Water Resources Research最新文献

{"title":"A Simple Model of Flow Reversals in Florida’s Karst Springs","authors":"Harald Klammler, James W. Jawitz, Matthew J. Cohen","doi":"10.1029/2023wr035987","DOIUrl":"https://doi.org/10.1029/2023wr035987","url":null,"abstract":"North Florida's karst springs are among the largest and most abundant in the world. Despite relatively stable spring discharges, flow reversals can episodically occur in some springs when river waters backflow into the aquifer during flood events. Reversals are normal features of the springs along the Suwanee River, but the changing incidence of these reversals in response to anthropogenic activities or climate change remains unclear and the mechanisms responsible for these reversals remain poorly described. Here we develop a reduced-complexity hydrogeological model of the Suwannee River catchment to explore conditions needed to induce spring flow reversals. Our model demonstrates that reversals require two conditions: (a) a hydrogeological setting that combines an upstream catchment with rapid hydrological responses to meteorological drivers, which freely drains to a downstream catchment containing the karst aquifer (i.e., the spring-fed river segment); and (b) meteorological conditions that create sufficient temporal variability in recharge. Given both conditions, recharge events can propagate from the upstream catchment and fill the downstream river segment faster than it can drain, causing river stage to rise above the aquifer head, resulting in temporary spring flow reversal (or bank storage). Our model accurately predicts significant post-flood increases in spring flow as bank storage recedes, and using measured electrical conductivity at a major river-adjacent spring we also quantify the enhancement of limestone dissolution (cave enlargement) due to reversal events. A comprehensive assessment of the incidence and duration of reversal events shows a predominant influence of climate and vegetation changes over that of groundwater pumping.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Monte Carlo Propagation of the Full Variance-Covariance of GRACE-Like Level-2 Data With Applications in Hydrological Data Assimilation and Sea-Level Budget Studies","authors":"Fan Yang, Ehsan Forootan, Shuhao Liu, Maike Schumacher","doi":"10.1029/2023wr036764","DOIUrl":"https://doi.org/10.1029/2023wr036764","url":null,"abstract":"Understanding mass (re-)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time-variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO satellite missions provide time-variable gravity fields with full variance-covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance-Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE-like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(-FO) data assimilation for hydrological applications and sea-level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Habitat Suitability and Larval Drift Modeling for Spawning-To-Nursery Functional Habitat Connectivity Analysis in Rivers","authors":"David Farò, Christian Wolter","doi":"10.1029/2023wr036827","DOIUrl":"https://doi.org/10.1029/2023wr036827","url":null,"abstract":"Habitat suitability modeling is a commonly used methodology to plan and assess in-stream habitat enhancement in rivers, such as for the key fish life stages spawning and juvenile development. However, their use only allows modeling the spatial distribution of habitats, but not their connectivity. By integrating micro-scale habitat modeling and a larval drift model, we assess the functional connectivity between spawning and larval nursery habitats for four rheophilic and litophilic fish species in a channelized and hydropower impacted reach of the lower Inn River (Bavaria, Germany), in which two restoration measures, a bypass channel and an island side-channel system, have been constructed to improve longitudinal connectivity and habitat conditions. The study aims to (a) map spawning and larval nursery habitats, (b) quantify their connectivity, and (c) optimize functional habitat connectivity through an alternative bypass channel location. Results show that the channel's morphological complexity influences quantity and quality of available spawning and nursery habitats and their connectivity. Despite the presence of nursery habitats across the analyzed channel, the slow lateral larval dispersion during drift limits their accessibility to only the left river bank, making only 33% of available habitats useable. The degree of functional connectivity and hence the percentage of useable habitats can however be increased up to 95.3 % when considering different spatial configurations of habitats, which were explored in two alternative restoration scenarios. The results demonstrate the importance of considering functional habitat connectivity in habitat assessments and restoration planning.