Detailed Meteorological Data Research Articles

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Microalgae bio-reactive façade: Location and weather-based systematic optimization

This article explores the design of microalgae biofaçade thanks to a numerical model describing the system’s thermal and biological behavior. Design options (single vs. double glazing, radiation-selective film, culture reservoir thickness) and operation strategies (light transmission, microalgae strain, sparged air origin, boiler fumes reuse) are screened systematically. The context for this investigation was the city of Marseille over a 10-year period (2013 to 2022) for which detailed meteorological data were available (air temperature, wind velocity and orientation, cloud cover, at a 3-hour resolution). These investigations revealed a very high level of interaction between the parameters (assessed by H2 Friedman index), making the system notoriously complicated to design and optimize. Nevertheless, some conclusions can be drawn. In France, fitting double glazing equipped with a greenhouse radiation-selective film is essential to harness as much heat as possible (＋16.7 % and ＋6.5 % of operation duration over year 2018, respectively). Similarly, a thermophilic strain is not relevant because overheating events are inexistent in well-designed systems. Further refinements, such as the origin of the air blown into the culture reservoir and the reuse of combustion fumes from the boiler (＋2.1 % performance), were evaluated from a technical point of view. However, their implementation would also be subjected to other considerations, such as the financial costs and environmental benefits. Finally, the model was applied to design the most efficient possible microalgae biofaçade system in the case of a hypothetical official building renovation.

Calibration and Evaluation of Empirical Methods to Estimate Reference Crop Evapotranspiration in West Texas

Evapotranspiration is an essential component of the hydrologic cycle, and its accurate quantification is crucial for managing crop water requirements and the operation of irrigation systems. Evapotranspiration data is key to hydrological and water management research investigations, including studying the impact of various climatic factors on crop water requirements. It has been estimated as the product of the reference crop evapotranspiration and crop coefficient. Daily reference crop evapotranspiration (ETo) can be determined by several methods and equations. The Food and Agriculture Organization Penman-Monteith equation requires complete weather data, whereas empirical equations such as Hargreaves and Samani, Valiantzas, Priestley-Taylor, Makkink, and Stephens-Stewart require limited weather data. This work evaluated different empirical equations for West Texas using the standard FAO Penman-Monteith method and calibrated their parameters to improve ETo estimation. Detailed meteorological data from West Texas Mesonet and high resolution (800 m) Parameter-elevation Regressions on Independent Slopes Model (PRISM) datasets from 2007 to 2016 were used. Daily ETo calculated using the standard FAO Penman-Monteith equation was compared to ETo estimated based on different empirical methods. The results show that all original empirical equations underestimated ETo. Calibration improved the performance of tested equations; however, there seems to be underestimation of ETo in the 8–16 mm range. Overall, the monthly Hargreaves and Samani equation with either original or calibrated values of its parameters outperformed all tested models. This equation seems to be a reasonable estimator, especially under limited weather data conditions.

Physical-based model for exposure coefficient and its validation towards the second generation of Eurocode EN 1991-1-3 for roof snow loads

In standards for structural design, models for snow loads on flat roofs generally provide the basis for determination of snow loads on any roof shapes. They are typically derived from ground snow loads and the predicted effects of exposure of flat roofs primarily due to wind, often conveyed through the exposure coefficient. The paper provides background information on the revision of the model for exposure coefficient in the second generation of the Eurocode on snow loads on structures, prEN 1991-1-3:2020. With the aim to provide an easy-to-use procedure with a minimum amount of input data, but to cover the main physical phenomena, a new formulation is proposed in prEN reflecting up-to-date scientific findings and the model provided in ISO 4355:2013. The prEN model corrects the main deficiency of the model in EN 1991-1-3:2003 where no account for wind velocities expected at the site during a snow season is taken. The prEN formulation additionally requires information on mean wind velocity for the coldest month at the site, that is commonly easily accessible.The contribution starts off by introducing the prEN model. To validate the new model, a parametric study is presented, considering plausible ranges of the main parameters influencing the determination of exposure coefficient in Europe. The outcomes of the new simplified prEN formulation are then compared with an advanced theoretical model. The prEN model is further applied considering detailed meteorological data representative for various climates. The results obtained by the prEN provisions are critically compared with those based on the advanced theoretical model and meteorological data, and with the estimates of the models in ISO 4355:2013 and in the JCSS Probabilistic Model Code.The presented validation demonstrates that the prEN model reflects well typical European snow and wind climates. The model captures a clear decreasing tendency of exposure coefficient with an increasing mean wind velocity. Mean wind velocity in the coldest month of 4.5 m/s appears to provide a reasonable threshold to make distinction between normal and exposed conditions.

