Inter-monthly Variation Research Articles

Response of spectral vegetation indices to Erannis jacobsoni Djak. damage in larch forests

Erannis jacobsoni Djak. (EJD), a typical pest of coniferous forests in Mongolia, has severely threatened forest areas in recent years owing to its rapid development and spread. EJD feeds on needles and leaves, killing many trees and causing severe damage to forest ecosystems，which results in substantial local economic losses. The rapid and effective monitoring of forest pests is crucial for preventing or controlling infestations in a timely manner. To this end, in this study, we calculated spectral vegetation indices using UAV multispectral data, assessed ground survey data to determine the degree of pest damage, and conducted sensitivity analysis on the spectral vegetation indices. Nine sensitive spectral vegetation indices were selected to analyze the intramonthly and intermonthly variations in the spectral vegetation indices of forests during EJD infestation: the chlorophyll red-edge parameter index (CIreg), corrected NIR/IR simple ratio (GMSR), intensity index (Int and Int2), improved NIR/red-edge simple ratio (MSRreg), normalized difference NIR vegetation index (NDSI), soil adjusted vegetation index (SAVI), and salinity index (SI2reg and SI3). The results demonstrated that the variance F values of the sensitive spectral vegetation indices after screening using the successive projection algorithm were highly significant at the α=10−10 level, suggesting that these indices are highly sensitive to the level of pest damage. The intramonthly results were as follows: in June, CIreg, GMSR, Int, Int2, MSRreg, SAVI, SI2reg, and SI3 decreased with increasing pest damage, whereas NDSI increased; in August, the difference in index values between light, medium, and heavy damage and healthy stands was not significant; and in September, most of the index differences changed to mild > moderate > severe. Regarding the intermonthly results, the magnitude of the vegetation index values for each sensitive spectrum at different hazard levels was ranked as June > September > August, and the overall difference varied as δ3>δ2>δ1. The spectral vegetation indices apparently responded to different levels of pest damage, making them suitable for quickly and accurately monitoring the occurrence and development of forest pests. These results provide a reference for the monitoring of forest pests at spatial and temporal scales.

Orographic effects on droughts in a monsoon climate with the world's highest rainfall

AbstractDrought, a recurring natural phenomenon in South Asia's monsoon climate, presents challenges in delineating its spatiotemporal patterns within complex topographies. This study investigated the impact of the orographic barrier in the rice‐dominated agricultural region of northeastern India and Bangladesh on drought characteristics during 1951–2020, employing the relative Standardized Precipitation Index (rSPI) and relative Standardized Precipitation‐Evapotranspiration Index (rSPEI) across 3‐, 6‐ and 12‐month scales. The results indicate that even in the rainiest region of the world, droughts extend beyond the limits of the dry season inherent in the monsoon regime. These mostly regional droughts exhibit weak correlations with the core of the Indian subcontinent and other parts of Bangladesh. The region's orographic barrier has a greater influence on drought intensity than on frequency. The rSPI index, which depends solely on rainfall, may overestimate drought intensity and frequency in regions with high seasonal/annual rainfall and substantial intermonthly variability. In contrast, the rSPEI index, which depends on both rainfall and potential evapotranspiration (PET), better reflects the spatial variation of drought in complex terrain, identifying the leeward hinterland of the orographic barrier as the most drought‐prone area. The two indices give similar results for drought characteristics away from the barrier. Furthermore, the orographic barrier exerts a negligible influence on the trends in rSPI and rSPEI. Principal component analysis (PCA) highlights the influences of the rainfall coefficient of variation and elevation on rSPI, while the PET coefficient of variation strongly influences rSPEI. Strategies to minimize the adverse effects of drought in complex topography and year‐round cropping should be local and season‐specific. These include using shorter‐growing, drought‐resistant rice varieties and adjusting planting schedules in rain shadow areas during the summer monsoon. These efforts should be complemented by integrating indigenous irrigation methods with modern practices such as roof water harvesting and tube wells in winter.

Effects of Spring Dust Aerosols on Direct Radiative Forcing in China from 2000 to 2020

