EXPLORING THE EFFECTS OF TECHNOLOGY PROFESSIONAL DEVELOPMENT ON THE 8TH GRADE NAEP MATHEMATICS ACHIEVEMENT

The purpose of this study was to examine if teachers’ participation in the educational technology professional development was a significant predictor in the 8th grade students’ mathematics achievement on the 2013 National Assessment of Educational Progress (NAEP). This non-experimental study analyzed selected variables from the 2013 NAEP 8th grade mathematics restricted dataset. The study considered students’ socio-economic status (SES), gender, race/ethnicity, teachers’ educational technology professional development, tenure-certification, and professional development on mathematic peer collaboration. Factors were created in principal component analysis with promax rotation method. The researcher has also used hierarchical regression analysis to explain how much of student achievement can be predicted through the independent variables. The study was guided by the amalgamation of two theoretical framework with Bandura’s self-efficacy (2013): Schulman’s Pedagogical Content Knowledge (PCK) from 1987 and Mishra and Koehler’s Technological Pedagogical Content Knowledge (TPACK) from 2006. The researcher used multiple regression analysis and hierarchical regression to assess the effects of SES, gender, race/ethnicity teachers’ educational technology professional development, tenure-certification, and professional development on mathematic peer collaboration with mathematics achievement serving as the dependent variable. In the initial step of the regression analysis, SES was considered as independent variable. In the following step, the students’ gender was included as an additional variable. This permitted the researcher to identify the influence of the students’ gender on the amount of explained variance. In the next step, the factor of educational technology professional development along with other factors were added to the analysis. The researcher was able to isolate the effect of the factor the amount of variance explained by repeating the process and filtering it for each of the race/ethnic subgroups reported in the NAEP. All these factors were then analyzed in hierarchical regression with mathematics achievement as the dependent variable to check the level predictability by each independent variable. Teachers should possess the necessary skills, including proficiency in technology, to develop lessons that effectively incorporate technology in instructional practices. The present study adds to the existing research that highlights the significance of technology-focused professional development for educators

A road map to IndOOS-2 better observations of the rapidly warming Indian Ocean

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(11), (2020): E1891-E1913, https://doi.org/10.1175/BAMS-D-19-0209.1The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.We thank the World Climate Research Programme (WCRP) and its core project on Climate and Ocean: Variability, Predictability and Change (CLIVAR), the Indian Ocean Global Ocean Observing System (IOGOOS), the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), the Integrated Marine Biosphere Research (IMBeR) project, the U.S. National Oceanic and Atmospheric Administration (NOAA), and the International Union of Geodesy and Geophysics (IUGG) for providing the financial support to bring international scientists together to conduct this review. We thank the members of the independent review board that provided detailed feedbacks on the review report that is summarized in this article: P. E. Dexter, M. Krug, J. McCreary, R. Matear, C. Moloney, and S. Wijffels. PMEL Contribution 5041. C. Ummenhofer acknowledges support from The Andrew W. Mellon Foundation Award for Innovative Research.2021-05-0

A sustained ocean observing system in the Indian Ocean for climate related scientific knowledge and societal needs

