MBE Values Research Articles

Assessing Solar Radiation Forecasting: a WRF-Solar Model Evaluation in Kupang, Indonesia

In response to the challenges faced by solar power plants in Kupang, Indonesia – particularly during the peak of the rainy season – this study aims to enhance the WRF-Solar (Weather Research and Forecasting–Solar) numerical weather prediction model. Accurate short-term solar radiation forecasting is crucial for managing electricity supply disruptions and controlling operational costs in the City of Kupang and its surroundings. The unpredictable weather patterns – characterized by intense cloud activity during these seasons – pose significant challenges for reliable solar energy generation, thereby making precise weather modeling an essential tool for effective power plant management. Spanning 2020–2022, the research focused on the peak rainy seasons, utilizing a specifically configured WRF-Solar model to simulate solar radiation. Validation of the WRF-Solar model using observational data from the Automatic Solar Radiation Stations at the Kupang Climatological Station showed correlation values consistently above 0.50. However, the model exhibited a tendency to overestimate radiation intensity for GHI and DNI parameters (correlation 0.78 and 0.54) and underestimate for DHI (correlation 0.80), with RMSE values ranging from 47–320 W/m² and MBE values from –41.84 to 116.34 W/m². The model's accuracy was notably higher under clear conditions, with lower RMSE and MBE values but decreased significantly under overcast conditions, hence indicating sensitivity to cloud cover. This study highlights the WRF-Solar model's capability in capturing the diurnal pattern and hourly fluctuations of solar radiation while also underscoring the need for improved cloud representation to enhance accuracy under diverse climatic conditions.

Avaliação do modelo AquaCrop para simulação da resposta da soja sob estresse hídrico

ABSTRACT: Lately, irrigated soybean production has increased; therefore, tools that can aid water resources management must be improved. Two experiments were carried out, the first one from May to August and the second one from September to December 2019. The experimental design included randomized blocks with five treatments and four replicates. In the control treatment (SE), irrigation was carried out to meet the crop’s water demand during all growth stages. In the other treatments, irrigation was interrupted at specific crop growth stages (TI = VC-V4, TII = V5-R1, TIII = R1-R5, and TIV = R5-R7), and then restored. After calibration, the model satisfactorily estimated the canopy cover, biomass, and soybean yield, with high values of determination coefficients (r² > 0.90), low RMSE and MBE values, and high values of EF. In experiment 1, the soil water content was overestimated in the SE, TI, and TIV treatments by 0.24%, 0.78%, and 0.23%, respectively, and underestimated by 3.3% and 5.5% in the TII and TIII treatments, respectively. In experiment 2, in the SE, TI, TII, TIII, and TIV treatments, the soil water content was underestimated by 6%, 3%, 4.6%, 5.9%, and 6.7%, respectively. Real evapotranspiration was overestimated in all treatments in both experiments, with low r² values in both experiments 1 (0.11- 0.23) and 2 (0.04 - 0.21).

UAV-based automated 3D modeling framework using deep learning for building energy modeling

Many approaches to the Urban Building Energy Model (UBEM) have been developed to analyze urban-scale energy demand patterns. The main goal of UBEM is to minimize manual work and improve modeling accuracy when building 3D modeling procedures that deal with vast amounts of data. To do so, it is important to build an automated process and increase the modeling efficiency throughout the process. This study proposes a new framework for automatically generating 3D models for building energy modeling. This framework collects geographic coordinate system (GCS) data by applying algorithms based on a convolutional neural network (CNN), Haversine formula, unmanned aerial vehicle (UAV), and geographic information system (GIS) information. The collected GCS data were used to generate a 3D model using EnergyPlus, resulting in a 3D model capable of a Level of Detail 3. Subsequently, we compared and verified the size and energy performance of the actual building with those of the generated model. The model size errors generated without drawing information are as follows: buildings, 3.69%, windows1 16.75%, and windows2 19.43%. The error range evidenced through the energy performance evaluation indicator showed that the MBE values for the cooling and heating energies were 5.54% and 5.77%, respectively.

