Global Cycle Research Articles

Global Warming Potential and Life Cycle Cost of Conventional and Potentially Low-Carbon Philippine Swine Production Systems

This study identified and evaluated conventional and low-carbon swine production systems through a review of literature and visits to commercial farrow-to-finish swine farms in the Philippines, aiming to recommend a swine production system that cost-effectively reduces GHG emissions. Parameters assessed were greenhouse gas (GHG) emission, carbon and payback period, and return on investment through conduct of life cycle assessment. The identified conventional technologies are open-sided housing system, slatted flooring, modified manual feeding system, nipple drinker, and scrapper and power sprayer as cleaning systems. The identified low carbon technologies are low protein swine feed formulation supplemented with amino acids and partial substitution of soybean meal with Protein-Enriched Copra Meal (PECM), open-sided housing with cemented flooring, manual feeding system, bite ball valve, scrapper combined with power sprayer as cleaning system, and biogas digester as manure management system. These entailed a GHG emission reduction potential of 31.93% in reference to the conventional system. The low carbon production system accounted for higher return on investment of 36.75% and shorter payback period at 2.72 years, compared to the conventional system that yielded 19.96% and 5.01 years, respectively. The identified low carbon swine farm production system can be a cost-effective alternative to the conventional production system.

Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision

Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14% from the original E datasets, the proposed method achieves up to 20% WC residual reduction on the most favorable dataset.

Tracking two decades of global gallium stocks and flows: A dynamic material flow analysis

Gallium (Ga) has recently garnered significant attention as a crucial byproduct element due to its crucial role in advancing low-carbon technologies and its globally imbalanced supply-demand. Understanding the global Ga flows and stocks can contribute to discovering ways to mitigate potential supply risks. In this study, we applied a material flow analysis to map the global Ga cycle from 2000 to 2021. Our results reveal the following results: (1) Over the past two decades, a cumulative 15.3% (313240.8 tons) of the total Ga reserves has been consumed through host metals (i.e., bauxite and zinc ores) mining. However, affected by actual demand, only 5549.7 tons of Ga have been extracted. Moreover, China has gradually become the dominant supplier of primary Ga globally, relying on its robust alumina industry. (2) The consumption pattern of global Ga is becoming increasingly complex. Driven by the diffusion of low-carbon technologies, primary Ga consumption has grown from 101 tons in 2000 to 432 tons in 2021. With a low material efficiency, only 2143.4 tons of Ga ultimately enter final products from semi-products. (3) The global Ga stock reached 956.7 tons in 2021 with significant recycling potential. However, commercial recycling systems were not established, and Ga resources in end-of-life (EoL) final products are mostly wasted. Given the anticipated growth of Ga demand, more coordinated efforts among different regions to promote technological innovation and improve recycling and circularity rates are urgently needed to ensure global Ga supply and a shared green future.

Analyzing trends and change points in hydro-meteorological parameters and groundwater level in the Barak river basin in India

Climate change has a substantial influence on a range of meteorological factors and the global hydrological cycle. This paper presents an analysis of the annual rainfall (P), temperature (including maximum temperature: Tmax, minimum temperature: Tmin, and average temperature: Tavg), stream flow (Q) data from 6 gauging sites, and groundwater level (GL) data from 7 groundwater stations in and around the Barak River Basin in India from 1986 to 2020. To identify patterns in the time series, the simple linear regression (SLR) approach, the Mann-Kendall (MK) test, the Hamid Mann-Kendall (HMK) test and the Spearman's Rho (SRO) tests have been employed. Pettit test has been used to identify the change points in the time series. The results indicated an increase in the Tmax and the Tmin across the study area, while no clear patterns were found in the Tavg time series. Except for the ‘Hailakhandi’ station, the annual total rainfall series showed an increasing trend. Tmax and Tmin showed a directional change point between 1999 and 2005, and between 2001 and 2007, respectively, according to change point analysis. Except for two stations, overall declining trends in the annual stream flow were found. Moreover, a total of five out of seven groundwater stations indicated a rise in groundwater levels. The findings presented here offer valuable insights into the current condition of the Barak basin and emphasize the importance of implementing sustainable water management practices.

