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1.
Abstract

The spatio-temporal variations of reference crop evapotranspiration (ETref) reflect the combined effects of meteorological variables, primarily wind speed, relative humidity, net radiation and air temperature. This study investigated the spatial distribution and temporal trends of ETref (calculated by the FAO-56 Penman-Monteith equation), pan evaporation (Epan) and pan coefficient (Kp) in a 140?×?103 km2 semi-humid to semi-arid area in China. The results show that: (i) although the spatial distributions of ETref and Epan are roughly similar and their spatial correlation is high over the growing season, Kp varied considerably in space due to high humidity in the east of the region and low humidity in the southwest; (ii) the monthly variations of ETref and Epan are similar to that of net radiation and opposite to that of relative humidity, while the monthly variation of Kp is similar to that of relative humidity and opposite to that of wind speed, and the long-term trend is slightly increasing for ETref and Epan, while significantly (10% significance level) increasing for Kp; and (iii) generally, the time series of ETref and Epan from 1951 to 2001 could be divided into three phases due to variations of meteorological variables.

Citation Liang, L.-Q., Li, L.-J. & Liu, Q. (2011) Spatio-temporal variations of reference crop evapotranspiration and pan evaporation in the West Songnen Plain of China. Hydrol. Sci. J. 56(7), 1300–1313.  相似文献   

2.
Abstract

Acceleration of the global water cycle over recent decades remains uncertain because of the high inter-annual variability of its components. Observations of pan evaporation (Epan), a proxy of potential evapotranspiration (ETp), may help to identify trends in the water cycle over long periods. The complementary relationship (CR) states that ETp and actual evapotranspiration (ETa) depend on each other in a complementary manner, through land–atmosphere feedbacks in water-limited environments. Using a long-term series of Epan observations in Australia, we estimated monthly ETa by the CR and compared our estimates with ETa measured at eddy covariance Fluxnet stations. The results confirm that our approach, entirely data-driven, can reliably estimate ETa only in water-limited conditions. Furthermore, our analysis indicated that ETa did not show any significant trend in the last 30 years, while short-term analysis may indicate a rapid climate change that is not perceived in a long-term perspective.

Editor Z.W. Kundzewicz; Associate editor D. Gerten

Citation Lugato, E., Alberti, G., Gioli. B., Kaplan, J.O., Peressotti, A., and Miglietta, F., 2013. Long-term pan evaporation observations as a resource to understand the water cycle trend: case studies from Australia. Hydrological Sciences Journal, 58 (6), 1287–1296.  相似文献   

3.
Evapotranspiration (ET) is one of the major processes in the hydrological cycle, and its reliable estimation is essential to water resources management. Numerous equations have been developed for estimating ET, most of which are complex and require numerous items of weather data. In many areas, the necessary data are lacking, and simpler techniques are required. Evaporation pans are used throughout the world because of the simplicity of technique, low cost, and ease of application. In this study, the radial basis function (RBF) network is applied for pan evaporation to evapotranspiration conversions. The adaptive pan‐based RBF network was trained using daily Policoro data from 15 May 1981 to 23 December 1983. The RBF network obtained, Christiansen, FAO‐24 pan, and FAO‐56 Penman–Monteith equations were verified in comparison with lysimeter measurements of grass evapotranspiration using daily Policoro data from 25 February to 18 December 1984. Based on summary statistics, the RBF network ranked first with the lowest RMSE value (0·433 mm day?1). The RBF network obtained on the basis of the daily data from Policoro, Italy and pan‐based equations were further tested using mean monthly data collected in Novi Sad, Serbia, and Kimberly, Idaho, USA. The overall results favoured use of the RBF network for pan evaporation to evapotranspiration conversions. The use of the RBF network is very simple and does not require any knowledge of ANNs. Users require only code (RBF network), Epan data and corresponding Ra data. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

