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The quest for highly active and efficient ligands in coordination polymer-based catalytic applications is paramount. The new porous cross-linked polymer manganese composites, which featured a benzimidazole-pyrimidine backbone, were designed, synthesized and fully characterized through several modern means. The resulting manganese composites revealed good catalytic activity in the selective synthesis of bisindolylmethane derivatives with high selectivity. Furthermore, the porous cross-linked polymer matrix composites displayed impressive recyclability, demonstrating their economic and environmental merits, with mechanistic studies shedding light on these transformative processes. The new porous cross-linked polymer manganese composites, which featured a benzimidazole-pyrimidine backbone. The resulting manganese composites revealed good catalytic activity in the selective synthesis of bisindolylmethane derivatives with high selectivity. Furthermore, the porous cross-linked polymer matrix composites displayed impressive recyclability, demonstrating their economic and environmental merits, with mechanistic studies shedding light on these transformative processes. [ABSTRACT FROM AUTHOR]

Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, permafrost degradation, and snow cover reduction. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, this issue is still not adequately addressed, particularly in mountainous catchments. We developed an integrated cryospheric–hydrologic model, the FLEX-Cryo model, to comprehensively consider glaciers, snow cover, and frozen soil and their dynamic impacts on hydrological processes. Taking the mountainous Hulu catchment located in the upper Heihe River of China as a case study, we utilized the state-of-the-art climate change projection data under two scenarios (SSP2-4.5 and SSP5-8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that under the medium-emission scenario (SSP2-4.5) and high-emission scenario (SSP5-8.5), by the end of the 21st century, the glacier will completely melt out around the years 2051 and 2045, respectively. The annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 d. The freeze onset of seasonally frozen soil is expected to be delayed by 10 and 22 d, while the thaw onset of permafrost is likely to advance by 19 and 32 d. Moreover, the maximum freeze depth of seasonally frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, catchment total runoff exhibits a decreasing trend, and the tipping point of glacier runoff occurs approximately between 2019 and 2021. Permafrost degradation will likely reduce the duration of low runoff in the early thawing season; the discontinuous baseflow recession gradually transitions into linear recessions, and the baseflow increases. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

Phosphaphenalenes, extended π conjugates with the incorporation of phosphorus, are attractive avenues towards molecular materials for the applications in organic electronics, but their electron accepting ability have not been investigated. Herein we present systematic studies on the reductive behavior of a representative phosphaphenalene and its oxide by chemical and electrochemical methods. The chemical reduction of the phosphaphenalene by alkali metals reveals the facile P−C bond cleavage to form phosphaphenalenide anion, which functions as a transfer block for structure modification on the phosphorus atom. In contrast, the pentavalent P‐oxide reacts with one or two equivalents of elemental sodium to form stable radical anion and dianion salts, respectively. [ABSTRACT FROM AUTHOR]

We derive the exact solutions for electrically charged black holes both in the absence and presence of a cosmological constant in the gravitational theory with Lorentz violation induced by a background Kalb–Ramond (KR) field. The corresponding thermodynamic properties are investigated. It is found that the standard first law of thermodynamics and the Smarr formula remain valid for the charged KR black holes. Nevertheless, the Lorentz-violating effect influences their ranges of local thermodynamic stability and the first- and second-order phase transition points. Furthermore, to examine the impact of Lorentz violation on the motion of test particles in the spacetime, we analyze the shadow and the innermost stable circular orbit (ISCO) of these black holes. Our results reveal that both the shadow and ISCO radii exhibit a high sensitivity to the Lorentz-violating parameter ℓ , with a decrease observed as ℓ increases. [ABSTRACT FROM AUTHOR]

