共查询到18条相似文献，搜索用时 156 毫秒
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在贝叶斯理论框架下,根据一种可结合多个水文模型给出模拟或预报结果的IBUNE方法探讨了水文模型的输入、参数以及结构的不确定性问题。将SCEM-UA算法和EM算法嵌入新安江和TOPMODEL水文模型用于参数优化和模型平均,进而将输入与参数的综合不确定性处理后得到的预报量后验分布进行多模型综合,据此对水文模型的不确定性及其对水文模拟结果的影响进行评价。以湖南洣水流域龙家山水文站以上集水区域为例进行了应用研究,结果表明,IBUNE方法能够有效估计水文模型的不确定性,并能给出合理的概率预报区间。 相似文献
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"异参同效"现象是水文模型参数率定过程中的一个难题。为了减少需要率定的模型参数个数,从而降低模型参数的相互作用,构建了利用土壤属性直接估计VIC(Variable Infiltration Capacity)模型3个基流参数的框架,并在3个位于不同水文气候条件下的典型流域中作了实例研究。传统的VIC模型参数估计方法需要率定6个参数;而在新的参数估计框架下,需要率定的模型参数从6个减少为3个。蒙特卡罗模拟结果表明新的参数估计框架提高了参数的敏感性。同时,新的参数估计方法模拟的径流过程和之前通过率定得到的径流过程差别很小。GLUE(Generalised Likelihood Uncertainty Estimation)方法分析得到径流模拟的不确定性结果表明:新的参数估计框架计算径流的90%置信区间要明显小于传统方法,而且对于低流量的模拟效果更加明显,也就是说采用新的参数估计框架可以显著降低径流模拟的不确定性。这种基流参数估计方法可以被应用于其他相似的水文模型中。 相似文献
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水文模型的参数优化率定一直以来是水文预报领域的重要研究内容,当水文模型的结构确定后,水文模型参数的选择对水文模型整体性能和水文预报结果的好坏有着至关重要的影响.针对传统水文模型参数优选采用单一目标不能充分全面挖掘水文观测资料中蕴含的水文特征信息的缺陷,本文以新安江三水源模型为例,尝试采用多目标优化算法优化率定水文模型,算例应用分析表明,通过合理的选择目标函数的种类和数目,采用多目标进化算法优化率定模型参数,可以获得相对于单目标率定模型参数更优的结果.进一步,研究工作针对模型参数优化的结果进行分析,可以明显看出模型参数优化中存在“异参同效”现象,为后续模型参数不确定性分析等相关研究工作的开展做好了铺垫. 相似文献
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水文模型是模拟水循环过程重要手段,依靠单个模型进行模拟往往存在很大的不确定性,使通过多模型进行组合模拟成为必然趋势。选取3个集总式水文模型应用贝叶斯模型平均(BMA)进行流域月径流量的多模型模拟,采用期望最大化算法推求BMA分布参数以得到BMA均值模拟序列和90%不确定性区间。以武烈河实测数据为例进行分析,结果表明:BMA方法既能通过均值模拟提供更高精度的模拟效果,还可通过不确定性置信区间定量评价模型结构不确定性,为径流模拟提供丰富信息。 相似文献
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选取具有物理基础的分布式水文模型MIKE SHE来模拟大尺度资料稀缺地区水文过程。以塔里木河主要源区之一开都河流域为研究区域,将流域内气象水文站点观测数据与遥感数据相结合,运用GIS空间分析方法修正数据输入。利用气象、土壤类型、土地利用和地表覆盖、数字高程和降雨等资料,研究大气、陆面、地表水和地下水的相互作用机理,通过模型敏感性分析确定了5个"自由"参数,并依据出山口水文站数据对模型进行率定和验证。结果表明,MIKE SHE能在水文、气象站点稀少,土壤及水文地质数据缺乏的条件下,模拟开都河流域的日径流过程,模型效率系数达到0.7以上,率定期与验证期水量平衡误差均小于3%,模拟径流与实测径流高度相关。 相似文献
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以深度学习为核心的数据驱动方法逐步应用于地球科学领域,但这类方法在模型的可解释性和物理一致性等方面还存在挑战。在遥感大数据背景下,如何结合深度学习和数据同化方法,发展陆地水循环过程模拟与预报的新技术和新方法成为地球科学领域的重要研究方向。重点梳理了近年来深度学习在改善陆地水循环要素观测数据质量以及深度学习如何减少物理模型不确定性方面的最新进展,从观测、模型和系统集成3个方面凝练出深度学习融合遥感大数据的陆地水文数据同化研究的关键科学问题：(1)深度学习反演遥感产品时,如何增强样本的时空代表性？(2)如何发展数据同化框架下物理导引的深度学习新方法？(3)如何通过“数据—模型”双驱动提升陆地水循环的可预报性？开展相关研究和探索将有助于推动“数据—模型”混合建模方法在水文领域的深入应用,提高陆地水循环过程的模拟和预测能力。 相似文献
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水文模拟不确定性长期以来是制约寒区水文发展的瓶颈问题。水稳定同位素示踪为认识冰川流域径流过程提供了重要“指纹”信息,但仍缺乏有效模型将该信息与冰川水文模型耦合,而且同位素信息对冰川流域水文模型不确定性的约束效果也有待检验。将水稳定同位素信息（δ18O）与冰川流域水文模型FLEXG相耦合,实现对冰川流域水稳定同位素和径流过程的耦合建模（FLEXG-iso）,并在乌鲁木齐河源1号冰川流域进行模拟检验和径流分割。结果表明：模型不仅对2013—2016年径流过程有良好的模拟效果,还可以重现水稳定同位素、冰川物质平衡等重要过程。利用水稳定同位素这一辅助数据,提高了模型参数的识别能力,减少了模拟过程中各水源的相互妥协效应和不确定性范围。2013—2016年1号冰川断面径流32%~34%来自融雪,48%~51%来自融冰,0%~7%来自地下水,12%~15%来自降雨径流。水稳定同位素对雪和冰川相关中间过程有明显的约束能力,原有模型对融冰贡献的模拟偏高约7%。FLEXG-iso模型的建立有助于推动寒区水文学理论和方法的发展,以及寒区水资源、生态环境保护等相关决策制定。 相似文献
