A data-driven model is commonly employed for partitioning eddy covariance (EC) CO2 fluxes (NEE) into ecosystem respiration (ER) and gross primary productivity (GPP) fluxes. However, current data-driven solely utilizing one sub-neural network to estimate above-ground respiration (ERa) and below-ground respiration (ERb), leading to substantial uncertainty. To address this issue, this research introduces a hybrid four-sub-deep neural network (HFSD) for partitioning NEE into GPP and ER. The HFSD employs dual sub-deep neural networks (DNNs) to independently estimate ERa and ERb. Additionally, the HFSD incorporates GPP and various environmental variables to predict vegetation transpiration (T). The GPP and ER partitioned by the HFSD model are constrained by EC-derived T and NEE. Comparison between the partitioned GPP and ER by the HFSD model and the nighttime (NT) and daytime (DT) temperature-driven methods is conducted across three EC towers. The results indicate that the dual sub-DNNs architecture enhances the accuracy of ER simulations, while integrating EC-derived T as a constraint improves the accuracy of GPP simulations. Furthermore, the HFSD model exhibits the capability to simulate GPP and ER under extreme scenarios and demonstrates strong generalization potential. Correlation analyses suggest that seasonal variations in GPP and ER are primarily influenced by solar radiation (Ra) and air temperature (Ta) during wet seasons, while GPP and ER are highly sensitive to soil moisture (SM) during dry seasons. This study advances the biophysical description of data-driven models for NEE partitioning and enhances the accuracy of GPP and ER estimates.

Stereochemical modifications (SCMs), mostly present in the form of d-amino acid substitution, have been increasingly identified from a wide range of neuropeptides and disease-associated biomarker proteins. Traditional mass spectrometry-based SCM identification has been effectively enhanced with technological and strategic advancements in ion mobility spectrometry. With the additional separation provided by ion mobility, SCM-induced structural changes can be probed both in theory and in practice, although the structural resolution for low-abundance SCMs still requires further improvement to enable accurate quantification or unambiguous identification of stereoisomers. Herein, we present a multi-component-enabled multidimensional ion mobility-mass spectrometry (3M-IM-MS) analytical workflow, based upon the metal-enhanced chiral amplification strategy we proposed previously (Nat. Commun., 2019, 5038). Notably, the 3M-IM-MS strategy comprises and features the powerful mathematical tools of continuous wavelet transform and Gaussian fitting-enabled peak splitting. Consequently, the resolving capability of ion mobility spectrometry for SCM analysis has been significantly enhanced, providing mobility profiles with baseline separation and more than fivefold improvement in resolving power and overall resolution. This study represents an alternative toward ultrahigh-resolution structural interrogation of mixtures with very small differences, featuring an important and long-lasting topic in chemical measurement.

Investigating the structures of water on metal oxides is helpful for understanding the mechanism of the adsorptions in the presence of water. In this work, the structures of adsorbed water molecules on anatase TiO2 (101) were studied by diffuse reflectance near-infrared spectroscopy (DR-NIRS). With resolution enhanced spectrum by continuous wavelet transform (CWT), the spectral features of adsorbed water at different sites were found. In the spectrum of dried TiO2 powder, there is only the spectral feature of the water adsorbed at 5-coordinated titanium atoms (Ti5c). With the increase of the adsorbed water, the spectral feature of the water at 2-coordinated oxygen atoms (O2c) emerges first, and then that of the water interacting with the adsorbed water can be observed. When adenosine triphosphate (ATP) was adsorbed on TiO2, the intensity of the peaks related to the adsorbed water decreases, indicating that the adsorbed water is replaced by ATP due to the strong affinity to Ti5c. Therefore, there is a clear correlation between the peak intensity of the adsorbed water and the adsorbed quantity of ATP. Water can be a NIR spectroscopic probe to detect the quantity of the adsorbed ATP. A partial least squares (PLS) model was established to predict the content of adsorbed ATP by the spectral peaks of water. The recoveries of validation samples are in the range of 92.00-114.96% with the relative standard deviations (RSDs) in a range of 2.13-5.82%.

