Multiple Time-scale Characteristics of Runoff Variations in Middle Reaches of Huolin River and Their Effects

The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyun- hushuo Hydrologic Station, which is located in the middle reaches of the Huolin River, were analyzed by using wavelet analysis. Main objective was to discuss the periodic characteristics of the runoff, and examine the temporal patterns of the Huolin River recharging to the floodplain wetlands in the lower reaches of the river, and the corresponding effects of recharging variation on the environmental evolution of the wetlands. The results show that the annual runoff varied mainly at three time scales. The intensities of periodical signals at different time scales were strongly characterized by local distribution in its time frequency domain. The interdecadal variation at a scale of more than 30yr played a leading role in the temporal pattern of runoff variation, and at this scale, the runoff at Baiyunhushuo Hydrologic Station varied in turn of flood, draught and flood. Accordingly, the landscape of the floodplain wetlands presented periodic features, es- pecially prominent before the 1990s. Compared with intense human activities, the runoff periodic pattern at middle (10-20yr) and small (1-10yr) scales, which has relatively low energy, exerted unobvious effects on the environmental evolution of the floodplain wetlands, especially after the 1990s.

Climate Change and Its Effects on Runoff of Kaidu River, Xinjiang, China:A Multiple Time-scale Analysis

This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, precipitation in flood season, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipi- tation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipita- tion showed little increase over the period of 1957-2002. By changing the time scale from 32-year to 4-year, we ob- served nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.

Multiple Time Scale Analysis of River Runoff Using Wavelet Transform for Dagujia River Basin, Yantai, China

Based on monthly river runoff and meteorological data, a method of Morlet wavelet transform was used to analyze the multiple time scale characteristics of river runoff in the Dagujia River Basin, Yantai City, Shandong Prov- ince. The results showed that the total annual river runoff in the Dagujia River Basin decreased significantly from 1966 to 2004, and the rate of decrease was 48×106m3/10yr, which was higher than the mean value of most rivers in China. Multiple time scale characteristics existed, which accounted for different aspects of the changes in annual river runoff, and the major periods of the runoff time series were identified as about 28 years, 14 years and 4 years with decreasing levels of fluctuation. The river runoff evolution process was controlled by changes in precipitation to a certain extent, but it was also greatly influenced by human activities. Also, for different time periods and scales, the impacts of cli- mate changes and human activities on annual river runoff evolution occurred at the same time. Changes in the annual river runoff were mainly associated with climate change before the 1980s and with human activities after 1981.

Simulation on the Time Progress of the Non-Point Source Pollution Load in Initial Stage Runoff for Small Watershed

Taking a reservoir in South China as an example, we use rainfall-runoff unit hydrograph method to analyze the time changing process of surface runoff inflow, which generated by typical design rainfall. On the basis of time series data of flow and water quality in control section of the main rivers in Xili Reservoir, we establish mathematical response relation between non-point source pollutants flux, such as flux of COD, flux of NH3-H, in catchment area of control section and runoff. Then we simulate the time dynamic change progress of non-point source pollution load which generate with the initial stage runoff that generated by design rainfall and flow into reservoir. It can provide technical parameters for the design of non-point source which generate from early runoff treatment project.

闽江流域多时间尺度降雨—径流关系变化与成因

为进一步揭示闽江流域降雨—径流关系的时空变化规律,识别主要驱动因子,基于1960—2019年降雨和径流数据,应用TFPW—MK方法,按照年以及年内的汛期与非汛期、前汛期和后汛期、最大月和最小月等不同时间尺度,检测闽江上游三大支流、干流及全流域等空间尺度的降雨—径流演变趋势;结合Sen trend方法,从气候变化、土地利用和水库工程建设3方面,分析影响降雨—径流关系变化的成因。结果表明:1)闽江流域及各支流年降雨—径流均呈不显著上升,趋势变化一致;2)除支流建溪流域,闽江及其他支流非汛期降雨—径流上升趋势显著;汛期内部趋势变化分化明显,前汛期降雨—径流略减少,后汛期降雨—径流大部分显著增加;3)极端降雨—径流的变化趋势不一致,主要受大型水库工程建设的影响。基于多时间尺度的分析,更深入揭示闽江流域降水、径流的演变及气候变化与人类活动的影响,对区域水资源管理与水土保持具有重要参考价值。