In the context of global warming, escalating water cycles have led to a surge in drought frequency and severity. Yet, multidecadal fluctuations in drought and their multifaceted influencing factors remain inadequately understood. This study examined the multide-cadal changes in drought characteristics (frequency, duration, and severity) and their geographical focal points within China's north-south transitional zone, the Qinling-Daba Mountains (QDM), from 1960 to 2017 using the Standardized Precipitation Evapotranspiration Index (SPEI). In addition, a suite of eight scenarios, correlation analysis, and wavelet coherence were used to identify the meteorological and circulation factors that influenced drought characteristics. The results indicate the following: (1) From 1960 to 2017, the QDM experienced significant interdecadal variations in drought frequency, duration, and severity, the climate was relatively humid before the 1990s, but drought intensified thereafter. Specifically, the 1990s marked the period of the longest drought duration and greatest severity, while the years spanning 2010 to 2017 experienced the highest frequency of drought events. (2) Spatially, the Qinling Mountains, particularly the western Qinling Mountain, exhibited higher drought frequency, duration, and severity than the Daba Mountains. This disparity can be attributed to higher rates of temperature increase and precipitation decrease in the western Qinling Mountain. (3) Interdecadal variations in droughts in the QDM were directly influenced by the synergistic effects of interdecadal fluctuations in air temperature and precipitation. Circulation factors modulate temperature and precipitation through phase transitions, thereby affecting drought dynamics in the QDM. The Atlantic Multidecadal Oscillation emerges as the primary circulation factors influencing temperature changes, with a mid-1990s shift to a positive phase favoring warming. The East Asian Summer Monsoon and El Niño-Southern Oscillation are the main circulation factors affecting precipitation changes, with positive phase transitions associated with reduced precipitation, and vice versa for increased precipitation. [ABSTRACT FROM AUTHOR]

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin. [ABSTRACT FROM AUTHOR]

Herein, we report an efficient and straightforward approach for the synthesis of N-alkylated aminoquinoline derivatives by recyclable Cd-containing coordination polymer-catalyzed reactions of aminoquinolines with primary alcohols via the borrowing hydrogen strategy. In this work, a new type of coordination polymer [Cd(CIA)(phen)2(H2O)]n was successfully designed and fabricated. The molecular structure was corroborated by single-crystal X-ray diffraction and fully characterized by PXRD, FT-IR, TGA, and XPS. Importantly, this polymer revealed high catalytic activity for the N-alkylation reaction of 2-aminoquinoline and 8-aminoquinoline with inexpensive and low-toxicity alcohols as alkylating agents in excellent yields up to 95%. Interestingly, the present synthetic protocol was successfully applied for the gram-level synthesis of several biologically active compounds. In addition, several control reactions were carried out to investigate the possible mechanisms of this transformation. Finally, recycling experiments indicated that the cadmium coordination polymer showed good recovery performance for borrowing hydrogen reactions. [ABSTRACT FROM AUTHOR]

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables. [ABSTRACT FROM AUTHOR]

The ice phenology of alpine lakes on the Tibetan Plateau (TP) is a rapid and direct responder to climate changes, and the variations in lake ice exhibit high temporal frequency characteristics. MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution. However, the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes. This work used Sentinel-1 synthetic aperture radar (SAR) data to derive highresolution ice maps (about 6 days), then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP (e.g. Selin Co, Ayakekumu Lake, and Nam Co). The results showed that three lakes had an average annual ice period of 125–157 days and a complete ice cover period of 72–115 days, from 2018 to 2022. They exhibit different ice phenology patterns. Nam Co is characterized by repeated episodes of freezing, melting, and refreezing, resulting in a prolonged freeze-up period. Meanwhile, the break-up period of Nam Co lasts for a longer duration (about 19 days), and the break-up exhibits a smooth process. Similarly, Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period, with deviations of up to 28 days observed among different years. Compared to the other two lakes, Selin Co experienced a relatively short freeze-up and break-up period. In short, Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP. Particularly, this data facilitates quantification of the freeze-melt dynamics. [ABSTRACT FROM AUTHOR]

Herein, we describe how computational mechanistic understanding has led directly to the discovery of new 2H‐phosphindole for C−CAr bond activation and dearomatization reaction. We uncover an unexpected intramolecular C−H bond activation with a 2H‐phosphindole derivative. This new intriguing experimental observation and further theoretical studies led to an extension of the reaction mechanism with 2H‐phosphindole. Through DFT calculations, we confirm that within a five‐membered ring, the polarizable PC3 unit orchestrates the formation of an electrophilic phosphorus atom (P+) and a nucleophilic carbon atom (C−). This kinetically accessible ambiphilic phosphorus/carbon couple is spatially separated by geometric constraints, and their reactivity is modulated through structural resonance. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions. [ABSTRACT FROM AUTHOR]

