NASA's Power Research Articles

Traditional Dual Tank Water Management: A Study of Kolakanatham in Perambalur District, Tamil Nadu

Societies make rainwater harvesting structures to collect and store rain water to cater for their immediate and future needs. In a remote village in the Perambalur district, the people use the dual temple tank, nearly 350 years old, for their drinking purposes. This study tries to understand the traditional knowledge embedded in the dual pond structures, cascade aerators, and an open well connected with the ground aquifer inside the tank. This study delves into projected rainfall trends using NASA POWER data for 2050, revealing a noticeable increase and an assessment of pond water quality parameters demonstrating adherence to acceptable limits set by the Bureau of Indian Standards (BIS). However, despite meeting these standards, the water quality index deems the water unsuitable for direct human consumption, though it remains suitable for domestic household use. Moreover, analysis of land cover and land use changes by Sentinel2A data over the past 22 years underscores significant growth in settlement areas, indicative of a rising demand for water resources. The study proposes the restoration and utilization of the dual tank system as a potential solution to address this burgeoning water demand, offering a reliable water source and preserving traditional practices within the local community.

Dynamic stochastic model of allometric equations and cumulative distribution for biomass-carbon in Pinus hartwegii Lindl., facing climate change

Objective: to construct a dynamic stochastic model with validated biospheric-interaction, estimating allometric equations for the total volumetric increase and cumulative distribution of biomass for Pinus hartwegii Lindl in the states of Mexico and Puebla, considering climate change. Design/ Methodology/ Approach: the methodology included the use of SiBiFor numerical databases, NASA Power data, Ordinary Least Squares mathematical models, the Random-Forest software, Ridge model with regression, R algorithms and Newton volumetric estimation equations. Results: estimated allometric equations were obtained for the total volume of trees in 2023, highlighting the importance of linear regression models and the validity of the variables used. Newton's mathematical equations and theoretical models for excurrent tree-form types were found to have the best accuracy in estimating the total volume of the barked tree. Limitations of the study/ Implications: this study has limitations in terms of generalization to other forest types and the availability of data in Mexico. However, it highlights the importance of understanding forest responses to environmental changes and the need for validated dynamic stochastic models to estimate allometric equations and assess carbon sequestration. Findings/ Conclusions: this study highlights the importance of understanding and assessing the carbon storage capacity of forests, especially in the context of climate change. In addition, it underlines the usefulness of linear regression models and variable validation to estimate carbon sequestration in Pinus hartwegii forests.

Comparison of Climate Data from NASA POWER Reanalysis and Data Measured at Surface Weather Stations for Application in Brazilian Paving Projects

Comparison of Climate Data from NASA POWER Reanalysis and Data Measured at Surface Weather Stations for Application in Brazilian Paving Projects

Statistical analysis of NASA POWER meteorological data for the assessment of climate variability in Adamawa State

Statistical analysis of NASA POWER meteorological data for the assessment of climate variability in Adamawa State

Evaluation of drought events using multiple drought indices under climate change in the Upper Indus Basin.

Spatiotemporal variations in drought events were examined through multiple drought indices in the Upper Indus Basin (UIB) during the 1980-2020 period using observed climate data of precipitation, temperature (T max., T min., and T mean), and potential evapotranspiration (PET) from 16 meteorological stations. Software like ClimPACT2 (to quality check data and generate SPI, SPEI, and CDD drought indices), DrinC (to generate AI, eRDI and PD drought indices), CMhyd (to bias correct NASA POWER gridded data), and ArcGIS (to map extreme drought years) were used in this study. It was revealed that precipitation decreased at 30mm/decade and maximum and minimum temperatures increased at 0.132°C/decade and 0.23°C/decade, aridity increased by decreasing aridity index at 0.15/decade, and number of consecutive dry days increased at 2days/decade. All drought indices reflected decreasing trends, indicating a warming and drying climatic regime. The eRDI drought index analysis revealed that droughts have occurred for 12.6years in the last four decades, and mild droughts (15%) are more common, followed by moderate droughts (9.4%), severe droughts (4.7%), and extreme droughts (2.4%). The study provides comparisons of drought patterns under changing climate in three distinct climatic and physical regions of UIB, i.e., Jammu, Kashmir, and Ladakh, and reveals that the UIB is not free from droughts.

