Global Land Research Articles

Knowledge evolution learning: A cost-free weakly supervised semantic segmentation framework for high-resolution land cover classification

Despite the success of deep learning in land cover classification, high-resolution (HR) land cover mapping remains challenging due to the time-consuming and labor-intensive process of collecting training samples. Many global land cover products (LCP) can reflect the low-level commonality (LLC) knowledge of land covers, such as basic shape and underlying semantic information. Therefore, we expect to use LCP as weakly supervised information to guide the semantic segmentation of HR images. We regard high-level specialty (HLS) knowledge as HR information unavailable in the LCP. We believe LLC knowledge can gradually evolve into HLS knowledge through self-active learning. Hence, we design a knowledge evolution learning strategy from LLC to HLS knowledge and correspondingly devise a knowledge evolution weakly supervised learning framework (KE-WESUP) based on LCP. KE-WESUP mainly includes three tasks: (1) Abstraction of LLC knowledge. KE-WESUP first adopts a training method based on superpixel to alleviate the inconsistency between LCP and HR images and directly learns the LLC knowledge from LCP according to the feature-fitting capacity of convolutional neural networks. (2) Automatic exploration of HLS knowledge. We propose a dynamic label optimization strategy to obtain a small number of point labels with high confidence and encourage the model to automatically mine HLS knowledge through the knowledge exploration mechanism, which prompts the model to adapt to complexHR scenes. (3) Dynamic interaction of LLC and HLS knowledge. We adopt the consistency regularization method to achieve further optimization and verification of LLC and HLS knowledge. To verify the effectiveness of KE-WESUP, we conduct experiments on USDA National Agriculture Imagery Program (1 m) and GaoFen-2 (1 m) data using WolrdCover (10 m) as labels. The results show that KE-WESUP achieves outstanding results in both experiments, which has significant advantages over existing weakly supervised methods. Therefore, the proposed method has great potential in utilizing the prior information of LCP and is expected to become a new paradigm for large-scale HR land cover classification.

Deriving the Terrestrial Water Storage Anomaly From GRACE Spherical Harmonic Coefficients Using a Convolutional Neural Network

AbstractTerrestrial water storage anomaly (TWSA), derived from Gravity Recovery and Climate Experiment (GRACE) satellites, has been widely used in hydrology studies. The inversion is commonly achieved by truncating and filtering spherical harmonic coefficients (SHC), whereby the result is characterized by leakage error and low resolution. It remains unclear whether machine learning methods can help resolve this challenging issue. In this study, we present a convolutional neural network (CNN) approach to correct TWSA from GRACE SHC by leveraging the knowledge of the leakage effect determined from global hydrological models (GHMs) and land surface models (LSMs). The CNN approach is implemented in three representative regions in China, that is, the human‐impacted Haihe River Basin, the nature‐impacted Yangtze River Basin and the model‐limited Tibetan Plateau. The results show the following: (a) The recovery performance of CNN at the basin scale is better than that at the grid scale, and the grid‐scale recovery is significantly influenced by the spatial heterogeneity of TWSA and the input GHM/LSMs; (b) The more accurate the GHM/LSMs used for training, the better the recovery performance of CNN; and (c) The trained model retains comparable performance in deriving the TWSA time series from GRACE SHC when compared to that derived from other methods (i.e., scaling factor and mass concentration solutions) with average r = 0.90 and RMSE = 21.50 mm. This study highlights the potential of machine learning to supplement conventional correction methods when deriving the TWSA from GRACE SHC by utilizing signal restoration knowledge learned from multiple accurate GHMs/LSMs.

Global impacts of vegetation clumping on regulating land surface heat fluxes

The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m−2 (∼18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m−2 (∼65%), resulting in a total sensible heat decrease of 1.0 W m−2 (∼3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m−2 (∼2%), primarily due to increased evapotranspiration (up to 11 W m−2) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. These changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.

Relative contribution of photodegradation to litter breakdown in Australian grasslands.