Changes in Qinghai Lake Area and Their Interactions with Climatic Factors

Lakes play a crucial role in the global water cycle and significantly contribute to enhancing regional ecological environments and simulating economic growth. In this study, based on the data from the Landsat TM 4-5, Landsat 7 ETM SLC-off, and Landsat 8-9 OLI/TIRS C2 L2 satellites, the surface area of Qinghai Lake is obtained by using the Normalized Difference Water Index (NDWI) method. Additionally, leveraging the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation land surface reanalysis dataset (ERA5-Land), we analyzed the interplay between lake area and related climate factors by using the Noise Assisted–Multivariate Empirical Mode Decomposition (NA-MEMD) and wavelet coherence analysis method. The surface area of Qinghai Lake showed an overall expansion trend from 1986 to 2022, with an expansion rate of 2.89 km2/a. Precipitation, temperature, and evapotranspiration (ET) also showed an increasing trend, with the largest increasing trend in autumn, summer, and summer, respectively. The area of Qinghai Lake did not demonstrate distinct periodic patterns from 1986 to 2022, in contrast to the marked 8–16 month oscillations observed in precipitation, temperature, and ET. In the phase of lake area expansion between 2008 and 2016, changes in the lake’s surface area were observed to trail behind variations in precipitation and temperature by approximately three months. Furthermore, the shift in ET was found to lag behind alterations in the lake area, displaying a delay of 3–6 months.

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Radiation and temperature drive diurnal variation of aerobic methane emissions from Scots pine canopy.

Methane emissions from plant foliage may play an important role in the global methane cycle, but their size and the underlying source processes remain poorly understood. Here, we quantify methane fluxes from the shoots of Scots pine trees, a dominant tree species in boreal forests, to identify source processes and environmental drivers, and we evaluate whether these fluxes can be constrained at the ecosystem-level by eddy covariance flux measurements. We show that shoot-level measurements conducted in forest, garden, or greenhouse settings; on mature trees and saplings; manually and with an automated CO2-, temperature-, and water-controlled chamber system; and with multiple methane analyzers all resulted in comparable daytime fluxes (0.144 ± 0.019 to 0.375 ± 0.074 nmol CH4 g-1 foliar d.w. h-1). We further find that these emissions exhibit a pronounced diurnal cycle that closely follows photosynthetically active radiation and is further modulated by temperature. These diurnal patterns indicate that methane production is associated with diurnal cycle of sunlight, indicating that this production is either a byproduct of photosynthesis-associated biochemical reactions (e.g., the methionine cycle) or produced through nonenzymatic photochemical reactions in plant biomass. Moreover, we identified a light-dependent component in stand-level methane fluxes, which showed order-of-magnitude agreement with shoot-level measurements (0.968 ± 0.031 nmol CH4 g-1 h-1) and which provides an upper limit for shoot methane emissions.

The Salinity of Coastal Waters as a Bellwether for Global Water Cycle Changes

AbstractWhile the global water cycle has been studied previously based on land and open ocean studies, here we use coastal satellite sea surface salinity (SSS) data to show that in global aggregate, SSS variations near coasts are strongly correlated with global water cycle variability driven by El Niño Southern Oscillation (ENSO). This is a significant finding as we demonstrate that open ocean SSS variability is not as sensitive to ENSO and global water cycle variability as the coastal oceans at interannual timescales. Aggregated global coastal SSS could therefore be used as a proxy for detection of changes in the large‐scale cycling of water between the oceans and continents. Moreover, we identify major potential “hotspots” on land and in the coastal ocean that tend to drive global coastal salinity variability, and which may consequently be most sensitive to future physical and biological impacts of water cycle changes on the coastal oceans.

Uncertainty Analysis and Data Fusion of Multi-Source Land Evapotranspiration Products Based on the TCH Method

Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania.

