Land-atmosphere Interactions Research Articles

Enhanced soil moisture–temperature coupling could exacerbate drought under net-negative emissions

Our understanding of drought evolution and land-atmosphere interactions under climate mitigation scenarios remains limited. Here, we analyzed future drought under net-zero and net-negative emission scenarios using the Community Earth System Model version 2, particularly focusing on three atmospheric CO2 states: linearly increases, decreases, and a return to the initial state. Interestingly, results revealed that net-zero emissions are more effective for drought mitigation than net-negative targets. Drying trends and drought characteristics — such as the duration, frequency, intensity, and area expansion are prominently increased under net-negative emissions due to higher potential evapotranspiration (PET). This is because the soil moisture and temperature couplings are stronger over drought regions and years, especially under net-negative forcing, with notable impacts in Central Africa and South Asia. Nevertheless, both target scenarios offer regional benefits, such as weakened dryness. This suggests that mitigating CO2 alone may not be sufficient to manage future droughts, highlighting the need for advanced water management strategies.

Improved simulation of compound drought and heat extremes in Eastern China through CWRF downscaling

Abstract Given their profound socio-economic impact and increasing occurrence, compound drought and heat extremes (CDHEs) have become a focal point of widespread concern. Studies have attempted to reproduce and predict these extremes using general circulation models (GCMs); however, the performance of these models in capturing compound events remains controversial. This study presents an improved simulation of CDHE trends over eastern China by using the regional Climate-Weather Research and Forecasting model (CWRF) to downscale the projections of two GCMs that participated in the Coupled Model Intercomparison Project Phase 6. The results show that CWRF downscaling significantly improved the underestimation of CDHE trends in GCM historical simulations, aligning better with observed trends. Moreover, the improvements of CWRF downscaling in simulating CDHEs are more pronounced than those for univariate events, i.e., extreme drought and extreme heat events. This enhancement largely results from CWRF’s better representation of land-atmosphere interaction processes, as indicated by the more realistic spatial distributions and intensities of the land-atmosphere coupling strength index. Under the SSP245 and SSP585 scenario, CWRF downscaling again predicts a more rapid increase in regional mean CDHE frequency compared to GCMs, with values nearing or exceeding 0.4 by the mid-21st century, suggesting a significant future threat to the study region. This study highlights the important role of land-atmosphere interactions in shaping CDHEs and the efficacy of regional climate models to reduce uncertainty in compound event simulations.

Investigation of land–atmosphere interaction at a high altitude (5330 m a.s.l.) snow covered station of East Himalaya

Investigation of land–atmosphere interaction at a high altitude (5330 m a.s.l.) snow covered station of East Himalaya

Hysteresis in seasonal land-atmospheric interactions over India and its characteristics across croplands and forests

Abstract Satellite-derived Vegetation Optical Depth and Soil Moisture (SM) data reveal the critical role of the soil-vegetation continuum in storing rainwater during the Indian Summer Monsoon and supporting evapotranspiration (ET) during the dry non-monsoon season. During the non-monsoon drier period, the climatologically estimated spatial mean of ET exceeds precipitation input, a phenomenon known as the Soil-Vegetation Capacitor (SVC) effect, which is pivotal in maintaining ecosystem productivity. Notably, our analysis reveals significant variations in the capacitor period between croplands and forests, with croplands exhibiting a ~77 days longer due to dual crop seasons influenced by regional precipitation. The well-recognized hysteresis curves, observed in magnetization and soil-water characteristic curves (SWCC), highlight phenomena where a system's state is influenced by its historical inputs or states and are integral to our findings. We report a previously undocumented seasonal hysteresis in the relationship between the evaporative fraction (EVF) and SM for Indian croplands and forests. We further found that the croplands SM-EVF relation exhibits a reversal in hysteresis in the case of root-zone SM. The surface SM-EVF hysteresis is not present in forests with large root depths and reduced soil evaporation due to high canopy shading, and yet it is present for the root-zone SM. With its reversal for croplands, the newly found hysteresis must be addressed in redefining the critical SM threshold to demarcate the energy and water-limiting regimes. It should be incorporated in the land surface modeling parameterization. Additionally, we observed hysteresis in the soil moisture-gross primary productivity (SM-GPP) relationship across both land covers and soil profiles (surface and root-zone), underscoring the need to investigate such processes to consider their dynamics in future ecological and hydrological models.

Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather

Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.

