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Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values. [ABSTRACT FROM AUTHOR]

This study investigates the diurnal, seasonal, monthly and temporal variation of land surface temperature (LST) and surface urban heat island intensity (SUHII) over the Isfahan metropolitan area, Iran, during 2003–2019 using MODIS data. It also examines the driving factors of SUHII like cropland, built-up areas (BI), the urban–rural difference in enhanced vegetation index (ΔEVI), evapotranspiration (ΔET), and white sky albedo (ΔWSA). The results reveal the presence of urban cool islands during the daytime and urban heat islands at night. The maximum SUHII was observed at 22:30 p.m., while the minimum was at 10:30 a.m. The summer months (June to September) show higher SUHII compared to the winter months (February to May). The daytime SUHII demonstrates a robust positive correlation with cropland and ΔWSA, and a negative correlation with ΔET, ΔEVI, and BI. The nighttime SUHII displays a negative correlation with ΔET and ΔEVI. [ABSTRACT FROM AUTHOR]

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales. [ABSTRACT FROM AUTHOR]

The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics. [ABSTRACT FROM AUTHOR]

The refined allocation of water resources and pollutant loads in a basin according to seasonal changes is an important measure for sustainable management. This study proposes a monthly water resource and pollutant load allocation model that is based on the water footprint and fallback bargaining. First, the water utilization and pollutant discharge demand and allocable resources are accounted for by taking their water footprints as indicators. Subsequently, various initial allocation schemes are designed based on several typical bankruptcy rules. Finally, with the goal of resource sustainability, the initial schemes are optimized by applying the fallback bargaining approach. The Huangshui River basin, which is located in Qinghai, China, is a typical seasonal basin with water use conflicts and it is considered for verifying the proposed methodology. The results show that the monthly allocation framework can effectively balance the water use and pollutant discharge demand of regions upstream and downstream in different seasons, improve the overall resource utilization efficiency in the basin, and ensure that the allocation each month reaches the Pareto optimum. [ABSTRACT FROM AUTHOR]

Research Highlights: A detailed picture of the seasonality in fine root biomass (FRB), necromass (FRN), and the biomass/necromass ratio (FRBN) throughout the whole year is crucial to uncover profound effects of long-term environmental changes on fine root dynamics. Materials and Methods: We used meta-analysis to characterize the variability of FRB, FRN and FRBN, and determined their relations with climatic (monthly versus annual), edaphic and geomorphic factors for tropical, temperate and boreal forest biomes across the Northern Hemisphere. Results: Boreal forests exhibited the highest FRB and FRN, while tropical forests yielded the lowest FRN, and thus the greatest FRBN. FRB and FRN significantly decreased with sampling depth, but increased with soil organic carbon content and elevation, while an opposite pattern was found for FRBN. Temperature and precipitation at different time scales (monthly versus annual) and latitude had varying influences on fine roots. High FRB and FRN were observed during dry season for tropical forests, but in the late growing season for temperate forests. The three forest biomes exhibited the high root activity (measured as FRBN) in June or July. Conclusions: It is crucial to realize the universal and specific responses of fine roots to multiple environmental factors when attempting to incorporate these parameters into fine root monthly dynamic models in forest ecosystems. The biome-specific fluctuation of fine roots contributes to identify the influence factors on fine root seasonal patterns throughout the whole year. Our analysis is expected to improve the understanding of the key role of fine roots at monthly level in modeling and predicting carbon budget of various forest biomes under future climate change. [ABSTRACT FROM AUTHOR]

Vitamin D is involved in bone metabolism and in many various extra-skeletal diseases such as malabsorption syndromes, cardiovascular and metabolic diseases, cancer, and autoimmune and neurological diseases. However, data on the optimal route of administration are not consistent. The aims of our study were to analyze not only the influence of daily vs. monthly administration of vitamin D on bone metabolism and bone turnover, but also the effects of different routes of administration on fat mass in a cohort of adults with low levels of 25(OH) vitamin D3 at baseline. We analyzed 44 patients with hypovitaminosis at baseline and after six months of two different regimens of administration: seven drops (1750 IU)/day vs. 50,000 IU/month. We found that the two regimens were equivalent; 36 out of 44 patients reached the normal range of vitamin D after six months of treatment. Interestingly, the main determinant of vitamin D at baseline was the waist circumference. In addition, 22 patients treated by monthly regimen were evaluated after 18 months of treatment. At the end of follow-up, patients showed normal levels of vitamin D, with increased calcium levels and decreased bone turnover. Waist circumference also decreased. Our results support the efficacy of vitamin D3 given monthly both for correcting hypovitaminosis and for maintaining vitamin D levels. The relationship between serum 25(OH)vitamin D3 concentration and waist circumference supports vitamin D having a protective role in the current setting, since waist size is directly associated with the risk of cardiovascular and metabolic diseases. [ABSTRACT FROM AUTHOR]