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1.
Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957–2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all lead-times and the models’ worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models.  相似文献   

2.
Abstract

Abstract Generating pulses and then converting them into flow are two main steps of daily streamflow generation. Three pulse generation models have been proposed on the basis of Markov chains for the purpose of generating daily intermittent streamflow time series in this study. The first one is based on two two-state Markov chains, whereas the second uses a three-state Markov chain. The third model uses harmonic analysis and fits Fourier series to the three-state Markov chain. Results for a daily intermittent streamflow data series show a good performance of the proposed models.  相似文献   

3.
Storage is a fundamental but elusive component of drainage basin function, influencing synchronization between precipitation input and streamflow output and mediating basin sensitivity to climate and land use/land cover (LULC) change. We compare hydrometric and isotopic approaches to estimate indices of dynamic and total basin storage, respectively, and assess inter-basin differences in these indices across the Oak Ridges Moraine (ORM) region of southern Ontario, Canada. Dynamic storage indices for the 20 study basins included the ratio of baseflow to total streamflow (baseflow index BFI), Q 99 flow and flow duration curve (FDC) slope. Ratios of the standard deviation of the streamflow stable isotope signal relative to that of precipitation were determined for each basin from a 1 year bi-weekly sampling program and used as indicators of total storage. Smaller ratios imply longer water travel times, smaller young water fractions (F yw, < ~2–3 months in age) in streamflow and greater basin storage. Ratios were inversely related to BFI and Q 99, and positively related to FDC slope, suggesting longer travel times and smaller F yw for basins with stable baseflow-dominated streamflow regimes. Inter-basin differences in all indices reflected topographic, hydrogeologic and LULC controls on storage, which was greatest in steep, forest-covered headwaters underlain by permeable deposits with thick and relatively uniform unsaturated zones. Nevertheless, differential sensitivity of indices to controls on storage indicates the value of using several indices to capture more completely how basin characteristics influence storage. Regression relationships between storage indices and basin characteristics provided reasonable predictions of aspects of the streamflow regime of test basins in the ORM region. Such relationships and the underlying knowledge of controls on basin storage in this landscape provide the foundation for initial predictions of relative differences in streamflow response to regional changes in climate and LULC.  相似文献   

4.
The growing concern for health‐related problems deriving from pollutants leaching is driving national and international administrations to support the development of tools for evaluating the effects of alternate management scenarios and identifying vulnerable areas. Cropping systems models are powerful tools for evaluating leachates under different environmental, social, and management conditions. As percolating water is the transport vehicle for pollutants transport in soil, a reliable evaluation of water balance models is a fundamental prerequisite for investigating pesticides and nitrate fate. As specific approaches for the evaluation of multi‐layer evolution of state variables are missing, we propose a fuzzy‐based, integrated indicator (ISWC: 0, best; 1, worst) for a comprehensive evaluation of soil water content (SWC) simulations. We aggregated error metrics with others quantifying the homogeneity of errors across different soil layers, the capability of models to reproduce complex dynamics function of both time and soil depth, and model complexity. We tested ISWC on a sample dataset where the models CropSyst and CERES‐Wheat were used to simulate SWC for winter wheat systems. ISWC revealed that, in the explored conditions, the global assessment of the two models' performances allowed identification of CropSyst as the best (average ISWC = 0·441, with a value of 0·537 obtained by CERES‐Wheat), although each model prevailed for some of the metrics. CropSyst presented the highest accuracy (average agreement module = 0·400), whereas CERES‐Wheat's accuracy was slightly worse, although achieved with a simplified modelling approach (average Akaike Information Criterion = − 230·44), thereby favouring large‐area applicability. The non‐univocal scores achieved by the models for the different metrics support the use of multi‐metric evaluation approaches for quantifying the different aspects of water balance model performances. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

5.
Abstract

New wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. 

