Search Results (83)

Long-term soil-moisture trends on the Chinese Loess Plateau are inferred from gridded products, but product choice can alter whether change is read as drying or wetting. We audited trends from the Global Land Data Assimilation System (GLDAS), ECMWF Reanalysis v5 Land (ERA5-Land), Soil Moisture of China by in situ data (SMCI), and Global Land Evaporation Amsterdam Model root-zone soil moisture (GLEAM SMrz) for 2001–2022, with an endpoint-extension test to 2025. We compared product-specific trends, spatial agreement, core-product medians, product-set bridge and leave-one-out sensitivities, and associations with precipitation, vapor pressure deficit, forest–shrub–grass cover change, and a potential-storage proxy. The core products shared substantial detrended interannual variability, yet their regional trend point estimates did not converge in sign. GLDAS gave a positive regional Sen-slope estimate, whereas ERA5-Land, SMCI, and GLEAM SMrz gave negative estimates. The core-product median was negative over 72.7% of the study area, but unanimous decline occurred over only 24.9%, and 63.5% showed mixed product signs. Only 36.9% of the area retained direction across all five robustness checks, and no environmental variable achieved repeatable same-direction support across all core products. Hydrologically, these results indicate that apparent Loess Plateau wetting or drying should be interpreted as product-dependent evidence of soil-water availability rather than as a universally robust soil-moisture trend or direct environmental response. Full article

Actual evapotranspiration (ETa) is a key component of the hydrological cycle, and its partitioning into soil evaporation (Es) and vegetation transpiration (Ec) is essential for understanding hydrological processes. Focusing on the Yiluo River Basin during 1960–2020, this study developed a hybrid framework combining the physically based Penman–Monteith–Leuning (PML) model with machine learning to dynamically parameterize the soil evaporation coefficient f. ERA5-Land reanalysis data were used to drive the model, while the Pettitt change-point test and ridge regression were applied to identify potential change points and quantify driving factors. The results show that the framework improved the agreement of Es simulations with the GLEAM-derived reference product (R² and NSE > 0.8) and reduced the difference in ETa estimates by approximately 10% within the product-constrained modeling framework. ETa exhibited a significant upward trend (0.28 mm·yr⁻¹) with a potential change point around 2004, while its components responded earlier, with Ec and Es changing in 1994 and 2002. Ec dominated ETa, accounting for about 70% of the total. Net radiation, temperature and leaf area index were primary controls, while increasing vapor pressure deficit, together with changes in relative humidity and precipitation, jointly regulated the identified shifts. These findings provide a process-based understanding of ETa dynamics and improve the representation of ETa components in hydrological modeling. Full article

Terrestrial evapotranspiration (ET) plays a vital role in the water cycle, comprising components such as transpiration, interception loss, bare-soil and open-water evaporation, etc. This study has validated the GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) product with eddy covariance ET data. The spatiotemporal variations in total ET and its components in China during 1980–2024, derived from the GLEAM model, and their relations with air temperature, precipitation and solar radiation in the context of climate change have been studied. During the study period, a significant increase in total ET was found over the southeast of China, especially in spring and summer. The different ET components showed somewhat different trends. While transpiration and interception losses increased significantly in humid and transitional zones, bare-soil evaporation declined markedly in humid regions but remained stable or increased slightly in the northwest and the Tibetan Plateau. Precipitation accounts for the largest share of total ET variability in arid regions, whereas transpiration in humid regions shows the strongest association with available energy. In transitional zones and the Tibetan Plateau, total ET reflects the synergistic regulation of both water and energy availability. Recent enhancements in total ET are primarily associated with rising precipitation in the Tibetan plateau and increasing air temperature in transitional zones. Full article

