Analysis of photosynthetically active radiation and applied parameterization model for estimating of PAR in the North China Plain

As photosynthetically active radiation (PAR) variability and PAR estimating methods play an important role in climate change and ecological process research, PAR variation trends and broadband global solar radiation (R _s ) ratios (PAR/R _s ) in the North China Plain (NCP) are examined using in situ PAR and R _s observed data for 2005 to 2011. The annual average PAR value found in the NCP is 22.9 mol m^?2 d^?1. The highest and lowest values were recorded at Changwu and Luancheng sites, respectively. The highest PAR/R _s value was found in Jiaozhouwan due to large water vapor volumes present in this area. PAR/R _s levels have increased in the NCP due to a decrease in fine aerosols and increase in water vapor concentration. From these analysis results, a parameterization model that can be applied to all sky conditions was checked. Empirical estimation model comparisons for obtaining PAR values indicate that model was least accurate when R _s was used independently. When the model included R _s, the clearness index (K _s) and the solar zenith angle, the model estimated PAR values with acceptable accuracy. A parameterization model was constructed by considering K _s and attenuation factors of PAR under clear weather conditions (ρ _clear). The improved parameterization model more accurately predicts values for local sites and for various observation sites.     

The climatological characteristics of photosynthetically active radiation in arid and semi-arid regions of China

Photosynthetically active radiation (Q _p ) is a key variable in models of net primary productivity and carbon cycle modelling. The relationship between broadband global solar radiation (R _s) and Q _p is investigated using 6?years of radiation data collected at 9 sites in arid and semi-arid regions of China. The dependence of Q _p /R _S on aerosol optical depth (AOD) and water vapour content are also discussed. A simple and efficient all-weather empirically derived model is developed to estimate Q _p from R _s. The annual average daily Q _p in arid and semi-arid areas is 29.9?±?11.7 and 27.3?±?10.1?mol?m^-2 d^-1, respectively. The highest value (31.9?±?11.3?mol?m^-2 d^-1) appears at Linze in the arid area. The lowest value (24.3?±?9.7?mol?m^-2 d^-1) appears at Ansai in the semi-arid area. The results show that the monthly variation of the Q _p /R _s ratio ranges from 1.69?±?0.19?mol?MJ^-1 in Aksu to 1.91?±?0.08?mol?MJ^-1 in Fukang. There is a small decreasing trend of the ratio of Q _p to R _s (PAR fraction) in arid and semi-arid regions because of the recent increase in fine aerosols. A simple and efficient empirically model suit for all-weather condition was developed to estimate Q _p from R _s. The slope a and intercept b of the regression line between estimated and measured values is close to 1 and zero, respectively. The application of the model to data collected from different locations also results in reasonable estimates of Q _p .     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

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.     

Measurements and estimations of photosynthetically active radiation in Beijing

The measurements of the photosynthetic photon flux density (Q_p) and other solar components have been in Beijing for 2-year period. The Q_p, broadband solar radiation (R_s) and the PAR fraction (Q_p / R_s) showed similar seasonal features that peaked in value during the Summer and reached their lowest value during the Winter. The PAR fraction ranged from 1.68 E M J^− 1 (Winter) to 1.98 E M J^− 1 (Summer) with an annual mean value of 1.83 E M J^− 1. The analysis of the hourly values also revealed a diurnal pattern, with higher values of Q_p and R_s being observed around noon. The PAR fraction increased from 1.78 to 1.89 μE J^− 1 (hourly values), as the sky conditions changed from clear to cloudy. The monthly mean hourly PAR fraction also revealed a diurnal variation, however, with lower values being observed around noon during most months. In November, the diurnal variations showed an opposite feature in comparison with other months. This is mainly attributed to the diurnal variations in the water vapor concentration.Two models were developed to estimate Q_p from R_s. The models consisted of atmospheric parameters that were found to cause substantial changes to the PAR fraction, such as sky clearness, brightness and path length. The estimated Q_p obtained via different equations was much closer to the observed values, with relative errors below 20% in Beijing. The Q_p and R_s data collected at three stations with featuring different climate types from within Beijing were used for verifying the transferability of the models. The correlation coefficients between the measured and estimated Q_p values decreased at these stations, and the relative error increased. This indicates that the estimation models need to be modified accordingly for the local climatic conditions.     

