Evapotranspiration In Arid Regions Research Articles

Predictive Modeling of Daily Evapotranspiration in Arid Regions Using Artificial Neural Networks

Predictive Modeling of Daily Evapotranspiration in Arid Regions Using Artificial Neural Networks

Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions

Reference evapotranspiration (ET0) is critical in agriculture and irrigation water management, particularly in arid and semi-arid regions. Our study aimed to develop an accurate and efficient model for estimating ET0 using various climatic variables as predictors. This research evaluated two model techniques, i.e., stepwise regression and artificial neural networks (ANNs), to identify the most effective model for calculating ET0. The two models were developed and tested based on climate data obtained from the whole climatic station of Egypt. The CLIMWAT 2.0 program was used to acquire the climate data for Egypt from a total of 32 stations. This software is a dedicated meteorological database created specifically to work with the CROPWAT computer program. The models were developed using average climate data spanning 29 years, from 1991 to 2020. The obtained data were utilized to compute reference evapotranspiration using CROPWAT 8, based on the Penman–Monteith equation. The results showed that the ANN model demonstrated superior performance in ET0 calculations compared to other methods, achieving a coefficient of determination (R2) of 0.99 and a mean absolute percentage error (MAPE) of 2.7%. In contrast, the stepwise model regression yielded an R2 of 0.95 and an MAPE of 8.06. On the other hand, the most influential climatic variables were maximum temperature, humidity, solar radiation, and wind speed. The findings of this study could be applied in various fields, such as agriculture, irrigation, and crop water requirements, to optimize crop growth under limited water resources and global environmental changes. Furthermore, our study identifies the limitations and challenges of applying these models in arid regions, such as data availability constraints and model complexity. We discuss the need for more extensive and reliable datasets and suggest future research directions, including ensemble modeling, remote sensing data integration, and evaluating climate change’s impact on ET0 estimation. Overall, this study contributes to the understanding of ET0 estimation in arid regions and provides valuable insights into the applicability of regression models and ANNs. The superior performance of ANNs offers potential advancements in water resource management and agricultural planning, enabling more accurate and informed decision-making processes.

Analysis of a steady-state model of groundwater discharge in a river valley without and with evapotranspiration

Riparian evapotranspiration is an important process that constrains water exchanges within a well-connected river–aquifer–vegetation hydrological system. In this study, we developed an extended analytical Dupuit–Forchheimer (DF) model to consider the additional water flow resistances due to both the vertical deformation of flow lines caused by evapotranspiration and discharge into the river screened by bottom sediments in which a river does not fully penetrate the aquifer. This analytical model can describe the groundwater discharge process in the presence of riparian evapotranspiration. We validated our analytical results with numerical simulations of a 2-D flow model that explicitly considers vertical hydraulic resistances, demonstrating that the surface water–groundwater exchanges in such hydrological systems are strongly dependent on the hydrogeological parameters, river width, riparian zone width, and parameters related to the hydraulic connection between evapotranspiration and groundwater. A sensitivity analysis of our extended DF model reveals that the water balance in hyperarid regions is determined by riparian evapotranspiration, and evapotranspiration most likely causes river flow losses. However, in humid regions, most groundwater (80% or more) is discharged into rivers rather than via riparian evapotranspiration. Our results confirm that riparian evapotranspiration in arid regions is an important process that affects surface water–groundwater exchanges and that evapotranspiration from riparian zones not only reduces groundwater discharge into rivers but also potentially causes disconnections within river–aquifer systems. The obtained analytical dependencies allow the grid-dependent parameters of the RIV and EVT packages to be calculated in the MODFLOW simulations of regional groundwater flow discharge in a river valley using coarse grids.

A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm

Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions.