Impacts of tropical cyclones on the global water budget

Tropical cyclones (TCs) require substantial amounts of moisture for their genesis and development, acting as important moisture drivers from the ocean to land and from tropical to subtropical and extratropical regions. Quantifying anomalous moisture transport related to TCs is crucial for understanding long-term TC-induced changes in the global hydrological cycle. Our results highlight that, in terms of the global water budget, TCs enhance moisture transport from evaporative regions and precipitation over sink regions, leading to predominantly anomalous positive surface freshwater flux areas over the tropics and more regionally concentrated negative areas over the Intertropical Convergence Zone. Furthermore, we detected seasonal variability in the impact of TC on the hydrological cycle, which is closely related to the annual and seasonal TC frequency. Our analysis also revealed a global statistically significant drop (~40 mm year−1) in TC-induced surface freshwater fluxes from 1980 to 2018 in response to the increasing sea surface temperature and slightly decrease in global TC frequency and lifetime in the last two decades. These findings have important implications for predicting the impacts of TCs on the hydrological cycle under global warming conditions.

Variability of sedimentary pyrite δ34S records: A case study of slope shale of the Green Point Formation in western Newfoundland, Canada

The Green Point Formation in western Newfoundland, GSSP of the Cambrian-Ordovician (Є-O) boundary, is dominated by slope rhythmites of alternating lime mudstone and shale interbeds. This formation was deposited in a semi-restricted basin with varying connectivity to the open ocean. In the current study, we investigate textures and bulk δ34S signatures of pyrite (δ34Spy) in the shale to better understand factors influencing the sedimentary δ34Spy fluctuation. Petrographic and SEM examinations reveal two major types of pyrite: (1) framboidal pyrite and (2) anhedral to euhedral pyrite. The latter is further categorized into two subtypes: type 2a anhedral to subhedral pyrite characterized by relict framboidal textures and larger sizes (∼10 to 300 μm), and type 2b smaller (typically <10 μm) subhedral to euhedral pyrite. Type 1 pyrite was precipitated near the sediment-water interface (SWI), whereas type 2b pyrite was formed in sediments below the SWI with limited access to the overlying seawater sulfate. Type 2a pyrite was evolved from framboids during early and burial diagenesis. The bulk δ34Spy values, marked by a significant scatter (1σ =10.62‰), range broadly from −17.6 to +22.4‰ (VCDT) and exhibit a pronounced positive excursion of ∼20‰ near the Є-O boundary. The abundance of type 2b pyrite generally mimics the changes in δ34Spy, suggesting that the substantial δ34Spy dispersion could be partially attributed to differing proportions of type 2b pyrite within the samples. Moreover, notable negative correlations exist between the δ34Spy values and the abundances of Al, Th, ∑REE, and Fe, indicating that riverine fluxes might have influenced the Δ34Sseawater – pyrite by modulating the regional seawater sulfate and iron reservoir sizes. Therefore, rather than being indicative of oceanic redox oscillations, the positive δ34Spy excursion of ∼20‰ of this interval was probably driven by decreased sulfate and iron levels in the local waterbody. The decline in terrestrial input during this δ34Spy shift might have also contributed to a negative δ13Ccarb excursion by reducing nutrient supply and inhibiting primary productivity. Collectively, the bulk sedimentary δ34Spy variability recorded by the Green Point shale may be attributed to a combination of changes in regional terrigenous input and varying amounts of pyrite formed at different diagenetic stages within the samples. The general opposing trends between the δ34Spy signals and the abundances of Al, Th, ∑REE, and Fe, however, imply that fluctuations in riverine influxes might exert a stronger influence on the major δ34Spy trend. These findings suggest that bulk sedimentary δ34Spy variations may not be reliable evidence for perturbations of the global sulfur cycle.

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.

Satellite-Based Global Sea Surface Oxygen Mapping and Interpretation with Spatiotemporal Machine Learning.

The assessment of dissolved oxygen (DO) concentration at the sea surface is essential for comprehending the global ocean oxygen cycle and associated environmental and biochemical processes as it serves as the primary site for photosynthesis and sea-air exchange. However, limited comprehensive measurements and imprecise numerical simulations have impeded the study of global sea surface DO and its relationship with environmental challenges. This paper presents a novel spatiotemporal information embedding machine-learning framework that provides explanatory insights into the underlying driving mechanisms. By integrating extensive in situ data and high-resolution satellite data, the proposed framework successfully generated high-resolution (0.25° × 0.25°) estimates of DO concentration with exceptional accuracy (R2 = 0.95, RMSE = 11.95 μmol/kg, and test number = 2805) for near-global sea surface areas from 2010 to 2018, uncertainty estimated to be ±13.02 μmol/kg. The resulting sea surface DO data set exhibits precise spatial distribution and reveals compelling correlations with prominent marine phenomena and environmental stressors. Leveraging its interpretability, our model further revealed the key influence of marine factors on surface DO and their implications for environmental issues. The presented machine-learning framework offers an improved DO data set with higher resolution, facilitating the exploration of oceanic DO variability, deoxygenation phenomena, and their potential consequences for environments.

