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环境变化影响下流域径流的精确模拟对洪涝灾害防治与区域水资源管理都具有重要意义。在径流模拟研究中，现有机器学习模型未能充分考虑水文中间状态变量对降雨-径流过程的影响，本研究基于集合卡尔曼滤波(EnKF)更新水文状态变量，结合主成分分析(PCA)提取预报因子的主要特征，采用长短时记忆神经网络(LSTM)构建考虑水文中间变量的机器学习水文模型EnKF-PCA-LSTM。以赣江流域为例，评估EnKF-PCA-LSTM模型的径流模拟效果，同时将模拟结果与LSTM模型、物理水文模型HYMOD做对比分析。结果表明，EnKF-PCA-LSTM模型模拟径流的纳什效率系数、Kling-Gupta效率系数和对数纳什效率系数分别为0.954、0.971和0.972,比LSTM模型和HYMOD模型具有更好的模拟性能，说明考虑水文状态变量可有效提高机器学习模型的径流模拟精度及稳定性。研究成果可为流域径流模拟提供技术参考。 相似文献
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提出了一种基于水文系统理论的分布式水文模拟方法,它将集总的水文非线性系统模型通过GIS平台,结合单元水文模拟,拓展到分布式流域水文模拟。它既具有分布式水文概念性模拟的特征,同时又具有水文系统分析适应能力强的优点。实例分析表明:在黄河板桥流域,通过考虑时变增益因子G的空间变异性,该模型吸纳了局部高强度降水信息,对分布式空间降水输入能够产生积极的响应,获得了比集总模型更好的峰值模拟效果;在华北潮白河流域,通过分析不同森林覆盖率下的G值变化曲线,初步揭示了潮白河流域土地利用变化的水文响应趋势,即在相同的土壤湿度条件下,随着森林覆盖率的增加,G值呈减小趋势,地表产流能力降低。 相似文献
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《地下水》2021,(1)
数字高程模型(DEM)空间分辨率的精准确定,对于提高分布式水文模型输入水文信息的可靠性和有效性意义显著。文章选择北洛河流域典型区域作为研究对象,基于7种不同的DEM空间分辨率,利用ARCGIS软件,分别提取水文特征信息;在新安江模型物理参数意义研究的基础上,利用槽蓄曲线的坡度K和流量比重系数X之间的相关关系,确定参数,进行水文模拟;根据交口、状头水文站的实测数据,对模拟结果进行对比验证,结果表明:DEM空间分辨率越低,提取的水文特征信息越少;洪峰的模拟精度在分辨率200m之前减低缓慢,200m之后大幅降低。对比分析不同分辨率对水文模拟的影响,进而确定满足实际洪水预报的DEM分辨率大小。 相似文献
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土地利用变化对水文系统的影响研究 总被引：22，自引：0，他引：22
以秦淮河流域为研究区域，利用现代遥感、地理信息系统等多种数字技术，获取流域空间分布的下垫面信息和中间状态信息，细致地刻画了流域内的水文过程，建立了研究区域数字水文模型。利用所建立的数字水文模型，对定义的土地利用变化情景进行了模拟。模拟结果表明，模型可以定性和定量地反映各种土地利用变化对水资源系统尤其是对水量平衡和防洪情势的影响程度。 相似文献
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基于物理过程分布式流域水文模型尺度依赖性 总被引：3，自引：0，他引：3
通过变化基于物理过程分布式参数流域水文模型的单元格大小(Grid size)和时间步长(Timestep)研究流域水文过程的尺度依赖性,进而寻求最佳模拟尺度和模拟效果,可以为实现相对准确的流域土地利用变化/植被变化以及气候变异的水文响应预测,制定适应性流域管理措施提供科学基础.本文以基于物理过程的分布式水文模型MIKES眦为工具,以位于西北黄土高原甘肃省天水市吕二沟流域的实测次降水一径流为输入对模型进行校正后,采用多尺度检验的方法探讨分析了单元格及步长变化的水文影响.结果表明:单元格变化对峰值及模拟径流总量有影响.单元格从10m增大到200m,模拟峰值及径流总量均增大,分别变化为1.72～8.56m3/s和1.10～3.68×105m3,这主要源于单元格变化引起流域特征变化,10m和200m单元格对应模拟面积分别较原流域面积变化-0.09%和20.57%,河流链接"rivet link"分别较原沟系长变化27.23%和5.48%.此外,模型中运动波近似方程有限差分像元△x与单元格水流交换总量之间的关系一定程度也解释了单元格变化对模拟结果的影响.分析认为本研究60～100m为较适宜采用的模拟单元格.步长变化引起单位步长内降水强度的变化,从而影响峰值模拟,但对径流总量无影响.步长减小,模拟峰值增高,0.5h步长和24h步长对应模拟峰值分别为2.38m3/s和1.72m3/s.由于各时段观测记录分辨率不一,研究中步长的选择确定具有很大任意性,使得任一既定采用的模拟步长并不适用于另一观测时段,模拟效果不一. 相似文献
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DEM是构建分布式水文模型的重要输入。以岷江上游为研究区,基于8种不同空间分辨率DEM构建SWAT模型,研究分析DEM空间分辨率对流域水文特征提取及径流模拟影响。研究表明,DEM空间分辨率越低,洪峰模拟精度越低,模拟流量与实测流量间的误差越大,曲线吻合度越低;DEM空间分辨率由25m降低到200m时,径流模拟误差变化不明显,而DEM空间分辨率由200m降低到3 200m时模拟误差显著增大;另外,DEM空间分辨率与径流总量的模拟精度间未表现出显著相关性。 相似文献
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水文响应单元(Hydrological Response Units,HRU)是SWAT模型模拟的基本单元,传统方法划分的水文响应单元在空间分布上不连续且难以确定其明确的空间位置,不能反映HRU间的相互作用和进行精确空间分析。利用GIS工具对土地利用和土壤类型数据进行概化处理,提出了HRU空间离散化的方法,实现了水文响应单元在空间上的准确定位。在此基础上,针对SWAT模型中同一子流域所有HRU采取相同延迟的弱点进行改进,并选择太湖地区西苕溪流域对改进的SWAT模型进行水文模拟验证。改进后,校正期港口站Nash效率系数ENS(Nash-Sutcliffe Efficiency)从0.64提高到0.67,验证期ENS系数从0.70提高到0.76。研究表明:修正后的SWAT模型更能反映流域的水文特征,可以达到非常好的效果,考虑到HRU距离因素的径流延迟更为准确地刻画径流过程。实现HRU空间离散化将为模型改进和更小尺度的空间分析提供数据基础。 相似文献
