共查询到18条相似文献，搜索用时 142 毫秒
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栅格新安江模型在天津于桥水库流域上游的应用 总被引：3，自引：1，他引：2
栅格新安江模型是在概念性新安江模型的理论基础上,以栅格为计算单元,结合地形地貌和下垫面特性构建出来的水文模型.在于桥水库流域上游的水平口流域应用栅格新安江模型,研究该地区洪水要素的空间变化以及洪水形成过程,讨论洪水模拟效果来验证模型在半湿润地区的适用性.选取水平口流域1978-2012年的洪水进行模型计算,模拟结果较好地反映了流域产流面积的时空变化,且均达到乙级以上精度.初步表明栅格新安江模型在半湿润地区有较好的适用性. 相似文献
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基于卫星遥感的太湖蓝藻水华时空分布规律认识 总被引：14，自引：6，他引：8
由于大尺度水文模型和无资料区水文研究是当前国际水文研究的重点和难点,通过参数区域化方法来估计大尺度区域和无资料区的模型参数值成为了研究的热点之一将HBV模型应用于东江流域及其子流域,采用代理流域法和全局乎均法来估计该区域内无资料流域的模型参数研究表明：HBV模型能较好得用于东江流域径流模拟;交叉检验中,较小的序和ME值对应的参数,其转移效果不一定比较大的R^2和ME值对应的参数转移效果差;全局平均法中,面积权重平均值和泰森多边形插值后平均并不能明显改进子流域算术平均值估计无资料流域的模型参数的模拟结果;两者都能有效用于东江流域无资料流域的参数估计,且效果相差不大。 相似文献
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在半湿润半干旱地区,下垫面条件复杂,产流机制混合多变,而现有的水文模型由于其固定的结构和模式,无法灵活地模拟不同下垫面特征的洪水过程.本文利用CN-地形指数法将流域划分为超渗主导子流域和蓄满主导子流域.将新安江模型(XAJ)、新安江-Green-Ampt模型(XAJG)和Green-Ampt模型(GA)相结合,在子流域分类的基础上构建空间组合模型(SCMs),并在半湿润的东湾流域和半干旱的志丹流域进行检验.结果表明:东湾流域的参数由水文模型来主导;而志丹流域的参数受主导径流影响很大.在东湾流域,偏蓄满的模型模拟结果优于偏超渗的模型,且SCM2模型(XAJ和XAJG的组合模型)的模拟效果最好(径流深合格率为75%,洪峰合格率75%);而SCM5模型(GA和XAJG的组合模型)在以超渗产流为主的志丹流域模拟最好(径流深合格率53.3%,洪峰合格率53.3%).在半干旱半湿润流域,SCMs模型结构灵活,在地形和土壤数据的驱动下,具有更合理的模型结构和参数,模拟精度较高,适应性较强. 相似文献
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阿克苏河(中吉国际河流)现已成为塔里木河的主河源,它对塔里木河干流的形成、发展和演变过程起着决定性作用.随着国家西部开发战略--塔里木河流域综合治理的深入开展和实施,阿克苏河流域的水文特征、水文预报等研究成为热点.特别是在干旱区中纬度高海拔流域的河流中,阿克苏河是以冰雪融水补充为主河流的典型代表,对阿克苏河流域径流进行预报研究具有理论和现实意义.鉴于此:(i)结合干旱区无资料或少资料的现状,利用现有的水文气象资料,尝试并构建日尺度水文预报方法;(ii)采用高空气温代替地面实测气温与日径流相关关系法、AR(p)预报模型、气温降雨修正的AR(p)预报模型和NAM降雨径流模型,对阿克苏流域的两大支流进行日径流模拟和预报;(iii)对4种方法模拟结果进行对比分析,表明利用气温和降雨修正后的AR(p)模型所用水文气象资料少、应用简便、预报精度较高、比较适用于资料较缺乏的阿克苏流域的短期径流预报.该研究以日尺度进行水文预报,在该流域尚属首次,不仅为阿克苏河、塔里木河的水文预报、洪水防治和全流域的水量调度等提供基础,也为干旱区其他流域的水文预报提供了参考方法. 相似文献
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阿克苏河(中吉国际河流)现已成为塔里木河的主河源,它对塔里木河干流的形成、发展和演变过程起着决定性作用.随着国家西部开发战略--塔里木河流域综合治理的深入开展和实施,阿克苏河流域的水文特征、水文预报等研究成为热点.特别是在干旱区中纬度高海拔流域的河流中,阿克苏河是以冰雪融水补充为主河流的典型代表,对阿克苏河流域径流进行预报研究具有理论和现实意义.鉴于此(i)结合干旱区无资料或少资料的现状,利用现有的水文气象资料,尝试并构建日尺度水文预报方法;(ii)采用高空气温代替地面实测气温与日径流相关关系法、AR(p)预报模型、气温降雨修正的AR(p)预报模型和NAM降雨径流模型,对阿克苏流域的两大支流进行日径流模拟和预报;(iii)对4种方法模拟结果进行对比分析,表明利用气温和降雨修正后的AR(p)模型所用水文气象资料少、应用简便、预报精度较高、比较适用于资料较缺乏的阿克苏流域的短期径流预报.该研究以日尺度进行水文预报,在该流域尚属首次,不仅为阿克苏河、塔里木河的水文预报、洪水防治和全流域的水量调度等提供基础,也为干旱区其他流域的水文预报提供了参考方法. 相似文献
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本文旨在将实时监测得到的土壤墒情转化为流域水文模型可以直接使用的土壤含水量,论证将实时土壤墒情资料用于实时预报的可行性;利用实时监测土壤墒情,改进传统的模型结构,设计基于实测土壤墒情的降雨径流水文预报模型.采用土壤含水量误差抗差估计技术以抵御观测资料粗差的影响,提高系统的稳定性;并在此基础上提出了土壤含水量系统响应修正方法,以提高模型计算精度.将该模型应用于实验流域——宝盖洞流域进行应用检验,洪水模拟合格率达到92.3%,整体模拟精度达到甲级. 相似文献
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水文非线性系统与分布式时变增益模型 总被引：5，自引：0，他引：5
论述了以Volterra泛函级数表达的流域降雨-径流非线性系统理论与概念性模拟方法. 依据流域数值高程模型、遥感信息和单元水文过程, 提出了水文非线性系统理论的时变增益模型(TVGM)和推广应用到流域时空变化模拟的分布式时变增益模型(DTVGM). 研究表明, 除了常用的非线性系统分析方法之外, 从复杂水文关系研究中另辟蹊径, 提出一种简单关系的非线性系统分析是完全有可能的. 时变增益水文模型的提出及其与一般性水文非线性系统的联系就是一个例证. 水文非线性系统方法与分布式流域水文模拟结合的DTVGM模型, 能够发挥水文系统方法与分布式水文模拟方法相结合的优点, 探索环境变化下的流域水文模拟问题. 将DTVGM分别应用到河西走廊干旱地区的黑河流域和华北地区潮白河流域实例研究, 模拟了水文时空变化以及陆面覆被变化与水文影响分析, 取得了较好的效果, 说明了其特色和应用价值. 相似文献
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以淮河流域吴家渡水文站作为试验站点,采用基于贝叶斯平均法(BMA)的集合预报模型处理来源于马斯京根法、一维水动力学方法、BPNN(Back Propagation Neural Network)的预报流量序列,通过分析BMA的参数以及其预报结果,对各方法在淮河典型站点流量预报中的适用性进行验证与分析.经2003—2016年19场洪水模拟检验可知,BMA模型能够有效避免模型选择带来的洪水预报误差放大效应,可以提供高精度、鲁棒性强的洪水预报结果.通过进一步比较各模型统计最优的频率与BMA权重值之间的相关性,发现权重值不适用于对单场洪水预报精度评定,而适用于描述多场洪水预报中,模型为最优的统计频率;基于大量先验信息,提前获取BMA的权重等参数,将是指导模型选择、降低洪水预报不确定性、改进洪水预报技术的有效手段. 相似文献