Purpose: Extensive research has demonstrated the influence of job resources on mitigating the impact of high job demands on individual well-being. Still, little is known about how individuals use job resources to cope. This study advances job demands-resources (JD-R) theory by investigating the process of job resource utilization from a coping perspective. Design/methodology/approach: This study is based on in-depth interviews with 32 Chinese senior executives using a Critical Incident Technique (CIT) for data collection and thematic analysis for data analysis. Findings: Data analysis reveals a 2x2 matrix model of resource utilization, highlighting the relevance of internal and external job resources in coping and resource creation. Practical implications: The findings suggest that work stress intervention should look beyond the organizational domain and assist senior executives in exploring and developing external resources for coping. Additionally, organizations should equip senior executives with sufficient support to create new job resources that effectively manage complicated job demands. Originality/value: The results of this study extend the understanding of job resources by distinguishing internal and external job resources. It also provides a dynamic view of resource utilization, emphasizing the role of job crafting in creating adaptable job resources to meet job demands. [ABSTRACT FROM AUTHOR]

An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi‐global land by combining three independent precipitation products. The performance of the ETCC‐merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC‐merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth. Plain Language Summary: Precipitation plays a significant role in global atmospheric and hydrological modeling systems. Owing to uncertainties in precipitation estimates from a single satellite or reanalysis product, it is necessary and challenging to blend multi‐source precipitation information to improve the quality of precipitation estimates. The present study proposed a new merging method, called Extended Triple Collocation for maximized Correlation (ETCC), which aims at maximizing the correlation of the merged product against unknown truth. The ETCC‐merged product combined three independent precipitation information derived from the satellite and reanalysis products, and then, was evaluated using three reference data sets. Meanwhile, we compared ETCC's performance with that of the original Triple Collocation (TC) merging. Results show that ETCC method not only performs better than each contributor, but also is more effective in improving precipitation accuracy relative to the TC approach especially in central North America and mainland China. Key Points: A new method is proposed for maximizing the correlation between the merged product and unknown truthThe merged product is more accurate than each contributorThe new method performs better than the original triple collocation [ABSTRACT FROM AUTHOR]

Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, reduction in snow cover area, and permafrost degradation. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, there is still a lack of systematic research that evaluates the variation of cryospheric elements in mountainous catchments and their impacts on future hydrology and water resources. In this study, we developed an integrated cryospheric-hydrologic model, referred to as the FLEX-Cryo model. This model comprehensively considers glaciers, snow cover, frozen soil, and their dynamic impacts on hydrological processes in the mountainous Hulu catchment located in the Upper Heihe river of China. We utilized the state-of-the-art climate change projection data from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that the two glaciers in the Hulu catchment will completely melt out around the years 2045–2051. By the end of the 21st century, the annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 days. The freeze onset of seasonal frozen soil is expected to be delayed by 10 and 22 days, while the thaw onset of permafrost is likely to advance by 19 and 32 days. Moreover, the maximum freeze depth of seasonal frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, runoff exhibits a decreasing trend until the complete melt-out of glaciers, resulting in a total runoff decrease of 15.6 % and 18.1 %. Subsequently, total runoff shows an increasing trend, primarily due to an increase in precipitation. Permafrost degradation causes the duration of low runoff in the early thawing season to decrease, and the discontinuous baseflow recession gradually transitions into linear recessions, leading to an increase in baseflow. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

Under global warming, flooding has become one of the most destructive natural disasters along the China–Pakistan Economic Corridor (CPEC), which significantly jeopardizes the construction and ongoing stability of the CPEC. The assessment of regional flood potential is, therefore, crucial for effective flood prevention and relief measures. In light of this, our study applied MIKE 11 hydrodynamic model for the Indus River Basin of Pakistan to achieve a comprehensive analysis of the flood-affected locations and depths under typical scenarios. The flood risk zones along the CPEC were evaluated using the indicator system method in conjunction with the combination weighting method. The results show that the hydrodynamic model has a Nash–Sutcliffe efficiency of 0.86, allowing for the investigation of floods at more precise temporal and spatial scales. Punjab, Sindh, and Balochistan Provinces are the main inundation areas under a 100-year flood scenario, with inundation depths ranging from 1 to 4 m. The coastal regions of Sindh and Hafizabad in Punjab witnessed the most severe floods, with maximum inundation depths exceeding 8 m. Flooding predominantly impacts the southeastern region of the CPEC. The medium- to high-risk zones comprise 25.56% of the region, while high-risk areas constitute 4.18%. Particularly, the eastern and southern regions of Punjab, along with the central and southern regions of Sindh, have been pinpointed as high-risk areas, primarily due to their dense population and riverine characteristics. Overall, our findings provide a scientific basis for informed decision making pertaining to disaster reduction and flood prevention. [ABSTRACT FROM AUTHOR]

