Modelling spatial,altitudinal and temporal variability of annual precipitation in mountainous regions: The case of the Middle Zagros,Iran

Relationships between precipitation and elevation are difficult to model for mountainous regions, due to complexities in topography and moisture sources. Attempts to model these relationships need to be tested against long-term location specific meteorological data, and hence require a case-study approach. This study uses artificial neural networks to model these relationships for the Middle of Zagros region, in semi-arid western Iran. Precipitation data for the region were collected for 1995–2007. Annual precipitation was designated as the target variable for the network, which additionally included variables significantly related to precipitation for the region, including longitude, latitude, elevation, slope, distance from the ridge, and relative distance from moisture. Long-term changes in annual precipitation for the region are investigated for 1961–2010. The artificial neural network (ANN) model explains 76% of the spatial variability of precipitation in the Middle Zagros. Precipitation predominantly increases with elevation on the windward slope, to a maximum height of 2500 m.asl, and thereafter either remains constant or decreases slowly to the ridge. Precipitation in the region has decreased significantly over the study period, with fluctuations driven by AO, NAO, ENSO and variability in the strength of pressure centers. Spectral analysis reveals significant oscillations of 2–4 and 5 yr periods, which correspond temporally with cycles in macro-scale circulation, ENSO and the Mediterranean Low pressure.     

Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998–2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316–0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983–2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.     

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.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004

This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950–2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature (T _min) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature (T _max) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of T _min decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of T _min and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century.     

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.     

Soil temperature modeling at different depths using neuro-fuzzy,neural network,and genetic programming techniques

The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models’ accuracy was also investigated. Including periodicity component in models’ inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.     

Estimating net radiation at surface using artificial neural networks: a new approach

This study describes the results of artificial neural network (ANN) models to estimate net radiation (R _n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R _n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R _n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m^?2 and correlation coefficient (R ²) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m^?2 and R ² between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R _n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R _n prediction using commonly measured meteorological parameters.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R ²) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.     

Spatial patterns and temporal trends of precipitation in Iran

Spatial patterns of monthly, seasonal and annual precipitation over Iran and the corresponding long-term trends for the period 1951–2009 are investigated using the Global Precipitation Climatology Centre gridded dataset. Results suggest that the spatial patterns of annual, winter and spring precipitation and the associated coefficients of variation reflect the role of orography and latitudinal extent between central-southern arid and semi-arid regions and northern and western mountainous areas. It is also shown that precipitation occurrence is almost regularly distributed within the year in northern areas while it is more concentrated in a few months in southern Iran. The spatial distribution of Mann–Kendal trend test (Z statistics) for annual precipitation showed downward trend in north-western and south-eastern Iran, whereas western, central and north-eastern exhibited upward trend, though not statistically significant in most regions. Results for winter and autumn revealed upward trend in most parts of the country, with the exception of north-western and south-eastern where a downward trend is observed; in spring and summer, a downward trend seems to prevail in most of Iran. However, for all seasons the areas where the detected trend is statistically significant are limited to a few spot regions. The overall results suggest that the precipitation is decreasing in spring and summer and increasing in autumn and winter in most of Iran, i.e. less precipitation during the warm season with a consequent intensification of seasonality and dryness of the country. However, since the detected trends are often not statistically significant, any stringent conclusion cannot be done on the future tendencies.     

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.     

神经网络模型预报湖北汛期降水量的应用研究

使用人工神经网络方法建立了湖北省汛期 (6～ 8月 )总降水量的短期气候预测模型 ,该神经网络模型的输入是汛期前期 (2～ 4月 )的北半球月平均 5 0 0 h Pa高度场、海平面气压场和太平洋海温场的扩展自然正交展开 (EEOF)的前几个主要模态的时间系数 ,输出了湖北汛期降水场的自然正交展开 (EOF)的前 2个主要模态的时间系数。41 a历史资料的交叉检验表明 :样本试验的预报技巧评分平均为 0 .2 4 6 ,虽然该模型对各年的预报效果仍存在一定的不稳定性 ,但它可为湖北汛期降水的短期气候预测提供一种具有明显统计预报正技巧的预报方法     

Precipitation drives interannual variation in summer soil respiration in a Mediterranean-climate, mixed-conifer forest

Predictions of future climate change rely on models of how both environmental conditions and disturbance impact carbon cycling at various temporal and spatial scales. Few multi-year studies, however, have examined how carbon efflux is affected by the interaction of disturbance and interannual climate variation. We measured daytime soil respiration (R _s) over five summers (June–September) in a Sierra Nevada mixed-conifer forest on undisturbed plots and plots manipulated with thinning, burning and their combination. We compared mean summer R _s by year with seasonal precipitation. On undisturbed plots we found that winter precipitation (PPT_w) explained between 77–96% of interannual variability in summer R _s. In contrast, spring and summer precipitation had no significant effect on summer R _s. PPT_w is an important influence on summer R _s in the Sierra Nevada because over 80% of annual precipitation falls as snow between October and April, thus greatly influencing the soil water conditions during the following growing season. Thinning and burning disrupted the relationship between PPT_w and R_s, possibly because of significant increases in soil moisture and temperature as tree density and canopy cover decreased. Our findings suggest that R _s in some moisture-limited ecosystems may be significantly influenced by annual snowpack and that management practices which reduce tree densities and soil moisture stress may offset, at least temporarily, the effect of predicted decreases in Sierran snowpack on R _s.     