Evaluation of Infiltration Modeling in the Cisadane Watershed in Indonesia: Existing and New Approach Equation

This study aims to evaluate the accuracy of semi-empirical models for determining infiltration based on the existing equations (Horton, Kostiakov and Kostiakov–Lewis) and a new form equation with optimized parameters which has good accuracy (lowest RMSE and MBE), sufficient linearity (highest R2), and a Willmott index close to 1. The location of this study was the Cisadane watershed, which is a priority watershed in Indonesia. The lowest values of average RMSE and MBE and highest values of R2 and Willmott index are given by Equation 1, which indicated that the model was closest to the field infiltration conditions. The distribution of the RMSE values for all survey points using Equation 1 resulted in small RMSE values ranging from 0.09 to 1.83. The distribution of R2 values for all survey points using Equation 1 yielded R2 values close to 1 (high linearity) with values ranging from 0.96 to 1. Based on these results, it can be concluded that Equation 1 is the most accurate infiltration model for the Cisadane watershed compared to the existing infiltration rate equation. This research can be used as an initial idea to create the infiltration rate model using other forms of equations with optimization parameters to produce a more representative model with field data.

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

Actual crop evapotranspiration (ETa) is measured or estimated using different methods, and its accuracy is critical for water management under precision agriculture. The objective of this study was to compare maize ETa estimated by the two-step approach using a locally developed crop coefficient curve with satellite-retrieved evapotranspiration by six models incorporated in the OpenET to identify the best evapotranspiration estimation alternatives to the two-step approach for water management in northern New Mexico. Maize (Zea mays L.) was planted at the NMSU Agricultural Science Center at Farmington from 2017 to 2022 and uniformly managed across years. Water management in plants was based on maize’s actual evapotranspiration estimated as the product of the reference evapotranspiration and the local crop coefficient, which is described as a third-order polynomial function of the accumulated heat units by maize plants. For the same growing seasons, maize ETa was retrieved from satellite, and was estimated by six models listed within the OpenET from 2017 to 2022. The results show that maize daily ETa was consistently smaller when measured by SIMS and PT-JPL during maize initial and actively growing stages, while ETc(kc), SIMS and eeMETRIC showed similar maize daily ETa during maize full canopy development and mid-season, and which overcome the evapotranspiration estimated by DisALEXI, PT-JPL, geeSEBAL, and SSBop. ETc(kc) drastically dropped and became the lowest value among all ETa estimation models after the first fall snow or the first killing frost. Regarding the seasonal average, all six models included in OpenET showed smaller maize evapotranspiration. Maize seasonal evapotranspiration varied from 589.7 to 683.2 mm. eeMETRIC compares most similarly to the ETc(kc) model, followed by SIMS, with percent errors of 2.58 and 7.74% on a daily basis and 2.43 and 7.88% on a seasonal basis, with the lowest MBE and RMSE values, respectively, and could be used as an alternative for maize actual daily evapotranspiration for water management in northern New Mexico. The results of this study could be used by water managers and crop growers to improve water management in the Four Corners region, using eeMETRIC for crop water use to improve water management and conservation under sustainable agriculture.

Comparative Estimation of Global Solar Radiation over Two Nigerian Cities, Using Artificial Neural Network and Empirical Models

The estimation of solar radiation intensity has been a focus of many researchers due to the cost of setting up its actual measurements. While many of them employed empirical models, this study utilizes the artificial neural network for the analysis and estimation of global solar radiation over two Nigerian cities. The model developed using sunshine hours, temperatures and relative humidity were compared with the existing empirical models. Model performance indicators comparing the measured data and the computed data for the derived and selected models, using the same number of input meteorological parameters showed that ANN having average values of RMSE, MBE, and MPE of 0.0744 MJm-2day-1, -0.0020 MJm-2day-1, and -0.0043%, respectively, performed slightly better. When different number of input meteorological parameters were used, the ANN gave the following error indicators for RMSE, MBE, MPE of 0.0394MJm-2day-1, -0.0023MJm-2day-1 and -0.0144% respectively. Also, in the result of solar radiation in Abuja, using the same number of meteorological parameters, the model with the best performance in the estimation of solar radiation is the ANN model with average values of RMSE, MBE, MPE of 0.1301MJm-2day-1, 0.0053MJm-2day-1 and 0.0441% respectively. Hence, the models are versatile for predicting global solar radiation in locations in the same climatic zones as locations studied in this study, where direct measurements of solar radiation is scarce and widely separated but there is availability of commonly measured meteorological parameters such as sunshine duration, minimum temperature, maximum temperature and relative humidity.

Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum.

The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the models to estimate the actual evapotranspiration (ETr) in agricultural crops irrigated by center pivots. The spectral bands of Sentinel 2A and 2B satellite and vegetation indices formed the first scenario. The second scenario was formed by performing the normalized ratio procedure between bands (NRPB) and joining the variables applied in the first scenario. The models were generated to predict the ETf using six regression algorithms and then compared with ETf calculated by the Simple Algorithm For Evapotranspiration Retrieving (SAFER) algorithm, was considered as the standard. The results possible to select the best model, which in both scenarios was Cubist. Subsequently, ETf was estimated only for the center pivots present in the study area and the classification of land use and cover was accessed through the MapBiomas product. Land use was necessary to enable the calculation of ETr in each scenario, in the center pivots with sugarcane and soybean crops. ETr was estimated using two ETo approaches (EToBrazil and Hargreaves-Samani). It was found that the Hargreaves-Samani equation overestimated ETr with higher errors mainly for center pivots with sugarcane, where systematic error (MBE) ranged from 0.89 to 2.02 mm d-1. The EToBrazil product, on the other hand, presented statistical errors with MBE values ranging from 0.00 to 1.26 mm d-1 for both agricultural crops. Based on the results obtained, it is observed that the ETr can be monitored spatially and temporally without the use of the thermal band, which causes the estimation of this parameter to be performed with greater temporal frequency.

Simulation of Diffuse Solar Radiation with Tree-Based Evolutionary Hybrid Models and Satellite Data

Diffuse solar radiation (Rd) provides basic data for designing and optimizing solar energy systems. Owing to the notable unavailability in many regions of the world, Rd is traditionally estimated by models through other easily available meteorological factors. However, in the absence of ground weather station data, such models often need to be supplemented according to satellite remote sensing data. The performance of Himawari-7 satellite inversion of Rd was evaluated in the study, and hybrid models were established (XGBoost_DE, XGBoost_FPA, XGBoost_GOA, and XGBoost_GWO), so as to improve the satellite data and achieve a better utilization effect. The meteorological data of 14 Rd stations in mainland China from 2011 to 2015 were used. Four input combinations (L1–L4) and eight input combinations (S1–S8) of meteorological factors corresponding to satellite remote sensing data were used for model simulation, while two optimal combinations (S7 and S8) were selected for cross-station application. The results revealed that the accuracy of Himawari-7 satellite Rd data was low, with RMSE, R2, MAE, and MBE values of 2.498 MJ·m−2·d−1, 0.617, 1.799 MJ·m−2·d−1, and 0.323 MJ·m−2·d−1, respectively. The performance of these coupled models based on satellite data was significantly improved. The RMSE and MAE values increased by 15.5% and 9.4%, respectively, while the R2 value decreased by 10.9 %. Compared with others based on satellite data, the XGBoost_GOA model exhibited optimal performance. The mean values of RMSE, R2, and MAE were 1.63 MJ·m−2·d−1, 0.76 and 1.21 MJ·m−2·d−1, respectively. The XGBoost_GWO model exhibited optimal performance in the cross-station application, and the average RMSE value was reduced by 2.3–10.5% compared with the other models. The meteorological factors input by the models exhibited different levels of significance in different scenarios. Rd_s was the main meteorological parameter that affected the model based on satellite data, while RH exhibited a significant improvement in the XGBoost_FPA and XGBoost_GWO models based on ground weather stations data. Accordingly, the present authors believe that the XGBoost_GOA model has excellent ability for simulating Rd, while the XGBoost_GWO model allows for cross-station simulation of Rd from satellite data.

