Estimating The Spatial Distribution of Snow Water Equivalent Using in situ and Remote Sensing Observations

Mountain snowpack is one of the primary surface water sources for about one-sixth of the global population. More than 75% of the total runoff originates from mountain snowpacks in the Western U.S. Snowmelt water recharges reservoirs and aquifers gradually in the melting season, providing vital water supplies for urban and agricultural areas. Therefore, accurately monitoring the spatial and temporal distribution of mountain snowpack &ndash; often measured as snow water equivalent (SWE) &ndash; is crucial for effective water management. While existing SWE estimation approaches remain highly uncertain, particularly when applied over large mountainous regions, the remotely-sensed snow data provide new opportunities to better characterize the spatial distributions of mountain snowpack. This dissertation investigates the approaches that optimally blend satellite, airborne, and ground snow observations to improve (near) real-time SWE estimation over mountainous terrain. The second chapter of this dissertation evaluates the accuracy of existing SWE estimation models in Sierra Nevada California. Five large-scale SWE datasets at fine spatial resolutions (&lt;= 1000 m) are comprehensively validated and compared with the Airborne Snow Observatory (ASO) SWE data in the Tuolumne River Basin (2013-2017), and ground snow pillow and snow course SWE observations across the Sierra Nevada (2004-2014). These SWE datasets include REC-INT, REC-ParBal, a Sierra Nevada SWE reanalysis (REC-DA), and two operational SWE datasets from the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE), respectively. The results show that the REC-DA overall provides the most accurate SWE estimates across the Sierra Nevada (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%), followed by the REC-ParBal (R2 = 0.73, MAE = 83 mm, PBIAS = -6.4%), which is the least biased SWE estimates. Generally, SNODAS (R2 = 0.66, MAE = 106 mm, PBIAS = 9.3%) and REC-INT (R2 = 0.61, MAE = 131 mm, PBIAS = -28.3%) exhibit comparable but lower accuracy than the earlier mentioned two datasets, while NWM-SWE (R2 = 0.49, MAE = 142 mm, PBIAS = -25.2%) shows the least accuracy among the five SWE datasets. Given that REC-DA is not applicable in real-time, in the third chapter, a SWE data-fusion framework is developed, which integrates the historical SWE patterns derived from REC-DA into a statistically-based linear regression model (LRM) to estimate SWE in real-time. To investigate the influence of satellite-observed daily mean fractional snow-covered area (DMFSCA) on SWE estimation accuracy, two LRMs are compared: a baseline regression model (LRM-baseline) in which physiographic data and historical SWE patterns are used as independent variables, and an FSCA-informed regression model (LRM-FSCA) in which the DMFSCA from MODIS satellite imagery is included as an additional independent variable. By incorporating DMFSCA, LRM-FSCA outperforms LRM-baseline with improved R2 from 0.54 to 0.60, and reduced PBIAS from 2.6% to 2.2% in snow pillow cross-validation. The improvement in LRM-FSCA&rsquo;s performance is more significant during snow accumulation periods than during the snowmelt seasons. Compared to the ASO SWE, the LRM-FSCA explains 85% of the variance on average, which is at least 21% higher than the operational SNODAS (R2 = 0.64) and NWM-SWE (R2 = 0.33) in comparison. In chapter 4, a SWE bias correction framework (SWE-BCF) is developed that incorporates the ASO SWE and machine learning (ML) algorithms to further improve LRM SWE estimates in real-time. The performance of a wide range of commonly used machine learning algorithms is examined in the SWE-BCF including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results indicate that all ML algorithms are capable of improving LRM-SWE accuracy substantially. While no single model performs significantly better than others, GPR, overall, shows the best performance with a 20% (0.14) increase in mean R2 value, a 31% (51 mm) reduction in mean RMSE, and a 61% (18.0%) reduction in absolute PBIAS compared with the original LRM using ASO SWE data for model validation. RF shows the most robust and stable performance in SWE bias correction with a 10% (0.08) increase in median R2 and a 41% (50 mm) reduction in median RMSE compared with the original LRM.</p