Editor D. Koutsoyiannis; Associate editor L. See

Citation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324.  相似文献   

6.
This is the second of two companion papers on inelastic design spectra (for strength, displacement, hysteretic and input energy) for systems with a prescribed ductility factor. All the spectra are consistent (interrelated and based on the same assumptions). This paper deals with two quantities related to cumulative damage: hysteretic and input energy. The input data for the procedure are the characteristics of the expected ground motion in terms of a smooth elastic pseudo-acceleration spectrum and the time integral of the square of the ground acceleration ∫a2 dt. Simple, approximate expressions for two dimensionless parameters (the parameter γ and the hysteretic to input energy ratio EHEI) have been proposed. The parameter 7, which controls the reduction of the deformation capacity of structures due to low-cycle fatigue, depends on the natural period of the system, the prescribed ductility factor, the hysteretic behaviour and the ground motion characteristics. The ratio EH/EI is influenced by damping, the ductility factor and the hysteretic behaviour. Very good approximations to the inelastic spectra for hysteretic and input energy can be derived from the elastic spectrum using the spectra for the reduction factor R, proposed in the companion paper, and the proposed values for γ and EH/EI  相似文献   

7.
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8.
In this paper, a method is proposed in order to obtain a simplified representation of hysteretic and input energy spectra. The method is based on the evaluation of the equivalent number of cycles correlated to the earthquake characteristics by the proposed seismic index ID. This procedure allows us to obtain peak values of the hysteretic and input energy that depend on the demanded ductility, on the seismic index ID and on the peak pseudo‐velocity. The assessment of the input energy represents a first step towards the definition of a damage potential index capable of taking into account the effect of the duration of the ground motions. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

9.
Stochastic models are often fitted to historical data in order to produce streamflow scenarios. These scenarios are used as input data for simulation/optimization models that support operational decisions for water resource systems. The streamflow scenarios are sampled from probability distributions conditioned on the available information, such as recent streamflow data. In this paper we introduce a procedure for further conditioning the probability distributions by considering the recent measurements of climatic variables, such as sea temperatures, that are used to describe the occurrence of El Ni?o. We adopt an auto-regressive model and use the “El Ni?o information” to refine the parameter estimation process for each time step. The corresponding methodology is tested for the monthly energy time series, “inflowing” to the power plants of Colombia. This is a linear combination of streamflow values for the 18 most important rivers of the country.  相似文献   

10.
Provision of reliable scientific support to socio‐economic development and eco‐environmental conservation is challenged by complexities of irregular nonlinearities, data uncertainties, and multivariate dependencies of hydrological systems in the Three Gorges Reservoir (TGR) region, China. Among them, the irregular nonlinearities mainly represent unreliability of regular functions for robust simulation of highly complicated relationships between variables. Based on the proposed discrete principal‐monotonicity inference (DPMI) approach, streamflow generation in the Xingshan Watershed, a representative watershed in this region, is examined. Based on system characterization, predictor identification, and streamflow distribution transformation, DPMI parameters are calibrated through a two‐stage strategy. Results indicate that the modelling efficiency of DPMI is satisfactory for streamflow simulation under these complexities. The distribution transformation method and the two‐stage calibration strategy can deal with non‐normality of streamflow and temporally unstable accuracy of hydrological models, respectively. The DPMI process and results reveal that both streamflow uncertainty and its rising tendency increase with flow levels. The dominant driving forces of streamflow generation are daily lowest temperature and daily cumulative precipitation in consideration of performances in global and local scales. The temporal heterogeneity of local significances to streamflow is insignificant for meteorological conditions. There is significant nonlinearity between meteorological conditions and streamflow and dependencies among meteorological conditions. The generation mechanism of low flows is more complicated than medium flows and high flows. The DPMI approach can facilitate improving robustness of hydro‐system analysis studies in the Xingshan Watershed or the TGR region. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