Dynamics of photosynthetic photon flux density (PPFD) and estimates in coastal northern California

Plants require solar radiation for photosynthesis and their growth is directly related to the amount received, assuming that other environmental parameters are not limiting. Therefore, precise estimation of photosynthetically active radiation (PAR) is necessary to enhance overall accuracies of plant growth models. This study aimed to explore the PAR radiant flux in the San Francisco Bay Area of northern California. During the growing season (March through August) for 2?years 2007?C2008, the on-site magnitudes of photosynthetic photon flux densities (PPFD) were investigated and then processed at both the hourly and daily time scales. Combined with global solar radiation (R _S) and simulated extraterrestrial solar radiation, five PAR-related values were developed, i.e., flux density-based PAR (PPFD), energy-based PAR (PARE), from-flux-to-energy conversion efficiency (fFEC), and the fraction of PAR energy in the global solar radiation (fE), and a new developed indicator??lost PARE percentages (LPR)??when solar radiation penetrates from the extraterrestrial system to the ground. These PAR-related values indicated significant diurnal variation, high values occurring at midday, with the low values occurring in the morning and afternoon hours. During the entire experimental season, the overall mean hourly value of fFEC was found to be 2.17???mol?J^?1, while the respective fE value was 0.49. The monthly averages of hourly fFEC and fE at the solar noon time ranged from 2.15 in March to 2.39???mol?J^?1 in August and from 0.47 in March to 0.52 in July, respectively. However, the monthly average daily values were relatively constant, and they exhibited a weak seasonal variation, ranging from 2.02?mol?MJ^?1 and 0.45 (March) to 2.19?mol?MJ^?1 and 0.48 (June). The mean daily values of fFEC and fE at the solar noon were 2.16?mol?MJ^?1 and 0.47 across the entire growing season, respectively. Both PPFD and the ever first reported LPR showed strong diurnal patterns. However, they had opposite trends. PPFD was high around noon, resulting in low values of LPR during the same time period. Both were found to be highly correlated with global solar radiation R _S, solar elevation angle h, and the clearness index K _t. Using the best subset selection of variables, two parametric models were developed for estimating PPFD and LPR, which can easily be applied in radiometric sites, by recording only global solar radiation measurements. These two models were found to be involved with the most commonly measured global solar radiation (R _S) and two large-scale geometric parameters, i.e., extraterrestrial solar radiation and solar elevation. The models were therefore insensitive to local weather conditions such as temperature. In particular, with two test data sets collected in USA and Greece, it was verified that the models could be extended across different geographical areas, where they performed well. Therefore, these two hourly based models can be used to provide precise PAR-related values, such as those required for developing precise vegetation growth models.     

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.     

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.     

Carbon and water flux responses to physiology by environment interactions: a sensitivity analysis of variation in climate on photosynthetic and stomatal parameters

Sensitivity of carbon uptake and water use estimates to changes in physiology was determined with a coupled photosynthesis and stomatal conductance (g _s) model, linked to canopy microclimate with a spatially explicit scheme (MAESTRA). The sensitivity analyses were conducted over the range of intraspecific physiology parameter variation observed for Acer rubrum L. and temperate hardwood C₃ (C₃) vegetation across the following climate conditions: carbon dioxide concentration 200–700 ppm, photosynthetically active radiation 50–2,000 μmol m^?2 s^?1, air temperature 5–40 °C, relative humidity 5–95 %, and wind speed at the top of the canopy 1–10 m s^?1. Five key physiological inputs [quantum yield of electron transport (α), minimum stomatal conductance (g ₀), stomatal sensitivity to the marginal water cost of carbon gain (g ₁), maximum rate of electron transport (J _max), and maximum carboxylation rate of Rubisco (V _cmax)] changed carbon and water flux estimates ≥15 % in response to climate gradients; variation in α, J _max, and V _cmax input resulted in up to ~50 and 82 % intraspecific and C₃ photosynthesis estimate output differences respectively. Transpiration estimates were affected up to ~46 and 147 % by differences in intraspecific and C₃ g ₁ and g ₀ values—two parameters previously overlooked in modeling land–atmosphere carbon and water exchange. We show that a variable environment, within a canopy or along a climate gradient, changes the spatial parameter effects of g ₀, g ₁, α, J _max, and V _cmax in photosynthesis-g _s models. Since variation in physiology parameter input effects are dependent on climate, this approach can be used to assess the geographical importance of key physiology model inputs when estimating large scale carbon and water exchange.     

