FAO Model Research Articles

COMPARATIVE EVALUATION OF THREE PENMAN-MONTEITH VARIANT MODELS FOR GROUNDWATER LEVEL ESTIMATION IN JEJU ISLAND

Accurate estimation of groundwater levels is crucial for effective water resource management, particularly in regions like Jeju Island, where groundwater is the primary water source. This study emphasizes the importance of preprocessing meteorological observations to address the temporal disconnect between rapid meteorological fluctuations and the slower responses of groundwater systems. Key factors such as cumulative effects, delayed reactions, and seasonal variations were considered during preprocessing to improve the reliability of evapotranspiration estimates. Three Penman-Monteith evapotranspiration models (PM, FAO-24, FAO-56) were evaluated using pre-processed data, including accumulated precipitation (100-day), temperature (20-day), and other meteorological parameters. Validation against 2023 groundwater level data demonstrated that the FAO-56 model preprocessing achieved the best performance, with the highest correlation (r = 0.89), lowest mean squared error (MSE = 0.10), and smallest error range (overestimation: +1.1m, underestimation: -1.4m). These results highlight that accurate groundwater level estimation relies on proper preprocessing of observations rather than solely optimizing model operations. The findings provide valuable insights for enhancing groundwater monitoring and sustainable water management in areas with complex geological and hydrological conditions.

Vitamin A-related nutrition knowledge gaps and predictors among caregivers of preschool children in Eastern Uganda: a cross-sectional study

BackgroundVitamin A (VA) remains a core micronutrient as VA Deficiency (VAD) in children has persisted as a public health problem in parts of Africa with adverse effects. Caregivers of children are essential in the control of VAD; however, there is a paucity of data on their knowledge of VA, dietary sources, and VAD. This study sought to assess the level of VA-related nutrition knowledge (VANK) and its predictors among caregivers of preschool children in Eastern Uganda.MethodsA cross-sectional analytical design was used. Both socio-demographic and knowledge and attitude (KA) data were collected using a structured questionnaire partly adapted from the FAO model Knowledge, Attitude and Practice (KAP) questionnaire. A sample size of 256 was used. Caregivers of 24–59 months-old children were selected from Bukwo District in Eastern Uganda using purposive and random sampling methods. Knowledge scores (%) based on responses to ten questions were determined and eventually classified as low (≤ 40%) and moderate or high (˃40%). Descriptive and inferential statistics were computed using SPSS (version 24). Logistic regression was used to identify predictors with p < 0.05 considered significant.ResultsThe study had 247 caregivers with a mean age of 30.9 ± 7.7 years. The majority were female (90%), married, subsistence crop farmers and had primary-level education or lower. The mean VANK score was 18.9 ± 24.7%. Overall, most of the caregivers had low VANK as only about 20% had moderate or high. The proportions that knew the different aspects of VANK were correspondingly small. About half of the caregivers (46.6%) knew VA itself and only 27% knew any of its sources. Those who knew VAD, its causes, signs/symptoms and prevention measures were 31, 22, 13 and 24% respectively. The caregivers’ VANK was significantly associated with their overall VA-related attitude, age and level of education. However, education and age were the significant predictors.ConclusionCaregivers had very low VANK. They barely knew VA and its food sources or VAD. The main predictors of VANK were caregiver age and level of education. The study recommends education of caregivers about VA for effective VAD control which contributes to achievement of the Sustainable Development Goal (SDG) 2.

