基于多源遥感土壤湿度与模型数据同化的流域径流模拟

为提升径流模拟精度,以秦淮河流域为例,采用集合平均法将SMAP、SMOS、AMSR2卫星遥感土壤湿度融合并利用地形湿度指数进行空间降尺度处理,采用卡尔曼滤波算法和栅格新安江模型进行遥感融合土壤湿度同化。对2016—2018年秦淮河流域3个流量站记录的11场洪水进行模型数据同化的结果表明：日尺度率定期洪峰、径流深相对误差合格率均为71.43%,验证期洪峰、径流深相对误差合格率分别为66.67%和100%;经同化后,8场洪水径流深误差减小,平均误差降低29.01%;8场洪水确定性系数增大,范围在0.01～0.09之间,模拟精度最高可提升11.84%;同化多源遥感土壤湿度能有效改善土壤湿度估计的准确性,进而提升径流模拟精度。     

基于EnKF的无实测资料区间支流反分析

区间入流误差是河道洪水演算不确定性来源之一。为此,一部分研究基于水动力模型和数据同化方法对区间总入流误差进行动态修正,但无法推算出某条支流的单独入流过程;另一部分研究通过从下游往上游反算水流的方式推算区间支流入流,但反算结果稳定性差,对边界条件误差敏感,推算的入流过程误差较大。针对上述问题,本文提出了基于集合卡尔曼滤波(EnKF)的区间支流反分析方法。方法由一维河道水动力模型正、反向水流演算初步估算支流入流,并构建监测断面滞时矩阵,计算水流扰动传播时间,从而确定用于支流入流校正的流量监测值。当EnKF校正的结果仍然存在较大误差时,可再次运用EnKF对首次校正结果进一步校正。将该方法应用于理想案例和西江实例,推算的支流入流过程与实测过程十分接近,入流结果 R2和NSE皆在0.99以上,相对RMSE也小于0.05。结果表明,本文提出方法可准确计算出无实测资料的区间支流入流过程,研究结果对于提高河道洪水演算精度具有重要意义。     

基于两阶段粒子群优化算法的新型逐步分解集成径流预测模型

传统分解集成径流预测模型首先将整个径流序列分解成若干个子序列,再将这些子序列划分为训练期和验证期进行建模,错误地将验证期内预报因子数据视作已知数据处理,难以应用于实际径流预报工作中。并且,这类模型的预测结果仅为一个确定数值,难以描述由于径流序列随机性和波动性而导致的预测不确定性。为解决以上问题,本文结合变分模态分解方法、支持向量机模型和核密度估计方法,提出了一种可同时进行点预测和区间预测的新型逐步分解集成(VMD-SVM-KDE)模型,并提出了一种两阶段粒子群优化(TSCPSO)算法来优化模型参数。选用黄河流域月径流数据评估模型性能,研究结果表明:(1)VMD-SVM-KDE模型将单一SVM-KDE模型的确定系数(R²)和纳什效率系数(NSE)值由0.145~0.630提升至0.872~0.921,区间平均偏差(INAD)值由0.046~95.844降低至0.005~0.034,说明VMD-SVM-KDE模型显著改进了单一SVM-KDE模型的点预测和区间预测性能;(2)相较于一阶段PSO算法,TSCPSO优化算法将单一模型的R²和NSE值由0.145~0.480提升至0.309~0.630,INAD值由48.813~95.844降低至0.046~0.195,将分解集成模型的R²和NSE值由0.872~0.912提升至0.876~0.921,INAD值由0.007~0.034降低至0.005~0.014,说明TSCPSO优化算法可以克服SVM的过拟合问题,并能提高单一模型和分解集成模型的预测精度;(3)VMD-SVM-KDE-TSCPSO有效解决了传统分解集成预测模型存在的错误使用验证期内预报因子数据的问题,并在各站的R²和NSE值均约为0.9,INAD值的范围为0.005~0.014,具有更高的点预测和区间预测精度。文中模型可为优化径流预测模型和非平稳非线性水文序列预报提供新思路。     

