Calculating Reference Evapotranspiration Research Articles

Agweather-qaqc: An Interactive Python Package for Quality Assurance and Quality Control of Daily Agricultural Weather Data and Calculation of Reference Evapotranspiration

agweather-qaqc: An Interactive Python Package for Quality Assurance and Quality Control of Daily Agricultural Weather Data and Calculation of Reference Evapotranspiration

Impact of Climate Change on the Development of Viticulture in Central Poland: Autoregression Modeling SAT Indicator

Ongoing climate change is having a profound impact on agriculture, which is attracting attention from the scientific community. One of its effects is an increase in average temperature, which is a key factor in grape cultivation. This may increase the popularity of viticulture in central Europe. The aim of this study was to assess the potential for the development of viticulture in central Poland based on SAT changes from 1975 to 2021, in addition to changes in evapotranspiration, occurrence of late spring and early autumn frosts and frosty days in selected years from this period as an important factors relating to climate change. The research utilized data obtained from the Institute of Meteorology and Water Management—National Research Institute. The Bai–Perron test was used to determine the direction of temperature changes. An AR(1) autoregression model was used to predict SAT changes in central Poland for the years 2022–2026, based on the results of the Bai–Perron test. As part of the in-depth research on the SAT index, reference evapotranspiration calculations were also made as a second factor that is considered an important indicator of climate change. The Sum of Active Temperatures from 1975 to 2021 in the provinces of central Poland showed an increasing trend of 0.07% per year. The average SAT in central Poland in 2022–2026 is expected to range from 2700 °C to 2760 °C. Considering the current thermal conditions in central Poland and the forecasts for the coming years, it can be expected that vineyard cultivation will develop in this region. However, the research shows that the observed increasing trend in evapotranspiration, both in total in individual years and in the period of the greatest vegetation, i.e., in the months from May to the end of August, will result in an increasing need in central Poland to ensure adequate irrigation in developing vineyards.

Revisiting reference evapotranspiration calculation under regional advection and its effect on single and dual crop coefficients: An empirical approach for quinoa crop

AbstractAdvection is a prevailing meteorological phenomenon in the arid and semi‐arid environments that directly affects the crop and soil hydrology. It could have a great impact on the rate of standard crop evapotranspiration (ETc) in the irrigated areas. Therefore, it is required to take it into consideration while computing crop water requirement through the physically based meteorological procedures. The objectives of this study are (1) simple modification of the Penman–Monteith (PM) equation in calculation of the grass reference evapotranspiration (ETo) to implement the local advection, (2) comparing the single (Kc) and dual crop coefficient (Kcb) of two quinoa cultivars (Titicaca, and Q5) with and without advection correction and (3) presenting a simple model to calculate the advection factor using the maximum air temperature and mean relative humidity for the future crop growth modelling studies. Both Q5 and Titicaca showed unexpectedly very high ETc and potential transpiration (Tp) as 1568 mm and 1003 mm, and 1156 mm and 829 mm, respectively, due to occurrence of regional advection, whereas very high unrealistic Kc and Kcb values for Q5 revealed the impact of strong local advection and external energy. Therefore, we modified ETo to implement the advection effect through introducing the advection factor, ETc/Rn (Rn is the net radiation), as a function of maximum air temperature and mean relative air humidity during the growing season [ETc/Rn = exp (0.025Tmax – 0.015RHavg)] which resulted in higher ETo values, and consequently lower and more realistic Kc and Kcb. As a result, modified maximum Kc values of 1.14 and 1.55 and the modified maximum Kcb of 0.94 and 1.0 were obtained for Titicaca and Q5 cultivars, respectively. This procedure leads to a more accurate site‐specific Kc estimation and indirectly reliable ETc estimation as a function of advection factor and its multiplication by the non‐modified ETo. Furthermore, for direct estimation of ETc through the PM equation, the coefficients of aerodynamic and canopy resistance components of PM equation were modified for estimation of ETc by the non‐modified Rn, which resulted in accurate estimation of ETc.

Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data

Water resource management and crop growth control require the calculation of reference evapotranspiration (ET0), but meteorological data can often be incomplete, necessitating models with minimal inputs. This study was conducted in Iran’s arid synoptic stations of Sirjan and Kerman, where data scarcity is severe. Penman–Monteith FAO-56 was selected as the target data for modeling artificial neural network (ANN), fuzzy neural adaptive inference system (ANFIS), and ANN-gray wolf optimization (ANN-GWO). The performance of these models was evaluated using an input dataset consisting of the current station’s minimum and maximum temperatures, ET0, and the wind speed of the nearby station (external data) in three different combinations. The models’ accuracy was assessed using two widely used criteria: root mean square error (RMSE) and coefficient of determination (R2), as well as the empirical Hargreaves equation. In the absence of climatic data, the ANFIS, ANN, and ANN-GWO methods using minimum and maximum temperatures, which are relatively easier to estimate, outperformed the empirical Hargreaves equation method in both stations. The results demonstrate that the ANFIS method performed better than ANN and ANN-GWO in all three input combinations. All three methods showed improvement when external data (wind speed and ET0 of the adjacent station) were used. Ultimately, the ANFIS method using minimum and maximum temperatures and the adjacent station’s ET0 in Kerman and Sirjan yielded the best results, with an RMSE of 0.33 and 0.36, respectively.

A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO–HFS due to their consistently high accuracy (RMSE and R2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.

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.

Estimation of reference evapotranspiration based on machine learning models and timeseries analysis: a case study in an arid climate

Water scarcity is a major challenge for irrigated agriculture, particularly in developing countries where access to meteorological data for calculating reference evapotranspiration (ETo) is limited. Thus, this study explores the potential of two machine learning models (random forest (RF) and long short-term memory (LSTM)) and autoregressive integrated moving average (ARIMA) to forecast ETo. The investigation was conducted for four weather stations in Egypt, from 1982 to 2020. The machine learning models were evaluated using four combinations of inputs: maximum and minimum temperature, relative humidity, and wind speed. The best results for both RF and LSTM models were achieved with the first set of inputs that included all four variables at both regional and local scales. For the regional scale, RF and LSTM models achieved R2 values of 0.85 and 0.86, respectively, with RMSE values of 0.69 and 0.68 mm/day. At the local scale, RF and LSTM models exhibited R2 values ranging from 0.92 to 0.95 and 0.93 to 0.95, respectively, while RMSE ranged between 0.38 and 0.46 mm/day and 0.37–0.43 mm/day, respectively. Additionally, ARIMA models were employed for tim series analysis of the same ETo data. ARIMA (2,1,4) and ARIMA (2,1,3) were found to be the most suitable models for the local-scale analysis while ARIMA (2,1,4) was identified as the optimal model for the regional-scale analysis. For the local-scale analysis, R2 values ranged from 0.86 to 0.91 and RMSE values ranged from 0.26 to 0.38. The regional scale analysis yielded an R2 value of 0.89 and an RMSE value of 0.58 mm/day. The developed models can be used in places where meteorological data for forecasting ETo are limited.

Investigation of the effect of climate change on reference evapotranspiration in Tekirdag, Turkiye

It is important to determine the impact of global climate change on water resources. In the effective use of water resources, the amount of water required for agricultural irrigation should be planned under these conditions. In this study, the trend analysis of the climate data required for the calculation of reference evapotranspiration (ET0) between 2011 and 2020 for the meteorology station of Tekirdag, Turkey was carried out. Analysis of change in the study was carried out according to the methods of Linear Regression Test, Mann-Kendall Test and Sen T Test. As a result of the change analysis, it was determined that the reference evapotranspiration (ET0) values had a decreasing trend in June and July and this decrease was statistically significant. As a result of the analysis of the changes in the climate parameters used in the calculation of reference evapotranspiration (ET0), it was determined that the values of maximum temperature, average relative humidity and average sunshine duration were in a decreasing trend, while minimum temperature and wind speed values were in an increasing trend. In the study, the change in monthly precipitation amounts during the measurement periods was also examined. As a result of the analysis of the changes, it was determined that the monthly precipitation amounts have a decreasing trend and the changes especially in April, September, October and December are statistically significant. It is thought that the results obtained will contribute to the future agricultural production planning in the regional conditions.

Evaluation of the evapotranspiration rate of lacustrine wetland macrophytes in Lake Tana, Ethiopia

