新安江模型参数的不确定性分析

水文模型的不确定性研究是水文科学研究的重要课题。模型参数的不确定性分析是水文模型不确定性研究的重要内容之一。本文采用GLUE方法分析新安江模型参数的不确定性,结论基于对不同水文特征流域的长时间径流模拟,研究发现大量"等效性"参数组存在。据此将参数总结为三类:第一类为非敏感参数,如上层张力水容量UM等。它们对似然判据,及确定性系数(R²)影响小。第二类为敏感性参数,如河网蓄水消退系数CS等,其特点是对R²的影响大。第三类为区域性敏感参数,如张力水蓄水容量曲线的方次B等,它们对R²的影响力跟流域特征密切相关。这些结论有助于理解新安江模型参数,为今后流域水文模拟提供参考。文中还展望了未来水文模型不确定性研究的发展方向。     

气候变化情景下青海湟水流域径流变化的HIMS模拟分析

基于国产HIMS(Hydro-Informatic Modeling System)模型,以青海湟水流域为研究区域,利用1986-2000年33个雨量站和8个气象站的逐日降水和气温数据,对其径流变化进行模拟;选取流域内6个水文站同期的实测径流数据,进行参数率定及验证。结果表明:HIMS模型日、月率定及验证结果良好,在湟水流域具有良好的适用性。在此基础之上,分析了湟水流域1961-2010年降水及气温的变化趋势,并对不同气候变化情景下的水文响应(径流量)进行模拟分析。结果显示气候变化对湟水流域径流量变化趋势影响显著,随气温升高和降水量的减少,径流量呈明显的减少趋势,反之,呈增加趋势。     

模型多参数灵敏度与不确定性分析

以潮白河为研究区域,探讨了与模型参数及模型模拟性能有关的多参数灵敏度及不确定性分析方法(Multi-Parameter Sensitivity and Uncertainty Analysis, MPSUA)。基于Monte Carlo模拟的多参数灵敏度分析,可以评价模型中多个参数的相对重要性。GLUE不确定性分析则能对模型性能进行量化评估。实例研究表明,通过MPSUA方法,可以减少优化参数的个数。而且,在没有对模型进行参数优化之前,基于MPSUA就可以确定模型的模拟精度。例如同样的模型在潮河可以获得比在白河更高的模拟精度。这种同一模型在不同流域所体现的差异性,一方面是源于模型结构本身的不完善,另一方面则与用于建模的数据误差有关。SCE-UA参数优化结果与MPSUA结果几乎一致,说明本文的参数灵敏度与模型总体性能评估方法比较合理。     

SCE-UA算法在流溪河模型参数优选中的应用

流溪河模型是一个主要用于流域洪水预报的分布式物理水文模型,目前采用手工试错法优选模型参数,虽然该方法在过去的研究中取得了较好的效果,但参数优选过程较为繁琐,需时较长.文中以广州市流溪水库流域为例,采用SCE-UA算法对流溪河模型优选参数,并使用优选结果进行模拟检验,取得了较好的效果.研究表明,SCE-UA算法能够快速、有效地进行流溪河模型参数的优选,相比手工试错法具有简单、方便、高效的特点,可应用于分布式流域水文模型的参数自动化优选工作.     

甘肃南小河流域土壤水分扩散率单一参数模型的建立及应用

非饱和土壤的水力性质是进行土壤中水分与污染物运移模拟的物理参数,其中土壤水分扩散率是定量模拟土壤水分流动和溶质运移的最重要的参数之一。通过对甘肃省南小河沟流域不同地貌、不同土地利用方式的土壤分层取样,采用水平土柱吸渗法测定各地类的非饱和土壤水分扩散率;运用土壤水分扩散率的单对数模型和双对数模型对试验结果进行分析。结果表明:单对数模型参数具有明显的线性相关,可以建立表征描述南小河沟各地类的土壤水分扩散率的单一参数的单对数模型;进一步从全流域及划分地类后得到的模型参数B与A的关系分析表明它们具有较好的线性关系,可对模型进一步简化为单一参数模型。分析单一参数模型B的统计特征表明:参数B随着地貌类型的变化具有一定的变化特征,可作为土壤水分扩散率空间变异的变异系数。因此,可以用参数B作为反映地貌类型的指标,应用于区域土壤水分扩散率的估计中。     

SLEUTH模型的参数行为研究

为探索SLEUTH模型的参数行为,利用场景分析方法,并以东莞市寮步镇为例,通过SLEUTH模型构建11个场景,预测研究区到2030年的城市扩张情况,进而研究SLEUTH模型的繁衍系数与蔓延系数对城市扩张数量及空间布局的影响.研究结果表明,繁衍系数与蔓延系数对城市扩张的推动作用存在一个最低阈值,当大于该阈值时,城市用地将不断向外扩张,并随时间推移趋于稳定,呈现较强的相关关系,相关系数高达0.9以上,从而充分体现了城市系统的突变性、自组织性与非线性等复杂性特征.研究成果可为深刻理解SLEUTH模型特征提供有利的分析工具.     