Comparison of evapotranspiration upscaling methods from instantaneous to daytime scale for tea and wheat in southeast China

Accurately converting instantaneous evapotranspiration (λETi) at satellite over-passing time into daily evapotranspiration (λETd) is a key issue of applying remotely sensed data to estimate regional evapotranspiration (λET) from remote sensing satellites, which plays an important role for effective water resource management. The scaling methods that take advantage of the relationship between λET and other environmental factors and can be used to convert λETi into λETd. In this study, five scaling methods of converting λETi into λETd, including the evaporative fraction method (Eva-f method), revised evaporative fraction method (R-Eva-f method), crop coefficient method (Kc-ET0 method), revised crop coefficient method (R-Kc-ET0 method) and direct canopy resistance method (Direct-rc method), were evaluated based on the detailed meteorological data measured from 2016 to 2018 in a tea field and 2018 to 2020 in a wheat field in southeast China. The estimated λETd was compared with the measured λETd by the Bowen ratio energy balance (BREB) method. The results indicated that the Eva-f and R-Eva-f methods with the mean root mean square error (RMSE) and coefficient of efficiency (ε) equaled 9.02 W m−2 and 0.92; 12.06 W m−2 and 0.89, respectively, were superior to the Kc-ET0, R-Kc-ET0 and Direct-rc method; the Kc-ET0 method with the mean RMSE and ε equaled 20.62 W m−2 and 0.79 was also a good option for simulating the λETd of tea and wheat; while the R-Kc-ET0 method simulated the λETd well for the wheat with mean RMSE and ε equaled 36.29 W m−2 and 0.71, but significantly overestimated the tea λETd with the mean RMSE and ε values of 39.61 W m−2 and 0.59 for tea; the Direct-rc method overestimated λETd of tea and wheat for the most of intervals with the mean RMSE and ε of 39.58 W m−2 and 0.62, and was not recommended to use in the present study areas.

Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method

The main objective of this paper is to present a novel approach for determining PV potential through an optimization method. The novel approach considers the importance of technical and economic potential simultaneously for determining the optimal configuration of PV systems using a digital surface model. The integration of photovoltaic systems is conditioned mainly by the location and type of installation - configuration. Thus, more and more photovoltaic systems are being integrated into urban areas. For the further successful integration of photovoltaic systems into networks and the successful establishment of appropriate policies and directives, it is essential to properly assess the photovoltaic potential. Based on the described methodology, the potential determination is made for a completed area of 75,537 m2 using a digital surface model of the observed area. The annual values of physical, geographical, technical, and economic potentials were 19.57 GWh, 7.54 GWh, 875.50 MWh, and 19.64 kWh, respectively. The methodologies presented in the paper are based on detailed meteorological data for 20 years, a digital surface model of the observed area, a novel optimization approach, and multi-year data of electricity prices on markets. The presented results can be an excellent basis for further analyzes of determining the photovoltaic potential.

National research & education network of meteorological monitoring in V. I. Vernadsky Crimean Federal University

The article describes the experience in organizing work of the meteorological monitoring network across the Republic of Crimea based on the best available technologies. V.I. Vernadsky Crimean Federal University applied approaches of the meteorological monitoring network for scientific and educational purposes. According to the studies, technical requirements, methodological approaches in organizing meteorological measurements and educational and practical base for students training and retraining were formed. The monitoring system allows collecting detailed meteorological data in the ground layer and studying the local formation features of hydrometeorological parameters across various natural climatic zones and landscapes in Crimea. The article also provides methodological requirements and results on creating system for collecting, storing and processing information on a personal server by organizing a thematic geoportal and visualizing data using web gis. According to the research results, we received appropriate patents, created an educational platform and a competence centre for training and re-training staff in subject-matter and related specialties in the Crimea in order to form knowledge, skills and abilities in the field of regional meteorological monitoring.

