Evaluation of Reference Evapotranspiration Methods in Arid,Semiarid, and Humid Regions

It is often necessary to find a simpler method in different climatic regions to calculate reference crop evapotranspiration (ET_o) since the application of the FAO‐56 Penman‐Monteith method is often restricted due to the unavailability of a comprehensive weather dataset. Seven ET_o methods, namely the standard FAO‐56 Penman‐Monteith, the FAO‐24 Radiation, FAO‐24 Blaney Criddle, 1985 Hargreaves, Priestley‐Taylor, 1957 Makkink, and 1961 Turc, were applied to calculate monthly averages of daily ET_o, total annual ET_o, and daily ET_o in an arid region at Aksu, China, in a semiarid region at Tongchuan, China, and in a humid region at Starkville, Mississippi, United States. Comparisons were made between the FAO‐56 method and the other six simple alternative methods, using the index of agreement D, modeling efficiency (EF), and root mean square error (RMSE). For the monthly averages of daily ET_o, the values of D, EF, and RMSE ranged from 0.82 to 0.98, 0.55 to 0.98, and 0.23 to 1.00 mm/day, respectively. For the total annual ET_o, the values of D, EF, and RMSE ranged from 0.21 to 0.91, ?43.08 to 0.82, and 24.80 to 234.08 mm/year, respectively. For the daily ET_o, the values of D, EF, and RMSE ranged from 0.58 to 0.97, 0.57 to 0.97, and 0.30 to 1.06 mm/day, respectively. The results showed that the Priestly‐Taylor and 1985 Hargreaves methods worked best in the arid and semiarid regions, while the 1957 Makkink worked best in the humid region.     

Climatic Controls on Watershed Reference Evapotranspiration Varied during 1961–2012 in Southern China

Reference evapotranspiration (ET_o) is an important hydrometeorological term widely used in understanding and projecting the hydrological effects of future climate and land use change. We conducted a case study in the Qinhuai River Basin that is dominated by a humid subtropical climate and mixed land uses in southern China. Long‐term (1961–2012) meteorological data were used to estimate ET_o by the FAO‐56 Penman–Monteith model. The individual contribution from each meteorological variable to the trend of ET_o was quantified. We found basin‐wide annual ET_o decreased significantly (p < 0.05) by 3.82 mm/yr during 1961–1987, due to decreased wind speed, solar radiation, vapor pressure deficit (VPD), and increased relative humidity (RH). However, due to the increased VPD and decreased RH, the ET_o increased significantly (p < 0.05) in spring, autumn, and annually at a rate of 2.55, 0.56, and 3.16 mm/yr during 1988–2012, respectively. The aerodynamic term was a dominant factor controlling ET_o variation in both two periods. We concluded the key climatic controls on ET_o have shifted as a result of global climate change during 1961–2012. The atmospheric demand, instead of air temperature alone, was a major control on ET_o. Models for accurately predicting ET_o and hydrological change under a changing climate must include VPD in the study region. The shifts of climatic control on the hydrological cycles should be considered in future water resource management in humid regions.     

Causes of decreased reference evapotranspiration and pan evaporation in the Jinghe River catchment, northern China

This study focused on the changes of reference evapotranspiration (ET₀) and pan evaporation (ET_pan) to study the impacts of climate change on the hydrological cycle in the Jinghe River catchment. Based on the Penman–Monteith equation, the ET₀ was calculated. The temporal trend and spatial distribution of ET₀ and E_pan measured with a 20-cm pan were examined at the 14 stations during 1957–2005. The effects of meteorological factors on the variation of ET₀ were determined by analyzing the trends in themselves with comparison between original climate and detrended climate scenarios and then their sensitivity to ET₀. Both the ET₀ and E_pan showed remarkable decreasing trends from 1957 to 2005 and their decreasing rate was 40.9 and 17.7 mm per 10 years, respectively. Trend analysis of meteorological factors exhibited that the reduction in ET₀ and ET_pan was principally caused by both significant decreases in wind speed and sunshine hours. Furthermore, the decreasing trend of ET₀ was mainly dominated by the significant decrease in wind speed with high sensitivity, to a less extent, by the decrease in net radiation. Although relative humidity is one of the most sensitive variables, its effect on ET₀ was negligible because of its temporal constancy. The contribution of wind speed reduction to decreased ET₀ has increased from 50 to 76.1%, but net radiation, by contrast, decreased from 50 to 23.9%.     

