditions influence the rate of the reaction.It is shown how such rate functions can be used to afford reasonable estimates of the initial rates ofthe reaction.The initial reaction rates thus estimated can be used to determine the kinetic order of thereactants and also to provide estimates of the activation energy of the reaction.A thorough discussion of how canonical analysis of the rate function may assist in the elucidation ofreaction mechanisms is given.     

Simulation and prediction of monthly accumulated runoff,based on several neural network models under poor data availability

Most previous research on areas with abundant rainfall shows that simulations using rainfall-runoff modes have a very high prediction accuracy and applicability when using a back-propagation(BP), feed-forward, multilayer perceptron artificial neural network(ANN). However, in runoff areas with relatively low rainfall or a dry climate, more studies are needed. In these areas—of which oasis-plain areas are a particularly good example—the existence and development of runoff depends largely on that which is generated from alpine regions. Quantitative analysis of the uncertainty of runoff simulation under climate change is the key to improving the utilization and management of water resources in arid areas. Therefore, in this context, three kinds of BP feed-forward, three-layer ANNs with similar structure were chosen as models in this paper.Taking the oasis–plain region traverse by the Qira River Basin in Xinjiang, China, as the research area, the monthly accumulated runoff of the Qira River in the next month was simulated and predicted. The results showed that the training precision of a compact wavelet neural network is low; but from the forecasting results, it could be concluded that the training algorithm can better reflect the whole law of samples. The traditional artificial neural network(TANN) model and radial basis-function neural network(RBFNN) model showed higher accuracy in the training and prediction stage. However, the TANN model, more sensitive to the selection of input variables, requires a large number of numerical simulations to determine the appropriate input variables and the number of hidden-layer neurons. Hence, The RBFNN model is more suitable for the study of such problems. And it can be extended to other similar research arid-oasis areas on the southern edge of the Kunlun Mountains and provides a reference for sustainable water-resource management of arid-oasis areas.     

Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin,China

Drought is one of the most frequent and widespread natural disasters and has tremendous agricultural, ecological, societal, and economic impacts. Among the many drought indices, the standardized precipitation index(SPI) based on monthly precipitation data is simple to calculate and has multiscale characteristics. To evaluate the applicability of high spatiotemporal resolution satellite precipitation products for drought monitoring, based on the Tropical Rainfall Measuring Mission(TRMM) products and station-based meteorological data, the SPI values at different time scales(1, 3, 6, and 12 months) were calculated for the period of 1998–2016 in the middle and lower reaches of the Yangtze River Basin(MLRYRB). The temporal correlations show that there is a high degree of consistency between calculations at the different time scales(1, 3, 6 and 12 months) based on the two data sources and that the amplitude of fluctuations decreases with increasing time scale. In addition, the Mann-Kendall(MK) test method was applied to analyze the trends from 1998 to 2016, and the results suggest that wetting trends clearly prevailed over drying trends. Moreover, a correlation analysis of the two data sources based on 60 meteorological stations was performed with the SPI values at different time scales. The correlation coefficients at the short time scales(1, 3, and 6 months) are all greater than 0.7, and the correlation coefficient at the long time scale(12 months) is greater than 0.5. In summary, the results demonstrate that the TRMM 3 B43 precipitation product provides a new data source that can be used for reliable drought monitoring in the MLRYRB.     