湖泊藻类动态模型数据同化模式的改进

以太湖为研究区域,采用2014—2016年的水环境生态监测数据,率定了三维水生态动力学模型(3DHED),模拟了太湖蓝藻生物量的时空变化;通过融入遥感数据建立了基于集合卡尔曼滤波(EnKF)的蓝藻生物量预测数据同化(DA)模式,同时提出了一种改进数据同化(mDA)的策略,降低了遥感数据不确定性的影响,显著提升了模型模拟精度。结果表明:相比3DHED蓝藻生物量的模拟结果,DA模拟结果的均方根误差均值降低了10.4%,IOA均值增加了48.8%;mDA在DA基础上对蓝藻生物量的模拟精度进一步提升,其均方根误差均值为1.16 mg/L,在DA基础上降低了8.6%,IOA均值为0.71,在DA基础上增加了10.9%,并有效提升了对蓝藻生物量峰值的捕捉能力,表明提出的mDA方法能有效减小原DA模式中遥感观测数据误差的影响,提升水华模拟精度。     

基于小波包分解的AJS-GMDH月径流时间序列预测研究

为提高月径流时间序列预测精度,建立基于小波包分解(WPD)、人工水母搜索(AJS)算法、数据分组处理方法(GMDH)的WPD-AJS-GMDH月径流时间序列预测模型。采用WPD将月径流时序数据分解为若干子序列分量;选取6个典型函数在不同维度条件下对AJS算法进行仿真测试;利用AJS算法优化GMDH网络关键参数,建立WPD-AJS-GMDH模型,并构建基于支持向量机(SVM)、BP神经网络及完全集合经验模态分解(CEEMD)、小波分解(WD)的17种对比分析模型;最后利用云南省龙潭站1952年~2016年780组的月径流时间序列数据对所建立的18种模型进行检验。结果表明,在不同维度条件下,AJS算法均具有较好的寻优效果;WPD-AJS-GMDH模型预测误差均小于其他17种模型;对于月径流时序数据分解,WPD分解效果优于CEEMD、WD方法;AJS算法能有效优化GMDH网络参数,提高预测性能。     

基于RBFNN数据延拓和CEEMDAN方法的径流序列组合预测

为提高径流预测精度,采用径向基神经网络(RBFNN)数据延拓技术处理完全集合经验模态分解(CEEMDAN)方法中的端点效应问题,并根据分解结果特点构建RBFNN-ARIMA组合预测模型。以1957—2013年黄河源区唐乃亥水文站年径流数据为例,先将选定的序列采用RBFNN进行延拓,然后进行CEEMDAN分解,对得到的分解分量运用RBFNN-ARIMA组合模型进行预测重构得到年径流量预测结果。研究表明,原始序列经过RBFNN数据延拓后再进行CEEMDAN分解,其所得分量可以有效反映不同时间尺度上的波动特征;ARIMA模型对高频IMF1分量的拟合效果较差,对其他中低频分量拟合效果较好;RBFNN-ARIMA组合模型预测结果的平均相对误差为5.23%,相较于RBFNN模型和ARIMA模型预测精度分别提高了9.88%和5.62%。因此,运用基于CEEMDAN方法的"分解-预测-重构"模式进行水文预测,对原始序列进行合理延拓并针对各分量特点进行组合预测可有效提高预测精度。     

基于SWAT模型的北江飞来峡流域径流模拟

为满足北江飞来峡流域非点源污染负荷核算需要,利用SWAT模型对研究区1969-2011年日径流过程进行模拟。基于飞来峡流域水文、气象、地形、土地利用和土壤类型等资料构建SWAT径流模型,并运用SWAT-CUP中的SUFI-2方法对模型中的14个径流参数进行敏感性分析及参数率定,再进行径流模拟效果定量评价。结果表明:对径流过程有显著影响的参数主要为SCS径流曲线系数、主河道曼宁系数、地下水滞后系数以及地表径流滞后时间等;日径流率定期和验证期的效率系数均为0.83,相对误差分别为1.40%和0.58%,且大部分模拟数据落在不确定性区间内,模拟结果的不确定性较小,表明所构建的SWAT径流模型具有较高的精度,在北江飞来峡流域适用性良好。     