This study quantified the total evapotranspiration (ET) flux of three major types of macrophytes that cover lacustrine fringe wetlands in Lake Tana, Ethiopia during the dry season, April and May, 2022. This study was conducted to understand lacustrine wetland macrophytes' ET rates and provide an important guideline for designing their conservation and restoration plans. To assess ET rates during the peak dry season, three macrophytes of lacustrine wetlands in Lake Tana were chosen: Eichhornia crassipes (water hyacinth), Cyperus papyrus, and Echinochloa stagnina. Three replicate lysimeters for each of the three treatment types (macrophytes) and one control type (open water) were applied to measure ET. To calculate reference evapotranspiration (ETo), FAO's ETo Calculator Version 3.2 was applied. All statistical analyses were carried out using R software. The results of ET rates from wetland macrophytes (9.05mm/day) were considered to be significantly higher than open water evaporation (5.90mm/day). There was a significant difference in mean ET among the macrophytes [F (3, 488) = 194.6, p < 2e-16 ***]. In this study, crop coefficient (Kc1) (1.99 ± 0.65) referenced to FAO Penman–Monteith ETo is greater than Kc2 (1.58 ± 0.59) referenced to open water evaporation (Eow). In terms of wetland macrophyte type, E. crassipes had the maximum mean Kc values (Kc1 = 2.51±0.70 and Kc2 = 2.02±0.67). E. stagnina had the minimum mean Kc values (Kc1 = 1.52±0.32 and Kc2 = 1.19±0.24). Except for open water which strongly correlated with sunshine hours (r = 0.61) and solar radiation (r = 0.60), the correlation among daily ET values and meteorological variables was found to be weak. The result of our research indicated that the presence of floating macrophytes of E. crassipes will increase the ET flux during the dry season more than emergent macrophytes and open water surfaces. Our findings could provide an important guideline for designing conservation and restoration plans for C. papyrus and E. stagnina to reverse the spread of E. crassipes and maintaining lacustrine fringe wetlands for fish breeding ground and sediment control.

Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions.

Version 1.1.0—pyfao56: FAO-56 evapotranspiration in Python

The pyfao56 software package is a Python-based implementation of the standardized evapotranspiration (ET) methodologies described in Irrigation and Drainage paper No. 56 of the Food and Agriculture Organization of the United Nations, commonly known as FAO-56. This update improved pyfao56 by (1) fixing a major bug related to testing for availability of weather data, (2) expanding options for reference ET calculations including hourly estimates, (3) adding functionality to specify variable soil characteristics with profile depth, (4) including optional water balance enhancements that consider soil water depletion for both the dynamic and maximum root zones, and (5) enabling the use of either constant or variable depletion fraction (p). The updates increase software versatility and expand options for use, while also maintaining the core functionality of the original software design.

Climate Change Impacts on Water Resources in Arid and Semi-Arid Regions: A Case Study in Saudi Arabia

In the coming years, climate change is predicted to impact irrigation water demand considerably, particularly in semi-arid regions. The aim of this research is to investigate the expected adverse impacts of climate change on water irrigation management in Saudi Arabia. We focus on the influence of climate change on irrigation water requirements in the Al Quassim (97,408 ha) region. Different climate models were used for the intermediate emission SSP2-4.5 and the high emission SSP5-8.5 Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. The FAO-CROPWAT 8.0 model was used to calculate reference evapotranspiration (ETo) using weather data from 13 stations from 1991 to 2020 and for both the SSP2-4.5 and SSP5-8.5 scenarios for the 2040s, 2060s, 2080s, and 2100s. The findings indicated that, for the 2100s, the SSP2-4.5 and SSP5-8.5 scenarios forecast annual average ETo increases of 0.35 mm/d (6%) and 0.7 mm/d (12.0%), respectively. Net irrigation water requirement (NIWR) and growth of irrigation water requirement (GIWR) for the main crops in the Al Quassim region were assessed for the current, SSP2-4.5, and SSP5-8.5 scenarios. For SSP5-8.5, the GIWR for the 2040s, 2060s, 2080s, and 2100s are expected to increase by 2.7, 6.5, 8.5, and 12.4%, respectively, compared to the current scenario (1584.7 million m3). As a result, there will be higher deficits in 2100 under SSP5-8.5 for major crops, with deficits of 15.1%, 10.7%, 8.3%, 13.9%, and 10.7% in the crop areas of wheat, clover, maize, other vegetables, and dates, respectively. Optimal irrigation planning, crop pattern selection, and modern irrigation technologies, combined with the proposed NIWR values, can support water resources management. The findings can assist managers and policymakers in better identifying adaptation strategies for areas with similar climates.