A new method for examining maritime mobility of direct crossings with contrary prevailing winds in the Mediterranean during antiquity

The use of detailed meteorological data with sailing software, in conjunction with sailing the Ma'agan Mikhael II replica ship, has engendered the development of a method for examining maritime mobility of single-masted square sail Mediterranean merchantmen in the Graeco-Roman period, with the initial objective of mapping direct, open sea, return sailing routes from the Levant that a priori lie contrary to the prevailing wind. Many quantitative works have used averaged winds as input, and evaluated sailing passages based on climatological averages, losing information on the intra- and inter-diurnal variability of the winds. Thus their sole measure of mobility has been a representation of sailing speed on direct crossings. Moreover, these studies have not considered the difference between physical and practical mobility, the latter driven by human factors. For instance, the choice of whether to sail, or wait for better conditions. The proposed method uses climatological resources at high spatial and temporal resolutions, with the premise that using high-resolution data reveals the recurring wind variabilities and patterns that are key to mobility, especially on routes lying contrary to the prevailing winds. The method generated a large set of over 5400 simulated sailing outcomes for each route segment, permitting well-established statistical analysis. Inclusion of criteria-based human factors of the mariners of the period provides a measure of mobility, representing not only sailing speed, but also waiting time, and the probability of conducting a feasible passage at a given time of the year. This new method provides deeper insight into maritime mobility and the understanding of seafaring in the Mediterranean, and is applicable to numerous scenarios, providing a practical and improved measure of maritime mobility.

Impact analysis on change in climatic conditions due to the development of highway roads and other infrastructures at Coimbatore - an evidence

Global warming, a major threat is mainly due to the increase in population. This leads to an increase in infrastructural facilities, and vehicles. The major reasons for the increase in temperature may be due to pollutant gases from Industries and Vehicles, other Infrastructural development, etc. Because of this, trees are being cut down which increases the temperature thereby reducing the rainfall. Also, rainfall decreases because of the destruction of trees leading to decreased groundwater levels. The proposed study primarily focuses on the change in climatic conditions such as temperature, relative humidity, and rainfall due to the development of infrastructures such as highway roads, bridges, etc. The above indicators were being addressed by many authors earlier, but there exists a gap in addressing the growth of infrastructure on the above-said factors. This proposed study presents a chance to address the gap that exists in the earlier literature based on the indicators specified above due to the infrastructural developments. Hence an Impact analysis is required to address the issues of infrastructure development especially for Coimbatore – Pollachi highway road. The detailed meteorological data of the past five years are taken for analysis and suggestions for Smart City projects, Policymaking decisions are addressed.

Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky

The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. This is particularly crucial for regions more prone to recurring droughts with limited ground monitoring stations. A three-year (2016–2018) flux ET dataset from a pastureland in north central Kentucky was used to validate the Operational Simplified Surface Energy Balance (SSEBop) model at monthly and annual scales. Flux and SSEBop ET track each other in a consistent manner in response to seasonal changes. The mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 5.47, 21.49 mm mon−1, 30.94%, and 0.87, respectively. The model consistently underestimated ET values during winter months and overestimated them during summer months. SSEBop’s monthly ET anomaly maps show spatial ET distribution and its accurate representation. This is particularly important in areas where detailed surface meteorological and hydrological data are limited. Overall, the model estimated monthly ET magnitude satisfactorily and captured it seasonally. The SSEBop’s functionality for remote ET estimation and anomaly detection, if properly coupled with ground measurements, can significantly enhance SSEBop’s ability to monitor drought occurrence and prevalence quickly and accurately.

Evaluating of eight evapotranspiration estimation methods in arid regions of Iran