Generation of the Quasi‐Biweekly Oscillation in the Surface Potential Vorticity Over the Tibetan Plateau During Boreal Summer: A Case Study of 2014

AbstractThe atmospheric circulation around the Tibetan Plateau (TP) exhibits a substantial 10–20‐day quasi–biweekly oscillation (QBWO), profoundly impacting weather and climate locally and remotely. Understanding the factors influencing the generation of QBWO over the TP (QBWOTP) and its physical mechanism is crucial. This study has investigated the influence of multi–timescale and land–atmosphere interactions on the generation of the QBWOTP in surface potential vorticity (SPV), a valuable tool for characterizing the mechanical and thermal variabilities in mountain forcing, based on a 2014 case study. Results indicate that in the free atmosphere, the summer monsoon onset over the Bay of Bengal induces a northward shift in the westerly jet toward the TP, manifested as an increase in low‐frequency zonal winds. This shift facilitates the propagation of wave trains, leading to atmospheric quasi–biweekly potential temperature anomalies (θa) over the TP through a multi‐timescale interaction. Additionally, the TP's surface thermal forcing and arrival of wave trains trigger anomalous upward motion and increase cloud cover. The resultant decrease in net short‐wave radiations and increase in net long‐wave radiations contribute to variations in surface potential temperature (θs) over the TP. As θa and θs evolve, the difference between them enlarges, resulting in the generation of the SPV QBWOTP. Given the relationship between the QBWOTP and downstream rainfall, this study could provide novel insights into understanding and predicting downstream rainfall QBWO.

Warming-induced soil moisture stress threatens food security in India.

Soil moisture (SM) interconnects various components of the Earth system and drives the land-atmosphere feedbacks and food production. However, around 40% of global vegetated land experiences SM drying. India is one of the global hotspots of land-atmosphere interactions and an extensively agrarian economy, but underexplored in terms of SM dynamics and its ramifications on food security. Here, we examine the mechanism of SM drying and its implications on cropland productivity in India based on remote sensing measurements and land surface model simulations in recent decades (2000-2019) and future projection of the 21st century. We find SM reduction predominantly in monsoon (4.5%) and winter (3%) seasons that areinthemajor agricultural seasonsofKharif and Rabi, respectively. Machine learning (ML)-based random forest (RF) reveals that temperature (T, 30.76%) is the dominant driver of SM variability, and then precipitation (P, 26.34%), evapotranspiration (ET, 26.08%) and surface greenness (16.82%). Concurrently, India experiences severe warming in terms of land (0.59 ℃/dec), soil (0.48 ℃/dec) and soil heat flux (SHF, 0.16 W/m2/dec) during 2000-2019. Partial correlation analysis between SM and T limiting the influence of P reveals a strong negative (> - 0.5) relationship in the agriculture intensive regions of Indo-Gangetic Plain (IGP) and South India (SI). Drying owing to warming and increased SHF, termed as warming-induced moisture stress, reduces gross primary productivity (GPP) (i.e. browning) and yield of major food crops of wheat, rain-fed rice, maize and soyabean, predominantly in SI and eastern IGP. Granger Causality shows that warming-induced soil moisture stress has a maximum temporal lag of 1month. In a warming world, the ever-growing population demands more food, and therefore, the warming-induced soil moisture stress is a serious threat to food security in India and similar agro-climatic regions of the world. This calls for climate-resilient agriculture, better agronomic management, improved irrigation and adoption of water-efficient crops.

Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces

Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.

An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.

Understanding tropical cyclone persistence over land: A case study of Cyclone Gulab

Abstract Tropical Cyclone (TC) Gulab originated in the Bay of Bengal during the post-monsoon season of 2021. Following an unusual westward trajectory, TC Gulab made landfall on the east coast of India, traversed central India, and reintensified over the Arabian Sea into TC Shaheen. This study employed an online Eulerian water vapor tracer (WVT) tool embedded within the Weather Research and Forecasting (WRF) model to investigate the role of land in terms of soil moisture and Land Use Land Cover (LULC) in persistance of TC Gulab over central India. We quantified the contributions of sub-regions and dominant LULC types within the Indian subcontinent to the precipitation associated with TC Gulab. Our findings indicate that evapotranspiration across the entire Indian subcontinent contributed approximately 30% to the post-landfall precipitation associated with TC Gulab. The highest contribution originated from the northwestern region, followed by the northeast. Croplands, the dominant LULC type, were the primary contributors, followed by forests. Despite changing the dominant land cover from cropland to forest, the low-pressure system persisted over land while altering the spatial patterns of precipitation. Sensitivity experiments demonstrated a linear decrease in precipitation originating from land with decreasing soil moisture (SM). Notably, there is a disproportionate decline in total precipitation with decreasing SM. Further analysis separating atmospheric vapor sources into land and ocean contributions using WVT revealed a compensatory mechanism. The contribution of oceanic vapor to atmospheric vapor over land increased when SM decreased due to increase in the latent heat over ocean. However, the total atmospheric vapor remained low, ultimately leading to a lower total precipitation. Although SM plays a role, our results highlight the importance of oceanic and atmospheric processes in determining TC persistence over land. This study offers valuable insights into the dynamics of land-atmosphere interactions during low-pressure systems of TCs.