This study investigates the early-age behaviour of continuously reinforced concrete pavement (CRCP) on the semi-rigid base, including structural behaviour during transverse crack propagation and curling behaviour both in the longitudinal and transverse direction. Field experiments are operated on a CRCP section in Wuhan, China. Vibrating wire strain gauges were installed at mid-slab and slab edge in the longitudinal and transverse direction for concrete strain and temperature measurement. Testing results showed the complexity of early-age behaviour of CRCP on semi-rigid base and curing with wet burlap sheets. An early-age transverse crack may originate from the bottom of the concrete slab due to the water absorption of the dry semi-rigid base. The development of a transverse crack took several cooling cycles. Curling behaviour in the longitudinal direction did not correspond to the temperature gradient after transverse crack occurrence. Typical curling motion in the transverse direction was found in the mid-slab rather than the slab edge. The findings of this study would lead to a better understanding of the early-age structure behaviour of CRCP on semi-rigid base and its impact on later road performance. [ABSTRACT FROM AUTHOR]

An iridium complex based on the unsymmetrical pyrenyl‐thiophene‐benzothiazole ligand was synthesized, and the iridium complex was supported on reduced graphene oxide successfully. The developed iridium catalyst PTB‐Ir@rGO was characterized through SEM, TEM, XPS, EDS. Meanwhile, we found that this heterogeneous iridium catalyst had high catalytic activity in N‐alkylation of benzamide with alcohols via borrowing hydrogen strategy. Moreover, several control reactions and preliminary mechanism explorations were carried out to study this transformation. Finally, the recycling experiment showed that the catalyst could be reused at least five times without a significant decrease in catalytic activity. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Isoindigo (IID) is widely used as organic dye and conjugated unit in opto‐electronic materials. Functionalization of IID to increase its structural complexity is demanding for obtaining diversity properties. Herein, we developed a direct C—H/N—H activation method of IIDs via double alkyne annulations and synthesized π‐extended IIDs with two pairs of 5/7 membered rings. The structure of the π‐extended IIDs was characterized and confirmed by 1H NMR, 13C NMR, HRMS and X ray crystal analysis. Their physical properties were characterized by UV‐vis absorption, cyclic voltammetry and thermogravimetric analysis. The absorption coefficient of the annulated products enhanced significantly compared with the non‐annulated analogue. [ABSTRACT FROM AUTHOR]

The heavier main group multiple bonds offer an effective tool for small molecule activation. Transient 2H‐phosphinidole working as a reactive phosphadiene system undergoes phospha‐Diels‐Alder reaction with a wide range of non‐activated aromatic carbocycles and heterocycles, including naphthalene, anthracene, phenanthrene, furan, thiophene, pyrrole, pyridine, and benzo‐fused heterocycles, affording concise access to a range of polycyclic fused rings feature with phosphorus at the bridgehead. These results demonstrate that non‐activated (hetero)arenes are capable of acting as 2π systems in [4+2] cycloaddition with highly reactive 2H‐phosphindole complex. [ABSTRACT FROM AUTHOR]

A new class of phosphorus-doped triptycenes was designed and synthesized via a Diels–Alder reaction between alkynylphosphonates and anthracene, followed by oxidative cyclization. The packing interaction and molecular alignment in the single crystals revealed that the weak C–H⋯π (2.825 Å) interaction guides the self-assembly of phosphindole oxide iptycenes. The photophysical and electrochemical properties of these photoluminescent phosphorus-doped iptycenes were characterized to gain a deeper understanding of their fluorescence tunability. The presence of functional groups on the phenyl ring of the P-doped fin and the chemical environment of the P atom both had an effect on the fluorescence emission. [ABSTRACT FROM AUTHOR]

