Topography and precipitation intensity are important factors that affect the quality of satellite precipitation products (SPPs). A clear understanding of the accuracy performance of SPPs over complex terrains and its relationship with topography is valuable for further improvement of product algorithms. The objective of this study is to evaluate three SPPs—the Climate Prediction Center morphing method bias corrected product (CMORPH CRT), Global Precipitation Measurement Integrated MultisatellitE Retrievals (IMERG), and Tropical Rainfall Measuring Mission 3B42V7 (TRMM 3B42V7) against a high-density network of 104 rain gauges over the Taihang Mountains from 1 January 2016 to 31 December 2017, with special focus on the reliability of products’ performance at different elevation and precipitation intensity. The results show that three SPPs slightly overestimate daily precipitation, compared to rain gauge observations, with bias ratios (β) from 1.02 to 1.06 over the entire regions. In terms of accuracy, 3B42 slightly outperforms CRT and IMERG over the Taihang Mountains. As for different elevation ranges, three SPPs show better performance in terms of accuracy in low and moderate elevation (0–500 m) regions. Similar performances of precipitation detection capability can be found for three products over the whole areas, with detection scores ranging from 0.53 to 0.58. Better precipitation detecting performance of three SPPs was discovered in high-elevation (>1000 m) regions. We adopted a linear regression (LR) model and Locally Weighted Regression (LWR) model in an attempt to discover the linear/non-linear relationships between SPPs’ performances and topographic variations. In the accuracy statistical metrics, the errors of 3B42 and CRT showed significantly positive correlations (p < 0.01) with elevation variations. The critical success index for three products gradually increased with elevation variation based on the LR model. The correlation coefficient and probability of detection for three products showed significant non-linear trends in the LWR model. The probability distribution function for the three products in different elevation regions is similar to that over the entire regions. Three SPPs slightly overestimated the frequency of heavy rain events (6.9 < precipitation intensity (PI) ≤ 19.6 mm/d); CRT and 3B42 tended to underestimate the frequency of no rain events (PI < 0.1 mm/d), while IMERG generally overestimated the frequency of no rain events. Our results not only give a detailed assessment of mainly current SPPs over the Taihang Mountains, but also recommend that further improvement on retrieval algorithm is needed by considering topographical impacts for SPPs in the future.

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This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

Objective: Serum magnesium (Mg2+) levels are associated with insulin resistance, hypertension, lipid abnormalities, and inflammation. However, limited studies have indicated the relationship between Mg2+ and multiple system indexes. The purpose of this study was to investigate the association between Mg2+ and allostatic load (AL) in hemodialysis patients.Methods: A cross-sectional survey was conducted on hemodialysis patients from different centers in Anhui Province, China, between January and December 2020. A total of 3,025 hemodialysis patients were recruited. Their clinical data were measured before hemodialysis. Information was collected by an online self-reported questionnaire and medical record. Serum Mg2+ was divided into three groups by tertiles. A score of AL greater than or equal to 3 was defined as high AL. A binary logistic regression model was applied to examine the relationship between serum Mg2+ and AL.Results: A total of 1,222 patients undergoing hemodialysis were included, 60% of whom were males (733/1,222). The mean (standard deviation) age of patients was 55.90 (12.75). The median level of serum Mg2+ was 1.22 mmol/L. The rate of high AL levels was 23.4%. Serum Mg2+ was negatively correlated with body mass index, fasting blood glucose (Glu), and C-reactive protein and positively correlated with high-density lipoprotein, low-density lipoprotein, total cholesterol, diastolic blood pressure (DBP), and serum phosphorus. After adjusting for gender, anxiety, diabetes, family residence, lipid-lowering agents, antihypertensive medications, albumin, and Glu, the binary logistic regression model showed that patients with lower levels of serum Mg2+ were more likely have high AL (OR for the T1 group of serum Mg2+:1.945, 95% CI: 1.365–2.773, and OR for the T2 group of serum Mg2+:1.556, 95% CI: 1.099–2.201).Conclusion: Our data support the hypothesis that higher serum Mg2+ concentrations may contribute to lower health risk in hemodialysis populations. Further randomized controlled trials and cohort studies are warranted to verify whether Mg2+ supplementation could be part of routine examinations in hemodialysis populations.

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Temperature impacts on crop yield are known to be dependent on concurrent precipitation conditions and vice versa. To date, their confounding effects as well as the associated uncertainties are not well quantified at the global scale. Here, we disentangle the separate and confounding effects of temperature and precipitation on global maize yield under 25 climate scenarios. Instead of relying on a single type of crop model as pursued in most of previous impact assessments, we utilize machine learning, statistical and process-based crop models in a novel approach that allows for reasonable inter-method companions and uncertainty quantifications. Through controlling precipitation, 1℃ warming could cause a global yield loss of 6.88%, 4.86% and 5.61% by polynomial regression, Long-short term memory (LSTM) and process-based crop models, respectively. With a 10% increase in precipitation, such negative temperature effects could be mitigated by 3.98%, 1.05% and 3.10%, respectively. When temperature is fixed at the baseline level, 10% increase in precipitation alone could lead to a global yield growth of 0.23%, 1.43% and 3.09% by polynomial regression, LSTM and process-based crop models, respectively. Further analysis demonstrates substantial uncertainties in impact assessment across crop models, which show larger discrepancy in predicting temperature impacts than precipitation effects. Overall, global-scale assessment is more uncertain under drier conditions than wet conditions, while a diverse uncertainty pattern is found for the top 10 maize producing countries. This study highlights the important role of climate interactions in regulating yield response to changes in a specific climate factor and emphasizes the value of using both machine learning, statistical and process crop models in a consistent manner for more realistic estimate of uncertainty than would be provided by a single type of model.