Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

Research on Pattern Matching Method of Multivariate Hydrological Time Series

The existing pattern matching methods of multivariate time series can hardly measure the similarity of multivariate hydrological time series accurately and efficiently.Considering the characteristics of multivariate hydrological time series,the continuity and global features of variables,we proposed a pattern matching method,PP-DTW,which is based on dynamic time warping.In this method,the multivariate time series is firstly segmented,and the average of each segment is used as the feature.Then,PCA is operated on the feature sequence.Finally,the weighted DTW distance is used as the measure of similarity in sequences.Carrying out experiments on the hydrological data of Chu River,we conclude that the pattern matching method can effectively describe the overall characteristics of the multivariate time series,which has a good matching effect on the multivariate hydrological time series.

Research on Hydrological Time Series Prediction Based on Combined Model

Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.

TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING

Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.

HYDROLOGIC SERIES CHARACTERISTICSANALYSIS OF THE MAJOR RIVERS AROUNDTHE TAKLIMAKAN DESERT

This paper firstly analyses the hydrologic characteristics of the major rivers around the Taklimakan Desert with the method of mathematical statistics. Secondly, structure features of annual runoff series of these rivers are discussed both in the domain and in frequency domain with the method of time series analysis. Fain the analysis, it can be learnt that the nature quantity of water in the rivers in this area is generally steady and the annual runoff series of rivers is mostly independment stationary random sequence. Therefore, this paper can Provide scientific basis for runoff variation law research and reasonal exploitation and utilization of water resource in this area.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability.Despite the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the literature.In this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns.To this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)archives.As these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale.We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower limit.We then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features,among others.We further(i)show that,if such descriptive information is available for a monthly hydroclimatic time series,we can even foretell the quality of its future forecasts with a considerable degree of confidence,and(ii)rank the features according to their efficiency in explaining and foretelling forecastability.We believe that the obtained rankings are of key importance for understanding forecastability.Spatial forecastability patterns are also revealed through our experiments,with East Asia(Europe)being characterized by larger(smaller)monthly temperature time series forecastability and the Indian subcontinent(Australia)being characterized by larger(smaller)monthly precipitation time series forecastability,compared to other continental-scale regions,and less notable differences characterizing monthly river flow from continent to continent.A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible.Indeed,continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters(because of their essential differences in terms of descriptive features).

应用MQ Series实现广域网接收北斗卫星水情数据

在简要介绍基于卫星通信的水情自动测报系统和MQSeries的基本概念、原理及特点的基础上,叙述了应用MQSeries实现广域网接收该水情自动测报系统数据的方法。

Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Uyo-Nigeria Using Non-Stationary Approach

This study aims at establishing if climate change exists in the Niger Delta environment using non-stationary rainfall Intensity-Duration-Frequency (IDF) modelling incorporating time-variant parameters. To compute the intensity levels, the open-access R-studio software was used based on the General Extreme Value (GEV) distribution function. Among the four linear parameter models adopted for integrating time as a covariate, the fourth linear model incorporating scale and location with the shape function constant produced the least corrected Akaike Information Criteria (AICc), varying between 306.191 to 101.497 for 15 and 1440 minutes, respectively, selected for calibration of the GEV distribution equation. The non-stationary intensities yielded higher values above those of stationary models, proving that the assumption of stationary IDF models underestimated extreme events. The difference of 13.71 mm/hr (22.71%) to 14.26 mm/hr (17.0%) intensities implies an underestimation of the peak flood from a stationary IDF curve. The statistical difference at a 95% confidence level between stationary and non-stationary models was significant, confirming evidence of climatic change influenced by time-variant parameters. Consequently, emphasis should be on applying shorter-duration storms for design purposes occurring with higher intensities to help reduce the flood risk and resultant infrastructural failures.

长江干线中下游航道枯水位变异特性研究

为深入认识长江中下游枯水情势发生的复杂变化,采用水文变异诊断系统对宜昌、沙市、汉口、大通等重要控制站点的枯水位进行分月变异特性研究。结果表明:宜昌站枯水期发生中、强的变异;沙市站发生强、巨的变异,变异程度最强;汉口站发生纯随机(无变异)至强变异;大通站发生纯随机(无变异)至中变异;各站点10—11月均发生向下的变异,与2003年相比,2021年汛后宜昌、沙市、汉口站水位(流量)分别下降了0.76 m(6000 m^(3)/s)、2.82 m(7000 m^(3)/s)、1.66 m(1万m^(3)/s),大通站则未发生明显变化,其对航运的潜在影响需加强分析。