Viruses under the Antarctic Ice Shelf are active and potentially involved in global nutrient cycles

Viruses play an important role in the marine ecosystem. However, our comprehension of viruses inhabiting the dark ocean, and in particular, under the Antarctic Ice Shelves, remains limited. Here, we mine single-cell genomic, transcriptomic, and metagenomic data to uncover the viral diversity, biogeography, activity, and their role as metabolic facilitators of microbes beneath the Ross Ice Shelf. This is the largest Antarctic ice shelf with a major impact on global carbon cycle. The viral community found in the cavity under the ice shelf mainly comprises endemic viruses adapted to polar and mesopelagic environments. The low abundance of genes related to lysogenic lifestyle (<3%) does not support a predominance of the Piggyback-the-Winner hypothesis, consistent with a low-productivity habitat. Our results indicate a viral community actively infecting key ammonium and sulfur-oxidizing chemolithoautotrophs (e.g. Nitrosopumilus spp, Thioglobus spp.), supporting a “kill-the-winner” dynamic. Based on genome analysis, these viruses carry specific auxiliary metabolic genes potentially involved in nitrogen, sulfur, and phosphorus acquisition. Altogether, the viruses under Antarctic ice shelves are putatively involved in programming the metabolism of ecologically relevant microbes that maintain primary production in these chemosynthetically-driven ecosystems, which have a major role in global nutrient cycles.

Direct ammonia oxidation (Dirammox) is favored over cell growth in Alcaligenes ammonioxydans HO-1 to deal with the toxicity of ammonium.

Bacteria capable of direct ammonia oxidation (Dirammox) play important roles in global nitrogen cycling and nutrient removal from wastewater. Dirammox process, NH3 → NH2 OH → N2 , firstdefined in Alcaligenes ammonioxydans HO-1 and encoded by dnf gene cluster, has been found to widely exist in aquatic environments. However, because of multidrug resistance in Alcaligenes species, the key genes involved in the Dirammox pathway and the interaction between Dirammox process and the physiological state of Alcaligenes species remain unclear. In this work, ammonia removal via the redistribution of nitrogen between Dirammox and microbial growth in A. ammonioxydans HO-1, a model organism of Alcaligenes species, was investigated. The dnfA, dnfB, dnfC, and dnfR genes were found to play important roles in the Dirammox process in A. ammonioxydans HO-1, while dnfH, dnfG, and dnfD were not essential genes. Furthermore, an unexpected redistribution phenomenon for nitrogen between Dirammox and cell growth for ammonia removal in HO-1 was revealed. After the disruption of the Dirammox in HO-1, more consumed NH4 + was recovered as biomass-N via rapid metabolic response and upregulated expression of genes associated with ammonia transport and assimilation, tricarboxylic acid cycle, sulfur metabolism, ribosome synthesis, and other molecular functions. These findings deepen our understanding of the molecular mechanisms for Dirammox process in the genus Alcaligenes and provide useful information about the application of Alcaligenes species for ammonia-rich wastewater treatment.

Does cycle superposition amplify bank liquidity risk under different levels of financial openness? Empirical evidence from China

PurposeThis paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.Design/methodology/approachEmploying the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.FindingsCycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.Originality/valueFirstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

Groundwater is a hidden global keystone ecosystem.

Groundwater is a vital ecosystem of the global water cycle, hosting unique biodiversity and providing essential services to societies. Despite being the largest unfrozen freshwater resource, in a period of depletion by extraction and pollution, groundwater environments have been repeatedly overlooked in global biodiversity conservation agendas. Disregarding the importance of groundwater as an ecosystem ignores its critical role in preserving surface biomes. To foster timely global conservation of groundwater, we propose elevating the concept of keystone species into the realm of ecosystems, claiming groundwater as a keystone ecosystem that influences the integrity of many dependent ecosystems. Our global analysis shows that over half of land surface areas (52.6%) has a medium-to-high interaction with groundwater, reaching up to 74.9% when deserts and high mountains are excluded. We postulate that the intrinsic transboundary features of groundwater are critical for shifting perspectives towards more holistic approaches in aquatic ecology and beyond. Furthermore, we propose eight key themes to develop a science-policy integrated groundwater conservation agenda. Given ecosystems above and below the ground intersect at many levels, considering groundwater as an essential component of planetary health is pivotal to reduce biodiversity loss and buffer against climate change.