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hermes, J. C., Masumoto, Y., Beal, L. M., Roxy, M. K., Vialard, J., Andres, M., Annamalai, H., Behera, S., D'Adamo, N., Doi, T., Peng, M., Han, W., Hardman-Mountford, N., Hendon, H., Hood, R., Kido, S., Lee, C., Lees, T., Lengaigne, M., Li, J., Lumpkin, R., Navaneeth, K. N., Milligan, B., McPhaden, M. J., Ravichandran, M., Shinoda, T., Singh, A., Sloyan, B., Strutton, P. G., Subramanian, A. C., Thurston, S., Tozuka, T., Ummenhofer, C. C., Unnikrishnan, A. S., Venkatesan, R., Wang, D., Wiggert, J., Yu, L., & Yu, W. (2019). A sustained ocean observing system in the Indian Ocean for climate related scientific knowledge and societal needs. Frontiers in Marine Science, 6, (2019): 355, doi: 10.3389/fmars.2019.00355.The Indian Ocean is warming faster than any of the global oceans and its climate is uniquely driven by the presence of a landmass at low latitudes, which causes monsoonal winds and reversing currents. The food, water, and energy security in the Indian Ocean rim countries and islands are intrinsically tied to its climate, with marine environmental goods and services, as well as trade within the basin, underpinning their economies. Hence, there are a range of societal needs for Indian Ocean observation arising from the influence of regional phenomena and climate change on, for instance, marine ecosystems, monsoon rains, and sea-level. The Indian Ocean Observing System (IndOOS), is a sustained observing system that monitors basin-scale ocean-atmosphere conditions, while providing flexibility in terms of emerging technologies and scientificand societal needs, and a framework for more regional and coastal monitoring. This paper reviews the societal and scientific motivations, current status, and future directions of IndOOS, while also discussing the need for enhanced coastal, shelf, and regional observations. The challenges of sustainability and implementation are also addressed, including capacity building, best practices, and integration of resources. The utility of IndOOS ultimately depends on the identification of, and engagement with, end-users and decision-makers and on the practical accessibility and transparency of data for a range of products and for decision-making processes. Therefore we highlight current progress, issues and challenges related to end user engagement with IndOOS, as well as the needs of the data assimilation and modeling communities. Knowledge of the status of the Indian Ocean climate and ecosystems and predictability of its future, depends on a wide range of socio-economic and environmental data, a significant part of which is provided by IndOOS.This work was supported by the PMEL contribution no. 4934

Sustainable data-driven insights: Statistical analysis and artificial intelligence-driven modelling of aerosol concentrations in Hyderabad district, India

Air pollution stands as a pressing issue in contemporary times, leading to the loss of millions of lives and exerting detrimental effects on the economy. The aerosols especially particulate matter, which are dispersions of matter in air medium play an important role in manipulating the climatological variables in an area. The current study was developed in response to the need to study aerosols and particulates on annual levels using 20-year (2002–2021) daily mean Aerosol Optical Depth (AOD) product released by Moderate Resolution Imaging Spectrometer (MODIS) sensors, and to generate prediction models for AOD using artificial intelligence (AI) techniques for Hyderabad district in India. The results of daily mean analysis revealed a rising trend in the number of days with severe AOD (> 1). Yearly mean AOD distribution showed a percentage increase of 45.31 % from 2002 to 2021. Furthermore, factor analysis was carried out to check for correlations of AOD and PM2.5 with various meteorological and pollutant variables. It was observed that both PM2.5 and AOD had significant weak to moderate (p < 0.05; r < 0.5) correlations with both pollutants and meteorological variables. The hybrid deep learning-based CNN-LSTM was identified as the best-fit model to predict AOD, outperforming MLP – ARIMA and MLP models. CNN – LSTM showed an R2 of 0.70, MAE of 0.08, MSE of 0.02 and RMSE of 0.14

Watershed Prioritisation of Drainage Basins Based on Geomorphometric Parameters, Neyyar Watershed, India