A preliminary observational study on the characteristics of surface turbulent fluxes over the South China Sea Islands

AbstractIn recent years, there has been a rise in human activities in oceanic areas, making the land–atmosphere interactions over islands a major scientific concern on a global scale. Examining the observation data from offshore areas enables a more comprehensive understanding of the turbulent fluxes in offshore atmospheric environments, patterns of momentum, energy and material exchange between the atmosphere and underlying surface in an oceanic boundary layer, and development of a heterogeneous atmospheric boundary layer. The related findings will assist in developing theoretical models and parameterization schemes to simulate the influence of heterogeneous surfaces on land–atmosphere interactions on the South China Sea Islands. Existing studies on the turbulent fluxes over the South China Sea Islands were mainly conducted on the Nansha Islands, whereas studies on the waters of the South China Sea are scarce. In this study, we used 10 Hz high‐frequency turbulence measurements to calculate the latent and sensible heat fluxes over the South China Sea Islands using the eddy correlation method. These findings were then compared with data from the Dunhuang Gobi, Ordos desert, and Xilingol grassland regions in inland China, along with the observed net radiation and surface heat fluxes. The findings indicate that the energy fluxes over the South China Sea in summer exhibit prominent diurnal variations. The magnitude of either latent or soil heat flux is low, and the net radiation is predominantly transformed into sensible heat flux, which warms the atmosphere. Furthermore, the daily variation curves of sensible and latent heat fluxes are influenced by intermittent turbulence on the islands and reefs, resulting in a less smooth pattern compared with soil heat flux. Although the South China Sea Islands have small land areas and are surrounded by the sea, the land–atmosphere interactions over the underlying surface of this region are similar to those over the underlying surface of grasslands in inland China during summer. The daily mean sensible heat flux on the islands is higher than that in an inland area, and the time lag in its response to sunrise is longer than that in inland areas by approximately 1 h. The overall energy balance ratio is approximately 0.75, c which is in line with the average level, but an energy balance residual of approximately 25% still exists. Furthermore, extreme weather conditions, such as typhoons, can disrupt the diurnal variations of sensible and latent heat fluxes, and the cyclical patterns are subsequently restored.

Surface energy fluxes in a drip-irrigated agroecosystem: Unique advection effect of oasis

Surface energy fluxes, mainly encompassing the net radiation (Rn), latent heat flux (LE), sensible heat flux (Hs), and soil heat flux (Gs), play an important role in the land-atmosphere interactions. However, almost all sites face the problem of energy imbalance, and advection fluxes associated with large inhomogeneous surfaces have been ignored, especially in arid oasis areas. In this study, a three-year continuous measurement of energy fluxes with an eddy covariance system was conducted in a drip-irrigated oasis agroecosystem in Northwest China. Reanalysis data including air temperature (Ta), soil moisture (θ), and leaf area index (LAI) in our cropland and surrounding deserts were also collected. The results showed that multi-year mean turbulent fluxes (LE+Hs) accounted for 75 ± 8 % (mean ± standard deviation) of available energy (Rn–Gs). To be precise, LE took up 72 ± 10 % of available energy, and 7.8 ± 2.8 % of it was induced by higher sensible heat advection, proving a pronounced advection effect in this study. When advection was present, most likely during the heading stage, the threshold value for the Priestley–Taylor parameter α, an indicator to reflect the advection effect, fell in the range of 0.88–1.34. Additionally, after a significant irrigation event, α showed a good linear relationship with differences in air temperature (ΔTa), soil moisture (Δθ), and leaf area index (ΔLAI) between our cropland and surrounding deserts. It's worth mentioning that Δθ was the most significant factor, showing a negative correlation with the advection effect. This study has deepened our understanding of the energy balance in oasis agriculture, emphasizing that the advection effect should not be overlooked.