Analysis of changes in the relationship between precipitation and streamflow in the Yiluo River, China

The Yiluo River is the largest tributary of the middle and lower Yellow River below the Sanmenxia Dam. Hydro-climatic variables have changed in the Yiluo River during the last half century. In this study, the trends in the annual precipitation and streamflow were analyzed in the Yiluo River during 1960–2006. The results indicated that both the annual precipitation and streamflow decreased significantly (P?<?0.05) from 1960 to 2006. Pettitt’s test shows that there was a change point for annual streamflow series around the year 1986 (P?<?0.05), while there was no change point identified for the annual precipitation series from 1960 to 2006. Annual streamflow decreased more significantly than annual precipitation since 1986. The relationship between the annual precipitation and streamflow presented a non-stationary state since 1986. This non-stationary relationship was mainly influenced by human activities. The average annual amount of water diversion from the Yiluo River increased significantly since the mid-1980s, accounting for 31.3 % of the total streamflow decrease from 1986 to 2006. In addition, land use/cover change (LUCC) contributed to 27.1–29.8 % of the decrease in streamflow. Human activities, including water diversion and LUCC, together contributed to 58.4–61.1 % of the decrease in streamflow and led to the non-stationary relationship between the annual precipitation and streamflow from 1986 to 2006. This study detected the changes in the precipitation–streamflow relationship and investigated the possible causes in the Yiluo River, which will be helpful for the understanding of the changes in streamflow in the Yellow River Basin.     

Least squares support vector machine for short-term prediction of meteorological time series

The prediction of meteorological time series plays very important role in several fields. In this paper, an application of least squares support vector machine (LS-SVM) for short-term prediction of meteorological time series (e.g. solar irradiation, air temperature, relative humidity, wind speed, wind direction and pressure) is presented. In order to check the generalization capability of the LS-SVM approach, a K-fold cross-validation and Kolmogorov–Smirnov test have been carried out. A comparison between LS-SVM and different artificial neural network (ANN) architectures (recurrent neural network, multi-layered perceptron, radial basis function and probabilistic neural network) is presented and discussed. The comparison showed that the LS-SVM produced significantly better results than ANN architectures. It also indicates that LS-SVM provides promising results for short-term prediction of meteorological data.     

GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions

Accurate estimation of reference evapotranspiration (ET₀) becomes imperative for better managing the more and more limited agricultural water resources. This study examined the feasibility of developing generalized artificial neural network (GANN) models for ET₀ estimation using weather data from four locations representing different climatic patterns. Four GANN models with different combinations of meteorological variables as inputs were examined. The developed models were directly tested with climatic data from other four distinct stations. The results showed that the GANN model with five inputs, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed, performed the best, while that considering only maximum temperature and minimum temperature resulted in the lowest accuracy. All the GANN models exhibited high accuracy under both arid and humid conditions. The GANN models were also compared with multivariate linear regression (MLR) models and three conventional methods: Hargreaves, Priestley–Taylor, and Penman equations. All the GANN models showed better performance than the corresponding MLR models. Although Hargreaves and Priestley–Taylor equations performed slightly better than the GANN models considering the same inputs at arid and semiarid stations, they showed worse performance at humid and subhumid stations, and GANN models performed better on average. The results of this study demonstrated the great generalization potential of artificial neural techniques in ET₀ modeling.     

An investigation of near surface wind speed trends in arid and semiarid regions of Iran

Wind plays an important role on the ecosystems and hydrological cycles besides other meteorological parameters such as temperature, precipitation, sunshine, and relative humidity. It strongly affects evapotranspiration, especially in arid and semiarid regions where there are serious problems in regard to water resource management. Evaluating the wind speed trend can provide good information for future agricultural planning. This study was conducted in order to investigate the wind speed trends over 24 synoptic meteorological stations located in arid and semiarid regions of Iran from 1975 to 2005. Near-surface wind speed was trended by nonparametric Mann–Kendall test spatially and temporally in three time scales (annual, seasonal, and monthly). Then, Sen’s slope estimator was used to determine the amount of the changes; furthermore, 10-year moving average low-pass filter was applied to show general trends. Finally, the smoothed time series derived from the mentioned filter were classified in three clusters for each time series and then mapped to show their spatial distribution pattern. Results showed insignificant and significant, increasing and decreasing trends during the surveyed time. Wind speeds in less than 50 % of stations changed statistically in all time scales, and in most cases, the frequency of the upward trends was more than that of downward ones. The spatial distribution of significant wind speed showed that the increase mostly occurred in eastern part. Clustering gave us the turning point around 1990. Clearly, when clusters were mapped, they indicated the same pattern as the Z value maps derived from Mann–Kendall test which meant that the outputs of the mentioned method confirmed the other one. As the wind speed trends in different stations likely to follow the previous evapotranspiration (ET₀) trend results in Iran, it confirms that wind speed was an effective parameter on ET₀, even though other parameters should be considered too.