Changes of Hydrological Components in Arctic Rivers Based on Multi-Source Data during 2003–2016

The hydrological cycle of the Arctic river basin holds an important position in the Earth’s system, which has been significantly disturbed by global warming. This study analyzed recent changes in the hydrological components of two representative Arctic river basins in Siberia and North America, the Lena River Basin (LRB) and Mackenzie River Basin (MRB), respectively. The trends were diagnosed in hydrological components through a comparative analysis and estimations based on remote sensing and observational datasets during 2003–2016. The results showed that the annual precipitation decreased at rates of 1.9 mm/10a and 18.8 mm/10a in the MRB and LRB, respectively. In contrast, evapotranspiration (ET) showed increasing trends, with rates of 9.5 mm/10a and 6.3 mm/10a in the MRB and LRB, respectively. Terrestrial water storage (TWS) was obviously decreased, with rates of 30.3 mm/a and 18.9 mm/a in the MRB and LRB, respectively, which indicated that more freshwater was released. Contradictive trends of the runoffs were found in the two basins, which were increased in the LRB and decreased in the MRB, due to the contributions of the surface water and base flow. In addition, the mean annual cycles of precipitation, ET, TWS, runoff depth, surface flow and base flow behaved differently in both magnitudes and distributions in the LRB and MRB, the trends of which will likely continue with the pronounced warming climate. The current case studies can help to understand the recent changes in the Arctic hydro-climatology and the consequence of global warming in Arctic river basins.

TimeSpec4LULC: A Global Deep Learning-driven Dataset of MODIS Terra-Aqua Multi-Spectral Time Series for LULC Mapping and Change Detection

Land Use and Land Cover (LULCs) mapping and change detection are of paramount importance to understand the distribution and effectively monitor the dynamics of the Earth’s system. An unexplored way to create global LULC maps is by building good quality LULC-models based on state-of-the-art deep learning networks. Building such models requires large global good quality time series LULC datasets, which are not available yet. This paper presents TimeSpec4LULC (Khaldi et al., 2021), a smart open-source global dataset of multi-Spectral Time series for 29 LULC classes. TimeSpec4LULC was built based on the 7 spectral bands of MODIS sensor at 500 m resolution from 2002 to 2021, and was annotated using a spatial agreement across the 15 global LULC products available in Google Earth Engine. The 19-year monthly time series of the seven bands were created globally by: (1) applying different spatio-temporal quality assessment filters on MODIS Terra and Aqua satellites, (2) aggregating their original 8-day temporal granularity into monthly composites, (3) merging their data into a Terra+Aqua combined time series, and (4) extracting, at the pixel level, 11.85 million time series for the 7 bands along with a set of metadata about geographic coordinates, country and departmental divisions, spatio-temporal consistency across LULC products, temporal data availability, and the global human modification index. To assess the annotation quality of the dataset, a sample of 100 pixels, evenly distributed around the world, from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing and evaluating various machine learning models, including deep learning networks, to perform global LULC mapping and change detection.

The Biodiversity of Ecological Interactions: Challenges for recording and documenting the Web of Life

Biodiversity is more than a collection of individual species. It is the combination of biological entities and processes supporting life on Earth: no single species persists without interacting with other species. A full account of biodiversity on Earth needs to document the essential ecological interactions that support Earth’s system through their functional outcomes. Quantifying biodiversity’s interactome (the whole suite of interactions among biotic organisms) is challenging not just because of the daunting task of describing ecosystem complexity, it’s also limited by the need to define and establish a proper grammar to record and catalog species interactions. Actually, a record of a pairwise interaction between two species can be identified as a "tetranomial species", with just a concatenation of the two Latin binomials. Thus sampling interactions requires solving exactly the same constraints and problems we face when sampling biodiversity. In real interaction webs, the number of actual pairwise interactions among species in local assemblages scales exponentially with species richness. I discuss the main components of these interactions and those that are key to properly sample and document them. Interactions take the form of predation, competition, commensalism, amensalism, mutualism, symbiosis, and parasitism and, in all cases, involve reciprocal effects for the interacting species and build into highly complex networks (Fig. 1). The type of metadata required to document ecological interactions between partner species depends on interaction type; yet a fraction of these metadata is shared with those of the partner species. The interaction type sets limits to between-species encounters (actually, encounters between individuals of the partner species) and, more importantly, sets the type of outcome emerging from the interactions. There is a broad range of information that can eventually be acquired when recording an ecological interaction: from its simple presence (the interaction exists, it's been just recorded) to an estimate of its frequency, to obtaining data about its outcome or per-interaction effect (e.g., number of flowers pollinated in a visit by a pollinator to a plant). In addition, the types of interaction data can be quite diverse, reflecting the variety of sampling methods: interaction records from direct observation in the field; camera-traps; DNA-barcoding; bibliographic sources; surveys of image databases, etc. Interaction biodiversity inventories may require merging information coming from these distinct data sources. All these components need to be properly defined in order to build informative metadata and to document ecological interaction records. We are just starting to delineate the main components needed to catalog and inventory ecological interactions as a part of biodiversity inventories.

