Journal of Agrometeorology

Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing

2024-06-03T04:57:30+00:00

Accurate evapotranspiration (ET) assessment is crucial for agricultural water management, encompassing crop water requirements, irrigation scheduling, water budgeting and drought monitoring. This study integrates remote sensing-based surface energy balance model with in-situ ET measurements to evaluate surface energy fluxes and ET in Pantnagar, Tarai region. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model, using high-resolution remote sensing data, was validated against observations from an ET station equipped with large aperture scintillometer and micrometeorological tower, situated in sugarcane farm at Govind Ballabh Pant University of Agriculture & Technology (GBPUA&T), Pantnagar. On November 13, 2021, METRIC and Landsat-9 satellite data estimated an instantaneous ET of 7.39 mm day^-1, closely aligned with the observed value of 6.72 mm day^-1 recorded by the ET station. The findings confirm the METRIC model's high accuracy for spatial ET estimation and its associated micrometeorological variables. This study underscores the utility of the METRIC model, ET station and remote sensing in determining ET and energy flux which may be further utilised in the estimation of crop water requirement, energy fluxes and irrigation water management for sugarcane cultivation in the Tarai region.

Influence of atmospheric stability conditions on wind energy density in Ali Al-Gharbi region of Iraq

2024-05-13T17:37:22+00:00

Atmospheric stability is considered as one of the most important factors affecting the increase or decrease in wind speed in the atmosphere and thus affects the wind energy density. This study aims to analyze the stability of the atmospheric conditions in southern part of Iraq, specifically in the Ali Al-Gharbi region, using one of methods to determine atmospheric stability called Monin-Obukhov length . Field data of horizontal and vertical wind speed and air temperature collected at three heights (10m, 30m, and 50m) in 2017 from tower installed in Ali Al-Gharbi region. The results show that the vertical wind speed increases with height up to 30 m and decreases at 50 m, while the horizontal wind speed increases as the height increases. The friction velocity , surface heat flux , momentum flux and shear stress were calculated and the stability conditions were determined. Results revealed that stable atmospheric condition was the most frequent with about 59% occasions occurring during the year followed by unstable (40%) and neutral (1%) conditions. The highest wind energy density was in stable conditions (0 ≤ L< 200), with percentage (58% - 57%) in spring season at both heights 10m and 50m, respectively, followed by unstable conditions (-200 < L ≤ 0), while the lowest wind energy density was in neutral conditions (-200 ≤ L ≥ 200) with percentage (1.3% - 0.8%) in autumn season.Atmospheric stability is considered as one of the most important factors affecting the increase or decrease in wind speed in the atmosphere and thus affects the wind energy density. This study aims to analyze the stability of the atmospheric conditions in southern part of Iraq, specifically in the Ali Al-Gharbi region, using one of methods to determine atmospheric stability called Monin-Obukhov length . Field data of horizontal and vertical wind speed and air temperature collected at three heights (10m, 30m, and 50m) in 2017 from tower installed in Ali Al-Gharbi region. The results show that the vertical wind speed increases with height up to 30 m and decreases at 50 m, while the horizontal wind speed increases as the height increases. The friction velocity , surface heat flux , momentum flux and shear stress were calculated and the stability conditions were determined. Results revealed that stable atmospheric condition was the most frequent with about 59% occasions occurring during the year followed by unstable (40%) and neutral (1%) conditions. The highest wind energy density was in stable conditions (0 ≤ L< 200), with percentage (58% - 57%) in spring season at both heights 10m and 50m, respectively, followed by unstable conditions (-200 < L ≤ 0), while the lowest wind energy density was in neutral conditions (-200 ≤ L ≥ 200) with percentage (1.3% - 0.8%) in autumn season.

Plant stress index (PSI) based irrigation scheduling of wheat in Punjab, India

2024-05-20T16:30:22+00:00

A field experiment was caried out over a period of two years (2022-23 and 2023-24) at Lovely Professional University, Phagwara, Punjab with eight irrigation treatments (based on PSI, soil moisture depletion & critical growth stages) and four replications in RBD Design. The different irrigation levels had an impact on plant growth, parameters contributing to yield, grain and straw production, as well as irrigation water use efficiency (IWUE). Among all the PSI based irrigation treatments, schedule irrigation at 0.50 PSI was found the best irrigation level for growing wheat with significant grain yield (5.67 t ha^-1), IWUE (0.092 t ha^-1cm) and gave 11.16% water saving over I_{50% FC} (irrigation as per farmer practices). To schedule irrigation as per the soil moisture depletion approach, irrigation levels I_{50% FC} and I_{75% FC} result in maximum grain yield over PSI & critical growth stage-based irrigation treatments, but this practice does not support sustainable wheat production in water-scarce regions. Therefore, irrigation can be tailored for wheat crops based on 0.5 PSI in water-scarce and water-abundant regions of Punjab.

