Urban Weather Data to Estimate Reference Evapotranspiration for Rural Irrigation Management

2012 ◽  
Vol 138 (9) ◽  
pp. 837-842 ◽  
Author(s):  
Yufeng Luo ◽  
Yunlu Jiang ◽  
Shizhang Peng ◽  
Shahbaz Khan ◽  
Xueliang Cai ◽  
...  
Keyword(s):  
Reference Evapotranspiration ◽  
Irrigation Management ◽  
Weather Data ◽  
Urban Weather

scholarly journals Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate

2009 ◽  
Vol 41 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Slavisa Trajkovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Management ◽  
Weather Data ◽  
Canopy Resistance ◽  
Rbf Network ◽  
Artificial Neural ◽  
Two Parameters

Numerous approaches have been developed for estimating hourly reference evapotranspiration ET0, most of which require numerous meteorological data. In many areas, the necessary data are lacking and new techniques are required. The objectives of this study are: (1) to develop artificial neural networks for estimating hourly reference evapotranspiration from limited weather data; (2) to evaluate the reliability of obtained artificial neural networks (ANNs) and Food and Agricultural Organization—56 Penman Monteith (FAO-56 PM) equation compared to the lysimeter measurements; (3) to test the performance of the FAO-56 PM equation for hourly daytime periods using rc=70 s m−1 (PM70) and using a lower rc=50 s m−1 (PM50); and (4) to evaluate the reliability of obtained ANNs compared to the FAO-56 PM equation using an hourly dataset from a variety of locations. The accuracy of two reduced-set artificial neural networks (ANNTR and ANNTHR) and two FAO-56 Penman-Monteith equations with different canopy resistance values (PM50 and PM70) was assessed using hourly lysimeter data from Davis, California. The ANNTR required only two parameters (temperature and radiation) as inputs. Temperature, humidity and (Rn−G) term were used as inputs in the ANNTHR. The ANNTR and PM50 were best at estimating hourly grass ET0. The ANNTR approach was additionally tested using hourly FAO-56 PM ET0 data from California Irrigation Management Information System (CIMIS) dataset. The overall results recommended Radial Basis Function (RBF) network for estimating hourly ET0 from limited weather data. Also, the results support the introduction of new value for canopy resistance (rc=50 s m−1) in the hourly FAO-56 PM equation.

scholarly journals A Simple Procedure to Estimate Reference Evapotranspiration during the Irrigation Season in a Hot-Summer Mediterranean Climate

2021 ◽  
Vol 13 (1) ◽  
pp. 349
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Reference Evapotranspiration ◽  
Simple Procedure ◽  
Irrigation Management ◽  
Weather Data ◽  
Maximum Temperature ◽  
Adjustment Coefficient ◽  
Irrigation Season ◽  
Rural Regions ◽  
Wide Range ◽  
Agricultural Organization

The Food and Agricultural Organization of the United Nations (FAO) Penman–Monteith (PM) method is widely regarded as the most effective reference evapotranspiration (ETo) estimator; however, it requires a wide range of data that may be scarce in some rural regions. When feasible relative humidity, solar radiation and wind speed data are unavailable, a temperature-based method may be useful to estimate ETo and provide suitable data to support irrigation management. This study has evaluated the accuracy of two ETo estimations methods: (1) a locally and monthly adjusted Hargreaves–Samani (HS) equation; (2) a simple procedure that only uses maximum temperature and a temperature adjustment coefficient (MaxTET). Results show that, if a monthly adjusted radiation adjustment coefficient (kRs) is calibrated for each site, acceptable ETo estimations (RMSE and R2 equal to 0.79 for the entire region) can be achieved. Results also show that a procedure to estimate ETo based only on maximum temperature performs acceptably, when compared with ETo estimation using PM equation (RMSE = 0.83 mm day−1 and R2 = 0.77 for Alentejo). When comparing these results with the ones attained when adopting a monthly adjusted HS method, the MaxTET procedure proves to be an accurate ETo estimator. Results also show that both methods can be used to estimate ETo when weather data are scarce.

scholarly journals Irrigation management strategies for winter wheat using AquaCrop model