River transport of dissolved organic carbon (DOC) to the ocean is a crucial but poorly quantified regional carbon cycle component. Large uncertainties remaining on the riverine DOC export from China, as well as its trend and drivers of change, have challenged the reconciliation between atmosphere‐based and land‐based estimates of China's land carbon sink. Here, we harmonized a large database of riverine in‐situ measurements and applied a random forest model, to quantify riverine DOC fluxes (FDOC) and DOC concentrations (CDOC) in rivers across China. This study proposes the first DOC modeling effort capable of reproducing well the magnitude of riverine CDOC and FDOC, as well as its trends, on a monthly scale and with a much wider spatial distribution over China compared to previous studies that mainly focused on annual‐scale estimates and large rivers. Results show that over the period 2001–2015, the average CDOC was 2.25 ± 0.45 mg/L and average FDOC was 4.04 ± 1.02 Tg/year. Simultaneously, we found a significant increase in FDOC (+0.044 Tg/year2, p =.01), but little change in CDOC (−0.001 mg/L/year, p >.10). Although the trend in CDOC is not significant at the country scale, it is significantly increasing in the Yangtze River Basin and Huaihe River Basin (0.005 and 0.013 mg/L/year, p <.05) while significantly decreasing in the Yellow River Basin and Southwest Rivers Basin (−0.043 and −0.014 mg/L/year, p =.01). Changes in hydrology, play a stronger role than direct impacts of anthropogenic activities in determining the spatio‐temporal variability of FDOC and CDOC across China. However, and in contrast with other basins, the significant increase in CDOC in the Yangtze River Basin and Huaihe River Basin is attributable to direct anthropogenic activities. Given the dominance of hydrology in driving FDOC, the increase in FDOC is likely to continue under the projected increase in river discharge over China resulting from a future wetter climate. [ABSTRACT FROM AUTHOR]

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson's correlation coefficients (e.g., abs(Pearson's correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson's correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide. [ABSTRACT FROM AUTHOR]

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products. [ABSTRACT FROM AUTHOR]

A coordination polymer Ni-CIA has been designed, synthesized, and characterized via Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectrometry (XPS), energy-dispersive X-ray spectroscopy (EDS), and thermal gravimetric analysis (TGA). The resulting polymer reveals a slice layer stacked combination structure and good stability. Importantly, this polymer exhibited high catalytic activity for the synthesis of oxindole and quinoline derivatives via borrowing hydrogen and dehydrogenation reactions. Meanwhile, several control reactions were carried out to investigate the possible mechanisms of this transformation. Finally, the results of the recycling experiment indicate that the coordination polymer Ni-CIA could be recovered and reused at least five times. [ABSTRACT FROM AUTHOR]

Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China's mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations. [ABSTRACT FROM AUTHOR]

The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Climate change has caused significant impacts on water resource redistribution around the world and posed a great threat in the last several decades due to intensive human activities. The impacts of human water use and management on regional water resources remain unclear as they are intertwined with the impacts of climate change. In this study, we disentangled the impact of climate‐induced human activities on groundwater resources in a typical region of the semi‐arid North China Plain based on a process‐oriented groundwater modelling approach accounting for climate‐human‐groundwater interactions. We found that the climate‐induced human effect is amplified in water resources management ('amplifying effect') for our study region under future climate scenarios. We specifically derived a tipping point for annual precipitation of 350 mm, below which the climate‐induced human activities on groundwater withdrawal will cause significant 'amplifying effect' on groundwater depletion. Furthermore, we explored the different pumping scenarios under various climate conditions and investigated the pumping thresholds, which the pumping amount should not exceed (4 × 107 m3) in order to control future groundwater level depletion. Our results highlight that it is critical to implement adaptive water use practices, such as water‐saving irrigation technologies in the semi‐arid regions, in order to mitigate the negative impacts of groundwater overexploitation, particularly when annual precipitation is anomalously low. [ABSTRACT FROM AUTHOR]