Improved water use efficiency of vegetation due to carbon fertilization not translating to increased soil moisture in India

Soil moisture (SM) plays a critical role in land–atmosphere interaction. Literature shows that the CO2 fertilization effect (CFE) governs vegetation dynamics and its interactions with the atmosphere. However, the impacts of changing vegetation properties due to CFE, on SM, specifically in India, remains unexplored. Comparing Land-hist (observed CO2 concentration) and Land-cCO2 (constant preindustrial CO2 concentration) simulations with the same meteorological forcings from the Land Use Model Inter-comparison Project (LUMIP), we found that changing vegetation due to CFE impacts SM following two pathways. Enhanced water use efficiency (WUE) due to CFE seeks to ameliorate SM by regulating transpiration. Conversely, elevated CO2 increases leaf area index and plant water use, ultimately leading to a decrease in SM due to the combined impacts of CFE in India. This reduction is most prominent in arid regions, where we observe a decline in SM particularly during dry periods. Our study highlights the need to consider plant physiology in land surface modeling under increased CO2 concentration.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Temporal accumulation and lag effects of precipitation on carbon fluxes in terrestrial ecosystems across semi-arid regions in China

Precipitation (PRE) plays a vital role in hydrological processes, ecological vegetation, and land-atmosphere interactions in semi-arid regions. Previous research has mainly focused on the impact of PRE on large-scale regional climate change and ecological evolution. However, there have been few studies on the long-term effects of PRE on carbon fluxes in these regions, especially the time-accumulation and -lag effects. Here, we employed observational data from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and integrated multiple data sources, including remote sensing and carbon flux simulation data, to quantitatively assess the lagged response of carbon fluxes to PRE and elucidate the underlying mechanisms from multiple perspectives. Characterization of PRE, soil water content (SWC) and carbon fluxes at SACOL qualitatively reveals the existence of a time-delayed response of carbon fluxes to PRE, both on monthly and finer daily temporal scales. The average lagged response of net ecosystem exchange (NEE) and gross primary productivity (GPP) to accumulated PRE (APRE) is approximately 42 days. When considering time-accumulation and -lag effects, the combined direct and indirect effects of APRE on NEE and GPP increase by 0.37 and 0.58, respectively. Notably, preceding APRE primarily exerts a direct effect on current carbon fluxes, whereas the impact of SWC at a depth of 0.1 m is primarily mediated through the memory effect of preceding APRE, resulting in an indirect effect on carbon fluxes. These findings emphasize the importance of preceding APRE. Significantly, our subsequent study indicates that the delay in NEE and GPP responses to APRE also extends to approximately 40 to 50 days at the regional scale. Our findings emphasize the significant time effects of APRE on carbon fluxes, and considering these effects will contribute to a better understanding of the interplay between PRE and vegetation over semi-arid regions in China.

Divergent response of energy exchange to heatwaves from flux tower observations among various vegetation types

Abstract The increasing frequency of European heatwaves and the associated impacts on ecosystems have raised widespread concern during the last two decades. The partitioning of surface energy between latent and sensible heat fluxes plays a pivotal role in regulating heat and water exchange between the land surface and the atmosphere. However, the responses of surface energy partitioning during heatwave events and the contributions of changes in energy partitioning to heatwave development have been underexplored. Here, we investigated the responses of surface energy exchange to temperature extremes during four devastating European heatwaves (2003, 2010, 2018, and 2022) based on long‒term observations from 31 flux towers. Our results demonstrated that the divergent responses of surface energy exchange to heatwaves were modulated by vegetation type and background climate in Europe. Forests maintained similar latent heat fluxes as the climatological mean but largely increased sensible heat under heat‒stressed conditions. While grasslands and croplands tended to increase sensible heat by suppressing latent heat during heatwaves, especially under water‒stressed conditions. Furthermore, the changes in surface energy partitioning strengthened positive land‒atmosphere feedbacks during the heatwave period, leading to unprecedented temperature extremes. This study highlights the importance of surface energy partitioning in land‒atmosphere interactions and heatwave developments.

