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1.
Abstract

Alternative approaches to estimating monthly and annual potential evapotranspiration (PE) are explored in cases where daily climate data are not routinely recorded. A database consisting of data from 222 weather stations, representing a wide variety of climatic conditions, is used to draw general conclusions. In addition, two PE formulae with different data requirements are used: the standard FAO-56 Penman-Monteith equation, and a simple temperature-based equation. First, we tested the degree of bias introduced by using climate data averaged over long time periods instead of daily data. Second, we explored the sensitivity of PE estimation with respect to variations in sampling frequency of climate variables. The results show that using mean weather data has only a limited effect on monthly and annual PE estimates. Conversely, imperfect sampling of weather data may bias monthly and to a lesser extent annual PE estimates if the sampling period exceeds 5 and 10 days, respectively. Finally, we tested the impact of erroneous weather data on the simulations of annual actual evapotranspiration obtained with the Budyko model. The impact on the Budyko model outputs depends more on the dryness index of a given location than on annual PE; for regions under water stress, the errors in estimation of actual evapotranspiration are very limited, compared to humid regions where available energy is the dominating factor and the propagation of PE errors is important.

Citation Oudin, L., Moulin, L., Bendjoudi, H. & Ribstein, P. (2010) Estimating potential evapotranspiration without continuous daily data: possible errors and impact on water balance simulations. Hydrol. Sci. J. 55(2), 209–222.  相似文献   

2.
ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R2 > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R2 > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.  相似文献   

3.
ABSTRACT

In order to understand and adequately manage hydrological stress, it is necessary to simulate groundwater levels accurately. In this research, gene expression programming (GEP) and M5 model tree (M5) are used to simulate monthly groundwater levels. The models are combined with wavelet transform to produce two hybrid models: wavelet gene expression programming (WGEP) and wavelet M5 model tree (WM5). For the simulation, groundwater level, temperature and precipitation values from three observation wells and one meteorological station, located in Iran, are used. The results indicate that the hybrid models, WGEP and WM5, lead to a better performance than the simple models, GEP and M5. The performance of the two hybrid models is similar. It is also observed that selecting a suitable time lag for inputs plays an important role in the accuracy of the simple models. The selection of a suitable decomposition level strongly affects the accuracy of hybrid models.  相似文献   

4.
A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mineral salt composition with TDS. In the present study, four artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast TDS in river water over a period of 18 years at seven different sites. Five different GEP, ANFIS and ANN models comprising various combinations of water quality and flow variables from Zarinehroud basin in northwest of Iran were developed to forecast TDS variations. The correlation coefficient (R), root mean square error and mean absolute error statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the GEP, ANFIS-GP, ANFIS-SC and ANN models could be employed successfully in forecasting TDS variations. A comparison was made between these artificial intelligence approaches which emphasized the superiority of GEP over the other intelligent models.  相似文献   

5.
《水文科学杂志》2012,57(15):1843-1856
ABSTRACT

An integrated data-intelligence model based on multilayer perceptron (MLP) and krill herd optimization – the MLP-KH model – is presented for the estimation of daily pan evaporation. Daily climatological information collected from two meteorological stations in the northern region of Iran is used to compare the potential of the proposed model against classical MLP and support vector machine models. The integrated and the classical models were assessed based on different error and goodness-of-fit metrics. The quantitative results evidenced the capacity of the proposed MLP-KH model to estimate daily pan evaporation compared to the classical ones. For both weather stations, the lowest root mean square error (RMSE) of 0.725 and 0.855 mm/d, respectively, was obtained from the integrated model, while the RMSE for MLP was 1.088 and 1.197, and for SVM it was 1.096 and 1.290, respectively.  相似文献   

6.
Evaporation of water from free water surfaces or from land surfaces is one of the main components of the hydrological cycle, and its occurrence is governed by various meteorological and physical factors. There is a multitude of models developed for estimating daily evaporation values by using weather data. This paper evaluates a Gene Expression Programming (GEP) model for estimating evaporation through spatial and temporal data scanning techniques. It is by using ‘leave‐one‐out’ procedures, a complete scan of the possible train and test set configurations is carried out according to temporal and spatial criteria. Comparison of the GEP model with empirical‐physical models shows that daily evaporation values computed by the GEP model are more accurate. Further, local calibration of the GEP model may not be needed, if enough climatic data are available at other stations. The performance of the GEP model fluctuates throughout the period of study and between stations. A suitable assessment of the model should consider a complete temporal and/or spatial scan of the data set used. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

7.
In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L?1) and Cl (meq L?1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively.  相似文献   

