Showing papers by "International Institute for Applied Systems Analysis published in 2023"

Underground Gravity Energy Storage: A Solution for Long-Term Energy Storage

Abstract: Low-carbon energy transitions taking place worldwide are primarily driven by the integration of renewable energy sources such as wind and solar power. These variable renewable energy (VRE) sources require energy storage options to match energy demand reliably at different time scales. This article suggests using a gravitational-based energy storage method by making use of decommissioned underground mines as storage reservoirs, using a vertical shaft and electric motor/generators for lifting and dumping large volumes of sand. The proposed technology, called Underground Gravity Energy Storage (UGES), can discharge electricity by lowering large volumes of sand into an underground mine through the mine shaft. When there is excess electrical energy in the grid, UGES can store electricity by elevating sand from the mine and depositing it in upper storage sites on top of the mine. Unlike battery energy storage, the energy storage medium of UGES is sand, which means the self-discharge rate of the system is zero, enabling ultra-long energy storage times. Furthermore, the use of sand as storage media alleviates any risk for contaminating underground water resources as opposed to an underground pumped hydro storage alternative. UGES offers weekly to pluriannual energy storage cycles with energy storage investment costs of about 1 to 10 USD/kWh. The technology is estimated to have a global energy storage potential of 7 to 70 TWh and can support sustainable development, mainly by providing seasonal energy storage services.

Is destabilisation risk increasing in land carbon sinks?

Abstract: Global net biome production (NBP), or net land carbon uptake, has been repeatedly shown to increase during recent decades. However, whether the temporal variability and autocorrelation of NBP has changed during this period remains elusive. Answering this question is particularly relevant given that an increase in both could indicate destabilising C sinks and potentially lead to abrupt changes. We investigated the trends and controls of net land C uptake and its temporal variability and autocorrelation, from 1981 to 2018, using two atmospheric inversion models, the amplitude of the seasonal cycle of atmospheric CO2 derived from nine monitoring stations distributed across the Pacific Ocean, and 12 dynamic global vegetation models. Spatially, we found that plant biodiversity presented a convex parabolic relationship with NBP and its temporal variability at the global scale while nitrogen deposition generally increased annual NBP. We also found that annual NBP and its interdecadal temporal variability globally increased, but temporal autocorrelation decreased. Regions characterized by increasingly variable NBP were usually with warmer and with increasingly variable temperatures, and lower and weaker trends in NBP compared to those where NBP variability did not increase, where NBP became stronger. Annual temperature increase and its increasing temporal variability were the most important drivers of declining NBP and increasingly its variability. Our results show that increasing regional NBP variability can be mostly attributed to climate change.

Simulating the effects of sea level rise and soil salinization on migration and adaptation in coastal Mozambique

Abstract: Sea level rise (SLR) causes increasing salt deposition in the soil and groundwater of coastal regions. This will affect coastal farmers, since salinity levels will reduce crop yield, which leads to loss in net annual income of farmer communities. To minimize the impacts and income loss, farmers often adopt adaptation measures, such as irrigation, adding manure and gypsum, switching to salt tolerant crops, or buying less saline lands. When these options are not feasible,  farmers may migrate to inland areas to minimize future impacts and damages.We adopt an agent-based model (ABM) to simulate adaptation and migration decisions by farmers in Mozambique under sea level rise. . The ABM is coupled to a salinization module for simulating the relation between soil salinity and sea level rise. The decision rules in the model (DYNAMO-M) are grounded in economic theory of subjected expected utility where household maximize their welfare by deciding 1) to stay and face loss from salinization and flooding, 2) stay and adapt (irrigation or buy land) or 3) migrate to safer inland areas. The model runs with a yearly timestep (2020-2100) for simulating salinity levels, but accounts for dynamics in the growing season. (Future-) soil salinity levels are derived from ISRIC (2012) and Hassani et al. (2020). Projections in salinity levels are converted into (reduced-) yield levels following Maas and Hoffman (1977). Country statistics and census data are then used to estimate farmers income from expected yields. The model finally simulates adaptation decisions based on the cost (expected yield loss + adaptation investments) against the benefit (expected yield). Results show how many farming households have stayed with the damage, adapted with a measure, and migrated to inland areas over time and space.   Keywords: sea level rise, soil salinization, coastal farmers, agent-based model, migration

Comment on egusphere-2022-1504

Abstract: Abstract. We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ~25 km × 25 km resolution, using spatially allocated national UNFCCC reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up estimate (+103 % for gas, +53 % for waste, +49 % for livestock, −14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0–5.5) Tg a⁻¹; best estimate and uncertainty range), Turkmenistan (4.4 (2.8–5.1) Tg a⁻¹), Saudi Arabia (4.3 (2.4–6.0) Tg a⁻¹), Algeria (3.5 (2.4–4.4) Tg a⁻¹), Egypt (3.4 (2.5–4.0) Tg a⁻¹) , and Turkey (3.0 (2.0–4.1) Tg a⁻¹). Most oil/gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil/gas emission hotspots in Turkmenistan, Algeria, Oman, and offshore in the Persian Gulf. We show that oil/gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil/gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil/gas emission inventories. We examined the methane intensity, defined as the upstream oil/gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil/gas sector, and using as reference the 0.2 % target set by industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through capture of associated gas, modern infrastructure, and concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil/gas upstream emissions and a 26 % decrease of total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.