Vegetation Dynamics and Climatological Drivers in Ethiopia at the Turn of the Century

Global change, particularly climate change, poses a risk of altering vegetation composition and health. The consequences manifest throughout Earth’s system as a change in ecosystem services and socioecological stability. It is therefore critical that vegetation dynamics are monitored to establish baseline conditions and detect shifts. Africa is at high risk of environmental change, yet evaluation of the link between climate and vegetation is still needed for some regions. This work expands on more frequent local and multinational scale studies of vegetation trends by quantifying directional persistence (DP) at a national scale for Ethiopia, based on the normalized difference vegetation index (NDVI) between 2000 and 2016. The DP metric determines cumulative change in vegetation greenness and has been applied to studies of ecological stability and health. Secondary analysis utilizing panel regression methodologies is carried out to measure the effect of climate on NDVI. Models are developed to consider spatial dependence by including fixed effects and spatial weights. Results indicate widespread cumulative declines in NDVI, with the greatest change during the dry season and concentrated in northern Ethiopia. Regression analyses suggest significant control from climatic variables. However, temperature has a larger effect on NDVI, which contrasts with findings of some previous studies.

Will Plant Genome Editing Play a Decisive Role in “Quantum-Leap” Improvements in Crop Yield to Feed an Increasing Global Human Population?

Growing scientific evidence demonstrates unprecedented planetary-scale human impacts on the Earth’s system with a predicted threat to the existence of the terrestrial biosphere due to population increase, resource depletion, and pollution. Food systems account for 21–34% of global carbon dioxide (CO2) emissions. Over the past half-century, water and land-use changes have significantly impacted ecosystems, biogeochemical cycles, biodiversity, and climate. At the same time, food production is falling behind consumption, and global grain reserves are shrinking. Some predictions suggest that crop yields must approximately double by 2050 to adequately feed an increasing global population without a large expansion of crop area. To achieve this, “quantum-leap” improvements in crop cultivar productivity are needed within very narrow planetary boundaries of permissible environmental perturbations. Strategies for such a “quantum-leap” include mutation breeding and genetic engineering of known crop genome sequences. Synthetic biology makes it possible to synthesize DNA fragments of any desired sequence, and modern bioinformatics tools may hopefully provide an efficient way to identify targets for directed modification of selected genes responsible for known important agronomic traits. CRISPR/Cas9 is a new technology for incorporating seamless directed modifications into genomes; it is being widely investigated for its potential to enhance the efficiency of crop production. We consider the optimism associated with the new genetic technologies in terms of the complexity of most agronomic traits, especially crop yield potential (Yp) limits. We also discuss the possible directions of overcoming these limits and alternative ways of providing humanity with food without transgressing planetary boundaries. In conclusion, we support the long-debated idea that new technologies are unlikely to provide a rapidly growing population with significantly increased crop yield. Instead, we suggest that delicately balanced humane measures to limit its growth and the amount of food consumed per capita are highly desirable for the foreseeable future.