Forecasting the impact of meteorological parameters on air pollutants in Andhra Pradesh using machine learning techniques

In the 21st century, air pollution has emerged as a significant problem all over the globe due to a variety of activities carried out by humans, such as the acceleration of industrialization and urbanization. SO₂, NO₂, and NH₃ are the key components contributing to air pollution. Moreover, these air pollutants have a significant connection to several climatic characteristics, such as the speed of the wind, the relative humidity, the temperature, the amount of precipitation, and the surface pressure. As a result, machine learning (ML) is regarded as a more effective strategy for predicting air quality than more conventional approaches such as probability and statistics, among others. In the research, Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Multi-Linear Regression (MLR) algorithms are used to make predictions about air quality, and MSE (Mean Squared Error), RMSE (Root Mean Square Error), MAE (Mean Squared error), and R² are used to determine how accurate the predictions are.     

The use of an atmospheric dispersion model to determine influence regions in the Prince George,B.C. airshed from the burning of open wood waste piles

A means of determining air emission source regions adversely influencing the city of Prince George, British Columbia, Canada from potential burning of isolated piles of mountain pine beetle-killed lodge pole pine is presented. The analysis uses the CALPUFF atmospheric dispersion model to identify safe burning regions based on atmospheric stability and wind direction. Model results show that the location and extent of influence regions is sensitive to wind speed, wind direction, atmospheric stability and a threshold used to quantify excessive concentrations. A concentration threshold based on the Canada Wide PM_2.5 Standard is used to delineate the influence regions while Environment Canada's (EC) daily ventilation index (VI) is used to quantify local atmospheric stability. Results from the analysis, to be used by air quality meteorologists in assessing daily requests for burning permits, are presented as a series of maps delineating acceptable burning locations for sources placed at various distances from the city center and under different ventilation conditions. The results show that no burning should be allowed within 10 km of the city center; under poor ventilation conditions, no burning should be allowed within 20 km of the city center; under good ventilation conditions, burning can be allowed within 10–15 km of the city center; under good to fair ventilation conditions, burning can be allowed beyond 15 km of the city center; and if the wind direction can be reliably forecast, burning can be allowed between 5 and 10 km downwind of the city center under good ventilation conditions.     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.     

深圳大气污染特征模拟及环境容量估算

本文根据深圳市8 个监测站点2013 年的逐日PM₁₀ 和PM_2.5 浓度监测数据、气象数据,统计风向、风速、稳定度联合频率等,利用污染物在大气中输送扩散模式,由实测的浓度值反推出污染物的产生量或排放量的方法,重点分析龙华新区PM₁₀ 和PM_2.5 的污染特征,并依据环境目标值,估算该区域剩余环境容量。研究结果表明,龙华新区全年盛行东风、南风,其风频分别为16.7%、13.2%,风速约为1.6m/s,PM₁₀、PM_2.5 浓度均呈现出季节性变化,秋、冬季浓度值较高,尤其在10 月到次年1 月份,其排放强度主要受本地污染源的影响。除此以外,其西、北部的污染源对其污染物浓度有一定的影响。新区PM₁₀ 和PM_2.5 的剩余环境容量均呈现负值,尤其以PM_2.5 最为突出,须大力加强减排控制,以达到环境目标值     

The effects of sinusoidal leading edge of turbine blades on the power coefficient of horizontal-axis wind turbine (HAWT)

In order to improve the aerodynamic performance of horizontal-axis wind turbine (HAWT), a sinusoidal shape is applied to turbine blade. In this study, four types of modified blades were chosen based on variations in amplitude and wavelength of protuberance along the leading edge. Compared with the baseline model, the power coefficients (C_p) of HAWT with modified blades were improved, especially at low tip speed ratios. At low wind speed (V = 6 m/s), blades with short wavelength obtain significant improvement in C_p compared with the baseline model. As wind speed increases, this improvement decreases. In addition, turbine blade with large amplitude and long wavelength obtains better C_p values at higher wind speeds than lower ones, which have a great potential to be more superior at relatively higher wind speeds.     

Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1

Abstract: Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short‐term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km²) located in Southern Québec, Canada. Streamflow (Q), precipitations (rain R and snow S, or total P), temperature (T) and snow lying (A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow (Q_SM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long‐term averages, seasonal data, and Q_SM failed to improve the networks.     

A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing,Malaysia

Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R² determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m², and SSW is the most common wind direction for this data.     