11.
It is known that construction of large sewers based on consideration of flow with non-deposition without a bed deposit is not economical. Sewer design based on consideration of flow with non-deposition with a bed deposit reduces channel bed slope and construction cost in which the presence of a small depth of sediment deposition on the bed increases the sediment transport capacity of the flow. This paper suggests a new Pareto-optimal model developed by the multigene genetic programming (MGGP) technique to estimate particle Froude number (Frp) in large sewers with conditions of sediment deposition on the bed. To this end, four data sets including wide ranges of sediment size and concentration, deposit thickness, and pipe size are used. On the basis of different statistical performance indices, the efficiency of the proposed Pareto-optimal MGGP model is compared to those of the best MGGP model developed in the current study as well as the conventional regression models available in the literature. The results indicate the higher efficiency of the MGGP-based models for Frp estimation in the case of no additional deposition onto a bed with a sediment deposit. Inasmuch as the Pareto-optimal MGGP model utilizes a lower number of input parameters to yield comparatively higher performance than the conventional regression models, it can be used as a parsimonious model for self-cleansing design of large sewers in practice.  相似文献   

12.
An a priori, analytical model in system identification of vibrating structures is improved by input and output measurements with least square fitting. The structure is modelled by the finite element method. Finite element models usually need a large number of degrees of freedom to simulate a small number of lower spectrum eigenfrequencies with accuracy. The large finite element model is reduced to a subspace of significant eigenfrequencies. The size of the subspace is chosen with regard to the frequency content of the measured data and the accuracy of the large analytical model. An identification method is formulated for the large analytical model. This procedure improves system parameters in the matrices of the large model by measured input and output data with a least square functional. The objective function is consistently reduced, so that the whole identification procedure can be performed in the small subspace. The proposed reduction method permits very large and accurate analytical models to be used, and it decreases the computational cost of the identification procedure significantly. The computational efficiency is demonstrated on an in situ experiment of a radar tower.  相似文献   

13.
A continuous Soil Conservation Service (SCS) curve number (CN) method that considers time‐varied SCS CN values was developed based on the original SCS CN method with a revised soil moisture accounting approach to estimate run‐off depth for long‐term discontinuous storm events. The method was applied to spatially distributed long‐term hydrologic simulation of rainfall‐run‐off flow with an underlying assumption for its spatial variability using a geographic information systems‐based spatially distributed Clark's unit hydrograph method (Distributed‐Clark; hybrid hydrologic model), which is a simple few parameter run‐off routing method for input of spatiotemporally varied run‐off depth, incorporating conditional unit hydrograph adoption for different run‐off precipitation depth‐based direct run‐off flow convolution. Case studies of spatially distributed long‐term (total of 6 years) hydrologic simulation for four river basins using daily NEXRAD quantitative precipitation estimations demonstrate overall performances of Nash–Sutcliffe efficiency (ENS) 0.62, coefficient of determination (R2) 0.64, and percent bias 0.33% in direct run‐off and ENS 0.71, R2 0.72, and percent bias 0.15% in total streamflow for model result comparison against observed streamflow. These results show better fit (improvement in ENS of 42.0% and R2 of 33.3% for total streamflow) than the same model using spatially averaged gauged rainfall. Incorporation of logic for conditional initial abstraction in a continuous SCS CN method, which can accommodate initial run‐off loss amounts based on previous rainfall, slightly enhances model simulation performance; both ENS and R2 increased by 1.4% for total streamflow in a 4‐year calibration period. A continuous SCS CN method‐based hybrid hydrologic model presented in this study is, therefore, potentially significant to improved implementation of long‐term hydrologic applications for spatially distributed rainfall‐run‐off generation and routing, as a relatively simple hydrologic modelling approach for the use of more reliable gridded types of quantitative precipitation estimations.  相似文献   