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选取具有物理基础的分布式水文模型MIKE SHE来模拟大尺度资料稀缺地区水文过程.以塔里木河主要源区之一开都河流域为研究区域,将流域内气象水文站点观测数据与遥感数据相结合,运用GIS空间分析方法修正数据输入.利用气象、土壤类型、土地利用和地表覆盖、数字高程和降雨等资料,研究大气、陆面、地表水和地下水的相互作用机理,通过模型敏感性分析确定了5个"自由"参数,并依据出山口水文站数据对模型进行率定和验证.结果表明,MIKE SHE能在水文、气象站点稀少,土壤及水文地质数据缺乏的条件下,模拟开都河流域的日径流过程,模型效率系数达到0.7以上,率定期与验证期水量平衡误差均小于3%,模拟径流与实测径流高度相关. 相似文献
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无资料地区降水径流模拟是水文学研究的国际前沿和热点问题。水文模型参数移植是无资料地区降水径流模拟的重要方法,对径流模拟精度具有重要的影响。利用核密度估计和蒙特卡罗随机模拟等方法,构建了一种水文模型参数移植误差驱动的无资料地区径流模拟不确定性定量评估框架。以广西壮族自治区42个有水文监测站点的典型中小河流为研究对象,率定新安江模型参数并模拟日径流和洪水过程,将42个典型流域依次假定为无资料流域,利用基于回归分析、相似流域和机器学习的参数移植方法,模拟无资料地区的洪水过程并识别最优的参数移植方法,分析移植法估算的模型参数值和直接率定值相比误差的概率分布特征,定量评估模型参数移植误差带来的径流模拟不确定性。研究结果表明：(1)基于回归分析的参数移植法模拟无资料地区洪水过程的精度优于相似流域法,优选的机器学习算法比传统回归分析法和相似流域法的计算精度提高了7%～15%;(2)与模型参数率定值相比,移植方法计算的模型参数具有一定的误差,对洪水模拟敏感参数的误差小于不敏感参数;(3)受模型参数移植误差的影响,利用蒙特卡罗法随机模拟的洪水过程具有一定的不确定性,洪量和洪峰相对误差的主要区间分别为10... 相似文献
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模型不确定性研究是水文科学的重要课题。以尼泊尔Bagmati流域为案例,采用了马尔科夫链蒙托卡罗(Markov Chain Monte Carlo)、蒙托卡罗(Monte Carlo)和拉丁超立方体(Latin Hypercube)等三种方法,分析了水箱模型输出成果的不确定性,并将三种方法所获得参数不确定性进行了比较。另外,运用Meta-Gaussian模型计算了总体不确定性,在基于所采用的似然函数基础上,对由参数导致模型输出的不确定性和模型输出的总体不确定性进行了比较。结果显示,模型的不确定性比参数不确定性更为重要,同时也表明,尽管蒙托卡罗和拉丁超立方体两种模拟方法产生几乎相同的结果,但两者都与马尔科夫链蒙托卡罗方法有很大的不同。 相似文献
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Qiuhong Tang Xingcai Liu Zhe Li Xiaobo Yun Xuejun Zhang Qiang Yu Jun Li Yongyong Zhang Huijuan Cui Siao Sun Chi Zhang Yin Tang Guoyong Leng 《地球科学进展》2019,34(2):115-123
The terrestrial water cycle is influenced by a wide range of climatic variables and human disturbances. In the era of the Anthropocene, when humans drive the changes in atmospheric and hydrological processes in river basins, there is an urgent need to include human impacts in the study of the terrestrial water cycle. This paper focused on the large-scale hydrological modeling which takes account of human impacts, reviewed the research progress of the natural and human-induced changes in the terrestrial water cycle and the development of comprehensive terrestrial hydrological models in recent years, and proposed that an integrated water system model with human-related processes such as crop water demand model, engineering regulation and social water demand, be the key to large-scale water cycle simulations under changing environment. Based on the existing large-scale land surface hydrological model, there is a need to put forward the integration of the human-related processes. A comprehensive integrated water system model that considers multi-processes can help us to understand the key mechanisms of how climate change and human activity influence the regional water cycle. It also provides a theoretical and practical basis for investigating the causes and effects of changes in terrestrial water cycle under a changing environment, and thus offers scientific support for climate change adaptation in the water sector. 相似文献
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陆地水循环过程的综合集成与模拟 总被引：1，自引：1，他引：0