Earth’s Time-Variable Gravity from GRACE Follow-On K-Band Range-Rates and Pseudo-Observed Orbits

During its science phase from 2002–2017, the low-low satellite-to-satellite tracking mission Gravity Field Recovery And Climate Experiment (GRACE) provided an insight into Earth’s time-variable gravity (TVG). The unprecedented quality of gravity field solutions from GRACE sensor data improved the understanding of mass changes in Earth’s system considerably. Monthly gravity field solutions as the main products of the GRACE mission, published by several analysis centers (ACs) from Europe, USA and China, became indispensable products for quantifying terrestrial water storage, ice sheet mass balance and sea level change. The successor mission GRACE Follow-On (GRACE-FO) was launched in May 2018 and proceeds observing Earth’s TVG. The Institute of Geodesy (IfE) at Leibniz University Hannover (LUH) is one of the most recent ACs. The purpose of this article is to give a detailed insight into the gravity field recovery processing strategy applied at LUH; to compare the obtained gravity field results to the gravity field solutions of other established ACs; and to compare the GRACE-FO performance to that of the preceding GRACE mission in terms of post-fit residuals. We use the in-house-developed MATLAB-based GRACE-SIGMA software to compute unconstrained solutions based on the generalized orbit determination of 3 h arcs. K-band range-rates (KBRR) and kinematic orbits are used as (pseudo)-observations. A comparison of the obtained solutions to the results of the GRACE-FO Science Data System (SDS) and Combination Service for Time-variable Gravity Fields (COST-G) ACs, reveals a competitive quality of our solutions. While the spectral and spatial noise levels slightly differ, the signal content of the solutions is similar among all ACs. The carried out comparison of GRACE and GRACE-FO KBRR post-fit residuals highlights an improvement of the GRACE-FO K-band ranging system performance. The overall amplitude of GRACE-FO post-fit residuals is about three times smaller, compared to GRACE. GRACE-FO post-fit residuals show less systematics, compared to GRACE. Nevertheless, the power spectral density of GRACE-FO and GRACE post-fit residuals is dominated by similar spikes located at multiples of the orbital and daily frequencies. To our knowledge, the detailed origin of these spikes and their influence on the gravity field recovery quality were not addressed in any study so far and therefore deserve further attention in the future. Presented results are based on 29 monthly gravity field solutions from June 2018 until December 2020. The regularly updated LUH-GRACE-FO-2020 time series of monthly gravity field solutions can be found on the website of the International Centre for Global Earth Models (ICGEM) and in LUH’s research data repository. These operationally published products complement the time series of the already established ACs and allow for a continuous and independent assessment of mass changes in Earth’s system.

Long-Term Global Ground Heat Flux and Continental Heat Storage from Geothermal Data

<p>Energy exchanges among climate subsystems are of critical importance to determine the climate sensitivity of the Earth's system to changes in external forcing, to quantify the magnitude and evolution of the Earth's energy imbalance, and to make projections of future climate. Additionally, climate phenomena sensitive to land heat storage, such as permafrost stability and sea level rise, are important due to their impacts on society and ecosystems. Thus, ascertaining the magnitude and change of the Earth's energy partition within climate subsystems has become urgent in recent years. </p><p>Here, we provide new global estimates of changes in ground surface temperature, ground surface heat flux and continental heat storage derived from geothermal data using an expanded database and new techniques developed in the last two decades. This new dataset contains 253 recent borehole profiles that were not included in previous estimates of global continental heat storage. In addition, our analysis considers additional sources of uncertainty that were not included in previous borehole studies. Results reveal markedly higher changes in ground heat flux and heat storage within the continental subsurface during the second half of the 20th century than previously reported, with a land mean temperature increase of 1 K and continental heat gains of around 12 ZJ relative to preindustrial times. Half of the heat gained by the continental subsurface since 1960 have occurred in the last twenty years. These results may be important for estimates of climate sensitivity based on energy budget constrains, as well as for the evaluation of global transient climate simulations in terms of the Earth’s heat inventory and energy-dependent subsurface processes. Our estimate of land heat storage is included in the new assessment of the components of the Earth’s heat inventory recently released (von Schuckmann et al. 2020), together with the oceans, the atmosphere and the cryosphere.</p>

Long-term global ground heat flux and continental heat storage from geothermal data

Energy exchanges among climate subsystems are of critical importance to determine the climate sensitivity of the Earth's system to greenhouse gases, to quantify the magnitude and evolution of the Earth's energy imbalance, and to project the evolution of future climate. Thus, ascertaining the magnitude of and change in the Earth's energy partition within climate subsystems has become urgent in recent years. Here, we provide new global estimates of changes in ground surface temperature, ground surface heat flux, and continental heat storage derived from geothermal data using an expanded database and new techniques. Results reveal markedly higher changes in ground heat flux and heat storage within the continental subsurface than previously reported, with land temperature changes of 1 K and continental heat gains of around 12 ZJ during the last part of the 20th century relative to preindustrial times. Half of the heat gain by the continental subsurface since 1960 has occurred in the last 20 years.