Grassy ecosystems cover ~40% of the global land surface and are an integral component of the global carbon (C) cycle. Grass litter decomposes via a combination of photodegradation (which returns C to the atmosphere rapidly) and biological decomposition (a slower C pathway). As such, decomposition and C storage in grasslands may vary with climate and exposure to solar radiation. We investigated rates of grass litter decomposition in Australian temperate grasslands along a climate gradient to uncouple the relative importance of photodegradation and climate on decomposition. Litterbags containing leaf litter from two common native grass species (Poa labillardierei, Themeda triandra) were deployed at six grassland sites across a precipitation gradient (380-890 mm) in south-eastern Australia. Bags were retrieved over 39 weeks to measure mass loss from decomposition. We used shade treatments on the litter of one species (T. triandra) to partition photodegradation from biological decomposition. The shade treatment reduced the rate of decomposition of T. triandra relative to the full-sun treatment at all sites, by an average of 38% at 39 weeks; the effect size of the shade treatment was not correlated with site productivity. The rate of decomposition in both species was positively correlated with rainfall midway through the experiment, but there were no significant differences in total decomposition among sites after 39 weeks. By week 39, total decomposition of T. triandra was significantly greater than for P. labillardierei. In general, we observed relatively linear decomposition rather than the strong negative exponential decay observed in many global litter decomposition studies. Synthesis: We found that solar radiation exposure was a strong contributor to litter decomposition in temperate Australian grasslands across a broad climate gradient, which may be related to a period of photopriming prior to further biotic decomposition. This study highlights the importance of litter composition and solar radiation exposure in our understanding of how decomposition patterns contribute to global C cycling.

Polar Amplification in the Earth’s Three Poles Based on MODIS Land Surface Temperatures

Polar amplification appears in response to greenhouse gas forcing, which has become a focus of climate change research. However, polar amplification has not been systematically investigated over the Earth’s three poles (the Arctic, Antarctica, and the Third Pole). An index of polar amplification is employed, and the annual and seasonal variations of land surface temperature over the Earth’s three poles are examined using MODIS (Moderate Resolution Imaging Spectroradiometer) observations for the period 2001–2018. As expected, the warming of the Arctic is most conspicuous, followed by the Third Pole, and is weakest in Antarctica. Compared to the temperature changes for the global land region, positive polar amplification appears in the Arctic and the Third Pole on an annual scale, whereas Antarctic amplification disappears, with a negative amplification index of −0.72. The polar amplification for the Earth’s three poles shows seasonal differences. Strong Arctic amplification appears in boreal spring and winter, with a surface warming rate of more than 3.40 times the global mean for land regions. In contrast, the amplification of the Third Pole is most conspicuous in boreal summer. The two poles located in the Northern Hemisphere have the weakest amplification in boreal autumn. Differently from the positive amplification for the Arctic and the Third Pole in all seasons, the faster variations in Antarctic temperature compared to the globe only appear in austral autumn and winter, and the amplification signal is negative in these seasons, with an amplification index of −1.68 and −2.73, respectively. In the austral winter, the strong negative amplification concentrates on West Antarctica and the coast of East Antarctica, with an absolute value of amplification index higher than 5 in general. Generally, the polar amplification is strongest in the Arctic except from June to August, and Antarctic amplification is the weakest among the Earth’s three poles. The Earth’s three poles are experiencing drastic changes, and the potential influence of climate change should receive attention.

Climate gradient and leaf carbon investment influence the effects of climate change on water use efficiency of forests: A meta-analysis.

Forest ecosystems cover a large area of the global land surface and are important carbon sinks. The water-carbon cycles of forests are prone to climate change, but uncertainties remain regarding the magnitude of water use efficiency (WUE) response to climate change and the underpinning mechanism driving WUE variation. We conducted a meta-analysis of the effects of elevated CO2 concentration (eCO2 ), drought and elevated temperature (eT) on the leaf- to plant-level WUE, covering 80 field studies and 95 tree species. The results showed that eCO2 increased leaf intrinsic and instantaneous WUE (WUEi, WUEt), whereas drought enhanced both leaf- and plant-level WUEs. eT increased WUEi but decreased carbon isotope-based WUE, possibly due to the influence of mesophyll conductance. Stimulated leaf-level WUE by drought showed a progressing trend with increasing latitude, while eCO2 -induced WUE enhancement showed decreasing trends after >40° N. These latitudinal gradients might influence the spatial pattern of climate and further drove WUE variation. Moreover, high leaf-level WUE under eCO2 and drought was accompanied by low leaf carbon contents. Such a trade-off between growth efficiency and defence suggests a potentially compromised tolerance to diseases and pests. These findings add important ecophysiological parameters into climate models to predict carbon-water cycles of forests.

Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model: implementation and effects on simulated water dynamics

Abstract. Vegetation largely controls land surface–atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analyzed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture–evaporation feedbacks during vegetation-water-stressed periods with interannually varying LAI in combination with interannually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation water stress periods.

Assessing the invasion risk of Chelydra serpentina in China under current and future climate change scenarios

Chelydra serpentina, a species introduced to China for aquaculture purposes, is commonly found in its natural habitats within the country. The invasion of C. serpentina poses potential threats to both the biodiversity of China and human health. The potential distribution of C. serpentina has been simulated using the species distribution model – MaxEnt, incorporating global distribution data, climate, and land cover variables. Our simulations encompasses both current conditions and four future climate change scenarios. Currently, the potential distribution is concentrated in central, eastern, and southeastern regions of China, with the central and eastern regions facing the highest risk of invasion. Under future climate change scenarios, the distribution area may expand by 30–90%, and multiple provinces will face a more severe threat of invasion. This study presents the inaugural simulation of the potential invasion range of C. serpentina under current climatic conditions. Moreover, it reveals that climate change is likely to contribute to the expansion of its invasive range, thus furnishing a reference foundation for scientific prevention and control measures. We propose integrating citizen science and eDNA technologies into species monitoring to enhance the efficiency of detecting invasive species. This research has filled the gap in the research on the invasive distribution range of C. serpentina in China and globally, while also providing novel perspectives on the invasion control of this species.

The impact of social interaction and information acquisition on the adoption of soil and water conservation technology by farmers: Evidence from the Loess Plateau, China

The adoption of soil and water conservation (SWC) technology is a key strategy for reducing global land degradation and improving agricultural productivity. This study uses survey data from households in the Loess Plateau in 2017 to evaluate the impact of social interaction on the decision-making process of SWC adoption by farmers. It measures the mediating effect of information acquisition and the moderating effect of internet use and deploys the probit model, general decomposition (KHB) model, and moderated effect model for analysis. The study found that the level of SWC adoption by farmers in the Loess Plateau of China remains relatively low, with only 57.3% of farmers adopting SWC, whereas social interaction increases the possibility of farmers adopting SWC by 10.0%. Accordingly, the paper argues that social interaction can encourage farmers to adopt SWC by improving their ability to acquire information, while internet use can positively moderate the impact of social interaction on farmers' adoption of SWC. Furthermore, the study found the positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies. Therefore, the research results provide a new perspective for promoting SWC in China, emphasizing the importance of enhancing social interaction improving farmers' ability to acquire information, and accelerating internet use.

Producing context specific land cover and land use maps of human-modified tropical forest landscapes for infectious disease applications

Satellite-based land cover mapping plays an important role in understanding changes in ecosystems and biodiversity. There are global land cover products available, however for region specific studies of drivers of infectious disease patterns, these can lack the spatial and thematic detail or accuracy required to capture key ecological processes. To overcome this, we produced our own Landsat derived 30 m maps for three districts in India's Western Ghats (Wayanad, Shivamogga and Sindhudurg). The maps locate natural vegetation types, plantation types, agricultural areas, water bodies and settlements in the landscape, all relevant to functional resource use of species involved in infectious disease dynamics. The maps represent the mode of 50 classification iterations and include a spatial measure of class stability derived from these iterations. Overall accuracies for Wayanad, Shivamogga and Sindhudurg are 94.7 % (SE 1.2 %), 88.9 % (SE 1.2 %) and 88.8 % (SE 2 %) respectively. Class classification stability was high across all three districts and the individual classes that matter for defining key interfaces between human habitation, forests, crop, and plantation cultivation, were generally well separated. A comparison with the 300 m global ESA CCI land cover map highlights lower ESA CCI class accuracies and the importance of increased spatial resolution when dealing with complex landscape mosaics. A comparison with the 30 m Global Forest Change product reveals an accurate mapping of forest loss and different dynamics between districts (i.e., Forests lost to Built-up versus Forests lost to Plantations), demonstrating an interesting complementarity between our maps and the % tree cover Global Forest Change product. When studying infectious disease responses to land use change in tropical forest ecosystems, we recommend using bespoke land cover/use classifications reflecting functional resource use by relevant vectors, reservoirs, and people. Alternatively, global products should be carefully validated with ground reference points representing locally relevant habitats.