Management of the Yeh Luwi Estuary Area as a Disaster Mitigation Strategy for Climate Change

Abstract The Yeh Luwi Estuary in Tibubeneng, Bali, faces escalating environmental threats exacerbated by climate change. Rising sea levels intensified precipitation patterns, and increased erosion and abrasion risks pose significant challenges to this critical ecological and socio-economic area. Climate-induced impacts endanger local cultural sites like Perancak Temple and vital infrastructure such as Berawa Beach. Addressing these challenges requires urgent implementation of comprehensive climate adaptation and mitigation strategies. Sustainable land-use practices, including preserving natural buffers and integrating cultural and ecological tourism development, are essential to enhancing coastal resilience. Effective management strategies, informed by SWOT analysis and climate data from sources like NASA POWER, are crucial for mitigating flood and erosion risks and safeguarding the estuarine ecosystem and community livelihoods. Collaborative efforts involving stakeholders and leveraging local cultural strengths are pivotal in building resilience and ensuring the sustainable future of the Yeh Luwi Estuary amidst ongoing climate change impacts.

A scientific-based protocol for the calculation of two standardized drought indices using NASA POWER data

A scientific-based protocol for the calculation of two standardized drought indices using NASA POWER data

Integrated machine learning models for enhancing tropical rainfall prediction using NASA POWER meteorological data

ABSTRACT This research evaluates the performance of deep learning (DL) models in predicting rainfall in George Town, Penang, utilizing the open-source NASA POWER meteorological data, which includes variables such as rainfall, dew point, solar radiation, wind speed, relative humidity, and temperature. This study introduces a newly developed hybrid DL based on the integration of a 2D convolutional neural network (CNN2D) with a bidirectional recurrent neural network (BRNN) and a bidirectional gated recurrent unit (BGRU). The proposed models, CNN2D–BGRU and BRNN–BGRU, were compared against standalone models CNN2D, BRNN, and BGRU. The results indicate that the BRNN–BGRU model is the most effective, with a root mean square error (RMSE) value of 2.59, a mean absolute error (MAE) value of 1.97, a Pearson correlation coefficient (PCC) value of 0.79, and a Willmott index (WI) value of 0.88. In a 3-day prediction, the BRNN–BGRU model also performed the best, with a test WI value of 0.83, a PCC value of 0.69, a RMSE value of 3.02, and MAE value of 2.34. The hybrid BRNN–BGRU model consistently excels in predicting multi-step rainfall in tropical regions using the NASA POWER dataset. These findings can contribute to the development of advanced rainfall-predicting systems for more effective management of water resources and flooding in urban areas.

Heat stress on dairy cattle

Climate change significantly impacts the production, health, welfare, and survival of farm animals. The prolonged heat stress is a major concern in tropical and subtropical regions. Heat tolerance enables livestock to maintain thermal balance under extreme climatic conditions, thus ensuring productivity and animal welfare. There is a feasibility of genetic selection for heat tolerance to improve production and reproduction traits. This genetic selection may provide enduring advantages in maintaining productive and reproductive performances as heat stress is a heritable trait. To identify and integrate traits associated with thermal tolerance into breeding programs, it is imperative to have a robust animal performance records and meteorological data. Temperature humidity index (THI) is a widely used biomarker for assessing heat stress in dairy cows, as it combines air temperature and relative humidity. In regions with sparse weather station coverage, NASA POWER satellite data is a viable alternative for obtaining meteorological information to calculate THI and evaluate heat stress impacts. There are multiple models for calculating THI and those focusing on humidity being more effective in humid climates. Genetic models, particularly reaction norm models based on random regressions, were introduced for estimating genetic parameters for heat tolerance in cattle. These models help to understand genotype-environment interactions and non-linear relationship between THI and milk yield reduction. The continued genetic selection for milk production without considering heat tolerance traits increases susceptibility to heat stress, emphasizing a need for comprehensive selection objectives. This review underscores the importance of incorporating heat tolerance traits into genetic selection for dairy cattle and highlights various biomarkers and genetic models used to estimate heat tolerance. Incorporating these traits into breeding goals is essential to improve thermotolerance while maintaining productivity, bolstering resilience of cattle to climate change.

Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments.

Deep Learning Approaches for Wildfire Severity Prediction: A Comparative Study of Image Segmentation Networks and Visual Transformers on the EO4WildFires Dataset

This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1, and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. These data cover 45 countries with a total of 31,730 wildfire events from 2018 to 2022. All of these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test the effectiveness of different deep learning architectures in predicting the size and shape of wildfire-burned areas. This study incorporates both image segmentation networks and visual transformers, employing a consistent experimental design across various models to ensure the comparability of the results. Adjustments were made to the training data, such as the exclusion of empty labels and very small events, to refine the focus on more significant wildfire events and potentially improve prediction accuracy. The models’ performance was evaluated using metrics like F1 score, IoU score, and Average Percentage Difference (aPD). These metrics offer a multi-faceted view of model performance, assessing aspects such as precision, sensitivity, and the accuracy of the burned area estimation. Through extensive testing the final model utilizing LinkNet and ResNet-34 as backbones, we obtained the following metric results on the test set: 0.86 F1 score, 0.75 IoU, and 70% aPD. These results were obtained when all of the available samples were used. When the empty labels were absent during the training and testing, the model increased its performance significantly: 0.87 F1 score, 0.77 IoU, and 44.8% aPD. This indicates that the number of samples, as well as their respectively size (area), tend to have an impact on the model’s robustness. This restriction is well known in the remote sensing domain, as accessible, accurately labeled data may be limited. Visual transformers like TeleViT showed potential but underperformed compared to segmentation networks in terms of F1 and IoU scores.

Solar irradiation forecast enhancement using clustering based CNN-BiLSTM-attention hybrid architecture with PSO

Accurate solar irradiation forecasting is essential for optimising solar energy use. This paper presents a novel forecasting approach: the ‘Clustering-based CNN-BiLSTM-Attention Hybrid Architecture with PSO’. It combines clustering, attention mechanisms, Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (BiLSTM) networks, and Particle Swarm Optimisation (PSO) into a unified framework. Clustering categorises days into groups, improving predictive capabilities. The CNN-BiLSTM model captures spatial and temporal features, identifying complex patterns. PSO optimises the hybrid model’s hyperparameters, while an attention mechanism assigns probability weights to relevant information, enhancing performance. By leveraging spatial and temporal patterns in solar data, the proposed model improves forecasting accuracy in univariate and multivariate analyses with multi-step predictions. Extensive tests on real-world datasets from various locations show the model’s effectiveness. For example, with NASA power data, the model achieves a Mean Absolute Error (MAE) of 24.028 W/m2, Root Mean Square Error (RMSE) of 43.025 W/m2, and an R 2 score of 0.984 for 1-hour ahead forecasting. The results show significant improvements over conventional methods.

Benchmark of estimated solar irradiance data at high latitude locations

Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76°N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65°N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65°N, the median rMAE of the different data products improves 3.2 – 9.4 %abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow-affected stations, the median rMAE improves 1.9 – 8.1 %abs, depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division.

Unlocking development of green hydrogen production through techno-economic assessment of wind energy by considering wind resource variability: A case study

Generating hydrogen from renewable energy sources is widely recognized as an effective strategy for addressing critical challenges in the energy sector while contributing to the reduction of greenhouse gas emissions. This study aims to assess the wind energy potential for electricity and hydrogen production in five locations on Sumba Island using 10 years (2011–2020) of data from the NASA's POWER Project. A wind variability assessment was performed on five wind turbines ranging from 500 kW to 1.5 MW and using electrolysis technology. The results indicated a hydrogen production potential of 25–57 tons per year. Economic analysis of hydrogen production for Haharu reveals an attractive internal rate of return (IRR) of 43% with a payback period (PBP) of 2.25 years and a net present value (NPV) of 1.3 MM USD, the levelized cost of electricity (LCOE) of 0.0241 USD/kWh, and the levelized cost of Hydrogen (LCOH) of 1.01 USD/kg. Sensitivity analysis underscores the critical influence of investment and revenue on project profitability, highlighting their substantial impact on determining a project's success or failure. These findings emphasize the potential of Sumba Island as a hub for sustainable energy production to meet the growing demand for clean and renewable energy sources.

