Environmental Science Data Research Articles

Land Use Change in a Typical Transect in Northern China and Its Impact on the Ecological Environment

This paper uses seven periods of land use/cover data from 1990 to 2020 to investigate the spatio-temporal features of land use change in a typical transect in northern China. By using the land use transfer matrix, transect analysis, and ecological contribution rate, image interpretation data obtained from the Chinese Academy of Sciences Resource and Environmental Science Data Platform were analyzed using a spatial overlay to quantitatively examine the transect’s land use changes and their impact on the eco-environment. The results indicate that (1) the transect’s land use is dominated by cropland and grassland. (2) Cropland, forest land, and waters experienced significant transitions in 1995 and 2000, which was linked to socio-economic development and policy factors. (3) The total dynamic percentage of land use change is 4.52%, with built-up land and cropland showing the highest change. (4) The transect’s eco-environmental quality (EQ) has significantly declined, with the quality index dropping from 0.3839 to 0.3773. The transformation of cropland to forest land improves the eco-environment, while the transformation to built-up land has negative impacts. Human activities adversely affect the EQ. The findings are promising for leading the development of conserving the eco-environment and supporting the formation of regionally differentiated paths of ecological civilization construction in the transect.

Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world’s most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners’ comfort and confidence with technical and nontechnical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM. Supplementary materials for this article are available online.

Trends in Science Humanities Convergence Education in Japanese Universities and their Implications

As science and technology advance, fundamental changes are taking place in individual lives and society. At this time, the dichotomous thinking that excessively distinguishes between science and humanities, as is prevalent in Korea and Japan, hinders the nurturing of the next generation’s capabilities. Since the 21st century, the Japanese government has continued to promote transversal and/or convergence education in science and humanities to overcome the limitations of early specialization in secondary education and the resulting limitations in college education. With the help of government financial and policy support, Japanese universities offer transversal education in a variety of ways, such as degree programs in interdisciplinary fields such as environmental studies and data science, a problem-solving-oriented curriculum, the activation of double major and minor systems, and bachelor degree programs in the liberal arts. Also, a trend began to redefine education goals to fit its nature and strengthen the liberal arts and sciences. This article explores the background and process of promoting transversal education through the Japanese government's official documents and articles, showing that it is being carried out within the larger framework of the pursuit of liberal arts in the 21<sup>st</sup> century. Lastly, this study shows that these trends fit into the context of liberal education and demonstrates how this field is reemerging globally.

Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

Opening a conversation on responsible environmental data science in the age of large language models

Abstract The general public and scientific community alike are abuzz over the release of ChatGPT and GPT-4. Among many concerns being raised about the emergence and widespread use of tools based on large language models (LLMs) is the potential for them to propagate biases and inequities. We hope to open a conversation within the environmental data science community to encourage the circumspect and responsible use of LLMs. Here, we pose a series of questions aimed at fostering discussion and initiating a larger dialogue. To improve literacy on these tools, we provide background information on the LLMs that underpin tools like ChatGPT. We identify key areas in research and teaching in environmental data science where these tools may be applied, and discuss limitations to their use and points of concern. We also discuss ethical considerations surrounding the use of LLMs to ensure that as environmental data scientists, researchers, and instructors, we can make well-considered and informed choices about engagement with these tools. Our goal is to spark forward-looking discussion and research on how as a community we can responsibly integrate generative AI technologies into our work.

Environmental data science: Part 2

SummaryEnvironmental data science is a multi‐disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two‐part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses common research themes that appear in the contributions to Part 2, which focuses on applications. These include spatio‐temporal modeling; the problem of aggregation and sparse sampling; the importance of community‐building and training for the next generation of specialists in environmental data science; and the need to look forward at the challenges that lie ahead for the discipline. This editorial complements that of Part 1, which largely focuses on statistical methodology; see Zammit‐Mangion, Newlands, and Burr (2023).

Environmental data science: Part 1

SummaryEnvironmental data science is a multi‐disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two‐part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses common strands of research that appear in the contributions to Part 1, which largely focus on statistical methodology. These include temporal, spatial and spatio‐temporal modeling; statistical computing; machine learning and artificial intelligence; and the critical question of decision‐making in the presence of uncertainty. This editorial complements that of Part 2, which largely focuses on applications; see Burr, Newlands, and Zammit‐Mangion (2023).

2022 Editorial Collaborators

EnvironmetricsVolume 34, Issue 1 e2790 EDITORIAL COLLABORATORS 2022 Editorial Collaborators First published: 29 January 2023 https://doi.org/10.1002/env.2790Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue1Special Issue: Environmental Data Science: Part 1February 2023e2790 RelatedInformation

Front Cover Image, Volume 34, Number 1, February 2023

The cover image is based on the Research Article Large-scale environmental data science with ExaGeoStatR by Sameh Abdulah et al., https://doi.org/10.1002/env.2770. Image Credit: Xavier Pita, KAUST.