Estimating urban evapotranspiration (ET) is of great significance for urban water resource allocation and assessing the urban heat island effect. However, most current urban ET models are based on the energy balance theory to estimate urban ET. These models lead to significant errors in urban ET simulation due to the surface heterogeneity and the existence of anthropogenic heat fluxes in urban areas. To solve this issue, this study proposes a modified machine learning-based urban ET method that can estimate urban ET at the site and regional scales. To better characterize the heterogeneity of urban surfaces, the flux footprint of in-situ ET and physical mechanism of ET process are integrated into the convolutional neural network (CNN) model. The modified CNN model is tested in a fast-developing city: Shenzhen, China based on two Eddy Correlation (EC) observations. The verification results indicated that coupling flux footprint and physical mechanism into the CNN model could effectively improve the accuracy of urban ET simulation at the site scale. The modified CNN model significantly reduced the root-mean-square-error (RMSE) of 25.8 W/m2 and increased the determination coefficient (R2) of 0.17 compared to the CNN-O model (The CNN-O model is defined as the CNN model do not integrate flux footprint and physical mechanism of ET). Further analyses suggested that fusing flux footprint data into a machine learning model helps enhance ET estimation in regions with high heterogeneity and highly variable wind directions. Moreover, the integration of physical mechanisms significantly enhanced the model capability to simulate extreme ET events. The modified CNN model is further applied to map the spatial distribution of urban ET and reconstruct long-term urban ET changes. The spatial pattern of urban ET exhibited large spatial variability, where the urban ET in water bodies (mean lambda ET larger than 480 W/m2) and vegetation-covered areas (mean lambda ET larger than 260 W/m2) are substantially higher than the impervious surfaces (mean lambda ET less than 30 W/m2). Long-term trend analyses demonstrated that urbanization resulted in decline in urban ET. The average decreasing rate of urban ET is 1.61 mm/yr (P < 0.05), with a 18 % decrease relative to the long-term ET average. The leading causes for the decline of urban ET are the increased impervious surfaces and the decreased radiation. This study improved the simulation accuracy of urban ET and revealed the response of urban ET to urbanization.

Accurate estimation of groundwater recharge is a precondition for assessing its spatial variation at different scales, especially field scale. In the field, the limitations and uncertainties of different methods are first evaluated based on site-specific conditions. In this study, we evaluated field variation in groundwater recharge via multiple tracers in the deep vadose zone on the Chinese Loess Plateau. Five deep soil profiles (approximately 20 m deep) were collected in the field. Soil water content and particle compositions were measured to analyse soil variation, and soil water isotope (3H, 18O, and 2H) and anion (NO3- and Cl-) profiles were used to estimate recharge rates. Distinct peaks in soil water isotope and nitrate profiles indicated a vertical one-dimensional water flow in the vadose zone. Although the soil water content and particle composition were moderately variable, no significant differences were observed in recharge rates among the five sites (p > 0.05) owing to the identical climate and land use. The recharge rates did not show a significant difference (p > 0.05) between different tracers' methods. However, recharge estimates by the chloride mass balance method indicated higher variations (23.5 %) than those by the peak depth method (11.2 % to 18.7 %) among five sites. Moreover, if considering the contribution of immobile water in vadose zone, groundwater recharge would be overestimated (25.4 % to 37.8 %) using the peak depth method. This study provides a favourable reference for accurate groundwater recharge and its variation evaluated using different tracers' methods in deep vadose zone.

Temperature proxies for paleoclimate reconstruction have been made typically via ice cores, tree rings, stalagmites, and lake sediments. While extremely useful, these proxies can be limited spatially. Here we sampled a 98 m "soil core" from Loess Plateau of China and examined the relationship between pore water isotopic values and hydroclimate history. We extracted soil pore water for delta O-18, delta H-2, and H-3 and measured chloride concentration. The H-3-peak at 6 m and chloride mass balance were used to turn depth into calendar year. A 1000 year span was revealed. delta O-18 and delta H-2 values between 14-50 m were anomalously low-bracketing well the Little Ice Age period from 1420 to 1870. The identification was consistent with other standard proxies in the region and showed the same temporal dynamics of temperature anomalies. Our study shows the potential of stable isotopes of soil water for paleoclimate reconstruction in deep soils.