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A shortage of water has limited the socio-economic development of the Heihe River Basin in northwestern China and has led to many ecological and economic problems. Only the development and application of integration tools can effectively represent the functionality of the watershed and aid in strategic decision making. In the framework of SME, modular and hierarchical model building can simplify the research work. According to the main ideas and key processes commonly used in distributed hydrological models, and combined with the spatial characteristics of the mountainous region of the Heihe River Basin, a unit hydrological model was developed using stella, which was an icon-based software package specifically designed for dynamic systems modeling, and an algorithm for spatial distribution in SME was introduced. This paper describes only the model structure and basic equations, whereas in the next paper, model calibration results using the data measured at meteorological stations, together with land use data and soil data, will be further introduced. 相似文献
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Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km2. The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds. 相似文献
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波致瞬态液化渗流导致海床内细粒沉积物向海水中运移,这一过程对海底沉积物再悬浮的贡献率不容忽视,但是贡献率的准确估计和预测比较困难。本研究将黄河水下三角洲的观测数据(包括水深、有效波高、有效波周期、实验舱内悬沙浓度、实验舱外悬沙浓度)作为模型输入数据集,基于长短时记忆循环神经网络建立了瞬态液化对再悬浮贡献率的深度学习预测模型。为了客观评价模型的性能,以平均绝对百分比误差、均方根误差和平均平方误差-标准偏差为评判标准,将该深度学习模型与其他预测模型(支持向量回归模型、人工神经网络)的预测结果进行了比较。结果表明,基于长短时记忆循环神经网络的深度学习模型对3.5d以内的瞬态泵送再悬浮贡献率预测误差最小,其平均绝对百分比误差、均方根误差和平均平方误差-标准偏差分别为5.87%、1.6730、0.1574。因此,该模型可以有效地减少机器学习方法在连续预测中产生的误差叠加问题。 相似文献
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为开展河川径流的水源解析,构建过程描述和本构参数两方面均有较强物理性的分布式水文模型。以雅鲁藏布江为对象,利用水文分区曲线对降雨、融雪和融冰等不同水源主导的流量过程进行划分,以划分的流量过程线子集对相应水文过程参数进行分步率定,提高了水文模型参数的物理性,以此构建了雅鲁藏布江流域分布式水文模型及参数集,内部多个水文站点和流域雪水当量的验证表明模型具有良好的性能。基于模型解析了2001-2015年间雅鲁藏布江的径流水源组成,降雨、融雪、融冰水源对总径流量贡献的比例分别为66%、20%和14%。本文方法对高山寒区径流的水源解析有普遍意义,结果对理解气候变化下雅鲁藏布江径流变化趋势有参考价值。 相似文献
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为开展河川径流的水源解析，构建过程描述和本构参数两方面均有较强物理性的分布式水文模型。以雅鲁藏布江为对象，利用水文分区曲线对降雨、融雪和融冰等不同水源主导的流量过程进行划分，以划分的流量过程线子集对相应水文过程参数进行分步率定，提高了水文模型参数的物理性，以此构建了雅鲁藏布江流域分布式水文模型及参数集，内部多个水文站点和流域雪水当量的验证表明模型具有良好的性能。基于模型解析了2001—2015年间雅鲁藏布江的径流水源组成，降雨、融雪、融冰水源对总径流量贡献的比例分别为66%、20%和14%。本文方法对高山寒区径流的水源解析有普遍意义，结果对理解气候变化下雅鲁藏布江径流变化趋势有参考价值。 相似文献
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东北半干旱地区流域分布式水文模拟——以洮儿河流域为例 总被引：1，自引：0，他引：1