Spatio-temporal variation in leaf area index in the Yan Mountains over the past 40 years and its relationship to hydrothermal conditions

Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection.

A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022

Droughts cause multiple ecological and social damages. Drought indices are key tools to quantify drought severity, but they are mainly limited to timescales of monthly or longer. However, shorter-timescale (e.g., daily) drought indices enable more accurate identification of drought characteristics (e.g., onset and cessation time) and help timely potential mitigation of adverse effects. Here, we propose a dataset of a daily drought index named daily evapotranspiration deficit index (DEDI), which is produced for global land areas from 1979 to 2022 using actual and potential evapotranspiration data. Validation efforts show that the DEDI dataset can well identify dry and wet variations in terms of spatial patterns and temporal evolutions when compared with other available drought indices on a daily scale. The dataset also has the capability to capture recent drying trends and to detect ecology- or agriculture-related droughts. Overall, the DEDI dataset is a step forward in facilitating drought monitoring and early warning at higher temporal resolution than other compared existing products.

The mediating role of governance in handling diverse and conflicting land interests in the peri-urban Ethiopia: A structural equation modeling analysis

ABSTRACT The dynamic transformation of peri-urban landscapes from agricultural uses to urban built up is a common practice in developing countries like Ethiopia, which generally does not yield the desired results. Therefore, this study aims to examine the role of governance in mediating the relationship between selected governance success factors and peri-urban socioeconomic conditions. Structural equation modeling is used to support analytical frameworks derived from the Land Governance Assessment Framework and the Global Land Indicators. The study employs mixed research methods and data from relevant participants. The results demonstrate that peri-urban land governance plays a mediating role between the constructed exogenous and endogenous variables. However, the results show that the mediating effect of current peri-urban land governance is weak due to the acknowledged insufficiency of governance success factors. This study argues that integrating success factors into good governance principles offers opportunities for robust peri-urban land governance and thereby resilient socioeconomic conditions.

Identification of dry and wet events in karst and non-karst areas of Guangxi based on a novel optimization method involving the antecedent precipitation attenuation coefficient

The determination of the traditional antecedent precipitation (AP) attenuation coefficient k is somewhat random and empirical, which is not conducive to the evaluation of AP influence in different regions and the comparison of results. In this study, two traditional calculation methods of AP are coupled to derive the formula for k applicable to different time scales in karst and non-karst areas. A novel optimization method incorporating an artificial neural network for determining the number of antecedent days N and the value of k is proposed. Dry and wet events in karst and non-karst areas of Guangxi based on hydrometeorological data at four time-scales (3 h, 6 h, 12 h, and 24 h) obtained from the Global Land Data Assimilation System (GLDAS) are identified. The results show that: (1) the mean optimal N value (Nopt) in karst areas of Guangxi is approximately 28 days, which is lower than the mean N value (approximately 59 days) in non-karst areas; the optimal k value (kopt) in karst areas from January to March and from August to December is lower than the k value in non-karst areas, whereas the kopt in karst areas in May and June is larger than the kopt in non-karst areas; (2) the recognition ability of kopt at the daily scale for drought events is better than the recognition ability of the other three time-scales, and kopt has better recognition ability than the traditional fixed k value for dry and wet events; (3) this study innovatively proposes that the kopt optimization method can be effectively applied to the recognition of dry and wet events in karst and non-karst areas of Guangxi, which has important theoretical value and practical significance and can serve as an effective reference for related research in other regions.

Twenty-Year Spatiotemporal Variations of TWS over Mainland China Observed by GRACE and GRACE Follow-On Satellites