Balanço de Radiação sobre água de Baixa Turbidez Purificada Artificialmente para Irrigação de Fumo Cultivado sob Sombreamento. II. Análise do Albedo da água e Modelagem da Radiação Líquida

Abstract The albedo of a water surface and the energy available for evaporation are strongly correlated and studying such processes is of paramount importance for water security at farm and regional levels. In this paper, water albedo (αw) and net all-wave radiation (Rn) were analyzed after being measured above the surface of an artificially cleaned and low-turbidity water used for tobacco irrigation. It was observed that αw decreased as the sun elevation (θ) increased, especially for clear and near clear skies. The results showed that αw can be reasonably predicted with a power law model either in terms of θ or Sg (incoming solar radiation) across different cloud cover conditions. From this study, a mean daily albedo of 0.05 is recommended. Three approaches were considered for estimation of daily Rn. In the first, a linear regression model strongly fitted Rn data in terms of Sg solely. The second option based on the definition of Rn was [0.95Sg - Lnet(56)], where Lnet(56) is net longwave (LW) radiation as used in the FAO56 model for reference evapotranspiration estimation, and the third was [0.95Sg - Lnet(MLR)], where MLR stands for multiple linear regression. The disadvantages of approaches (1) and (3), based on regressions, is that they are constrained to the type of water stored in the farm and the climatic conditions of the region. The performance of approach (2), where Lnet(56) is a widely used model, was comparable to the others can potentially be improved with a site-specific calibration. All three approaches for estimating daily Rn proposed in this study can possibily be extended to clear water that did not go through any filtration process.

Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used. Moreover, the effect of ETo model selection on sweet corn irrigation water use was investigated by integrating simulated ETo data from empirical and ML models using the Decision Support System for Agrotechnology Transfer (DSSAT) model at two locations (Citra and Homestead) in Florida. Furthermore, a linear bias correction calibration technique was employed to improve the performance of empirical models. Results were consistent in that the NN and RF models outperformed the empirical models. The empirical models tended to underestimate and overestimate small and high daily ETo values, respectively, with the HS model exhibiting the least accuracy. However, calibrated PT and TU models performed comparably to the ML models. Results also revealed that using an inappropriate ETo model could lead to over-irrigation by up to 54 mm during a single crop season. Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance. In areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed. It is important to highlight that distinct plant species exhibit varying transpiration characteristics and, consequently, have different water requirements. These differences play a pivotal role in shaping the overall impact of ETo models on crop water use.

Modeling actual water use under different irrigation regimes at district scale: Application to the FAO-56 dual crop coefficient method

The modeling of irrigation in land surface models are generally based on two soil moisture parameters SMthreshold and SMtarget at which irrigation automatically starts and stops, respectively. Typically, both parameters are usually set to optimal values allowing to fill the soil water reservoir with just the estimated right amount and to avoid crop water excess at all times. The point is that agricultural practices greatly vary according to many factors (climatological, crop, soil, technical, human, etc.). To fill the gap, we propose a new calibration method of SMthreshold and SMtarget to represent the irrigation water use in any (optimal, deficit or even over) irrigation regime. The approach is tested using the dual-crop coefficient FAO-56 model implemented at the field scale over an 8100 ha irrigation district in northeastern Spain where the irrigation water use is precisely monitored at the district scale. Both irrigation parameters are first retrieved at monthly scale from the irrigation observations of year 2019. The irrigation simulated by the FAO-56 model is then evaluated against observations at district and weekly scale over 5 years (2017–2021) separately. The performance of the newly calibrated irrigation module is also assessed by comparing it against three other modules with varying configurations including default estimates for SMthreshold and SMtarget. The proposed irrigation module obtains systematically the best performance for each of the 5 years with an overall correlation coefficient of 0.95 ± 0.02 and root-mean square error of 0.27 ± 0.07 hm3/week (0.64 ± 0.17 mm/day). Unlike the three irrigation modules used as benchmark, the new irrigation module is able to reproduce the farmers’ practices throughout the year, and especially, to simulate the actual water use in the deficit and excess irrigation regimes occurring in the study area in spring and summer, respectively.