牛栏江—滇池补水工程水源区不同时间尺度径流变化特征

为做好补水工程水资源调度、配置、流域水安全治理和区域防汛抗旱工作,充分发挥工程效益,基于牛栏江—滇池补水工程水源区代表站长系列水文资料,采用集中度、不均匀系数、Hurst系数、M-K趋势检验、滑动T检验等多种方法分析了牛栏江—滇池补水工程水源区不同时间尺度径流变化特征。结果表明,水源区不同时间尺度径流年际变化大、丰-平-枯周期明显;年内分配向均匀性和坦化趋势演变;最小1、7 d径流显著上升,其他7个径流趋势变化不显著,均发生突变;年、丰水期、极端最大等5个径流指标突变后径流量减少明显,枯水期和极端最小等4个径流指标突变后为增长趋势;年径流突变后8~10月来水量减少明显;预计补水工程水源区未来一段时间不易发生极端枯水事件,但总来水量可能会进一步衰减。

考虑记忆时间的LSTM模型在赣江流域径流预报中的应用

在气候变化条件下,准确的径流预测对水资源的规划与管理十分重要。本文基于长短时记忆神经网络(LSTM)模型,采用赣江流域外洲、峡江以及栋背水文站的逐日流量以及CN05.1日降水数据构建3个不同面积流域的径流预测模型,并通过设置不同情景分析:模型的有效预见期与不同流域平均产汇流时间之间的关系,有效预见期内LSTM径流预测模型精度与记忆时间之间的关系,不同长度的预见期与模型最佳记忆时间之间的关系,同时探讨LSTM径流预测所需的记忆时间与流域面积的关系。结果表明:(1)综合考虑降水和前期径流情景下的径流预测效果最好,当预见期为1 d时,外洲、峡江、栋背站的纳什效率系数(NSE)分别可达0.98、0.96以及0.90;且其有效预见期与仅考虑降水信息的有效预见期相同,均与流域平均产汇流时间相近。(2)随着预见期的延长,不同情景下的预测精度均有不同程度的下降,其中仅考虑前期径流情景的下降率最大,说明降水信息较前期径流对径流预测效果的提升更重要。同时,随着流域面积的增加,相同预见期内径流预测精度均有所提升。(3)当预见期相同时,随记忆时间的延长,不同径流预测模型的预测精度均先上升至最高,接着具有下降趋势,最后逐渐趋于稳定。且在有效预见期内,随着预见期的延长,最佳记忆时间均有增大趋势,当达到最长的有效预见期时,对应的最佳记忆时间均为14 d。此外,在赣江流域的模拟结果表明,随着流域面积的增大,LSTM的最佳记忆时间减小。研究结果可为赣江流域的径流预报提供参考,同时有助于推求其他流域采用机器学习进行径流预测所需的最佳记忆时间。

基于数据分解与斑马算法优化的混合核极限学习机月径流预测

为提高月径流预测精度,改进混合核极限学习机(HKELM)预测性能,提出小波包分解(WPT)-斑马优化算法(ZOA)-HKELM组合模型。利用WPT处理月径流时序数据,构建局部高斯径向基核函数和全局多项式核函数相混合的HKELM;通过ZOA优化HKELM超参数(正则化参数、核参数、权重系数),建立WPT-ZOA-HKELM组合模型,并构建WPT-遗传算法(GA)-HKELM、WPT-灰狼优化(GWO)算法-HKELM、WPT-鲸鱼优化算法(WOA)-HKELM、WPT-ZOA-极限学习机(ELM)、WPT-ZOA-最小二乘支持向量机(LSSVM)、ZOA-HKELM作对比模型,通过黑河流域莺落峡、讨赖河水文站月径流时间序列预测实例对各模型进行检验。结果表明:(1)莺落峡、讨赖河水文站月径流时间序列WPT-ZOA-HKELM模型预测的平均绝对百分比误差分别为1.054%、0.761%,决定系数均达0.999 9,优于其他对比模型,具有更高的预测精度,预测效果更好。(2)利用ZOA优化HKELM超参数,可提高HKELM预测性能,优化效果优于GWO、WOA、GA。(3)预测模型能充分发挥WPT、ZOA和HKELM优势,提高月径流预测精度;在相同分解和优化情形下,HKELM的预测性能优于ELM、LSSVM。