4.
The estimation of evapotranspiration (E) in forested areas is required for various practical purposes (e.g. evaluation of drought risks) in Japan. This study developed a model that estimates monthly forest E in Japan with the input of monthly temperature (T). The model is based on the assumptions that E equals the equilibrium evaporation rate (Eeq) and that Eeq is approximated by a function of T. The model formulates E as E (mm month−1) = 3·48 T ( °C) + 32·3. The accuracy of the model was examined using monthly E data derived using short‐term water balance (WB) and micrometeorological (M) methods for 15 forest sites in Japan. The model estimated monthly E more accurately than did the Thornthwaite and Hamon equations according to regression analysis of the estimated E and E derived using the WB and M methods. Although the model tended to overestimate monthly E, the overestimation could be reduced by considering the effect of precipitation on E. As T data are commonly available all over Japan, the model would be a useful tool to estimate forest E in Japan. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

5.
ABSTRACT

The potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models – CEEMDAN-ANN and CEEMDAN-M5-MT – with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error.  相似文献   

6.
The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T max), minimum temperature (T min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.  相似文献   

7.
Evapotranspiration was studied at a salt marsh site in the Hunter River estuary, NSW, Australia, during 1996–8. Estimates of actual evapotranspiration (Ea) were obtained for three sites using the eddy correlation method. These values were compared with results obtained with the Penman and Penman–Monteith equations, and with pan evaporation. The Penman–Monteith method was found to be most reliable in estimating daily and hourly evapotranspiration. Surface resistance values averaging 12 s m?1 were derived from the eddy correlation estimates. Recent tidal flooding and rainfall were found to decrease surface resistance and increase Ea/Ep ratios. Estimates of evapotranspiration obtained using the Penman–Monteith method were shown to be sensitive to changes in surface resistance, canopy height and the method used to estimate net radiation from incoming solar radiation. These results underline the importance of accurately estimating such parameters based on site‐specific data rather than relying on empirical equations, which are derived primarily for crops and forests. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

8.
The aim of this study was to validate evaporation models that can be used for palaeo‐reconstructions of large lake water levels. Lake Titicaca, located in a high‐altitude semi‐arid tropical area in the northern Andean Altiplano, was the object of this case study. As annual evaporation is about 90% of lake output, the lake water balance depends heavily on the yearly and monthly evaporation flux. At the interannual scale, evaporation estimation presents great variability, ranging from 1350 to 1900 mm year?1. It has been found that evaporation is closely related to lake rainfall by a decreasing relationship integrating the implicit effect of nebulosity and humidity. At the seasonal scale, two monthly evaporation data sets were used: pan observations and estimations derived from the lake energy budget. Comparison between these data sets shows that (i) there is one maximum per year for pan evaporation and two maxima per year for lake evaporation, and (ii) pan evaporation is greater than lake evaporation by about 100 mm year?1. These differences, mainly due to a water depth scale factor, have been simulated with a simple thermal model θw(h, t) of a free‐surface water column. This shows that pan evaporation (h = 0·20 m) is strongly correlated with direct solar radiation, whereas the additional maximum of lake evaporation (h = 40 m) is related to the heat restitution towards the atmosphere from the water body at the end of summer. Finally, five monthly evaporation models were tested in order to obtain the optimal efficiency/complexity ratio. When the forcing variables are limited to those that are most readily available in the past, i.e. air temperature and solar radiation, the best results are obtained with the radiative Abtew model (r = 0·70) and with the Makkink radiative/air temperature model (r = 0·67). Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

