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1.
A non-linear perturbation model considering catchment wetness and its application in river flow forecasting 总被引：5，自引：0，他引：5
A non-linear perturbation model for river flow forecasting is developed, based on consideration of catchment wetness using an antecedent precipitation index (API). Catchment seasonality, of the form accounted for in the linear perturbation model (the LPM), and non-linear behaviour both in the runoff generation mechanism and in the flow routing processes are represented by a constrained non-linear model, the NLPM-API. A total of ten catchments, across a range of climatic conditions and catchment area magnitudes, located in China and in other countries, were selected for testing daily rainfall-runoff forecasting with this model. It was found that the NLPM-API model was significantly more efficient than the original linear perturbation model (the LPM). However, restriction of explicit non-linearity to the runoff generation process, in the simpler LMP-API form of the model, did not produce a significantly lower value of the efficiency in flood forecasting, in terms of the model efficiency index R2. 相似文献
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This paper compares artificial neural network (ANN), fuzzy logic (FL) and linear transfer function (LTF)‐based approaches for daily rainfall‐runoff modelling. This study also investigates the potential of Takagi‐Sugeno (TS) fuzzy model and the impact of antecedent soil moisture conditions in the performance of the daily rainfall‐runoff models. Eleven different input vectors under four classes, i.e. (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff and (iv) rainfall, runoff and antecedent moisture content are considered for examining the effects of input data vector on rainfall‐runoff modelling. Using the rainfall‐runoff data of the upper Narmada basin, Central India, a suitable modelling technique with appropriate model input structure is suggested on the basis of various model performance indices. The results show that the fuzzy modelling approach is uniformly outperforming the LTF and also always superior to the ANN‐based models. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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Abstract The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A linear multiple-input single-output (MISO) model coupled with the ANN is shown to provide a better representation of the rainfall-runoff relationship in such large size catchments compared with linear and nonlinear MISO models. The present model provides a systematic approach for runoff estimation and represents improvement in prediction accuracy over the other models studied herein. 相似文献
4.
A model of rainfall–runoff relationships is an essential tool in the process of evaluation of water resources projects. In this paper, we applied an artificial neural network (ANN) based model for flow prediction using the data for a catchment in a semi‐arid region in Morocco. Use of this method for non‐linear modelling has been demonstrated in several scientific fields such as biology, geology, chemistry and physics. The performance of the developed neural network‐based model was compared against multiple linear regression‐based model using the same observed data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modelling appears to be a promising technique for the prediction of flow for catchments in semi‐arid regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
5.
A rainfall‐runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall‐runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km2) in the Nile basin is selected here as a case study. The catchment is classified into seven subcatchments, and the mean areal precipitation over those subcatchments is computed as a main input to the ANN model. The available daily data (1992–99) are divided into two sets for model calibration (1992–96) and for validation (1997–99). The results of the ANN model are compared with one of physical distributed rainfall‐runoff models that apply hydraulic and hydrologic fundamental equations in a grid base. The results over the case study area and the comparative analysis with the physically based distributed model show that the ANN technique has great potential in simulating the rainfall‐runoff process adequately. Because the available record used in the calibration of the ANN model is too short, the ANN model is biased compared with the distributed model, especially for high flows. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
6.
Hydrological model comparison and assessment: criteria from catchment scales and temporal resolution
ABSTRACTThis study examines the performance of three hydrological models, namely the artificial neural network (ANN) model, the Hydrologiska Byråns Vattenbalansavdelning-D (HBV-D) model, and the Soil and Water Integrated Model (SWIM) over the upper reaches of the Huai River basin. The assessment is done by using databases of different temporal resolution and by further examining the applicability of SWIM for different catchment sizes. The results show that at monthly scale the performance of the ANN model is better than that of HBV-D and SWIM. The ANN model can be applied at any temporal scale as it establishes an artificial precipitation–runoff relationship for various time scales by only using monthly precipitation, temperature and runoff data. However, at daily scale the performance of both HBV-D and SWIM are similar or even better than the ANN model. In addition, the performance of SWIM at a small catchment size (less than 10 000 km2) is much better than at a larger catchment size. In view of climate change modelling, HBV-D and SWIM might be integrated in a dynamical atmosphere-water-cycle modelling rather than the ANN model due to their use of observed physical links instead of artificial relations within a black box.