长江流域水域及消落区现状、变迁与渔业资源变动

采用中-大尺度遥感监测手段,对长江流域水域及消落区开展调查与分析,重点阐述长江流域从自然水体为主向人工水面为主的变化趋势,及其对长江流域天然渔业资源衰退的潜在影响。结果显示,近40年全长江流域历史最大水面约63360 km^(2),最小水面约26396 km^(2),历史最大消落面积约36964 km^(2)。2019—2020年“一江两湖七河”最大水面约为19663 km^(2),最小水面约为14281 km^(2),消落区面积6337 km^(2),其中反季节性消落区633km^(2)。2001—2020年和1984—2000年两时段相比,地表水减少水面中超过80%来自于具有自然水文情势的消落区,而新增水面中,由于水库充填导致的河流水面增加达5500 km^(2),致使长江流域水域类型组成结构发生了巨大转变,自然水体占比不足20世纪80年代的一半,而同时期的鱼类资源现存量也下降为20世纪80年代的一半。本研究首次明确反季节性消落区的概念,探讨了长江流域河流梯级水库充填形成的反季节性消落区与鱼类“三场”(产卵场、索饵场和越冬场)关键栖息地丧失的关系。

流域变化环境下水文非平稳异方差序列随机建模研究进展

气候变化和高强度人类活动的显著影响使流域水文序列出现异方差性,破坏了Box-Jekins法ARMA模型建立的前提条件。基于国内外相关研究进展和存在的主要科学问题,提出了目前几个主要研究内容和研究途径。金融学、经济学和信号处理等领域的观测序列与流域变化环境下水文非平稳序列异方差性波动特性相似,而且这些领域中已取得了许多成功的案例。应用金融学、经济学和信号处理等领域的异方差性原理和方法,结合流域水文特性,开展变化环境下水文非平稳异方差序列随机建模是可行的,与分布式水文模型相比,该方法是一种实用的建模途径。文中提出的一些思路可为变化环境下精确描述流域水文要素值的变化规律、涉水工程的规划设计与科学管理以及干旱风险调控提供依据。

水文计算中伽玛函数的计算方法研究

伽玛函数作为水文频率及洪水计算中应用广泛的函数,计算方法众多,适用范围和计算精度各不相同。为探索适合水文计算的高精度伽玛函数计算方法,采用斯特林级数及其衍生的近似式等不同伽玛函数渐进展开公式进行计算分析,比较各种方法的适用范围及计算精度。结果表明,分段多项式法截断误差最小,成果精度最高;其次为Ramanujan渐近展开式和stirling前四项式。推荐的高精度伽玛函数计算方法可提高水文成果的精度,为各类涉水工程规划、设计中确定工程规模和管理决策提供快速精准的成果。

新河湖关系下石臼湖水文节律变化及其生态环境效应

为量化分析长江下游及湖泊水文节律受上游梯级水库开发和三峡工程运用的影响,采用M-K检验、累积距平法、小波相干谱法等水文时间序列趋势分析法和STL等时间序列分解法统计分析了石臼湖蛇山站49年的历史水位资料,解析了石臼湖在新河湖关系下的水文节律的长期变化趋势和规律,探析了水文节律变化和本地气象因子的相干关系和新形势下水文节律变化的生态效应。结果表明,新河湖关系下,枯季更早更长且平均水位更高,极端低水位更易出现,变化更平缓,难以出现高水位;汛期极值高水位更低更晚,汛期平均水位更低,结束更早,变幅更平缓。枯季提前有利于增加水生植物的生物量,但枯季水位变幅减小可能降低枯季生物多样性。

考虑径流非一致性的水库分期旱限水位优化研究

【目的】分析变化环境下水文序列的非一致性,为龙羊峡水库分期旱限水位优化调度模型的建立提供参考。【方法】采用Mann-Kendall检验法、有序聚类分析法和水文序列振动中心重构法,对龙羊峡水库年径流序列进行一致性分析及修正,然后利用水库旱限水位分级和Fisher最优分割法,对径流序列修正前后不同干旱等级下的水库分期旱限水位进行确定,最后通过耦合分期旱限水位建立以缺水指数最小为目标函数的龙羊峡水库优化调度模型,对径流序列修正前后两种情况下的分期旱限水位进行对比研究。【结果】一致性检验发现,龙羊峡水库径流序列具有明显下降的趋势,并在1989年发生突变。经一致性修正后计算龙羊峡水库7-10月、11-12月、1-4月、5-6月4个分期的旱限水位,在轻旱条件下分别为2530.00,2534.91,2539.10和2535.13 m,中旱条件下分别为2530.00,2535.44,2539.80和2536.70 m,重旱条件下分别为2530.00,2535.87,2540.35和2537.89 m;特旱条件下分别为2530.00,2536.06,2540.58和2538.12 m。采用不同调度模型对龙羊峡水库进行优化调度,结果显示修正后的调度效果较好,其总缺水量、缺水指数相比修正前分别降低5.37%和6.03%,其中枯水时段平均缺水量降低了10.71%,缺水程度改善明显。【结论】非一致性径流对龙羊峡水库旱限水位的确定有一定影响,一致性修正后的径流使水库旱限水位的确定更加可靠,水库的抗旱能力得以提升,可为水库供水、抗旱应急管理决策提供参考。