9.
Sustainable water management in semi-arid agriculture practices requires quantitative knowledge of water fluxes within the soil-vegetation-atmosphere system. Therefore, we used stable-isotope approaches to evaluate evaporation (Ea), transpiration (Ta), and groundwater recharge (R) at sites in Senegal's Groundnut basin and Ferlo Valley pasture region during the pre-monsoon, monsoon, and post-monsoon seasons of 2021. The approaches were based upon (i) the isothermal evaporation model (for quantifying Ea); (ii) water and isotope mass balances (to partition Ea and Ta for groundnut and pasture); and (iii) the piston displacement method (for estimating R). Ea losses derived from the isothermal evaporation model corresponded primarily to Stage II evaporation, and ranged from 0.02 to 0.09 mm d−1 in the Groundnut basin, versus 0.02–0.11 mm d−1 in Ferlo. At the groundnut site, Ea rates ranged from 0.01 to 0.69 mm d−1; Ta was in the range 0.55–2.29 mm d−1; and the Ta/ETa ratio was 74%–90%. At the pasture site, the ranges were 0.02–0.39 mm d−1 for Ea; 0.9–1.69 mm d−1 for Ta; and 62–90% for Ta/ETa. The ETa value derived for the groundnut site via the isotope approach was similar to those from eddy covariance measurements, and also to the results from the previous validated HYDRUS-1D model. However, the HYDRUS-1D model gave a lower Ta/ETa ratio (23.2%). The computed groundwater recharge for the groundnut site amounted to less than 2% of the local annual precipitation. Recommendations are made regarding protocols for preventing changes to isotopic compositions of water in samples that are collected in remote arid regions, but must be analysed days later. The article ends with suggestions for studies to follow up on evidence that local aquifers are being recharged via preferential pathways.  相似文献   

10.
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   

11.
Is the total evaporation from a wetland surface (including: open water evaporation, plant transpiration and wet/dry soil evaporation) similar, lower, or higher than evaporation from an open water surface under the same climatic conditions? This question has been the subject of long debate; the literature does not show a consensus. In this paper we contribute to the discussion in two steps. First, we analyse the evaporation from a wetland with emergent vegetation (Ea) versus open water evaporation (Ew) by applying the Penman–Monteith equation to identical climate input data, but with different biophysical characteristics of each surface. Second, we assess the variability of measured Ea/Ew through a literature review of selected wetlands around the globe.We demonstrate that the ratio Ea/Ew is site-specific, and a function of the biophysical properties of the wetland surface, which can also undergo temporal variability depending on local hydro-climate conditions. Second, we demonstrate that the Penman–Monteith model provides a suitable basis to interpret Ea/Ew variations. This implies that the assumption of wetland evaporation to behave similar to open water bodies is not correct. This has significant implications for the total water consumption and water allocation to wetlands in river basin management.  相似文献   

12.
Accurate and reliable river flow forecasts attained with data-intelligent models can provide significant information about future water resources management. In this study we employed a 50-model ensemble of three data-driven predictive models, namely the support vector regression (SVR), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree) to forecast river flow data in a semiarid and ecologically significant mountainous region of Pailugou catchment in northwestern China. To attain stable and accurate forecast results, 50 different models were trained by randomly sampling the entire river flow data into 80% for training and 20% for testing subsets. To attain a complete evaluation of the ensemble-model based results, the global mean of six quantitative statistical performance evaluation measures: the coefficient of correlation (R), mean absolute relative error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), relative RMSE, and the Willmott’s Index (WI), and Taylor diagrams, including skill scores relative to a persistence model, were selected to assess the performances of the developed predictive models. The results indicated that all of the averaged R value attained was higher than 0.900 and all of the averaged NS values were higher than 0.800, representing good performance of the SVR, MARS and M5Tree models applied in the 1-, 2- and 3-day ahead modeling horizon, and this also accorded with the deductions made through an assessment of the Willmott’s Index. However, the M5Tree model outperformed both the SVR and MARS models (with NS?=?0.917 vs. 0.904 and 0.901 for 1-day, 0.893 vs. 0.854 and 0.845 for 2-day, and 0.850 vs. 0.828 and 0.810 for 3-day forecasting horizons, respectively), which was in concurrence with the high value of WI. Therefore, based on the ensemble of 50 models, the performance of the M5Tree can be considered as superior to the SVR and MARS models when applied in a problem of river flow forecasting at multiple forecast horizon. A detailed comparison of the overall performance of all three models evaluated through Taylor diagrams and boxplots indicated that the 1-day ahead forecasting results were more accurate for all of the predictive models compared to the 2- and 3-day ahead forecasting horizons. Data-intelligent models designed in this study indicate that the M5Tree method could successfully be explored for short-term river flow forecasting in semiarid mountainous regions, which may have useful implications in water resources management, ecological sustainability and assessment of river systems.  相似文献   