Assessing Satellite and Ground‐Based Potential Evapotranspiration for Hydrologic Applications in the Colorado River Basin

This study evaluates a remotely sensed and two ground‐based potential evapotranspiration (PET) products for hydrologic application in the Upper Colorado River Basin (UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product (MODIS‐PET) is a continuous, daily time series with 250 m resolution derived using the Priestley‐Taylor (P‐T) equation. The MODIS‐PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System (NLDAS) and a Hargreaves PET derived from DAYMET variables (DAYMET‐PET; 1 km, daily). Compared to point‐scale PET computed using regional flux tower data, the MODIS‐PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET‐PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS‐PET values, while bias in NLDAS temperature, downward shortwave (SW↓), and downward longwave (LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS‐PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite‐based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB.     

SO2 Dispersion and Monthly Evaluation of the Industrial Source Complex Short-Term (ISCST32) Model at Mina Al-Fahal Refinery,Sultanate of Oman

Modeling of air pollutant dispersion has been undertaken for emissions of sulfur dioxide (SO₂) at the Mina Al-Fahal refinery in the Sultanate of Oman. The study was conducted during the period of November 1999 to October 2000. The Industrial Source Complex Short-Term (ISCST32) air pollution model was adopted to predict the ground level concentration of SO₂ in and around the refinery. The modeling results were validated against measured data during the study period. The comparison, based on the monthly average measurements, showed that the model underestimates the observed SO₂ concentrations. However, the predicted ground level concentrations of SO₂ during the months of September, October, November, and June were in better agreement with the observations. The predicted SO₂ values are presented in the form of concentration contours to determine the spatial distribution of SO₂ and to assess the impact on air quality over the survey area. Predicted SO₂ concentrations were found lower than the World Health Organisation (WHO) guideline value of 365 μg/m³, with the maximum ground level concentrations being found to occur relatively close to the sources of emission. Moreover, concentration contour patterns for the modeled area vary with changes in meteorological conditions. On the basis of this study, the refinery is not likely to cause any significant deterioration in air quality, and predicted concentrations of SO₂ are well below those likely to influence health.     

LAKE SHORE PROTECTION AGAINST WIND-GENERATED WAVES1

ABSTRACT: A method is reported for estimating the height of wind waves in any lake for a given wind condition. Maximum wind speeds from five climatological stations in and around Ilinois for the period of 1950–1972 were analyzed and the maximum wind speed for various durations and return periods were presented. Statistical analysis of wind wave data collected from Carlyle Lake indicated the Rayleigh distribution fitted the wave height distribution reasonably well and that the nondimensional energy spectra followed the (f/f_m)^-5 rule in the equilibrium range of frequencies. From a consideration of various forces and physical properties of riprap particles and water, a relationship was developed to estimate the stable weight of riprap particles. A practical design criteria is proposed to stabilize lake shores against wind waves.     

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with T_max, T_min, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

RELATIONSHIPS BETWEEN WIND VELOCITY AND UNDERWATER IRRADIANCE IN A SHALLOW LAKE (LAKE OKEECHOBEE,FLORIDA, USA)1

ABSTRACT: Relationships between wind velocity and the vertical light attenuation coefficient (K₀) were determined at two locations in a large, shallow lake (Lake Okeechobee, Florida, USA). K₀ was significantly correlated with antecedent wind conditions, which explained as much as 90 percent of the daily variation in K₀. Sub-surface irradiance began to change within 60 to 90 minutes of the time when wind velocity exceeded or dropped below a threshold value. Maximum one hour changes in K₀ were > 50 percent, however, 20 to 30 percent changes were more common. The magnitude of change in K₀ varied spatially based on differences in sediment type. K₀ never exceeded 2.8 at a location where bottom sediments were dominated by a mixture of coarse sand and shells. In comparison, K₀ exceeded 9 during episodic wind events where the bottom sediment was comprised of fine grain mud. Underwater irradiance data can be used to determine threshold wind velocity and account for the influence sediment type has on K₀. Once a threshold velocity has been established, the frequency, rate, and duration of expected change in underwater irradiance can be evaluated. This is critical information for scientists who are studying algal productivity or other light-related phenomena.     