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Following the COVID-19 restrictions, there was a sharp decline in global air quality and related environmental metrics. Due to the limited availability of in situ atmospheric data in Bangladesh, this study collected data on various air pollutants (NO2, SO2, CO, and PM2.5), greenhouse gases (CO2, CH4, and O3), as well as meteorological variables like Land Surface Temperature (LST), Relative Humidity (RH), Precipitation, surface albedo and Aerosol Optical Depth (AOD) from different datasets by Google Earth Engine (GEE), the International Energy Agency (IEA), NASA Giovanni, and NASA Power Access Viewer, covering periods before (2019), during (2020), and after (2021–2023) the COVID-19 lockdown in Bangladesh. GIS-based assessment alongside Principal Component Analysis (PCA) has been performed to explore the patterns, trends and correlations among the observed variables. Results showed in 2020 compared to 2019, NO2, SO2, CO, PM2.5, and CO2 concentrations decreases by 1.94, 16.67, 1.95, 2.08, and 6 %, respectively, while CH4 and O3 continued to rise. Meteorological variables exhibited a 0.16 °C decreases in LST, 6.4 % increases in RH, a 6 % reduction in AOD, and 6.36 % declines in surface albedo. Post-lockdown in 2021, air pollutants surged (NO2, SO2, CO, and PM2.5 increases by 17.3, 23.6, 0.6, and 8.3 %, respectively), with CO2, LST, and AOD rising by 8.5 %, 0.13 °C, and 8.3 %, and a slight 0.46 % decrease in RH compared to 2019 due to resuming more economic activities, transportation and industrial production works. The years 2022–2023 saw slight improvements in most variables except CH4, though concentrations did not revert to those of 2019. The findings of correlation coefficients revealed that pollutants and GHG are highly correlated with the meteorological variables specially with RH. This study underscores the substantial shifts in atmospheric variables from pre-to post-lockdown periods, offering valuable insights for more effective management of the greenhouse effect and air pollution control strategies.

Geostatistical evaluation of the impact of climate variability on malaria incidence In the South-West of Nigeria

Malaria remains a significant health concern in Nigeria, particularly in the South-West region. This study assesses the impact of temperature and rainfall on malaria incidence and prevalence in South-West Nigeria using remotely sensed and modelled data sourced from the Malaria Atlas Project and NASA's POWER database covering 2000 to 2020. The study adopts the Geographically Weighted Regression geostatistical model to establish the relationship between malaria and rainfall and temperature in the study area. The result shows a rising oscillating annual mean temperature trend of 0.0088oC/yr-1 from 2000 to 2020. The malaria incidence exceeds 8 million cases annually, peaking in 2020 at almost 10 million cases. The rising trend of malaria incidence highlights the inadequacy of the malaria intervention programmes to meet their goal of reducing malaria incidence by 40% by 2020. The study highlights the spatial variations, with high incidence in urban centres like Lagos and Ibadan metropolises, their satellite towns, as well as other prominent and capital towns including Oshogbo, Ilesa, Akure, Ijebu-Ode and Abeokuta. Contrary to this, the greater malaria prevalence was recorded in less densely populated areas of Oyo state, Imeko-Afon, Odeda, Yewa and Ijebu-Waterside areas in Ogun state as well as Ose and Idanre in Ondo state. The Geographically Weighted Regression equation model shows a strong positive correlation between malaria prevalence and temperature at a significance of 0.76 compared to rainfall which exhibits no association indicating the relevance of temperature as an explanatory indicator of malaria. With the continuous endemicity of malaria in the South-West, malaria management and control efforts should be focused on high-incidence areas in the South-West and Nigeria in general to fulfil the Sustainable Development Goal of Good health and well-being and the eradication of malaria by 2030.