The principles and guidelines for designing long-term agronomic experiments

Many of the important questions facing farming systems in the world today require long-term studies to provide meaningful information and answers. A long-term agronomic experiment (LTAE) should (1) have long-term objectives; (2) study important soil processes or ecological processes; and (3) be related to the productivity and sustainability of systems. A well established LTAE can provide both insights into how the system operates and foresight into where the system goes. The prerequisites for setting up a LTAE are the secured land, continuous funding and dedicated scientists. A number of principles must be considered carefully when establishing a LTAE, (1) the site must be representative of large areas; (2) the treatments should be simple, but focusing on the big questions; (3) the plots should be large enough to allow subsequent modification of the experiment if this becomes necessary; (4) crop rotations should minimise, wherever possible, the risk of build-up of pests and diseases, and rotational phase should be considered in a rotational experiment; (5) a clearly defined experimental protocol should be developed to ensure data collected is scientifically valid and statistically analysable, but with flexibility to allow essential changes; (6) soil samples, possibly plant samples, should be achieved to provide better answer to the original questions when new, perhaps more accurate analytical techniques are developed, or answer new research questions that were not considered in the original design. The MASTER experiment in Australia was used as a case study to demonstrate how these principles are implemented in practice.     

Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

The Soil and Water Assessment Tool(SWAT) was implemented in a small forested watershed of the Soan River Basin in northern Pakistan through application of the sequential uncertainty fitting(SUFI-2) method to investigate the associated uncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period(1991–2000) with an initial 4-year warm-up period(1987–1990), and was validated for the subsequent 10-year period(2001–2010). The model evaluation indices R~2(the coefficient of determination), NS(the Nash-Sutcliffe efficiency), and PBIAS(percent bias) for stream flows simulation indicated that there was a good agreement between the measured and simulated flows. To assess the uncertainty in the model outputs, p-factor(a 95% prediction uncertainty, 95PPU) and r-factors(average wideness width of the 95 PPU band divided by the standard deviation of the observed values) were taken into account. The 95 PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factor was 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients(R~2 and NS) for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78 and 0.74, respectively. Meanwhile, the 95 PPU covered more than 60% of the observed sediment data during calibration and validation. Moreover, improved model prediction and parameter estimation were observed with the increased number of iterations. However, the model performance became worse after the fourth iterations due to an unreasonable parameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertainty during the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessment of water balance components, climate change studies, and land use management practices.     

引发意大利托斯卡纳区滑坡的区域降水阈值体系的定义——统计与环境分析(英文)

The aim of this work is the determination of regional-scale rainfall thresholds for the triggering of landslides in the Tuscany Region(Italy).The critical rainfall events related to the occurrence of 593 past landslides were characterized in terms of duration(D) and intensity(I).I and D values were plotted in a log-log diagram and a lower boundary was clearly noticeable:it was interpreted as a threshold representing the rainfall conditions associated to landsliding.That was also confirmed by a comparison with many literature thresholds,but at the same time it was clear that a similar threshold would be affected by a too large approximation to be effectively used for a regional warning system.Therefore,further analyses were performed differentiating the events on the basis of seasonality,magnitude,location,land use and lithology.None of these criteria led to discriminate among all the events different groups to be characterized by a specific and more effective threshold.This outcome could be interpreted as the demonstration that at regional scale the best results are obtained by the simplest ap-proach,in our case an empirical black box model which accounts only for two rainfall pa-rameters(I and D).So a set of thresholds could be conveniently defined using a statistical approach:four thresholds corresponding to four severity levels were defined by means of the prediction interval technique and we developed a prototype warning system based on rainfall recordings or weather forecasts.     

长沙市人居环境与经济协调发展的不确定性评价(英文)

The coordinated development of human settlement environment and economy is of vital significance to urban sustainable development and urban ecosystem health. Urban human settlement and economic systems exist in urban ecosystems, which are a structural complexity. Therefore the research is being challenged by some uncertain factors between human settlements and economic systems. However most of the researches were focused on its determinate objective aspects and qualitative analyses while less concern on the quantitative evaluation of coordinated development of urban human settlement environment and economy, especially little on its uncertain aspect. At present, the urgent task is to study the coordinated development of urban settlement environment and economy in terms of the effect of uncertainty. This study analyzed the uncertain characteristics, which would be confronted at different stages, such as confirming the index categories, their bound values, and their construction rate, etc. According to the actual urban conditions, many construction principles based on uncertainties are put forward and an indicating system for human settlement and economic evaluation is established. Moreover, the application of fuzzy mathematics presents a new method and a calculation model for the comprehensive assessment of the coordinated development of urban human settlement environment and economy. The application of the method and model in Changsha city of China showed that the assessment results can reflect not only the overall coordination degree of the city, but also the mode of interactive mechanism between urban economic system and human settlement environment.     