The molecular mechanism underlying inhibition of ice growth by polyproline (PPro), a minimal antifreeze glycoprotein mimic, remains unclear. In this work, the change in the structure of water during the growth of ice in PPro solutions was investigated using a combination of near-infrared spectroscopy and molecular dynamics (MD) simulations. The results show that only high concentrations of PPro solutions can effectively inhibit ice growth, as indicated by the variation in the spectral intensity of ice with time. When PPro exhibits an antifreeze effect, the spectral intensity of hydrated water associated with PPro in a solution is weakened. The experiments and MD simulations reveal that the quantity of the interfacial water between the ice crystal and the hydrophobic groups of PPro progressively reaches a plateau. Most significantly, we present clear evidence that the stable existence of this interfacial water is critical for the antifreeze activity of PPro.

The determination of the evapotranspiration (ET) and its components in urban woodlands is crucial to mitigate the urban heat island effect and improve sustainable urban development. However, accurately estimating ET in urban areas is more difficult and challenging due to the heterogeneity of the underlying surface and the impact of human activities. In this study, we compared the performance of three types of classic two-source ET models on urban woodlands in Shenzhen, China. The three ET models include a pure physical and process-based ET model (Shuttleworth–Wallace model), a semi-empirical and physical process-based ET model (FAO dual-Kc model), and a purely statistical and process-based ET model (deep neural network). The performance of the three models was validated using an eddy correlation and stable hydrogen and oxygen isotope observations. The verification results suggested that the Shuttleworth–Wallace model achieved the best performance in the ET simulation at main urban area site (coefficient of determination (R2) of 0.75). The FAO-56 dual Kc model performed best in the ET simulation at the suburb area site (R2 of 0.77). The deep neural network could better capture the nonlinear relationship between ET and various environmental variables and achieved the best simulation performance in both of the main urban and suburb sites (R2 of 0.73 for the main urban and suburb sites, respectively). A correlation analysis showed that the simulation of urban ET is most sensitive to temperature and least sensitive to wind speed. This study further analyzed the causes for the varying performance of the three classic ET models from the model mechanism. The results of the study are of great significance for urban temperature cooling and sustainable urban development.

Estimating global land surface evapotranspiration (ET) is of great significance for assessing the impact of climate change on the global hydrological cycle and energy balance. In this study, we propose a surface energy balance constrained deep learning (DL-SEB) model for simulating global land surface evapotranspiration (ET). The accuracy of the DL-SEB model in estimating ET was tested using FLUXNET observations. The results suggested that the proposed DL-SEB model significantly enhanced the simulation capability of extreme ET events compared with the original deep learning model (without being coupled with the energy balance equation). The DL-SEB model was further applied to reconstruct global ET changes during 2000-2019 based on meteorological, soil, vegetation, and flux data sets. The annual average global land surface ET was 613 mm/yr during the period 2000-2019 (exclude Antarctica and deserts). The global land surface ET exhibited a significant upward trend with average increase rate of 1.16 mm/yr during the past two decades, which corresponds to approximately 3.8% increase above the mean global ET during 2000-2019. The positive trend of global land surface ET was driven by the combined effect of air temperature (Ta), soil moisture (SM), net radiation flux (Rn) and leaf area index (LAI). The natural climatic events such as El Nin similar to o events significantly altered short-term global ET variation, but did not changed the long-term increase trend of global ET. This study enhanced the understanding of the impact of climate change on the global land surface ET. The proposed DL-SEB model achieved a physicsbased, smart and reliable ET simulation at global and regional scales.

