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1.
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
2.
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
3.
选择7种水文模型分别在中国北部3个半湿润流域做模拟对比,分析不同水文模型在各流域的适用性,并使用贝叶斯模型平均法对不同模型集合,比较各种集合方法的优势,研究贝叶斯模型平均法的应用效果.研究结果表明,以蓄满产流模式为主的模型在半湿润流域应用效果较好,针对不同流域特点对传统模型进行改进可以提高模拟精度.贝叶斯模型平均法能提供较好的确定性预报结果和概率预报结果,仅对少数模拟效果好的模型进行集合,并不能有效提高预报精度,适当增加参与集合的模型数量能使贝叶斯模型平均法更好地综合各模型优势,提高预报结果的精度. 相似文献
4.
空间集总式水文模型的洪水预报精度会受到面平均雨量估计误差的严重影响.点雨量监测值的误差类型、误差大小以及流域的雨量站点密度和站点的空间分布都会影响到面平均雨量的计算.为提高实时洪水预报精度,本文提出了一种基于降雨系统响应曲线洪水预报误差修正方法.通过此方法估计降雨输入项的误差,从而提高洪水预报精度.此方法将水文模型做为输入和输出之间的响应系统,用实测流量和计算流量之间的差值做为信息,通过降雨系统响应曲线,使用最小二乘估计原理,对面平均雨量进行修正,再用修正后的面平均雨量重新计算出流过程.将此修正方法结合新安江模型使用理想案例进行检验,并应用于王家坝流域的16场历史洪水以及此流域不同雨量站密度的情况下,结果证明均有明显修正效果,且在雨量站密度较低时修正效果更加明显.该方法是一种结构简单且不增加模型参数和复杂度的实时洪水修正的新方法. 相似文献
5.
复杂河道洪水预报系统研究——以淮河王家坝至小柳巷区间流域为例 总被引：2，自引：1，他引：1
随着社会经济的快速发展,洪水灾害造成的损失日益严重.洪水预报作为一项重要的防洪非工程措施,对防洪、抗洪工作起着至关重要的作用.淮河洪水危害的严重性和洪水演进过程的复杂性使得淮河洪水预报系统的研究长期以来受到高度重视.本文以王家坝至小柳巷区间流域为例,以河道洪水演算为主线,采用新安江三水源模型进行子流域降雨径流预报,概化具有行蓄洪区的干流河道,进行支流与干流、行蓄洪区与干流的洪水汇流耦合计算,采用实时更新的基于多元回归的方法确定水位流量关系,并以上游站点降雨径流预报模型提供的流量作为上边界条件、以下游站点的水位流量关系作为下边界条件,结合行蓄洪调度模型,建立具有行蓄洪区的河道洪水预报系统,再与基于K-最近邻(KNN)的非参数实时校正模型耦合,建立淮河中游河道洪水预报系统.采用多年资料模拟取得了较好的预报效果,并以2003和2007年大洪水为例进行检验,模拟结果精度较高,也证明了所建预报系统的合理性和适用性. 相似文献
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与传统确定性预报相比,洪水概率预报能够为防洪调度决策提供更为丰富的信息。以大渡河猴子岩水库以上流域为研究区,建立新安江次洪模型,并采用动态系统响应曲线进行实时洪水预报校正。在确定性预报校正基础上,建立基于水文不确定性处理器（HUP）的次洪概率预报模型,定量分析预报不确定性,实现入库洪水概率预报。结果表明：(1)利用猴子岩流域2009 2019年水文气象资料,建立的新安江次洪模型整体精度较高,率定期和验证期的洪量和洪峰相对误差均在±20%以内,平均确定性系数分别为0.69和0.72;经动态系统响应曲线校正后,洪峰和洪量误差均有降低,率定期和验证期的确定性系数分别提高0.13和0.09。(2)以2020年3场洪水未来48 h预报降雨为输入,新安江模型预报精度不高,且随着预见期增长而降低,但经动态系统响应曲线校正后,整体预报精度有所提高,洪量相对误差减小幅度超50%,确定性系数提高幅度超60%。(3)HUP次洪概率预报模型提供的分布函数中位数Q50的预报精度在一定程度上优于校正后的确定性预报;提供的90%置信区间覆盖率均在90%左右,离散度均在0.40以下,能以相对较窄的区间覆盖大部分实测值... 相似文献
8.