Estimating crop coefficients and actual evapotranspiration in citrus orchards with sporadic cover weeds based on ground and remote sensing data

Accurate estimations of actual crop evapotranspiration are of utmost importance to evaluate crop water requirements and to optimize water use efficiency. At this aim, coupling simple agro-hydrological models, such as the well-known FAO-56 model, with remote observations of the land surface could represent an easy-to-use tool to identify biophysical parameters of vegetation, such as the crop coefficient Kc under the actual field conditions and to estimate actual crop evapotranspiration. This paper intends, therefore, to propose an operational procedure to evaluate the spatio-temporal variability of Kc in a citrus orchard characterized by the sporadic presence of ground weeds, based on micro-meteorological measurements collected on-ground and vegetation indices (VIs) retrieved by the Sentinel-2 sensors. A non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as the normalized difference vegetation index, NDVI, and the normalized difference water index, NDWI, is suitable to represent the spatio-temporal dynamics of the investigated environment, characterized by sparse vegetation and the sporadic presence of spontaneous but transpiring soil weeds, typical of winter seasons and/or periods following events wetting the soil surface. The Kc values obtained in each cell of the Sentinel-2 grid (10 m) were then used as input of the spatially distributed FAO-56 model to estimate the variability of actual evapotranspiration (ETa) and the other terms of water balance. The performance of the proposed procedure was finally evaluated by comparing the estimated average soil water content and actual crop evapotranspiration with the corresponding ones measured on-ground. The application of the FAO-56 model indicated that the estimated ETa were characterized by root-mean-square-error, RMSE, and mean bias-error, MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated soil water contents, SWC, were characterized by RMSE equal to 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of soil water content and actual crop evapotranspiration under the investigated field conditions.

Development of an Energy Efficient and Fully Autonomous Low-Cost IoT System for Irrigation Scheduling in Water-Scarce Areas Using Different Water Sources

Politicians and the general public are concerned about climate change, water scarcity, and the constant reduction in agricultural land. Water reserves are scarce in many regions in the world, negatively affecting agricultural productivity, which makes it a necessity to introduce sustainable water resource management. Nowadays, there is a number of commercial IoT systems for irrigation scheduling, helping farmers to manage and save water. However, these systems focus on using the available fresh water sources, without being able to manage alternative water sources. In this study, an Arduino-based low-cost IoT system for automated irrigation scheduling is developed and implemented, which can provide measurements of water parameters with high precision using low-cost sensors. The system used weather station data combined with the FAO56 model for computing the water requirements for various crops, and it was capable of handling and monitoring different water streams by supervising their quality and quantity. The developed IoT system was tested in several field trials, to evaluate its capabilities and functionalities, including the sensors’ accuracy, its autonomous controlling and operation, and its power consumption. The results of this study show that the system worked efficiently on the management and monitoring of different types of water sources (rainwater, groundwater, seawater, and wastewater) and on automating the irrigation scheduling. In addition, it was proved that the system is can be used for long periods of time without any power source, making it ideal for using it on annual crops.

Reference crop evapotranspiration in distinct agricultural regions of Southern Brazil: a comparison of improved empirical models

The FAO56 Penman-Monteith model is globally accepted for the accurate determination of reference evapotranspiration (ETo). However, a lack of appropriate data encouraged the improved model’s approach to estimate ETo. This study compared the performance of 10 empirical models of ETo estimation (Penman, Priestley &amp; Taylor, Tanner &amp; Pelton, Makkink, Jensen &amp; Haise, Hargreaves &amp; Samani, Camargo, Benevides &amp; Lopes, Turc, and Linacre) contrasted with the FAO56 model in two regions in Southern Brazil. Data were collected from automatic stations of the Brazilian National Institute of Meteorology (INMET) from December 21, 2019, to February 28, 2021. The determination coefficient (R²), mean square error (nRMSE), mean bias error (MBE), Willmott index (d), and Pearson’s correlation coefficient (r), clustering, and Principal Component Analysis (PCA) were performed. For the different regions, the radiation-based model proposed by Penman was the best alternative for estimating ETo. The model showed the most appropriated values for R2 (0.9015) and r (0.9494). The clustering and PCA analyses indicated the interrelations of the meteorological data and the combination of the models according to the parameters used for the determination of ETo.