Impacts on air pollution in urban areas

Environmental management involves controlling various forms of pollution to levels that do not pose a threat to the health of the people and the environment in general. This paper presents a framework to analyze sources of local air pollution in cities. Using an OLS model, an investigation is performed of the relationship among the concentrations of air pollutants [more precisely, concentrations of sulfur dioxide (SO₂), dust, nitric oxide (NO), nitrogen dioxide (NO₂), carbon oxide (CO) and ozone (O₃)], economic activities, and meteorology. Time series analysis leads to a model, that explains a high degree of the variance in the air pollution data. The model is applied to daily time series from three measurement stations in innsbruck, Austria. Estimation results of the model generally fit with the expected relations. Space heating influences SO₂, dust, and NO, while NO₂ levels are primarily affected by traffic. These results also indicate interdependent relations among the pollutants NO, NO₂, O₃, and CO; O₃ levels depend on temperature and sunshine.     

The impact of urban growth on regional air quality surrounding the Langat River Basin, Malaysia

Studies of air quality were carried out in the towns of Kajang, Nilai and Banting in the Langat River Basin, southern region of Kuala Lumpur to determine the status and trend of air quality. The determination of air quality was based on several parameters such as suspended solids with diameters less than 10???m (PM₁₀) and gaseous pollutants of sulphur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). Primary concentration data of air pollutants were compiled through fieldwork studies and combined with secondary data obtained from the regular monitoring data as collected by Alam Sekitar Malaysia Sdn. Bhd. (ASMA) on behalf of Malaysian Department of Environment (DOE) at their stations in Kajang and Nilai. Results showed that the average concentrations of PM₁₀, SO₂, NO₂, O_3, and CO at all sampling stations were still below the permissible values recommended by the Malaysian DOE. The level of gaseous pollutants of NO₂, O_3, and CO was recorded at statistically higher levels (p?<?0.05) than values recorded at the control station at Pangsun Recreational Area. These pollutants were suspected to have originated mainly from exhaust systems of motor vehicles. Data for the years 1996 to 2006 as obtained from ASMA showed long-term air quality trends of increasing O₃ and NO₂ concentrations in Kajang whilst concentrations of PM₁₀ recorded at both Kajang and Nilai stations were mostly expected coming from transboundary sources especially biomass burning and the development activities around the study areas.     

成都市温江边界层风场特征的研究

着眼于成都地区污染气象特征,利用温江2004年~2012年的地面观测资料及同期的探空资料,对该地区风场的统计特征进行了分析。结果表明:(1)从风频而言,温江地面风春、夏、秋3季均以偏北风为主,冬季则以东北风为主,因此,全年的主导风为偏北风。(2)从风速而言,区域静风和小风频率较高,占全年的68.7%;近10年来,温江年和4季的平均风速均呈现出减小趋势。(3)基于修正的帕斯奎尔稳定度分级法,利用幂指数律公式拟合了风随高度的变化,发现风廓线指数比国标值偏高,并随稳定度的增加而增大。(4)风速、稳定度联合频率的大值区主要出现在风速小于3m/s、稳定度为D~E类。上述研究成果对区域大气环境规划和工程治理具有重要的参考价值。     

ESTIMATING REFERENCE EVAPORATION IN THE CENTRAL VALLEY OF CALIFORNIA USING THE LINACRE MODEL1

ABSTRACT: The Linacre (1988) model for calculating evaporation from open water or well-watered surfaces only requires inputs of air temperature, latitude and elevation, and windspeed if it is available. The model was developed using data collected at a large number of sites in different climatic regions of the world, while independent tests of the model have shown it to be suitable for estimating evaporation in a variety of locations. This study was intended to contribute to the broad goal of evaluating temperature-based evaporation models for use in California by testing the Linacre model in the agriculturally intensive Central Valley. Observed monthly mean reference evaporation (E_o) and meteorological data for periods ranging up to 72 months were obtained from 25 California Irrigation and Management Information System (CIMIS) stations distributed throughout the Central Valley. Uncalibrated and calibrated Linacre models were used to estimate monthly mean reference evaporation, and the performance of each model was evaluated using indices that quantified the random and systematic errors and overall model performance. The accuracy of the radiation and ventilation components of the model were evaluated separately. The uncalibrated model was found to systematically overestimate E_o with most of the model error being attributed to the ventilation component. Calibration of the radiation and ventilation components removed most of the systematic model errors, and the root mean square error for monthly mean E_o was 0.676 mm day^?1 (16.8 percent of the mean observed value). (KEY TERMS: reference evaporation; Linacre model; irrigation scheduling.)