Elucidating the effect of heat stress on milk production and composition in Jersey crossbred cows using test day records integrated with NASA POWER satellite data.

The present study was undertaken to determine the effect of heat stress on milk production (test day milk yield) and compositional traits (fat%, protein%, fat yield, protein yield) as well as to observe the pattern of response to increasing heat load on these traits in Jersey crossbred cows, maintained at ICAR-National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal, India. The weather information, obtained from the NASA POWER database based on the location of the farm latitude and longitude, was used to calculate the Temperature Humidity Index (THI). To analyze the data, a linear model was fitted to the milk production and compositional records, which were adjusted for additive genetic effect of animal, permanent environmental effect of animals and known environmental sources of variations. Subsequently, a segmented linear regression model was fitted, and the least squares estimates of production and composition traits in different classes of THI were used as the dependent variable. Two THI break-points (BP) for milk yield and one THI BP for fat yield, protein %, and protein yield were found. The first and second BP for milk yield was at THI 59 and 77, respectively, with a significant decline in milk yield of -0.04kg/unit of THI at second BP. The BP for fat and protein yield was at THI 76, with a decline rate of -1.18 and - 0.61g/unit of THI increase, respectively. The findings revealed the significant adverse effects of THI on milk production and composition traits in Jersey crossbred cattle.

An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.

An integrated framework for rainfall prediction and analysis using a Stacked Heterogeneous Ensemble Model (SHEM)

Recent climate conditions in India have led to numerous disasters, including floods, overflowing rivers, broken dams, and reduced vegetation. Machine learning aims to train models to make predictions based on historical data. Although many studies have focused on predictions and model building, this research introduces a novel integrated framework using a Stacked Heterogeneous Ensemble Model (SHEM). It aims to enhance prediction accuracy by employing another novel approach for imputing missing values, the Variable Specific Hot Deck (VSHD) imputation method. The outcomes were contrasted with established machine learning techniques, including random forest, decision tree, k-nearest neighbor, and support vector machine. After completing the imputation, we proceeded to implement the SHEM model. Real-time climate data for the Cuddalore location in Tamil Nadu state, South India was collected from the NASA Power Access viewer portal to verify the accuracy level of the proposed model. Performance analysis indicates that the proposed imputation outperforms all four alternative models with a 30% average improvement in accuracy. Moreover, the developed SHEM model has a reduced RMSE value of 0.321 and an R-squared value of 0.952 and shows a 9% improvement in accuracy compared to the base model’s performance. Furthermore, these prediction results are compared to an LSTM (Long Short Term Memory), a deep learning model to calculate accuracy and loss, showing that the proposed achieves high accuracy and significant loss during validation. The obtained results can serve as a guideline for atmospheric scientists and various weather forecasting applications, helping them to choose the most appropriate machine learning method for their prediction task.

Assessment of Crop water requirement using FAO CROPWAT 8.0 for Groundnut in South Odisha

Water has a major impact on crop growth, output, and quality and increased production with less water consumption is crucial due to increased demand from domestic and industrial sectors. With evaporative demand-based irrigation scheduling the water consumption gets reduced and improves water productivity. The type of soil, growing season, weather, and crop type affect specific crops' water needs. To compute crop water requirement, irrigation requirement, and its schedule for groundnut cultivated during the rabi season in south Odisha, India, the FAO CROPWAT 8 program was used, with the meteorological data of Centurion University, Paralakhemundi, and NASA power. The water requirement for groundnut was 362.1 mm, the irrigation requirement for groundnut crops was 323.7 mm, and crop evapotranspiration (ETc) was 373.4 mm during rabi season. At 50% critical depletion of soil moisture and soil replenishment to field capacity, the yield did not decrease. These results indicate that irrigation schedules can be practiced for different regions' crops without experimental results by estimating crop water requirements and irrigation schedules obtained through the FAO CROPWAT 8 model.