Identification of Suitable Sites for Jatropha curcas L. Bioenergy Plantation Using the AquaCrop Model

Jatropha curcas (JC), as a biofuel plant, has been reported to have various desired characteristics such as high oil content seeds (27–40%), fast-growth, easy cultivation, drought tolerance, and can be grown on marginal soil and wasteland, requiring fewer nutrients and management and does not interfere with existing food crops, insects, and pest resistance. This investigation was the first study of its type to use climatological data, blue/green water footprints, and JC seed production to identify suitable sites for JC bioenergy plantation using the AquaCrop FAO model across the Khyber Pakhtunkhwa province in northwest Pakistan. The JC seed yield (10 ton/ha) was at a maximum in the districts of Bannu, Karak, Hangu, Kurram, North Waziristan, Lakki Marwat, South Waziristan, and Dera Ismail Khan, in addition to its frontier regions, Tank, Peshawar, Mohmand, Orakzai, Khyber, Kohat, Charsadda, Mardan, Swabi, and Nowshera, respectively. Green water footprint (264 m3/ton of JC seed) and blue water footprint (825 m3/ton) was less in these areas. Furthermore, the results revealed that, depending on climatological circumstances, the southern part of the Khyber Pakhtunkhwa province is more appropriate for JC bioenergy plantation than the northern region. The districts of Bannu, Karak, Hangu, Kurram, North Waziristan, Lakki Marwat, South Waziristan, Dera Ismail Khan, and its frontier regions, Tank, Peshawar, Mohmand, Orakzai, Khyber, and Kohat, in Khyber Pakhtunkhwa province were identified to be the most ideal places for JC bioenergy plantation. As a result, under the Billion Tree Afforestation Project (BTAP) and the Green Pakistan Project, the Forest Department of Khyber Pakhtunkhwa should consider planting JC species in the province’s southern region. Furthermore, this research will provide scientific information to government and private sector officials for better management and optimum yield of the JC biofuel crop, as well as for the promotion of energy forestry in Pakistan.

Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions

Accurate estimation of evapotranspiration (ET) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since ET represents more than 85% of the total water budget. FAO-56 is one of the widely used formulations to estimate the actual crop evapotranspiration (ETc act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ETc act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualKc. Surface SM observations were assimilated into the soil evaporation (Es) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (Tc act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (Ks) as a proxy of LST (LSTproxy). The FAO-Ks was corrected by assimilating LSTproxy derived from Landsat data based on the variances of predicted errors on Ks estimates from FAO-56 model and thermal-derived Ks. The proposed approach was tested over a semi-arid area in Morocco using first, in situ data collected during 2002–2003 and 2015–2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ETc act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualKc using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ETc act measurements.

The use of terrestrial LiDAR to monitor crop growth and account for within-field variability of crop coefficients and water use

Monitoring the spatio-temporal distribution of crop height and biomass is important for crop management in terms of applying irrigation, fertilizers and pesticides. This paper reports the performance of a terrestrial laser scanner (TLS) for measuring crop height and biomass of a bean crop (Phaseolus vulgaris., cv. ‘Contender’); and crop height for barley (Hordeum vulgare., cv. ‘Carfields CKS1′), and pea (Pisum sativum., cv. ‘Massey’) crops, grown under field conditions. In addition, a method is presented that improves spatio-temporal estimates of crop coefficient (Kc) and crop evapotranspiration (ETc) using TLS data combined with NDVI data (“TLVI method”), compared with standard FAO56 model estimates. Evidence is provided to show that the TLVI method gives accurate estimates of crop growth patterns due to spatial variations in soil, microclimate and plant health, providing a valuable tool for precision crop management.The TLS-derived canopy height method showed a significantly high correlation and low biases and errors with the manually measured canopy height (R2 = o.95, RMSE = 5.85 for barley; R2 = 0.93, RMSE = 3.01 for pea; R2 = 0.91, RMSE = 1.82 for bean). The TLS method also showed potential to estimate bean biomass (R2 = 0.70, RMSE = 37.56). In-field sensor measurements of canopy height and NDVI were used to estimate site specific crop factors required for accurate estimation of daily evapotranspiration losses. These estimates showed general agreement with those predicted by standard dual FAO56 crop coefficients, but in addition were able to detect the spatial variability in crop growth and water requirements which were largely controlled by soil differences.Overall, the results showed that the TLS method has the potential to measure crop height surface and that the TLVI method provides estimates of ETc with a high spatio-temporal resolution for different crops. This approach can be considered a very promising tool for site-specific management.