Validation of the improved GOES-16 aerosol optical depth product over North America

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) every 5 min. However, the current version of the official GOES-16 ABI AOD product is affected by diurnally varying biases. An improved ABI AOD product based on an empirical bias correction algorithm has become available recently. This study aims to comprehensively validate this improved ABI AOD product over land and ocean. Three years of Aerosol Robotic Network (AERONET) ground-based measurements at 95 stations over North America are used for the validation. Results suggest the product performs moderately well with an overall R2 of 0.46, an MBE of 0.04, a slope of 0.97, and ∼70.0% retrievals falling within the expected error (EE) envelope. A slight overestimation is found at a majority of ground sites: ≈87% of those sites have MBE values between 0 and 0.1. The analysis shows that the retrievals perform better over water and forests, but perform poorly over urban areas, grasslands and shrublands, and is worst over mountains. Besides, the present results indicate that the combination of the retrieval and bias correction algorithms yields a systematic underestimation of AOD for coarse particles over croplands, as well as large uncertainties for smaller-size particles over shrublands. At seasonal scale, the AOD retrievals perform better in summer and autumn than in spring and winter. Despite the empirical bias correction algorithm, a systematic overestimation of 0.07 at 1700 UTC and 0.08 during 1000–1300 UTC still exist. The sampling rate of the top-2 quality AOD retrievals are 14%, 12%, 10% and 11% for the four seasons, respectively, which constitutes a significant reduction of valid data in comparison with the sampling rate of all four quality retrievals. The limited availability and accuracy of the top-2 bias-corrected ABI AOD product raises concerns about its application in air quality and energy-related applications, in particular. This study also suggests a few potential improvements to the aerosol retrieval algorithm.

Convective drying of unripe plantain: a comparative response surface methodology and genetic algorithm optimization study, certainty and sensitivity analysis

The requirement for reduction of post-harvest losses increased production, and cost-effectiveness of foods are driving continuous food process investigations. In this study, Response Surface Methodology (RSM) was utilized to understand, model, and optimize the effect of selected process factors on the moisture content (MC) of convectively dried unripe plantain fruit. For technical accuracies, Multi Gene Genetic Programming (MGGP) was also used to model the process and both MGGP and RSM models were statistically compared. Furthermore, Monte Carlo Simulation (MCS) was used to conduct sensitivity analysis of unripe plantain’s MC to each selected process factor. Results showed that increased sample thickness increased the MC while increased drying temperature and drying time decreased the MC of unripe plantain. RSM model had Chi-square, MBE, t-value, RMSE and R2 values of 15.2131, 0.7531, 7.6170, 0.9193 and 0.9674, respectively; while MGGP model had 3.0415, 0.2563, 2.6871, 0.4111 and 0.9956, respectively. Sensitivity analysis showed that sample thickness, drying temperature and drying time had +89.5 %, -10.2 % and -0.3 % contribution to the variances of MC, respectively. These results are useful in unripe plantain drying process prediction, optimization, and product standardization.

Evaluation of Lethal and Sublethal Effects of Methyl Benzoate on the Generalist Predator Orius laevigatus (Fieber).

Methyl benzoate (MBe), a volatile organic molecule, has been shown to have insecticidal effects on a variety of agricultural, stored products, and urban arthropod pests in recent investigations. However, the toxicity of MBe against nontarget organisms has rarely been investigated. This study investigated the lethal and sublethal effects of MBe on the generalist predator Orius laevigatus (Fieber) (Hemiptera: Anthocoridae) via different exposure routes. This species is an important natural enemy of thrips, aphids, and mites in biological control programs globally. Acute toxicity bioassays conducted on O. laevigatus showed that the lethal median concentration (LC50) values of MBe for topical and residual toxicity were 0.73 and 0.94%, respectively, after 24 hr of exposure. Importantly, a sublethal concentration of MBe (LC30 = 0.51%) did not affect the survival and reproduction of O. laevigatus. In addition, prey consumption by O. laevigatus under different exposure conditions with varying densities of Aphis gossypii (Glover) (Hemiptera: Aphididae) adults demonstrated a good fit for a Type II functional response. The sublethal concentration of MBe did not affect the attack rate and handling time of O. laevigatus compared to untreated insects, nor did it affect the longevity and fecundity of O. laevigatus females. Thus, according to the International Organization for Biological Control, the sublethal MBe concentration for O. laevigatus is categorized as harmless and may be used in conjunction with this predator species for integrated control of many agricultural insect pests.

Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/m2/d, and 0.01 kWh/m2/d. The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/m2/d, and 0.01 kWh/m2/d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/m2/d, and 0.13 kWh/m2/d, while the reduction for yearly was 91.6%, 0.11 kWh/m2/d, 0.2 kWh/m2/d. The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/m2/d, and 0.16 kWh/m2/d, while the yearly values were 0.47%, 0.18 kWh/m2/d, and 0.004 kWh/m2/d.

Evaluation of Pedotransfer Functions for Estimating Soil Water Retention Curve of Ap Horizon Soils for Various Soil Series of Reclaimed Tidal Flat Soil

This investigation was to evaluate the applicability and prediction accuracy of pedotransfer function (PTF) to estimate the water retention curves of Ap horizon soils for five soil series which cannot be directly used to cultivate upland crops other than rice because of their high salinity. Soil water retention curves (SWRCs) were obtained from 150 undisturbed soil samples collected from the Ap horizons of which soil textures were grouped into sandy loam with high contents of sand (>60%) and low clay contents (<10%) and silt loam with relatively high silt content (>60%) and low sand content (<10%). Soil-water retention characteristics between 0 to −50 kPa and −50 to −1500 kPa were measured using the sandbox, kaolin-plate, and pressure chamber methods, respectively. The SWRCs were also estimated by Rawls and Brakensiek PTF (RB-PTF). The measured SWRCs of sandy loam and silt loam were compared against those obtained from RB-PTF. The SWRCs estimated with the modeled data closely fit the measured data of sandy loam and silt loam although saturated volume water content (θs) and residual volume water content (θr) of RB-PTF were higher than those of the measured mean for both soil textures. The air entry point (α) and the steepness of the water-retention curve (n) were higher in sandy loam than those of silt loam. SWRCS estimated by the RB-PTF yielded the best fit of all soil samples from the five series, whereas the MBE values less than zero indicate that θv measured at the water potentials is under-predicted. The r2 values greater than 0.9 for sandy loam and silt loam represent RB-PTF and are suitable for predicting SWRCs in RTFS because the measured data points do not vary around the estimated regression line.

Larvicidal Activity of Methyl Benzoate, a Volatile Organic Compound, Against the Mosquitoes Aedes albopictus and Culex pipiens (Diptera: Culicidae).

Methyl benzoate (MBe) is a volatile organic molecule found in various plants; it is used as an insect semiochemical. MBe also has a biorational insecticidal effect against various agricultural and urban arthropod pests. The present study was the first to assess the larvicidal potential of MBe against fourth-instar larvae of the mosquitoes Aedes albopictus (Skuse) and Culex pipiens (L.). A positive association was observed between MBe concentrations and larval mortality in both the species. The highest mortality recorded was 100% for Ae. albopictus and 56% for Cx. pipiens after 24 h of exposure to 200 ppm MBe. The lethal median concentration (LC50) values of MBe against fourth-instar larvae of Ae. albopictus and Cx. pipiens were 61 ppm and 185 ppm, respectively. These results suggest that MBe has great potential for use as an environmentally friendly larvicidal agent for mosquito control.

