• Energy Research

Abstract This study explores how major climatic shifts, together with socioeconomic factors over the past two millennia, influenced buffer crop selection, focusing on five crops: rye, millet, buckwheat, oat, and hemp. For this study, we analyzed archaeobotanical data from 135 archaeological contexts and historical data from 242 manor inventories across the northeastern Baltic region, spanning the period from 100 to 1800 AD. Our findings revealed that rye remained a main staple crop throughout the studied periods reflecting environmental adaptation to northern latitudes. The drought-tolerant and thermophilic millet crop exhibited resilience during the adverse dry climatic conditions of the Medieval Climatic Anomaly while showing a significant decline during the Little Ice Age. During the period of post-1500 AD, a significant shift towards cold-resilient summer crops such as buckwheat and hemp is recorded. This study enhances our understanding of how historical agricultural systems responded to both socioeconomic factors and climatic change in northern latitudes, offering notable potential solutions for modern agricultural practices in the face of future climate variability trends.

AbstractEurope witnessed a strong increase in climate variability and enhanced climate-induced extreme events, such as hot drought periods, mega heat waves, and persistent flooding and flash floods. Intensified land degradation, land use, and landcover changes further amplified the pressure on the environmental system functionalities and fuelled climate change feedbacks. On the other hand, global satellite observations detected a positive spectral greening trend—most likely as a response to rising atmospheric CO2 concentrations and global warming. But which are the engines behind such shifts in surface reflectance patterns, vegetation response to global climate changes, or anomalies in the environmental control mechanisms? This article compares long-term environmental variables (1948–2021) to recent vegetation index data (Normalized Difference Vegetation Index (NDVI), 2001–2021) and presents regional trends in climate variability and vegetation response across Europe. Results show that positive trends in vegetation response, temperature, rainfall, and soil moisture are accompanied by a strong increase in climate anomalies over large parts of Europe. Vegetation dynamics are strongly coupled to increased temperature and enhanced soil moisture during winter and the early growing season in the northern latitudes. Simultaneously, temperature, precipitation, and soil moisture anomalies are strongly increasing. Such a strong amplification in climate variability across Europe further enhances the vulnerability of vegetation cover during extreme events.

Overview This dataset is the repository for the following paper submitted to Data in Brief: Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023). The Data in Brief article contains the supplement information and is the related data paper to: Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023). Description/abstract The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant. Folder structure The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files: “code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data. “MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023. “mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”). “yield_productivity” contains .csv files of yield information for all countries listed above. “population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods). “GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data. “built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”. Code structure 1_MODIS_NDVI_hdf_file_extraction.R This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent. 2_MERGE_MODIS_tiles.R In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged"). 3_CROP_MODIS_merged_tiles.R Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. The repository provides the already clipped and merged NDVI datasets. 4_TREND_analysis_NDVI.R Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique. 5_BUILT_UP_change_raster.R Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022. 6_POPULATION_numbers_plot.R For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration. 7_YIELD_plot.R In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R. 8_GLDAS_read_extract_trend The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables. (9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis. ___ Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf ___ Acknowledgements I would like to thank three anonymous reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia at the University of Basel under grant number TMPFP2_217358. __ All data underlying the results of this article are publicly available on the internet: GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023); Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023); FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023); Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023); Population development: FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023); the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023); Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023); Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023); MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023). Competing interests statement:The author declares no conflict of interest.The author has no relevant financial or non-financial interests to disclose.Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text.Ethical approval: This article does not contain any studies with human participants performed by any of the authors.Informed consent: This article does not contain any studies with human participants performed by any of the authors.

Fighting land degradation of semi-arid and climate-sensitive grasslands are among the most urgent tasks of current eco-political agenda. Particularly, northern China and Mongolia are prone to climate-induced surface transformations, which were reinforced by the heavily increased numbers of livestock during the 20th century. Extensive overgrazing and resource exploitation amplified regional climate change effects and triggered intensified land degradation that forced policy-driven interventions to prevent desertification. In the past, however, the regions have been subject to continuous shifts in environmental and socio-cultural and political conditions, which makes it particularly difficult to distinguish into regional anthropogenic impact and global climate change effects. This article presents analyses of historical written sources, palaeoenvironmental data, and Normalized Difference Vegetation Index (NDVI) temporal series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to compare landcover change during the Little Ice Age (LIA) and current spectral greening trends over the period 2001–2020. Results show that decreasing precipitation and temperature records triggered increased land degradation during the late 17th century in the transition zone from northern China and Inner Mongolia Autonomous Region to Mongolia. From current climate change perspectives, modern vegetation shows enhanced physical vegetation response related to an increase in precipitation (Ptotal) and temperature (T). Vegetation response is strongly related to Ptotal and T and an increase in physical plant condition indicates local to regional grassland recovery compared to the past 20-year average.

The Levant is highly vulnerable to climate change and experiences prolonged heat waves that have led to societal crises and population displacement. In addition, the region has been impacted by further socio-political turmoil at least since 2010, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and unregulated construction activity. This article uses climate data, satellite imagery, and land cover information in a multicomponent trend analysis to illustrate the substantial increase in construction activity and to highlight the intricate relationship between climate change predictions and current socio-political development in the Levant. The analyses were performed using annual and seasonal composites of MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) datasets with a spatial resolution of 250 m compared to climate indices of the GLDAS (Global Land Data Assimilation System) Noah Land Surface Model L4 dataset for the period 2001-2023. Surface reflectance and climatic parameters were then evaluated on the basis of socio-cultural factors, such as population dynamics, governmental decision-making, water withdrawal regulations, and built-up change as a result of large-scale migration processes. All analyses were conducted using R-software and can be reproduced and replicated using the code and the data provided in this article and the repository.