Quantifying Food Loss and Waste in Saudi Arabia

Using the FAO model calculations proposed by Gustavsson et al. (2013) and FAO (2014), food loss and waste (FLW) is measured in Saudi Arabia with a special focus on wheat, rice, dates, poultry, vegetables, fruits, fish, and meat. Results show that the overall FLW rate is 33.1%, where the food loss rate is 14.2%, and the food waste rate is 18.9%. Acceding to the disaggregated results, we find that FLW rates are distributed as follows: 29.7% for wheat, 33.6% for rice, 21.4%, for dates 29.1% for poultry, 39.5% for vegetables, 39.6% for fruits, 33% for fish, and 31.3% for meat. The Sustainable Development Goal (SDG 12.3) target is to reduce the rates of food loss and waste by 50% in 2030, and to help achieve that goal, we employed a nonlinear optimisation simulation model with the objective function of reducing FLW by 50% over the period 2020–2030. Based on the findings achieved, recommendations are made to cover the various aspects of the whole food supply chain (FSC) and to aim at more efficiency and higher levels of productivity. Our findings have significant implications by estimating the FLW baseline indicator and providing the different stakeholders of FSC with the optimal actions to do to reduce FLW rates.

Comparing Evapotranspiration Estimates from the GEOframe-Prospero Model with Penman–Monteith and Priestley-Taylor Approaches under Different Climate Conditions

Evapotranspiration (ET) is a key variable in the hydrological cycle and it directly impacts the surface balance and its accurate assessment is essential for a correct water management. ET is difficult to measure, since the existing methods for its direct estimate, such as the weighing lysimeter or the eddy-covariance system, are often expensive and require well-trained research personnel. To overcome this limit, different authors developed experimental models for indirect estimation of ET. However, since the accuracy of ET prediction is crucial from different points of view, the continuous search for more and more precise modeling approaches is encouraged. In light of this, the aim of the present work is to test the efficiency in predicting ET fluxes in a newly introduced physical-based model, named Prospero, which is based on the ability to compute the ET using a multi-layer canopy model, solving the energy balance both for the sunlight and shadow vegetation, extending the recently developed Schymanski and Or method to canopy level. Additionally, Prospero is able to compute the actual ET using a Jarvis-like model. The model is integrated as a component in the hydrological modelling system GEOframe. Its estimates were validated against observed data from five Eddy covariance (EC) sites with different climatic conditions and the same vegetation cover. Then, its performances were compared with those of two already consolidated models, the Priestley–Taylor model and Penman FAO model, using four goodness-of-fit indices. Subsequently a calibration of the three methods has been carried out using LUCA calibration within GEOframe, with the purpose of prediction errors. The results showed that Prospero is more accurate and precise with respect to the other two models, even if no calibrations were performed, with better performances in dry climatic conditions. In addition, Prospero model turned to be the least affected by the calibration procedure and, therefore, it can be effectively also used in a context of data scarcity.