洪涝灾害是世界主要自然灾害之一,优化洪水预报方案对防洪决策至关重要,然而传统水文模型存在参数多、调参受人为因素影响,泛化能力弱等问题。针对上述问题,本文提出基于改进的鲸鱼优化算法和长短期记忆网络构建自动优化参数的WOA-LSTM模型,通过优化神经网络结构进一步增强该模型的稳定性和精确度,并且建立不同预见期下的洪水预报模型来分析讨论神经网络结构与预报期之间的关系。以横锦水库流域1986—1997年洪水资料为例,其中以流域7个雨量站点的降雨以及横锦站水文资料为输入,不同预见期下洪水过程作为输出,以1986—1993年作为模型的率定期,1994—1997年作为模型的检验期,研究结果表明:(1)以峰现时差、确定性系数、径流深误差和洪峰流量误差作为评价指标,相比较于LSTM模型和新安江模型对检验期的模拟结果表明WOA-LSTM模型拥有更高的精度、预报结果更稳定;(2)结合置换特征值和SHAP法分析模型特征值重要性,增强了神经网络模型的可解释性;(3)通过改变神经网络结构在一定程度避免由于预见期增加和数据关联性下降而导致的模型预报精度下降的问题,最终实验表明该模型在预见期1~6 h下都可以满足横锦水库的洪水预报要求,可以为当地的防洪决策提供依据。 相似文献
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为考虑洪水预报误差的空间变化,提出一种基于微分响应的流域产流分单元修正方法.该方法建立了各单元流域产流与流域出口流量之间的微分响应关系,采用正则化最小二乘法结合逐步迫近进行反演求解,将产流误差估计量分配给相应单元流域实现流域产流分单元修正.将构建的方法应用于大坡岭流域和七里街流域进行新安江模型产流修正,比较分析了流域产流分单元修正、流域面平均产流修正和自回归修正的效果.结果表明：流域产流分单元修正效果优于流域面平均产流修正;随着预见期的增大,产流微分响应修正效果优于自回归修正.该方法通过汇流系统将流域出口断面流量信息进行分解用于修正各单元流域产流,有利于提高实时洪水预报精度. 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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Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Xiaolei Fu Zhongbo Yu Lifeng Luo Haishen Lü Di Liu Qin Ju Tao Yang Feng Xu Huanghe Gu Chuanguo Yang Jingwen Chen Ting Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):681-693
Accurate soil moisture information is useful in agricultural practice, weather forecasting, and various hydrological applications. Although land surface modeling provides a viable approach to simulating soil moisture, many factors such as errors in the precipitation can affect the accuracy of soil moisture simulations. This paper examined how precipitation rate and evapotranspiration rate affect the accuracy of soil moisture simulation using simple biosphere model with and without data assimilation through ensemble Kalman filter (EnKF). For each of the two variables, seven levels of relative errors (?20, ?10, ?5, 0, 5, 10 and 20 %) were introduced independently, thus a total of 49 combined cases were investigated. Observations from Wudaogou Hydrology Experimental site in the Huaihe River basin, China, were used to drive and verify the simulations. Results indicate that when the error of precipitation rate is within 10 % of the observations, the resulting error in soil moisture simulations is less significant and manageable, thus the simulated precipitation can be used to drive hydrological models in poorly gauged catchments when observations are not available. When the error of evapotranspiration rate is within 20 % of the observations, which is partly caused by model structural and parameterization errors, its impact on soil moisture simulation is less significant and can be acceptable. This study also demonstrated that the EnKF can perform consistently well to improve soil moisture simulation with less sensitivity to precipitation errors. 相似文献
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We present a method for real-time forecasting of water table depth and soil moisture profiles. The method combines a simple form of data-assimilation with a moving window calibration of a deterministic model describing flow in the unsaturated zone and local as well as regional drainage. The local drainage level is calibrated on-line using a moving window calibration. Assigning more weight to the last available measurements then yields a form of model adaptation that is in between on-line calibration and data-assimilation (i.e. a simplified form of Newtonian nudging). Five-day hydrological forecasts are performed based on 5-day weather forecasts, while on-line observations of phreatic level and soil moisture content are assimilated on a daily basis. Advantages of the proposed method are that it improves the real-time forecasts compared to off-line calibration and ordinary moving window calibration and that it yields physically consistent soil moisture profiles. 相似文献
14.
