Landscape transformation after irrigation development in and around a semi-arid wetland ecosystem

The objectives of this study are to determine the spatial and temporal land use/cover changes in a semi-arid agricultural basin (Develi Basin) after the implementation of an irrigation project and to understand how these changes affected the wetlands (Sultan Marshes) located in the basin. The changes were determined using multitemporal Landsat Thematic Mapper and Landsat 8 Operational Land Imager imagery taken in 1987, 1998, 2007, and 2013. The images were classified into six information classes (grasslands/shrublands, croplands, permanent wetlands, water bodies, barren, and urban/built-up) using a hybrid classification method. Post-classification change detection was applied to determine the changes between different years. Overall, the accuracy of the classified images ranged from 85 to 94%. Grasslands/shrublands covered the largest area in the basin (63% in 2013), followed by croplands (32% in 2013). The area covered by water bodies, permanent wetlands, barren, and urban/built-up was 5% (in 2013). From 1987 to 2013, croplands expanded by 56%, while grasslands/shrublands declined by 15%. The areas occupied by water bodies decreased by 88% and permanent wetlands decreased by 4%. Urban/built-up areas expanded by 140%. The hydrologic regime of the Sultan Marshes wetland changed, which resulted in declines in water volumes by 85% and in water inflows by 55% from 2000 to 2015. Climatic variations during the 1987–2013 period were low and there was no apparent trend in precipitation and air temperature, which ruled out climatic conditions as one of the drivers of wetland changes. Economic and institutional factors supported the expansion of irrigated agriculture and animal husbandry in the basin and accelerated the expansion of croplands and conversion to industrial and fodder crops and orchards from traditional non-irrigated crops. Expansion of croplands and irrigated agriculture were the major drivers of the changes in the Sultan Marshes.

     

Effect of ring width, cambial age, and climatic variables on the within-ring wood density profile of Norway spruce Picea abies (L.) Karst.

Studying the effects of dendrometric and climatic variables on within-ring density variations needs flexible and interpretable models. We described the within-ring density profile using a piecewise linear regression and studied its dependence on (1) dendrometric variables such as cambial age (CA) and ring width (RW), and (2) climatic variables. Based on X-ray analysis of 5,191 Norway spruce rings, a six-parameter three-segmented model was fitted on each within-ring density profile. Each model parameter was related to dendrometric and climatic variables using multiple linear regressions. Then, these models were assembled in two models relating the within-ring density profile to (1) RW and CA (model M1), and (2) climatic variables (model M2). M1 showed an R ² of 83.4 % and a residual standard error of 68.5 kg m^?3. Larger rings were associated with a decrease of latewood proportion and mean ring density. Rings with high CA were characterised by high maximum ring density. M2 showed an R ² of 60.9 % and a residual standard error of 94.9 kg m^?3. Warm summers increased the maximum ring density. Years with favourable water status decreased mean ring density. The piecewise linear models allowed the classification of within-ring density profiles in three types. Considering CA and RW led to the most explicative model since RW described many processes such as silviculture or climate. Earlywood density was impacted by water status while latewood density was conditioned by both temperatures and water status. Our approach may be used for the identification of within-ring density fluctuations or to assess the effects of silviculture or global change on the within-ring density profile.     

Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato

An artificial neural network (ANN) was used to analyze photometric features extracted from the digitized images of leaves from in vitro-regenerated potato plants for non-invasive estimation of chlorophyll content. A MATLAB®-based, feed-forward, backpropagation-type network was developed for an input layer (three input elements), with one hidden layer (one node) and one output layer representing the predicted chlorophyll content. A significant influence of training function during optimization of ANN modeling was observed. Among the 11 training functions tested, “trainlm” was found to be the best on the basis of comparative analysis of root-mean-square error (RMSE) at zero epoch. A significant correlation between the model-predicted and Soil-Plant Analysis Development (SPAD) meter-measured relative chlorophyll contents was obtained when the mean brightness ratio (rgb) parameters were used. Compared to a red (R), green (G), and blue (B) color space model, the rgb model exhibited better performance with a significant correlation (R ² = 0.85). Incorporation of photometric features, such as luminosity (L), blue (B)/L, and green (G)/L, with rgb failed to improve the performance of the network. The developed Intelligent image analysis (IIA) system was able to estimate in real time the chlorophyll content of in vitro-regenerated leaves for assessment of plant nutrient status during micropropagation.     