Prioritisation of sub-watersheds (SWs) is becoming increasingly important in the conservation of natural resources, particularly in watershed planning. In this study, sub-watershed for the Neyyar basin was prioritised using three methods: morphometric analysis, principal component analysis (PCA) and hypsometric analysis. Morphometric analysis and hypsometric analysis were carried out using remote sensing (RS) and geographic information system (GIS) techniques, while PCA was performed for dimensionality reduction of morphometric parameters. The watershed was divided into 11 sub-watersheds (SW1–SW11), and each sub-watershed was given priority. To rank and prioritise SWs, 15 morphometric parameters were selected from the quantitative measures of morphometric analysis, including linear, relief, and areal. PCA was used to rank and prioritise SWs based on three highly correlated morphometric parameters. The hypsometric integral (HI) values were determined using the elevation relief ratio approach, and HI values were utilised to prioritise SWs. For both methods, such as morphometric analysis and PCA, a higher priority has been given to SW1. Using hypsometric analysis, higher priorities have been assigned to SW1, SW7, SW8, SW9, SW10 and SW11. The most common SWs that belong to the same priority of SWs and have a high correlation between them among the three methods are SW1, SW2, and SW5.The results of this analysis indicate that SW1 is a common high priority area with a significant risk of soil erosion, runoff and peak discharge. Therefore, decision-makers may utilise the high-priority sub-watershed to guide planning and development, measure conservation efforts and manage the land to prevent

Detection of land use/land cover changes in a watershed: A case study of the Murredu watershed in Telangana state, India

Land-use change refers to a change in how a particular area of land is utilised or managed by humans. Land-cover change refers to a change in some continuous features of the land, such as vegetation type, soil conditions, and so on. For the purpose of identifying change-vulnerable areas and creating sustainable ecosystem services, mapping and quantifying the state of land use/land cover (LULC) changes and change-causing factors are crucial. The present research utilizes a geographic information system (GIS) and remote sensing (RS) techniques to categorise and identify changes in a Murredu watershed in Telangana state, India, between 1996 and 2019. Five major LULC categories (agricultural land, forest, barren land, built-up area, and waterbodies) from satellite images of 1996 to 2019 were mapped. The maximum likelihood approach was used to supervise the classification process, and high-resolution Google Earth Pro was used to evaluate the accuracy of the classified map. The accuracy of the mapping was evaluated using the error matrix and Kappa statistics. Overall classification accuracy for the classified image of 2019 was found to be 90 % with overall kappa statistics of 85.98%. From these findings, change detection analysis shows that the area used for agricultural land, barren land, forest, built-up areas, and waterbodies has increased by 5.17%, 3.39%, 0.84%, and 0.26%, respectively, between 1996 and 2019. The forest area has decreased by 9.67% at the same time. Therefore, this research anticipates that the findings might provide information to planners, land managers, and decision-makers for the sustainable management and development of the natural resource

Delineation of groundwater potential zones and identification of artificial recharge sites in the Kinnerasani Watershed, India, using remote sensing-GIS, AHP, and Fuzzy-AHP techniques

The sustainable management of groundwater resources is crucial for ecological diversity, human health, and economic growth. This study employs scientific concepts and advanced techniques, including the analytic hierarchy process (AHP) and Fuzzy-AHP, to identify groundwater potential zones (GWPZs). Thematic maps representing drainage density, elevation, soil, geomorphology, slope, land use and land cover, and rainfall are used to delineate the GWPZs. Both techniques are employed to assign weights to these thematic maps based on their characteristics and water potential. The study revealed that in the investigated area, 17.76 and 18.27% of the final GWPZs (AHP and Fuzzy-AHP) can be classified as having poor potential, while 72.79 and 71.07% are categorized as having moderate potential. Moreover, 9.45 and 10.69% of the final GWPZs are identified as having high potential using the AHP and Fuzzy-AHP models, respectively. Receiver operating characteristics (ROCs) analysis is employed to validate these findings, demonstrating that the Fuzzy-AHP technique achieves an accuracy of 74% in identifying GWPZs in the region. This study utilizes the best method derived from both models to identify 26 suitable locations for artificial recharge sites. The reliable findings of this research offer valuable insights into decision-makers and water users in the Kinnerasani Watershed. HIGHLIGHTS The novel Fuzzy-AHP method for identifying GWPZs.; Suitable locations for artificial recharge sites were determined by selecting the best model between AHP and Fuzzy-AHP.; The obtained results are valuable for making informed decisions and facilitating sustainable groundwater management planning.