Development of the crop coefficient for vertically trained cucumber vines grown in soilless media under naturally ventilated greenhouse conditions

AbstractIn the wake of climate change and water scarcity issues, protected cultivation combined with precise irrigation management allows effective water resource utilization. This research was intended to find the actual crop evapotranspiration (ETc) of vertically trained cucumber vines in a soilless medium and develop crop coefficients (Kc) under greenhouse conditions. Experiments were performed in three micro‐lysimeters (volume 0.015 m3) placed inside a naturally ventilated greenhouse from April to June 2018 (season I) and September to December 2019 (season II) with a planting density of 0.32 m2/plant. The FAO‐56 Penman–Monteith equation was used to calculate reference evapotranspiration (ETo), and lysimeters were used to calculate the actual ETc. The average actual ETc of cucumber vines in season II (147.81 mm) was almost half of the ETc in season I (275.24 mm). The higher ETc in season I was due to a higher ETo of 493 mm during season I compared to 150 mm in season II inside the greenhouse. Kc values for season I were 0.3, 0.74, 1.11 and 0.68 compared to 0.3, 0.77, 1.38 and 0.79 in season II during the initial development, mid and end stages of cucumber vines, respectively, under greenhouse conditions.

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

The accurate calculation of reference evapotranspiration (ET0) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (Rs) data, the machine learning methods obtain better results in their estimation of ET0. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U2) data, using RHCLD and U2CLD from the CLDAS to estimate ET0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

Evapotranspiration dynamics in aerated and non-aerated subsurface flow treatment wetlands

This study reports the seasonal dynamics of evapotranspiration (ET) and evaporation (E) in different subsurface flow treatment wetlands operating in a temperate European climate. Daily water balances were compiled over the course of ten years (August 2010–July 2020). The study includes non-aerated horizontal flow wetlands (25 cm deep and 50 cm deep) as well as horizontal flow and vertical flow wetlands. The pilot systems were operated in planted and unplanted pairs, enabling Phragmites evapotranspiration rates (for planted systems) and evaporation rates (for unplanted systems) to be calculated. Evapotranspiration rates are highly seasonal. Aeration was observed to increase both evaporation and evapotranspiration rates. The overall percentage of inflow lost to ET was highest in non-aerated wetlands, due to the lower hydraulic load that they received compared to the aerated systems. Plant coefficients (Kp) relate measured evapotranspiration with the calculated reference evapotranspiration ETo. Wetlands planted with Phragmites display dynamic and highly seasonal values of Kp which are well-characterized by a sinusoidal curve during the growing season paired with a minimum (stable) value in the non-growing season. Aeration was observed to increase both evapotranspiration and evaporation rates. The concept of a Plant Scaling Factor (PSF) is introduced as a way of quantifying the “clothesline effect” observed in small treatment wetlands. Whereas unplanted systems effectively have a PSF of zero, the systems in this study (ranging in size from 5.6 to 6.2 m2) exhibited PSF values between 3.8 and 4.8 when the vegetation was fully mature.

Estimation of Reference Evapotranspiration in Bhavanisagar Block of Tamil Nadu Using FAO ETo Calculator

The FAO Penman-Monteith equation is used by the ETo calculator software to calculate reference evapotranspiration – ETo from meteorological data. FAO chose this technique as the standard because it is physically based, roughly approximates grass ETo in the studied region, and explicitly considers both physiological and aerodynamic aspects. The application can handle climate data on a daily, ten-day, and monthly basis. The data may be supplied in a variety of units, and it can be analyzed using regularly used meteorological parameters. In this study, ETo calculator version 3.2 is used to estimate the ETo in Bhavanisagar. The results revealed that, it is possible to estimate ETo by using the ETo calculator. The concept of the reference evapotranspiration was introduced in this study and the result obtained are used further to estimate the evaporative demand of the atmosphere independently of crop type, crop development and management practices.

Optimal Selection of Empirical Reference Evapotranspiration Method in 36 Different Agricultural Zones of China

The Food and Agriculture Organization has proposed the current version of the Penman–Monteith method (FAO56-PM) as the standard for calculating reference evapotranspiration (ET0); however, high meteorological data requirements limit its application in many areas. There is thus an urgent need to identify the best alternative empirical method to accurately calculate ET0 in regions that lack sufficient meteorological data. In this study, three temperature-based methods and five radiation-based methods were evaluated using ET0 values generated using the FAO56-PM method in 36 agricultural zones in China based on meteorological data from 823 stations, measured between 2011 and 2020. The results showed that the optimal temperature-based method and radiation-based method differed for different agricultural zones, and no one temperature method or radiation method could be suitable for all agricultural zones. The eight empirical methods were regionally calibrated to improve the ET0 calculation accuracy in the different zones. The relationship between the optimal methods and climatic conditions showed that the most reliable empirical method could be selected according to the local annual mean temperature and aridity index. The results provide useful guidance for the selection of reliable empirical ET0 methods in agricultural zones outside China.