Comparative Assessment of the Artificial Neural Network and Response Surface Modelling Efficiencies for Biohydrogen Production on Sugar Cane Molasses

This study comparatively evaluates the modelling efficiency of the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN). Twenty-nine biohydrogen fermentation batches were carried out to generate the experimental data. The input parameters consisted of a concentration of molasses (50–150 g/l), pH (4–8), temperature (35–40 °C) and inoculum concentration (10–50 %). The obtained data were used to develop the RSM and ANN models. The ANN model was a committee of networks with a topology of 4-(6-10)-1 structured on multilayer perceptrons. RSM and ANN models gave R ² values of 0.75 and 0.91, respectively, with predicted optimum conditions of 150 g/l, 8 and 35 °C for molasses, pH and temperature, respectively, with differences in inoculum concentrations (10.11 and 15 %) for RSM and ANN, respectively. Upon validation, 15.12 and 119.08 % prediction errors on hydrogen volume were found for ANN and RSM, respectively. These findings suggest that ANN has greater accuracy in modelling the relationships between the considered process inputs for fermentative biohydrogen production and thus, is more reliable to navigate the optimization space.     

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R ²) and adjusted R ² values for the model. Although the R ² value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R ², adjusted R ², AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.     

多源多角度遥感数据反演森林叶面积指数方法

利用北京1号和Landsat多源数据组合成4个角度多波段数据集, 在考虑森林三维垂直分布特点的基础上, 结合INFORM几何光学与辐射传输混合模型, 通过聚类+神经元网络方式, 建立相应的多源多角度LAI反演模型。最后利用实地LAI测量数据和MODIS LAI产品, 对不同角度组合和噪声水平下的LAI反演结果进行验证。结果表明: 在保证数据质量的条件下, 通过增加角度可以提高森林的LAI反演精度, 最终R²=0.713, RMSE=0.957, 比单个角度的反演精度平均提高约20%。     

The application of Artificial Neural Network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands

Denitrification and its regulating factors are of great importance to aquatic ecosystems, as denitrification is a critical process to nitrogen removal. Additionally, a by-product of denitrification, nitrous oxide, is a much more potent greenhouse gas than carbon dioxide. However, the estimation of denitrification rates is usually clouded with uncertainty, mainly due to high spatial and temporal variations, as well as complex regulating factors within wetlands. This hampers the development of general mechanistic models for denitrification as well, as most previously developed models were empirical or exhibited low predictability with numerous assumptions. In this study, we tested Artificial Neural Network (ANN) as an alternative to classic empirical models for simulating denitrification rates in wetlands. ANN, multiple linear regression (MLR) with two different methods, and simplified mechanistic models were applied to estimate the denitrification rates of 2-year observations in a mesocosm-scale constructed wetland system. MLR and simplified mechanistic models resulted in lower prediction power and higher residuals compared to ANN. Although the stepwise linear regression model estimated similar average values of denitrification rates, it could not capture the fluctuation patterns accurately. In contrast, ANN model achieved a fairly high predictability, with an R² of 0.78 for model validation, 0.93 for model calibration (training), and a low root mean square error (RMSE) together with low bias, indicating a high capacity to simulate the dynamics of denitrification. According to a sensitivity analysis of the ANN, non-linear relationships between input variables and denitrification rates were well explained. In addition, we found that water temperature, denitrifying enzyme activity (DEA), and DO accounted for 70% of denitrification rates. Our results suggest that the ANN developed in this study has a greater performance in simulating variations in denitrification rates than multivariate linear regressions or simplified nonlinear mechanistic model.     

Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches

Phytoplankton biomass is an important indicator for water quality, and predicting its dynamics is thus regarded as one of the important issues in the domain of river ecology and management. However, the vast majority of models in river systems have focused mostly on flow prediction and water quality with very few applications to biotic parameters such as chlorophyll a (Chl a). Based on a 1.5-year measured dataset of Chl a and environmental variables, we developed two modeling approaches [artificial neural networks (ANN) and multiple linear regression (MLR)] to simulate the daily Chl a dynamics in a German lowland river. In general, the developed ANN and MLR models achieved satisfactory accuracy in predicting daily dynamics of Chl a concentrations. Although some peaks and lows were not predicted, the predicted and the observed data matched closely by the MLR model with the coefficient of determination (R ²), Nash–Sutcliffe efficiency (NS), and the root mean square error (RMSE) of 0.53, 0.53, and 2.75 for the calibration period and 0.63, 0.62, and 1.94 for the validation period, respectively. Likewise, the results of the ANN model also illustrated a good agreement between observed and predicted data during calibration and validation periods, which was demonstrated by R ², NS, and RMSE values (0.68, 0.68, and 2.27 for the calibration period, 0.55, 0.66 and 2.12 for the validation period, respectively). According to the sensitivity analysis, Chl a concentration was highly sensitive to dissolved inorganic nitrogen, nitrate–nitrogen, autoregressive Chl a, chloride, sulfate, and total phosphorus. We concluded that it was possible to predict the daily Chl a dynamics in the German lowland river based on relevant environmental factors using either ANN or MLR models. The ANN model is well suited for solving non-linear and complex problems, while the MLR model can explicitly explore the coefficients between independent and dependent variables. Further studies are still needed to improve the accuracy of the developed models.     