在气候变化与人类活动的影响下,陆地水循环过程发生了明显改变,并导致了一系列资源环境问题。深入认识陆地水循环过程的变化机理,发展陆地水循环过程综合集成模拟技术,预估未来陆地水循环的变化趋势,是当前水循环研究面临的重要任务。主要关注人类活动影响下的大尺度(大河流域或大陆尺度)陆地表层系统水循环模拟,梳理了近年来陆地水循环过程综合集成与模拟相关的研究进展,指出当前大尺度陆地表层系统水循环模拟模型的主要问题是对自然过程与人类活动过程间相互作用描述不足,以及人类活动参数化方案的不完善。因此,完善人类活动参数化方案,构建陆地水循环过程的综合集成模型,是模拟研究的重要发展方向之一。同时,考虑多要素过程的综合集成模型有助于解释气候变化与人类用水活动影响水循环变化的关键机制,为探索变化环境下陆地水循环变化成因及其效应提供理论与实践基础,其结果将为区域水资源配置及应对全球变化的战略决策提供科学依据。 相似文献
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Anzhou Zhao 《Arabian Journal of Geosciences》2016,9(15):664
Hydrological process modeling depends on the soil data spatial resolution of the watershed. Especially, in a large-scale watershed, could a higher resolution of soil data contribute to a more accurate result? In this study, two soil datasets with different classification systems FAO (World Reference Base) and GSCC (the Genetic Soil Classification of China) were used as inputs for the SWAT model to study the effects of soil datasets on hydrological process modeling in Weihe River basin, China. Results show that the discharge simulated using FAO soil data was better than one simulated using GSCC soil data before model calibration, which indicates that FAO soil data needed less effort to calibrate. After model calibration, discharges were simulated better by both of FAO and GSCC soil data but statistical parameters demonstrate that we can make a relatively more accurate estimation of discharge using the GSCC rather than FAO soil data. Soil water content (SW) simulated using GSCC soil data was statistically significantly higher than those simulated using FAO soil data. However, variations in other hydrological components (surface runoff (SURQ), actual evapotranspiration (ET), and water yield (WYLD) were not statistically significant. This might be because SW is more sensitive to soil properties. For studies aiming to simulate or compare SW, merely calibrating and validating models using river discharge observations is not enough. The hydrological modelers need to identify the key hydrological components intrinsic to their study and weigh the advantages and disadvantages before selecting suitable soil data. 相似文献
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全球气候变化影响了气象水文要素的时空分布特性,气象水文干旱事件的转化关系及风险传播特征亟待研究。基于站点、栅格观测资料和CMIP5（Coupled Model Inter-comparison Project Phase5）的19个气候模式输出数据,采用新安江等4个水文模型模拟了中国135个流域历史（1961—2005年）和未来时期（2011—2055年,2056—2100年）的水文过程,计算了SPI（Standard Precipitation Index）和SRI（Standard Runoff Index）干旱指标,通过游程理论识别了气象干旱与水文干旱事件,利用Copula函数与最大可能权函数度量二维干旱风险特征,定量评估了气象干旱至水文干旱的潜在风险传播特性。结果表明:①气象-水文干旱对气候变化响应强烈,华北和东北地区的干旱联合重现期增大,干旱潜在风险减小,华中和华南地区的干旱联合重现期减少60%~80%,干旱潜在风险增加;②气象干旱与水文干旱风险在历史和未来时段均存在显著的正相关关系,相关系数超过0.99;③各流域水文干旱风险变化对气象干旱风险变化的敏感程度不会随气候变暖发生较大变化,但未来北方地区水文干旱同气象干旱同时发生的概率将会小幅度增加。 相似文献