Functional relationships reveal differences in the water cycle representation of global water models

Abstract: Global water models are widely used for policy-making and in scientific studies, but substantial inter-model differences highlight the need for additional evaluation. Here we evaluate global water models by assessing so-called functional relationships between system forcing and response variables. The more widely used comparisons between observed and simulated fluxes provide insight into model behavior for the representative area of an observation, and can therefore potentially improve the model for that area. Functional relationships, by contrast, aim to capture how system forcing and response variables co-vary across large scales, and thus offer the potential for model improvement over large areas. Using 30-year annual averages from 8 global water models, we quantify such functional relationships by calculating correlations between key forcing variables (precipitation, net radiation) and water fluxes (actual evapotranspiration, groundwater recharge, total runoff). We find strong disagreement for groundwater recharge, some disagreement for total runoff, and the best agreement for evapotranspiration. Observation- and theory-derived functional relationships show varying agreements with models, indicating where model representations and our process understanding are particularly uncertain. Overall, our results suggest that model improvement is most important for the representation of energy balance processes, recharge processes, and generally for model behavior in dry and cold regions. We argue that advancing our ability to simulate global hydrology requires a better perceptual understanding of the global water cycle. To evaluate if our models match that understanding, we should explore alternative evaluation strategies, such as the use of functional relationships.

Including soil erosion and sediment delivery to surface waters in a high-resolution global hydrological model

Abstract: Soil erosion and sediment delivery to surface waters impact the human-water cycle in several ways. Eroded soil, along with nutrients, travels from intensive crop and grazing systems to waterbodies, reducing the fertility of agricultural land, degrading the integrity of freshwater ecosystems, and negatively impacting water quality on which the human population and other sectors depend. Furthermore, sedimentation reduces the functional capacity of vital energy and agricultural infrastructure, such as reservoirs and irrigation canals, resulting in reduced productivity and profitability of food and energy production systems. A few hydrologic models have accounted for the effects of soil erosion on water quality by implementing soil erosion models into their simulations. To our knowledge, soil erosion has not yet been included in large-scale hydrologic models. Our research adds an erosion-sediment transport module to the hydrological Community Water Model (CWatM). That is to assess soil erosion on a regional to a global scale and to simulate the concentration of suspended sediments in surface waters. CWatM is a fully-distributed, large-scale, open-source hydrological model. It runs on a daily time step and high resolution of up to 30 arc seconds (approximately 1 km at the equator). The model can account for human activity and management of water systems, including reservoir operations, water demand, and crop-specific irrigation requirements. A global dataset of 5 arc minutes is available for an easy simulation setup at a catchment, region, and global scale. The implementation of soil erosion and sediment delivery from terrestrial sources will rely on the Modified Universal Soil Loss Equation (MUSLE) and the stream network density within each grid cell. Further, simulating instream erosion uses a power law approach. Finally, the routing algorithm would move suspended soil particles downstream. For that purpose, we combine input datasets with CWatM variables, e.g., surface runoff. We apply this module to a case study in Uganda’s share of the Victoria Lake Basin at five arc minute resolution, where high soil erosion rates challenge the ecological integrity of the natural environment, as well as agricultural productivity and water quality. We further discuss the model sensitivity to input parameters’ variation (e.g., the fraction of daily rainfall in the half-hour of highest intensity).

Reply on RC1

Abstract: Abstract. The Global Runoff Data Centre provides time series of observed discharges that are valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology that has not been satisfactorily solved, though investigated several times. To compare simulated and observed discharge, grid-based hydrological models must fit reported station locations to the resolution-dependent gridded river network. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid-scale, reducing the errors in assigning stations to the correct upstream basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arc seconds) river network and provide source codes and high- and low-resolution watershed boundaries. The dataset is stored on Zenodo with the associated DOI https://doi.org/10.5281/zenodo.6906577.

Evaluating the global wastewater’s untapped irrigation potential

Abstract: Wastewater has been increasingly considered an untapped resource rather than a waste. It can be used for water supply, aquifer recharge, energy generation, and fertilizer production, contributing concurrently to achieving multiple Sustainable Development Goals. Currently, treated wastewater is used for irrigation in different water-scarce countries like Israel and Spain. Recent estimates suggest that treated wastewater irrigates 6 million hectares (Mha) of cropland worldwide, with global potential for irrigating 31-42 Mha. Nevertheless, these estimates often do not account for environmental, social, and economic conditions that may limit this potential, including irrigated crops’ spatial distribution, water quality requirements, and water conveyance options.Our study advanced a spatial-explicit analysis of four different technological scenarios and estimated the untapped potential of wastewater irrigation at a global, regional, and national scale. We found that utilizing treated wastewater for irrigation can satisfy up to 4% of the global irrigation demand. Considering only water-scarce regions, reduce this global potential to roughly 2%. However, intensification and expansion strategies or scenarios can increase this irrigation share. For example, in ten countries, delivering treated wastewater to croplands by canals adds over 5% to the share of treated wastewater relative to total irrigation demand. An increase of over 1% is evident in 30 countries, mainly in the Middle East, North Africa, and Western Europe.Increased wastewater treatment capacity (i.e., expansion strategy) potentially increases the share of global irrigation demand satisfied with treated wastewater to 6% -12%. Some regions’ potential exceeds 20% of their irrigation demand, e.g., China and North America. An increase in potential wastewater irrigation becomes significant when considering expansion scenarios (e.g., increased capacities to treat the wastewater).Considering the changes in different regions’ water scarcity, we find that the potential share of irrigation demand satisfied by treated wastewater is significantly increased in North America, the United Kingdom, Europe, and Western Asia. We conclude that irrigation with treated wastewater in these regions can be an important climate change adaptation measure. Besides, we show that intensifying and expanding wastewater reclamation systems can help manage droughts in regions with high drought risk, like the Italian Po and French Loire River basins.By exploring expansion and intensification strategies to reclaim treated wastewater, this work establishes an essential step towards planning the global and regional pathways to utilize this untapped resource, abating current and future water scarcity risks.

WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping

Abstract: Abstract. The challenge of global food security in the face of population growth, conflict and climate change requires a comprehensive understanding of cropped areas, irrigation practices and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereals maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. The presented products are fully validated, e.g., global user's and producer's accuracies for the annual temporary crop product are 88.5 % and 92.1 %, respectively. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023).

Roles of diversity and adaptation in the eco-evolutionary responses of biodiverse plant communities to climate change

Abstract: Climate change is projected to cause not only higher mean temperatures but also higher climate variability. Although elevated CO2 concentrations can potentially increase the productivity of some ecosystems, higher temperatures and more frequent droughts may lead to increased respiration and mortality, possibly negating these productivity gains. The capacity of global forests to adjust to climate change depends on their functional diversity and the ecosystem’s adaptive capacity.The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperatures, and droughts. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities.First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 conditions and in the absence of nutrient limitation, our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we simulate the calibrated model with hypothetical future drought regimes to investigate three key features of ecosystem responses: 1) the change in species composition and ecosystem functioning in response to altered conditions, 2) the timescales of ecosystem response to new regimes, 3) the influence of functional diversity on the timescale of ecosystem adaptation and its consequences for ecosystem collapse.Our eco-evolutionary vegetation modelling strategy presents a powerful approach to leverage the power of natural selection to simulate ecosystem dynamics under novel conditions that plants may have never experienced before.

Agent-Based Simulation of Low Carbon Emissions Product Diffusion

Abstract: Actors in a complex adaptive system (CAS) are heterogeneous, adaptive, and interact with one another, and it is difficult to analyze such systems using traditional approaches. Agent-based simulations can help generate knowledge regarding CASs. There have been increasing calls to develop low carbon emissions products. The diffusion of low emissions products results from interactions among different heterogeneous and adaptive actors and could be viewed as the emergence of a CAS. This study develops an agent-based model on the diffusion of low emissions products. The model’s innovative features include: (1) peer effect among consumers as well as interactions between consumers and producers, (2) producers are path-dependent when developing new products, and (3) actors are influenced by the product’s emissions after the industry is operational for a certain time period. With the model, we first simulate how different the timing of the introduction of the emissions attribute influences the diffusion of low emissions products, then simulating how different peer effect among consumers and social network structure influences the diffusion of low emissions products, and finally, how different policies influence the diffusion of low emissions products. Simulations using the model can help to expand insights and aid the development of strategic intuition regarding the complexity of the diffusion of low emissions products.

GEB: A large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model

Abstract: Humans play a key role in the hydrological system, and their decisions influence the entire water system from tributary to river mouth. To fully comprehend how the human-natural water system evolves over space and time, and to investigate the systemic effects of climate change and human interventions, it is important to consider human behaviour and feedbacks to the hydrological system simultaneously at the local household- and large basin scales.Therefore, we present GEB (Geographical, Environmental and Behavioural model); an agent-based model coupled to a fully distributed hydrological model that can simulate the behaviour and daily bi-directional interaction of more than 10 million individual farm households and reservoir operators with the hydrological system. Through this coupling, each individual farmer with unique characteristics and location can make daily decisions, such as irrigating their crops from surface-, reservoir-, or groundwater, planting and harvesting crops, investing in adaptation options (e.g., irrigation wells and sprinkler irrigation). All these decisions can be based on the available water in their environment, the status of their crops, their risk perception, crop price, water price, and weather conditions etc. Similarly, reservoir operators can regulate the availability of water for irrigation, and downstream releases of water based on the state of the hydrological system as well as communication with farmer agents.GEB is dynamically linked with the spatially distributed hydrological model CWatM at 30&#8217;&#8217; grid resolution (< 1km at the equator). Because many small-holder crop fields are much smaller, CWatM was specifically adapted to implement dynamically sized hydrological response units at field scale / sub-grid level, providing each agent with an independently operated hydrological environment.While the model could be applied anywhere, we show an implementation with local and basin-wide feedbacks in the heavily managed Krishna basin in India, encompassing ~8% of India&#8217;s land area and ~12.1 million farmers. Here, we quantify bi-directional feedbacks such as the reservoir paradox and test various policies, such as providing subsidies for adaptation options (e.g., irrigation wells, sprinkler irrigation), and quantify effects on the hydrological system as well as downstream farmers.In this implementation, GEB uses approximately 15 GB of RAM memory and can thus be used on an above average personal laptop. Computational requirements scale linearly with basin size, assuming similar farm-size distribution.

The benefits of rural electrification to improve water access and irrigation in Sub-Saharan Africa, a water-energy-land assessment framework applied to Zambia.

Abstract: Sub-Saharan Africa has a large portion of the population with no access to electricity, piped drinking water, or sanitation services. The lack of these basic services also affects farmers that mostly rely on rainfed agriculture instead of irrigation. Given the expected population growth and potential changes in hydrology and crop yield response due to climate change, future development for the region needs to be carefully studied to achieve increased access to basic services and, potentially, synergetic economic benefits in agriculture.Within the LEAP-RE 4 AFRI project, we developed a framework that combines three high-resolution, single-sector simulation models (crop water requirements, electricity demand and rural electricity dispatchment) to a long-term water-energy-land integrated assessment model to explore different scenarios of future development for Zambia. This helps understand which regions would benefit of better water access and irrigation potential due to improved rural electrification.We compare two scenarios of moderate and universal electricity-water access with a current trend scenario, we compare the expected costs and benefits for the rural population, including the economic benefits achievable by improving irrigation standards and crop yields.Although Zambia is a relatively water-abundant region, we focus on it as a case study with a framework that can be transferred to any other country in Sub-Saharan Africa, where climate change impact might have a significant impact on water scarcity, electricity generation potential and crop yields.