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

To better reduce the gas explosion hazard, a method of explosion prevention was developed, through which the explosion was totally suppressed. The corresponding system was designed to quench the explosion flame and terminate the propagation by extinguishing the agent sprayed after an explosion. The lower limit quantity of agent of ABC powder (NH4H2PO4 powder) required to quench the flame was considered as the difficulty standard for explosion prevention. The main experimental duct was 3.0 m long and horizontal arrangement. The rear duct was 0.5 m long, which was placed at different angles with the horizontal main duct. And its cross-section was varied in different experiments. The results showed that the 90° duct structure was most conducive to preventing the explosion, in which 10 g ABC powder was needed, while 50 g powder was needed in 180° duct structure. The cross-section reduction of the rear duct was also conducive to explosion prevention. At the positions where the nozzles were set up, the slower the flame spread, the easier the explosion prevention could be achieved. When the nozzles were set at different positions, the amount of powder required to prevent the explosion varied greatly, ranging from 5 g to more than 80 g. [ABSTRACT FROM AUTHOR]

The Qushandao Granite, mainly composed of alkali‐feldspar granite, is situated in the eastern Zhejiang province of coastal southeast China. In this paper, we present whole‐rock geochemistry, zircon U–Pb geochronology, and Hf isotopes to constrain the age, magma sources, and geodynamic setting of the Qushandao Granite. LA‐ICP‐MS zircon U–Pb dating results revealed that the Qushandao Granite was emplaced in the Late Cretaceous (101–98 Ma). Geochemically, the Qushandao Granite exhibits relatively high silica and alkali contents, metaluminous to weakly peraluminous (A/CNK = 0.98–1.02), and low abundances of phosphorus, titanium, magnesium, and calcium. It is also characterized by enrichment in Rb, K, Th, and depletion in Nb, Ta, P, Ti, and Sr with moderately to weakly negative europium anomalies (Eu/Eu* = 0.71–0.87). Furthermore, the Qushandao Granite displays lower FeOT/MgO, 104 × Ga/Al, and Zr + Nb + Ce + Y values relative to typical A‐type granites. Therefore, we classify the Qushandao Granite as calc‐alkaline I‐type granite based on a synthesis of geological and geochemical characteristics. The Qushandao Granite shows variable zircon Hf isotopic compositions (εHf(t) = −7.6 to +2.3) and TDM2 model ages of 1.40–0.83 Ga with a mean value of 1.17 Ga. We argue that the Qushandao Granite was most likely generated by mixing of mantle‐derived mafic magma and crust‐derived felsic magma in the lower crust, and that it was formed during post‐collisional extension in the Late Cretaceous, related to the gradually increasing subduction angle of the Paleo‐Pacific plate. [ABSTRACT FROM AUTHOR]

Crop production security is an essential guarantee for the prosperity of country and the well‐being of people. In the context of frequent occurrence of extreme weather events, how to comprehensively evaluate the risk of multiple hazards and quantitatively describe spatial differences will be one of the significant topics in agricultural disaster mitigation. In this study, based on the gridded meteorological data and other datasets (crop, socioeconomic, disaster loss, and actual planting location) from 2000 to 2020, multihazard risk model for different growth period was built to assess the comprehensive risk of winter wheat in the upper Huaihe River basin (UHRB), China. The conclusions were as follows: (a) The spatial distribution of continuous rain risk increased gradually from north to south; the area of the highest lodging risk was located in the east; the spatial trend of drought risk was the highest in the south and the lowest in the northeast. (b) The weights of continuous rain, lodging, and drought disasters were 41, 31, and 28%, respectively, which indicated that winter wheat in UHRB was most affected by continuous rain during the harvest period. (c) The spatial distribution of comprehensive disaster risk assessment showed that the higher risk areas were located in the central and southern parts (Huaibin, Linquan, Luoshan). After verification by historical disasters information, the results are credible, providing a concrete scientific basis for implementing regional agricultural disaster response measures. [ABSTRACT FROM AUTHOR]