2013 ◽  
Vol 3 ◽  
pp. 106-113
Author(s):  
M.H. Ali ◽  
I. Abustan
Keyword(s):  
Winter Wheat ◽  
Irrigation Management ◽  
Weather Data ◽  
Water Model ◽  
High Yield ◽  
Management Decision ◽  
Soil Conditions ◽  
Irrigation Frequency ◽  
Aquacrop Model ◽  
Daily Weather Data

Many regions of the world face the challenge to ensure high yield with limited water supply. This calls for utilization of available water in an efficient and sustainable manner. Quantitative models can assist in management decision and planning purposes. The FAO’s newly developed crop-water model, AquaCrop, which simulates yield in response to water, has been calibrated for winter wheat and subsequently used to simulate yield under different sowing dates, irrigation frequencies, and irrigation sequences using 10 years daily weather data. The simulation results suggest that “2 irrigation frequency” is the most water-efficient schedule for wheat under the prevailing climatic and soil conditions. The results also indicate decreasing yield trend under late sowing. The normal/recommended sequence of irrigation performed better than the seven-days shifting from the normal. The results will help to formulate irrigation management plan based on the resource availability (water, and land availability from previous crop).

scholarly journals Urban weather generation: the intercomparison of three emerging models

2021 ◽  
Author(s):  
Csilla Gal
Keyword(s):  
Land Use ◽  
Wind Speed ◽  
Air Temperature ◽  
Numerical Models ◽  
Weather Generator ◽  
Weather Data ◽  
Urban Air ◽  
Area Of Interest ◽  
Built Form ◽  
Urban Weather

<p>Cities modify the background climate through the surface-atmosphere interaction. This modification is function of urban design features, such as the configuration of buildings and the amount of vegetation. Compared to the undisturbed climate of the region, the climate of cities is characterized by higher temperature and lower wind speed. This modification is especially pronounce in dense urban areas. The climate modification of cities is not static, but varies in space and time. The spatial variations are governed by land use and built form differences, as well as by the presence or absence of green and blue infrastructures. Due to the spatial complexity of cities and the general lack of urban weather station networks in most places, the amount of available urban weather data is limited. As a consequence, planners, engineers and public health professionals can only approximate the climate impact of built environments in their respective fields.</p><p>Over the past years, several numerical simulation models have emerged that are able to model the influence of built areas on the atmosphere at the local scale and thus, deliver urban weather data for an area of interest. The aim of this study is to assess the performance of three numerical models with an ability to predict site-specific urban air temperature. The evaluated models are the Urban Weather Generator (UWG), the Vertical City Weather Generator (VCWG) and the Surface Urban Energy and Water Balance Scheme (SUEWS). Although the models differ in their scopes, modeling approaches and applications, they all derive the urban weather data from rural observations considering the land use and built form characteristics of the site.</p><p>The models are evaluated against air temperature measurements from the dense, 13<span><sup>th</sup></span> District of Budapest (Hungary). The field measurement utilized simple air temperature and relative humidity loggers placed in non-aspirated solar radiation screens at four shaded sites. The two week measurement period encompassed a five-day-long anticyclonic period with clear sky and low wind speed.<strong> </strong>Preliminary results indicate a good general agreement between modeled and observed values with root mean square error below or at 2ºC and index of agreement between 0.92-0.96. During the anticyclonic period most models slightly overestimate the daily maximum and underestimated the daily minimum urban air temperature.</p>

scholarly journals A Building Retrofit and Sensitivity Analysis in an Automatically Calibrated Model Considering the Urban Heat Island Effect in Abu Dhabi, UAE

2019 ◽  
Vol 11 (24) ◽  
pp. 6905 ◽  
Author(s):  
Lindita Bande ◽  
Adalberto Guerra Cabrera ◽  
Young Ki Kim ◽  
Afshin Afshari ◽  
Mario Favalli Ragusini ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Urban Heat Island Effect ◽  
Abu Dhabi ◽  
Weather Data ◽  
Mean Bias Error ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat ◽  
Urban Weather

Villas are a very common building typology in Abu Dhabi. Due to its preponderance in residential areas, studying how to effectively reduce energy demand for this type of building is critical for Abu Dhabi, and many similar cities in the region. This study aims to show the impact of proposed energy efficiency measures on a villa using a calibrated model and to demonstrate that to be accurate, the model must be driven using urban weather data instead of rural weather data due to the significance of the urban heat island effect. Available data for this case study includes construction properties, on-site (urban) weather data, occupancy-related loads and schedules and rural weather data. Four main steps were followed, weather data customisation combining urban and rural weather variables, model calibration using a genetic algorithm-based tool and simulating retrofit strategies. We created a calibrated model for electricity demand during 2016–2017 with a 4% normalized mean bias error and an 11% coefficient of variation of the mean square error. Changing from none to all retrofit strategies results in a 34% reduction in annual energy consumption. According to the calibrated model, increased urban temperatures cause a 7.1% increase in total energy consumption.