Applicative limitations of sediment transport on predictive modeling in geomorphology

Sources of uncertainty or error that arise in attempting to scale up the results of laboratory-scale sediment transport studies for predictive modeling of geomorphic systems include: (i) model imperfection, (ii) omission of important processes, (iii) lack of knowledge of initial conditions, (iv) sensitivity to initial conditions, (v) unresolved heterogeneity, (vi) occurrence of external forcing, and (vii) inapplicability of the factor of safety concept. Sources of uncertainty that are unimportant or that can be controlled at small scales and over short time scales become important in large-scale applications and over long time scales. Control and repeatability, hallmarks of laboratory-scale experiments, are usually lacking at the large scales characteristic of geomorphology. Heterogeneity is an important concomitant of size, and tends to make large systems unique. Uniqueness implies that prediction cannot be based upon first-principles quantitative modeling alone, but must be a function of system history as well. Periodic data collection, feedback, and model updating are essential where site-specific prediction is required.     

大尺度水循环模拟系统不确定性研究进展

水循环过程受众多自然因素和人为因素影响,决定了水循环系统的变化性和复杂性。水循环系统模型作为研究流域水文循环过程及演变规律的重要工具,必然也存在较大的不确定性,特别是对于大尺度陆-气耦合下的水循环模拟系统,其不确定性来源包括输入和参数不确定性、结构不确定性、方法不确定性以及初始和边界条件不确定性。本文在分析不确定性量化方法和传统水文模型不确定性研究基础上,重点评述当前大尺度水循环系统模拟的不确定性研究进展和存在的瓶颈问题,并介绍一种针对大型复杂动力系统的不确定性量化解决方案和工具系统-PSUADE,基于此讨论PSUADE在大尺度水循环模拟系统不确定性量化过程中的优势。     

土壤风力侵蚀研究现状与进展

土壤风蚀实质上是土壤颗粒在风力作用下发生位移的自然过程,它包含了土壤夹带起沙、空间输移及沉降淀积等三个阶段。风蚀研究的根本任务是对土壤风蚀的范围、强度及数量进行监测、评价以及预测预报。为此,科学家在断面尺度、地块(图斑)尺度以及区域尺度上,以年、月、日、小时等时间尺度展开了研究。当前的风蚀研究主要有以下四个方向:实验室和野外风洞实验研究、野外观测与网络监测、风蚀评价以及风蚀估算与过程模拟研究等。实验室和野外风洞实验有助于人们深入理解风蚀的基本过程;而网络监测数据对于实现风蚀研究从局部到整体的尺度转换具有重要意义;在风蚀评价方面,对风蚀发源地的风蚀评价研究卓有成效,但针对风蚀物运移过程及沉降过程的研究成果还不多见;在风蚀估算和过程模拟方面,一些模型或应用系统已经在不同的区域以不同的时空尺度取得良好的效果,但是要将这些模型和系统在不同的时空尺度上做进一步推广还有许多工作要做。遥感和GIS等现代地理信息技术在区域尺度的风蚀研究中有着显著的优势,并贯穿了风蚀研究的全过程。     

Mass Balance of the Greenland Ice Sheet: Can Modern Observation Methods Reduce the Uncertainty?