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Daily global solar irradiation (R _s) is one of the main inputs in environmental modeling. Because of the lack of its measuring facilities, high-quality and long-term data are limited. In this research, R _s values were estimated based on measured sunshine duration and cloud cover of our synoptic meteorological stations in central and southern Iran during 2008, 2009, and 2011. Clear sky solar irradiation was estimated from linear regression using extraterrestrial solar irradiation as the independent variable with normalized root mean square error (NRMSE) of 4.69 %. Daily R _s was calibrated using measured sunshine duration and cloud cover data under different sky conditions during 2008 and 2009. The 2011 data were used for model validation. According to the results, in the presence of clouds, the R _s model using sunshine duration data was more accurate when compared with the model using cloud cover data (NRMSE = 11. 69 %). In both models, with increasing sky cloudiness, the accuracy decreased. In the study region, more than 92 % of sunshine durations were clear or partly cloudy, which received close to 95 % of total solar irradiation. Hence, it was possible to estimate solar irradiation with a good accuracy in most days with the measurements of sunshine duration.     

Sensitivity of ADOM dry deposition velocities to input parameters: A comparison with measurements for SO2 and NO2 over three land use types

Abstract

Dry deposition velocity measurements of SO₂ and NO₂ over a deciduous forest, a carrot field and a snow surface are compared with estimates obtained from the dry deposition module in the regional Eulerian Acid Deposition and Oxidant Model (ADOM). The comparison with measurements taken in the fall and winter shows large model overestimates, sometimes as large as a factor of 5. The NO₂ estimates are particularly poor and support existing evidence that models that employ the constant flux assumption for NO₂ are inadequate. The canopy and the snow surface resistances are the largest contributors to the total resistances for SO₂ and NO₂, except for situations in which some of the snow turns into liquid water, when the aerodynamic resistance becomes important.

Increasing the magnitudes, taken from measurements, of the ADOM original values for the stomatal, cuticle, ground and snow resistances and decreasing the NO₂ mesophyll resistance and the Leaf Area Index (LAI) yield improved model results, particularly for SO₂, reducing the error by almost a factor of 5 at times. The new estimates compare favourably with those from a model that includes Wesely's canopy resistance parametrization. Over snow the NO₂ estimates are improved by as much as a factor of 6. Observed deposition velocities for SO₂ vary from 0 to 0.65 cm s^?2 over a deciduous forest, 0 to 0.60 cm s^?2 over a carrot field and are generally less than 0.05 cm s^?2 over snow.     

Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Recent trends in annual snowline variations in the northern wet outer tropics: case studies from southern Cordillera Blanca,Peru

TL-moments approach has been used in an analysis to identify the best-fitting distributions to represent the annual series of maximum streamflow data over seven stations in Johor, Malaysia. The TL-moments with different trimming values are used to estimate the parameter of the selected distributions namely: Three-parameter lognormal (LN3) and Pearson Type III (P3) distribution. The main objective of this study is to derive the TL-moments (t ₁,0), t ₁?=?1,2,3,4 methods for LN3 and P3 distributions. The performance of TL-moments (t ₁,0), t ₁?=?1,2,3,4 was compared with L-moments through Monte Carlo simulation and streamflow data over a station in Johor, Malaysia. The absolute error is used to test the influence of TL-moments methods on estimated probability distribution functions. From the cases in this study, the results show that TL-moments with four trimmed smallest values from the conceptual sample (TL-moments [4, 0]) of LN3 distribution was the most appropriate in most of the stations of the annual maximum streamflow series in Johor, Malaysia.     

