Earths Future最新文献

Fresh coastal groundwater is a valuable water resource of global significance, but its quality is threatened by saltwater intrusion. Excessive groundwater abstraction, sea-level rise (SLR), land subsidence and other climate-related factors are expected to accelerate this process in the future. The objective of this study is to (a) quantify the impact of projected climate change and (b) explore the role of individual hydrogeological boundaries on groundwater salinization of low-lying coastal groundwater systems until 2100 CE. We employ numerical density-dependent groundwater flow and salt transport modeling for this purpose, using Northwestern Germany as a case. Separate model variants are constructed and forced with climate data, that is, projected SLR and groundwater recharge, as well as likely ranges of other hydrogeological boundaries, including land subsidence, abstraction rates and drain levels. We find that autonomous salinization in the marsh areas, resulting from non-equilibrium of the present-day groundwater salinity distribution with current boundary conditions, is responsible for >50% of the salinization increase until 2100 CE. Sea-level rise, land subsidence and drain levels are the other major factors controlling salinization. We further show that salinization of the water resources is a potential threat to coastal water users, including water suppliers and the agrarian sector, as well as coastal ecosystems. Regional-scale uplifting of drain levels is identified as an efficient measure to mitigate salinization of deep and shallow groundwater in the future. The presented modeling approach highlights the consequences of climate change and anthropogenic impacts for coastal salinization, supporting the timely development of mitigation strategies.

The middle reaches of the Lancang-Mekong River Basin (M-LMRB) experienced a record-breaking drought event in 2019, resulting in significant economic losses of approximately 650 million dollars and affecting a population of 17 million. However, the anomalous circulation and transportation processes of water vapor, which may have played a crucial role in inducing the extreme drought, have not been fully studied. In this study, we analyze the water vapor circulation during the 2019 drought event using the land-atmosphere water balance and a backward trajectory model for moisture tracking. Our results indicate that the precipitation in the M-LMRB from May to October 2019 was only 71.9% of the long-term climatological mean (1959–2021). The low precipitation during this drought event can be attributed to less-than-normal external water vapor supply. Specifically, the backward trajectory model reveals a decrease in the amount of water vapor transported from the Indian Ocean, the Bay of Bengal, and the Pacific Ocean, which are the main moisture sources for precipitation in the region. Comparing the atmospheric circulation patterns in 2019 with the climatology, we identify anomalous anticyclone conditions in the Bay of Bengal, anomalous westerlies in the Northeast Indian Ocean, and an anomalous cyclone in the Western Pacific Ocean, collectively facilitating a stronger export of water vapor from the region. Therefore, the dynamic processes played a more significant role than thermodynamic processes in contributing to the 2019 extreme drought event.

The 2021 heatwave over Western North America (WNA) led to record-breaking air temperatures and human-perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6-day means of humidex and temperature (HX-6 and TX-6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June-September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi-model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX-6 and HX95) show faster and larger increases than temperature indices (TX-6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi-model ensemble. At 3.0°C global warming from pre-industrial, the multi-model ensemble projects occurrences of HX-6, TX-6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C.

We present a framework for strategic dam planning under uncertainty, which includes GHG emissions mitigation as a novel objective. We focus on the Mekong River Basin, a fast-developing region heavily relying on river-derived ecosystem services. We employ a multi-objective evolutionary algorithm to identify strategic dam portfolios for different hydropower expansion targets, using process-related and statistical models to derive indicators of sediment supply disruption and GHG emissions. We introduce a robust optimization approach that explores variations in optimal portfolio compositions for more than 5,000 state-of-the-world configurations, regarding sediment origins and trapping and GHG emissions. Thus, we can rank dam projects' attractiveness based on their frequency of inclusion in optimal portfolios and explore how uncertainty affects these rankings. Our results suggest that developing dams in the upper Mekong would be a more robust option for near-term development than, for example, the lower Mekong and its tributaries, for both environmental and energy objectives. Our work presents a novel approach to better understand the basin-scale cumulative impacts of dam development in high-uncertainty, data-scarce contexts like the Mekong Basin.

Record high temperatures were documented in the McMurdo Dry Valleys, Antarctica, on 18 March 2022, exceeding average temperatures for that day by nearly 30°C. Satellite imagery and stream gage measurements indicate that surface wetting coincided with this warming more than 2 months after peak summer thaw and likely exceeded thresholds for rehydration and activation of resident organisms that typically survive the cold and dry conditions of the polar fall in a freeze-dried state. This weather event is notable in both the timing and magnitude of the warming and wetting when temperatures exceeded 0°C at a time when biological communities and streams have typically entered a persistent frozen state. Such events may be a harbinger of future climate conditions characterized by warmer temperatures and greater thaw in this region of Antarctica, which could influence the distribution, activity, and abundance of sentinel taxa. Here we describe the ecosystem responses to this weather anomaly reporting on meteorological and hydrological measurements across the region and on later biological observations from Canada Stream, one of the most diverse and productive ecosystems within the McMurdo Dry Valleys.