密云水库流域降水径流非平稳特征识别及归因

为更准确揭示和认识变化环境下密云水库流域水文过程的非平稳特征,选取该流域1960—2019年降水和径流资料,采用多种时间序列分析方法综合诊断其趋势、突变点和周期等非平稳特征并进行成因分析。结果显示:该流域降水变化主要表现出较明显的随机特性,径流相比降水的下降趋势更显著,且在1979年发生了向下跳跃的强变异;相比气候变异影响,人类活动是密云水库流域径流减少的主导因素;1980—1998年流域水土保持措施与水利工程兴建对径流变化的贡献率为-111.40%,抵消了气候变异11.4%的增水效应;1999—2019年,塘坝建造和用地类型转变对径流变化的贡献率为-66.60%,同时叠加气候变异-33.40%的减水效应,导致近20年来该流域径流呈现显著减少的态势。研究结果可为密云水库安全运行、洪水调度以及流域水资源管理决策提供科学支撑。

考虑时间效应的VMD-BiLSTM径流量预测模型研究

首先采用VMD对非稳态的径流量进行变量分解,提取了径流量的多尺度特征信息;然后采用BiLSTM算法通过反向学习实现了对径流量时间效应的信息捕捉和传递,并采用Dropout技术解决了BiLSTM模型在训练过程中存在过拟合的问题,从而构建了考虑时间效应的径流量的预测模型。由工程案例可知,与其他常规模型相比,该模型的预测精度最高,可靠性较高,预测精度水平能够满足工程实际的应用。该模型为径流量的预测研究提供借鉴。

改进LSTM在金沙江流域中长期径流预报研究

径流非平稳性及超参数率定容易陷入“维数灾”的问题限制了神经网络(Long Short-Term Memory,LSTM)在中长期径流预报中的应用,针对上述问题,采取“分解-预测-重构”的思路,在数据预处理阶段采用变分模态分解(Variational Mode Decomposition,VMD)进行分解重构,耦合PSO(Particle Swarm Optimization)、SSA(Sparrow Search Algorithm)优化算法长短期记忆神经网络(LSTM),构建VMD-PSO-LSTM、VMD-SSA-LSTM、VMD-LSTM和LSTM四类模型进行对比试验研究,并探讨时滞步长对预报精度的影响。将模型应用于金沙江流域石鼓水文站月平均径流预报:(1)VMD-PSO-LSTM预报精度略高于VMD-SSA-LSTM,其次是VMD-LSTM,最后是LSTM。VMD-PSO-LSTM预报成果的MAE、MAPE、NSE、QR、RMSE分别为160.4、0.135、0.949、0.811、252.8;VMD-PSO-LSTM预报成果评价指标MAE、MAPE和RMSE分别比LSTM模型减小53.92%、50.67%、55.86%;NSE、QR分别比LSTM模型提升28.34%、84.48%;(2)在一定范围内,时滞步长越大,MAE、MAPE和RMSE增大,NSE、QR下降。【结论】对径流时间序列预处理,能够降低时间序列的非平稳性;加入优化算法能够在超参数空间中寻找到较优超参数组合,使得LSTM拟合能力较强,同时保持一定的泛化能力,从而提高预报精度。此外,时滞步长是影响预报精度的重要因素,加入优化算法能降低模型对时滞步长的敏感性。

基于分布式时变增益水文模型和CN05.1降水数据的赣江流域径流模拟研究

水文模型是径流模拟的重要手段,降水是水文循环的重要组成部分,模型的选择和降水数据的质量对流域径流模拟都具有重要意义。以赣江流域为研究区域,通过对比CN05.1降水数据与站点线性插值降水数据,分析CN05.1降水数据的可靠性,评估基于分布式时变增益水文模型(DTVGM)和CN05.1降水数据在赣江流域径流模拟的适用性。研究表明:(1)CN05.1降水和站点插值降水多年月平均降水年内变化过程类似,空间分布相关性很高,R均值为0.90,CN05.1日降水与站点插值日降水相关性较高,RMSE均小于等于7.52 mm,R均大于等于0.81;(2)基于CN05.1降水和站点插值降水数据模拟了赣江流域新田等6个水文站1961—2009年的流量,模拟流量在多数大流量处大于实测流量,表明模型对于大流量的模拟存在高估现象;(3)无论是日径流还是月径流,基于CN05.1降水数据的径流模拟精度均较高,且月径流模拟要优于日径流。因此,基于DTVGM模型和CN05.1降水数据在赣江流域径流模拟具有较高的适用性。