Terrestrial water storage (TWS) is a pivotal component of the global water cycle, profoundly impacting water resource management, hazard monitoring, and agriculture production. The Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-On (GFO), have furnished comprehensive monthly TWS data since April 2002. However, there are 35 months of missing data over the entire GRACE/GFO observational period. To address this gap, we developed an operational approach utilizing singular spectrum analysis and principal component analysis (SSA-PCA) to fill these missing data over mainland China. The algorithm was demonstrated with good performance in the Southwestern River Basin (SWB, correlation coefficient, CC: 0.71, RMSE: 6.27 cm), Yangtze River Basin (YTB, CC: 0.67, RMSE: 3.52 cm), and Songhua River Basin (SRB, CC: 0.66, RMSE: 7.63 cm). Leveraging two decades of continuous time-variable gravity data, we investigated the spatiotemporal variations in TWS across ten major Chinese basins. According to the results of GRACE/GFO, mainland China experienced an average annual TWS decline of 0.32 ± 0.06 cm, with the groundwater storage (GWS) decreasing by 0.54 ± 0.10 cm/yr. The most significant GWS depletion occurred in the Haihe River Basin (HRB) at −2.07 ± 0.10 cm/yr, significantly substantial (~1 cm/yr) depletions occurred in the Yellow River Basin (YRB), SRB, Huaihe River Basin (HHB), Liao-Luan River Basin (LRB), and Southwest River Basin (SWB), and moderate losses were recorded in the Northwest Basin (NWB, −0.34 ± 0.03 cm/yr) and Southeast River Basin (SEB, −0.24 ± 0.10 cm/yr). Furthermore, we identified that interannual TWS variations in ten basins of China were primarily driven by soil moisture water storage (SMS) anomalies, exhibiting consistently and relatively high correlations (CC > 0.60) and low root-mean-square errors (RMSE < 5 cm). Lastly, through the integration of GRACE/GFO and Global Land Data Assimilation System (GLDAS) data, we unraveled the contrasting water storage patterns between northern and southern China. Southern China experienced drought conditions, while northern China faced flooding during the 2020–2023 La Niña event, with the inverse pattern observed during the 2014–2016 El Niño event. This study fills in the missing data and quantifies water storage variations within mainland China, contributing to a deeper insight into climate change and its consequences on water resource management.

Global soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm

The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and nonlinear trends were observed in the global land areas that have experienced statistically significant changes. The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20% agreement, demonstrating the robustness and reliability of the observed trends.

Characteristics of boundary layer height and its influencing factors in global monsoon regions

Monsoon precipitation changes has led to substantial changes in the spatial pattern of land surface energy in most monsoon regions of the world, which subsequently affect the structure and development of the atmospheric boundary layer (ABL) over land. Based on the ERA-Interim reanalysis data (1979–2018), the present study analyzes the long-term variability characteristics of Boundary layer height (BLH) and its influencing factors in the global land monsoon region. The results show that the long-term annual mean BLH averaged over all the monsoon regions across the globe is approximately 653.2 m, but there exist great differences between various regions. The highest BLH occurs in the Australian (AUS) region, followed by that in South Africa (SAF), North Africa (NAF), North America (NAM) and South America (SAM) regions, and the lowest BLH occurs in the Asian (ASN) monsoon region. The differences in BLH between various regions reflect significant regional differences in surface energy distribution. A larger sensible heat flux SHF usually corresponds to a higher BLH, while NAM with the largest latent heat flux LHF corresponds to not the lowest BLH, which is related to its smaller cloud radiation cooling. In the past 40 years, the spatial variation of BLH in ASN region is the most special among all monsoon regions, which is mainly manifested in the increasing trend in the east (east of 100°E) and the decreasing trend in the west (west of 100°E), partly due to the distribution of more precipitation in the east and less precipitation in the west in recent decades. The differences in BLH distribution in different monsoonal regions are mainly caused by SHF. Wind shearSand vertical sensible heat advectionHmv, as the main driving sources of ABL development, play a non-negligible role in the variances of BLH structure in different monsoon areas, although their contributions are much smaller than that of SHF.The role of S in promoting or inhibiting the development of ABL is not only related to S size, but also closely related to the atmospheric turbulence environment. Additionally, each monsoon region's Hmv influence on BLH is the most complex, and changes in near-surface temperature and annual precipitation can only partially explain the observed occurrences. The complicated process must also take into account the significance of local vertical motion brought on by weather variations and surface irregularities in various monsoon regions.