A novel bivariate Lomax-G family of distributions: Properties, inference, and applications to environmental, medical, and computer science data

<abstract><p>This paper presents a novel family of bivariate continuous Lomax generators known as the BFGMLG family, which is constructed using univariate Lomax generator (LG) families and the Farlie Gumbel Morgenstern (FGM) copula. We have derived several structural statistical properties of our proposed bivariate family, such as marginals, conditional distribution, conditional expectation, product moments, moment generating function, correlation, reliability function, and hazard rate function. The paper also introduces four special submodels of the new family based on the Weibull, exponential, Pareto, and log-logistic baseline distributions. The study establishes metrics for local dependency and examines the significant characteristics of the proposed bivariate model. To provide greater flexibility, a multivariate version of the continuous FGMLG family are suggested. Bayesian and maximum likelihood methods are employed to estimate the model parameters, and a Monte Carlo simulation evaluates the performance of the proposed bivariate family. Finally, the practical application of the proposed bivariate family is demonstrated through the analysis of four data sets.</p></abstract>

Intensified Warming and Aridity Accelerate Terminal Lake Desiccation in the Great Basin of the Western United States

AbstractTerminal lakes in the Great Basin (GB) of the western US host critical wildlife habitat and food for migrating birds and can be associated with serious human health and economic consequences when they desiccate. Water levels have declined dramatically in the last 100+ years due to diversion of inflows, drought and climate change. Satellite‐derived environmental science data records (ESDRs) from the MODerate‐resolution Imaging Spectroradiometer (MODIS) (snow cover, evapotranspiration (ET) and land surface temperature (LST)), enable a unique approach to evaluate the effects of aridification on terminal lakes and to study their individual vulnerabilities. Surface and air temperatures in the GB are rising dramatically, with a sharp rise in the rate of increase observed beginning around 2011, while the number of days of snow cover is declining especially in the western mountainous part of the GB as exemplified in Mono Basin, California. Rising temperatures coincide with fewer days of snow cover, a decrease of inflow to the lakes and greater evaporation of water from the lakes. MODIS ESDRs show strong and statistically significant increasing surface temperature (LST) in the GB, a reduction in the number of days of snow cover, and mixed results in ET. ET declined slightly in the more arid parts of the GB due to greater moisture restrictions to evaporation from extended drought, while ET increased in the more‐vegetated, wetter, mountainous northeastern parts as temperatures have risen. Severe and costly ecological, human health and economic consequences are expected if the lakes continue to decline as predicted.

Know Before You Go: Data-Driven Beach Water Quality Forecasting.

Forecasting environmental hazards is critical in preventing or building resilience to their impacts on human communities and ecosystems. Environmental data science is an emerging field that can be harnessed for forecasting, yet more work is needed to develop methodologies that can leverage increasingly large and complex data sets for decision support. Here, we design a data-driven framework that can, for the first time, forecast bacterial standard exceedances at marine beaches with 3 days lead time. Using historical data sets collected at two California sites, we train nearly 400 forecast models using statistical and machine learning techniques and test forecasts against predictions from both a naive "persistence" model and a baseline nowcast model. Overall, forecast models are found to have similar sensitivities and specificities to the persistence model, but significantly higher areas under the ROC curve (a metric distinguishing a model's ability to effectively parse classes across decision thresholds), suggesting that forecasts can provide enhanced information beyond past observations alone. Forecast model performance at all lead times was similar to that of nowcast models. Together, results suggest that integrating the forecasting framework developed in this study into beach management programs can enable better public notification and aid in proactive pollution and health risk management.

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

Large‐scale environmental data science with ExaGeoStatR

AbstractParallel computing in exact Gaussian process (GP) calculations becomes necessary for avoiding computational and memory restrictions associated with large‐scale environmental data science applications. The exact evaluation of the Gaussian log‐likelihood function requires storage and operations, where is the number of geographical locations. Thus, exactly computing the log‐likelihood function with a large number of locations requires exploiting the power of existing parallel computing hardware systems, such as shared‐memory, possibly equipped with GPUs, and distributed‐memory systems, to solve this exact computational complexity. In this article, we present ExaGeoStatR, a package for exascale geostatistics in R that supports a parallel computation of the exact maximum likelihood function on a wide variety of parallel architectures. Furthermore, the package allows scaling existing GP methods to a large spatial/temporal domain. Prohibitive exact solutions for large geostatistical problems become possible with ExaGeoStatR. Parallelization in ExaGeoStatR depends on breaking down the numerical linear algebra operations in the log‐likelihood function into a set of tasks and rendering them for a task‐based programming model. The package can be used directly through the R environment on parallel systems without the user needing any C, CUDA, or MPI knowledge. Currently, ExaGeoStatR supports several maximum likelihood computation variants such as exact, diagonal super tile and tile low‐rank approximations, and mixed‐precision. ExaGeoStatR also provides a tool to simulate large‐scale synthetic datasets. These datasets can help assess different implementations of the maximum log‐likelihood approximation methods. Herein, we show the implementation details of ExaGeoStatR, analyze its performance on various parallel architectures, and assess its accuracy using synthetic datasets with up to 250K observations. The experimental analysis covers the exact computation of ExaGeoStatR to demonstrate the parallel capabilities of the package. We provide a hands‐on tutorial to analyze a sea surface temperature real dataset. The performance evaluation involves comparisons with the popular packages GeoR, fields, and bigGP for exact Gaussian likelihood evaluation. The approximation methods in ExaGeoStatR are not considered in this article since they were analyzed in previous studies.