Clarifying the water-root-carbon nexus in the entire root zone is crucial for unlocking the potential of afforestation in mitigating climate change. But the nexus in deep soil (depth > 1 m) remains poorly understood. Here we report contrasts in deep soil water and root distributions across 72 paired sites of adjacent farmlands, representing typical pre-afforestation conditions, and tree plantations, representing modern afforestation across the Loess Plateau of China. Ranging from 6 to 25 m of depth, these profiles included plantations of 13 tree species ranging from 1 to 25 years of age. The observations revealed sustained water mining in deep soil following afforestation with mean soil water decline of 75.2 +/- 9.8 mm yr 1 that were accompanied by root deepening rates of 1.00 +/- 0.06 m yr(-1) with an associated biomass input of 0.18 +/- 0.04 Mg C ha(-1) yr(-1). A water for carbon trade-off in deep soil become evident, likely involving a single pulse of C gains and water losses as no signs of soil rewetting under tree plantations where observed total soil water exhaustion that accumulated the equivalent of up to 2.8 years of mean annual precipitation inputs. The reported water-root-carbon nexus reveals overlooked hydrological costs and, more importantly, over-optimistic expectations of sustained C sequestration under afforestation that may rather represent a single-pulsed C gain supported by deep soil water exhaustion.

It is well known that the dry/wet boundaries of land surface temperature fractional vegetation coverage (LSTfc) trapezoid framework vary linearly with vegetation coverage (f(c)). In this study, the theoretical end-members algorithm is modified to continuously estimate the dry/wet end-members under varying vegetation conditions, causing the theoretical dry/wet boundaries to become non-linear. The findings revealed that the non-linear dry/wet boundaries were generally below the conventional linear dry/wet boundaries. Furthermore, the non-linear boundary scheme adopted herein provided better performance in estimating the latent heat flux (LE) and vegetation latent heat flux fraction (LEv/LE) compared to the linear boundary scheme. The parametric schemes of aerodynamic and thermodynamic roughness length and the aerodynamic resistance were the major drivers that result in dry/wet boundaries characteristics being highly non-linear. This study enhanced the physical process description in the LST-f(c) trapezoid framework and improved the prediction accuracy of regional LE and its components.

Urban evapotranspiration is an important component linking the urban hydrological cycle and energy balance. Previous studies on urban evapotranspiration (ET) mainly focused on evaporation from the soil, vegetation, and water surfaces. However, the urban Ei process was overlooked mainly due to its low amount. In this study, the urban Ei was determined by eddy correlation (EC) observations combined with a flux footprint model and urban land use information for two urban EC sites. The evaluation of flux data quality indicated that more than 76 % of observed flux data can be used for Ei analysis at the two stations during rainy periods. The daily urban Ei exhibited high intermittent temporal patterns. The Ei value was 0 during the non-rainy period and peaked on the following 1-2 days after the rainy event, then gradually decreased to 0. The average daily Ei accounted for 11-14 % of the total ET for the two EC stations during the rainy period. Further analysis indicated that urban Ei significantly altered the urban energy balance and turbulent transport processes. The urban Ei reduced sensible heat flux (H) and Bowen ratio (BR) during the rainfall period, thereby playing a vital role in mitigating urban heat land effect. Moreover, urban Ei improved the turbulent transport efficiency of latent heat flux (rwq) and restored turbulent transport similarity in an urban area. The temporal characteristics of urban Ei exhibited a large discrepancy among different impervious surface materials, which mainly depend on the water retention and thermodynamic properties of the impervious surface. Gravel had the best water retention capability and resulted in the highest spatially averaged Ei, while the concrete surface had the best thermal storage capability and resulted in the longest duration time of Ei. The spatial average of urban Ei and its duration time were positively correlated with the water retention and heat storage capabilities of impervious surface materials. This study gained insight into urban Ei, including its determination method, temporal characteristics, controlling factors, and impacts of urban Ei on the regional energy balance and turbulent transport process.

