A Modified SEBAL Modeling Approach for Estimating Crop Evapotranspiration in Semi-arid Conditions

Remote sensing methods are becoming attractive to estimate crop evapotranspiration, as they cover large areas and can provide accurate and reliable estimations; intensive field monitoring is also not required, although some ground-truth measurements can be helpful in interpreting satellite images. For the purposes of this paper, modeling and remote sensing techniques were integrated for estimating actual evapotranspiration of groundnuts (Arachishypogaea, L.) that is cultivated near Mandria Village in Paphos District of Cyprus. The Surface Energy Balance Algorithm for Land (SEBAL) was adopted for the first time in Cyprus, employing the essential adaptations for local soil and meteorological conditions. Landsat-5 TM and 7 ETM+ images were used to retrieve the needed spectral data. The SEBAL model is enhanced with empirical equations determined as part of the present study, regarding crop canopy factors, in order to increase its accuracy. Maps of ET_a were created using the SEBAL modified model (CYSEBAL) for the area of interest. The results have been compared to the measurements from an evaporation pan (which was used as a reference) and those of the original SEBAL model. The statistical comparison has shown that the modified SEBAL yields results that are comparable to those of the evaporation pan. T-test application has revealed that the statistical difference between SEBAL and CYSEBAL is significant and quite crucial, especially in a place with limited surface and underground water resources.     

遥感估算蒸腾蒸发量的时空尺度推演方法及应用

本文提出了适合任意蒸腾蒸发量(ET)的时空尺度推演方法,建立了不同土地类型区域的水分盈亏计算方法。利用NOAA/AVHRR和Landsat TM遥感数据分别反演出影像,获取当天的实际蒸腾蒸发量,通过Penman-Monteith公式并使用气象数据计算出参照蒸腾蒸发量,然后用空间和时间的推演法,形成高分辨率、长时间序列的遥感反演ET结果,同时对降雨量进行样条插值得到数字图像。以此为基础,通过区域水量平衡分析方法对通县1999年的区域水分盈亏状况进行了研究。     

基于SEBAL 模型的锡林浩特蒸散量反演研究

陆面蒸散作为反映土地利用/覆盖变化所引起地表水热变化的敏感因子是当今全球气候变化研究的焦点之一.本文以内蒙古锡林郭勒盟锡林浩特市为研究区域,基于SEBAL（Surface Energy Balance Algorithm for Land）模型,采用研究区2005年8月17日的Landsat TM遥感影像对其地表蒸散量进行反演研究,分析蒸散量的空间变化格局.结果表明：锡林浩特日蒸散量平均值为4.3mm,最大值达到了7.5mm;日蒸散量呈现出西北低、东部高的空间分布格局;日蒸散量较少的区域主要分布于锡林浩特市的西部、北部以及最南部的部分区域,中部较少部分地区的蒸散量较低（接近于0）.     

Application of SEBAL and Markov Models for Future Stream Flow Simulation Through Remote Sensing

Watershed hydrology, including the volumes of stream flow is widely considered to be influenced by global climate change. Traditional studies using the (GWLF) model to estimate stream flows have relied on evapotranspiration cover coefficient (Kc) obtained from published references. Other factors, such as future land-use status and evapotranspiration (ET) change, are usually not considered. This study aims to improve on traditional studies by including remote sensing techniques to estimate the Kc, as well as integrating the SEBAL model, the CGCM1 model, and the Markov model to predict land-use and ET changes. The chosen study area was in the north of Taiwan. The processes include land-use classification using hybrid approach and Landsat-5 TM images, a comparison of stream flow simulations using the GWLF model with two Kc values derived from remote sensing and traditional methods, and finally the prediction of future land-use and Kc parameters for assessing the effect of land-use change and ET change. The results indicated that the study area was classified into seven land-use types with 89.09% classification accuracy. The stream flows simulated by two estimated Kcs were different, and the simulated stream flows using the remote sensing approach presented more accurate hydrological characteristics than a traditional approach. In addition, the consideration of land-use change and ET change indeed affected the predicted stream flows under climate change conditions. These results imply that the integration of remote sensing, the SEBAL model, the CGCM1 model, and the Markov model is a feasible scheme to predict future land-use, ET change, and stream flow. Therefore, these models will improve future studies of predictions in water resource management and global environmental change.     

Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods

Evapotranspiration has a highlighted role in agricultural and forest meteorology researches, hydrological cycle, irrigation scheduling, and water resources management. There are many models to estimate the evapotranspiration including mass transfer, radiation, temperature, and pan evaporation-based models. This study aims to compare temperature-based models to detect the best model under different weather conditions. For this purpose, weather data were gathered from 181 synoptic stations in 31 provinces of Iran. The evapotranspiration was estimated using 11 temperature-based models and was compared with the FAO Penman-Monteith model. The results showed that the Modified Hargreaves-Samani models estimate the evapotranspiration better than other models in the most provinces of Iran (25 provinces). However, the values of R² were less than 0.98 for 15 provinces of Iran. Therefore, the models were calibrated and preciseness of estimation was increased. However, the estimation was improved only in 14 provinces. The new temperature-based models estimated the evapotranspiration in the eastern (RK, NK, SB, and KE) provinces of Iran (with a various temperature range 14–20 °C) better than other provinces. The best precise methods were the Modified Hargreaves-Samani 1 method for AL (before calibration) and the Modified Hargreaves-Samani 3 method for KE (after calibration). Finally, a list of the best performance of each model has been presented to use other regions and next researches according to values of mean, maximum, and minimum temperature, elevation, minimum and mean relative humidity, sunshine, precipitation, and wind speed. The results are also useful for selecting the best model when we must apply temperature-based models because of type of available data.     

黄河三角洲湿地蒸发蒸腾及其对土地利用/覆被变化的响应特征研究

以黄河三角洲为研究区,基于1986年6月5日和2009年6月4日的Landsat TM影像进行土地利用/覆被分类及变化特征研究,同时,基于SEBAL模型估算区域蒸发蒸腾量,进而分析湿地蒸发蒸腾的数量特征、空间格局特征及其对土地利用/覆被变化的响应特征。结果表明:23年间,58.74%的区域土地利用/覆盖类型发生了变化,未利用地大幅减少,水体和建设用地大幅增加,显著的土地垦殖与耕地荒废则同时存在;蒸发蒸腾强度大小按土地利用/覆被类型排序依次为水体>滩涂>苇地>林草地>耕地>建设用地>未利用地,研究区未利用土地变为水体和耕地是导致区域蒸发蒸腾整体提高的主导原因,而耕地流失则是导致区域蒸发蒸腾整体下降的主要原因,但23年间实际变化以前一种情形占优势,因而区域蒸发蒸腾总体呈现为增加趋势。研究结果对区域土地利用规划和水资源管理有重要的理论和实践指导意义。     

气象数据缺测条件下参照腾发量的计算方法

利用河北雄县和望都两气象站的数据，对FAO推荐的气象数据缺测时参照腾发量的计算方法进行了检验和评价，分析了参照腾发量对各气象要素的敏感性，提出了在气象数据缺测条件下适应我国北方平原区气候条件的参照腾发量的计算方法。     

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.     

基于气象预报的参考作物蒸发蒸腾量的神经网络预测模型

参考作物蒸发蒸腾量(ET0)是进行实时灌溉预报和农田水分管理的主要参数,BP神经网络能够较好地反映ET0与诸影响因素间复杂的非线性关系。本文将ET0看作时间序列,选取前3日ET0作为影响因子,以天气预报可测因子包括最高、最低和日平均温度、反映天气类型的阴晴指数、日序数和风力等级进行修正,建立了三层BP神经网络模型。选取江苏射阳站2003与2004年气象资料,应用Matlab神经网络工具箱,采用trainer算法进行模型训练与预测。结果证明,所建模型能够很好地反映诸多影响因子与ET0之间的关系,具有较高的模拟精度和较好的泛化能力。     

