Earths Future最新文献

Burn severity is fundamental to post-fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics-informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out-of-sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science-based insights for managers and diverse stakeholders.

Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM-FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold-induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

The future evolution of the Amazon rainforest remains uncertain not only due to uncertain climate projections, but also owing to the intricate balance between tree growth and mortality. Many Earth System Models inadequately represent forest demography processes, especially drought-induced tree mortality. In this study, we used ORCHIDEE-CAN-NHA, a land surface model featuring a mechanistic hydraulic architecture, a tree mortality sub-model linked to a critical loss of stem conductance and a forest demography module for simulating regrowth. The model was forced by bias-corrected climate forcing data from the ISIMIP-2 program, considering two scenarios and four different climate models to project biomass changes in the Amazon rainforest until 2100. These climate models display diverse patterns of climate change across the Amazon region. The simulation conducted with the HadGEM climate model reveals the most significant drying trend, suggesting that the Guiana Shield and East-central Amazon are approaching a tipping point. These two regions are projected to transition from carbon sinks to carbon sources by the mid-21st century, with the Brazilian Shield following suit around 2060. This transition is attributed to heightened drought-induced carbon loss in the future. This study sheds light on uncertainties in the future carbon sink in the Amazon forests, through a well-calibrated model that incorporates tree mortality triggered by hydraulic damage and the subsequent recovery of drought-affected forests through demographic processes.

Beside climate-change-induced sea-level rise (SLR), land subsidence can strongly amplify coastal risk in flood-prone areas. Mapping and quantifying contemporary vertical land motion (VLM) at continental scales has long been a challenge due to the absence of gridded observational products covering these large domains. Here, we fill this gap by using the new European Ground Motion Service (EGMS) to assess the current state of coastal VLM in Europe. First, we compare the InSAR-based EGMS Ortho (Level 3) with nearby global navigation satellite systems (GNSS) vertical velocity estimates and show that the geodetic reference frame used to calibrate EGMS strongly influences coastal vertical land velocity estimates at the millimeter per year level and this needs to be considered with caution. After adjusting the EGMS vertical velocity estimates to a more updated and accurate International Terrestrial Reference Frame (ITRF2014), we performed an assessment of VLM in European low elevation coastal flood plains (CFPs). We find that nearly half of the European CFP area is, on average, subsiding at a rate faster than 1 mm/yr. More importantly, we find that urban areas and populations located in the CFP experience a near −1 mm/yr VLM on average (excluding the uplifting Fennoscandia region). For harbors, the average VLM is even larger and increases to −1.5 mm/yr on average. This demonstrates the widespread importance of continental-scale assessments based on InSAR and GNSS to better identify areas at higher risk from relative SLR due to coastal subsidence.

In this study, we present a machine-learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food-insecurity risk. We present a XGBoost machine-learning model to predict food-security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current-situation estimates to train the machine-learning model. Food-security dynamics were captured effectively by the model up to 3 months in advance (R² > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets in agro-pastoral regions (n = 22) with a 3-month lead time. We also compared our 8-month model predictions to the 8-month food-security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro-pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. With the well-established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine-learning methods into operational systems like FEWS NET.

Groundwater in north India remains a vital food and water security resource for more than one billion people. Both summer monsoon drying, and winter warming pose considerable challenges for rapidly declining groundwater. However, their impacts on irrigation water demands and groundwater storage under the observed and projected future climate remain unexplored. Using in situ observations, satellite data, and a hydrological model that considers the role of irrigation and groundwater pumping, we show that summer monsoon drying and winter warming accelerate groundwater depletion in north India during the observed climate, which will continue in the projected future climate. Summer monsoon precipitation has significantly (P-value = 0.04) declined (∼8%) while winters have become warmer in north India during 1951–2021. Both satellite (GRACE/GRACE-FO) and hydrological model-based estimates show a rapid groundwater depletion (∼1.5 cm/year) in north India with a net loss of 450 km³ of groundwater during 2002–2021. The summer monsoon drying followed by winter warming cause a substantial reduction in groundwater storage due to reduced groundwater recharge and enhanced pumping to meet irrigation demands. Summer monsoon drying and winter warming will continue to affect groundwater storage in north India in the future. For instance, summer monsoon drying (10%–15% deficit for near-far periods) followed by substantial winter warming (1–4°C) in the future will further accelerate groundwater depletion by increasing (6%–20%) irrigation water demands and reducing groundwater recharge (6%–12%). Groundwater sustainability measures including reducing groundwater abstraction and enhancing the groundwater recharge during the summer monsoon seasons are needed to ensure future agricultural production.

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 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.

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.

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.