以东北半干旱地区典型流域-洮儿河流域为研究对象,应用SWAT模型对流域水文过程进行了模拟研究;选择流域上游子流域和中下游子流域分别进行参数敏感性分析,识别出影响模拟结果的敏感参数,研究发现部分参数敏感性存在空间变异性,分析主要原因在于气候和下垫面的空间异质性导致了流域上下游产流模式存在差异。采用1988-1997年水文气象数据进行模型率定和验证,结果表明：干流水文站月流量过程率定期Nash-Sutcliffe 效率系数平均值为0.78,验证期为0.72,相关系数都达到0.86以上,水量误差大多在20%以内,对日过程的模拟也有较高的精度;枯水年模拟结果较差,主要是因为流域降水站数量不够,难以反映降水的时空分布。对于水文、气象等资料相对缺乏的东北半干旱地区,SWAT模型的模拟结果总体令人满意,可以应用于与流域径流相关的各种模拟分析,研究成果对进一步加强洮儿河流域水资源综合管理提供了依据和手段。 相似文献
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Florian Kobierska Tobias Jonas Jan Magnusson Massimiliano Zappa Mathias Bavay Thomas Bosshard Frank Paul Stefano M. Bernasconi 《Applied Geochemistry》2011
A comprehensive hydrological modeling study in the drainage area of a hydropower reservoir in central Switzerland is presented. Two models were tested to reproduce the measured discharge dynamics: (1) a detailed energy-balance model (ALPINE3D) primarily designed for snow simulations; (2) a conceptual runoff model system (PREVAH), including a distributed temperature-index snow and ice melt model. Considerable effort was put into distributing available meteorological station data to the model grids as forcing data. The recent EU regional climate modeling initiative ENSEMBLES provided up-to-date climate predictions for two 30-a periods in mid and late 21st century. These were used to estimate evolutions in the water supply of the hydropower reservoir in response to expected climate changes. The simulations suggest a shift of spring peak-flow by almost two months for the end of the century. Warmer winter temperatures will cause higher winter base-flow. Due to glacier retreat, late-summer flow will decrease at the end of the century. 相似文献
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Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system 总被引：1，自引：0，他引：1
The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics. 相似文献
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Soil temperature has an important role in agricultural, hydrological, meteorological and climatological studies. In the present research, monthly mean soil temperature at four different depths (5, 10, 50 and 100 cm) was estimated using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). The monthly mean soil temperature data of 31 stations over Iran were employed. In this process, the data of 21 and 10 stations were used for training and testing stages of used models, respectively. Furthermore, the geographical information including latitude, longitude and altitude as well as periodicity component (the number of months) was considered as inputs in the mentioned intelligent models. The results demonstrated that the ANN and ANFIS models had good performance in comparison with the GEP model. Nevertheless, the ANFIS generally performed better than ANN model. 相似文献