分布式双源蒸散发模型及其在南广河流域运用研究

鉴于传统蒸散发模型基于单点计算,不能考虑流域下垫面条件对蒸散发影响的局限性,利用卫星遥感数据源,以四川境内的南广河流域作为研究对象,构建基于栅格单元的分布式双源蒸散发模型,实现研究区域蒸散发时空全过程计算。研究表明:基于遥感数据源的区域蒸散发计算值与蒸发站点实测蒸发值具有较好的一致性和相关性,相关系数为0.744,在相似的气象和下垫面条件下,蒸散发能力排序为:林地>灌木丛>水田>旱地>草地,模型研究成果对于流域蒸散发空间计算,以及定量分析土地利用变化对流域蒸散发影响提供一定的参考价值。     

三江平原主要生态类型耗水分析和水分盈亏状况研究

利用SEBAL模型并集成MODIS卫星数据,计算了三江平原2006年生长季5—10月的地表蒸散量,结合降水量数据,借助GIS技术对主要生态类型的耗水和水分盈亏状况进行分析。计算结果表明:森林、湿地、旱田和水田生态系统在2006年生长季的耗水量分别为697.14、562.21、486.17和523.52mm,森林生态系统的蒸散量大于其他生态系统;从2006年生长季总体来看,三江平原北部和中部区域耗水量要小于降水量,东部、南部和西部则相反。森林、湿地、水田和旱田生态系统在2006年生长季水分盈亏量平均值分别为-193.46、-23.38、-1.86和37.59mm;从湿地、水田和旱田的水分盈亏情况对比来看,湿地开垦为水田比湿地开垦为旱田更适于当地的自然条件;对于农作物来说,从整个生长季来看三江平原降雨量可以基本满足地表蒸散的需求,但是时空不匹配,在特定的月份上,会出现干旱。     

Relating Crop Water Consumption to Irrigation Water Supply by Remote Sensing

A new set of irrigation performance indicators based on remote-sensing estimates of evapotranspiration is introduced. These evapotranspiration indicators are the relative evapotranspiration or crop stress and the water efficiency as well as their uniformity. With a remote-sensing evapotranspiration algorithm (SEBAL) maps of actual crop water consumption are derived. These maps are one of the inputs in the evapotranspiration indicators, together with GIS data (digitized irrigation unit boundaries) and field data (irrigation delivery schedule and water flow). This approach is applied on the Rio Tunuyan irrigation scheme, Mendoza, Argentina, which is served by surface water and privately owned ground water pumps. A homogeneous pattern of actual crop water consumption is detected from the highest irrigation level till the lowest (farm) level (coefficient of variance from 8.6% to 6.1% and 14.0% of secondary, tertiary and pixel level, respectively). Considering that a rotational irrigation schedule at tertiary and farm level is present, the results indicate that ground water supply through extraction and capillary rise equalize the spatial patterns in crop water consumption. The latter is proved by a comparison between (i) the areal water consumption from remote-sensing measurements, (ii) the areal water supply and (iii) additional field information on ground water extraction and capillary rise.     

Forecasting of Remotely Sensed Daily Evapotranspiration Data Over Nile Delta Region, Egypt