The Early Cretaceous geology of Northwestern (NW) Peru was characterized by emplacement of voluminous felsic volcanic rocks (e.g. the Oyotún Fm.), which have important implications for the understanding of Early Cretaceous crust-mantle interactions in the Central Andes. We present U-Pb-Hf isotopic compositions and whole-rock and trace elemental compositions of a series of Early Cretaceous volcanic rocks from the Western Cordillera and the Eastern Cordillera of the Central Andes. The volcanic rocks outcrop sporadically with limited thickness and uniform volcanic lithofacies in the western region, while the eastern region has distributions of larger scale volcanic rocks with more complex volcanic lithofacies. LA-ICP-MS zircon U-Pb dating defines the age of the Oyotún Fm. range from 138.8 Ma to 131.0 Ma in the Western Cordillera, and from 139.7 Ma to 132.3 Ma in the Eastern Cordillera. In situ Hf isotope analysis on dated zircon yield an εHf(t) range of +3.37 to +8.42 and a TDM2(Hf) range from 983 Ma to 650 Ma with a peak at ca. 840 Ma. The Early Cretaceous felsic magmas are predominantly dacites and rhyolites with minor components of basalt and andesite, and show relatively homogeneous Hf isotopic compositions of zircon in single samples. Both the εHf(t) of zircons and the magma temperature increase from the bottom to the top of the Early Cretaceous volcanic rocks in each studied area. All these facts imply an increasing contribution of contemporaneous underplated hot and dry mantle-derived magmas with time, both as a heat source inducing crustal melting and a source of material (melt) that variably mixed with the ancient continental crustal materials. [ABSTRACT FROM AUTHOR]

The lakes in the arid or semi-arid regions of western China are more sensitive to climate changes, and lake levels are considered as a direct indicator of regional climate variability. In this study, we combined satellite altimetry data from ICESat-1 with a smaller footprint and higher accuracy (compared to radar altimetry) and CryoSat-2 with a higher resolution in the along-track direction to monitor lake levels in western China and their trends over a long time period from 2003 to 2021. Our satellite altimetry derived lake levels were well-validated by comparing them against in situ measurements for a lake and independent altimetry-derived product from the DAHITI database for the common lakes. Furthermore, the commonly used linear model was applied to our derived lake level time-series to estimate the overall change trends in 67 typical lake levels over western China. Our results showed that 55 (82%) of these lakes displayed an increasing tendency in water levels, and the remaining 12 (18%) lakes showed a decreasing trend. Overall, the mean water level changing rate in western China was +0.15 m/yr (−1.40 to +0.58 m/yr) during the studied time period. The spatial patterns of the lake level variations can be grouped into three subregions: lake level changes between 2003 and 2021 showed general rising lake levels for the central–northern TP (Tibetan Plateau) endorheic region and Xinjiang, but declining levels for the southern TP exorheic region. The seasonal characteristic of lake level changes showed a significant increase during the summer monsoon season, followed by decreases during the non-monsoon season. The precipitation variations play a leading role in the lake level changes in the context of warm and humid climate states. There were good correspondences between the monthly variations in the lake level and monthly mean precipitation. Additionally, the lake levels also showed a relationship with the air temperature change, in particular, the lake level increase showed a small degree of hysteresis behavior compared with the rising temperatures. Geographically, the precipitation increase in the westerlies regions led to widespread lake expansion in the central–northern TP and Xinjiang. Conversely, precipitation decrease in the Indian monsoon regions caused lake shrinkage in the exorheic region of the southern TP. This study helps us achieve a better understanding of the spatial-temporal patterns of lake level changes in the arid or semi-arid region of western China. [ABSTRACT FROM AUTHOR]

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations. [ABSTRACT FROM AUTHOR]

Atmospheric levels of atomic mercury pollution were measured using spectroscopic techniques in the city of Guangzhou, Guangdong Province, China. Assessments were mainly performed at ground level using a portable (vehicle mounted or hand carried) Zeeman modulation correlation spectrometer (Lumex RA-915M), and the results are given in easily comprehensible diagrams. Measurements were made with continuous recording in car traverses along major roads which cross the city, but also at selected spots, such as at a university campus with laboratory buildings. Further, pollution levels at different locations were recorded when walking through a major and a small hospital. While concentrations in the city in the range 3–10 ng/m3 were typical, and strongly dependent on the traffic situation, very high concentrations (up to 1300 ng/m3) were found at certain indoor hospital locations, again drawing attention to the fact that high mercury levels due to inadequate handling routines can remain undetected but could readily be eliminated by adequate measurements and subsequent sanitation. [ABSTRACT FROM AUTHOR]

Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides. [ABSTRACT FROM AUTHOR]

Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith's Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m3) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices. [ABSTRACT FROM AUTHOR]

Porous copper and cobalt polymers were designed, synthesized, and characterized by scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), X-ray photoelectron spectrometry (XPS), Fourier-transform infrared (FT-IR) spectroscopy, and X-ray diffraction (XRD). The resulting copper/cobalt composites exhibited a much higher catalytic activity towards the borrowing hydrogen reactions of benzenesulfonamides with benzyl alcohols and carboxamides with alcohols than copper polymers. In addition, copper/cobalt composites could be easily recovered and reused at least six times. Mechanism investigations were conducted to study Cu/Co composites and these transformations. [ABSTRACT FROM AUTHOR]