13.
A modified Jarvis–Stewart model of canopy transpiration (Ec) was tested over five ecosystems differing in climate, soil type and species composition. The aims of this study were to investigate the model's applicability over multiple ecosystems; to determine whether the number of model parameters could be reduced by assuming that site‐specific responses of Ec to solar radiation, vapour pressure deficit and soil moisture content vary little between sites; and to examine convergence of behaviour of canopy water‐use across multiple sites. This was accomplished by the following: (i) calibrating the model for each site to determine a set of site‐specific (SS) parameters, and (ii) calibrating the model for all sites simultaneously to determine a set of combined sites (CS) parameters. The performance of both models was compared with measured Ec data and a statistical benchmark using an artificial neural network (ANN). Both the CS and SS models performed well, explaining hourly and daily variation in Ec. The SS model produced slightly better model statistics [R2 = 0.75–0.91; model efficiency (ME) = 0.53–0.81; root mean square error (RMSE) = 0.0015–0.0280 mm h‐1] than the CS model (R2 = 0.68–0.87; ME = 0.45–0.72; RMSE = 0.0023–0.0164 mm h‐1). Both were highly comparable with the ANN (R2 = 0.77–0.90; ME = 0.58–0.80; RMSE = 0.0007–0.0122 mm h‐1). These results indicate that the response of canopy water‐use to abiotic drivers displayed significant convergence across sites, but the absolute magnitude of Ec was site specific. Period totals estimated with the modified Jarvis–Stewart model provided close approximations of observed totals, demonstrating the effectiveness of this model as a tool aiding water resource management. Analysis of the measured diel patterns of water use revealed significant nocturnal transpiration (9–18% of total water use by the canopy), but no Jarvis–Stewart formulations are able to capture this because of the dependence of water‐use on solar radiation, which is zero at night. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

14.
Climatic variations over Eastern Asia, including the Tibetan Plateau, were analysed using meteorological data for 32 points in the period 1971 to 2000. Changes in heat and water balances were examined using potential evaporation EP, and a wetness index WI, as suggested by Kondo and Xu ( 1997a,b ). Climate zones, including the humid, semi‐humid, semi‐arid and arid climate types, in Eastern Asia identified by the wetness index matched the vegetation distribution. Average monthly temperatures increased over the 30 years, with the sharpest increase in February. In general, temperature increases were larger in the north than in the south. Air temperature increased by more than 0·05 K yr−1 in northern China. The data showed that diurnal temperature ranges have decreased in recent years. From the Tibetan Plateau, through central China, to southern northeast China, there has been an increase in potential evaporation and pan evaporation, which may be related to both higher temperatures and a lack of surface water. Increasing long‐wave radiation flux is apparent in every month and in the interannual trends. This is in contrast to the solar radiation flux. On the other hand, trends for relative humidity and cloud cover were negative, but positive for water vapour pressure. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