Daily evapotranspiration is a major component in crops water consumption management plans. Consequently, forecasting of daily evapotranspiration is the keystone of any effective water resources management plans in fragile environment similar to the Nile Delta region. The estimation of daily evapotranspiration was carried out using Surface Energy Balance System (SEBS), while the forecasting of the daily evapotranspiration was carried out using Auto Regressive Integrated Moving Average (ARIMA) and its derivative Seasonal ARIMA. Remote sensing data were downloaded from European Space Agency (ESA) and used to estimate daily evapotranspiration values. Remote sensing data collected from August 2005 till December 2009 on a monthly basis for daily evapotranspiration estimation. The application of the most adequate ARIMA (2,1,2) to the evapotranspiration data set failed to sustain the forecasting accuracy over a long period of time. Although, time series analysis of daily evapotranspiration data set showed a seasonality behavior and thus, using seasonal ARIMA [(2,1,2) (1,1,2)6] was the optimum to forecast the daily evapotranspiration over the study area and sustain the forecasting accuracy. A linear regression model was established to test the correlation between the forecasted daily evapotranspiration values using S-ARIMA model and the actual values. The forecasting model indicates an increase of the daily evapotranspiration values with about 1.3 mm per day.     

基于广义互补原理的嘉陵江流域实际蒸散发时空分布特征及影响因素

为深入了解嘉陵江流域实际蒸散发特征及其变化原因,基于1956—2000年嘉陵江流域13个气象站点逐日观测资料以及流域逐年径流数据,运用广义互补相关原理模型,分析嘉陵江流域实际蒸散发量E_Ta时空分布特征,通过分析Pearson相关系数研究实际蒸散发量和气象因子的相关性,探讨其变化原因。结果表明:①广义互补相关原理理论模型适用于嘉陵江流域,且E_Ta估算精度高,平均绝对误差为6.79 mm,平均相对误差仅为1.42%;②1961—2000年嘉陵江流域实际蒸散发量时间分布上呈现缓慢降低趋势,下降速率达-5.3 mm/(10 a),空间分布表现为北高南低、东高西低,最大值649.86 mm,出现在甘肃省迭部县;最低值188.26 mm,位于四川省松潘县;③1961—2000年嘉陵江流域日照时数和日较差的下降导致辐射能量项的降低,致使实际蒸散发量减少,日最高温、日最低温、实际水汽压的增加以及2 m风速的下降造成空气动力学项的减少,缓解了实际蒸散发量的减弱。首次将广义互补相关原理理论模型运用到嘉陵江流域E_Ta的计算,取得了较好的估算精度,为嘉陵江流域的水资源评价、制定正确的水资源规划及未来实现水资源持续发展提供科学依据。     

Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

This study applied a time series evapotranspiration (ET) data derived from the remote sensing to evaluate Soil and Water Assessment Tool (SWAT) model calibration, which is a unique method. The SWAT hydrologic model utilized monthly stream flow data from two US Geological Survey (USGS) stations within the Big Sunflower River Watershed (BSRW) in Northwestern, Mississippi. Surface energy balance algorithm for land (SEBAL), which utilized MODerate Resolution Imaging Spectro-radiometer (MODIS) to generate monthly ET time series data images were evaluated with the SWAT model. The SWAT hydrological model was calibrated and validated using monthly stream flow data with the default, flow only, ET only, and flow-ET modeling scenarios. The flow only and ET only modeling scenarios showed equally good model performances with the coefficient of determination (R²) and Nash Sutcliffe Efficiency (NSE) from 0.71 to 0.86 followed by flow-ET only scenario with the R² and NSE from 0.66 to 0.83, and default scenario with R² and NSE from 0.39 to 0.78 during model calibration and validation at Merigold and Sunflower gage stations within the watershed. The SWAT model over-predicted ET when compared with the Modis-based ET. The ET-based ET had the closest ET prediction (~8% over-prediction) as followed by flow-ET-based ET (~16%), default-based ET (~27%) and flow-based ET (~47%). The ET-based modeling scenario demonstrated consistently good model performance on streamflow and ET simulation in this study. The results of this study demonstrated use of Modis-based remote sensing data to evaluate the SWAT model streamflow and ET calibration and validation, which can be applied in watersheds with the lack of meteorological data.     