Evaluation of long-term climate change impact on the growing season and water use of mature pecan in Lower Rio Grande Valley

Pecan is a major crop in the Lower Rio Grande Valley (LRGV), New Mexico. New Mexico is currently the largest pecan producer in the United States. Pecan is also considered a major water user which can consume as much as 1200–1300 mm of water during the growing season. Climate change can have significant potential impact on the water requirement of pecan as well as its productivity and cost of production. This study evaluates the climate change effects on the duration of growing season (DGS) and water requirement of pecan in LRGV over the period of 1901–2019. Temperature data for the study period was used to identify the DGS. Hargreaves–Samani model was used to calculate reference evapotranspiration (ET0). Daily crop coefficient (Kc) over the study period was calculated using a developed regression model relating growing season Kc to cumulative growing degree days (GDD). The Mann-Kendall and Sen’s methods were applied to determine if there was a significant positive or negative trend in the DGS, ET0, and actual evapotranspiration (ETa). Statistical results showed a decreasing trend in the DGS over 1901–1950 whereas an increasing trend in the DGS was detected for the period of 1950–2019. Like the DGS variations, decreasing and increasing trends for both seasonal ET0 and ETa were observed for the first 50 years and the rest of the period with 99% confidence level, respectively. Results showed an increase of 124 mm in seasonal ETa between 1950 and 2019. The study showed that climate change effects in LRGV have been occurring for the study period, continuous increases in both DGS and ETa may bring changes in crop water requirement, and economics of pecan production as well as other horticultural challenges such as insufficient chill period for flowering, increased vivipary, and delayed or prolonged harvest.

Conditioning point and gridded weather data under aridity conditions for calculation of reference evapotranspiration

Meteorological data are often collected in dry, desert regions where the local environments exhibit effects of aridity caused by the lack of evapotranspiration (ET). In contrast, the standard reference ET (ETo) calculations of FAO56 and ASCE assume that the surfaces underlying collected weather data are well-watered so that near-surface meteorological measurements reflect the cooling and humidifying effects of an evaporating surface. In this study, we develop a weather data conditioning process and algorithms to adjust for biases in meteorological data that exhibit aridity effects. The conditioning process is intended to adjust the weather data to better exhibit characteristics of data collected over a well-watered vegetated surface prior to use of the data to estimate reference ET. The procedure involves the extrapolation of air temperature, vapor pressure, and wind speed profiles to and from a regional blending height using standard surface energy balance equations and flux-profile relationships and employing ET estimated for the ambient, dry conditions of an arid weather station and reference ET that should co-exist with weather measurements used to calculation reference ET. Example applications are given with 24-hour weather data and hourly weather data in Idaho and Nevada. Results indicate that reference ETo can be overstated by as much as 25% in southern Idaho and 8% in eastern Nevada. The methodology is intended to be transferrable to other regions and climates and is self-aware of the need for conditioning of weather data according to differences in ambient ET and the reference ET estimates.

The effectiveness of detention basin in Soursoup (Annona muricata L.) farming land of Selopamioro village – Bantul, Yogyakarta

Soursop fruit is rich in health benefits. The soursop plants (Annona muricata L.) have a great potential to be cultivated in Indonesia. It requires an enough water availability in the land to optimize biomass production through photosynthesis. Soursoup farming in dry land Selopamioro Village – Bantul, Yogyakarta needs irrigation. Due to geomorphological condition, water resources for irrigation mainly from harvested rainfall in detention basin (local name “rorak’”). This study was aimed to determine the effectiveness of detention basin Soursop farming land in storing the rainfall which later used them for irrigation. The material includes the coordinates of the research location, Soursop farming land boundaries, and detention basins. Furthermore, the daily climate data in the form of rainfall, temperature, net solar radiation, wind speed, atmospheric pressure and relative humidity were used to calculate reference evapotranspiration. The reference evapotranspiration (ETo) was computed by Penman-Monteith approach. Later, it was used to determine plant evapotranspiration (ETc) using the soursop plant coefficient (Kc) from previous study. The effectiveness of detention basins was evaluated from water balance analysis. It was applied to assign annual irrigation water requirement in Soursop farming land. This study defined average ETo of Soursop farming land in Nawungan orchard was 5.0 mm/day, while the average ETc was 3.0 mm/day. There were three detention basins in Soursop farming land, laid on the upper slope and two on the middle slope. Annual irrigation water requirement was 4287 m3;. During this period, three detention basins could meet the water need of plant for 84 days. So, more detention basin should be built in Soursop farming land - Nawungan orchard.