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Streamflow Prediction in Human-Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities","authors":"Arken Tursun, Xianhong Xie, Yibing Wang, Dawei Peng, Yao Liu, Buyun Zheng, Xinran Wu, Cong Nie","doi":"10.1029/2023wr036853","DOIUrl":"https://doi.org/10.1029/2023wr036853","url":null,"abstract":"Accurate streamflow prediction in human-regulated catchments remains a formidable challenge due to the complex disturbance of hydrological processes. To consider human disturbance in hydrological modeling, this study introduces a novel static attribute collection that combines river-reach attributes with catchment attributes, referred to as multiscale attributes. The attribute collection is assembled into two deep learning (DL) methods, that is, the Long Short-Term Memory (named as Multiscale LSTM) and the Differentiable Parameter Learning (DPL) model, and the performance is evaluated across 95 human-regulated catchments in the United States (USA) and 24 catchments in the Yellow River Basin in China. In the USA, the Multiscale LSTM and the DPL models achieve similar performance with median Kling-Gupta Efficiency (KGE) of 0.78 and 0.71, respectively. However, in the Yellow River Basin, the KGE values are 0.58 for Multiscale LSTM and 0.24 for DPL. These results highlight the DL models' ability to leverage multiscale attributes for improved performance compared to traditional catchment attributes. The performance of Multiscale LSTM and DPL models is predominantly influenced by river-scale attributes, encompassing factors such as connectivity status index (CSI), degree of regulation (DOR), sediment trapping (SED), and number of dams. Additionally, satellite-derived attributes such as mean and maximum river width (Width), slope and mean water surface elevation (WSE) from the Surface Water and Ocean Topography River Database (SWORD) contribute valuable insights into anthropogenic influences. Moreover, our study highlights the significance of selecting the appropriate training data period, which emerges as the most dominant factor affecting model performance across human-regulated catchments. The diversity of data during the training period enables the model to capture a broad spectrum of hydrological signatures within these catchments. Consequently, this study emphasizes the advantages of Multiscale LSTM and underscores the significance of considering both natural and anthropogenic signatures to enhance hydrological predictions within human-regulated environments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Sensing of River Discharge From Medium-Resolution Satellite Imagery Based on Deep Learning","authors":"Zhen Hao, Naier Xiang, Xiaobin Cai, Ming Zhong, Jin Jin, Yun Du, Feng Ling","doi":"10.1029/2023wr036880","DOIUrl":"https://doi.org/10.1029/2023wr036880","url":null,"abstract":"Accurate monitoring of river discharge variations is essential for managing floods and droughts and understanding the response of global river systems to climate change. Remote sensing of discharge (RSQ) offers a timely and efficient alternative for widespread monitoring, particularly in ungauged areas. Current methods often struggle with accuracy, especially when estimating the width of narrow rivers from medium-resolution images. We first observe that, although estimating the width variation of narrow rivers can be challenging from medium-resolution satellite imagery, river discharge still correlates with river surface color or reflectance. However, existing methods can only correlate river surface reflectance with discharge in gauged rivers. Here, we introduce a novel method employing an advanced Transformer architecture to map river discharge variations directly from time-series reflectance imagery. Our model, trained on quality-checked data from 2,036 discharge gauges, outperforms existing methods in discharge estimation accuracy and is less affected by the need for precise river width estimation. The proposed model yields positive Kling-Gupta Efficiency (KGE) in 68.6% of ungauged rivers, a substantial improvement over the BAM and geoBAM methods, which show positive KGEs in only 28.4% and 33.1% of rivers, respectively. Notably, this performance is achieved despite two-thirds of the rivers being less than 100 m wide, a range where traditional RSQ methods typically struggle, and the RSQ performance does not show degradation for braided rivers. Our approach suggests a significant shift toward more efficient, extensive, and adaptable space-based river discharge monitoring.