遥感蒸散发数据应用对SWAT模型径流和蒸散发模拟精度的影响

为探讨遥感蒸散发数据补充研究区水文资料的能力,及其在SWAT模型应用中对径流和蒸散发模拟精度的影响。以淮河上游息县控制流域为研究区建模,并利用实测径流资料与遥感蒸散发数据(MOD16A2)设置3种参数率定情景:仅实测径流率定参数(S1)、仅遥感蒸散发率定参数(S2)、径流与蒸散发同时率定参数(S3),分析不同情景下径流与蒸散发过程的模拟效果。结果表明:从径流模拟而言,S2、S3较S1的模拟精度(NS系数)均有不同程度的降低,但S3在S2的基础上有较明显的改善;从子流域尺度上的蒸散发模拟而言,S1至S3模拟精度呈现出逐渐上升的趋势,在采用径流与蒸散发同时率定时,S3比S2情景NS系数上升的子流域个数占总数的46%。通过逐步深入探究遥感蒸散发数据在SWAT模型中的应用,以及对参数率定的影响,从而分析其对径流与蒸散发的模拟精度产生的变化,此方法也可推广到其他水文模型,在区域尺度水资源管理与利用上具有较好的参考价值。     

An Experimental Study on Evapotranspiration Data Assimilation Based on the Hydrological Model

The accurate estimation of watershed evapotranspiration (ET) has been a research hotspot in the field of hydrology and water resources for a long time. This study aims to develop a new comprehensive method integrating the advantages of the hydrological model and remote sensing data for improving the daily ET processes simulation. For the purpose, a data assimilation (DA) approach was established on the basis of a physical-based hydrological model, Distributed Time Variant Gain Model (DTVGM). Due to the calculation of ET by using soil moisture recurrence relations in distributed hydrology model, ET was expressed by state variables, in combine with the remote sensing data of ET through a two-layer model, by using ensemble Kalman filter (EnKF) for ET assimilation and constructed a ET assimilation system based on DTVGM, obtained more accurate continuous time series values of ET. Applied in Beijing Shahe River Basin, the DA approach made the simulation shift towards the remote sensing results. According to the verification based on the measurement data of the flux station, the mean absolute percentage error of the DA-based ET was reduced from 25.8 to 8.2 % in No. 1 hydrological unit. The DA approach improved the ET simulation accuracy of hydrological model, and provides a new effective method for daily ET estimation.     

基于 EnKF 的新安江模型参数和变量同步估计方法

数据同化方法可提高数值预报的时效性和准确性,且该方法已在水文领域得到应用,并得到快速发展。为了提高新安江模型径流模拟预报精度,采用集合卡尔曼滤波方法同化径流数据,对参数和状态变量进行同步校正估计。通过对三水源新安江模型进行理想条件下的数值实验,在同时考虑模型自身、模型参数以及观测数据的不确定性的情况下,分析了参数均值和方差改变、集合大小、同化参数的敏感性以及相关性分析对同化过程的影响。结果表明:集合卡尔曼滤波算法具有可行性,且参数均值越接近真值、方差适当增加,集合大小适中,同化参数敏感性较低以及参数与变量间相互独立时,能在一定程度上增加径流同化精度。该研究可为同类型参数同化估计提供一定参考依据。     

非饱和土壤水的集合卡尔曼滤波Ⅰ:状态向量与非饱和流算法的选择

土壤水运动是水分循环中的基本过程,但土壤水预测面临着参数获取难、预测精度差等挑战。数据同化技术为土壤水参数估计和精确预报提供了一种新的方法。本文建立了基于3种不同非饱和水流求解方法的集合卡尔曼滤波(En KF)算法,针对状态向量的选择和正演模型的选择两个问题,研究了非饱和土壤水En KF的计算性能。研究结果表明:对于非线性非饱和水流问题,同时更新水头和参数比仅仅更新水头能够取得更好的预测效果,特别是当多参数未知时;En KF本质上是Monte Carlo方法,极端样本容易导致Picard-h和Picard-mix算法的崩溃,因此传统的HYDRUS程序与复杂非饱和土壤水的数据同化兼容性不佳;当同时同化水头和参数时,如果极端的样本值能够快速得以更新,Picard-h和Picard-mix算法在数据同化模拟中的适用性能得以提升;但由于观测信息对参数的校正能力取决于特定的问题和条件,Ross算法是执行非饱和土壤水数据同化模拟的更好选择。     

Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction

We have developed a hybrid model that integrates chaos theory and an extreme learning machine with optimal parameters selected using an improved particle swarm optimization (ELM-IPSO) for monthly runoff analysis and prediction. Monthly streamflow data covering a period of 55 years from Daiying hydrological station in the Chaohe River basin in northern China were used for the study. The Lyapunov exponent, the correlation dimension method, and the nonlinear prediction method were used to characterize the streamflow data. With the time series of the reconstructed phase space matrix as input variables, an improved particle swarm optimization was used to improve the performance of the extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly streamflow prediction was obtained. The accuracy of the predictions of the streamflow series (linear correlation coefficient of about 0.89 and efficiency coefficient of about 0.78) indicate the validity of our approach for predicting streamflow dynamics. The developed method had a higher prediction accuracy compared with an auto-regression method, an artificial neural network, an extreme learning machine with genetic algorithm and with PSO algorithm, suggesting that ELM-IPSO is an efficient method for monthly streamflow prediction.