Root zone soil moisture (RZSM) is critical for understanding hydrological processes and monitoring agricultural drought, yet its accurate representation remains challenging in topographically complex regions. Using 40 cm in situ SM observations from 19 ground stations in Yunnan Province, China, during 2008–2012 as the reference, this study systematically evaluated the performance of five widely used multi-source soil moisture (SM) products and their different depth layers, including ERA5-Land, GLDAS Noah, GLEAM, ASCAT H141, and CCI SM. A CCI-derived RZSM proxy generated by exponential filtering, hereafter CCI RZSM, was also included. Product performance was assessed using original and deseasonalized time series, and the effects of land-use type, long-term wetness background, and short-term dry conditions on product performance were explicitly examined. The results showed that the intermediate and deeper layers of ERA5-Land and ASCAT H141, especially the 7–28 cm layers, exhibited better performance in capturing RZSM dynamics, achieving a favorable balance among temporal correlation (r > 0.6), random error and systematic bias. Surface-layer products showed limited direct representativeness, and effective RZSM representativeness differed substantially among nominal product layers. Deseasonalization showed that original-series correlations were partly supported by the shared seasonal wet–dry cycle, whereas most products had weaker skill in tracking non-seasonal RZSM anomalies. Environmental background substantially modulated error structures: stronger positive Bias generally occurred at drier stations, Grassland showed higher positive Bias, Cropland showed greater dispersion, and Forest displayed relatively balanced performance. Under dry conditions, temporal correlations declined for nearly all products, whereas increases in random error were mainly concentrated in surface layers. Exponential filtering improved the temporal consistency of CCI SM in representing RZSM, but the filtering with a fixed characteristic time parameter (T) performed worse than filtering with station-optimized T, indicating limited generalizability in ungauged regions. Overall, RZSM representativeness in Yunnan is jointly controlled by product structure, environmental background, and wet–dry conditions. ERA5-Land and ASCAT H141 intermediate-to-deep layers are therefore more suitable for RZSM anomaly and drought applications in Yunnan Province. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

Gridded meteorological data sources, such as reanalysis datasets, are increasingly used to estimate evapotranspiration, a key variable for surface water-budget analyses at regional and national scales and for assessing plant water requirements for irrigation. This study, conducted over mainland Portugal for the 44-year reference period from 1980 to 2023, first presents a comprehensive comparative analysis of the spatial patterns of potential ($Ep$) and reference ($Eto$) evapotranspiration at a 0.1° spatial resolution using daily data. Estimates derived from two high-resolution datasets (GLEAM and ERA5-Land) are compared with those obtained from the Thornthwaite, Hargreaves–Samani, and Penman–Monteith models. Secondly, trend analyses of $Eto$ magnitudes on a monthly and annual basis in a gridded format were conducted. The resulting spatial distributions of $Ep$ and $Eto$ show higher values in milder and flatter southern Portugal and lower values in the cooler and more mountainous northern regions, in agreement with existing knowledge. The Penman–Monteith model exhibited the highest reliability, while the Thornthwaite model generally underestimated evapotranspiration across the country, and the Hargreaves–Samani model showed underestimation in coastal areas. Trend analysis of $Eto$ indicates an overall increase in atmospheric evaporative demand over the full study period, with a more pronounced rise during the recent 22-year period (2002–2023) compared with the earlier period (1980–2001). These increases are statistically significant in August and October and may reflect a climate shift towards a progressively longer dry season. Understanding how changes in evapotranspiration affect hydrological processes—including surface water availability, river discharge, reservoir performance, and crop requirement—is critical. This study aims to contribute to addressing these emerging challenges. Full article