Coupling a global glacier model with a global hydrological model - benefits, challenges and limitations

Abstract: Glaciers are present in many large river basins and influence runoff variations considerably in many mountain areas. Due to climate change, annual runoff volumes originating from glaciers and the glacial melt seasonality are undergoing considerable changes. These changes can affect water availability in basins with glacier cover. Nevertheless, glaciers have been largely neglected in large-scale hydrological models so far, which is a crucial limitation in global climate impact studies on water resources.To include glacier runoff in large-scale hydrological studies, we have coupled two open-source and well-documented models: a global glacier model (OGGM, Maussion et al., 2019) and a large-scale hydrological model (CWatM, Burek et al., 2020). The coupling offers an explicit inclusion of glacier runoff in large-scale hydrological modeling, and thanks to the dynamic modelling of glaciers, changes in glacier area and volume are explictly considered.The coupling has been evaluated for selected large river basins, namely the Rhine, Rhone, Fraser and Gloma basins on 5arcmin resolution (~9km) and globally on 30arcmin (~50km) resolution, and differences in simulation results with and without coupling have been assessed. Simulations were run for the recent past (1990&#8211;2019) and for two scenarios (SSP1-2.6, SSP5-8.5) for the 21st century.Including glaciers explicitly in climate impact modelling of large river basins simulates larger future changes in summer discharge. Therefore, it is especially important to include glaciers in studies focusing on changes in summer water availability and its impacts. For the recent past, the contribution of glaciers to discharge at downstream stations of the selected river basins ranges from 7 to 37% for one month and between 2 and 8% annually. For the period 2070&#8211;2099, the projected contribution of glaciers drastically decreases to 2 to 13% for one month and 0.2 to 1.3% annually even under the low-emission scenario.Issues to tackle during the model coupling include precipitation data correction, different spatial and temporal resolutions in the models, &#160;different snow process representations, and the model calibration.Here, we give an overview of the benefits, challenges and limitations of coupling a global glacier model with a global hydrological model and focus on future discharge projections in large river basins.&#160;ReferencesBurek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., and Wada, Y.: Development of the Community Water Model (CWatM v1.04) &#8211; a high-resolution hydrological model for global and regional assessment of integrated water resources management, Geosci. Model Dev., 13, 3267&#8211;3298, https://doi.org/10.5194/gmd-13-3267-2020, 2020.Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K. et al..: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909&#8211;931, https://doi.org/10.5194/gmd-12-909-2019, 2019.

Leveraging Integrated Assessment Models to access climate feedbacks on Water, Energy, and Land Systems: An Evaluation of Regional and Sectoral Transformations for Achieving the Sustainable Development Goals

Abstract: This research investigates the interconnections between water, energy, and land systems in the context of a long-term assessment of transition paths to achieve the Sustainable Development Goals (SDGs). It highlights the importance of integrated methods and addresses the complexity, interdependence, and uncertainty of climate change's impacts on natural systems and technology in the water, energy, and land sectors. The research utilizes two Integrated Assessment Models (MESSAGEix-GLOBIOM and IMAGE) to assess the long-term resources, supply, and demand of these sectors, together with the regional and sectoral reforms required to achieve the SDGs. It demonstrates how various locations and sectors would be affected by climate feedback under various climate mitigation scenarios.&#160; The study concludes that changes in water availability, that influence agriculture, water and sanitation access, hydropower potential, and power plant cooling technologies, constitute the largest proportion of climate impacts and the prime source of uncertainty. Furthermore, scenario analysis is used to understand the relationship between the SDGs and climate impacts in the absence of climate policies. The findings demonstrate that considerable progress towards the trajectories of the nexus SDGs resulted in strong synergies and interactions across the energy-water and land nexus components, irrespective of climate factors. Additionally, the study demonstrates that ambitious and healthy dietary modifications and a reduction in food waste can result in a decrease in global food demand, irrigation withdrawals, and emissions. Changes in the land sector can reduce overall SDG policy costs and energy and water expenditures, while strengthening the needs of the poor. Improving wastewater treatment and establishing more efficient water management technologies has socioeconomic and environmental advantages and can alleviate stress on freshwater withdrawals in locations that are water stressed.&#160; The study also shows that some regions, such as the Middle East and South Asia, are more vulnerable to climate impacts on the water sector and may require more extensive investments in water efficiency. In addition, it stresses that supplying households with electricity and clean cooking services can stimulate energy demand in emerging economies, but widespread adoption would require an increase in household incomes, notably in South Asia and Sub-Saharan Africa. Overall, the study highlights the importance of exploring the effects of climate change on natural and technological systems in the water, energy, and land sectors, as well as the relevance of implementing a coordinated strategy to achieving the Sustainable Development Goals. It also demonstrates the inter - dependencies and potentials of various sectors to achieve the SDGs while addressing the challenges they face because of climate change.&#160;

Opening up FAIR in-situ land-use reference data: current gaps, obstacles and future challenges