Cohomology of semisimple local systems and the decomposition theorem

In this paper, we study the cohomology of semisimple local systems in the spirit of classical Hodge theory. On the one hand, we construct a generalized Weil operator from the complex conjugate of the cohomology of a semisimple local system to the cohomology of its dual local system, which is functorial with respect to smooth restrictions. This operator allows us to study the Poincaré pairing, usually not positive definite, in terms of a positive definite Hermitian pairing. On the other hand, we prove a global invariant cycle theorem for semisimple local systems. As an application, we give a new proof of Sabbah’s Decomposition Theorem for the direct images of semisimple local systems under proper algebraic maps, by adapting the method of de Cataldo-Migliorini, without using the category of polarizable twistor D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathscr {D}}$$\\end{document}-modules. This answers a question of Sabbah.

A comparative spectral assessment approach of SEBAL and SEBS for actual evaporation estimation in Ardabil Province

Evapotranspiration is a crucial process in the Earth's water and climate cycle, responsible for transforming water from liquid to water vapor. This transformation plays a vital role in the global water cycle and has a significant impact on the climate, weather patterns, and precipitation in various regions. In this study, actual evapotranspiration in the Mughan plain of Ardabil province has been estimated using spectral data from the OLI and TIRS sensors of the Landsat 8 satellite. The SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) methods have been employed to calculate these phenomena. SEBAL is an energy balance algorithm designed for land surfaces, utilizing satellite data to estimate actual evapotranspiration accurately. Similarly, SEBS is a surface energy balance system that provides a more precise estimation of evapotranspiration and transpiration rates. Data from the OLI and TIRS sensors of Landsat 8 were collected from the study area for this research. By applying the SEBAL and SEBS methods to these data, actual evapotranspiration values in the Mughan plain were obtained. The results indicated that SEBAL showed a broader range of actual evapotranspiration values (0.74 to 5.8 mm) compared to SEBS (1.25 to 8.85 mm), highlighting its ability to distinguish different regions with varying evapotranspiration rates. However, SEBAL's implementation is more computationally demanding than SEBS, making the calculation process time-consuming. Overall, both algorithms demonstrated relatively high capabilities in calculating instantaneous evapotranspiration using spectral data. The findings of this study can be valuable for water resources and agricultural management in the research area, as well as for water resource planning and environmental studies.

Arc Sediments are a Major source of Juvenile Sr in the Northern Cordillera's Taku River Basin: Potential Implications for the Global Sr Cycle

Dendritic network models have proven valuable in predicting variation in strontium concentrations [Sr] and isotope ratios (87Sr/86Sr) across large river systems. These models also support inference about sources and processes controlling Sr fluxes from such systems, although this capacity has not been exploited in previous work. Here, we apply these methods to model [Sr] and 87Sr/86Sr values of a large river draining accreted volcanic arc terranes of the northern Cordillera of North America. The [Sr] and 87Sr/86Sr river models have excellent fit to measured river waters with RMSE = 0.05: r2 = 0.67 and RMSE = 0.0004: r2 = 0.87, respectively. The results strongly indicate that mixed siliciclastic sedimentary units dominate the Sr weathering flux within these systems. Extrapolation using global arc sediment distributions suggests arc sedimentray units could source ∼30% of the modern riverine Sr flux, constituting a major source of juvenile Sr to the ocean. Because the weathering of arc sediments can lag arc growth and may persist over 100's of millions of years, we suggest that the preservation and accretion of mixed siliciclastic units formed in these settings should be considered when scaling arc weathering contributions through geologic time. Contributions from such sediments could amplify or smooth the arc-derived rock weathering and 87Sr/86Sr flux over long timescales, altering interpretations of biogeochemical cycles and the silicate weathering feedback.