To comply with new pastoral regulations in Benin, herders are increasingly adopting sedentary cattle systems, which may pose environmental risks if poorly managed. This study assessed greenhouse gas (GHG) emissions from three sedentary cattle farm types: zebu (SZF), taurine (STF), and crossbreed (SCF), across two vegetation zones: Sudanian (SZ) and Guineo-Congolian (GCZ) using the GLEAM-i model, online version. Irrespective of the farm type, the animals were exclusively fed on natural pasture. A total of 12 cattle herds were surveyed to collect input data (herd structure, demographic parameters, milk production and composition, and weight data) for the GLEAM-i. The fat and protein content of the milk (determined using a milkotester device), the live weight, and weight at slaughter of animals were entered into the GLEAM-i, which automatically determines the emission intensity values per kg of protein produced. The results revealed that CH₄ was the main GHG emitted (88%), followed by CO₂ (6–7%) and N₂O (6%). The highest and lowest total GHG emissions (kgCO₂-eq/year) were recorded in SZF (188,497) and STF (52,003) farms, respectively. With regard to emission intensity (kgCO₂-eq/kg protein), this varied from 506.59 to 3043.73 for meat and from 588.86 to 3043.73 for milk. Overall, preliminary trends suggest lower emission intensities for taurine in the GCZ and for zebu in the SZ. However, these results would be more meaningful and more accurate if emission values were directly measured from individual animals using the GreenFeed Technology under current production conditions, using various pasture resources and controlled allocation. These would allow us to make firm recommendations for breeding strategies to reduce GHG emissions in Benin’s extensive livestock production system. Full article

Evapotranspiration (ET) data products has greatly facilitated the hydrological research in complex basins, and various ET datasets have been produced and applied. The applicability and reliability of ET dataset is significant for regional studies. Therefore, this study compared ET datasets from multisource remote sensing (GLEAM, MOD16, GLASS, PML-V2, Han, Chen and Ma), machine learning (Jung) and reanalysis products (ERA5-Land, MERRA2) for the Yarlung Zangbo River basin (YZB). ET was estimated using the terrestrial water balance (TWB) and was taken as baseline for comparisons of different ET datasets in terms of spatial distribution and temporal variation. Results indicate that (1) the TWB-based ET estimates are rational with acceptable uncertainties; (2) the multi-source ET datasets exhibit good correlations with TWB-ET across the entire basin (r = 0.78–0.90) in term of annual variation, with GLEAM-ET performing the best (r = 0.88, RMSE = 14.24 mm, Rbias = 18.55%); (3) Spatially, PML-ET and Ma-ET show higher consistency with TWB-ET, and temporally, MOD16-ET and GLASS-ET better capture the changing trend; (4) A comprehensive evaluation using the linear weighted method reveals that GLASS-ET and GLEAM-ET perform relatively well in all aspects and are reliable datasets for ET research in the YZB. These findings provide a scientific basis for ET estimation and data selection in the YZB, offering important references for ET analysis and hydrological research. Full article

Sustainable agriculture in semi-arid regions like the Awash Basin is critically dependent on water availability, which is increasingly threatened by rapid land use and land cover (LULC) change. This study assesses the impact of multi-decadal LULC changes on water resources essential for agriculture. Using satellite-derived LULC scenarios (2001, 2010, 2020) to drive the WRF-Hydro/Noah-MP modeling framework, we provide a holistic assessment of water dynamics in Ethiopia’s Awash Basin. The model was calibrated and validated with observed streamflow (R² = 0.80–0.89). Markov analysis revealed rapid cropland expansion and urbanization (2001–2010), followed by notable woodland recovery (2010–2020) linked to national initiatives. Simulations show that early-period changes increased surface runoff, potentially enhancing reservoir storage for large-scale irrigation. In contrast, later changes promoted subsurface flow, indicating a shift towards enhanced groundwater recharge, which is critical for small-scale and well-based irrigation. Evapotranspiration (ET) trends, validated against GLEAM (monthly R² = 0.88–0.96), reflected these shifts, with urbanization suppressing water fluxes and woodland recovery fostering their resurgence. This research demonstrates that land use trajectories directly alter the partitioning of agricultural water sources. The findings provide critical evidence for designing sustainable land and water management strategies that balance crop production with forest conservation to secure irrigation water and support initiatives like Ethiopia’s Green Legacy Initiative. Full article