Projection of precipitation under RCP4.5 and RCP 8.5 in central and southern regions of Iraq

2024-05-12T22:26:10+00:00

Climate change has significant impacts on natural systems, especially in terms of altering hydrological patterns due to changing precipitation and melting snow and ice. This study examines projected changes in precipitation for central and southern Iraq under the RCP4.5 and RCP8.5 scenarios for the periods 2046–2065 and 2081–2100 using CMIP5 climate model HadGEM2-AO output. The results revealed that under RCP4.5, there was large spatial variation in the projected precipitation over the region during 2064-2065, ranging from as low as 27 mm in western part to as high as 1541 mm in the eastern part. The spatial variability as well as precipitation amount decreased considerably during 2081-2100 periods. Under RCP8.5 the projected precipitation was only 14-164 mm in 2046-2065 and 36-532 mm in 2081-2100 periods. Thus, under RCP8.5 scenario anticipated precipitation will be quite low.

Exploring the nexus of grain production and climate variability under nitrogen and hydrothermal regimes using CERES-wheat model

2024-05-18T22:03:42+00:00

Field experiment was conducted at Punjab Agricultural University, Bathinda from 2017-18 to 2019-20 to evaluate the growth and yield of wheat under varying sowing dates (Nov. 15, Nov. 30 & Dec 15), irrigation regimes (IW: CP = 0.6, 0.8, & 1.0), and nitrogen levels 80% RDN, RDN & 120% RDN). Timely sowing (15 November) resulted in significantly highest grain yield (5090 kg ha^-1) and delay in sowing caused reduction in yield by 2.83 % and 9.91 % in crop sown on 30 November and 15 December respectively. Yield and yield attributes were found to increase with increase in irrigation and nitrogen levels. The highest yield was achieved with 15 November sowing, IW: CPE ratio 1.0 and 120% RDN. The sensitivity of the validated CERES-wheat model to increase in temperature by 0.7, 1.2, 1.5 ⁰C and CO₂ concentration by 435, 460, 500 ppm, as projected for years 2030, 2040 and 2050 respectively, revealed reduction in the wheat yield. The combination of 15 November sown crop, irrigation at IW:CPE ratio 1.0, and recommended dose of nitrogen showed resilience against increasing CO₂ concentration and temperature rise.

Impact of GHG emission, temperature, and precipitation on rice production in Nepal

2024-06-10T16:26:12+00:00

Climate variables mainly greenhouse gas (GHG) emissions, temperature, precipitation, and rainfall are affecting crop production across the world. Nepal as a vulnerable country in terms of climate change, has raised the attention of researchers and policymakers in recent years. In this scenario, this study has attempted to find the impact of GHG emissions, temperature, and precipitation on rice production in Nepal. The study is based on time serried data from 1990 to 2019. The findings show that GHG emission has a significant positive impact on rice production. However, the annual average mean temperature has a significant negative impact on rice production. Besides having a negative coefficient, precipitation did not affect rice production significantly. The study recommends concrete climate change adaptation practices in the major rice production areas of Nepal, mainly in the Terai and Hilly belts.

Rainfall modeling with CMIP6-DCPP outputs and local characteristic information using eigenvector spatial filtering varying coefficient (ESF-VC)

2024-05-19T20:35:06+00:00

Estimating rainfall at a point or region is difficult because complex factors affect rainfall. A helpful strategy is to utilize the GCM output information from CMIP6-DCPP by forming a functional relationship between GCM output data and rainfall data at a certain point or region, called statistical downscaling. However, because the resolution of the GCM output is relatively low, the model could not explain the local effects since the heterogeneity is enormous. Based on this fact, the current research proposes to add some local characteristics in the downscaling model to improve the performance to predict the rainfall levels. Further, the rainfall levels have spatial dependencies among points. Therefore, this research employed the Eigenvector Spatial Filtering-Varying Coefficient (ESF-VC) as the methodology of the modeling. The objective of this research is to perform rainfall predictive modeling with CMIP6-DCPP output and some local characteristic information as predictors using ESF-VC methodology. The approach was implemented to predict the rainfall level in the Province of Riau in Indonesia. Based on the results, the ESF-VC model provides good performance in estimating rainfall in Riau. The variables that provide local effects are altitude, equator (location), equator (distance), and wet month dummy. While the variables ENSO and vegetation (NDVI) have a significant global effect on the model.