The future contribution to sea level change from the large ice sheets in Greenland and Antarctica is composed of two terms: (i) a background trend determined by the past climate and dynamic history of the ice sheets on a range of time scales (decadal, millennial or even longer); and (ii) a rise/fall related to future climate change, whether due to anthropogenic effects or natural climate variability. The accelerating development of remote sensing techniques for monitoring ice sheet behaviour, and the use of high-resolution general circulation models to estimate temperature and precipitation changes are likely to result in improved estimates of the sensitivity of ice sheet mass balance to climate change and thereby to narrow down the uncertainty of contribution (ii). Contribution (i) is much more difficult to assess, because the mass balance displays large temporal variability on year-to-year and even on decadal time scales that masks the long-term trend. So, although modern remote sensing techniques enable accurate measurement of ice sheet surface elevation change, the mass changes derived from such measurements, even if performed over a period of several years, might just reflect a statistical fluctuation around the long-term background trend, which we must know in order to assess the future ice sheet contribution to sea level change on century and longer time scales. The measured volume changes must therefore be evaluated on the background of short- and long-term accumulation rates (e.g. determined from ice cores and high-resolution ice radar) and dynamic model studies of ice sheet evolution on century, millennial and longer time scales. The problems are illustrated by using the Greenland ice sheet as an example.     

SWH双源蒸散模型模拟效果验证及不确定性分析

SWH模型是在经典Shuttleworth-Wallace双源蒸散模型的基础上发展起来的蒸散模型。过去的研究结果表明在站点尺度上SWH模型表现出较高模拟精度,但有关模型对主要参数及驱动变量的敏感性以及模型模拟的不确定性来源等缺乏深入理解与认识。本文通过与51个陆地生态系统站点多年的蒸散观测数据对比,在季尺度、年尺度上验证了全国范围内SWH模型的模拟效果,并分析了关键参数和驱动变量对模型不确定性的贡献大小。结果表明：SWH模型在区域尺度上取得了较好的模拟效果,模拟蒸散与实测值R²均在0.75以上。模型各参数中,冠层导度估算涉及的两个参数对蒸散模拟不确定性影响较大;驱动数据中,归一化植被指数对蒸散模拟不确定性影响较大。尽管部分数据（如降水）因插补存在较大的误差,但总体上气候驱动数据对蒸散模拟的不确定性的贡献仍低于NDVI。     

全球冰冻圈变化预测研究现状

冰冻圈是气候系统重要的圈层,对气候系统有强烈的反馈作用,在全球变暖的背景下,冰冻圈的变化和预测研究愈来愈受到科学界的重视。近年来,冰冻圈的预测研究已经取得重要进展,主要表现在:海冰、积雪冻土等子系统模式发展迅速,开展了不少模式比较计划,这些模式能重现大尺度的季节变化和年际变化特征,模拟能力较以前有了大幅度提高。但模式模拟的不确定性仍普遍存在,主要表现在:冰盖等子系统的模式对于其内部的热力过程、其底部与海洋的相互作用过程缺乏有效的观测手段,认识不够清楚,湖冰河冰模式主要还依赖统计相关模型。随着遥感技术以及资料同化技术的不断应用,各个子系统物理过程认识的不断深入,冰冻圈模式预测将日趋完善,逐渐缩小不确定性。     

Degradation of unconsolidated Quaternary landforms in the western North America

One of the major goals of geomorphology is to understand the rate of landscape evolution and the constraints that erosion sets on the longevity of land surfaces. The latter has also turned out to be vital in modern applications of cosmogenic exposure dating and interpretation of lichenometric data from unconsolidated landforms. Because the effects of landform degradation have not been well documented, disagreements exist among researchers regarding the importance of degradation processes in the dating techniques applied to exposures. Here, we show that all existing qualitative data and quantitative markers of landform degradation collectively suggest considerable lowering of the surface of unconsolidated landforms over the typical life span of Quaternary moraines or fault scarps. Degradation is ubiquitous and considerable even on short time scales of hundreds of years on steeply sloping landforms. These conservative analyses are based entirely on field observations of decreasing slope angles of landforms over the typical range of ages in western North America and widely accepted modeling of landscape degradation. We found that the maximum depth of erosion on fault scarps and moraines is on average 34% of the initial height of the scarp and 25% of the final height of the moraine. Although our observations are limited to fault scarps and moraines, the results apply to any sloping unconsolidated landform in the western North America. These results invalidate the prevailing assumption of no or little surface lowering on sloping unconsolidated landforms over the Quaternary Period and affirm that accurate interpretations of lichen ages and cosmogenically dated boulder ages require keen understanding of the ever-present erosion. In our view, the most important results are twofold: 1) to show with a large data set that degradation affects universally all sloping unconsolidated landforms, and 2) to unambiguously show that even conservative estimates of the total lowering of the surface operate at time and depth scales that significantly interfere with cosmogenic exposure and lichen dating.     