Radio-loud high-redshift quasars (RHRQs) provide crucial insights into the evolution of relativistic jets and their connection to the growth of supermassive black holes. Beyond the extensively studied population at $z\ge 5$, the cosmic morning epoch ($3\lesssim z\lesssim 5$) marks the peak of active galactic nucleus (AGN) activity and black hole accretion, yet remains relatively unexplored. In this work, we compiled the radio high-redshift quasar catalog (RHzQCat) by cross-matching the SDSS DR16Q catalog with four major radio surveys—FIRST, NVSS, RACS, and GLEAM. Our tier-based cross-matching framework and visual validation ensured reliable source identification across surveys with diverse beam sizes. The catalog included 1629 reliable and 315 candidate RHRQs, with radio luminosities uniformly spanning ${10}^{25.5}$–${10}^{29.3}$ W Hz⁻¹. About 95% of the confirmed sources exhibited compact morphologies, consistent with Doppler-boosted or young AGN populations at high redshifts. Our catalog increases the number of known RHRQs at $z\ge 3$ by an order of magnitude, representing the largest and most homogeneous catalog of radio quasars at cosmic morning, filling the observational gap between the early ($z>6$) and local Universe. It provides a robust reference for future statistical studies of jet evolution, AGN feedback, and cosmic magnetism with next-generation facilities such as the Square Kilometer Array (SKA). Full article

Agricultural production in Zamora Chinchipe is primarily focused on dairy farming, an activity that constitutes a key component of land use in the region. Accordingly, the objectives of this study were as follows: (a) to estimate greenhouse gas (GHG) emissions from dairy farms using the GLEAM model and (b) to evaluate the influence of altitude and livestock management practices on soil properties and the estimated GHG emissions associated with cattle production. This study encompassed 100 dairy farms, where the GLEAM methodology was applied to quantify emissions-related data. In addition, 300 soil samples (three per farm) were collected, and the perimeter of each farm, as well as the remaining forest areas, was mapped. The results indicate that although the farms generate CO₂-equivalent emissions associated with livestock activities, the remaining forest areas contribute to mitigation by storing carbon in the soil. Altitude was found to positively influence soil quality, increasing organic matter and nitrogen content, whereas overgrazing negatively affected key soil properties and was associated with higher levels of GHG emissions. These findings underscore the need to implement sustainable management strategies that integrate agricultural production with the conservation of ecosystem services. Full article

Accurate quantification of evapotranspiration (ET) is crucial for agricultural water management and climate change adaptation, especially in global warming and extreme climate events. Despite the availability of various ET products, their applicability across different scales and climatic conditions has not been comprehensively verified. This study evaluates nine ET products at grid, basin, and site scales in China from 2003 to 2014 under varying climatic conditions, including extreme temperatures, vapor pressure deficit (VPD), and drought. The main results are as follows: (1) At the grid scale, all products except the MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid (MOD16A2) product showed high consistency, with the Global Land Evaporation Amsterdam Model V4.2a (GLEAM) product exhibiting the highest comparability. The three-cornered hat (TCH) method revealed that GLEAM and the Synthesized Global Actual Evapotranspiration Dataset (Syn) had low uncertainties in multiple basins, while the Reliability Ensemble Averaging (REA) product and Penman–Monteith–Leuning Evapotranspiration V2 (PMLv2) product had the smallest uncertainties in the Songhua River and Hai River Basins. (2) At the basin scale, ET products were closely aligned with water-balance-based ET (WB-ET), with GLEAM achieving the smallest root mean square error (RMSE) (22.94 mm/month). (3) At the site scale, accuracy decreased significantly under extreme climatic conditions, with the coefficient of determination (R²) dropping from about 0.60 to below 0.30 and the mean absolute error (MAE) increasing by 110.30% (extreme high temperatures) and 101.40% (extreme high VPD). Drought conditions caused slight instability in ET estimations, with MAE increasing by approximately 12.00–40.00%. (4) Finally, using a small number of daily ET products as inputs for machine learning models, such as random forest (RF), greatly improved ET estimation, with R² reaching 0.91 overall and 0.81 under extreme conditions. GLEAM was the most important product for RF in ET estimation. This study provides essential guidance for selecting and improving ET products to enhance agricultural water-use efficiency and sustainable irrigation. Full article