Phytoextraction potential of water fern (Azolla pinnata) in the removal of a hazardous dye,methyl violet 2B: Artificial neural network modelling

This study investigated the potential of Azolla pinnata (AP) in the removal of toxic methyl violet 2B (MV) dye wastewater using the phytoextraction approach with the inclusion of an Artificial Neural Network (ANN) modelling. Parameters examined included the effects of dye concentration, pH and plant dosage. The highest removal efficiency was 93% which was achieved at a plant dosage of 0.8 g (dye volume = 200 mL, initial pH = 6.0, initial dye concentration = 10 mg L^?1). A significant decrease in relative frond number (RFN), a growth rate estimator, observed at a dye concentration of 20 mg L^?1 MV indicated some toxicity, which coincided with the plant pigments studies where the chlorophyll a content was lower than the control. There were little differences in the plant pigment contents between the control and those in the presence of dye (5 to 15 mg L^?1) indicating the tolerance of AP to MV at lower concentrations. A three-layer ANN model was optimized (6 neurons in the hidden layer) and successfully predicted the phytoextraction of MV (R = 0.9989, RMSE = 0.0098). In conclusion, AP proved to be a suitable plant that could be used for the phytoextraction of MV while the ANN modelling has shown to be a reliable method for the modelling of phytoextraction of MV using AP.     

Artificial neural networking to estimate the leaf area for invasive plant Wedelia trilobata

Leaf area are very important parameter for the understanding of growth and physiological responses of invasive plant species under different environmental factors. This study was conducted to build non-destructive leaf area model of Wedelia trilobata that were grown in greenhouse. Regression analysis and artificial neural network (ANN) approaches were used for the development of leaf area model with the help of leaf length and width of 262 plants samples. In selection of best method under both techniques, the lower value of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and higher value of R² were considered. According to the results it was found that ANN have higher value of (R² = 0.96) and lower value of error (MAE = 0.023, RMSE = 0.379, MAPE = 0.001) than regression analysis (R² = 0.94, MAE = 0.111, RMSE = 1.798, MAPE = 0.0005). It was concluded that error between predicted and actual value was less under ANN. Therefore, ANN model approach can be used as an alternating method for the estimation of leaf area. Through estimation of leaf area, invasive plant growth can predict under different environment conditions.     

Construction of Predictive Models to Describe Apparent and Complex Viscosity Values of O/W Model System Meat Emulsions Using Adaptive Neuro – Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN)

This paper introduces an adaptive neuro ?C fuzzy inference system (ANFIS) and artificial neural networks (ANN) models to predict the apparent and complex viscosity values of model system meat emulsions. Constructed models were compared with multiple linear regression (MLR) modeling based on their estimation performance. The root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R ²) statistics were performed to evaluate the accuracy of the models tested. Comparison of the models showed that the ANFIS model performed better than the ANN and MLR models to estimate the apparent and complex viscosity values of the model system meat emulsions. Coefficients of determination (R ²) calculated for estimation performance of ANFIS modeling to predict apparent and complex viscosity of the emulsions were 0.996 and 0.992, respectively. Similar R ² values (0.991 and 0.985) were obtained when estimating the performance of the ANN model. In the present study, use of the constructed ANFIS models can be suggested to effectively predict the apparent and complex viscosity values of model system meat emulsions.     