8.
Abstract

This paper develops an algorithm for computing spatially-distributed monthly potential evaporation (PE) over a mountainous region, the Lhasa River basin in China. To develop the algorithm, first, correlation analysis of different meteorological variables was conducted. It was observed that PE is significantly correlated with vapour pressure and temperature differences between the land surface and the atmosphere. Second, the Dalton model, which was developed based on the mass transfer mechanism, was modified by including the influence of the related meteorological variables. Third, the influence of elevation on monthly temperature, vapour pressure and wind velocity was analysed, and functions for extending these meteorological variables to any given altitude were developed. Fourth, the inverse distance weighting method was applied to integrate the extended meteorological variables from five stations adjacent to and within the Lhasa River basin. Finally, using the modified Dalton model and the integrated meteorological variables, we computed the spatially-distributed monthly PE. This study indicated that spatially-distributed PE can be obtained using data from sparse meteorological stations, even if only one station is available; the results show that in the Lhasa River basin PE decreases when elevation increases. The new algorithm, including the modified model and the method for spatially extending meteorological variables can provide the basic inputs for distributed hydrological models.
Editor Z.W. Kundzewicz  相似文献   

9.
The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

10.
《水文科学杂志》2012,57(15):1803-1823
ABSTRACT

A new methodology is proposed for improving the accuracy of groundwater-level estimations and increasing the efficiency of groundwater-level monitoring networks. Three spatio-temporal (S-T) simulation models, numerical groundwater flow, artificial neural network and S-T kriging, are implemented to simulate water-table level variations. Individual models are combined using model fusion techniques and the more accurate of the individual and combined simulation models is selected for the estimation. Leave-one-out cross-validation shows that the estimation error of the best fusion model is significantly less than that of the three individual models. The selected fusion model is then considered for optimal S-T redesign of the groundwater monitoring network of the Dehgolan Plain (Iran). Using a Bayesian maximum entropy interpolation technique, soft data are included in the geostatistical analyses. Different scenarios are defined to incorporate economic considerations and different levels of precision in selecting the best monitoring network; a network of 37 wells is proposed as the best configuration. The mean variance estimation errors of all scenarios decrease significantly compared to that of the existing monitoring network. A reduction in equivalent uniform annual costs of different scenarios is achieved.  相似文献   

11.
Özgür Kişi 《水文研究》2009,23(2):213-223
This paper reports on investigations of the abilities of three different artificial neural network (ANN) techniques, multi‐layer perceptrons (MLP), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) to estimate daily pan evaporation. Different MLP models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity were developed to evaluate the effect of each of these variables on pan evaporation. The MLP estimates are compared with those of the RBNN and GRNN techniques. The Stephens‐Stewart (SS) method is also considered for the comparison. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics. Based on the comparisons, it was found that the MLP and RBNN computing techniques could be employed successfully to model the evaporation process using the available climatic data. The GRNN was found to perform better than the SS method. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

12.
Gene Expression Programming (GEP) was used to develop new mathematical equations for estimating daily reference evapotranspiration (ET ref) for the Kingdom of Saudi Arabia. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith model was used as a reference target for evapotranspiration (ET) values, with h c varies from 5 to 105 cm with increment of a centimetre. Eight GEP models have been compared with four locally calibrated traditional models (Hargreaves-Samani, Irmak, Jensen-Haise and Kimberly-Penman). The results showed that the statistical performance criteria values such as determination coefficients (R 2) ranged from as low as 64.4% for GEP-MOD1, where the only parameters included (maximum, minimum, and mean temperature and crop height), to as high as 95.5% for GEP-MOD8 with which all climatic parameters included (maximum, minimum and mean temperature; maximum, minimum and mean humidity; solar radiation; wind speed; and crop height). Moreover, an interesting founded result is that the solar radiation has almost no effect on ET ref under the hyper arid conditions. In contrast, the wind speed and plant height have a great positive impact in increasing the accuracy of calculating ET ref. Furthermore, eight GEP models have obtained better results than the locally calibrated traditional ET ref equations.  相似文献   

13.
Gene expression programing (GEP) is used to estimate the suspended sediment yield (SSY) in Euphrates River. SSY is considered to be a function of (i) discharge and (ii) time‐lagged discharge and SSY. The proposed models were trained to extrapolate natural stream data collected from five stations in Middle Euphrates Basin. A detailed sensitivity analysis is done to select the time‐lagged discharge and sediment yield variables. GEP implicitly evaluates the contribution of each independent variable on the fitness of candidate solution and eliminates the variable having no contribution. In this study, all input variables are observed to be included in the proposed GEP models, which prove the significance of each variable. Also, standard and modified sediment rating curves and regression‐based formulae are developed for the five stations. In verification, the estimations of GEP formulae agree well with the measured ones. The GEP models are evaluated by the results of the rating curves and regression formulae. In general, the GEP formulae give better results compared to the rating curves and regression‐based formulae.  相似文献   

14.
Abstract

The Blaney-Criddle (BC) temperature-based equation is used in areas where the complete weather data to estimate reference evapotranspiration (ET0) by the Penman-Monteith FAO-56 (PMF-56) standard model is complex. In this study, the BC equation was first tested and calibrated against the ET0 values computed by the PMF-56 method using data from 17 weather stations in arid regions of Iran. Then, geographical information systems (GIS)-based spatially-distributed maps of ET0 were prepared by means of geographic/topographic factors derived from a digital elevation model (DEM) for all months, separately. The results indicate that the original BC equation overestimated PMF-56 ET0 by 4% at the study sites. The BC equation produced closer ET0 estimates to the PMF-56 method after it was calibrated. The error rate of <3% for the spatial modelling approach suggests that the developed ET0 maps are reliable.