逐日参照腾发量预测的改进方法

利用Penman Monteith方程计算并分析了山西洪洞、山东陡山、广西桂林、湖北漳河、云南景洪及昆明等国内6个不同经纬度和海拔站点16～31年不同长度系列的逐日ET0变化规律,将过去按旬统计的天气类型修正系数缩短到逐日,引入基于Zadeh算子的模糊隶属函数,进行天气类型修正系数的模糊集运算判别.改进的数学模型在昆明和景洪两个灌区分别用于2000年和1998年逐日ET0预测的结果表明,预测精度较此前的径向基函数法和年际均值校正法分别提高18.9%和10%以上.     

乌伦古河流域SWAT模型基础数据库构建

以西北内陆干旱区新疆乌伦古河流域为例,构建了乌伦古河流域SWAT模型土壤属性和气象数据库。土壤数据库的构建过程为：通过查阅《新疆土种志》获得所需基础土壤数据;运用MATLAB编程转换土壤粒径;采用SPAW软件计算土壤的水分参数。气象数据库的构建过程为：在中国气象共享服务网上下载地面气象数据并整理该数据为所需格式;运用SwatWeather．exe软件计算降水、气温、露点、风速和辐射数据,从而获得SWAT模型天气发生器参数。成功构建SWAT模型基础数据库,为模型在此流域的应用奠定基础。     

Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

The reference evapotranspiration (ET₀) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET₀ using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET₀ time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET₀ prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET₀ prediction in Serbia is the novelty of this study.

     

Are Weather Generators Robust Tools to Study Daily Reference Evapotranspiration and Irrigation Requirement?

Reference evapotranspiration (ETo) is one of the driving forces in crop simulation models and is very important to be estimated accurately. Moreover, weather generator (WG) models are widely used in combination with these crop models. As the quality of model output is related to the quality of weather data used as input, the evaluation of the sensitivity of model outputs to the quality of generated weather data is essential. In this study, eight different weather generator models were assessed and their outputs were used to estimate daily reference evapotranspiration and irrigation requirement. Two daily weather generator algorithms were combined with a monthly weather generator and/or an adjustment algorithm for low-frequency variances. Precipitation occurrence series was generated by an independent semi-empirical distribution. The daily weather generators outperformed the monthly models in reproducing daily statistics, while the monthly models performed better in simulating the monthly and yearly variations. After analyzing the model performances in simulating climatic variables, more assessments were carried out on ETo and irrigation requirement. The results depicted the strength of all the models in simulating daily ETo and irrigation requirement. Although all the studied models have comparable performances in simulating these two daily variables on daily and monthly scales, the monthly WGs outperform the daily models on yearly time scales and have better performances in simulating standard deviation values of yearly mean ETo and irrigation requirement. It can be concluded that WG models are robust tools for estimating these two daily variables if they can at least reproduce daily statistics (i.e. mean and standard deviation) well. But it must be taken in considerations that each WG model (including the one studied here) has different weaknesses and strengths and the best choice must be done according to the requirements.     

基于遥感的三江平原主要生态类型耗水分析和水分盈亏状况研究

利用SEBAL模型和MODIS的MOD11A1、MOD13A2、MOD43B3产品计算了2006年生长季5-9月三江平原地表蒸散量，并且结合降水量数据,借助GIS技术对主要生态类型的耗水和水分盈亏状况进行分析。结果表明：森林、湿地、旱田和水田生态系统在2006年生长季的耗水量分别为697.14、562.21、486.17和523.52mm，在本研究区，森林生态系统的蒸散量大于其他生态系统；在2006年生长季总体来看，三江平原北部和中部区域耗水量要小于降水量;东部、南部和西部则相反。森林、湿地、水田和旱田生态系统在06年生长季水分盈亏量平均值分别为-193.46、-23.38、-1.86和37.59mm；从湿地、水田和旱田的水分盈亏情况对比来看，湿地开垦为水田比湿地开垦为旱田更适应于当地的自然条件，对于农作物来说，从整个生长季来看三江平原降雨量可以基本满足地表蒸散的需求，但是时空不匹配，在特定的月份上，会出现干旱。