气候变化对汉江水华影响评价的参数模型分析方法及其应用研究(英文)

气候变化对河流和湖泊水环境的影响是当前国际国内关注的热点问题和学科前沿问题之一。但是由于其复杂性和不确定性,目前对该问题的科学研究和成果仍然十分有限。本文针对水质污染中的富营养化问题,以中国南水北调中线工程调水区的汉江流域为例,开发和应用了多元线性回归、多元非线性回归、人工神经网络及河流富营养化模型等多种评价方法,建立了与气象要素、水文要素、营养盐负荷相联系的多输入单输出富营养化系统参数模型,深入分析了在人类活动和经济发展所产生的影响以外,气候变化在此基础上对水体富营养化的增益作用。最后本文通过单因子和多因子分析法,甄别出不同情景下各要素对汉江水华的影响。通过计算得出,当其中某一项要素变化而其他两项不变时,其导致河流富营养化的贡献度依次为:污染负荷(14.82%)、水文要素(5.56%)、气象要素(3.7%);当污染负荷和水文要素同时变化时对水华的贡献度最大(20.37%),其次是当污染负荷和气象要素同时变化其贡献度为(15.82%),最后为水文要素和气象要素同时变化时的贡献度为(11.11%)。研究结果表明,对于中国这样的发展中国家来说,当控源和治污不能在短时间内达到良好的效果的时候,气候因素会增加水污染的风险性。即使水体内部污染源稳定,气候变化依然会通过改变水温和水文情势进而影响水体富营养化程度。最后本文通过多种方法比较,根据预测和评估得出的结果制定相应的防治对策,从而对今后的相关研究可起澄清概念和指明方向的作用。     

基于E-FAST的流溪河模型参数敏感性分析

基于E-FAST全局定量敏感性分析方法的思想,提出一个针对分布式物理水文模型——流溪河模型的参数敏感性分析方法,包括参数简化及取值范围确定、参数采样及采样次数的确定、敏感性评价指标的选取、参数敏感度计算4个部分.针对我国4个不同规模的流域(黄龙带水库流域、流溪河水库流域、长湖水库流域和新安江水库流域)开展了分布式物理水文模型参数敏感性分析,并采用4种不同的评价函数进行了对比计算.结果表明,流溪河模型饱和含水率和田间持水率是高度敏感参数,饱和水力传导率、土壤层厚度、土壤特性系数、边坡和河道糙率为敏感参数,其他参数为不敏感参数.     

基于少量典型样点土壤属性空间分布推测模型中的土壤属性参数敏感性分析

针对基于少量典型样点土壤属性空间分布推测模型中的土壤属性参数敏感性问题,以坡位渐变信息结合典型土壤样点的加权平均模型为例,利用东北地形平缓小流域的土壤表层有机质含量样点集,使用阶乘设计、箱线图分析、扰动分析法和本文新设计的MR指数评价该模型的参数敏感性。结果表明,该模型中土壤属性参数敏感性较大,其大小与典型样点空间分布有关。敏感性主要由应用该模型时采用的坡位分类体系的不确定性引起。该文的分析方法可用于对基于少量典型样点的土壤属性空间分布推测模型进行参数敏感性综合分析。     

流域水资源开发阈值模型及其在黄河流域的应用

流域水资源开发阈值是流域水资源合理利用的基础和流域生态系统健康的保证。随着水资源短缺引起的 问题日益突出, 人们对水资源开发阈值的认识也在不断加深, 从最初的“水资源总量”、“可利用量”和“可供水量” 等, 到考虑生态需水和水质保护的水资源可利用量。本文在这些理念的基础上, 提出一种新的流域水资源开发阈值 及其计算模型, 即考虑水资源量、不可利用的洪水量、被污染水量、生态需水量及重复计算水量, 综合计算流域水资 源开发阈值。黄河流域作为我国水资源严重短缺地区, 水资源开发面临严峻挑战。以黄河流域为例, 运用该阈值模 型对黄河流域水资源开发阈值进行初步计算, 得到开发阈值多年平均约为238 亿m3, 占黄河天然径流量的40%左 右, 符合国际公认的流域水资源开发标准。     

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。     

Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model

This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems.     

A case study of model selection and parameter inference by maximum likelihood with application to uncertainty analysis

One of the uses of geostatistical conditional simulation is as a tool in assessing the spatial uncertainty of inputs to the Monte Carlo method of system uncertainty analysis. Because the number of experimental data in practical applications is limited, the geostatistical parameters used in the simulation are themselves uncertain. The inference of these parameters by maximum likelihood allows for an easy assessment of this estimation uncertainty which, in turn, may be included in the conditional simulation procedure. A case study based on transmissivity data is presented to show the methodology whereby both model selection and parameter inference are solved by maximum likelihood.     

Multi-criteria spatial screening and uncertainty analysis applied to direct-use geothermal projects

ABSTRACT

The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems.     

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.     

喀斯特石漠化小流域景观的空间因子分析——以贵州清镇王家寨小流域为例

以贵州喀斯特地区的王家寨小流域为研究对象,基于多源信息,依托RS和GIS技术获取2005年该流域石漠化景观格局信息,以此为基础进行石漠化景观分布指数、石漠化综合指数、 -s平面分析模型以及三次曲线拟合等分析,旨在从小流域尺度上探讨石漠化景观在坡度、坡向、高程和与村庄距离等空间因子上的分布规律。结果表明:潜在、轻度石漠化景观受坡度影响最显著;其他类型石漠化景观受坡向影响最明显。石漠化程度先随坡度的增大而加重,27°后呈缓解趋势;各坡向中南、东南坡石漠化最严重;随高程增加石漠化加剧;距村庄越远石漠化越严重。初步推断各空间因子对石漠化程度的影响由强至弱的顺序为:坡度、坡向、高程、与村庄的距离。