Estimating effective net radiation for a mountainous Watershed

Net radiation estimates are frequently required in watershed research, e.g., in calculating evapotranspiration and snowmelt. In mountainous areas, the effective net radiation, i.e., the horizontal projection of the flux through a surface parallel to the slope, is a more accurate measure of the available energy than that measured with a horizontal sensor. In a non-homogeneous area, however, a basin average of effective net radiation is difficult to estimate.The annual curves for net and global solar radiation under clear skies at one point in the Marmot Creek Experimental Watershed in Alberta, Canada, show variations from 55 to 650 ly day^-1 for net radiation, and from 100 to 760 ly day^-1 for global radiation. A factor to convert measured net radiation at the point to a basin average of effective net radiation is obtained by comparing these curves with that for effective clear sky global radiation for the basin, and by considering the ratio of net to global radiation over the various types of vegetation in the basin. This conversion factor varies throughout the year with the elevation of the Sun and the basin albedo, ranging from a maximum of 1.27 in December to a minimum of 0.93 in April, and averaging 1.06 for the year.     

Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.     

Estimating hourly net radiation for leaf wetness duration using the Penman-Monteith equation

Summary Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity (RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn. The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to 1.02 and R² from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates were between 1.0 and 1.5 h, which is sufficient for use in plant disease management schemes. Authors’ addresses: Paulo C. Sentelhas, Agrometeorology Group, Department of Exact Sciences, ESALQ, University of S?o Paulo, P.O. Box 9, 13418-900, Piracicaba, SP, Brazil; Terry J. Gillespie, Agrometeorology Group, Department of Land Resource Science, Ontario Agricultural College, University of Guelph, NIG-2W1, Guelph, ON, Canada.     

Spatiotemporal modeling of monthly soil temperature using artificial neural networks

Soil temperature data are critical for understanding land–atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.     

An approach to the estimation of surface net radiation in mountain areas using remote sensing and digital terrain data

Summary An approach is proposed to estimate the net radiation load at the surface in mountain areas. The components of the radiation balance are derived using a radiative transfer model combined with remotely sensed and digital terrain data. Integrated shortwave (0.28–6.00 µm) and longwave irradiances (3.00–100.00 µm) are computed using a modified version of the Practical Improved Flux Method (PIFM) of Zdunkowski et al. (1982) which makes use of digital topographic data in order to account for slope, aspect, and shading effects. Surface albedo and thermal exitance estimates are obtained using Landsat Thematic Mapper (TM) and digital terrain data combined with the LOWTRAN 7 atmospheric model (Kneizys et al., 1988). LOWTRAN 7 is utilized together with a set of terrain modeling programs to compute direct and diffuse sky irradiance for selected TM bands, and to remove atmospheric effects within the visible, near-infrared, mid-infrared, and thermal infrared bands of Landsat TM. Model testing in the Colorado alpine show a generally good correspondence between estimated values and field measurements obtained over comparable tundra surfaces during several field campaigns. The method is finally used to produce 1) maps of the components of the radiation balance at the time of Landsat TM overflight and 2) maps of daily totals of shortwave irradiance and net shortwave radiation on a typical summer day in the Colorado Rocky Mountains (i.e. including cloud cover effects). The results indicate that the proposed approach is particularly suitable for obtaining estimates of net radiation at the surface from the toposcale to the regional scale.With 6 Figures     

Relative importance of parameters affecting wind speed prediction using artificial neural networks

In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz.     

Estimation of net radiation at sea

Abstract

Measurement of net radiation at sea is very difficult whereas, the measurement of global solar radiation or total downward radiation is much less complicated. Hence the dependencies of net radiation on global solar radiation and total downward radiation are studied from hourly Canadian GATE data.

Results show that net radiation can be estimated from measurements of incoming solar radiation or total downward radiation by empirical formulae to an accuracy comparable to that of measurement. However, these formulae must be established from measurements.     

云天地表总辐射和净辐射瞬时值的计算方法

为减少计算机时,满足实时预报要求,全球数值预报模式中的辐射计算频率通常设定为三小时。这样处理会大大减少计算量,但也同时导致较大辐射日变化偏差,并影响模式对地面能量平衡,对流及降水的模拟。为改进这一缺陷,我们开发了一种辐射快速计算方案,可用于计算瞬时地面太阳总辐射和净辐射,使到达地面的太阳辐射计算可与模式积分同步进行,从而改善地面太阳辐射日变化模拟。本文介绍云天的计算方法。该方案所用的输入变量均为预报模式或卫星观测所能提供的量。结果表明：该方案既可用于数值预报模式也可利用观测资料独立计算地面太阳辐射。经与美国能源部大气辐射观测资料检验,该方案的精度很高,地面总辐射瞬时值的平均计算误差小于7%。     

The total solar radiation time series simulation in Athens, using neural networks