Forest soil respiration reflects plant productivity across a temperature gradient in the Alps

Soil respiration (R _s) plays a key role in any consideration of ecosystem carbon (C) balance. Based on the well-known temperature response of respiration in plant tissue and microbes, R _s is often assumed to increase in a warmer climate. Yet, we assume that substrate availability (labile C input) is the dominant influence on R _s rather than temperature. We present an analysis of NPP components and concurrent R _s in temperate deciduous forests across an elevational gradient in Switzerland corresponding to a 6 K difference in mean annual temperature and a considerable difference in the length of the growing season (174 vs. 262 days). The sum of the short-lived NPP fractions (“canopy leaf litter,” “understory litter,” and “fine root litter”) did not differ across this thermal gradient (+6 % from cold to warm sites, n.s.), irrespective of the fact that estimated annual forest wood production was more than twice as high at low compared to high elevations (largely explained by the length of the growing season). Cumulative annual R _s did not differ significantly between elevations (836 ± 5 g C m^?2 a^?1 and 933 ± 40 g C m^?2 a^?1 at cold and warm sites, +12 %). Annual soil CO₂ release thus largely reflected the input of labile C and not temperature, despite the fact that R _s showed the well-known short-term temperature response within each site. However, at any given temperature, R _s was lower at the warm sites (downregulation). These results caution against assuming strong positive effects of climatic warming on R _s, but support a close substrate relatedness of R _s.     

Analysis of cylindrospermopsin- and microcystin-producing genotypes and cyanotoxin concentrations in the Macau storage reservoir

The Macau storage reservoir (MSR) has experienced algal blooms in recent years, with high levels of Cylindrospermopsis and Microcystis and detectable concentrations of cyanotoxins. To analyze the cyanotoxin-producing genotypes and relate the corresponding cyanotoxins to the water quality parameters, a quantitative real-time polymerase chain reaction was developed and applied to the water samples in three locations of MSR. Cylindrospermopsin polyketide synthetase (pks) gene and a series of microcystin synthetase (mcy) genes were used for identifying and quantifying cylindrospermopsin- and microcystin-producing genes, and the corresponding water parameters were measured accordingly. Our results showed that high concentrations of cylindrospermopsin and low concentrations of microcystin were measured during the study period. There was a strong correlation between the pks gene numbers and cylindrospermopsin concentrations (R ² = 0.95), while weak correlations were obtained between the mcy genes numbers and microcystin concentrations. Furthermore, the pks gene numbers were strongly related to Cylindrospermopsis (R ² = 0.88), cyanobacterial cell numbers (R ² = 0.96), total algae numbers (R ² = 0.95), and chlorophyll-a concentrations (R ² = 0.83), consistent with the dominant species of Cylindrospermopsis among the cyanobacteria existing in MSR. NH₄–N (R ² = 0.68) and pH (R ² = 0.89) were the water quality parameters most highly correlated with the pks gene numbers. These results contribute to monitoring for potential cyanotoxins in raw water.     

Soil CO2 efflux and soil carbon balance of a tropical rubber plantation

Natural rubber is a valuable source of income in many tropical countries and rubber trees are increasingly planted in tropical areas, where they contribute to land-use changes that impact the global carbon cycle. However, little is known about the carbon balance of these plantations. We studied the soil carbon balance of a 15-year-old rubber plantation in Thailand and we specifically explored the seasonal dynamic of soil CO₂ efflux (F _S) in relation to seasonal changes in soil water content (W _S) and soil temperature (T _S), assessed the partitioning of F _S between autotrophic (R _A) and heterotrophic (R _H) sources in a root trenching experiment and estimated the contribution of aboveground and belowground carbon inputs to the soil carbon budget. A multiplicative model combining both T _S and W _S explained 58 % of the seasonal variation of F _S. Annual soil CO₂ efflux averaged 1.88 kg C m^?2 year^?1 between May 2009 and April 2011 and R _A and R _H accounted for respectively 63 and 37 % of F _S, after corrections of F _S measured on trenched plots for root decomposition and for difference in soil water content. The 4-year average annual aboveground litterfall was 0.53 kg C m^?2 year^?1 while a conservative estimate of belowground carbon input into the soil was much lower (0.17 kg C m^?2 year^?1). Our results highlighted that belowground processes (root and rhizomicrobial respiration and the heterotrophic respiration related to belowground carbon input into the soil) have a larger contribution to soil CO₂ efflux (72 %) than aboveground litter decomposition.     

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg^?1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R² (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.     