Evapotranspiration (ET) plays an important role in efficient irrigation and water resources management. ET can be accurately estimated via the FAO–Penman–Monteith method (ETFPM), which is known as the standard reference method for the ET estimation. However, this model requires more detailed meteorological data than are generally available at most weather stations. Alternative methods for predicting daily ET should be found which are simpler to work with less input data without scarifying prediction accuracy. The main purpose of this study was to determine a reliable alternative ET models that requires fewer input data compared with the ETFPM as a reference method. Here, five radiation-based models including Makkink (ETMAK), Priestley and Taylor (ETPT), Abtew (ETABT), Jensen-Haise (ETJH), McGuinness and Bordne (ETMB), and three temperature-based models including Hargreaves and Samani (ETHS), Hamon (ETHAM), Linacre (ETLIN) were evaluated by comparing them with PETFPM on a daily and growing season scale, using long-term data from 13 meteorological stations in northeast Iran. The statistical analysis revealed that the ETHS, ETJH, ETHAM, and ETLIN were the best methods for predicting daily ET in 54%, 15%, 15% and 15% of areas, respectively. In general, the temperature-based methods outperformed the radiation-based-methods in the study area. The cumulative values of daily ET during the growth periods showed that the Jensen−Haise method performed the best for the warm growing season compared to other alternative methods, while the Hargreaves–Samani method was the best prediction method for the cool growing season. Nevertheless, divergence between estimations of the best alternative methods and the reference method showed that the best ET alternative methods might be unreliable in some regions, and could not be recommended for estimating crop water requirements. Accordingly, the spatiotemporal variability in predictability performance of ET models should be taken into account prior to use.

A numerical study of harmful gas dispersion based on CALPUFF model

Severe gas leakage accidents occasionally happened during the exploitation of high-sulfur gas, the consequent harmful gas dispersion could seriously impact the air quality, cause large-scale casualties and economic losses. Therefore, people’s comprehensive understanding of harmful gas dispersion in both spatial and temporal dimensions should be improved. This paper takes gas well blowout accident in Kai County, China, as the case study, simulates and analyzes the dispersion characteristics of the leaked gas, mainly focuses on H2S. The CALPUFF model is applied to execute the experiment, the detailed meteorological data, complex terrain data, wet deposition and chemical transformations are all under consideration. The result shows that due to different impact factors, such as complex terrain and gas density variations, H2S exposure risk was higher in the valley area than that on the mountain, which also explains why Xiaoyang and Gaowang villages have a great number of casualties. Besides, the analysis result is basically consistent with the historical local news and reported data, which demonstrated that our case study of H2S gas dispersion simulation is reasonable and reliable.

Three dimensional gas dispersion modeling using cellular automata and artificial neural network in urban environment

The gas dispersion simulation in complex urban environment posts challenges on consequence analysis. Though computational fluid dynamics (CFD) are general approaches to provide building-resolving estimates, the time consuming calculation and complex process of modeling limit their application for emergency response. In this paper, a cellular automata dispersion model is prompted to simulate continuous point release of propane in 3-D domain with ground obstructions. An artificial neural network is employed to calculate the temporal state transition of cellular automata. To provide data for the neural network to train, fire dynamic simulator (FDS) code is adopted to simulate 100 scenarios of propane release from a fixed position in pre-specific domain with different combinations of meteorological conditions and source parameters. A proportion of the simulation results is selected to train the artificial neural network with different transition rules derived from the advection-diffusion equation. The dispersion processes are eventually replicated with the proposed approach on the remaining scenarios that the artificial neural network has never encountered. Provided with detailed meteorological field data, the cellular automata model could calculate the gas dispersion process about 1.5 times faster than FDS. As to the model performance, in the long term evolution, decreases in model accuracy are observed due to the nature of cellular automata in explicit evolution and the unavailability of error compensation methods. The transition rule that takes source terms into consideration outperforms in estimating the concentration distributions.

Meteorological Factors that Affect Stroke Onset

To date, there have been many publications concerning relationship of meteorological condition and stroke onset. However, little is known about detailed meteorological factors that affect stroke onset. With hospital characteristics that most inpatients at our institution lived around Komaki, we examined the relationship between stroke onset and meteorological factors based on detailed meteorological data of Komaki. Between April 1, 2012 and March 31, 2015, 1351 stroke patients admitted to Komaki City Hospital were enrolled in this study. These patients were classified into hypertensive intracranial hemorrhage(ICH), subarachnoid hemorrhage(SAH), and cerebral infarction(CI). Meteorological conditions such as season, weather, temperature, atmospheric pressure, and humidity were evaluated as factors that affect stroke onset. The incidence of ICH was lower in summer, and the minimum daily temperature was significantly related to ICH onset. The incidence of SAH was significantly related to the temperature difference compared to that on the previous day. When the mean temperature was lower than that on the previous day, the incidence of SAH was higher. No meteorological factor was found to affected the incidence of CI in this study. This study indicated that seasons and temperatures affect stroke onset.

