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Ecosystem services (ES) emerge from the complex interactions of diverse ecosystem components. In turn, the provision of ES is influenced by how they are put to use and environmental drivers such as climate change and land use change (CC-LUC). Researches into CC-LUC impacts on ES have so far focused on biophysical supply rather than ES interactions, making it difficult to design appropriate strategies for natural capital investment and the consequent ecosystem benefits for human well-being. In this study, we built networks from correlations among climatic forcing, land-use intensity and ES for the Xijiang River Basin in southern China, a rapidly urbanizing region with rich endowments of ecosystem assets. The variations in network structure were evaluated under different climate and land use projections up to 2035. We found that the CC-LUC-ES networks were more sensitive to land use change which affected the network structure either by directly influencing biophysical ES supplies or indirectly through altering ES interactions. Sustainable land-use management (i.e., ecological protection priority scenario) coupled with the RCP4.5 emission scenario showed higher improvements for both water-erosion prevention and regulating services. Whereas increasing land-use intensity (i.e., economic development priority scenario) with RCP8.5 resulted in losses of ES synergies and a more heterogenous network of greater integrity. Our approach provides a comprehensive view of the relationships among multiple ES and how they respond to CC-LUC, which can be applied to advance mechanistic hypotheses for ES variations. The results of this study can inform sustainable management strategies by regulating ES interactions in watersheds.

Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programmes. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defence pathways in coffee. Seventy-one single nucleotide polymorphisms (SNPs) were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB-like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, such as the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex situ conservation strategies are discussed.

Droughts have increased in frequency, duration, and severity across most of the tropics but their effect on forest communities remain not fully understood. Here we assessed the effects of a severe El Niño-induced drought event on dominant and low abundance understory plant species and the consequent impacts on ecosystem functions in the South American Atlantic Forest. We established 20 permanent plots with contrasting vegetation structure and topography. In each plot, we measured the stem diameter at breast height (DBH) of every understory woody plant (i.e. 1 to 10 cm stem diameter) before and after a severe 4-year drought event to calculate relative growth and mortality rates after drought. Litter biomass, litter nutrient content and soil nutrients, as well as tree canopy cover, were also quantified. High stem density reduced survival to drought for both dominant and low abundance understory woody species. The growth rate of dominant and low abundance species was lower on steeper slopes during the drought. Dominant species were the main contributor of litter biomass production whereas low abundance species were important drivers of litter quality. Overall, our findings suggest that habitats with low tree density and larger trees on flat areas, such as in valleys, can act as refuges for understory plant species during drought periods. These habitats are resource-rich, providing nutrients and water during unfavorable drought periods and might improve forest resilience to climate change in the long term.

Wildfires can have strong negative effects on soil and water resources, especially in headwater areas. The spatially explicit OpenLISEM model was applied to a burned catchment in southern Portugal to quantify the individual and combined impacts of wildfire and rainfall on hydrological and erosion processes. The companion paper has calibrated and assessed model performance in this area before and after a fire. In this study, the model was applied with design storms of six different return periods (0.2, 0.5, 1, 2, 5, and 10 years) to simulate and evaluate pre- and post-wildfire hydrological and erosion responses at the catchment scale. Our results show that rainfall amount and intensity played a more important role than fire occurrence in the catchment discharge and sediment yields. Fire occurrence was found to be an important factor for peak discharge, indicating that high post-fire hydro-sedimentary responses are frequently related to extreme rainfall events. The results also suggest a partial shift from runoff to splash erosion after fire, especially for higher return periods. This can be explained by increased splash erosion in burned upstream areas saturating the sediment transport capacity of surface runoff, limiting runoff erosion in downstream areas. Therefore, the pre-fire erosion risk in the croplands of this catchment was partly shifted to a post-fire erosion risk in upper slope forest and natural areas, especially for storms with lower return periods, although erosion risks in croplands were important both before and after fires. These findings have significant implications to identify areas for post-wildfire stabilization and rehabilitation, which is particularly important given the predicted increase in the occurrence of fires and extreme rainfall events with climate change.

Soil organic matter is essential for preserving and maintaining a range of soil and ecosystem functions as well as supply and store carbon for climate change mitigation. Digital Soil Mapping techniques will be used to obtain a spatially continuous product, especially over permanently vegetated areas. Recently available satellite remote sensing data, with among other systems the Copernicus Sentinel, will be used as input for environmental covariates. Digital Soil Mapping, coupled together with Remote Sensing products, is a powerful tool to produce soil properties maps and monitoring the changes in soil conditions over time.