降雨初期冲刷效应及弃流时间分析——以徐州市城市道路为例

降雨初期冲刷效应的判断对城市径流污染控制的解决方向起决定性作用,而初期雨水的弃流时间对海绵城市雨水污染的源头控制在工程实际中的应用具有重要的参考价值。本文对徐州市城市道路径流中5种主要径流污染物——悬浮物(suspended solids,SS)、化学需氧量(chemical oxygen demand,COD)、氨氮(ammonia nitrogen,NH_3-N)、总氮(total nitrogen,TN)、总磷(total phosphorous,TP)进行分析,采用M(V)曲线法(污染物负荷累积率随径流量累积率的变化曲线)分析并对比相关水质标准,分析道路径流的初期效用及弃流时间。结果表明,徐州市城市道路径流存在明显的初期冲刷效应,且初期冲刷效应SS>COD> TP>NH_3-N>TN;SS初期冲刷效应随雨强增大而增加,COD、NH_3-N、TN、TP初期冲刷效应随雨强增大而减小;可将产流后20 min作为徐州市城市道路初期雨水的弃流时间,为海绵城市雨水源头控制提供参考依据。

Human impacts on runoff regime of middle and lower Yellow River

In this study, the 54-year （1950 to 2003） monthly runoff series from February, April, August, and November, as well as the annual runoff series, measured at both Huayuankou and Lijin hydrological stations were chosen as representative data, and the continuous wavelet transform （CWT） was applied to analyze the impacts of human activities on the runoff regime of the middle and lower Yellow River. A point of change in 1970 was first determined, and the observed series before 1970 were considered natural runoff while those after 1970 were restored according to linear trends. Then, the CWT was applied to both the observed and restored runoff series to reveal their variations at multi-temporal scales, including the five temporal ranges of 1-4, 6-8, 9-12, 16-22, and 22-30 years, and the trend at the temporal scale of 54 years. These analysis results are compared and discussed in detail. In conclusion, because of the impacts of human activities, there have been significant changes in the runoff regime in the middle and lower Yellow River since 1970. The decaying tendency of annual runoff has become more pronounced, and the inner-annual distribution of runoff has changed, but human activities have had little impact on the periodic characteristics of runoff.

崩岗崩积体坡面初始产流时间影响因素及其临界阈值

崩岗崩积体坡面原位模拟降雨试验研究还不多见。明确不同雨强、坡度和土体初始含水率对崩积体坡面初始产流时间的影响及其临界阈值,可加深对崩岗产汇流的认识,为合理配置水土资源和完善崩岗治理提供指导。以广东德庆县8个崩积体为试验地点,基于前期对崩积体土体理化性质的测定,采用便携式野外模拟降雨装置、无线自计气象仪和土壤剖面水分测定仪,在面积1 m×2 m的崩积体坡面径流场内,通过9种雨强(0.8—3.8 mm·min^(-1))、8种坡度(9°—38°)、12次10 cm深度土体初始含水率(0.2%—20.5%)的不同组合,进行了12场野外模拟降雨及崩积体坡面产流试验。结果表明,初始产流时间随降雨强度的持续增大而逐渐缩短,没有表现出明显的阈值现象。当坡度<26°—30°时,初始产流时间随坡度的增大而缩短;当坡度>26°—30°时,初始产流时间随坡度的增大而略有加长,初步判断26°—30°的坡度是影响初始产流时间的临界坡度。当10 cm深度土体初始含水率<7.8%—8.3%时,初始产流时间随10 cm深度土体初始含水率的变化没有明显的规律性;当10 cm深度土体初始含水率>7.8%—8.3%时,初始产流时间随10 cm深度土体初始含水率的升高而缩短,因而7.8%—8.3%的10 cm深度土体初始含水率可能是影响初始产流时间的临界阈值。在影响崩积体坡面初始产流时间的3个主要因素中,降雨强度对初始产流时间影响最大,土体初始含水率的影响次之,坡度的影响相对最小。土壤条件也是影响崩积体坡面产汇流的关键因素之一,开展不同区域土壤质地、土壤类型、土壤状况下崩积体坡面产汇流过程的对比分析,是今后需要加强的研究方向。该文初步发现的坡度和10 cm深度土体初始含水率的临界现象,也需要开展更多的试验和实证研究。

A Structural Overview through GR(s) Models Characteristics for Better Yearly Runoff Simulation