Univariate and multivariate imputation methods evaluation for reconstructing climate time series data: A case study of Mosul station-Iraq

2024-07-08T20:27:02+00:00

Comprehensive climate time series data is indispensable for monitoring the impacts of climate change. However, observational datasets often suffer from data gaps within their time series, necessitating imputation to ensure dataset integrity for further analysis. This study evaluated six univariate and multivariate imputation methods to infill missing values. These methods were applied to complete the subsets of time series data for precipitation, temperature, and relative humidity from Mosul station spanning 1980–2013. Artificial gaps of 5%, 10%, 20%, and 30% missing observations were introduced under scenarios of missing completely at random (MCAR) missing at random (MAR), and missing not at random (MNAR). Evaluation metrics including RMSE and Kling-Gupta Efficiency were utilized for performance evaluation. Results revealed that seasonal decomposition was the most effective univariate imputation method across all variables. For the multivariate imputation, kNN demonstrated superior performance in infilling the precipitation missing data under MCAR, while norm.predict exhibited optimal performance in the temperature missing data under all missing scenarios. Moreover, missForest was identified as the most suitable method for infilling missing relative humidity data. This study's methodology offers insights into selecting appropriate imputation methods for other climate stations, thereby enhancing the accuracy of the climate change effects analysis.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

2024-06-07T16:44:18+00:00

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Study and evaluation of wind power density for the use of small wind turbine under Baghdad conditions

2024-07-02T00:04:36+00:00

The main aim of this research is to analyze the characteristics of the wind speed and wind power density in Baghdad City within micro-scale meteorological conditions at Mustansiriyah University Meteorological Station (MUMS). Temperature, atmospheric pressure and wind speed data were taken for one year (2016) measured at a height of 18 m above the earth's surface. Hourly, monthly, and seasonal changes in wind speed at an altitude of 30 m were estimated using a power law. The mean diurnal and monthly air density was calculated. Different statistical distributions were used, including the Rayleigh, Gamma and Weibull distributions, and the best distribution function was selected to evaluate the wind power density in the study area. The results showed the highest monthly mean of wind speed recorded in June and July. Therefore, these months have the highest wind power density. The lowest monthly mean of wind speed and wind power density was observed in December. The maximum and minimum values of air density were recorded in December, January and July, August, respectively. The monthly variation of the shape parameter (k) ranges between 0.99 - 1.81, while the monthly variation of the scale parameter(c) ranges between 1.07 - 2.3 m s^-1. It was also found that the Weibull distribution was more accurate than the Rayleigh and Gamma distributions. The prevailing wind direction is northeast (NE) and east-northeast (ENE) most of the time. The research results showed that the study area is not suitable for using wind energy to generate energy.

Western disturbances: Occurrence and impact on wheat productivity in Punjab

2023-11-22T13:44:05+00:00

Western disturbances (WD) bring moderate to heavy rain in the northern parts of the country as well as heavy snow to the mountain areas of the Indian subcontinent. It is the source of most of the winter and post-monsoon rainfall in northwest India. Spatio-temporal analysis of western disturbances was carried out by using weather data recorded at Ballowal Saunkhri (sub-mountainous region), Ludhiana (central plain region) and Bathinda (south west region). The WD events per month were lowest in November and highest in March month averaged over all the agroclimatic regions. The highest numbers of WD events were found during February month in the Sub-mountainous region and during March in central plain region and south-western region. Winter rainfall quantity and number of rainfall events were higher in the sub-mountainous region followed by the central plain region and lowest in the south-western region. Higher number of WD during wheat growing season along with less and well distributed rainfall resulting in lower maximum temperature conditions were conducive for higher productivity in Punjab. The significantly positive correlation was observed between the number of western disturbances and wheat productivity during April month at central plain region and southwest region, whereas, the seasonal total number of WDs showed significant positive relationship at submontane region and southwest region.

Weather based paddy yield prediction using machine learning regression algorithms

2024-06-23T00:57:51+00:00

Paddy is a major crop in India which is highly affected by the weather variables resulting in drastic reduction of its yield; adverse all the variables drastically reduce the paddy yield. In this research, machine learning model was developed for prediction of paddy yield production by linear regression (LR), random forest regression (RFR), support vector regression (SVR), cat boost regression (CBR), and hybrid machine learning with variance inflation factor (VIF) LR-VIF, RFR-VIF, SVR-VIF, and CBR-VIF techniques. The dataset consists of variables (weather) for more than 15 years collected for the study area which is Madurai district, Tamil Nadu in India. Analysis was carried out by fixing 70% of data calibration & remaining 30% for validation in Jupyter notebook (Python programming). Results showed that CBR-VIF performed having nRMSE value 1.23 to 1.40% for Madurai South, nRMSE value 0.56 to 1.40% for Melur, nRMSE value 1.10 to 1.25% for Usilampatti, and nRMSE value 0.75 to 1.10% for Thirumangalam. The hybrid model of CBR along with VIF and then CBR model has shown improvement with high influenced weather variables such as maximum temperature, minimum temperature, rainfall normal, and actual rainfall.