Editor D. Koutsoyiannis; Associate editor D. Yang

Citation Tabari, H., Hosseinzadeh Talaee, P., and Shifteh Some'e, B., 2013. Spatial modelling of reference evapotranspiration using adjusted Blaney-Criddle equation in an arid environment. Hydrological Sciences Journal, 58 (2), 408–420.  相似文献   

15.
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   

16.
ABSTRACT

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (Tdew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting Tdew values.  相似文献   

17.
ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.  相似文献   

18.
Abstract

The applicability of two versions of the Bartlett Lewis rectangular pulse model, the original and the modified model, is discussed for describing the temporal and spatial variation of rainfall patterns observed at 15 raingauge stations in Peninsular Malaysia over the period 1971–2008; 17 different sets of moment combinations are fitted to these models based on the generalized method of moments approach. The common statistics included in all sets are the mean, variance, lag-1 autocorrelation and the probability of dry based on the hourly rainfall data. The analysis was carried out on hourly rainfall data from all 15 stations for all months of the year. Two stations, Petaling Jaya and Kemaman, located on the west and east coasts of the Peninsula, respectively, are considered for illustration of the results, taking the months of July and November, which correspond to the driest and wettest months, corresponding to the southwest monsoon (May–August) and northeast monsoon (November–February), respectively. The best moment combination found for the illustrative results is based on the common statistics, as well as the mean and variance based on 24-h aggregated rainfall data, the inclusion of which successfully improved the model performance; the errors were significantly reduced. It was also found that the performance of the fitted models based on the mean absolute deviate error varies according to the type of Bartlett Lewis model applied: errors are much smaller for the fitted model based on the modified model as compared to the original model. In addition, the fitted statistics: mean, lag-1 autocorrelation and probability of dry are quite well fitted for several aggregated time scales; however, the variances are underestimated in both models for all aggregated time scales, particularly in the case of the original model. The results of extreme value analysis indicate that the modified model failed to reproduce the annual hourly and daily rainfall extremes satisfactorily.
Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Hanaish, I.S., Ibrahim, K., and Jemain, A.A., 2013. On the potential of Bartlett Lewis rectangular pulse models for simulating rainfall in Peninsular Malaysia. Hydrological Sciences Journal, 58 (8), 1690–1703.  相似文献   

19.
ABSTRACT

The impact of climate change on hydrology and water salinity of a valuable coastal wetland (Anzali) in northern Iran is assessed using daily precipitation and temperature data from 19 models of Coupled Model Inter-comparison Project Phase 5. The daily data are transiently downscaled using the Long Ashton Research Station Weather Generator to three climatic stations. The temperature is projected to increase by +1.6, +1.9 and +2.7°C and precipitation to decrease by 10.4%, 12.8% and 12.2% under representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5 and RCP8.5, respectively. The wetland hydrology and water salinity are assessed using the water balance approach and mixing equation, respectively. The upstream river flow modelled by the Soil and Water Assessment Tool is projected to reduce by up to 18%, leading to reductions in wetland volume (154 × 106 m3), area (57.47 km2) and depth (2.77 m) by 34%, 21.1% and 20.2%, respectively, under climate change, while the mean annual total dissolved solids (1675 mg/L) would increase by 49%. The reduced volume and raised salinity may affect the wetland ecology.  相似文献   

20.
ABSTRACT

In this study, a data-driven streamflow forecasting model is developed, in which appropriate model inputs are selected using a binary genetic algorithm (GA). The process involves using a combination of a GA input selection method and two adaptive neuro-fuzzy inference systems (ANFIS): subtractive (Sub)-ANFIS and fuzzy C-means (FCM)-ANFIS. Moreover, the application of wavelet transforms coupled with these models is tested. Long-term data for the Lighvan and Ajichai basins in Iran are used to develop the models. The results indicate considerable improvements when GA selection and wavelet methods are used in both models. For example, the Nash-Sutcliffe efficiency (NSE) coefficient for Lighvan using FCM-ANFIS is 0.74. However, when GA selection is applied, the NSE is improved to 0.85. Moreover, when the wavelet method is added, the performance of new hybrid models shows noticeable enhancements. The NSE value of wavelet-FCM-ANFIS is improved to 0.97 for Lighvan basin.
Editor D. Koutsoyiannis Associate editor E. Toth  相似文献   

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