Abstract: It is becoming increasingly obvious that in order to address current global challenges and achieve the SDGs in the land-use sector, monitoring and evaluation using remote sensing technologies are essential. In particular, with the Copernicus program of the European Union, unprecedented free and open Earth observation data are becoming available. However, in order to improve our remotely sensed based machine learning models, training data in the form of in-situ or annotated land-use or land cover data which are based on the visual interpretation of aerial photographs or very high resolution satellite data are of utmost importance. Without sufficient training data, many land-use and land cover maps lack sufficient quality.The presentation will provide an overview of existing and open in-situ data in the field of land-use science. It will highlight what land-use data are currently available including data collected though crowdsourcing and the Geo-Wiki toolbox. In particular, it will provide insights into current gaps in land cover, land-use, livestock, forest as well as crop type information globally. It will draw on existing global data products such as those from the Copernicus global land monitoring service, and more recently generated products such as WorldCover and WorldCereal. Furthermore, tools to close those data gaps will be shown. The presentation will furthermore explore current obstacles and limitations to data sharing and debunk current arguments that are often put forth for not sharing in-situ data. These arguments include limited resources, quality issues, competition, as well as time constraints, etc. Specific attention will be given to the role of doners and funders in more clearly defining open and FAIR requirements for in-situ data. The presentation will close by making the audience aware of the LUCKINet consortium, which is trying to make more reference data openly accessible and to build a consistent global land-use change dataset as well as work done on in-situ data within the EU LAMASUS and OEMC project.&#160;

Modeling Sustainable Intensification of Agriculture under Resource Constraints

Abstract: Sustainable Intensification of agriculture is a means to meet increasing future food demand in regions with rapidly growing populations and economies. In many of these regions, productivity is currently low. Increasing productivity while limiting additional use of resources such as water and land is therefore key to providing demanded and nutritious food in these countries. To model sustainable intensification, we build an optimization model. In its main economic specification, the objective of the model is to maximize farmer profit while at the same time respecting ecological boundaries. Therefore, constraints of the model are given by land availability and agronomic and climatic constraints to crop growth from the Global Agroecological Zones (GAEZ) method, as well as water availability derived from the Global Hydro-Economic Model (ECHO). The framework can be applied for either a single crop or multiple crops. The results of our analysis provide guidance on where and under which circumstances production of a crop can most efficiently be intensified. This can include the spatial distribution of irrigation or the allocation of several crops to optimally meet estimated demand. Demand is derived from projections of human population, economic growth and income elasticities. We use scenario analysis to examine how optimal sustainable intensification strategies change under different trade regimes for agricultural products and under different levels of climate resilience. Application of the model focuses on the East African extended Lake Victoria Basin (eLVB). In eLVB agroecological conditions are favorable for a wide range of crops, sufficient water is available in several sub-basins and population and food demand are projected to rapidly expand.

Multi-crisis resilience gain: on the sustainable model-based paradigm of resource governance

Abstract: Global social transformations, induced by the digital revolution, impacting to sociocultural practices, changing human communication and behavior, varying and substantially localizing production, transportation and consuming practices.At the same time transformation processes relate to the crisis phenomena, such as technological disasters, conflicts, warfare, epidemies, etc. It is important to note, that transformative crisis is usually should be considering as the accelerator of the general transformation. But crisis is the producer and driver radicalization, fractioning, group competition, and conflict identity construction, which leads to local increasing of inequity, inequality and instability threats, dramatic increasing damages and losses.Reducing of losses and damages requires deep understanding of drivers of vulnerabilities, risks and nature of community resilience, including reducing inequity and inequality in the complex multi-agent environment, where every agent is producer, consumer and transmitter of resources.Such understanding can be grounded on the anthologically-based mathematical concept of varied types of social and natural resources management, which should include numerical measurement and analysis of behavior indicators.To construct a such concept, a traditional risk management paradigm might be used, with replacing the distribution, impact and perception functions with functions of availability, accessibility and utilizability of resources (from medical and education to critical water and energy resources with some limitations, specified by the complex of local sociocultural practices).In such approach the risk communication component should include multi-agent nature of natural-human-technological environment.In the study a formal model of resilience analysis is proposed. A formal model of resilience-oriented multi-agent risk communication demonstrates that (i) crisis impact is a reason of drastic clustering of environment, (ii) caused by successive crises reorganization of the subsystems structure from hierarchical to network leads to increasing of resilience, (iii) efficiency of governing depends of equity of agents, (iv) efficiency of crisis governing should be measured in terms of multiscale damage/losses, which should include issues of adaptation, protection, response and mitigation. Separately, it should be noted, that participatory models are more applicable for crisis management.Construction of such formal concept of the resource management in the transforming multi-agent dynamically clustered social environment aimed to increasing of the community resilience allow to propose algorithms to identify the threats of transformation conflicts and resources inequity management, to increase an efficiency of governing in terms of collective decision-making processes in the transformative multi-agent environment, to increase community resilience toward multi-crisis impact, risk management and mitigation strategies.