Developing pedotransfer functions using Sentinel-2 satellite spectral indices and Machine learning for estimating the surface soil moisture

To estimate the surface soil moisture (SM) using a combination of new spectral indices and methods of Random Forrest (RF) and Multiple Linear Regression (MLR), 11 pedotransfer functions (PTF1-11) were developed by combining basic soil properties (clay, silt/sand, and bulk density) and spectral indices of Sentinel-2 satellite. In this study, 124 surface soil samples were randomly taken from three regions including Telo in Tehran province, Ivaneki in Semnan province, and Borujerd in Lorestan province, Iran. The results showed that the accuracy of the RF method was considerably higher compared to the MLR method. The SM was better estimated using water spectral indices (such as Normalized Difference Water Index (NDWI) and Surface Water Capacity Index (SWCI)), along with the basic properties of soil as inputs of PTF7. In the training and testing steps, the Root Mean Square Error (RMSE) decreased from 0.041 and 0.05 (cm3 cm−3) in PTF1 to 0.028 and 0.039 in PTF7, respectively. The average values of RMSE, Akaike Information Criterion (AIC), coefficient of determination (R2) and Relative Improvement (RI) of the RF method were 0.028 (cm3 cm−3), −559, 0.79 and 0.001, and 0.038 (cm3 cm−3), −279, 0.73 and −0.006 for the training and testing steps of all PTFs, respectively. While, these values for the MLR method were 0.032 (cm3 cm−3), −542, 0.69 and 0.0003, and 0.043 (cm3 cm−3), −269, 0.63 and 0.002 for the training and testing steps, respectively. Due to the low values of MBE, it was possible to disregard the overestimation of the results. The evaluation of results for predictor importance indicated that among the basic properties, clay percent has a significant effect on estimation of SM. These results show that spectral indices alone are not suitable estimators for SM estimation. It suggests using basic soil properties and spectral indices to estimate the SM.

On the prediction of daily global solar radiation using temperature as input. An application of hybrid machine learners to the six climatic Moroccan zones

Forecasting of solar radiation intensity is a necessity for the establishment of solar energy projects and for decision-making in other related fields. Current prediction models/methods are site-dependent and their performance/accuracy outside the area of application is debatable. Temperature-based solar radiation models are highly recommended in areas where only air temperature data is available. Therefore, the purpose of this study is to evaluate the prediction accuracy of 42 existing temperature-based solar radiation models in forecasting the daily global solar radiation (DGSR) on horizontal surface for the six climatic zones of Morocco. In the first time, the models were assessed using only the least square method. Then, four Machine Learners models (SVM, Decision Tree, Gaussian Regression and Linear Regression) were employed as optimizers to improve the accuracy prediction of the models. The results differ from model to another based on their values of MBE, MSE, RMSE, σ and R2. Two methods were employed for ordering the studied models: the Performance score and the Taylor diagram. Long term meteorological data was used in the evaluation processes. The correlation R2 of the optimized models changes from 0.80 to 0.95 for all skies and from 0.95 to 0.98 for clear skies.

Comparative Valuation of Global Solar Radiation on Horizontal and Tilt Surfaces in Owerri, Nigeria

Comparative valuation of global solar radiation on horizontal and tilt surfaces with the use of Angstrom-Page and four isotropic models for Owerri has been carried out in this paper. Angstrom linear regression model is developed from thirty years (1992-2022) of sunshine hour and average monthly global solar radiation sourced from NIMET and NASA, respectively. The Angstrom regression coefficients 𝑎 and 𝑏determined for Owerri is 0.210 and 0.696, respectively. The developed model is used to estimate average monthly global solar radiation on horizontal surface and consequently, the amount of incident solar radiation received on tilt surface by the isotropic models. The MBE, RMSE and MPE values are estimated to be 0.0083, 0.0029 and -0.0041%, respectively, for the developed Angstrom-Page model. A strong coefficient of determination of 76.6% was obtained and Analysis of Variance (ANOVA) significant of 100% was observed, which shows the goodness of fitness of the model. Among the isentropic models studied, one of the models poorly estimated solar radiation on horizontal and tilt surfaces for the study location

Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region

Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production. In this study, the reliability of SSRD (surface solar radiation downward) forecast from the 51 ensemble members in the ECMWF (European Centre for Medium Range Forecast) long-range forecast to predict daily and monthly radiation in 5 climatological stations in Indonesia is evaluated. The global horizontal irradiance (GHI) data from the solar radiation observation network from January 2018 – December 2020 are used in the quantitative evaluation of the SSRD forecast. Post-processing methods are applied to the model output, namely the bilinear interpolation method and the empirical quantile mapping to reduce consistent biases in the model output. The evaluation was carried out for different cloud covers based on the calculation of clearness index (k_t). The cloud condition affects the performance of the model, where the highest correlation value is achieved during sunny days (0.18 – 0.65) and the lowest correlation happens in overcast days (0.05 – 0.35). Models also tend to underestimate radiation when the sky is clear and overestimate it in cloudy days, based on negative MBE values during clear days (-0.47 kWh/m2 – -1.29 kWh/m2). The spatial averaging method did not necessarily improve the accuracy of the forecast, but the empirical quantile mapping method provides better accuracy, which is indicated by a values (mean error ratio) lower than 1 in most stations. Information about the influence of cloud cover on model performance can be used in future application of the model output and the bias correction process carried out in this study can be applied to reduce bias in the model.

Analysis of red chilli drying kinetics affected by low-temperature long time blanching

The drying kinetics of red chillies in the effect of low-temperature long time blanching were investigated. The blanching process was carried out at 70 °C for 10, 15, and 20 minutes. The drying process lasts for 32 to 35 hours to reduce the moisture content from 82.24% (wb) to 8.84% (wb). The experimental data were fitted to three mathematical models of thin-layer drying, namely the Henderson and Pabis, Lewis, and Page models. The result shows that the Page model is the most suitable model for predicting moisture ratio in chilli drying with low-temperature long time blanching treatment. This suitability is evidenced by the highest R2 value, which ranges from 0.9709 to 0.9786, and also the lowest RMSE and MBE values range from 0.0503 to 0.0579 and 0.0025 to 0.0034, respectively. Meanwhile, the effective moisture diffusivity varies from 5.6300 × 10−9 m2/s to 6.5662 × 10−9 m2/s for the treatments studied.

The evaluation of DRAINMOD-S and AquaCrop models for simulating the salt concentration in soil profiles in areas with a saline and shallow water table

Soil salinity in the coastal areas of Mazandaran province in the north of Iran is a big problem that threats the cultivation of rice which is the major crop in this region. In this study, the latest version of DRAINMOD-S and AquaCrop models were used to simulate the salt concentration in the soil profile of paddy field with shallow and saline groundwater. This research was carried out in a physical model, located at the meteorological research center of agricultural and natural resources campus of University of Tehran in 2018. The physical model was divided into two boxes (each with dimension of 1*1*1.9 m) by an isolated sheet; one box was conditioned with shallow and saline groundwater (as SSG treatment) and another box with shallow and nonsaline groundwater as a control treatment (NSG). The concentration of saline groundwater was considered to be 20 dS m−1, equal to the salinity of groundwater in agricultural lands of the coastal zone of the Caspian Sea. Both boxes were irrigated with tap water with a salinity of 0.94 dS m−1. After evaluating the models for simulation of salinity in the soil profile of NSG treatment, the best values of MBE, RMSE, NRMSE, d, and R2 were 0.02 dS m−1, 0.07 dS m−1, 2.85%, 0.91 and 0.89 for DRAINMOD-S model and zero, 0.05 dS m−1, 2.11%, 0.93 and 0.84 for AquaCrop models. The results indicate a fairly good correlation between the observed and simulated data. Also, the parameters of MBE, RMSE, NRMSE, d, and R2 were calculated to evaluate the performance of the model in estimating salt concentration changes in three depths of soil. The best values for these parameters in the SSG treatment were −0.04 dS m−1, 0.10 dS m−1, 1.95%, 0.95 and 0.96, for DRAINMOD-S and −0.01 dS m−1, 0.09 dS m−1, 1.82%, 0.96 and 0.93 for AquaCrop, respectively. In general, comparing the measured and simulated soil salinity illustrated that DRAINMOD-S and AquaCrop could be used as a useful tools to predict and simulate the soil salinity changes trend. Also, the accuracy and performance of DRAINMOD-S in higher salinity levels was more than the ones of AquaCrop, because DRAINMOD-S model is a more specific model for salinity issues.