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconciling Global Terrestrial Evapotranspiration Estimates From Multi-Product Intercomparison and Evaluation","authors":"Yaoting Cai, Qingchen Xu, Fan Bai, Xueqi Cao, Zhongwang Wei, Xingjie Lu, Nan Wei, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Yonggen Zhang, Xueyan Li, Yongjiu Dai","doi":"10.1029/2024wr037608","DOIUrl":"https://doi.org/10.1029/2024wr037608","url":null,"abstract":"Terrestrial evapotranspiration (ET) is a vital process regulating the terrestrial water balance. However, significant uncertainties persist in global ET estimates. Focusing on the area between 60°, we performed an intercomparison of 90 state-of-the-art ET products from 1980 to 2014. These products were obtained from various sources or methods and were grouped into six categories: remote sensing, reanalysis, land surface models, climate models, machine learning methods, and ensemble estimates. It is shown that global ET magnitudes of categories differ considerably, with averages ranging from 518.4 to 706.3 mm yr⁻¹. Spatial patterns are generally consistent but with significant divergence in tropical rainforests. Global trends are mildly positive or negative (−0.10 to 0.37 mm yr⁻²) depending on categories but with distinct spatial variability. Evaluation against site measurements reveals various performances across land cover types; the ideal point error values range from 0.45 to 0.83, with wetlands performing the worst and open shrublands the best. Using the three-cornered hat method, there are spatial differences in ET uncertainty, with lower uncertainty for ensemble estimates, showing less than 15% relative uncertainty in most areas. The best global ET data set varies depending on the intended use and study region. Distinct spatial patterns of controlling factors across categories have been identified, with precipitation driving arid and semi-arid regions and leaf area index dominating tropical regions. It is suggested to include advancing precipitation inputs, incorporate vegetation dynamics, and employ hybrid modeling in future ET estimates. Constraining estimates using complementary data and robust theoretical frameworks can enhance credibility in ET estimation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142159017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity","authors":"Yao Rong, Weishu Wang, Peijin Wu, Pu Wang, Chenglong Zhang, Chaozi Wang, Zailin Huo","doi":"10.1029/2023wr036809","DOIUrl":"https://doi.org/10.1029/2023wr036809","url":null,"abstract":"Accurate evaluation of evapotranspiration (<i>ET</i>) is crucial for efficient agricultural water management. Data-driven models exhibit strong predictive <i>ET</i> capabilities, yet significant limitations like naive extrapolation hamper wider generalization. In this perspective, we explore a novel hybrid deep learning (<i>DL</i>) framework to integrate domain knowledge and demonstrate its potential for evaluating <i>ET</i> under the influence of soil salinity. Specifically, we integrated physical constraints from process models (Penman-Monteith or Shuttleworth-Wallace) and salinity-induced stomatal stress mechanisms into the <i>DL</i> algorithm, and evaluated its performance by comparing four diverse scenarios. Results demonstrate that hybrid <i>DL</i> framework offers a promising alternative for <i>ET</i> estimation, achieving comparable accuracy to pure <i>DL</i> during training and validation. Nonetheless, due to the limited available measurements, data-driven model may not adequately capture plant responses to salt stress, leading to significant prediction biases observed during independent testing. Encouragingly, the hybrid <i>DL</i> model (<i>DL-SS</i>) integrating Shuttleworth-Wallace and salinity-induced stomatal stress mechanisms demonstrated enhanced interpretability, generalizability, and extrapolation capabilities. During testing, <i>DL-SS</i> consistently showed optimal performance, yielding root mean square error (<i>RMSE</i>) values of 37.4 W m⁻² for sunflower and 39.2 W m⁻² for maize. Compared to traditional Jarvis-type approaches (<i>JPM</i> and <i>JSW</i>) and pure <i>DL</i> model during testing, <i>DL-SS</i> achieved substantial reductions in <i>RMSE</i> values: 51%, 33%, and 43% for sunflower, and 45%, 31%, and 35% for maize, respectively. These findings highlight the importance of integrating prior scientific knowledge into data-driven models to enhance extrapolation capability of <i>ET</i> modeling, especially in salinized regions where conventional models may struggle.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradient Information Enhanced Image Segmentation and Automatic In Situ Contact Angle Measurement Applied to Images of Multiphase Flow in Porous Media","authors":"Yang Gao, Sajjad Foroughi, Zhuangzhuang Ma, Sanyi Yuan, Lizhi Xiao, Branko Bijeljic, Martin J. Blunt","doi":"10.1029/2023wr036869","DOIUrl":"https://doi.org/10.1029/2023wr036869","url":null,"abstract":"A gradient-information-enhanced image segmentation method using convolutional neural networks is presented, and then combined with contact angle measurement to establish an automated processing workflow. For three-dimensional X-ray images, the segmentation accuracy at interfaces and sparsely distributed small objects directly influences the accuracy of the contact angle measurement. Leveraging reliable gradient information to train the neural network, this segmentation method addresses the issue of inaccurate segmentation of interfaces even at low resolution and with small objects present. Furthermore, memory requirements are reduced by performing analysis on orthogonal two-dimensional planes. The workflow was tested on water-wet Ketton limestone, as well as on both water-wet and mixed-wet sandstone and a reservoir carbonate. The results from both the segmentation and contact angle measurements underscore the effectiveness of the approach. Notably, the workflow shows considerable generalizability and robustness, even with varying wettability and lithology.