How climate change and groundwater extraction will shape the global terrestrial water cycle pattern

Human groundwater extraction alters hydrological processes, thereby influencing climate through land–atmosphere interactions. As socioeconomic development progresses, the escalating water demand exacerbates conflicts with limited water resources. Nevertheless, potential response of the global terrestrial water cycle to the combined impact of climate change and groundwater extraction under various future scenarios remain vaguely understood. Therefore, this study developed human water regulation projection schemes based on Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) integrated into a global climate model (CAS-FGOALS-g3). The results show that global water demand in 2100 under the SSP370 scenario almost doubled relative to 2010. The proportion of surface soil drying due to warming is 3%-11% more in the medium- and high-emission scenarios than in the low one. In major irrigated areas, excessive groundwater extraction changed the groundwater storage trend, especially in the Southern Plains of the United States, where the increasing groundwater storage trend became negative compared to scenarios without groundwater extraction. This study underscores the complex interplay between human water use and hydroclimate within an evolving environment, emphasizing the critical need for effective water resource management to mitigate future water stress.

Unveiling teleconnection drivers for heatwave prediction in South Korea using explainable artificial intelligence

Increasing heatwave intensity and mortality demand timely and accurate heatwave prediction. The present study focused on teleconnection, the influence of distant land and ocean variability on local weather events, to drive long-term heatwave predictions. The complexity of teleconnection poses challenges for physical-based prediction models. In this study, we employed a machine learning model and explainable artificial intelligence to identify the teleconnection drivers for heatwaves in South Korea. Drivers were selected based on their statistical significance with annual heatwave frequency ( | R | > 0.3, p < 0.05). Our analysis revealed that two snow depth (SD) variabilities—a decrease in the Gobi Desert and increase in the Tianshan Mountains—are the most important and predictive teleconnection drivers. These drivers exhibit a high correlation with summer climate conditions conducive to heatwaves. Our study lays the groundwork for further research into understanding land–atmosphere interactions over these two SD regions and their significant impact on heatwave patterns in South Korea.

Deep learning-based quantitative analyses of feedback in the land-atmosphere interactions over the Vietnamese Mekong Delta

During the past several decades, the Vietnamese Mekong Delta (VMD) has experienced many severe droughts, resulting in significant impacts on both agriculture and aquaculture. In the evolution and intensification of droughts, local feedbacks in the Land-Atmosphere (LA) interactions were considered to play a crucial role. It is critical to quantify the impact of LA variables on drought processes and severity with the feedback loop of water and energy balances (e.g., soil moisture-latent and sensible heat-precipitation). In this study, a deep learning model, named Long- and Short-term Time-series Network (LSTNet), was applied to simulate the LA interactions over the VMD. With the ERA5 data as modelling inputs, the role of each key variable (e.g., soil moisture, sensible and latent heat) in the LA interactions over the period of 2011–2020 was quantified, and the variations of their inter-relationships in the future period (2015–2099) were also investigated based on the CMIP6 data. The LSTNet model has demonstrated that the deep learning algorithm can effectively capture the relative importance of key variables in the LA interactions. We found that it is crucial to evaluate the effects of soil moisture and sensible heat on the LA interactions, particularly in the dry periods when negative anomalies in soil moisture and sensible heat would significantly reduce the amount of precipitation. In addition, the decline in soil moisture and the rise in sensible heat are anticipated to further diminish precipitation in the future under the changing climate.

Spatial-temporal variations in evapotranspiration across the continental United States: An atmospheric water balance perspective

Accurate estimation of terrestrial evapotranspiration (ET) is crucial for understanding land–atmosphere interactions, but is challenging on the regional scales. The water balance approach, including terrestrial and atmospheric water balance (TWB and AWB), provides a simple tool for estimating regional ET. This study estimated ET values based on the AWB approach (ETAWB) over the continental United States (CONUS) for the period 1979–2021. Validations using TWB-based ET estimates (ETTWB) suggest that ETAWB is accurate. ETAWB demonstrates comparable interannual variability with the other three long-term ET products over the CONUS and is consistent with ETTWB in the majority of basins. The 43-year CONUS averaged ETAWB is 548 ± 26 mm/year, with higher values in the eastern region and the coastal regions in the western CONUS, and lower values in the western arid regions. During 1979–2021, increasing trends of ETAWB are observed in the eastern CONUS, while decreasing trends occurred in the western regions. The intercomparison between ETAWB, GLEAM, Noah, and ERA5 illustrates similar spatial patterns and linear trends. In the water-limited arid basins, ETAWB anomalies over time show strong agreements with precipitation anomalies. The results of this study indicate that the AWB approach provides reasonable regional-to-continental terrestrial ET estimates over the CONUS, serving as a reference for hydrological and climatic modeling.