Mapping the water-economic cascading risks within a multilayer network of supply chains

Trade linkages within the supply chain can be mapped onto a complex network. Disruptions in regional resource supplies (i.e., water scarcity) have the potential to generate industrial losses in remote areas due to the interconnected flow of goods and services. While numerous studies have assessed the economic and virtual water supply networks, they assumed rapid and linear transmission of industrial risks in the network, without modeling the transmission process and vulnerability between nodes. Such oversights can lead to the misestimation of risks, especially in the context of climate change. Therefore, it is urgent to construct a cascading model for the supply economic and water supply network that consider step-by-step avalanche in nodes, to help identify vulnerable sectors and mitigate economic risks.In this research, we utilize the 2017 multi-region environmental multiregional input-output (E-MRIO) table in China to construct a comprehensive multilayer network. Each province is represented as a distinct layer within this network, incorporating 42 economic sectors(nodes). These layers and nodes are interconnected through trade linkages. To simulate the cascade process, we introduce the concept of net fragility for a node, calculated as the difference between the ratio of the sum of net inflows and net outflows of a node to its own total output and the threshold. Once a node fails (i.e., net fragility less than 1) the cascade process is triggered, then we quantify the total number of collapsed adjacent nodes, i.e., avalanche size. The bigger avalanche size refers to the province-sector higher vulnerability to economic shocks. Furthermore, we use the risk probability of province-sectors suffering from water scarcity as external shocks to describe the supply network response process under different water quantity and quality constraints. By comparing with the economic impact results, we can further identify vulnerable nodes affected by the dual restraints of water scarcity and economic shocks

Lake storage variation on the endorheic Tibetan Plateau and its attribution to climate change since the new millennium

Citation: Yao, F., Wang, J., Yang, K., Wang, C., Walter, B. A., & Crétaux, J.-F. (2018). Lake storage variation on the endorheic Tibetan Plateau and its attribution to climate change since the new millennium. Environmental Research Letters, 13(6), 064011. https://doi.org/10.1088/1748-9326/aab5d3Alpine lakes in the interior of Tibet, the endorheic Changtang Plateau (CP), serve as ‘sentinels’ of regional climate change. Recent studies indicated that accelerated climate change has driven a widespread area expansion in lakes across the CP, but comprehensive and accurate quantifications of their storage changes are hitherto rare. This study integrated optical imagery and digital elevation models to uncover the fine spatial details of lake water storage (LWS) changes across the CP at an annual timescale after the new millennium (from 2002–2015). Validated by hypsometric information based on long-term altimetry measurements, our estimated LWS variations outperform some existing studies with reduced estimation biases and improved spatiotemporal coverages. The net LWS increased at an average rate of 7.34 ± 0.62 Gt yr−1 (cumulatively 95.42 ± 8.06 Gt), manifested as a dramatic monotonic increase of 9.05 ± 0.65 Gt yr−1 before 2012, a deceleration and pause in 2013–2014, and then an intriguing decline after 2014. Observations from the Gravity Recovery and Climate Experiment satellites reveal that the LWS pattern is in remarkable agreement with that of regional mass changes: a net effect of precipitation minus evapotranspiration (P-ET) in endorheic basins. Despite some regional variations, P-ET explains ∼70% of the net LWS gain from 2002–2012 and the entire LWS loss after 2013. These findings clearly suggest that the water budget from net precipitation (i.e. P-ET) dominates those of glacier melt and permafrost degradation, and thus acts as the primary contributor to recent lake area/volume variations in endorheic Tibet. The produced lake areas and volume change dataset is freely available through PANAGEA (https://doi.pangaea.de/ 10.1594/PANGAEA.888706)

CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke

Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with this task, such as large range of stroke lesion scales and the tissue intensity similarity. The famous encoder-decoder convolutional neural network, which although has made great achievements in medical image segmentation areas, may fail to address these challenges due to the insufficient uses of multi-scale features and context information. To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images. Specifically, a Cross-Level feature Fusion (CLF) strategy was developed to make full use of different scale features across different levels; Extending Atrous Spatial Pyramid Pooling (ASPP) with CLF, we have enriched multi-scale features to handle the different lesion sizes; In addition, convolutional long short-term memory (ConvLSTM) is employed to infer context information and thus capture fine structures to address the intensity similarity issue. The proposed approach was evaluated on an open-source dataset, the Anatomical Tracings of Lesions After Stroke (ATLAS) with the results showing that our network outperforms five state-of-the-art methods. We make our code and models available at https://github.com/YH0517/CLCI_Net

Benzosiloles with Crystallization-induced Emission Enhancement of Electrochemiluminescence: Synthesis, Electrochemistry, and Crystallography

Crystallization-induced emission enhancement (CIEE) was demonstrated for the first time for electrochemilunimescence (ECL) with two new benzosiloles. Compared with their solution, the films of the two benzosiloles gave CIEE of 24 times and 16 times. The mechanism of the CIEE-ECL was examined by spooling ECL spectroscopy, X-ray crystal structure analysis, photoluminescence, and DFT calculation. This CIEE-ECL system is a complement to the well-established aggregation-induced emission enhancement (AIEE) systems. Unique intermolecular interactions are noted in the crystalline chromophore. The first heterogeneous ECL system is established for organic compounds with highly hydrophobic properties

Clinical evidence of acupuncture and moxibustion for irritable bowel syndrome: A systematic review and meta-analysis of randomized controlled trials

BackgroundAcupuncture and moxibustion have been widely used in the treatment of Irritable Bowel Syndrome (IBS). But the evidence that acupuncture and moxibustion for IBS reduction of symptom severity and abdominal pain, and improvement of quality of life is scarce.MethodsPubMed, Embase, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP), Wanfang Database, China Biomedical Literature Service System (SinoMed), and unpublished sources were searched from inception until June 30, 2022. The quality of RCTs was assessed with the Cochrane Collaboration risk of bias tool. The strength of the evidence was evaluated with the Grading of Recommendations Assessment, Development and Evaluation system (GRADE). Trial sequential analysis (TSA) was conducted to determine whether the participants in the included trials had reached optimal information size and whether the cumulative data was adequately powered to evaluate outcomes.ResultsA total of 31 RCTs were included. Acupuncture helped reduce the severity of symptoms more than pharmaceutical drugs (MD, −35.45; 95% CI, −48.21 to −22.68; I2 = 71%). TSA showed the cumulative Z score crossed O'Brien-Fleming alpha-spending significance boundaries. Acupuncture wasn't associated with symptom severity reduction (SMD, 0.03, 95% CI, −0.25 to 0.31, I2 = 46%), but exhibited therapeutic benefits on abdominal pain (SMD, −0.24; 95% CI, −0.48 to −0.01; I2 = 8%) compared to sham acupuncture. Moxibustion show therapeutic benefits compared to sham moxibustion on symptom severity (SMD, −3.46, 95% CI, −5.66 to −1.27, I2 = 95%) and abdominal pain (SMD, −2.74, 95% CI, −4.81 to −0.67, I2 = 96%). Acupuncture (SMD, −0.46; 95% CI, −0.68 to −0.24; I2 = 47%) and the combination of acupuncture and moxibustion (SMD, −2.00; 95% CI, −3.04 to −0.96; I2 = 90%) showed more benefit for abdominal pain compared to pharmacological medications as well as shams. Acupuncture (MD, 4.56; 95% CI, 1.46–7.67; I2 = 79%) and moxibustion (MD, 6.97; 95% CI, 5.78–8.16; I2 = 21%) were more likely to improve quality of life than pharmaceutical drugs.ConclusionAcupuncture and/or moxibustion are beneficial for symptom severity, abdominal pain and quality of life in IBS. However, in sham control trials, acupuncture hasn't exhibited robust and stable evidence, and moxibustion's results show great heterogeneity. Hence, more rigorous sham control trials of acupuncture or moxibustion are necessary.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=262118, identifier CRD42021262118