14.
ABSTRACT

Although it is conceptually assumed that global models are relatively ineffective in modelling the highly unstable structure of chaotic hydrologic dynamics, there is not a detailed study of comparing the performances of local and global models in a hydrological context, especially with new emerging machine learning models. In this study, the performance of a local model (k-nearest neighbour, k-nn) and, as global models, several recent machine learning models – artificial neural network (ANN), least square-support vector regression (LS-SVR), random forest (RF), M5 model tree (M5), multivariate adaptive regression splines (MARS) – was analysed in multivariate chaotic forecasting of streamflow. The models were developed for Australia’s largest river, the River Murray. The results indicate that the k-nn model was more successful than the global models in capturing the streamflow dynamics. Furthermore, coupled with the multivariate phase-space, it was shown that the global models can be successfully used for obtaining reliable uncertainty estimates for streamflow.  相似文献   

15.
Stochastic and deterministic upscaling techniques are developed that upscale saturated conductivity at the support of 0.04 m2 to representative actual infiltration (Ib) for support units (blocks) of 101–104 m2, as a function of steady state rainfall and runon to the block, under Hortonian runoff (infiltration excess overland flow). Parameters in the upscaling techniques represent the surface runoff flow pattern and the spatial probability distribution of saturated conductivity within the 101–104 m2 block. The stochastic upscaling technique represents the spatial process of infiltration and runoff using a simple process-imitating model, estimating Ib using Monte Carlo simulation. The deterministic upscaling technique aggregates these processes by a deterministic function relating rainfall and runon to Ib. The stochastic upscaling technique is shown to be capable to upscale saturated conductivity derived from ring infiltrometers to Ib values of plots (1 m2) corresponding to measured Ib values using rainfall simulators. It is shown that both upscaling techniques can be used to estimate Ib for each time step and each block in transient rainfall–runoff models, giving better estimates of cumulative runoff from a hillslope and a small catchment than model runs that do not use upscaling techniques.  相似文献   

16.
Abstract

Existing models for generating synthetic daily streamflow data are unsuitable for reproducing the predominant features of daily flows, the rising and recession of flood flows, the peaks of the floods, the volume of the waves and the range. In this paper, a model is presented which is able to reproduce the important features of daily flows. In the model the measured record of daily data is assumed to be the output of a linear system. The input of the system consists of pulses, occurring on certain days. The pulses are convoluted with the system function in order to produce the output. The form of the system function depends on the magnitude of the output. First, the days on which pulses occur, the magnitude of pulses, and the form of the system function as a function of the system output, are determined. Subsequently, a model was developed for the generation of the pulses. The model consists of a combination of two processes. Using a Markov chain model, the sequence of dry and wet days (days with and without pulses) is generated. Thereafter, a pulse of certain magnitude is assigned to each wet day. A modified first-order autoregressive process is used to produce these correlated pulses. The random components of the pulses are taken from a transformed exponential distribution. The periodicity of the flows within the year is reproduced by using different model parameters for each month of the year. The model yields good results for small and medium size basins, especially as far as peak flows, the volume of the waves, and the range are concerned. A sequence of daily flows from at least 20 years is required for input data.  相似文献   

17.
The variability of diurnal, day-to-day, and monthly average rates of the 557.7-nm atmospheric emission (I 557.7) is considered. We use 1997–2010 airglow observation data obtained for the upper atmosphere over Eastern Siberia (52°N, 103°E). The variation coefficient KV of corresponding quantities is taken as the variability index. For the 23rd solar cycle, we examine the resulting seasonal variation of KV of monthly averaged I 557.7, the dependence of monthly averaged I 557.7 on solar activity for each month, the variation coefficient of diurnal values of I 557.7 for different seasons of the year, the variability of I 557.7 during some geophysical events, and the correlation of I 557.7 variations with global climatic indices.  相似文献   

18.
A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework, involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model application and testing made with reference to some daily runoff series from Italian basins.  相似文献   

19.
A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework, involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model application and testing made with reference to some daily runoff series from Italian basins.  相似文献   

20.
ABSTRACT

Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow.  相似文献   

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