Determining hydrological regimes in an agriculturally used tropical inland valley wetland in Central Uganda using soil moisture,groundwater, and digital elevation data 
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下载免费PDF全文 Geofrey Gabiri Bernd Diekkrüger Constanze Leemhuis Sonja Burghof Kristian Näschen Immaculate Asiimwe Yazidhi Bamutaze 《水文研究》2018,32(3):349-362
Inadequate knowledge exists on the distribution of soil moisture and shallow groundwater in intensively cultivated inland valley wetlands in tropical environments, which are required for determining the hydrological regime. This study investigated the spatial and temporal variability of soil moisture along 4 hydrological positions segmented as riparian zone, valley bottom, fringe, and valley slope in an agriculturally used inland valley wetland in Central Uganda. The determined hydrological regimes of the defined hydrological positions are based on soil moisture deficit calculated from the depth to the groundwater table. For that, the accuracy and reliability of satellite‐derived surface models, SRTM‐30m and TanDEM‐X‐12m, for mapping microscale topography and hydrological regimes are evaluated against a 5‐m digital elevation model (DEM) derived from field measurements. Soil moisture and depth to groundwater table were measured using frequency domain reflectometry sensors and piezometers installed along the hydrological positions, respectively. Results showed that spatial and temporal variability in soil moisture increased significantly (p < .05) towards the riparian zone; however, no significant difference was observed between the valley bottom and riparian zone. The distribution of soil hydrological regimes, saturated, near‐saturated, and nonsaturated regimes does not correlate with the hydrological positions. This is due to high spatial and temporal variability in depth to groundwater and soil moisture content across the valley. Precipitation strongly controlled the temporal variability, whereas microscale topography, soil properties, distance from the stream, anthropogenic factors, and land use controlled the spatial variability in the inland valley. TanDEM‐X DEM reasonably mapped the microscale topography and thus soil hydrological regimes relative to the Shuttle Radar Topography Mission DEM. The findings of the study contribute to improved understanding of the distribution of hydrological regimes in an inland valley wetland, which is required for a better agricultural water management planning. 相似文献
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Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field‐scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach. 相似文献
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The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent. 相似文献
18.
A simple water balance model adapted for soil water repellency: application on Portuguese burned and unburned eucalypt stands 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 João Pedro Nunes Maruxa Malvar Akli Ait Benali María Ermitas Rial Rivas Jan Jacob Keizer 《水文研究》2016,30(3):463-478
Soil water repellency can impact soil hydrology, overland flow generation and associated soil losses. However, current hydrological models do not take it into account, which creates a challenge in repellency‐prone regions. This work focused on the adaptation for soil water repellency of a daily water balance model. Repellency is estimated from soil moisture content using site‐specific empirical relations and used to limit maximum soil moisture. This model was developed and tested using approximately 2 years of data from one long‐unburned and two recently burned eucalypt plantations in northern Portugal, all of which showed strong seasonal soil water repellency cycles. Results indicated important improvements for the burned plantations, with the Nash–Sutcliffe efficiency increasing from ?0.55 and ?0.49 to 0.55 and 0.65. For the unburned site, model performance was already good without the modification and efficiency only improved slightly from 0.71 to 0.74, mostly due to the better simulation of delayed soil wetting after dry periods. Results suggested that even a simple approach to simulate soil water repellency can markedly improve the performance of hydrological models in eucalypt forests, especially after fire. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
19.
Sanghyun Kim 《水文研究》2012,26(22):3434-3447
The vertical and lateral profiles of temporal variations in soil moisture are important for understanding the hydrological process along hillside transects. In this study, relationships among measured soil moistures were explored to configure the hydrological contributions of different flowpaths. All the measured soil moistures included a common stochastic structure because rainfall, the hydrometeological driver, is the main factor that determines the soil moisture response feature, and the infiltration process through the topsoil at a shallow depth is also common in all measured soil moisture histories. Therefore, the relationships between the measured series are also affected by both rainfall and topsoil infiltration. The common stochastic structure of the soil moisture series was removed via a prewhitening procedure. A systematic analysis procedure is presented to delineate the exclusive causal relationships among multiple soil moisture measurements. A monitoring system based on multiplexed time domain reflectometry was used to obtain soil moisture time series along two transects on a steep hillslope during the rainy season. The application of the proposed method for monitoring points in two adjacent locations provided 8, 12, 14, and 13, 16, 22 causal relationships for vertical, lateral in parallel, and diagonal directions, respectively, along the two transects. The point‐based contributions of the internal flowpath can be evaluated as the correlation is normalized in the context of inflow and outflow. The hydrological processes in the soil layer, vertical flow, lateral flow, downslope recharge, and return flow were quantified, and the relative importance of each hydrological component was determined to improve our understanding of the hydrological processes along the two transects of the study area. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
20.
Error distribution modelling of satellite soil moisture measurements for hydrological applications 下载免费PDF全文
下载免费PDF全文 Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献