基于长系列水文监测数据的台风对舟山岛降雨量的影响

以舟山长春岭雨量站作为参考点,利用长系列监测数据进行分析计算,对1980—2019年间发生在西北太平洋的台风对长春岭站降雨量的影响进行研究。分析结果显示长春岭站平均每年台风雨占台汛期降雨的72.55%,舟山登陆、浙江登陆、其他登陆及非登录台风平均每年给长春岭站带来降雨量29.45、126.23、364.68 mm,这3类台风的单个台风给长春岭站带来降雨量分别为300.65、157.78、71.28 mm。研究揭示了长春岭站总降雨量很大程度上取决于带来影响>50 mm降雨量的台风,特别是浙江省内登录的台风。除登陆点外,浙江登陆台风影响长春岭站降雨量最大因子是路径走向,其次是强度和移动速度。研究结果对进一步认识舟山岛水资源的形成具有一定的参考意义。

Auto-Regressive Models of Non-Stationary Time Series with Finite Length

To analyze and simulate non-stationary time series with finite length, the statistical characteris- tics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and stud- ied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments re- garded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.

Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Smoothing Non-Stationary Time Series Using the Discrete Cosine Transform

This paper considers the problem of smoothing a non-stationary time series(having either deterministic and/or stochastic trends) using the discrete cosine transform(DCT).The DCT is a powerful tool which has found fruitful applications in filtering and smoothing as it can closely approximate the optimal Karhunen-Loeve transform(KLT).In fact,it is known that it almost corresponds to the KLT for first-order autoregressive processes with a root close to unity:This is the case with most economic and financial time series.A number of new results are derived in the paper:(a) The explicit form of the linear smoother based on the DCT,which is found to have time-varying weights and that uses all observations;(b) the extrapolation of the DCT-smoothed series;(c) the form of the average frequency response function,which is shown to approximate the frequency response of the ideal low pass filter;(d) the asymptotic distribution of the DCT coefficients under the assumptions of deterministic or stochastic trends;(e) two news method for selecting an appropriate degree of smoothing,in general and under the assumptions in(d).These findings are applied and illustrated using several real world economic and financial time series.The results indicate that the DCT-based smoother that is proposed can find many useful applications in economic and financial time series.

Bayesian-combined wavelet regressive modeling for hydrologic time series forecasting

Wavelet regression(WR)models are used commonly for hydrologic time series forecasting,but they could not consider uncertainty evaluation.In this paper the AM-MCMC(adaptive Metropolis-Markov chain Monte Carlo)algorithm was employed to wavelet regressive modeling processes,and a model called AM-MCMC-WR was proposed for hydrologic time series forecasting.The AM-MCMC algorithm is used to estimate parameters’uncertainty in WR model,based on which probabilistic forecasting of hydrologic time series can be done.Results of two runoff data at the Huaihe River watershed indicate the identical performances of AM-MCMC-WR and WR models in gaining optimal forecasting result,but they perform better than linear regression models.Differing from the WR model,probabilistic forecasting results can be gained by the proposed model,and uncertainty can be described using proper credible interval.In summary,parameters in WR models generally follow normal probability distribution;series’correlation characters determine the optimal parameters values,and further determine the uncertain degrees and sensitivities of parameters;more uncertain parameters would lead to more uncertain forecasting results and hard predictability of hydrologic time series.

甘肃河西地区近50年气象和水文序列的变化趋势

应用累积滤波器和秩次相关法 ,对甘肃河西地区近 5 0年气象和水文序列的变化趋势进行了分析 .结果表明 ,该区年降水量、年平均气温和年平均流量变化趋势存在明显的地域分布 ,并受海拔高程的影响 .这可能是不同的下垫面对全球变暖的不同响应 .该区气候总体趋于暖湿 。