The urban heat island (UHI) effect is accelerated with urbanization and climate change, thus threatening human survival. The evaporation from water body (E) takes away energy through heat absorption process, thereby effectively play a role in temperature cooling and UHI effect alleviation. However, the response of UHI effect to urban E is still lacks study in the current UHI research. To address this issue, this work proposes a customized water body evaporation model in urban areas. The newly developed urban E model considers the contribution of anthropogenic heat flux (AHF) to the energy balance in urban areas. Meanwhile, AHF is also used to enhance the simulation of the water heat storage change (G) for urban water body. Validation results in two megacities in China indicate that the developed urban E model which considered AHF in the energy balance equation significantly improves the simulation performance of E in the main urban area (the root mean square error (RMSE) significantly decreased by 26.6 W/m2 compared with the original Penman formula for E in the main urban area (Eu) simulation). The consideration of AHF in the G determination improves the simulation performance of E in the deep water body (the RMSE significantly decreased by 33.3 W/m2 compared to the AHFPenman model that do not considering G for E simulation in deep water body). The developed urban E model is further used to evaluate the response of UHI to the Eu and E in the suburban area (Es). It is found that Eu effectively alleviates UHI, while Es aggravate UHI. Moreover, the cooling effect of E in the main urban areas (& UDelta;Tau) and suburbs (& UDelta;Tas) are increased with urbanization. The increasing rate of & UDelta;Tau is higher than & UDelta;Tas, indicate the contribution of evaporation cooling to the UHI alleviation is increased with urbanization. Further analysis demonstrate the urbanization process can explain approximately 90% of the enhanced ability of E to mitigate the UHI effect. Correlation analysis shows that the mitigation capability of E to UHI effect is mainly controlled by the volume and surface size of water body. Finally, future climate scenario-based urban E forecast confirms that & UDelta;Tau and & UDelta;Tas will continue to rise with climate change. The average increasing rate are 0.018 degrees C/ year and 0.013 degrees C/year for & UDelta;Tau and & UDelta;Tas, respectively, under the three representative concentration pathways. The increasing rate of & UDelta;Tau is larger than & UDelta;Tas, suggesting the mitigation of the UHI effect will benefit more from urban E under the future climate change. Generally, our findings highlight that the mitigation of the UHI effect mainly benefits from E in the main urban area rather than E in the suburban area. This study gains insight into E in urban areas, including its algorithm, interaction with the UHI effect, and responses to urbanization and climate change. The results of this study provide a good scientific basis for urban landscape water planning.

Resolution is always an obstacle to analyzing the fine structure of a spectrum. The problem is particularly serious in the analysis of the near-infrared (NIR) spectra of aqueous solutions, because the spectrum is generally composed of overlapping broad peaks making the understanding of the structures and the interactions notoriously difficult. In this work, wavelet packet transform (WPT) was adopted to enhance the resolution of the NIR spectra of aqueous mixtures. Due to the microscopic ability of WPT in both position and frequency, the fine details of a spectrum can be observed in the spectral components of different frequencies obtained by WPT decomposition. Ultra-high resolution spectrum can be obtained from the high-frequency component representing the spectral features. Spectral features of different hydrogen-bonded OH, as well as the OH in HOH and HOD, were identified from the high-resolution NIR spectra of water and heavy water mixtures and validated by the variation of the spectral intensity with the mole ratio of H2O and D2O. The high-resolution spectrum was further applied in analyzing the interaction of amine and water. The spectral features of the hydrogen bonding between CH/NH in tert-butylamine (TBA) and OH in water were observed. The structures of CH bonded to one water molecule, and the structures of NH connecting with one and two water molecules were identified.

Accurate determination of extra-cellular pH (pHe) and intra-cellular pH (pHi) is important to cancer diagnosis and treatment because tumor cells exhibit a lower pHe and a slightly higher pHi than normal cells. In this work, the characteristic absorption of water in the near-infrared (NIR) region was utilized for the determination of pHe and pHi. Dulbecco’s modified eagle medium (DMEM) and bis (2-ethylhexyl) succinate sodium sulfonate reverse micelles (RM) were employed to simulate the extra- and intra-cellular fluids, respectively. Continuous wavelet transform (CWT) was used to enhance the resolution of the spectra. Quantitative models for pHe and pHi were established using partial least squares (PLS) regression, producing relative errors of validation samples in a range of −0.74–2.07% and −1.40–0.83%, respectively. Variable selection was performed, and the correspondence between the selected wavenumbers and water structures was obtained. Therefore, water with different hydrogen bonds may serve as a good probe to sense pH within biological systems.