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基于改进型SIMTOP参数化径流方案和新安江模型的三层土壤水量平衡计算方法,本文构建了一个输入数据和率定参数较少、同时具有地形指数尺度转换机制、较好描述二维水文过程的简单高效的大尺度水文模型TOPX,并将其与区域环境系统集成模式RIEMS紧密耦合,以增强区域气候模式对大尺度流域径流量的定量数值模拟能力．TOPX模型在酉水河流域和泾河流域的离线测试表明：该模型对小尺度流域的径流量模拟精度较高,能够较好地描述流域水文变化过程;同时,该模型在大尺度上具有较强的分布式模拟能力,能够捕捉陆面水文过程的主要特征和时空演变特点．TOPX与RIEMS的耦合模式在泾河流域进行了在线测试,借助TOPX模型中的地形指数降尺度转换和水文过程产汇流机制,耦合模式实现了利用区域气候模式模拟的气象资料来驱动水文模型进行大尺度流域日径流量的模拟．进一步分析还表明：区域气候模式RIEMS模拟的降水时空分布数据的精度是影响耦合模式对径流量模拟效果的关键因素． 相似文献
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Streamflow forecasting methods are moving towards probabilistic approaches that quantify the uncertainty associated with the various sources of error in the forecasting process. Multi-model averaging methods which try to address modeling deficiencies by considering multiple models are gaining much popularity. We have applied the Bayesian Model Averaging method to an ensemble of twelve snow models that vary in their heat and melt algorithms, parameterization, and/or albedo estimation method. Three of the models use the temperature-based heat and melt routines of the SNOW17 snow accumulation and ablation model. Nine models use heat and melt routines that are based on a simplified energy balance approach, and are varied by using three different albedo estimation schemes. Finally, different parameter sets were identified through automatic calibration with three objective functions. All models use the snow accumulation, liquid water transport, and ground surface heat exchange processes of the SNOW17. The resulting twelve snow models were combined using Bayesian Model Averaging (BMA). The individual models, BMA predictive mean, and BMA predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best and the BMA variance was lowest at the colder sites with high winter precipitation and little mid-winter melting. An individual snow model would often outperform the BMA predictive mean. However, observed snow water equivalent (SWE) was captured within the 95% confidence intervals of the BMA variance on average 80% of the time at all sites. Results are promising that consideration of multiple snow structures would provide useful uncertainty information for probabilistic hydrologic prediction. 相似文献
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While seasonal time-varying models should generally be used to predict the daily concentration of ground-level ozone given its strong seasonal cycles, the sudden switching of models according to their designated period in an annual operational forecasting system may affect their performance, especially during the season’s transitional period in which the starting date and duration time can vary from year to year. This paper studies the effectiveness of an adaptive Bayesian Model Averaging scheme with the support of a transitional prediction model in solving the problem. The scheme continuously evaluates the probabilities of all the ozone prediction models (ozone season, nonozone season, and the transitional period) in a forecasting system, which are then used to provide a weighted average forecast. The scheme has been adopted in predicting the daily maximum of 8-h averaged ozone concentration in Macau for a period of 2 years (2008 and 2009), with results proved to be satisfactory. 相似文献
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Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis. 相似文献
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Shuai Li Lihua Xiong Hong-Yi Li L. Ruby Leung Yonas Demissie 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(1):251-269
Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging of four monthly water balance models was proposed. The method was applied to the Weihe River Basin, the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956–1990) and human-impacted period (1991–2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models. 相似文献
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Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application 
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下载免费PDF全文 Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments 总被引：5，自引：0，他引：5
This study attempts to assess the uncertainty in the hydrological impacts of climate change using a multi-model approach combining multiple emission scenarios, GCMs and conceptual rainfall-runoff models to quantify uncertainty in future impacts at the catchment scale. The uncertainties associated with hydrological models have traditionally been given less attention in impact assessments until relatively recently. In order to examine the role of hydrological model uncertainty (parameter and structural uncertainty) in climate change impact studies a multi-model approach based on the Generalised Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods is presented. Six sets of regionalised climate scenarios derived from three GCMs, two emission scenarios, and four conceptual hydrological models were used within the GLUE framework to define the uncertainty envelop for future estimates of stream flow, while the GLUE output is also post processed using BMA, where the probability density function from each model at any given time is modelled by a gamma distribution with heteroscedastic variance. The investigation on four Irish catchments shows that the role of hydrological model uncertainty is remarkably high and should therefore be routinely considered in impact studies. Although, the GLUE and BMA approaches used here differ fundamentally in their underlying philosophy and representation of error, both methods show comparable performance in terms of ensemble spread and predictive coverage. Moreover, the median prediction for future stream flow shows progressive increases of winter discharge and progressive decreases in summer discharge over the coming century. 相似文献
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Abstract This paper presents four different approaches for integrating conventional and AI-based forecasting models to provide a hybridized solution to the continuous river level and flood prediction problem. Individual forecasting models were developed on a stand alone basis using historical time series data from the River Ouse in northern England. These include a hybrid neural network, a simple rule-based fuzzy logic model, an ARMA model and naive predictions (which use the current value as the forecast). The individual models were then integrated via four different approaches: calculation of an average, a Bayesian approach, and two fuzzy logic models, the first based purely on current and past river flow conditions and the second, a fuzzification of the crisp Bayesian method. Model performance was assessed using global statistics and a more specific flood related evaluation measure. The addition of fuzzy logic to the crisp Bayesian model yielded overall results that were superior to the other individual and integrated approaches. 相似文献
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Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging 总被引：4，自引：0，他引：4
In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT. 相似文献