scholarly journals Evapotranspiration data to determine agro-climatic zones in Egypt

2017 ◽  
Vol 32 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Samiha A. H. Ouda ◽  
Tahany A. Norledin
Keyword(s):  
Nile Delta ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Weather Data ◽  
Climatic Zones ◽  
Average Value ◽  
Scheduling Model ◽  
Significant Difference ◽  
Difference Test ◽  
Climatic Zoning

Abstract The objective of this paper was to compare between agro-climatic zones developed from 10-year interval of weather data from 2005-2014, 20-year interval of weather data from 1995-2014 and the zoning developed by [NORELDIN et al. 2016] using 30-year interval from 1985-2014 in the old cultivated land of Egypt in the Nile Delta and Valley. Monthly means of weather data were calculated for each year, and then monthly values for 10-year and 20-years were calculated for each governorate. Basic Irrigation scheduling model (BISm) was used to calculate reference evapotranspiration (ETo). Analysis of variance was used and the means was separated and ranked using least significant difference test (LSD0.05). Our results showed that agro-climatic zoning using 20-year values of ETo was similar to the zones developed with 30-year values of ETo, with different values of average ETo in each zone. Furthermore, using 10-year values of ETo resulted in higher values of ETo in each zone, compared to 20-year and 30-year ETo values. However, the average value of ETo over the three classifications was close to each other. Thus, depending on the availability of weather data, either zoning can be sufficient to develop agro-climatic zones.

scholarly journals Heuristic methods applied in reference evapotranspiration modeling

2018 ◽  
Vol 42 (3) ◽  
pp. 314-324 ◽  
Author(s):  
Daniel Althoff ◽  
Helizani Couto Bazame ◽  
Roberto Filgueiras ◽  
Santos Henrique Brant Dias
Keyword(s):  
Reference Evapotranspiration ◽  
Performance Criteria ◽  
Superior Performance ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Computational Techniques ◽  
Heuristic Methods ◽  
Scarce Data

ABSTRACT The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

scholarly journals Performance of methods for estimation of table beet water requirement in Alagoas

2018 ◽  
Vol 22 (3) ◽  
pp. 189-193 ◽  
Author(s):  
Daniella P. dos Santos ◽  
Célia S. dos Santos ◽  
Leiliane M. da Silva ◽  
Márcio A. L. dos Santos ◽  
Cícero G. dos Santos
Keyword(s):  
Water Use ◽  
Standard Method ◽  
Water Scarcity ◽  
Performance Index ◽  
Reference Evapotranspiration ◽  
Irrigation Management ◽  
Crop Evapotranspiration ◽  
Abstract Optimization ◽  
Crop Coefficients ◽  
Drainage Lysimeter

ABSTRACT Optimization of water use in agriculture is fundamental, particularly in regions where water scarcity is intense, requiring the adoption of technologies that promote increased irrigation efficiency. The objective of this study was to evaluate evapotranspiration models and to estimate the crop coefficients of beet grown in a drainage lysimeter in the Agreste region of Alagoas. The experiment was conducted at the Campus of the Federal University of Alagoas - UFAL, in the municipality of Arapiraca, AL, between March and April 2014. Crop evapotranspiration (ETc) was estimated in drainage lysimeters and reference evapotranspiration (ETo) by Penman-Monteith-FAO 56 and Hargreaves-Samani methods. The Hargreaves-Samani method presented a good performance index for ETo estimation compared with the Penman-Monteith-FAO method, indicating that it is adequate for the study area. Beet ETc showed a cumulative demand of 202.11 mm for a cumulative reference evapotranspiration of 152.00 mm. Kc values determined using the Penman-Monteith-FAO 56 and Hargreaves-Samani methods were overestimated, in comparison to the Kc values of the FAO-56 standard method. With the obtained results, it is possible to correct the equations of the methods for the region, allowing for adequate irrigation management.

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