The Modeling of the Atmospheric Boundary Layer over Inhomogeneously Moistened Surface as a Tool for Evapotranspiration Estimation

The skills of a method to estimate evapotranspiration from a heterogeneous surface, including irrigated lands, based on the quantitative description of the deformation of vertical distributions of specific humidity at the windward and leeward boundaries of the site are considered in the framework of numerical regional climate modeling and a detailed description of the atmospheric boundary layer evolution. It is shown that the average values of evapotranspiration from a heterogeneous surface are mainly determined by the area of irrigated fields and do not almost depend on the number and size of inhomogeneities. Based on numerical experiments, we analyzed the wind speed effect on the spatial distribution of evapotranspiration in arid regions, including irrigated lands.

Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions

Recently, data driven machine learning techniques has been widely applied for modeling reference evapotranspiration (ETo) values under various climatic conditions taking into account the different number of sites and available data length. A major issue with applying those models is the proper selection of training/testing data sets. Although some spatial generalization approaches have been recommended for this purpose, there are no specified recommended local (temporal) data partitioning strategies for machine learning based ETo estimation. The present study evaluates different hold-out and k-fold validation temporal data partitioning strategies when using gene expression programming (GEP) technique to estimate daily ETo in arid regions. The k-fold validation strategies considered annual, monthly and growing season period patterns as test data sets. Although commonly used partitioning of the available patterns into training and testing sets gave accurate results, statistical analysis showed that the results obtained through k-fold validation assessment were more reliable. A two-block partitioning strategy with chronologic data selection for training and testing provided the most accurate results among the hold-out procedures (mean scatter index (SI) value of 0.162). Fixing the extreme ETo values as training data set in hold-out procedures provided the less accurate results with considerable over/underestimation of the ETo values (mean SI value was 0.506). Results on the basis of hold-out approaches can be biased or only partially valid depending on selection of the test data from the time series. K-fold validation yielded the lowest over/underestimations of ETo values. Further, considering monthly patterns as minimum affordable test size produced higher error magnitudes among k-fold validation strategies, while considering the complete patterns of one growing season provided more accurate results among k-fold validation strategies.

Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method

The Penman-Monteith (PM) method is the most widely used technique to estimate potential worldwide evapotranspiration. However, current research shows that there may be significant errors in the application of this method in arid areas, although questions remain as to the degree of this estimation error and how different surface conditions may affect the estimation error. To address these issues, we evaluated the uncertainty of the PM method under different underlying conditions in an arid area of Northwest China by analyzing data from 84 meteorological stations and various Moderate Resolution Imaging Spectroradiometer (MODIS) products, including land surface temperature and surface albedo. First, we found that when the PM method used air temperature to calculate the slope of the saturation vapor pressure curve, it significantly overestimated the potential evapotranspiration; the mean annual and July-August overestimation was 83.9 and 36.7 mm, respectively. Second, the PM method usually set the surface albedo to a fixed value, which led to the potential evapotranspiration being underestimated; the mean annual underestimation was 27.5 mm, while the overestimation for July to August was 5.3 mm. Third, the PM method significantly overestimated the potential evapotranspiration in the arid area. This difference in estimation was closely related to the underlying surface conditions. For the entire arid zone, the PM method overestimated the potential evapotranspiration by 33.7 mm per year, with an overestimation of 29.0 mm from July to August. The most significant overestimation was evident in the mountainous and plain nonvegetation areas, in which the annual mean overestimation reached 5% and 10%, respectively; during July, there was an estimation of 10% and 20%, respectively. Although the annual evapotranspiration of the plains with better vegetation coverage was slightly underestimated, overestimation still occurred in July and August, with a mean overestimation of approximately 5%. In order to estimate potential evapotranspiration in the arid zone, it is important that we identify a reasonable parameter with which to calibrate the PM formula, such as the slope of the saturation vapor pressure curve, and the surface albedo. We recommend that some parameters must be corrected when using PM in order to estimate potential evapotranspiration in arid regions.

Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions

Accurate estimates of evapotranspiration (ET) in arid ecosystems are important for sustainable water resource management due to competing water demands between human and ecological environments. Several empirical remotely sensed ET models have been constructed and their potential for regional scale ET estimation in arid ecosystems has been demonstrated. Generally, these models were built using combined measured ET and corresponding remotely sensed and meteorological data from diverse sites. However, there are usually different vegetation types or mixed vegetation types in these sites, and little information is available on the estimation uncertainty of these models induced by combining different vegetation types from diverse sites. In this study, we employed the most popular one of these models and recalibrated it using datasets from two typical vegetation types (shrub Tamarix ramosissima and arbor Populus euphratica) in arid ecosystems of northwestern China. The recalibration was performed in the following two ways: using combined datasets from the two vegetation types, and using a single dataset from specific vegetation type. By comparing the performance of the two methods in ET estimation for Tamarix ramosissima and Populus euphratica, we investigated and compared the accuracy of ET estimation at the site scale and the difference in annual ET estimation at the regional scale. The results showed that the estimation accuracy of daily, monthly, and yearly ET was improved by distinguishing the vegetation types. The method based on the combined vegetation types had a great influence on the estimation accuracy of annual ET, which overestimated annual ET about 9.19% for Tamarix ramosissima and underestimated annual ET about 11.50% for Populus euphratica. Furthermore, substantial difference in annual ET estimation at regional scale was found between the two methods. The higher the vegetation coverage, the greater the difference in annual ET. Our results provide valuable information on evaluating the estimation accuracy of regional scale ET using empirical remotely sensed ET models for arid ecosystems.

On the attribution of changing crop evapotranspiration in arid regions using four methods

Accurate evaluation of the relative contribution of climatic and vegetative drivers on land evapotranspiration (ET) is critical for interpreting the ET controlling mechanism, modeling and predicting. However, how to accurately separate and estimate the contribution of climatic and vegetative changes on ET variation remains uncertain. Our study attempted to interpret the interannual variation in ET during 2007–2013 over a maize field in northwest China, using four methods simultaneously, such as the Penman-Monteith (PM) model, the modified crop coefficient method, the Priestly-Taylor (PT) model and the regression linear model.Results indicate that compared to the ET in 2007, the ET decreased averagely by 12, 30, 29, and 73 W m−2 in 2008, 2011, 2012 and 2013, respectively. All models yielded similar results and showed that the vegetative controls played a more important role in regulating ET relative to climatic drives, and that more than half of decrease in ET was caused by vegetative factors, while the differences in net radiation, water vapor pressure deficit and air temperature among years were lesser source of variation in ET. Furthermore, the advantage and disadvantage of the four methods were discussed.Our study confirmed the great effect of vegetative drivers in regulating crop ET, highlighted the importance of estimating canopy conductance accurately in ET modeling and prediction, and provided new approaches for separating the climatic and vegetative contribution on ET changes.