On the nature and attribution of the 2022 annual record temperature in the UK and Ireland

Abstract: As climate changes to likely a warmer mean state by the end of this century than any time during the existence of humans, the world population has rapidly increased from about 1.3 billion in 1850 to 8 billion in November 2022. The latest IPCC AR6 reports attests that extreme events (e.g., heatwaves, floods, droughts, etc.) are occurring in many parts of the world at an increasing frequency and/or intensity due to global climate change, and are threatening human health, Earth&#8217;s biosphere, and the socio&#8208;economic fabric of our rapidly expanding and resource&#8208;hungry civilisation. On 19 July 2022 England, Wales and Scotland all experienced the hottest days on the record reaching 40.3 deg.C, 37.1 deg.C and 34.8 deg.C, respectively, and London Fire Brigade received the highest number of emergency calls since World War II. While in Dublin near-surface air&#160;temperature reached 33.0 deg.C on 18 July &#8211; the highest in the Ireland&#8217;s record. Furthermore, the annual mean temperature in 2022 was highest on the record in the UK and Ireland. This study uses a set of observations and reanalysis products combined with large ensembles of CMIP5/6 simulations to examine the structure of atmospheric circulation and the role of anthropogenic drivers leading to these extreme events on annual timescale. We also use large ensembles of specifically designed historical/factual and natural/counterfactual simulations of EC-Earth3 coupled climate model at the standard resolution and weather@home2 climate simulations performed by citizen scientists around the world to assesses to what extent anthropogenic forcing modified the probability and magnitude of this event. Moreover, we involve conditional perspective of the atmospheric circulation in our attribution estimates. The preliminary results points to a pronounced role of the global climate change in modifying likelihood and intensity of these annual extreme events.

A Transformer-Based Analysis of Tweets in Germany to Investigate the Appearance and Evolution of the 2021 Eifel Flood in Social Media

Abstract: In July 2021 several European countries were hit by severe floods. Estimates by SwissRe indicate that a flood event caused by the low-pressure area &#8220;Bernd&#8221; caused 227 deaths and economic losses of 41 billion USD in Central and Western Europe, with hotspots in Germany, Belgium, and the Netherlands. An increasing number of studies focus on understanding and modelling the causes and evolution of this event, developing reliable estimates of the losses it caused, and recommending improved disaster management strategies. However, risk communication and flood-related citizens&#8217; behaviors, attitudes, and perceptions before, during, and after the flood are currently understudied.Here, we develop an analytical framework to extract information on these human-related elements based on social media data. We ultimately aim to understand how flood warnings, intensity and impact are reflected in social media topics. To this extent, we analyze differences between topics arising on social media for an event like the 2021 flood compared to less devastating floods that occurred in the past. This requires homogeneous automatic assessment of Twitter data over time. We analyse the content of 42,000 tweets containing selected keywords related to flooding posted in Germany since 2014. Keywords refer to both fluvial and flash floods. Bidirectional Encoder Representations from Transformers (BERT) in combination with unsupervised clustering techniques are implemented to classify the tweets in different topic groups (BERTopic). Further, we extract the temporal evolution of topic patterns for different flood types and phases of flooding. Our analysis contributes to understanding the patterns of key topics, reflecting behaviors before, during and after the flooding event - thus how these topics change over time. Using the new framework and understanding these dynamics supports (i) modelling risk communication, behavioral drivers, and social interactions in relation to different types of floods with different intensities, and (ii) identifying indirect flood impacts that are not reported in traditional flood documentation. Finally, our approach can be extended for analysis of other natural hazards as well as compound events.

Global groundwater archetypes: a new typology of groundwater interactions with social and ecological systems and an outlook for sustainable development

Abstract: Groundwater resources do not exist in isolation but are deeply connected with social and ecological systems. As humans continue to modify the land surface, drive climate change, and place greater pressures on global freshwater resources, it is increasingly necessary to assess global groundwater resources through their relationships to these coupled systems. While several global classifications of physical groundwater systems exist, there is no data-driven global typology based on groundwater interactions with connected social and ecological systems. Though physical attributes remain hydrogeologically important, a more expansive systems-oriented classification is needed for policy development, applied research, and to develop the next generation of global hydrological models.We fill this gap by producing a spatially-explicit, moderate resolution (5 arcminute) global map of groundwater system archetypes based on groundwater interactions with social and ecological systems. These include interactions with streamflow, ecosystems, climate, agriculture, the economy, and water governance and management, all underpinned by existing global data. Archetypes, each with a unique set of interaction strengths and combinations, form a finite set of characteristic &#8220;fingerprints&#8221; that represent the dominant modes of interactions between groundwater and connected social and ecological systems. We find all WHYMAP large aquifer systems of the world are characterized by multiple social-ecological archetypes, suggesting that differentiated, context-appropriate approaches are necessary within large aquifers that are often assumed as uniform in global assessments and initiatives.We derive archetypes using multiple clustering algorithms and assign archetype membership based on majority agreement across clustering methods after cluster reclassification to create comparable maps. This multiple-method approach renders the archetypes more robust and less contingent on a single clustering algorithm while simultaneously enabling greater representation of archetype uncertainty.We additionally provide an outlook on sustainable development opportunities and challenges for each archetype. We summarize data sets that represent notable social-ecological outcomes&#160; related to the UN Sustainable Development Goals (SDGs), including: crop yield gaps (SDG 2), remotely sensed groundwater storage trends (SDG 6), economic inequality (SDG 10), human modification of terrestrial systems (SDG 15), and likelihood for hydropolitical interaction (SDG 16), among others.&#160;This work provides a number of useful contributions. First, the combination of archetyping (i.e., system characterization) and archetype-specific SDG outlook analysis provides a robust, data-driven overview of the role of groundwater in the global sustainability discourse. Secondly, the archetypes identify social-ecological system similarities across the globe, which may support interregional cooperation and networking, coordinated investment and interventions. Thirdly, as we harness the rapid growth in global data that document groundwater system interactions as the basis for our analysis, we simultaneously provide a synthesis and snapshot of the pertinent global data space. This snapshot can be used to identify the need for further data collection, especially on socio-economic interactions that remain underrepresented in global data. And finally, the archetypes raise awareness, build capacity, and shift mental models about the emerging perspective that it is necessary to conceptualize groundwater as a socially and ecologically connected resource.