“Future Earth”: Nigmatulin Hypothesis and Dynamic Model of Radiation Field of Ocean-Atmosphere System

The United Nations has proclaimed a Decade of Ocean Science for Sustainable Development (2021-2030) to support efforts to reverse the cycle of decline in ocean health and gather ocean stakeholders worldwide behind a common framework that will ensure ocean science can fully support countries in creating improved conditions for sustainable development of the Ocean. The marine realm is the largest component of the Earth’s system that stabilizes climate and support life on Earth and human well-being. Scientific understanding of the ocean’s responses to pressures and management action is fundamental for sustainable development. Planet Earth is a natural example of a dynamic system with nonlinear processes that is in continuous change. The Earth’s radiation field is a single physical field (electromagnetic radiation) and the unifying factor of the Earth dynamical system. The Earth’s climate system is a natural environment that includes the atmosphere, the hydrosphere (oceans, seas, lakes, rivers), the cryosphere (land surface, snow, sea and mountain ice, etc.), and the biosphere that unites all living things. According to the hypothesis of R.I. Nigmatulin “Ocean is a dictator of climate”. H2O and CO2 are competing climate influences. In this article, we propose original author’s mathematical models for radiation blocks with hyperspectral data on absorption by atmospheric components. The new models are based on the development of the theory of the optical transfer operator and the method of influence functions in the theory of radiation transfer and Boltzmann equations, as well as the iterative method of characteristics with iteration convergence accelerations.

Perceptions of Food Retailers Regarding Climate Change and Greenhouse Gas Emissions

Greenhouse gas (GHG) emission and its associated effects have been a debate in literature for many years (Hoffman, 2011:5; Williams & Schaefer, 2012:175; Whitmarsh, 2011:690). According to Jackson (2016), climate change is seen as a yearly change within the earth's climate that is a result of changes in its atmosphere, as well as interactions between the atmosphere and other chemical, geologic, geographic and biological factors within the earth's system. Climate change has primarily caused a warming effect of the earth's atmosphere that has affected all aspects of life (Pachauri & Reisinger, 2007:7). While there are limited studies that measure greenhouse gas emissions arising from the entire global food chain, there have been estimates of GHG emissions attributable to global agricultural production (Garnett, 2011:23). Energy consumption is one of the biggest challenges food retailers are facing as it not only increases overhead costs but also GHG emission (Tassou, Hadawey & Marriott, 2011). Garnett (2011) alleges that the food chain produces greenhouse gas (GHG) emissions at all stages in its life cycle, from the farming process and its inputs, through to manufacture, distribution, refrigeration, retailing, food preparation in the home and waste disposal. Technological improvements, while essential, will not be sufficient in reducing GHG emissions. The combination of population growth and rising per capita anticipated consumption of meat and dairy products will undermine the cuts that technological and managerial innovation can achieve. Over the last few years food retailers in South Africa started to focus their attention towards GHG emissions, but there is still no framework for food retailers to reduce GHG emissions in South Africa (Tassou et al. 2007:2988). Various studies have argued that the food and drink, transportation, and construction industry sectors are regarded as the most significant contributors to GHG emissions (European Commission, 2006; SEI, WWF & CURE, 2006 and UNEP, 2008). Significant changes in food production and increases in food transport have resulted. The production of food on farms has become increasingly mechanised, large-scale, and specialised; and food supply chains have become more complicated and transport-intensive (Roelich, 2008). Food retailers are contributing to GHG emissions by means of electricity usage through refrigerator equipment, lighting, heating, air conditioning, baking and other secondary services. There is no general strategy for food retailers to reduce GHG emission and minimal research has been done in this sector (Tassou et al, 2011). Keywords: climate change; food retailers; greenhouse gas emission; perceptions; strategies