Wetlands formed by natural sediment deposition account for a large proportion of new coastal lands, and these new wetlands usually have active ecosystems and obvious ecological effects. However, previous studies largely overlooked this sediment-caused wetland expansion, and the spatiotemporal variation in these wetlands and future response to sea-level rise (SLR) have not been determined. Here, we employed satellite observations to quantify the seaward expansion of coastal lands in China over the past two decades. A total land expansion of 6,651 km² was found, and wetlands and artificial surfaces dominated, accounting for 32% and 25%, respectively. Subsequently, we utilized an integrated model to estimate the response of these new wetlands to SLR in the 21st century, that is, we estimated the wetland gain from sediment deposition and loss due to SLR. The results indicate that under the current condition of sediment availability, the area of China's new coastal wetlands is projected to increase by 200%–261% compared to that in 2020 based on four SLR scenarios, despite the unavoidable impact of SLR. These increases are accompanied by the continuous enhancement of carbon accumulation. Wetland changes are influenced by factors such as sediment deposition, SLR and storm surges, as well as the continued effect of local natural and anthropogenic factors. These results show the importance of understanding the ecological effects of new wetlands and constructing specific protection measures for sustainable development.

Future sea level rise and changes in extreme weather will increase the frequency of flooding and intensify the risks for the millions of people living in low-lying coastal areas. Concerns about coastal adaptation have been broadened due to societal awareness of the threat from rising seas, leading to a large set of potential adaptation users with diverse needs for adequate sea level projections in coastal areas beyond the current state of the art regional projections. In this paper, we provide an overview of the potential steps for improvement of regional sea level projections along the global coastline, with specific focus on the contribution from ocean dynamics to seasonal-decadal variability of coastal sea level, and its implications for changes in frequency and magnitude of extreme sea levels. We discuss the key gaps in our knowledge and predictive capability of these dynamics as they relate to sea level variability on seasonal to decadal timescales, and conclude by suggesting ways in which these knowledge gaps could be addressed.

The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna-like state. Whilst previous work has suggested that forest resilience has declined in recent decades, that work was based only on local resilience indicators, and moreover was potentially biased by the employed multi-sensor and optical satellite data and undetected anthropogenic land-use change. Here, we show that the average correlation between neighboring grid cells' vegetation time series, which is referred to as spatial correlation, provides a more robust resilience indicator than local estimations. We employ it to measure resilience changes in the ARF, based on single-sensor Vegetation Optical Depth data under conservative exclusion of human activity. Our results show an overall loss of resilience until around 2019, which is especially pronounced in the southwestern and northern Amazon for the time period from 2002 to 2011. The results from the reliable spatial correlation indicator suggest that in particular the southwest of the ARF has experienced pronounced resilience loss over the last two decades.

Understanding how climate variability affects oilseed yields is crucial for ensuring a stable oil supply in regions such as China, where self-sufficiency in edible vegetable oils is low. Here, we found coherent patterns in the interannual variability of Sea Surface Temperature (SST) anomalies and percent crop yield anomalies in the three ocean basins, and then quantified the contribution of these SST modes to oilseed crop yield anomalies. Our analysis revealed that, at the national level, the six tropical SST modes collectively accounted for 51% of soybean, 52% of rapeseed, and 33% of peanut yield anomalies in China. Tropical Indian Ocean variability exerts the greatest impact on soybean and peanut yield variability, whereas the most significant impact on rapeseed yield anomalies is attributed to El Niño-Southern Oscillation. Finally, this study examined the specific ways in which changes in SST modes can affect oilseed crop yields using changes in local meteorological variables. Our findings revealed the relationship between tropical SST variability and oilseed crop yields, providing a detailed understanding of the diverse connections between SST modes and oilseed crop yield. This study deepens our knowledge of the influence of climate variability on agriculture, offering valuable insights for devising strategies to mitigate the adverse effects of climate variability on oilseed crop production in China.

Under persistent eutrophication of European water bodies and a changing climate, there is an increasing need to evaluate best-management practices for reducing nutrient losses from agricultural catchments. In this study, we set up a daily discharge and water quality model in Hydrological Predictions of the Environment for two agricultural catchments representative for common cropping systems in Europe's humid continental regions to forecast the impacts of future climate trajectories on nutrient loads. The model predicted a slight increase in inorganic nitrogen (IN) and total phosphorus (TP) loads under RCP2.6, likely due to precipitation-driven mobilization. Under RCP4.5 and RCP8.5, the IN loads were forecasted to decrease from 16% to 26% and 21%–50% respectively, most likely due to temperature-driven increases in crop uptake and evapotranspiration. No distinct trends in TP loads were observed. A 50% decrease in nutrient loads, as targeted by the European Green Deal, was backcasted using a combination of management scenarios, including (a) a 20% reduction in mineral fertilizer application, (b) introducing cover crops (CC), and (c) stream mitigation (SM) by introducing floodplains. Target TP load reductions could only be achieved by SM, which likely results from secondary mobilization of sources within agricultural streams during high discharge events. Target IN load reductions were backcasted with a combination of SM, fertilizer reduction, and CC, wherein the required measures depended strongly on the climatic trajectory. Overall, this study successfully demonstrated a modeling approach for evaluating best-management practices under diverging climate change trajectories, tailored to the catchment characteristics and specific nutrient reduction targets.