Agricultural and Environmental Data Science— Interdisciplinary Field for the Sustainable Management—An Overview

Agricultural and Environmental Data Science— Interdisciplinary Field for the Sustainable Management—An Overview

One script to solve it all – an open-source-based framework for a digital workflow based on WWTP data

Abstract The increasing use of digital technology has many advantages, but is insufficiently used in urban water management. This article describes a workflow based on detailed German rules and regulations for the use of routine data from wastewater treatment plants (WWTP) for design, assessment, benchmarking, and activated sludge modelling. Our workflows use data that are routinely collected. However, prior to use the data must first be checked. It includes data import, plausibility checks, data evaluation, and subsequent plant design. In addition, the data available in the system can be used to supply an activated sludge model of the plant as a basis for use in redesign and process optimisation. The main advantages of such an approach are time savings, increased quality, and a transparent and comprehensible procedure. Exemplary results are presented for individual elements of the workflow. We also provide a comprehensive classification of the question in the context of environmental data science. WWTP routine data bases are an important source for information needed for evaluation and optimisation.

Densely Connected Neural Networks for Nonlinear Regression.

Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimensions of the proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models, including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation with observations, which indicates that our model could advance environmental data science.

Intersection between environmental data science and the R community in Latin America

AbstractEnvironmental data science is used to solve environmental problems, and to help answer complex questions about biodiversity loss, climate change, and water scarcity, among others thereby allowing sustainable decision‐making using data‐driven approaches. In this opinion piece, I describe some recent Latin American experiences in the data science community, and what is needed for environmental data scientists to take advantage of those experiences. Specifically, I show the importance of being part of a diverse and local data science community.

Delivering net zero

What comes next? The 2021 Glasgow Climate Pact rightly expressed “alarm and utmost concern” at the impacts of climate change that are already being felt around the world, following repeated instances of death and destruction brought by extreme heat, floods and wildfires.11 United Nations Framework Convention on Climate Change (2021) Glasgow Climate Pact, UNFCCC. https://unfccc.int/sites/default/files/resource/cma2021_L16_adv.pdf It explicitly recognised that further impacts will be much lower at a 1.5C temperature increase than at 2C, and it stated that this means almost halving global carbon dioxide emissions this decade and reaching net zero around mid-century. More needs to be done to close the considerable gap between the ambition to limit climate change and the promised emission reductions. Pledges made by countries in Glasgow on issues such as deforestation, electric vehicles and methane need to be translated into real policy and action, accountability mechanisms need to be strengthened, and international climate finance needs to be bolstered. Nevertheless, a clear commitment to a global net-zero future has been made by global governments, mirrored throughout much of the private sector, and the principal challenge now is one of delivery on an accelerated timeframe. “This is both unimaginably daunting and eminently doable” “such investment must be fairly distributed and aligned to a broader strategy to deliver a just transition” “A critical part of the institutional infrastructure is ensuring horizontal and vertical connectivity in decision-making across local and national government” “emissions reduction commitments … remain woefully insufficient to limit a temperature rise to 1.5C” With the alarm bell still ringing from Glasgow, the single most important thing is that pledges are translated into accelerated whole-economy action so that this vision and the ambition of net zero rapidly become a global reality. Prof Emily Shuckburgh is Director of Cambridge Zero, the University of Cambridge's major climate change initiative. She is also Professor of Environmental Data Science at the Department of Computer Science and Technology and leads the UKRI Centre for Doctoral Training on the Application of AI to the study of Environmental Risks (AI4ER).

Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

The analysis of land use and land cover data provides invaluable support to a variety of land management and conservation activities. However, historically its application has been limited by high costs associated with data acquisition, analysis, and classification. In recent years, freely available satellite imagery and geospatial data sets and rapid advancement in data analysis capabilities provide immense opportunities to understand and solve the real-world environmental problems. Open-source platforms such as Google Earth Engine (GEE) provide a planetary-scale environmental science data and analyses capability at much greater efficiency and accuracy than the traditional workflows. We evaluated the GEE Python API utility for classifying the freely available NAIP aerial imagery of 2017 to derive the land use land cover (LULC) information of a Panhandle area of Florida, USA. We identified eight major LULC classes with an overall accuracy of 86% and Kappa value of 79%. We completed all remote sensing data analyses procedures including data retrieval, classification, and report preparation in the Jupyter notebook, an open-source web application. Computation time for the procedure was less than 15 min. Our results demonstrate the usefulness of this approach for conducting land use and land cover analysis using much less time, money, and human resources. The open-source nature of GEE Python API and its library of remote sensing data could benefit remote sensing projects throughout the world, especially where access to commercial image processing software packages and remote sensing data are limited.