西南近50年实际蒸散发反演及其时空演变

PDF HTML阅读 XML下载 导出引用 引用提醒 西南近50年实际蒸散发反演及其时空演变 DOI: 10.5846/stxb201805231129 作者: 作者单位: 中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划（2016YFC0502102）；中国科学院科技服务网络计划（KFJ-STS-ZDTP-036）；"西部之光"人才培养计划（A类）（〔2018〕X）；贵州省科技计划（2017-2966） Inversion and spatiotemporal evolution of actual evapotranspiration in southwest China for the past 50 years Author: Affiliation: Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences Fund Project: This research work was supported jointly by national key research program of China (No. 2016YFC0502102 & 2016YFC0502300), 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用CRU 4.0及GLDAS Noah 2.1数据集，采用随机森林算法对1966年至2016年中国西南陆面月尺度实际蒸散发（ETa）进行逐像元反演，结合袋外误差均方值（MSEOOB）、解释方差百分数（PVE）和均方根误差（RMSE）评价模型以及与其他典型数据集对比的方法对模型和反演结果进行精度评价，在对中国西南ETa的空间格局及时空演变特征进行分析的基础上，利用因子置换重要性评价模型（PIM）对特征因子进行重要性评价。结果表明：（1） MSEOOB均值为4.14，标准偏差仅为3.73，PVE均值为99.36%，标准偏差仅为0.33，模型基于2000年至2016年月尺度拟合结果的RMSE均值仅为1.04 mm/月，标准偏差为0.52，反演结果与GLDAS 2.1、2.0及MOD16数据的R2分别为0.99、0.89、0.95，总体而言模型及拟合结果可信度和精度较高；（2）西南地区ETa整体上表现出随着纬度的降低而增加的特征，从西北高原地区向东南沿海区域逐步增加，不同季节上西南的ETa空间分布差异较为明显，从春季到夏季先呈现出由东南向西北逐步增加的态势，夏季到冬季则呈现出从西北向东南减弱的特征，在每年的7、8月份左右各区域的ETa达到最大值，在1、2月份左右为最低值，并呈现起伏的周期特征；（3）以横断山脉为分界，横断山脉以南的丰水区的ETa主要受云覆盖百分数、月均气温日较差与月均日最高温共同驱动，而横断山脉以北的少水区域主要受云覆盖百分数、月霜日频率与月均水汽压共同驱动，而无论是在丰水区还是少水区，云覆盖百分数都是所有因素中最主要的驱动因子。 Abstract:In this study, we combined the CRU 4.0 and GLDAS Noah 2.1 datasets and used the random forest algorithm (RF) to calculate the monthly actual evapotranspiration (ETa) in southwestern China between 1966 and 2016. The accuracy of our model and the inversion results were evaluated by the MSEOOB, PVE, and RMSE indices and a comparison with other typical datasets. Based on this, the spatiotemporal distribution and evolution pattern of ETa over multiple time scales were fully discussed. We extended our research by using the PIM model to evaluate the importance of the feature factors in each pixel. We obtained the following results:1) the mean value and the standard deviation (Std Dev) of MSEOOB were 4.14 and 3.73, and those of PVE were 99.36% and 0.33; in addition, the mean RMSE of the monthly inversion results for 2000 to 2016 was 1.04 mm per month and the corresponding Std Dev was 0.52; moreover, the R2 values for the inversion results of ETa from GLDAS 2.1, GLDAS 2, and MOD16 were 0.99, 0.89, and 0.95, respectively. All these evaluation indices illustrated the credibility and precision of the model and the inversion results were sufficiently high. 2) Our inversion results indicated that ETa increased with a decrease in latitude and gradually increased from the northwest plateau to the southeast coastal area; in addition, the spatial distribution patterns in southwestern China in different seasons were quite different:from spring to summer, high ETa expanded from the southeast to the northwest; in contrast, in winter, ETa diminished remarkably from northwest to southeast. The maximum value was reached around July every year and had the lowest value was reached around February, which showed periodic characteristics with fluctuations. 3) We found that the Hengduan Mountains were the boundary of the driving factors for ETa in arid and humid regions. The ETa in the humid regions south of the mountains was jointly driven by the cloud cover percentage (CCP), diurnal temperature range (DTR), and monthly average daily maximum temperature (TMX). In contrast, ETa in arid regions north of the mountains was mainly affected by CCP, frost day frequency (FDF), and vapor pressure (VAP). Notability, CCP is the most important driving factor weather in both humid and arid areas. 参考文献 相似文献 引证文献

Water saving practices enhance regional efficiency of water consumption and water productivity in an arid agricultural area with shallow groundwater