Closing the loop between integrated assessment and climate risk research – rapid climate risk emulation

Abstract: Climate model emulation has long been and is increasingly applied to the results of integrated assessment modelling (IAM)models (IAMs), to determine the climate outcomes, primarily global mean surface temperature (GMST), of emissions pathways. Originally provided at the global level, more recently approaches have been developed to reproduce a growing number of climate variables, also with spatial, even gridded, resolution. Here we build on these approaches to demonstrate a workflow and post-processing package, that takes the GMST trajectory, e.g. from a simple climate model (SCM) and calculates a range of climate impacts and exposure indicators based on the GMST trajectory. To do this, we built a database of post-processed climate impacts from global climate [WM1]&#160;CMIP6 & ISIMIP-3 GCMs and impacts [WM2]&#160;models, and also calculated population and land area exposure to the indicators through time and for spatial units, e.g. countries. Indicators include temperature and precipitation extremes, heatwaves, degree days, drought intensity, water stress, and indicators of hydrological variability. Using a high-resolution temperature time timeslice approach, GMST trajectories are then mapped to the impact and exposure indicators to produce gridded maps of climate impacts through time, and trajectories of climate exposure by spatial unit. Using this approach we demonstrate the rapid post-processing of SCM [WM3]&#160;results such that ensembles of global IAM [WM4]&#160;mitigation pathways, such as those from AR6, can be accompanied by a new suite of climate impacts and risk information &#8211; and discuss related uncertainties and avenues for further research to incorporate vulnerabilities.

What about human behavior? The case of urbanization and rice in Africa

Abstract: Concurrent with an extensive population growth, the African continent has experienced a vast urbanization trend over the last decades. In 2000, around 35% of the population resided in urban areas. By 2020, this share has increased to around 44% and is projected to increase even further by 2050 following the Shared Socioeconomic Pathways (SSP) scenarios. Besides an important effect on local land use through urban expansion, this also affects food systems by shifting dietary patterns away from traditional diets towards imported or convenient goods. This is particularly the case for rice, which is predominantly imported from Southeast Asia, India, or Pakistan, and is gaining in popularity in African urban diets because of the low effort needed for cooking or storage &#8211; giving it a strong advantage over other staple crops. This dietary shift will alter trade dynamics, increase the pressure on local resources such as land, water, and fertilizer use, and subsequently also on biodiversity. In studies investigating the influence of urbanization, either the direct effect of urban expansion on land cover or the effects of dietary changes on demands are investigated, but rarely a combination or comparison of both. Particularly in impact studies or applications that focus on the synergy between water, land, and food-related issues, the dimension of human behavior, such as consumer preferences, is often overlooked.In this study, we provide an initial projection of the expected future effects of both sprawl and shifting preferences for rice caused by urbanization on rice availability, land &#8211; and input use, rice-specific emissions, and trade dynamics. By combining micro-level data from household surveys stemming from the Living Standards Measurements Study (LSMS) with the partial equilibrium Global Biosphere Management Model (GLOBIOM) at an African scale, we were able to identify the relative contribution of land cover effects stemming directly from urban expansion and indirectly from dietary shifts caused by rural-urban migration and a divergence in income between urban and rural areas.We indicate that while urban expansion only has a limited effect at the continental scale, the omission of any dietary shifts caused by urbanization substantially underestimates projections of African rice demand (by around 8% under an SSP2-scenario). This also results in subsequent underestimations of impacts on land use, trade dynamics, and rice-specific methane emissions. By this, our study exemplifies that consumer preferences are an essential component to understanding urbanization impacts, and that, by extension, human behavior is important to consider in impact and nexus studies.

Recent advancement in water quality indicators for eutrophication in global freshwater lakes

Abstract: Excessive nutrient (nitrogen and phosphorus) loadings to freshwater lakes cause eutrophication, which is a global water quality issue. Anthropogenic activities in lake basins emit nutrients, either as point- (e.g., sewage) or diffuse sources (e.g., agricultural runoff). Their typical impacts on lake water quality include the occurrence of harmful algal blooms, hypoxia and fish kills. These impacts are likely to worsen due to climate change, population growth and economic development. The response of lakes to a change in nutrient inputs depends on their interactions with the climate, land-use, hydrology and socio-economic conditions of a lake basin. These feedback mechanisms, however, are not often included in the eutrophication assessments for lakes. In this study, we present a new causal network of the drivers-pressure-state-impact-response (DPSIR) framework using a total of 58 sub-indicators to characterize all the DPSIR elements and systematically conceptualize the complex interactions of nutrients in freshwater lake basins. The network provides a holistic perspective on nutrient dynamics of multiple indicators and their interactive effects on water quality in lake basins, which is key to improving water quality management. Furthermore, we disentangle the complex eutrophication mechanisms using drivers and pressures, that represent different sources and nutrient pathways. The study highlights coupling of lake systems in water quality modeling frameworks and assessments which is required to understand its impact on water quality from human activities in the basin. The drivers and pressures can be used as proxies to provide meaningful information on nutrient emissions and biogeochemical pathways, that can fill the gap in water quality monitoring data, especially in data scarce regions such as Asia and Africa. These indicators can be used to set realistic water quality targets, and are, therefore, beneficial in long-term policy making and sustainable water quality management.