Drought assessment in Kabul River basin using machine learnings

2024-07-29T04:45:04+00:00

Droughts significantly impact water resources and agriculture, leading to economic losses and potential human fatalities. This study aims to predict droughts by analysing changes in the Standardised Precipitation Evapotranspiration Index (SPEI) for the Kabul River basin using data from 1981 to 2022. The research is divided into three phases: calculating SPEI, splitting the dataset into training (80%) and testing (20%) subsets, and evaluating model performance. Various machine learning algorithms, including XGBoost, Decision tree, AdaBoost, and KNN, were employed alongside different climatic variables. The models were assessed using statistical metrics such as R², RMSE, MAE, MSE for regression, and confusion matrix, accuracy, precision, recall, F1 score, ROC AUC, and Log loss for classification. Results showed strong performance, with R² values of 0.97, 0.86, 0.92, and 0.96 for XGBoost, KNN, Decision tree, and AdaBoost, respectively. SPEI demonstrated significant potential for drought forecasting, and spatial distribution mapping revealed persistent moderate drought occurrences.

Development of growth-stage specific crop coefficient for drip irrigated wheat crop grown in climatic conditions of Jalandhar, Punjab

2024-07-01T14:20:16+00:00

The crop coefficient (Kc) values given in FAO-56 report need to corrected for local conditions due to differences in climate, soil types, and water management practices. Therefore, present study was undertaken to develop growth-stage-specific Kc values of wheat grown under drip irrigated conditions for two years (2022-23 and 2023-24) at Jalandhar, Punjab. The developed K_c values are 0.36, 0.77, 1.05, 0.69, and 0.25 during initial, development, mid, late, and end growth stages, respectively. These average Kc values can be effectively utilized to schedule irrigation for drip-irrigated wheat crops in the Jalandhar region of Punjab. Irrigating wheat crop under drip irrigation with developed K_c values not only enhances grain yield by 36% but also improves IWUE by three times and saves 61% use of irrigation water as compared to conventional irrigation practices (flood irrigation).

Influence of weather parameters on rice blast disease progression in Tamil Nadu, India

2024-06-21T14:29:11+00:00

Rice cultivation in Madurai district, Tamil Nadu, spans distinct cropping seasons: Kar (May – Jun), Semi-dry (Jul – Aug), Samba/ Late Samba (Aug – Sep), and Navarai (Dec – Jan), each with unique weather conditions and rice varieties. This study explores the correlation between weather parameters temperature, rainfall, humidity, sunshine hours, and wind speed and rice blast disease severity from 2021 to 2023. Using multiple linear regression with ordinary least squares (OLS), the analysis achieves high predictive accuracy (R² = 0.98). Results show that the maximum temperatures correlated negatively with disease severity (r = -0.869 to -0.892), while rainfall (r = 0.768 to 0.804) and wind speed (r = 0.766 to 0.938) correlated positively during the semi-dry season. Relative humidity exhibits varying impacts across seasons. These findings underscore the importance of tailored disease management strategies, such as targeted fungicidal applications during warmer seasons and optimized water management in others. By elucidating these dynamics, the study enhances understanding of weather-disease interactions, providing actionable insights to optimize disease management and enhance crop resilience in Madurai district.

Agroclimatic conditions influencing wheatgrass (Agropyron pectinatum, M. Biev.) production in Volgograd region of southern Russia

2024-05-30T14:27:47+00:00

Response of drip irrigated spring capsicum crop to pan evaporation-based irrigation levels in combination with varying nitrogen doses

2024-06-28T22:06:20+00:00

Relationship between NDVI, LST and simulated wheat yield with district wise reported yield: a case study of Bathinda, Punjab

2024-04-10T22:38:20+00:00

Comparative analysis of wheat yield prediction through artificial intelligence, simulation modelling and statistical analysis in Central Punjab

2024-01-20T01:36:53+00:00

Evaluating precipitation patterns of Vellanikkara, Kerala using SPI approach under future climatic scenarios

2024-03-31T20:05:51+00:00

Performance comparison of linear regression and ANN models in estimating monthly reference evapotranspiration (ET0)

2024-06-28T22:22:35+00:00

Comparative study of meteorological drought indices for Adilabad district, Telangana, India

2024-06-07T14:19:38+00:00

Trend analysis of rainfall (1984-2023) of Tlawng River basin of Mizoram, India using Man-Kendall test

2024-07-27T15:52:09+00:00