Radiometrically determined skin temperature and scalar roughness to estimate surface heat flux. Part I: Parameterization of radiometric scalar roughness

A series of experiments carried out in a pasture field during a growing season, allowed a radiometric determination of the scalar roughness for sensible heatz _oh,r . The values ofz _oh,r are shown to vary over the range of 10^–1–10^–7m both diurnally and seasonally, and an existing theoretical model for the estimation of scalar roughness for sensible heat is found to be inappropriate for the precise estimation ofz _oh,r . To parameterizez _oh,r better, a multiple regression analysis was performed, with predictor candidates such as solar elevation, solar radiationR _s , leaf area index LAI, canopy height, the ratio of the solar radiation and the extraterrestrial radiationR _s /R _e , the ratio of the direct and the total solar radiationR _d /R _s , and the roughness Reynolds number among others. The best regression equation which usesR _s , LAI,R _s /R _e , andR _d /R _s is derived withr=0.75; with smaller numbers of predictors, values ofr tend to deteriorate gradually down tor=0.52 when only one predictor, LAI, was incorporated into the equation.     

On the vorticity and energy budgets of the cold vortex in Northeast China: a case study

This study examines the vorticity budgets, turbulent extended exergy and kinetic energy evolution equations to investigate the major dynamical and energy conversion processes contributing to the initiation and intensification of the cold vortex over Northeast China that occurred during June 19–22, 2009. The results show that the cyclonic vorticity was initiated in the lower troposphere due to the intense convergence of horizontal winds. The growth of cyclonic vorticity in the middle troposphere is mainly due to the vertical transportation of the vorticity, yet the increase of cyclonic vorticity in the upper troposphere primarily results from the horizontal advection of vorticity. Of special interest in this study is the evaluation of the role of thermal advections in the baroclinic development of the cold vortex. The results indicate that the rising of the air over relatively warm areas and the sinking of the air in relatively cold regions are favorable for releasing turbulent extended exergy $ \left( {e_{\text{t}} } \right) $ , which is later converted to turbulent kinetic energy $ \left( {k_{\text{t}} } \right) $ , and this process occurs during the initiation and intensification of the cold vortex. In addition, barotropic energy conversion is another important process that contributes to the growth of k _t, and it strengthens gradually after the initiation of the cold vortex. Other than frictional consumption, the flux of k _t in the vertical direction also depletes some of k _t. The fluxes of e _t, baroclinic energy conversions and diabatic generations are favorable factors for the growth of e _t, whereas it decreases with time as a result of a large amount of e _t that is released. Most of the energy conversion processes, including the baroclinic and the barotropic energy transformations and the energy conversions from e _t to k _t, as well as the fluxes of e _t, are stronger in the lower troposphere than the other areas during the formation of the cold vortex. This accounts for the initiation of the cyclonic vorticity in the lower troposphere. Finally, the fact that the turbulent extended exergy releases primarily in the middle troposphere through the vertical thermal circulation is consistent with our understanding based on the vorticity budget analyses.     

Estimating crop top microclimates from weather station data

Abstract

A mathematical model (Microsim) was developed to estimate the microclimate at the top of nearby crops using inputs of weather station data and some knowledge about crop characteristics, such as height, albedo, and leaf area index. The model was tested using data measured simultaneously over a weather station and over each of two crops ‐ corn and soybean. Temperatures at the top of unstressed, uniform crops on level terrain within 1600 m of a recording weather station were estimated within 1.0° C 96% of the time for a corn crop and 92% of the time for a soybean crop. Winds at crop top were estimated within 0.4 m s^?1 92% of the time for corn and 100% of the time for soybean. Energy balance flux density estimates for the corn crop resulted in correlation coefficients of r > 0.89 for each of R_n, LE, H and G. microsim worked well under atmospheric conditions that ranged from very stable to unstable.

An enhancement was made to the model to describe wind and temperature profiles based on the complete fetch characteristics of the sites. This resulted in significantly better wind estimates, but had the disadvantage of requiring more information about the crop and weather station surroundings.     