Accurate drought assessment is crucial for effective regional water resource management. While reanalysis and remote sensing products enable high-resolution drought assessment, their regional application requires rigorous local validation. This study evaluates nine data combinations, pairing three precipitation products with three evapotranspiration products, to identify the optimal combination for robust SPEI estimation and subsequently to investigate the spatiotemporal variations in drought conditions during 1980–2020 in Zhejiang Province, China. The results indicate that the choice of precipitation product is the dominant factor influencing SPEI accuracy, with the combination of CMFD V2.0 precipitation and GLEAM v4.2a evapotranspiration identified as the most reliable for SPEI estimation across multiple timescales (SPEI1/3/6/12). The long-term trend analysis of the SPEI derived from this optimal data combination reveals significant spatiotemporal heterogeneity: temporally, a pronounced “wetter winters, drier springs” seasonal pattern emerges, posing a substantial threat to agricultural water security; spatially, a distinct divergence shows central/northeastern areas wetting while southern/southeastern regions experience a significant drying trend, particularly for long-term hydrological drought (SPEI12). Additionally, the prevalence of light droughts across the province suggests a sustained baseline of water stress. Attribution analysis further demonstrates that precipitation is the dominant driver of droughts across all timescales. This study contributes both a validated, high-resolution data foundation for regional drought assessment and a scientific basis for targeted drought adaptation strategies. Full article

This investigation highlights the importance of adopting ethical and sustainable practices in chicken farming, in response to the increasing global demand for poultry products driven by the expanding world population. How ambient gases, such as hydrogen sulfide ($H_{2}S$), nitrous oxide ($N_{2}O$), ammonia ($N{H}_3$), carbon dioxide ($C{O}_2$), and methane ($C{H}_4$), affect the welfare of farm workers and poultry is investigated. The use of various gas sensor technologies is crucial for effective management and monitoring of these gases. The research emphasizes the vital importance of precise gas concentration measurements in mitigating environmental impact. It is noteworthy that there is a closely intertwined relationship between $C{O}_2$ levels and chicken health, requiring vigilant monitoring and care. There are potential risks associated with $N{H}_3$ exposure, and waste management and ventilation practices are necessary. Furthermore, the contribution of $C{H}_4$ sensors to environmental sustainability and safety is addressed. The review also examines $H_{2}S$ emissions, providing mitigation strategies to safeguard avian health. This study identifies an important gap between the limited use of commercially available Metal Oxide Semiconductor (MOS) sensors in the commercial Internet of Things (IoT) systems for poultry farms and their potential to detect a wider range of chemical gases. The pivotal role played by gas sensors in these sustainable efforts is highlighted. Full article

Understanding and predicting changes in land evapotranspiration (ET) during droughts is crucial for elucidating land-atmosphere interactions. While previous studies have primarily focused on overall ET or individual components, they often overlook the mutual influences among different ET components. To address this gap, this study presents the first global analysis of concurrent changes in multiple ET components during meteorological droughts. Utilizing advanced satellite-based and reanalysis-based datasets, including the Global Land Evaporation Amsterdam Model (GLEAM) and the ECMWF reanalysis v5 (ERA5-Land) for the period 2000–2020, we find that the average probability of drought-driven increases in ET (P(ET+)) was approximately 0.5 during drought events. In contrast, the probabilities of an increase for the primary components—bare soil evaporation (Eb), canopy interception evaporation (Ei), and transpiration (Et)—were below 0.4, while the probability of drought-driven increases in snow sublimation (Es) exceeded 0.6. Globally, ET decreased by an average of 20.5 mm/month during a given drought period, though it increased in humid regions and snow-covered areas. Mild droughts resulted in an overall ET reduction, with increases in Eb and Es partially offsetting decreases in Et and Ei. However, as drought intensity increased, ET shifted toward an increase, which was constrained under extreme droughts. These findings highlight the asymmetric and interdependent responses of ET components to drought, underscoring the critical need to understand these interactions for accurately predicting ET dynamics under drought stress. Full article