Türkiye’de Fındık İhracatının Box-Jenkins Yöntemiyle Modellenmesi ve İhracat Öngörüsü

Ülkemiz fındık üretim ve ihracatında Dünya’da ilk sırada yer alarak fındık pazarında söz sahibi ülke durumundadır. Bu nedenle fındık piyasasının şekillenmesinde önemli rolü vardır. Bu çalışmanın amacı Türkiye’de 1961-2018 yıllarına ait fındık ihracatının zaman serileri ile modellenerek 2023 yılına kadar olan fındık ihracat öngörüsünü yapmaktır. Fındık ihracatı veri tabanına FAO’dan ulaşılmıştır ve 2019-2023 yıllarını tahminlemek için ARIMA modeli kullanılmıştır. Yapılan analizler sonucu en uygun modelin ARIMA(1,1,11) olduğu tespit edilmiştir. Sonuçlara göre 2019 yılında Türkiye’nin fındık ihracatının 162 bin ton, 2023 yılına gelindiğinde ise 176 bin ton olacağı tahmin edilmiştir.

Survey of Wet and Dry Stress in Soybean in Japanese Farmers' Fields using Dry and Wet Indices Based on FAO56 Model

Survey of Wet and Dry Stress in Soybean in Japanese Farmers' Fields using Dry and Wet Indices Based on FAO56 Model

Assessment of Surface Irrigation Potential of the Dhidhessa River Basin, Ethiopia

Assessing available water resources and their potential for irrigation water use is vital for sustainable agricultural development and planning. This is particularly of interest in developing countries like Ethiopia, where a small portion of largely accessible land for surface irrigation applications has been utilized, despite the majority of the population relying on agricultural productivity. This study utilized the Dhidhessa River Basin (Ethiopia) as a case study and analyzed the main challenges to balance the sustainable water resources utilization and enhance agricultural productivity of the basin. The study mainly focused on estimating the available water resources and their potential for surface irrigation water use in the basin. This was achieved by utilizing Geographic Information System (GIS)-based tools, a hydrological Soil and Water Assessment Tool (SWAT) model, and a Crop Water and Irrigation Requirements Program of FAO (CROPWAT) model. While the SWAT estimated the water availability in the basin, GIS-tools such as Model Builder were used to map the irrigation potential of the basin. For irrigation water potential assessment, we selected six crops (cabbage, maize, tomato, pepper, groundnut and sugarcane) and estimated their irrigation water requirements using the CROPWAT model. We developed the SWAT model for the period from 1986 to 2012 using the available hydro-meteorological and geo-spatial data. Due to many parameters used in the model, we first performed a parameter sensitivity analysis and identified the most essential/sensitivity parameters via Sequential Uncertainty Fitting-II (SUFI-2). The identified sensitive parameters were subsequently used for model calibration (1989–2000) and validation (2001–2012) procedures achieved via SUFI-2. SWAT was able to reproduce the observed monthly streamflow values with a coefficient of determination (R2) and Nash-Sutcliffe Coefficient (NSE) of 0.85 and 0.87 for the calibration period and 0.91 and 0.89 for the validation period, respectively. The findings generally indicated a “good” performance of the model in simulating the hydrology. The annual available water of the basin is 9.26 billion cubic meters (BCM) whereas the 70% and 80% dependable flow is 7.56 and 6.97 BCM, respectively. Based on the Model Builder of ArcGIS, the SWAT estimated available water can potentially irrigate an area of 259,028 ha for slope less than 8%, 643,162 ha for slopes less than 15% and 1,023,581 ha for slopes less than 30%. Moreover, the irrigation water requirements were calculated by the CROPWAT model for the six selected crops indicated that although the need for irrigation water varies depending on the season, the potential irrigation area of the Dhidhessa River Basin is greater than its irrigated land. Therefore, it is concluded that the basin’s surface irrigation systems need to be expanded to enhance the agricultural productivity and improve the livelihood of the basin’s communities and similar basins elsewhere.