Quantifying the drivers of terrestrial drought and water stress impacts on carbon uptake in China

Droughts are one of the most damaging weather-related hazards and can have detrimental impacts on ecosystem carbon uptake. However, little is known about the physical mechanisms underlying drought evolution as well as their potential effects on the carbon budget under historical and future climates. Here, we first quantify the impacts of water stress on carbon uptake under climate change in China. While heat and water stress play a crucial role in carbon uptake, the effect of ecosystem complexity is also significant. Then, we employ a machine learning model to explore the driving mechanisms of droughts, which are identified through the depletion of terrestrial water storage (TWS). Our results indicate that TWS droughts tend to be governed by atmospheric dryness, with precipitation, relative humidity (RH) and temperature playing dominant roles in drought evolution across most land areas. Precipitation and RH control moisture supply and demand, while rising temperature signifies increasing evaporative demand and enhanced evapotranspiration, leading to soil moisture depletion and reduced surface runoff, thereby intensifying drought. Further, by combining satellite data, field measurements, six global hydrological models, a global land surface model and a dynamic vegetation model, we find that water and heat stress have negative impacts on gross primary productivity (GPP), total ecosystem respiration (TER) and net ecosystem productivity (NEP), under both current and future climates. By the end of the 21st century (2071–2100), drought occurrence is projected to increase by sixfold over more than 60 % of land areas, leading to disproportionate negative impacts on carbon assimilation. Negative anomalies of NEP under drought stress are projected to decline from -0.09 g·m−2·day−1 (historical period) to −0.16 g·m−2·day−1(future period) under SSP370, with even more severe effects on future carbon assimilation under higher emission pathways. Our results suggest that more severe drought conditions might challenge ecosystem sustainability, and highlight the necessity of improving ecosystem resilience to climate warming.

Global evapotranspiration models and their performance at different spatial scales: Contrasting a latitudinal gradient against global catchments

Actual evapotranspiration (AET) is a key variable in the global water balance, driving agricultural production and ecosystem health. It is a complex hydrologic process that depends on vegetation, climate, and available water conditions. Different moderate resolution global AET models have been developed to quantify water resources at large scales. In this work we evaluate five of these products, including MODIS, PML, SSEBop, TerraClimate, and a Synthesis AET using point and catchment-scale datasets based on flux towers. We also contrast water balance changes with total water storage (TWS) products. These comparisons cover different radiation and precipitation regimes over catchments around the world and along a strong climatic gradient in north-central Chile. We rank the models, contrast TWS datasets, and study differences related to scale in validation and the effect of rainfall and radiation on simulated values. Additionally, we use a Budyko framework to evaluate the AET products in terms of their agreement with expected water budgets. At different evaluation scales, AET estimates and observations agreed reasonably well, with the largest mean R2 of about 0.7 and errors of approximately 15% of the magnitude of the observed variables. MODIS and Synthesis AET had the highest R2 at the point (0.62) and at the catchment scales (0.71 and 0.59 for regional and global catchments), respectively, but were closely followed by PML. PML and TerraClimate led to the lowest magnitude errors at the point (RMSE = 0.78 mm day−1) and catchment scales (mean RMSE = 1.5 mm day−1), respectively. The rainfall gradient is reflected in a performance gradient. PML, MODIS, and TerraClimate gave consistent behaviour based on the Budyko curve, with a few arid catchments exceeding the water limit. The major conclusion is that remotely sensed AET outperforms flux tower AET extrapolation for water balance calculations at the catchment scale, which means that errors in satellite-based AET products tend to cancel out at larger spatial scales, which makes them viable alternatives for regional water balance studies. However, flux data integrated into AET models, such as the FluxCom model, leads to the lowest errors. The assimilation and downscaling of Gravity Recovery and Climate Experiment (GRACE) into the Global Land Data Assimilation System (GLDAS) leads to an improvement in regional results compared with other TWS products.

Is Cane Sugar “Canadian”? The Disavowal of Global Lives and Lands within Canadian Sugar Marketing

ABSTRACT This article considers the history of global supply chains in relation to Canadian sugar marketing. Now a dietary staple, refined sugar in Canada today is plentiful and affordable. Ninety percent of this sugar is made from imported cane. Yet within Canada little public awareness exists regarding cane sugars’ countries of origin. Inquiring into how Canadian refiners have portrayed their cane sugars’ provenance, this article sketches out the histories of land and labor disavowal within Canadian cane sugar marketing. Looking at advertising produced by the company Rogers Sugar between 1890 and 1931, it finds that during this time a tension existed between portraying Rogers’ sugars as sourced from within the British Empire and as emanating from Canada. As well, in most of its advertising, Rogers’ white Canadian refinery workers are highlighted. Due to these workers’ ubiquity, the omission of international contributors to Rogers’ sugars is especially noticeable.