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Numerous modeling approaches are available to provide insight into the relationship between climate change and groundwater recharge. However, several aspects of how hydrological model choice and structure affect recharge predictions have not been fully explored, unlike the well-established variability of climate model chains—combination of global climate models (GCM) and regional climate models (RCM). Furthermore, the influence on predictions related to subsoil parameterization and the variability of observation data employed during calibration remain unclear. This paper compares and quantifies these different sources of uncertainty in a systematic way. The described numerical experiment is based on a heterogeneous two-dimensional reference model. Four simpler models were calibrated against the output of the reference model, and recharge predictions of both reference and simpler models were compared to evaluate the effect of model structure on climate-change impact studies. The results highlight that model simplification leads to different recharge rates under climate change, especially under extreme conditions, although the different models performed similarly under historical climate conditions. Extreme weather conditions lead to model bias in the predictions and therefore must be considered. Consequently, the chosen calibration strategy is important and, if possible, the calibration data set should include climatic extremes in order to minimise model bias introduced by the calibration. The results strongly suggest that ensembles of climate projections should be coupled with ensembles of hydrogeological models to produce credible predictions of future recharge and with the associated uncertainties. 相似文献
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Over the last decade several computational models, and several types of model, have been developed to simulate the response of river systems to environmental change over time scales of decades to millennia: hydrological models, flood inundation models, channel morphology models, channel network models, models of river meandering and river braiding, alluvial stratigraphy models, and landscape evolution models. Each type of model simulates different aspects of a river's response to changes in environmental inputs such as climate and land-use — and to changes in these inputs. And each type of model has its abilities, advantages and limitations. We provide an overview of the different types of model that have been developed, and we evaluate their suitability for testing hypotheses about past environmental conditions, as well as for investigating the response of alluvial river systems to future environmental change. Additionally, we discuss the general issues and problems of computational modelling (e.g. scale and resolution, data availability, process representation, process parameterization, model calibration, non-linearity, and uncertainty), and the extent to which these hamper the usefulness of the models as a tool in environmental landscape studies. Finally, we identify trends in computational modelling research to outline possible future directions of the discipline. 相似文献