In rainfall-runoff modelling, a monthly timescale and an annual one are sufficient for the management of deductions. However, to simulate the flow at a large time-step (annual), we generally precede the use of a model working for a finer time-step (daily) while aggregating the desired outputs. The finest time-steps are considered, apriori, as the most performant. By passing from one time-step to another, and in order to work in the desired time-step (annual) and calculate the potential gains or loss, this article proposed a comparative study between the aggregation method of outputs of a modal working at a finer time step, and a method in which we use a conceived model from the beginning. To ensure this comparative and empirical approach, the choice has been focused on (GRs) models to a daily time-step (GR4J), monthly time step (GR2M) and annual time step (GR1A). The modelling platform used is the same for all three models taking into account the specificities of each one: the same data sample, the same optimization method, and the same function criterion are used during the construction of these models. Due to the moving between these time steps, results show that the best way to simulate the annual flow is to use an appropriate and designed modal initially conceived to this time step. Indeed, this simulation seems to be less effective when using a model at a finer time-step (daily).

Rainfall-runoff simulation and flood forecasting for Huaihe Basin

The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.

不同前处理方式对青藏高原冰川径流主要阳离子浓度的影响

青藏高原地理位置偏远,其冰川径流样品在采集完成后通常需要较长时间才能送达实验室开展测试研究,因而样品的不同储存和处理方式可能会影响原始信息的获取。为了探明不同前处理方式对青藏高原冰川径流主要阳离子的影响,采集青藏高原腹地纳木错流域曲嘎切径流样品,经过过滤与否、不同孔径滤膜过滤、加酸与否、不同储存温度以及储存时间等前处理,探究其对主要阳离子浓度的影响。结果表明:在常温和冷藏储存条件下,由于“河水-悬浮颗粒物”两相已达接近平衡的状态,Ca^(2+)、Mg^(2+)、K^(+)、Na^(+)和SiO_(2)浓度在2.5个月内基本稳定,是否过滤以及不同孔径过滤对冰川径流样品水化学的影响较小。冷冻或加酸储存后Ca^(2+)、Mg^(2+)、K^(+)、Na^(+)和SiO_(2)浓度都存在不同程度的偏离。与碳酸盐矿物含量相对丰富的积雪样品相比,不同前处理方式对冰川径流主要阳离子浓度的影响更小。建议冰川径流样品采集后冷藏或常温储存且在测试前完成过滤处理即可,不可加酸储存,并在两个月内完成测试。

基于VMD-PSR-BNN模型的月径流预测方法研究

径流过程是地球上水文循环中的关键一环,科学准确地预测月径流的来水量对于流域的水量调度、水资源规划及管理具有十分重要的意义。然而由于径流过程的复杂性以及人类活动的影响,在变化环境中精准捕捉月径流时间序列的变化规律变得十分困难。针对月径流时间序列预测中存在的对于样本数据中先验信息识别不够彻底以及对时间步长嵌入维度难以有效地自适应选取这两点问题,设计了基于VMD-PSR-BNN的月径流时间序列预测模型。基于变分模态分解(VMD)算法对噪声良好的鲁棒性和对时序信号精确分解的特性,将月径流时间序列视为一种时序信号,利用VMD方法将月径流时间序列分解为多个相对平稳的固有模态函数(IMF),再基于相空间重构(PSR)理论对各个IMF分别进行重构,对各个重构后的IMF分别采用基于变分推理的贝叶斯神经网络(BNN)进行预测,最后将各个BNN的预测结果进行聚合重构得到月径流时间序列的最终预测结果。选取渭河流域咸阳和华县两个水文站1953-2018年的月径流时间序列进行实例分析。结果表明:VMD对月径流时间序列具有很好的分解效果,两个水文站基于VMD-PSR-BNN模型的月径流预测结果均可达到水文预报的甲级标准,并且对于样本中的极端值具有较好的拟合效果,为月径流时间序列的预测提供了新的方法参考。

基于时序分解与机器学习的非平稳径流序列集成模型与应用

揭示变化环境下非平稳径流序列波动特征,可为提高径流预测精度和涉水工程规划提供支撑。针对径流序列具有非平稳性、周期性和异方差性的特征,收集长江流域攀枝花、城陵矶和大通站2008—2018年实测径流资料,基于周期趋势分解法(STL)将原始数据分解为周期项、趋势项和剩余项,结合各子序列特征采用多模型集成获取未来径流的综合预测值,并将预测结果与Prophet、LSTM和GARCH等单一模型进行对比。结果表明:联合机器学习和时序分解的集成模型在多个评价指标上均优于单一模型,且对异方差效应显著的站点模拟精度提升明显;验证期内3个站点的纳什效率系数分别为0.96、0.95和0.93,表明该模型能有效模拟长江流域径流波动过程。