15.
Measurements of water vapour flux from semi‐arid perennial woodland (mallee) were made for 3 years using eddy covariance instrumentation. There have been no previous long‐term, detailed measures of water use in this ecosystem. Latent energy flux (LE) on a half hourly basis was the measure of the combined soil and plant evaporation, ‘evapotranspiration’ (ELE) of the site. Aggregation over 3 years of the site measured rain (1136 mm) and the estimated evaporation (794 mm) suggests that 342 mm or 30% of rain had moved into or past the root zone of the vegetation. Above average rainfall during 2011 and the first quarter of 2012 (633 mm, 15 months) would likely have been the period during which significant groundwater recharge occurred. At times immediately after rainfall, ELE rates were the same or exceeded estimates of potential E calculated from a suitably parameterized Penman–Monteith (EPMo) equation. Apparent free water E from plant interception and soil evaporation was about 2.3 mm and lasted for 1.3 days following rainfall in summer, while in autumn, E was 5.1 mm that lasted over 5.4 days. The leaf area index (LAI) needed to adjust a wind function calibrated Penman equation (EPMe) to match the ELE values could be back calculated to generate seasonal change in LAI from 0.12 to 0.46 and compared well with normalized difference vegetation index; r = 0.38 and p = 0.0213* and LAI calculated from digital cover photography. The apparently conservative response of perennial vegetation evaporation to available water in these semi‐arid environments reinforces the conclusion that these ecosystems use this mechanism to survive the reasonably common dry periods. Plant response to soil water availability is primarily through gradual changes in leaf area. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

16.
A typical agricultural water reservoir (AWR) of 2400 m2 area and 5 m depth, located in a semi‐arid area (southern Spain), was surveyed on a daily basis for 1 year. The annual evaporation flux was 102·7 W m?2, equivalent to an evaporated water depth of 1310 mm year?1. The heat storage rate G exhibited a clear annual cycle with a peak gain in April (G ～ 45 W m?2) and a peak loss in November (G ～ 40 W m?2), leading to a marked annual hysteretic trend when evaporation (λE) was related to net radiation (Rn). λE was strongly correlated with the available energy A, representing 91% of the annual AWR energy loss. The sensible heat flux H accounted for the remaining 9%, leading to an annual Bowen ratio in the order of 0·10. The equilibrium and advective evaporation terms of the Penman formula represented 76 and 24%, respectively, of the total evaporation, corresponding to a annual value of the Priestley–Taylor (P–T) coefficient (α) of 1·32. The P–T coefficient presented a clear seasonal pattern, with a minimum of 1·23 (July) and a maximum of 1·65 (December), indicating that, during periods of limited available energy, AWR evaporation increased above the potential evaporation as a result of the advection process. Overall, the results stressed that accurate prediction of monthly evaporation by means of the P–T formula requires accounting for both the annual cycle of storage and the advective component. Some alternative approaches to estimating Rn, G and α are proposed and discussed. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

17.
Soil containing calcic nodules is widely present on the northern Loess Plateau of China owing to soil genesis under local climate conditions. In most studies, little attention is payed to the effect of calcic nodules on soil evaporation and ecoenvironment, resulting in inaccurate evaporation estimation in this kind of soil and further improper field water management measures and irrigation effects. In this paper, soil column experiments were conducted in order to investigate evaporation process in soil containing calcic nodules and the effect of calcic nodules on soil evaporation was determined. The results indicated that evaporation reduction was positively related to calcic nodule content (CNC = mass of calcic nodules/total mass), and could be estimated by the experiential equation: Esoil = E0 (1 – 0.4 CNC) (Esoil = actual evaporation, E0 = theory evaporation in soil without calcic nodules). When CNC was below 0.2, the impact could be neglected. While, as CNC exceeded 0.2, the impact needed to be considered during soil evaporation estimation. As CNC reached 0.5, soil evaporation could be reduced by 7.5 mm, accounting for around 10% of the total soil water. Water balance calculation in soil columns showed that water absorbed by calcic nodules was partially available to evaporation. Water available to evaporation was positively related to CNC, and this water could not exceed 63% of the water absorbed by calcic nodules. Generally, evaporation behavior was dominated by calcic nodule quantity and its water absorption. These results provide new ideas for irrigation measures in arid areas of the globe.  相似文献   

