Earths Future最新文献

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.

High-resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR-ESDM) is a computationally-efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi-Gaussian time series, gridded or point-based, into multi-decadal long-term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long-term predictor trends are bias-corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high-resolution bias-corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR-ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high-resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1-in-1000 hottest and coldest temperatures are <0.5°C and biases in the 1-in-1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR-ESDM represents a significant advance in generating consistent high-resolution projections to comprehensively assess climate risk and optimize resilience globally.

Aedes sp. mosquitoes are changing their geographic range in response to climate change. This is of concern because these mosquitoes can carry dengue fever and other viral diseases. Changing weather patterns can also increase the numbers of Aedes mosquitoes, leading to greater human exposure and enhancing population health risks. We project the geographic distribution of Aedes and associated changes in populations exposed to dengue in Asian metropolitan areas under warming scenarios from 1.5°C to 5.0°C above pre-industrial temperatures, using multi-model ensembles. With global warming, the southern part of the Arabian Peninsula, the coast of the Arabian Sea in southern Iran, southern Pakistan in West Asia, the Korean Peninsula, most of the Japanese islands, and parts of North China in East Asia are projected to become suitable for dengue transmission. The numbers of metropolitan areas exposed to dengue is projected to change from 142 (48%) in the reference period (1995–2014) to 211 (71%) at 5.0°C warming. With the combined impact of socioeconomic and climate change, population exposure to dengue in Asian metropolitan areas is projected to increase from 263 (multi-model range 252–268) million in 1995–2014 to 411 (394–432) million, 446 (420–490) million, 509 (475–601), 558 (493–685) and 587 (529–773) million, respectively, at 1.5°C, 2.0°C, 3.0°C, 4.0°C and 5°C warming, with an average of 2.9 million new people exposed to dengue fever in metropolitan areas each year.

Urbanization alters the thermal and dynamic environment of the local climate system, resulting in significant impacts on precipitation in both urban and adjacent areas. Nevertheless, there remains a significant gap in our understanding of urbanization-induced effects on asymmetrical, symmetrical, and other precipitation patterns in urban agglomerations (UAs) with divergent background climates and geographic regions at different timescales. Specifically, this asymmetrical change pattern is characterized by an increase in heavy (or light) rainfall and a decrease in light (or heavy) rainfall. Here, we assessed the effects of urbanization on precipitation patterns across 18 UAs situated in diverse background climates and geographical areas in China at different timescales. The results demonstrate that urbanization predominantly alters precipitation patterns in UAs located in the humid region. Specifically, urbanization amplified asymmetrical changes in Yangtze River Delta, Pearl River Delta, Beibu Gulf, Middle Yangtze River, and Guanzhong, but exacerbated symmetrical changes in precipitation in some regions such as Chengdu-Chongqing. Notably, the urbanization effect demonstrates greater significance at the hourly scale, as exemplified in the Yangtze River Delta, Pearl River Delta, and Middle Yangtze River, where the urban impact is nearly twice as pronounced when compared to the daily scale. Moreover, urbanization had either no effect or has a negative impact on precipitation patterns in UAs located within continental and arid regions. This is related to the intensity of urbanization, background climate and complex topography. This finding implies that urban managers should consider the impact of urbanization on precipitation patterns in different contexts to provide scientific guidance for urban planning.