Improving the efficiency of water consumption and water productivity is the key approach to satisfy sustainable water resource supply and food demand. As effective measures, water saving practices are implemented in arid and semi-arid regions. For areas with shallow groundwater, water used for irrigation is not entirely consumptively used. The majority of irrigation water infiltrations below the root zone are stored in shallow groundwater. This can be reused as groundwater based evapotranspiration (ETg) at the regional scale. Thus, actual regional efficiency of water consumption (REWC) based on all water within the hydrological system is greater than based on consumptive use only. Accurately evaluating the response of REWC and regional water productivity (RWP) to water saving practices is essential due to the complexity of the hydrological system. In this study, regional ETg and regional evapotranspiration (ET) of the past 20 years were reproduced in a typical arid irrigation district with shallow groundwater based on the water balance method. Furthermore, REWC and RWP were estimated to investigate the impact of water saving practices to regional water use. Simulation results show that groundwater is a significant water source of regional ET in arid regions with a shallow aquifer and contributes more than 16% of regional ET for the irrigation district. Water saving practice implementation enhances the contribution of groundwater to ET. After water saving practices implementation, annual REWC and RWP have been improved by 0.07 and 0.1kg/m3, respectively. Furthermore, negative correlation between REWC and I+P (the total water supply including rainfall and irrigation water diversion) and positive correlation between RWP and REWC demonstrate that water saving practices can reduce the non-beneficial water losses by evaporation and enhance productivity by a lower groundwater table. Overall, shallow groundwater plays an important role to enhance REWC and RWP and the contribution of groundwater to regional water use needs to be considered as part of a reasonable water saving strategy towards a sustainable agricultural system.

Diagnosis of evapotranspiration controlling factors in the Heihe River basin, northwest China

AbstractEstimation of actual evapotranspiration (ET) and diagnosis of its controlling factors contribute to addressing water scarcity challenges in arid regions. For detecting impacts of vegetation, precipitation, and evaporation capacity on ET, a monthly ET model was proposed on the basis of the three factors and applied in the upstream and midstream of the Heihe River basin, northwest China, an arid and semi-arid basin. Parameter sensitivity analysis, partial correlation analysis, and factor analysis were used to assess the controlling factors of ET. Results demonstrated that the proposed ET model can reconstruct monthly ET processes with satisfying performances and precipitation, evaporation capacity and vegetation are the controlling factors of ET in the study areas. The proposed ET model could be applicable with the three controlling factors to estimate ET in arid regions.

Parameter sensitivity analysis and optimization for a satellite‐based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data

AbstractGlobal and regional estimates of daily evapotranspiration are essential to our understanding of the hydrologic cycle and climate change. In this study, we selected the radiation‐based Priestly‐Taylor Jet Propulsion Laboratory (PT‐JPL) model and assessed it at a daily time scale by using 44 flux towers. These towers distributed in a wide range of ecological systems: croplands, deciduous broadleaf forest, evergreen broadleaf forest, evergreen needleleaf forest, grasslands, mixed forests, savannas, and shrublands. A regional land surface evapotranspiration model with a relatively simple structure, the PT‐JPL model largely uses ecophysiologically‐based formulation and parameters to relate potential evapotranspiration to actual evapotranspiration. The results using the original model indicate that the model always overestimates evapotranspiration in arid regions. This likely results from the misrepresentation of water limitation and energy partition in the model. By analyzing physiological processes and determining the sensitive parameters, we identified a series of parameter sets that can increase model performance. The model with optimized parameters showed better performance (R2 = 0.2–0.87; Nash‐Sutcliffe efficiency (NSE) = 0.1–0.87) at each site than the original model (R2 = 0.19–0.87; NSE = −12.14–0.85). The results of the optimization indicated that the parameter β (water control of soil evaporation) was much lower in arid regions than in relatively humid regions. Furthermore, the optimized value of parameter m1 (plant control of canopy transpiration) was mostly between 1 to 1.3, slightly lower than the original value. Also, the optimized parameter Topt correlated well to the actual environmental temperature at each site. We suggest that using optimized parameters with the PT‐JPL model could provide an efficient way to improve the model performance.

Evaluation of six potential evapotranspiration models for estimating crop potential and actual evapotranspiration in arid regions

Using potential evapotranspiration (PET) to estimate crop actual evapotranspiration (AET) is a critical approach in hydrological models. However, which PET model performs best and can be used to predict crop AET over the entire growth season in arid regions still remains unclear. The six frequently-used PET models, i.e. Blaney-Criddle (BC), Hargreaves (HA), Priestley-Taylor (PT), Dalton (DA), Penman (PE) and Shuttleworth (SW) models were considered and evaluated in the study. Five-year eddy covariance data over the maize field and vineyard in arid northwest China were used to examine the accuracy of PET models in estimating daily crop AET.Results indicate that the PE, SW and PT models underestimated daily ET by less than 6% with RMSE lower than 35Wm−2 during the four years, while the BC, HA and DA models under-predicted daily ET approximately by 10% with RMSE higher than 40Wm−2. Compared to BC, HA and DA models, PE, SW and PT models were more reliable and accurate for estimating crop PET and AET in arid regions. Thus the PE, SW and PT models were recommended for predicting crop evapotranspiration in hydrological models in arid regions.

Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India

This paper evaluates the ability of wavelet transform in improving the accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) models. In this study, the performance of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating daily evapotranspiration in arid regions was evaluated. Prior to the development of models, gamma test was used to identify the best input combinations that could be used under limited data scenario. Performance of the proposed hybrid models was compared to ANN, ANFIS, and conventionally used Hargreaves equation. The results revealed that use of wavelet transform as data preprocessing technique enhanced the efficiency of ANN and ANFIS models. Wavelet-ANN and Wavelet-ANFIS performed reasonably better than other models. Better handling of wavelet-decomposed input variables enabled Wavelet-ANN models to perform slightly better than the Wavelet-ANFIS models. W-ANN2 (RMSE = 0.632 mm/day and R = 0.96) was found to be the best model for estimating daily evapotranspiration in arid regions. The proposed W-ANN2 model used second-level db3 wavelet-decomposed subseries of temperature and previous day evapotranspiration values as inputs. The study concludes that hybrid Wavelet-ANN and Wavelet-ANFIS models can be effectively used for modeling evapotranspiration.

Calculating Sunshine Hours and Reference Evapotranspiration in Arid Regions When Solar Radiation Data are Limited

AbstractThe Penman–Monteith equation is often recommended as the standard for both computing the reference evapotranspiration (ET0) and evaluating other ET0 equations. The main shortcoming of this equation is it requires weather data, e.g. solar radiation or sunshine hours (n), that are not always available. Since neither solar radiation nor n data are available in Libya, the main objective of the present study was to develop a reliable equation for estimating the sunshine hours (ne) to estimate monthly ET0 in arid regions. In this study, three equations were developed for predicting ne based on mean monthly temperature, relative humidity and wind speed. The derived equations were used for calculating the monthly ET0 based on the Penman–Monteith equation. The ET0 estimates were compared with ET0 calculated using observed n. The results indicate that the ET0 calculated using ne matched well with the ET0 calculated by n.The ET0 calculated by the three equations were tested against ET0 calculated by both the California Irrigation Management Information System (CIMIS) and Hargreaves–Samani equation.The ET0 based on estimated sunshine was closest to ET0 obtained from CIMIS compared to the Hargreaves–Samani equation. In conclusion, the equation to estimate sunshine hours should provide good estimates of the ET0 for similar desert locations that lack sunshine hours or solar radiation data. Copyright © 2015 John Wiley & Sons, Ltd.

Estimation of hourly evapotranspiration in arid regions by a simple parameterization of canopy resistance

The Penman-Monteith (PM) equation for evapotranspiration (ET) estimation has been applied in the world. Its weak point is the estimation of the canopy resistance. This paper proposed a simple canopy resistance model for estimating ET on an hourly basis. A non-linear functional relationship between canopy resistance, aerodynamic resistance, and climatic resistance was proposed. For model validation, data was collected in an irrigated wheat field in the Hetao Irrigation District, which is the largest one along the Yellow River. Surface energy balance and near-surface weather variables were measured during the growth cycle of wheat. Measured hourly ET values were obtained by the Bowen ratio energy balance method. Regression of predicted versus measured ET data resulted in r2=0.97 with slope of 0.98 and intercept of 0.01 which are not different from 1 and 0 at 99% confidence. This study provided a simple way to estimate ET with a general available meteorological database which can be further applied in hydrologic models considering effects of variable canopy resistance.

Global Water Balance According to the Penman Approach*

Global Water Balance According to the Penman Approach*