     

Application of ensemble H-infinity filter in aquifer characterization and comparison to ensemble Kalman filter

Though the ensemble Kalman filter (EnKF) has been successfully applied in many areas, it requires explicit and accurate model and measurement error information, leading to difficulties in practice when only limited information on error mechanisms of observational instruments for subsurface systems is accessible. To handle the uncertain errors, we applied a robust data assimilation algorithm, the ensemble H-infinity filter (EnHF), to estimation of aquifer hydraulic heads and conductivities in a flow model with uncertain/correlated observational errors. The impacts of spatial and temporal correlations in measurements were analyzed, and the performance of EnHF was compared with that of the EnKF. The results show that both EnHF and EnKF are able to estimate hydraulic conductivities properly when observations are free of error; EnHF can provide robust estimates of hydraulic conductivities even when no observational error information is provided. In contrast, the estimates of EnKF seem noticeably undermined because of correlated errors and inaccurate error statistics, and filter divergence was observed. It is concluded that EnHF is an efficient assimilation algorithm when observational errors are unknown or error statistics are inaccurate.     

Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction

Hybrid data assimilation (DA) is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines (SVMs) and the ensemble Kalman filter (EnKF) technology was used for the prediction of soil moisture in different soil layers: 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches: the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.     

A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating

Streamflow estimation is highly significant for water resource management. In this work, we improve the accuracy and stability of streamflow estimation through a novel hybrid decompose-ensemble model that employs variational mode decomposition (VMD) and back-propagation neural networks (BPNN). First, the latest decomposition algorithm, namely, VMD, was used to extract multiscale features that were subsequently learned and ensembled by the BPNN model to obtain the final estimate streamflow results. The historical daily streamflow series of Laoyukou and Wushan hydrological stations in China were analysed by VMD-BPNN, by a single GBRT and BPNN model, ensemble empirical mode decomposition (EEMD) models. The results confirmed that the VMD outperformed a single-estimation model without any decomposition and EEMD-based models; moreover, ensemble estimations using the BPNN model development technique were consistently better than a general summation method. The VMD-BPNN model’s estimation performance was superior to that of five other models at the Wushan station (GBRT, BPNN, EEMD-BPNN-SUM, VMD-BPNN-SUM, and EEMD-BPNN) using evaluation criteria of the root-mean-square error (RMSE?=?2.62 m³/s), the Nash–Sutcliffe efficiency coefficient (NSE?=?0. 9792) and the mean absolute error (MAE?=?1.38 m³/s). The proposed model also had a better performance in estimating higher-magnitude flows with a low criterion for MAE. Therefore, the hybrid VMD-BPNN model could be applied as a promising approach for short-term streamflow estimating.

     

Streamflow Trends and Climate Variability Impacts in Poyang Lake Basin, China

Under the background of global warming, does the effect of the rising global surface temperature accelerate the hydrological cycle? To address this issue, we use the hydro-climatic data from five sub-basins in Poyang Lake basin in the southeast China over the past 50 years, to investigate the annual and seasonal trends of streamflow and the correlations between streamflow and climatic variables. The Theil–Sen Approach and the non-parametric Mann–Kendall test are applied to identify the trends in the annual and seasonal streamflow, precipitation and evapotranspiration series. It was found that annual and seasonal streamflow of all the stations had increasing trends except Lijiadu station in wet season. Only 37.5% hydro-stations in annual streamflow increased significantly, while most stations increased at 95% significance level in dry season. Trends in annual and seasonal precipitation during the whole period were generally not as significant as those in evapotranspiration. The correlations between streamflow and climate variables (precipitation and evapotranspiration) were detected by the Pearson’s test. The results showed that streamflow in the Poyang Lake basin are more sensitive to changes in precipitation than potential evapotranspiration.