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Flow Turbulence Metrics to Improve Bar Rack Guidance for Downstream Migrating Fish","authors":"X. Fang, S. Kumahor, M. F. Tachie, C. Katopodis, H. Ghamry","doi":"10.1029/2023wr034900","DOIUrl":"https://doi.org/10.1029/2023wr034900","url":null,"abstract":"Turbulent flows are investigated upstream of a bar rack system that is recommended as optimum in recent literature from tests with several fish species of different morphology, swimming ability, and behavior. Both two-dimensional two-component and two-dimensional three-component state-of-the-art particle image velocimetry were used to quantify and analyze hydrodynamic metrics important for downstream migrating species. The inclination angles of the bar and rack were 45° and 30°, respectively, and the thickness of the bottom overlay was 13% of the water depth. The two Reynolds numbers investigated, based on incoming velocity and bar thickness, were 4,000 and 6,000. The statistical and structural characteristics of turbulent flows in the streamwise-spanwise plane at 5% water depth, and the streamwise-vertical plane at channel mid-span are discussed. Upstream of the bottom overlay, the mean flow is deflected and accelerated toward the bypass, leading to an increase in the Reynolds stresses, while the turbulence eddies become smaller. For effective fish guidance, it is recommended that sweeping velocity (<i>V</i>_<i>p</i>) be larger than normal velocity (<i>V</i>_<i>n</i>), with <i>V</i>_<i>p</i> parallel and <i>V</i>_<i>n</i> perpendicular to the bar rack and bottom overlay. In the downstream half of the bar rack, <i>V</i>_<i>n</i> may increase sufficiently to surpass <i>V</i>_<i>p</i> near the bypass, possibly reducing effective guidance for some species and sizes. Upstream of the bars, the levels of streamwise mean velocity vary abruptly, which may deter fish from contacting the bars. Although inferences on passage effectiveness are made based on previous studies, tests with different species and sizes are needed to confirm fish responses.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrological Impact of Remotely Sensed Interannual Vegetation Variability in the Upper Colorado River Basin","authors":"Qianqiu Longyang, Ruijie Zeng","doi":"10.1029/2023wr035662","DOIUrl":"https://doi.org/10.1029/2023wr035662","url":null,"abstract":"Vegetation plays a crucial role in atmosphere-land water and energy exchanges, global carbon cycle and basin water conservation. Land Surface Models (LSMs) typically represent vegetation characteristics by monthly climatological indices. However, static vegetation parameterization does not fully capture time-varying vegetation characteristics, such as responses to climatic fluctuations, long-term trends, and interannual variability. It remains unclear how the interaction between vegetation and climate variability propagates into hydrologic fluxes and water resources. Multi-source satellite data sets may introduce uncertainties and require extensive time for analysis. This study developes a deep learning surrogate for a widely used LSM (i.e., Noah) as a rapid diagnosic tool. The calibrated surrogate quantifies the impacts of time-varying vegetation characteristics from multiple remotely sensed GVF products on the magnitude, seasonality, and biotic and abiotic components of hydrologic fluxes. Using the Upper Colorado River Basin (UCRB) as a test case, we found that time-varying vegetation provides more buffering effect against climate fluctuation than the static vegetation configuration, leading to reduced variability in the abiotic evaporation components (e.g., soil evaporation). In addition, time-varying vegetation from multi-source remote sensing products consistently predicts smaller biotic evaporation components (e.g., transpiration), leading to increased water yield in the UCRB (about 14%) compared to the static vegetation scheme. We also highlight the interaction between dynamic vegetation parameterization and static parameterization (e.g., soil) during calibration. Parameter recalibration and a re-evaluation of certain model assumptions may be required for assessing climate change impacts on vegetation and basin-wide water resources.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}