Morphometric analysis of watersheds: A comprehensive review of data sources, quality, and geospatial techniques

The analysis of morphometric parameters plays a crucial role in understanding and managing watersheds, making it a fundamental component of hydrological investigations. This review paper talks about how important it is to objectively evaluate morphometric parameters, with a focus on the evaluation of basins' relief, linear, and areal parameters. However, it is noted that there is a lack of a distinct standard classification and implication for each parameter in some research publications. Furthermore, the range and categories of values for each morphometric parameter have not been adequately addressed in previous studies. Many papers state whether a particular parameter's resultant value is high or low without providing specific value ranges or associated implications. Also, it is emphasised that the accuracy and sources of digital elevation models (DEMs) affect how well morphometric parameter analysis works, even when DEMs with the same resolution are used. The existing literature demonstrates that determining the value of each morphometric parameter poses significant challenges. Moreover, verifying the first and second Horton's laws and assessing the correlations between morphometric parameters have been lacking in some articles. The main objective of this review article is to address these gaps by providing an in-depth study of each parameter's categorization, including the range of values, the level of input data quality, the data products generated, and the applicability of the fundamental Horton's laws. By doing so, this review aims to enhance the understanding of morphometric parameters, their value ranges, and the significance of their application in watershed analysis and management

Analysis of cooling effect of water bodies on land surface temperature in nearby region: A case study of Ahmedabad and Chandigarh cities in India

Erratic and unplanned development of the urban area has posed a threat to the environment in a country like India where the development is haphazard mostly. Congested and unsustainable planning, reduction in green covers and increased emissions from industries and vehicles have given birth to many climatic issues. One such issue is land surface temperature (LST) variations giving rise to Urban Heat Island (UHI) phenomenon. This study evaluates and provides direct evidence with the help of remote sensing technique about how stream water features affect the temperature variation in urban areas where the land use condition is very distinct. Surface water bodies present in an area are responsible for prospective cooling through evaporation thereby reducing the heating effect. Landsat images of the years 2009, 2010 and 2011of 30-meter resolution has been processed to produce the LST of the study areas. Water bodies, i.e., Sukhna Lake in Chandigarh and Sabarmati River in Ahmedabad are the research points to find the variation in microclimate developed near these water bodies. An appreciable average temperature dip of 7.51 °C and 3.12 °C is observed during summer and winter, respectively for three years near the Sukhna Lake in Chandigarh city. An average dip of about 1.57 °C and 1.71 °C is observed during summer and winter, respectively on the right bank of Sabarmati river up to an influence distance of about 200 to 300 m. The average fall of temperature near the left bank is about 0.69 °C and 0.65 °C during summer and winter, respectively. Keywords: Urban heat island, Land surface temperature, Water bodies, Cooling effec

Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India

Land surface temperature (LST) is a key parameter for surface energy balance and urban climatology studies. LST is affected by the characteristics of the land surface such as vegetation cover and its type, land use-land cover and surface imperviousness. Incessant urbanization has resulted in many fold increase in the urban area and it has caused significant changes in the land surface. The difference in altitude of two points, that are located at different parts of a vast study area, may be large. The aim of the present study is to investigate the effect of change in elevation over LST. LST data from Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model from ASTER have been used. Consistent inverse linear trend is observed between LST and elevation for all the study seasons. High correlation (R2 = 0.73–0.87) is found between elevation and mean LST. It is seen that the change in LST due to elevation difference between two points separated in space in horizontal direction varies from 3.5 °C to 4.6 °C per 1000 m which is relatively lesser than the condition when two points are separated in vertical direction (5.0 °C–10.0 °C per 1000 m) i.e. along a vertical column of air. The study concludes that in any study related with spatial distribution of LST over a large area, effect of change in elevation at different locations shall also be considered and LSTs at different location shall be rationalized on the basis of their comparative elevations. Keywords: Land surface temperature, Elevation, MODIS, ASTE