Policy implications from aligning IPCC scenarios to national land emissions inventories

Abstract: Taking stock of global progress towards achieving the Paris Agreement requires measuring aggregate national action against modelled mitigation pathways. A key gap exists, however, in how scientific studies and national inventories account for the role of anthropogenic land-based carbon fluxes, resulting in a 5.5-6.0 GtCO2yr-1 difference between the respective present-day land-use estimates. Modelled pathways mainly include direct human-induced fluxes, while inventories submitted by countries to the UNFCCC (NGHGIs) generally include a wider definition of managed land area as well as the indirect removals on that land caused by environmental changes (e.g., the CO2 fertilization effect). This difference hinders comparability between targets set by countries and scientific benchmarks.&#160;Scenarios assessed in AR6 show that a combination of deep near-term gross emissions reductions and medium-term carbon removal from the atmosphere are needed to reach net-zero and eventually net-negative CO2 emissions to limit warming in line with the Paris Agreement temperature goal. However, scenarios lacked key information needed to estimate land-based removals and to align their LULUCF projections with NGHGIs. Here, we estimate the land-based removals consistent with NGHGIs using a reduced complexity climate model with explicit treatment of the land-use sector, OSCAR, one of the models used by the Global Carbon Project. Of the 1202 pathways that passed IPCC vetting, 914 provide sufficient land-use change data to allow us to fill this information gap and enable alignment between pathways and inventories.Across both 1.5&#176;C and 2&#176;C scenarios, pathways aligned with NGHGIs show a strong increase in the total land sink until around mid-century. However, the &#8216;NGHGI alignment gap&#8217; decreases over this period, converging in the 2050-2060s for 1.5&#176;C scenarios and 2070s-2080s for 2&#176;C scenarios. These dynamics lead to land-based emissions reversing their downward trend in most NGHGI-aligned scenarios by mid-century, and result in the LULUCF sector becoming a net-source of emissions by 2100 in about 25% of deep mitigation scenarios.Our results do not change any climate outcome or mitigation benchmark produced by the IPCC, but rather provide a translational lens to view those outcomes. We find that net-zero timings on average advance by around 5 years; however, this does not imply that 5 years have been lost in the race to net-zero, but rather that following the reporting conventions for natural sinks results in net-zero being reached 5 years earlier. Understanding how these different accounting frameworks can be mutually interpreted is a fundamental challenge for evaluating progress towards the Paris Agreement, given the reality that direct and indirect carbon removals cannot be estimated separately with direct observations.We propose three primary ways to address this science-policy gap. First, targets can be formulated separately for gross emission reductions, land-based removals, and technical carbon removals, allowing for nations to clearly define their expected contributions and to measure progress in each domain separately. Second, nations can clarify the nature of their deforestation pledges. Third, modelling teams can provide their assumptions for the NGHGI correction as part of their standard output which future IPCC assessments can use to vet scenarios.

Dynamic risk modelling of a coupled human-drought system under multi-drought conditions

Abstract: Drought risk is modified through hazard, vulnerability and exposure, and exacerbated by management shortcomings. A quantitative understanding of the combined effects of these drivers is required to effectively lower risk. Yet, knowledge about the dynamics and effects of risk drivers over time and space and the human-natural feedbacks that steer them is largely lacking. In this study, we propose using GEB, the first large-scale coupled-agent based hydrological model that simulates all individual farmers at field scale, to systemically quantify the relations between dynamic hazard, vulnerability, exposure, management and drought risk over a multi-drought period in the Bhima basin, India. First, we parametrized the coupled hydrological model with meteorological and hydrological data to capture hydrological drought &#160;conditions of different paired drought events. Next, we develop the agent based model part to simulate the drought management behavior of two million farmers and how they respond to drought events. To simulate this behavior, we applied the protection motivation theory, supplemented by theory of planned behavior, to describe farmer agent behavior. The parameters of these theories were parametrized with survey data of Indian farmers fitted to the statistical distribution of the two million Bhima basin farmers. To study the dynamic attribution of the three risk drivers, a Global Sensitivity Analysis of all factors was performed at consecutive time intervals, showing the interaction of drivers before and after each drought event, as well as between the two events. The results are expected to further the understanding of drought risk dynamics and what disaster risk reduction measures can optimally reduce impacts in the long term.

The more wastewater reclamation, the less water stress?

Abstract: Wastewater treatment removes water pollutants and wastewater reclamation provides an alternative water supply. It is believed that increasing the rate of wastewater reuse and reclamation can reduce water stress. This study aims at understanding whether reusing more wastewater can help mitigate water stress in China. Through scenario analysis, it is found that the potential for reuse of reclaimed water under a water conservation scenario is only 12-56% of the actual situation, but regional water stress under a water saving scenario is 10-82% lower than the current reality. The results show that a higher amount of reclamation does not necessarily lead to a lower water stress in one region. The potential for wastewater treatment and reuse is determined by return flows, which can reduce water use efficiency and exacerbate water stress. To effectively alleviate water stress, it is important to not only increase wastewater reuse, but also prioritize water conservation.

Predicting Spatiotemporal Soil Organic Carbon Responses to Management Using EPIC-IIASA Meta-Models

Abstract: The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 35 years of multifactorial, spatially-explicit simulation data from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM), we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soils across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-determined meta-models are highly accurate (R&#178; = .97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the meta-model&#8217;s performance compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-model accurately predicts broad SOC dynamics while it often&#160; underestimates&#160; the measured SOC responses to management.&#160; Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications.While more accurate input data, calibration, and validation will l be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.