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IVDD) is the most common contributor to low back pain (LBP). Recent studies have found that oxidative stress and reactive oxygen species (ROS) play an important role in IVDD. As a by‐product of aerobic respiration, ROS is mainly produced in the mitochondria by the electron transport chain and other mitochondrial located proteins. With the excessive accumulation of ROS, mitochondria are also the primary target of ROS attack in disc cells. A disrupted balance between intracellular ROS production and antioxidant capacity will lead to oxidative stress, which is the key contributor to cell apoptosis, cell senescence, excessive autophagy, and mitochondrial dysfunction. As the pivotal ingredient of oxidative stress, mitochondrial dysfunction manifests as imbalanced mitochondrial dynamics and dysregulated mitophagy. Mitochondria can alter their own dynamics through the process of fusion and fission, so that disabled mitochondria can be separated from the mitochondrial pool. Moreover, mitophagy participates by clearing these dysfunctional mitochondria. Abnormality in any of these processes either increases the production or decreases the clearance of ROS, leading to a vicious cycle that results in the death of intervertebral disc cells in large quantities, combined with degradation of the extracellular matrix and overproduction of matrix metalloproteinase. In this review, we explain the changes in mitochondrial morphology and function during oxidative stress‐mediated IVDD and highlight the important role of mitochondria in this process. Eventually, we summarize the IVDD therapeutic strategies targeting mitochondrial dysfunction based on current understanding of the role of oxidative stress in IVDD. [ABSTRACT FROM AUTHOR]

Precipitation, as an important component of the Earth's water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products. [ABSTRACT FROM AUTHOR]

The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. Thus, the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP), with a total lake area exceeding 50 000 km 2 , accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with areas from 0.2 to 4503 km 2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream-contributing area of each lake, and the inter-lake catchments, which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km 2 from the full catchment. There are six categories (i.e., lake body, topography, climate, land cover/use, soil and geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the time series of 16 hydrological and meteorological variables are extracted, which can be used to drive or validate lumped hydrological models and machine learning models for hydrological simulation. The dataset contains fundamental information for analyzing the impact of catchment-level characteristics on lake properties, which on the one hand, can deepen our understanding of the drivers of lake environment change, and on the other hand can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272026, Liu, 2022). [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data" published in the journal Applied Water Science. The correction addresses an error in Table 1 of the article, where data in the last row second column was incorrectly published. The correct table is provided in the correction notice. The original article has been corrected. [Extracted from the article]

One way of reducing environmental pollution is to reduce our dependence on fossil fuels by replacing them with solar radiation (Rs), which is one of the main sources of clean and renewable energy. In this study, daily Rs values at seven meteorological stations in Iran (Ahvaz, Isfahan, Kermanshah, Mashhad, Bandar Abbas, Kerman and Tabriz) over 2010–2019 were estimated using empirical models, support vector machine (SVM), SVM coupled with cuckoo search algorithm (SVM-CSA) and multi-model approach in the form of two structures. In structure 1, data from each station were divided into training and testing sets. In structure 2, data from the former four stations were used for model training, and those from the latter three stations were used to test the models. The results showed that using meteorological parameters improved estimation accuracy compared with the use of geographical parameters for both SVM and SVM-CSA models. Coupling the CSA to SVM did improve the accuracy of radiation estimates, reducing RMSE by up to 38% (Kermanshah station) and 36% (Tabriz station) for the first structure and about 42.4% (Tabriz station) for the second. Performance analysis of the models over three intervals including, the first, middle and last third of measured radiation values at each station showed that for both structures (except at Tabriz station), the best model performance in under- and over-estimation sets of radiation values was obtained, respectively, in the first third interval (first structure, Mashhad station, RMSE = 28.39 J.cm−2.day−1) and the last third interval (first structure, Bandar Abbas station, RMSE = 12.23 J.cm−2.day−1). Determining the effects of climate change on Rs estimation and using remotely sensed data as inputs of the models could be considered as future works. [ABSTRACT FROM AUTHOR]