Simulation of PSII-operating efficiency from chlorophyll fluorescence in response to light and temperature in chrysanthemum (<Emphasis Type="Italic">Dendranthema grandiflora</Emphasis>) using a multilayer leaf model

Chlorophyll fluorescence serves as a proxy photosynthesis measure under different climatic conditions. The objective of the study was to predict PSII quantum yield using greenhouse microclimate data to monitor plant conditions under various climates. Multilayer leaf model was applied to model fluorescence emission from actinic light-adapted (F') leaves, maximum fluorescence from light-adapted (Fm') leaves, PSII-operating efficiency (F_q'/F_m'), and electron transport rate (ETR). A linear function was used to approximate F' from several measurements under constant and variable light conditions. Model performance was evaluated by comparing the differences between the root mean square error (RMSE) and mean square error (MSE) of observed and predicted values. The model exhibited predictive success for Fq'/Fm' and ETR under different temperature and light conditions with lower RMSE and MSE. However, prediction of F' and Fm' was poor due to a weak relationship under constant (R² = 0.48) and variable (R² = 0.35) light.     

Role of coarse woody debris in the carbon cycle of Takayama forest,central Japan

Coarse woody debris (CWD) is an important component of the forest carbon cycle, acting as a carbon pool and a source of CO₂ in temperate forest ecosystems. We used a soda-lime closed-chamber method to measure CO₂ efflux from downed CWD (diameter ≥5 cm) and to examine CWD respiration (R _CWD) under field conditions over 1 year in a temperate secondary pioneer forest in Takayama forest. We also investigated tree mortality (input to the CWD pool) from the data obtained from the annual tree census, which commenced in 2000. We developed an exponential function of temperature to predict R _CWD in each decay class (R ² = 0.81–0.97). The sensitivity of R _CWD to changing temperature, expressed as Q ₁₀, ranged from 2.12 to 2.92 and was relatively high in decay class III. Annual C flux from CWD (F _CWD) was extrapolated using continuous air temperature measurements and CWD necromass pools in the three decay classes. F _CWD was 3.0 (class I), 17.8 (class II), and 13.7 g C m^?2 year^?1 (class III) and totaled 34 g C m^?2 year^?1 in 2009. Annual input to CWD averaged 77 g C m^?2 year^?1 from 2000 to 2009. The budget of the CWD pool in the Takayama forest, including tree mortality inputs and respiratory outputs, was 0.43 Mg C ha^?1 year^?1 (net C sink) owing to high tree mortality in the mature pioneer forest. The potential CWD sink is important for the carbon cycle in temperate successional forests.     

Potential of texture metrics derived from high-resolution PLEIADES satellite data for quantifying aboveground carbon of <Emphasis Type="Italic">Kandelia candel</Emphasis> mangrove forests in Southeast China

Evaluating accurate aboveground carbon (AGC) of mangrove forests is a challenging task owing to the complex canopy structure of mangroves and physiographic features. This study thoroughly assessed the potential ability of high-resolution PLEIADES texture metrics at different window sizes for quantifying AGC of mangrove forests in Jiulong River Estuary. Field measurements of AGC were obtained in 31 plots (10?×?10 m), and the data ranged from 78.747 to 143.393 t C ha^?1, with an average of 102.233 t C ha^?1 per plot. Various possibilities were examined, including spectrals bands, band ratios and various types of texture metrics, and regression modeling was applied in a five-step framework. To ensure good performance of model during the calibration process, we determined coefficients of determination (R²), p value of analysis of variance and root mean square errors (RMSE). Additionally, variable inflation factor was used to avoid the problem of multi-collinearity among the independent variables. Results showed that the spectral-based model only predicted AGC with an uncertainty (RMSE) of 9.14 t C ha^?1, and R² of 0.631. The texture-based model had a much better potential for AGC estimation with a higher R² value of 0.934, and a lower RMSE of 3.76 t C ha^?1. With increasing of window size for the texture calculations, the R² values increased and the RMSEs decreased. Additionally, we observed negative effects for AGC predictions, when spectral variables were added to texture variables during model development. Based on the calibrations, four texture-based models were selected and validated using another set of field data. Indicators including R², relative error, Nash–Sutcliffe efficiency (E_NS), and RMSE were examined to measure for deviations between estimated and observed data. Model 7 with an R² value of 0.878 was finally chosen for relatively accurate quantification of AGC. The AGC values at a selected site derived from the model ranged from 1 to 153 t C ha^?1.     

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs

Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m^?2 day^?1, and an average Nash–Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66 %. SADM overestimated daily GPP by about 15 % during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30 %, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.