Due to its geographical condition and geophysical location Bangladesh is one of the world’s most vulnerable country, which will become more vulnerable to the impact of climate change. According to the Global Climate Risk Index 2020 and Intergovernmental Panel on Climate Change (IPCC) 2011, Bangladesh is the seventh most climate change-affected nation in the world. This chapter elaborates on the possible impacts of climate change in Bangladesh through various natural disasters, i.e., increasing temperature, sea level rise, salinity intrusion, cyclone, storm surges, drought, etc. and also discusses the comprehensive disaster management approach in Bangladesh. It is now a worry in the scientific community that climate change could dramatically change weather patterns like the disease spread of epidemics (such as COVID-19) from vulnerable regions to invulnerable regions. All sectors will be affected by the impact of climate change, not only Bangladesh but also other South Asian countries. In Bangladesh, both the government and nongovernmental organizations (NGOs) are trying to prevent and alter the impacts of climate change by enhancing several adaptation and mitigation approaches. But still, coastal districts and northern areas in Bangladesh are facing many climatic issues, such as flash floods, super cyclones, salinity intrusion, storm surges, drought and riverbank erosion etc. Moreover, the government is taking the immediate response of shifting people in a cyclone center at the moment of extreme natural events but most of the peoples of the coastal districts in Bangladesh are illiterate so that they very careless about the awareness. On the basis of current information, it is suggested that the government should make some policy in disaster management for a sustainable solution for coastal areas in Bangladesh.

Photovoltaics (PV)-coupled battery energy storage systems (BESSs) are becoming more popular due to the reduction of BESS costs as well as the decreased remuneration of PV grid injection over the last few years. This paper compares the techno-economic system performance of two sizing approaches of PV-coupled BESSs - using mixed integer linear programming - for a case study in the Netherlands. The first approach uses a static sizing methodology to size the BESS based on the daily PV grid injection. While the second approach considers the BESS energy and power capacity as decision variables in an optimization problem to optimally size the BESS. The results demonstrate that the total costs can be reduced by 41% with our second approach - when the BESS is optimally sized considering annual demand and generation profiles.

A number of negative emission technologies (NETs) have been proposed to actively remove CO2 from the atmosphere, with enhanced silicate weathering (ESW) as a relatively new NET with considerable climate change mitigation potential. Models calibrated to ESW rates in lab experiments estimate the global potential for inorganic carbon sequestration by ESW at about 0.5–5 Gt CO2 year−1, suggesting ESW could be an important component of the future NETs mix. In real soils, however, weathering rates may differ strongly from lab conditions. Research on natural weathering has shown that biota such as plants, microbes, and macro-invertebrates can strongly affect weathering rates, but biotic effects were excluded from most ESW lab assessments. Moreover, ESW may alter soil organic carbon sequestration and greenhouse gas emissions by influencing physicochemical and biological processes, which holds the potential to perpetuate even larger negative emissions. Here, we argue that it is likely that the climate change mitigation effect of ESW will be governed by biological processes, emphasizing the need to put these processes on the agenda of this emerging research field.

Greenhouse gases do not generate heat. Bare ground is the furnace: vegetation and the water cycle provide the air-conditioning. Heat is emitted as infrared radiation; greenhouse gases in the atmosphere absorb some of this outgoing radiation which then heats the atmosphere or is reabsorbed by the land and, mainly, the oceans. So, control of greenhouse gas emissions is necessary and urgent–but it will not be enough to arrest global heating and does nothing for the water cycle. Clearance of natural landscapes for agriculture has driven global heating and disrupted the water cycle. Regenerative agriculture combats these hazards by avoiding bare soil, not ploughing, adopting a diverse crop rotation including perennial grasses and legumes, and creating windbreaks and wetlands. Speedier uptake of these society-saving practices requires a new social contract. The war in Ukraine provides an opportunity as well as urgent need.

The pig industry needs strategies to solve problems such as poor rearing environment, diseases, odours, and high energy loads. In this study, an air recirculated ventilation system (ARVS) was developed with optimally designed modules and an operating algorithm. Validation experiments conducted in winter and reported here but experiments carried out during the summer and during changes in season and reported in Kim et al. (2023). Environmental data (air temperature, relative humidity, carbon dioxide, ammonia, and ventilation rate) were automatically collected during winter and livestock disease and stress of piglets were evaluated by sampling. In winter the ARVS reused internal heat energy to satisfy the pig thermal demand. Each module of ARVS modules was designed and integrated based on the previous researches. Environmental factors were monitored in real time, and then ARVS was automatically controlled using developed algorithm. The ventilation rate of the ARVS was about 3 times more than that of the conventional ventilation system (CVS). Air temperature, relative humidity, and ammonia gas inside the ARVS piglet room were optimally maintained. Also, by reusing about 73% of internal energy, it was possible to reduce heating costs. The average concentrations of ammonia and odour measured at the outlet were 2.1 ppm and 251 OU. Piglets of the ARVS weighed 1.6 kg more than those of the CVS. The disease detection rate was <1% with beneficial bacteria increased and harmful bacteria decreased.