基于改进的双作物系数法估算辽河三角洲芦苇湿地蒸散量

PDF HTML阅读 XML下载 导出引用 引用提醒 基于改进的双作物系数法估算辽河三角洲芦苇湿地蒸散量 DOI: 10.5846/stxb201810272317 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学青年基金项目（41405109）；辽宁省科技厅重点研发计划指导计划项目（2018108004）；中国气象局气候变化专项（CCSF201819） Evapotranspiration estimation of Phragmites australis wetland in the Liaohe River Delta based on the improved dual crop coefficient method Author: Affiliation: Fund Project: 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:以辽河三角洲湿地芦苇群落为研究对象，利用涡动相关通量、小气候梯度要素、群落内水面蒸发量以及芦苇群落生长参数等数据，基于FAO-56模型的双作物系数法，分别计算作物系数Kc、基础作物系数Kcb和水面蒸发系数Kw，分析其日变化动态及主导影响因子，建立基于生物因子和环境因子的小时尺度双作物系数模型。结果如下：（1）芦苇生长初期，Kc和Kcb的日变化呈现早晚高、中午略低的多峰波动曲线；在快速生长期和稳定生长期，Kc和Kcb白天波动幅度较小，早晚波动幅度较大；生长末期，Kc和Kcb夜晚波动幅度较大，白天呈现多峰波动曲线；Kw白天较小、夜晚较高，生长初期白天的数值显著高于其他时期。（2）相关分析表明，气温、相对湿度、风速、株高和叶面积指数是Kc、Kcb和Kw的影响因子；基于生物因子和环境因子重新构建双作物系数模型，基于改进的双作物系数法模拟芦苇群落蒸散，决定系数R2达0.894。（3）利用改进的双作物系数模型和FAO-56模型，对辽河三角洲芦苇群落的蒸发与蒸腾过程进行模拟，实现芦苇群落蒸发过程与蒸腾过程的分离，解决了实际观测无法直接获取芦苇群落蒸腾量的问题，同时提高了芦苇群落蒸散的模拟精度。（4）调整了FAO推荐的芦苇单作物系数常数值，调整后的作物系数更适用于辽河三角洲芦苇湿地。 Abstract:Phragmites australis wetland in the Liaohe River Delta of Northeastern China was selected as a research object. Based on the dual crop coefficient of FAO-56 model and the observed data (including flux data, microclimate gradient data, water evaporation, growth parameters of P. australis, etc.) in Panjin Wetland Ecosystem Research Station, the crop coefficient (Kc), the basal crop coefficient (Kcb), and the water evaporation coefficient (Kw) were calculated. The diurnal dynamics of three coefficients and their influencing factors were analyzed. Dual crop coefficient model at hour scale was established using the biological factors and environmental factors. The results are as follows:(1) In the initial growth stage, the diurnal variation of Kc and Kcb showed multi-peak fluctuation with higher values in the morning and evening and lower values at noon; during the rapid development and stable stage, the fluctuation ranges of Kc and Kcb were smaller in the daytime and larger at night; at the end of growth season, Kc and Kcb fluctuated greatly at night and presented multi-peak curves in the daytime. Kw was smaller in the daytime and higher at night, with the daytime values in the initial stage higher than those of the other stages. (2) The correlation analysis showed that the influencing factors of Kc, Kcb, and Kw were air temperature, relative humidity, wind speed, plant height, and leaf area index. The dual coefficient model was reconstructed with the biological and environmental factors. Thus, the simulation of the evapotranspiration in P. australis wetland was improved with the determination coefficient R2 reaching 0.894. (3) Using the improved dual crop coefficient model and FAO-56 model, the evaporation and transpiration of P. australis wetland in the Liaohe River Delta were simulated. This could solve the problem that transpiration is unobtainable from direct observation and improve the accuracy of evapotranspiration simulation. (4) We adjusted the constant value of P. australis crop coefficient recommended by FAO which was more suitable for the Liaohe River Delta wetland. 参考文献 相似文献 引证文献

Quantitative and qualitative land suitability assessment for rice cultivation, north of Iran