Near infrared (NIR) spectroscopy has been used to analyze water structures due to the strong absorption of NIR energy by water. The spectral band around 6900 cm −1 , corresponding to the first overtone of the OH stretching vibration, is generally studied because the OH in the water molecule with different numbers of hydrogen bonds can be distinguished. In this work, the spectral band around 8600 cm −1 , corresponding to the combination of HOH bending and stretching vibration, ν 1 +ν 2 +ν 3 , was studied to extract spectral information about water structures. Continuous wavelet transform was used to enhance the resolution of the spectra. Seven peaks related to the possible molecular structures of water with different numbers of hydrogen bonds were identified based on the spectral changes with temperature. The identification was validated by varying the spectral peaks with molar ratio of H 2 O–D 2 O in mixtures and the effect of hydration around the cations on the structure of water. NIR spectroscopy is therefore proven to be a powerful technique for identifying water structures with different hydrogen bonds.

Evapotranspiration (ET) estimation models can be broadly classified as statistical or physical process based models. However, assuming the limitation of individual approaches, the integration of these two approaches has become a challenging task for ET simulation under varying surface and climatic conditions. To address this issue, a revised Penman-Monteith (PM) formula that uses a non-linear exponential Clausius-Clapeyron relationship was proposed in this study. The improved PM formula was further coupled into the loss function of the deep learning (DL) model, and subsequently, a hybrid DL model was formulated. The hybrid DL model with improved physical conceptualization considered the constraints of surface energy balance and turbulent diffusion processes in the ET simulation. The performance of the hybrid DL model was verified at 212 flux sites from the FLUXNET that contain ten types of underlying surfaces across the globe. The results revealed that as compared to the original DL model, the hybrid DL model improved the predictive capability of ET. The average root-mean-square-error (RMSE) and mean absolute percentage difference (MAPD) reduced by 12.1 W/m2 and 5.7%, respectively for latent heat flux (LE) simulation. Furthermore, the hybrid DL model also performed better than the original DL model in predicting the extreme events (such as ET under drought and heatwave conditions) which justifying its improved generalization capability. Sensitivity analysis outcomes showed that the vegetation parameters highest influence for ET simulations at the 212 flux sites, followed by soil parameters and meteorological parameters. The hybrid DL model was further applied to map the inter-seasonal distribution of global ET across twelve months of the year 2015 with five global ET products as the benchmark. Certainly, this research achieved the seamless integration of machine learning-based ET model and physical mechanism-based ET model and provided a new dimension for ET simulation. The hybrid DL model could be adopted to generate continuous ET datasets across regional and global scales.

系统回顾了基于热红外地表温度和基于阻抗过程的两类遥感双源蒸散发模型研究进展,综述了能量平衡模型、特征空间模型和基于阻抗过程模型的优缺点,提出在未来遥感蒸散发模型的开发过程中,应注重上述两类双源模型物理机制的结合,强化机器学习方法的应用,在基于阻抗过程的蒸散发模型中加强与碳循环的耦合,进一步开发适用于城市区域的遥感蒸散发模型.

The Shuttleworth-Wallace two-source (S-W) model has been widely applied to estimate evapotranspiration (ET) and its components in a variety of vegetation-covered surface conditions. However, significant uncertainties occur in calculation of vegetation canopy resistance (rsc) and soil surface resistance (rss). In this study, an enhanced version of the S-W model is proposed to simulate and partition ET. The deep learning (DL) approach which combined soil, vegetation, and meteorological observation data, is employed to simulate rsc. A two-objective Monte Carlo-based Bayesian parameter optimization (TOMCBP) is developed to determine the empirical parameters for the rss calculation. The enhanced S-W model was verified based on the three years of eddy correlation (EC) and stable water isotope observations in an urban forest land located in Tianjin, China. Results suggest the machine learning-based rsc parametric scheme effectively improved the performance of the S-W model for the ET simulation. The TOMCBP-based rss parameterization scheme can improve the performance of the S-W model for ET partition. Furthermore, this study used the Bayesian model evidence (BME) to evaluate the performance of different models. BME is shown to balance the model complexity and fitting accuracy when compared with traditional statistical parameters, thus showing superiority in model evaluation and selection. This study improves the physical mechanism and performance of the S-W model and proposes a new method for rsc and ET components simulation based on machine learning. The enhanced S-W model is more precise and provides guidance for irrigation measures in urban woodland areas.