Predicting the Severity of Motor Vehicle Accident Injuries in Adana-Turkey Using Machine Learning Methods and Detailed Meteorological Data

Traffic accidents are among the most important issues facing every nation in the world as they cause many deaths and injuries as well as economic losses every year. In this study, the traffic accidents that took place in Adana, have been classifiedaccording to injury severity (i.e. fatal or non-fatal) and the factors affecting the accident outcome are investigated. The study included the traffic accident reports kept by Regional Traffic Division and the weather data provided by the Regional Directorate of Meteorology during 2005-2015. Five major machine learning methods (i.e. k-Nearest Neighbor, Naive Bayes, Multilayer Perceptron, Decision Tree, Support Vector Machine) and one statistical method, Logistic Regression, were employed for prediction models and performances of the models as well as the effective parameters were compared. The main objective of the study is to determine how important weather and other phenomena are for the occurrence of traffic accidents. Decision Tree, k-Nearest Neighbor and Multilayer Perceptron based models yielded higher accuracy in classification of accidents compared to other models. Furthermore, in Area Under Curve based analysis of factor importance, it was determined that Mean Cloudiness, Existence of Traffic Control and Ground Surface Temperature had higher positive effects, while Maximum Temperature and Weather (kept by traffic officers) parameters decreased the accuracy of models.

Vertical Deformation Monitoring of the Suspension Bridge Tower Using GNSS: A Case Study of the Forth Road Bridge in the UK

The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts to reveal its deformation mechanism. Firstly, we designed a strategy for high-precision GNSS data processing aiming at facilitating deformation extraction and analysis. Then, 33 months of vertical deformation time series of the southern tower of the Forth Road Bridge (FRB) in the UK were processed, and the accurate subsidence and the parameters of seasonal signals were estimated based on a classic function model that has been widely studied to analyse GNSS coordinate time series. We found that the subsidence rate is about 4.7 mm/year, with 0.1 mm uncertainty. Meanwhile, a 15-month meteorological dataset was utilised with a thermal expansion model (TEM) to explain the effects of seasonal signals on tower deformation. The amplitude of the annual signals correlated quite well that obtained by the TEM, with the consistency reaching 98.9%, demonstrating that the thermal effect contributes significantly to the annual signals. The amplitude of daily signals displays poor consistency with the ambient temperature data. However, the phase variation tendencies between the daily signals of the vertical deformation and the ambient temperature are highly consistent after February 2016. Finally, the potential contribution of the North Atlantic Drift (NAD) to the characteristics of annual and daily signals is discussed because of the special geographical location of the FRB. Meanwhile, this paper emphasizes the importance of collecting more detailed meteorological and other loading data for the investigation of the vertical deformation mechanism of the bridge towers over time with the support of GNSS.

The Daily Erosion Project – daily estimates of water runoff, soil detachment, and erosion

AbstractWater runoff and sediment transport from agricultural uplands are substantial threats to water quality and sustained crop production. To improve soil and water resources, farmers, conservationists, and policy‐makers must understand how landforms, soil types, farming practices, and rainfall interact with water runoff and soil erosion processes. To that end, the Iowa Daily Erosion Project (IDEP) was designed and implemented in 2003 to inventory these factors across Iowa in the United States. IDEP utilized the Water Erosion Prediction Project (WEPP) soil erosion model along with radar‐derived precipitation data and government‐provided slope, soil, and management information to produce daily estimates of soil erosion and runoff at the township scale (93 km2 [36 mi2]). Improved national databases and evolving remote sensing technology now permit the derivation of slope, soil, and field‐level management inputs for WEPP. These remotely sensed parameters, along with more detailed meteorological data, now drive daily WEPP hillslope soil erosion and water runoff estimates at the small watershed scale, approximately 90 km2 (35 mi2), across sections of multiple Midwest states. The revisions constitute a substantial improvement as more realistic field conditions are reflected, more detailed weather data are utilized, hill slope sampling density is an order of magnitude greater, and results are aggregated based on surface hydrology enabling further watershed research and analysis. Considering these improvements and the expansion of the project beyond Iowa it was renamed the Daily Erosion Project (DEP). Statistical and comparative evaluations of soil erosion simulations indicate that the sampling density is adequate and the results are defendable. The modeling framework developed is readily adaptable to other regions given suitable inputs. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy

In recent years, the diffusion of electric plants based on renewable non-dispatchable sources has caused large imbalances between the power generation schedule and the actual generation in real time operations, resulting in increased costs for dispatching electric power systems. Although this type of source cannot be programmed, their production can be predicted using soft computing techniques that consider weather forecasts, reducing the imbalance costs paid to the transmission system operator (TSO). The problem is mainly that the forecasting procedures used by the TSO, distribution system operator (DSO) or large producers and they are too expensive, as they use complex algorithms and detailed meteorological data that have to be bought, this can represent an excessive charge for small-scale producers, such as prosumers. In this paper, a cheap photovoltaic (PV) production forecasting method, in terms of reduced computational effort, free-available meteorological data and implementation is discussed, and the economic results regarding the imbalance costs due to the utilization of this method are analyzed. The economic analysis is carried out considering several factors, such as the month, the day type, and the accuracy of the forecasting method. The user can utilize the implemented method to know and reduce the imbalance costs, by adopting particular load management strategies.

A Comparative Study of Potential Evapotranspiration Estimation by Eight Methods with FAO Penman–Monteith Method in Southwestern China

Potential evapotranspiration (PET) is crucial for water resources assessment. In this regard, the FAO (Food and Agriculture Organization)–Penman–Monteith method (PM) is commonly recognized as a standard method for PET estimation. However, due to requirement of detailed meteorological data, the application of PM is often constrained in many regions. Under such circumstances, an alternative method with similar efficiency to that of PM needs to be identified. In this study, three radiation-based methods, Makkink (Mak), Abtew (Abt), and Priestley–Taylor (PT), and five temperature-based methods, Hargreaves–Samani (HS), Thornthwaite (Tho), Hamon (Ham), Linacre (Lin), and Blaney–Criddle (BC), were compared with PM at yearly and seasonal scale, using long-term (50 years) data from 90 meteorology stations in southwest China. Indicators, viz. (videlicet) Nash–Sutcliffe efficiency (NSE), relative error (Re), normalized root mean squared error (NRMSE), and coefficient of determination (R2) were used to evaluate the performance of PET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in PET estimation varied among regions; HS, PT, and Abt overestimated PET, while others underestimated. In Sichuan basin, Mak, Abt and HS yielded similar estimations to that of PM, while, in Yun-Gui plateau, Abt, Mak, HS, and PT showed better performances. Mak performed the best in the east Tibetan Plateau at yearly and seasonal scale, while HS showed a good performance in summer and autumn. In the arid river valley, HS, Mak, and Abt performed better than the others. On the other hand, Tho, Ham, Lin, and BC could not be used to estimate PET in some regions. In general, radiation-based methods for PET estimation performed better than temperature-based methods among the selected methods in the study area. Among the radiation-based methods, Mak performed the best, while HS showed the best performance among the temperature-based methods.

Determination of crop and soil evaporation coefficients for estimating evapotranspiration in a paddy field

Accurate estimation of evapotranspiration is important in efficient water management for improving water use efficiency. In order to obtain evapotranspiration and evaporation beneath the canopy using the Food and Agriculture Organization (FAO) method, pan evaporation was used instead of reference evapotranspiration calculated by the Penman-Monteith equation with detailed meteorological data. The total crop coefficient and soil evaporation coefficient were determined using actual measured daytime evapotranspiration and evaporation by the Bowen ratio energy balance and lysimeter, respectively, in a rice paddy field in Japan. The average evapotranspiration was 5.3 mm/d, 4.4 mm/d, 7.4 mm/d and 6.3 mm/d and crop coefficient was 0.79, 1.18, 1.01 and 0.86 for the initial stage, development stage, middle-season stage and late-season stage, respectively. The evaporation was low and almost constant with an average value around 0.77 mm/d when the leaf area index (LAI) reached 3. The proposed average crop coefficients for different growing stages were applied to estimate daytime evapotranspiration and found suitable. A simple soil water evaporation coefficient model was developed using leaf area index for practical use and it was found that it could accurately estimate evaporation. Keywords: evapotranspiration, evaporation, paddy field, lysimeter, leaf area index (LAI), Bowen ratio energy balance DOI: 10.25165/j.ijabe.20171004.2290 Citation: Yan H F, Zhang C, Oue H, Peng G J, Darko R O. Determination of crop and soil evaporation coefficients for estimating evapotranspiration in a paddy field. Int J Agric & Biol Eng, 2017; 10(4): 130–139.