Editor D. Koutsoyiannis; Associate editor D. Hughes 相似文献
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Eight data-driven models and five data pre-processing methods were summarized; the multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition (WD) models were then used in short-term streamflow forecasting at four stations in the East River basin, China. The wavelet–artificial neural network (W-ANN) method was used to predict 1-month-ahead monthly streamflow at Longchuan station (LS). The results indicate better performance of MLR and wavelet–multiple linear regression (W-MLR) in analysing the stationary trained dataset. Four models showed similar performance in 1-day-ahead streamflow forecasting, while W-MLR and W-ANN performed better in 5-day-ahead forecasting. Three reservoirs were shown to have more influence on downstream than upstream streamflow and models had the worst performance at Boluo station. Furthermore, the W-ANN model performed well for 1-month-ahead streamflow forecasting at LS with consideration of a deterministic component. 相似文献
10.
The rainfall–runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall–runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall–runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall–runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance. 相似文献
11.
The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
12.
PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments 总被引：1，自引：0，他引：1
The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting. 相似文献
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The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (TBs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the TB signature. SSM/I TBs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover. 相似文献
14.
Hydrological studies across varied climatic and physiographic regions have observed small changes in the ‘states of wetness’; based on average soil moisture, can lead to dramatic changes in the amount of water delivered to the stream channel. This non-linear behaviour of the storm response has been attributed to a critical switching in spatial organization of shallow soil moisture and hydrologic connectivity. However, much of the analysis of the role of soil moisture organization and connectivity has been performed in small rangeland catchments. Therefore, we examined the relationship between hydrologic connectivity and runoff response within a temperate forested watershed of moderate relief. We have undertaken spatial surveys of shallow soil moisture over a sequence of storms with varying antecedent moisture conditions. We analyse each survey for evidence of hydrologic connectivity and we monitor the storm response from the catchment outlet. Our results show evidence of a non-linear response in runoff generation over small changes in measures of antecedent moisture conditions; yet, unlike the previous studies of rangeland catchments, in this forested landscape we do not observe a significant change in geostatistical hydrologic connectivity with variations in antecedent moisture conditions. These results suggest that a priori spatial patterns in shallow soil moisture in forested terrains may not always be a good predictor of critical hydrologic connectivity that leads to threshold change in runoff generation, as has been the case in rangeland catchments. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
15.
《水文科学杂志》2013,58(2)
Abstract Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance. 相似文献
16.
The analysis of the physical processes involved in a conceptual model of soil water content balance is addressed with the objective of its application as a component of rainfall–runoff modelling. The model uses routinely measured meteorological variables (rainfall and air temperature) and incorporates a limited number of significant parameters. Its performance in estimating the soil moisture temporal pattern was tested through local measurements of volumetric water content carried out continuously on an experimental plot located in central Italy. The analysis was carried out for different periods in order to test both the representation of infiltration at the short time‐scale and drainage and evapotranspiration processes at the long time‐scale. A robust conceptual model was identified that incorporated the Green–Ampt approach for infiltration and a gravity‐driven approximation for drainage. A sensitivity analysis was performed for the selected model to assess the model robustness and to identify the more significant parameters involved in the principal processes that control the soil moisture temporal pattern. The usefulness of the selected model was tested for the estimation of the initial wetness conditions for rainfall–runoff modelling at the catchment scale. Specifically, the runoff characteristics (runoff depth and peak discharge) were found to be dependent on the pre‐event surface soil moisture. Both observed values and those estimated by the model gave good results. On the contrary, with the antecedent wetness conditions furnished by two versions of the antecedent precipitation index (API), large errors were obtained. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
17.
Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs are removed. Then an adequate ANN model, which only includes highly relevant inputs, is constructed. Unlike the trial‐and‐error procedure, the proposed approach is more systematic and avoids unnecessary trials. To demonstrate the effectiveness of the proposed approach, an application to actual typhoon events is presented. The results show that the proposed ANN model, which is constructed by the proposed approach, has advantages over those obtained by the trial‐and‐error procedure. The proposed ANN model has a simpler architecture, needs less training time, and performs better. The proposed ANN model is recommended as an alternative to existing rainfall–runoff ANN models. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
18.
Transfer of atmospheric N deposition on shallow‐soil forested basins on the Canadian Shield to receiving water bodies may be enhanced by rapid preferential flow along the soil–bedrock interface (BR runoff) on basin slopes. Controls on BR runoff, partitioning of event and pre‐event water contributions to this flow, and implications of this partitioning for N fluxes in BR runoff were studied under natural and artificial inputs to an instrumented slope. BR runoff as a fraction of water inputs to the slope increased with antecedent soil wetness and input depth. Event water contributions to BR runoff initially increased with antecedent soil wetness, but then declined at large antecedent soil wetness. Export of applied NH4+ from the slope was maximized when event water contributions containing large NH4+ concentrations dominated BR runoff; however, there was no relationship between the fraction of NO3? application transported in BR runoff and either application input or the event water fraction of that runoff. The applicability of our results to other shallow‐soil areas of the Canadian Shield is limited by artificial N inputs to the slope in excess of natural loads and by low rates of N mineralization and negligible nitrification in the slope's soils. Nevertheless, the study reinforces the need to consider how the hydrologic, geometric and pedologic properties of forest slopes interact with biotic and abiotic soil processes to control N transport and transformation. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
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L. Palleiro M. L. Rodríguez‐Blanco M. M. Taboada‐Castro M. T. Taboada‐Castro 《水文研究》2014,28(4):1677-1688
Using data collected at the Mero catchment during three hydrological years (2005/06–2007/08), an analysis of rainfall–runoff relationships was performed at annual, seasonal, monthly, and event scales. At annual scale, the catchment showed low runoff coefficients (23–35%), due to high water storage capacity soils as well as high runoff inter‐annual variability. Rainfall variability was the main responsible for the differences in the inter‐annual runoff. At seasonal and monthly scales, there was no simple relationship between rainfall and runoff. Seasonal dynamics of rainfall and potential evapotranspiration in conjunction with different rainfall distribution during the study years could be the key factors explaining the complex relationship between rainfall and runoff at monthly and seasonal scale. At the event scale, the results revealed that the hydrological response was highly dependent on initial conditions and, to a lesser extent, on rainfall amount. The shapes of the different hydrographs, regardless of the magnitude, presented similar characteristics: a moderate rise and a prolonged recession, suggesting that subsurface flow was the dominant process in direct runoff. Moreover, all rainfall–runoff events had a higher proportion of baseflow than of direct runoff. A cluster‐type analysis discriminated three types of events differentiated mainly by rainfall amount and antecedent rainfall conditions. The study highlights the role of the antecedent rainfall and the need for caution in extrapolating relationships between rainfall amount and hydrological response of the catchment. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
20.
Observed rainfall and flow data from the Dongjiang River basin in humid southern China were used to investigate runoff changes during low‐flow and flooding periods and in annual flows over the past 45 years. We first applied the non‐parametric Mann–Kendall rank statistic method to analyze the change trend in precipitation, surface runoff and pan evaporation in those three periods. Findings showed that only the surface runoff in the low‐flow period increased significantly, which was due to a combination of increased precipitation and decreased pan evaporation. The Pettitt–Mann–Whitney statistical test results showed that 1973 and 1978 were the change points for the low‐flow period runoff in the Boluo sub‐catchment and in the Qilinzui sub‐catchment, respectively. Most importantly, we have developed a framework to separate the effects of climate change and human activities on the changes in surface runoff based on the back‐propagation artificial neural network (BP‐ANN) method from this research. Analyses from this study indicated that climate variabilities such as changes in precipitation and evaporation, and human activities such as reservoir operations, each accounted for about 50% of the runoff change in the low‐flow period in the study basin. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献