&lt;p&gt;Evaluation of arable lands and agricultural potential to support current and future agricultural uses is one of the best agricultural policies for food security in developing countries. Hence, studies related to land suitability evaluation can lead to sustainable land use. The aims of this research were qualitative land suitability evaluation, land production potential prediction and quantitative land suitability evaluation on the basis of the FAO model for rice in Sangar region, northern Iran. Qualitative evaluation was carried out using the maximum limitation and parametric methods. Land production potential was determined by the agro-ecological zoning (AEZ) model. Land suitability classes according to maximum limitation, Storie and square root parametric methods are determined S3, S3-N1 and S2-S3, respectively. Rice radiation-thermal production potential was calculated as 7.65 t/ha; mean land production potentials, using Storie and square root formulas were predicted respectively, as 3.69 and 4.52 t/ha and mean actual yield was estimated as 2.81 t/ha. The results of this study showed that use of the square root formula is more appropriate than Storie formula as far as land production potential calculation is concerned. Soil limitations and weak management level have caused majority of land units to have moderate quantitative suitability (S2) for rice cultivation. Comparison between qualitative and quantitative suitability classes demonstrates that quantitative suitability classes are significantly increased due to crop adaptation with climate agents.&lt;/p&gt;

Biophysical Parameters and Actual Evapotranspiration of Bean Culture by Means of Remote Sensing

Agricultural irrigation is involved in an important chain that involves all sectors of the economy, either directly, by increasing food production, or indirectly, by withdrawing large amounts of fresh water. The relevance of this theme forces the search for alternatives to make water use as rational as possible. Evapotranspiration estimation methods based in remote sensing, such as the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model, become extremely relevant in these scenarios, since it is possible to estimate this parameter in large scales. Therefore, the aim of this research was to apply the SAFER model in the estimation of bean crop actual evapotranspiration using Landsat-8 satellite image data. One of the parameters used as input in the SAFER model is the NDVI (Normalized Difference Vegetation Index), which presented a coefficient of determination (r&amp;sup2;) equal to 0.80 when compared to the crop coefficient. The actual evapotranspiration (ETa) estimated by the SAFER model were compared to the FAO 56 model estimates for later correlation between the models. This information is expected to assist the producer in a better management of water resources used in irrigation. The correlation between the two models presented a relevant coefficient of determination (r2 = 0.73), representing the potential of the SAFER model in relation to the FAO model 56.

Partitioning evapotranspiration of a drip-irrigated wheat crop: Inter-comparing eddy covariance-, sap flow-, lysimeter- and FAO-based methods

A precise estimate of the evapotranspiration (ET) partitioning is fundamental for determining the crop water needs and optimizing irrigation management. The plant transpiration (T) is generally considered to be the most desirable component, while reducing the soil evaporation (E) could be one of the most important water-saving actions in semi-arid agricultural regions. Given the lack of reference method to estimate the E/T partitioning of wheat crop, this study inter-compares four different methods based on eddy covariance, sap flow and lysimetry measurements and FAO modeling. The objectives are: i) to quantify the systematic and random uncertainty in E and T observations, ii) to evaluate the partitioning ratio (T/ET) at the daily/field scale and iii) to assess the performance of the FAO model over two drip irrigated wheat fields. Results indicate that despite the small surface sensed by mini-lysimeters, the partitioning ratio is evaluated more precisely (19% relative error) with lysimetry than with the other systems (any combination of eddy covariance, lysimetry and sap flow measurements). Moreover, stem-scale T measurements from sap flow sensors are subject to representativeness issues at the field scale, and to systematic errors during water-stress and senescence periods. The lysimeter-derived partitioning ratio increases from about 0.50 to 0.85 during the growth stage and rapidly drops towards 0 during senescence. Its dynamics is found to be significantly correlated (R>0.7) with the 5-cm soil moisture. By comparing FAO simulations with observations, it is found that the FAO method overestimates T and underestimates E, while keeping satisfying ET estimates for drip irrigated wheat. This study suggests that different independent measurement techniques should be implemented to both quantify and reduce uncertainties in the T/ET ratio, and that accurate observations are still needed to improve the modeling of E/T components.