Spatially and temporally distributed modeling of landslide susceptibility

Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km² Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.     

Challenges of calculating dunefield mobility over the 21st century

Attention has been directed towards both the impacts of future climate change on environmental systems and dunefield activity in the past, but there has been relatively little consideration of potential dune mobility in a future and possibly warmer world. This paper considers the use and limitations of four Global Circulation Models (GCMs) (Hadcm3, Hadcm2, CSIRO-mk2b and CGCM1), in combination with simple dune mobility indices to predict the activity of the Kalahari dunefield. It is clear that uncertainties surround GCM resolution and accuracy, mobility index robustness for the calculation of intra-annual dune mobility and data collection for mobility index calibration. Macro-scale studies that look at large areas of the world over long time scales are well suited to GCM and mobility index use, but dune mobility can be variable within a dunefield, and it is the extreme sand transporting events, occurring at high temporal resolutions, that are the most important for short term studies. To investigate intra-annual changes in dune mobility over a specific dunefield techniques such as downscaling, weather generators and probability curve fitting can help provide climate predictions for smaller areas over shorter time frames. However, these methods introduce uncertainty of their own, and they often rely on the accuracy of original GCM predictions or the climate parameter relationships observed at present. Analysis of intra-annual changes also requires mobility indices that can model monthly mobility patterns well, although existing indices have only been used for calculating annual dune mobility potential. When they are used for intra-annual predictions, the lack of lag response between precipitation decreases and the assumed vegetation dieback leads to an exaggerated amplitude pattern of dune mobility throughout the year. Calibration of dune mobility indices to dune mobility observed on the ground is therefore important but is hampered by a lack of observed measurements for individual months. Solutions are available to overcome some of the outlined problems, but they can provide their own set of uncertainties, which combine to further reduce the confidence given to future dune mobility predictions.     

Dynamics of coupled human-landscape systems

A preliminary dynamical analysis of landscapes and humans as hierarchical complex systems suggests that strong coupling between the two that spreads to become regionally or globally pervasive should be focused at multi-year to decadal time scales. At these scales, landscape dynamics is dominated by water, sediment and biological routing mediated by fluvial, oceanic, atmospheric processes and human dynamics is dominated by simplifying, profit-maximizing market forces and political action based on projection of economic effect. Also at these scales, landscapes impact humans through patterns of natural disasters and trends such as sea level rise; humans impact landscapes by the effect of economic activity and changes meant to mitigate natural disasters and longer term trends. Based on this analysis, human-landscape coupled systems can be modeled using heterogeneous agents employing prediction models to determine actions to represent the nonlinear behavior of economic and political systems and rule-based routing algorithms to represent landscape processes. A cellular model for the development of New Orleans illustrates this approach, with routing algorithms for river and hurricane-storm surge determining flood extent, five markets (home, labor, hotel, tourism and port services) connecting seven types of economic agents (home buyers/laborers, home developers, hotel owners/ employers, hotel developers, tourists, port services developer and port services owners/employers), building of levees or a river spillway by political agents and damage to homes, hotels or port services within cells determined by the passage or depth of flood waters. The model reproduces historical aspects of New Orleans economic development and levee construction and the filtering of frequent small-scale floods at the expense of large disasters.