18.
Continuous temperature measurements at 11 stream sites in small lowland streams of North Zealand, Denmark over a year showed much higher summer temperatures and lower winter temperatures along the course of the stream with artificial lakes than in the stream without lakes. The influence of lakes was even more prominent in the comparisons of colder lake inlets and warmer outlets and led to the decline of cold‐water and oxygen‐demanding brown trout. Seasonal and daily temperature variations were, as anticipated, dampened by forest cover, groundwater input, input from sewage plants and high downstream discharges. Seasonal variations in daily water temperature could be predicted with high accuracy at all sites by a linear air‐water regression model (r2: 0·903–0·947). The predictions improved in all instances (r2: 0·927–0·964) by a non‐linear logistic regression according to which water temperatures do not fall below freezing and they increase less steeply than air temperatures at high temperatures because of enhanced heat loss from the stream by evaporation and back radiation. The predictions improved slightly (r2: 0·933–0·969) by a multiple regression model which, in addition to air temperature as the main predictor, included solar radiation at un‐shaded sites, relative humidity, precipitation and discharge. Application of the non‐linear logistic model for a warming scenario of 4–5 °C higher air temperatures in Denmark in 2070‐2100 yielded predictions of temperatures rising 1·6–3·0 °C during winter and summer and 4·4–6·0 °C during spring in un‐shaded streams with low groundwater input. Groundwater‐fed springs are expected to follow the increase of mean air temperatures for the region. Great caution should be exercised in these temperature projections because global and regional climate scenarios remain open to discussion. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

19.
This study was undertaken to evaluate the effects of climatic variability on inter‐annual variations in each component of evapotranspiration (ET) and the total ET in a temperate coniferous forest in Japan. We conducted eddy covariance flux and meteorological measurements for 7 years and parameterized a one‐dimensional multi‐layer biosphere‐atmosphere model (Kosugi et al., 2006 ) that partitions ET to transpiration (Tr), wet‐canopy evaporation (Ewet), and soil evaporation (Esoil). The model was validated with the observed flux data. Using the model, the components of ET were estimated for the 7 years. Annual precipitation, ET, Tr, Ewet, and Esoil over the 7 years were 1536 ± 334 mm, 752 ± 29 mm, 425 ± 37 mm, 219 ± 34 mm, and 108 ± 10 mm, respectively. The maximum inter‐annual fluctuation of observed ET was 64 mm with a coefficient of variance (CV) of 2.7%, in contrast to relatively large year‐to‐year variations in annual rainfall (CV = 20.1%). Tr was related to the vapour pressure deficit, incoming radiation, and air temperature with relatively small inter‐annual variations (CV = 8.2%). Esoil (CV = 8.6%) was related mainly to the vapour pressure deficit. Ewet was related to precipitation with large inter‐annual variations (CV = 14.3%) because of the variability in precipitation. The variations in Ewet were counterbalanced by the variations in Tr and Esoil, producing the small inter‐annual variations in total ET. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

20.
Abstract

Evaporation is an important reference for managers of water resources. This study proposes a hybrid model (BD) that combines back-propagation neural networks (BPNN) and dynamic factor analysis (DFA) to simultaneously precisely estimate pan evaporation at multiple meteorological stations in northern Taiwan through incorporating a large number of meteorological data sets into the estimation process. The DFA is first used to extract key meteorological factors that are highly related to pan evaporation and to establish the common trend of pan evaporation among meteorological stations. The BPNN is then trained to estimate pan evaporation with the inputs of the key meteorological factors and evaporation estimates given by the DFA. The BD model successfully inherits the advantages from the DFA and BPNN, and effectively enhances its generalization ability and estimation accuracy. The results demonstrate that the proposed BD model has good reliability and applicability in simultaneously estimating pan evaporation for multiple meteorological stations.

Citation Chang, F.J., Sun, W., and Chung, C.H., 2013. Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan. Hydrological Sciences Journal, 58 (4), 813–825.  相似文献   

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