Summary  The present study describes a neural network approach for modeling and making short-term predictions on the total solar radiation time series. The future hourly values of total solar radiation for several years are predicted, by extracting knowledge from their past values, using feedforward backpropagation neural networks. The results are tested using various sets of non training measurements, the findings are very encouraging and the model is found able to simulate the future values of total solar radiation time series based on their past values. “Multi-lag” output predictions are performed using the predicted values to the input database in order to model future total solar radiation values with sufficient accuracy. Furthermore, an autoregressive model is developed for analysing and representing the total solar radiation time series. The predicted values of solar radiation are compared with the observed data series and it was found that the neural network approach leads to better predictions than the AR model. Received November 22, 1999 Revised February 17, 2000     

晴天地表总辐射和净辐射瞬时值计算方法

以较为精确的大气辐射传输模式为基础,研制出晴天地表总辐射和净辐射瞬时值的计算方案。与以往的经验计算方法不同,该方案将辐射传输带模式的思路引入地面太阳辐射计算,并尽可能将大气中吸收和散射物质对太阳辐射的影响考虑进去,从而使该方法具有较好的精确性和普适性。在此基础上采用了Kokhanovsky等人提出的大气气溶胶反射率和透过率参数化方案,使得气溶胶对地面总辐射和净辐射的影响得到较好的处理。采用的自变量都是数值预报模式或卫星观测能提供的气象要素,因此该方案即可用于数值预报模式或陆面过程模式计算地表辐射平衡,又可以利用卫星观测或再分析资料估算地面太阳能资源分布。利用美国能源部三个大气辐射观测站点2005年全年的观测资料及欧洲宇航局提供的卫星反演气溶胶资料对计算方案进行了检验。结果表明,该方法十分精确,所有点的平均相对误差都小于6%,误差的均方差都小于0.3 W•m^-2。     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

Estimating the world's potentially available cropland using a bottom-up approach

Previous estimates of the land area available for future cropland expansion relied on global-scale climate, soil and terrain data. They did not include a range of constraints and tradeoffs associated with land conversion. As a result, estimates of the global land reserve have been high. Here we adjust these estimates for the aforementioned constraints and tradeoffs. We define potentially available cropland as the moderately to highly productive land that could be used in the coming years for rainfed farming, with low to moderate capital investments, and that is not under intact mature forests, legally protected, or already intensively managed. This productive land is underutilized rather than unused as it has ecological or social functions. We also define potentially available cropland that accounts for trade-offs between gains in agricultural production and losses in ecosystem and social services from intensified agriculture, to include only the potentially available cropland that would entail low ecological and social costs with conversion to cropland. In contrast to previous studies, we adopt a “bottom-up” approach by analyzing detailed, fine scale observations with expert knowledge for six countries or regions that are often assumed to include most of potentially available cropland. We conclude first that there is substantially less potential additional cropland than is generally assumed once constraints and trade offs are taken into account, and secondly that converting land is always associated with significant social and ecological costs. Future expansion of agricultural production will encounter a complex landscape of competing demands and tradeoffs.     

Estimating solar radiation as a function of air temperature using Fourier series

Summary Due to the periodicity of variations in solar radiation and air temperature their dimensionless values are expanded in Fourier series. Fourier coefficients were determined using data recorded by weather stations in various Egyptian cities. In terms of ambient air temperature, these coefficients are used to calculate solar radiation for specific geographic locations near to weather stations for which solar radiation data are unavailable.Estimates of solar radiation calculated by means of Fourier coefficients are compared with observed solar radiation data based on the number of hours of sunshine for the stations where there were records of sunshine duration. The comparison shows a good agreement between estimated and observed.With 10 Figures     

Effects of complex terrain on net surface longwave radiation in China

Net surface longwave radiation (NSLR) is one of key meteorological factors and is strongly influenced by cloud cover, surface temperature, humidity, and local micrometeorological conditions as well as terrain conditions. Realistically estimating NSLR is vitally important for understanding surface radiation balance and investigating micrometeorological factors of air pollution dispersion, especially in regions with complicated terrain. In this study, we proposed a distributed model for estimating NSLR by considering effects of complex local terrain conditions in China. Meteorological data (including mean temperature, relative humidity, and sunshine percentage) and observed NSLR data from 1993 to 2001 together with the digital elevation model data were used to parametrize the model and account for the effects of atmospheric factors and surface terrain factors according to the isotropic principle. The monthly NSLR during 1961–2000 was estimated at a spatial resolution of 1 km. Topographic analysis suggests that the distribution characteristics of NSLR with elevation or slope are consistent with those of field observations. In particular, the estimated NSLR is favorably comparable with site-level observations on the Tibetan Plateau (average relative error < 11%). Our results indicate that this model can describe microscale distribution features in mountainous areas in detail and that this improved approach can be used for NSLR spatial estimation in other regions with complicated terrain.     

Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km², by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t _47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t _47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.