Comparative measurements and seasonal variations in energy and carbon exchange over forest and pasture in South West Amazonia

Summary Comparative measurements of radiation flux components and turbulent fluxes of energy and CO₂ are made at two sites in South West Amazonia: one in a tropical forest reserve and one in a pasture. The data were collected from February 1999 to September 2002, as part of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). During the dry seasons, although precipitation and specific humidity are greatly reduced, the soil moisture storage profiles down to 3.4m indicate that the forest vegetation continues to withdraw water from deep layers in the soil. For this reason, seasonal changes observed in the energy partition and CO₂ fluxes in the forest are small, compared to the large reductions in evaporation and photosynthesis observed in the pasture. For the radiation balance, the reflected short wave radiation increases by about 55% when changing from forest to pasture. Combined with an increase of 4.7% in long wave radiation loss, this causes an average reduction of 13.3% in net radiation in the pasture, compared to the forest. In the wet season, the evaporative fraction (E/R_n) at the pasture is 17% lower than at the forest. This difference increases to 24% during the dry season. Daytime CO₂ fluxes are 20–28% lower (in absolute values) in the pasture compared to the forest. The night-time respiration in the pasture is also reduced compared to the forest, with averages 44% and 57% lower in the wet and dry seasons, respectively. As the reduction in the nocturnal respiration is larger than the reduction in the daytime uptake, the combined effect is a 19–67% higher daily uptake of CO₂ in the pasture, compared to the forest. This high uptake of CO₂ in the pasture site is not surprising, since the growth of the vegetation is constantly renewed, as the cattle remove the biomass.     

Soil CO2 efflux, carbon dynamics, and change in thermal conditions from contrasting clear-cut sites during natural restoration and uncut larch forests in northeastern China

With the implementation of the Chinese Natural Forest Conservation Program (NFCP) in 1998, over millions of hectares of forest in northeastern China have been protected through natural restoration (closure of hills). The impact of this program on the carbon budget of soil has not been evaluated until now. This paper presents results from a 6-year study of total CO₂ efflux from both soil and litter (R _total), CO₂ flux from soil (R _soil), soil organic matter (SOM), soil microbe density, and litter input and root biomass at an uncut larch (Larix gmelinii) forest and at a natural restoration site. The natural restoration area is a clear-cut site that was formerly part of a continuous portion of the uncut larch forest. Our objectives were to: (1) quantify the magnitude of CO₂ efflux from typical sites in northeastern China; (2) explore the changes in thermal conditions, SOM, and annual CO₂ flux during the 6-year natural restoration, and (3) evaluate the impact of NFCP on soil carbon processes. The annual R _soil at the clear-cut site (58.6–68.2 mol m^???2 year^???1) was 113.6–228.4% (mean 141.5%) higher than that at the uncut larch site (29.6–58.4 mol m^???2 year^???1). At the same time, annual CO₂ from litter at the clear-cut site (2.0–14.2 mol m^???2 year^???1) was only 23.5–84.5% (mean 52.5%) of that at the uncut larch site (5.4–16.8 mol m^???2 year^???1). SOM at the surface layer of the clear-cut site was 75% of that at the uncut larch site, but the soil microbial biomass (carbon) at the clear-cut site was much higher than that at the larch site (p?<?0.05). The percentage of bacteria, fungi and actinomycetes also were largely different between both sites. Natural restoration at the clear-cut site strongly affected thermal conditions. Although the soil temperature (T _soil) and effective accumulated $T_{\rm soil} > 0^{\circ}$ C at the clear-cut site was much higher, the temperature sensitivity (Q ₁₀) was much lower than that at the uncut larch site, and their differences decreased linearly from 2001 to 2006 (p?<?0.05). Moreover, Q ₁₀ at the clear-cut site significantly increased with the progress of natural restoration, which diminished the Q ₁₀ difference between the two sites (slope?=???0.2792, r ²?=?0.4744, p?<?0.05). These data imply that the NFCP natural restoration process has positively recovered the thermal condition of the clear-cut site to the level of uncut larch forest during the 6-year period. However, linear regression analysis showed that the 6-year natural restoration only slightly affected the annual soil CO₂ efflux and SOM at both sites, and also did not diminish the differences between the two sites (p?>?0.10), indicating that a much longer time is necessary to restore the soil carbon in the clear-cut site.     

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.