In order to understand the mechanism of dust aerosol influence on regional climate change, it is crucial to quantify the radiative forcing effect of dust aerosols. However, studies on the direct radiative forcing of dust aerosols over long time series in China are still lacking. The direct radiative forcing effect of dust aerosols in China over the past 20 years was simulated and evaluated based on the WRF-Chem (Weather Research and Forecasting model coupled to Chemistry) model in conjunction with remote sensing satellites and ground-based observations. The results showed that dust aerosols exhibited an obvious inter-annual positive radiative forcing effect (about 0.38 W m−2) on net radiation at the top of the atmosphere, mainly in northwest China and the North China Plain, while at the atmosphere dust aerosols presented negative radiative forcing effects on shortwave radiation and positive effects on longwave radiation, with a value of 1.54 W m−2 of net radiative forcing, showing a warming effect. Dust aerosols have a net radiative forcing value of −1.16 W m−2 at the surface, indicating a cooling effect, with a positive forcing effect on longwave radiation and a negative forcing effect on shortwave radiation, both of which coincide with the geographical distribution of dust aerosol concentrations. In terms of inter-monthly variations, at both the atmosphere and top of the atmosphere, the dust aerosols net radiative forcing values showed an increasing trend, with March (−0.20 W m−2 and 0.68 W m−2) < April (0.48 W m−2 and 1.44 W m−2) < May (0.94 W m−2 and 2.42 W m−2). Meanwhile, at the surface, the dust aerosols net radiative forcing values displayed a decreasing trend, with March (−0.88 W m−2) > April (−0.96 W m−2) > May (−1.48 W m−2).

Temporal characteristics and trends of nitrogen loadings in lake Taihu, China and its influencing mechanism at multiple timescales

Climate warming impact on excessive nitrogen (N) load in sediment favours cyanobacterial blooms in eutrophic waters. The nitrate (NO3−-N) and ammonium (NH4+-N) are two forms of N loads that contribute to algae blooms. However, little attention is paid to the impact of environmental factors on N loads variations at different time scales. This paper used a well-calibrated and validated EFDC model to investigate the temporal patterns and trends of ammonium and nitrate from June 2016 to June 2017. This paper presented the relationship and effects between these variations and environmental factors using data from satellite and reanalysis-based observations obtained for six meteorological parameters. The relationship and effects between these variations and environmental factors were also examined at different timescales (i.e., daily, monthly and seasonal scales). Model calibration results indicated that measured values reasonably matched simulated values. The validation results revealed that relative error (RE) values were within an acceptable range. The REs of ammonium at East Taihu (S12) and Xu Lake (S23) sampling sites were 55.83% and 57.61%, while that of nitrate was 24.37% (S12) and 41.08%, respectively. The daily analysis of NH4+-N and NO3−-N variations was 7.318 ± 3.876 (g/m2/day) and 0.0275 ± 0.222 (g/m2/day), respectively. The monthly analysis showed NH4+-N and NO3−N range from 2.04 to 12.04 (g/m2/day) and 0.0008 to 0.064 (g/m2/day), respectively. The magnitude NH4+-N and NO3−-N varied and showed distinct inter-monthly variations. , The relationship between sediment fluxes and meteorological parameters showed the magnitude of correlation coefficient (r) and strength of correlation varied significantly. At daily scales, the relationship of NH4+-N and NO3−-N had a significant positive correlation with all meteorological parameters. At monthly, the correlation coefficient (r) of NH4+-N and NO3−N were heterogenous. At daily and monthly scales, air temperature and wind speed are the main drivers affecting sediment N loads' dynamics; however, the influence of relative humidity, precipitation, and evaporation on N loads are smaller. The study demonstrates the contribution of meteorological conditions to the magnitude and timing of N loadings variability in water bodies. The findings provide more insight into lake ecosystem protection and environmental remediation.

Contributions of meteorology and nutrient to the surface cyanobacterial blooms at different timescales in the shallow eutrophic Lake Taihu

Quantitative assessments of the contributions of various environmental factors to cyanobacterial blooms at different timescales are lacking. Here, the hourly cyanobacterial bloom intensity (CBI) index, a proxy for the intensity of surface cyanobacterial biomass, was obtained from the geostationary satellite sensor Geostationary Ocean Color Imager (GOCI) over the years 2011–2018. Generalized additive model was applied to determine the responses of monthly and hourly CBI to the perturbations of meteorological factors, water stability and nutrients, with variation partitioning analysis used to analyze the relative importance of the three groups of variables to the inter-monthly variation of diurnal CBI in each season. The effects of environmental factors on surface cyanobacterial blooms varied at different timescales. Hourly CBI increased with increasing air temperature up to 18 °C but decreased sharply above 18 °C, whereas monthly CBI increased with increasing air temperature up to 30 °C and stabilized thereafter. Among all the environmental factors, air temperature had the largest contribution to the intra-daily variation in CBI; water stability had the highest explanation rate for the inter-monthly variation of diurnal CBI during summer (42.3 %) and autumn (56.9 %); total phosphorus explained the most variation in monthly CBI (18.5 %). Compared with cyanobacterial biomass (CB) in the water column, high light and low wind speed caused significantly lower CBI in July and higher CBI in November respectively. Interestingly, cyanobacterial blooms at the hourly scale were aggravated by climate warming during winter and spring but inhibited during summer and autumn. Collectively, this study reveals the effects of environmental factors on surface cyanobacterial blooms at different timescales and suggests the consideration of the hourly effect of air temperature in short-term predictions of cyanobacterial blooms.

Inter-monthly Variability of Pineapple (Ananas comosus, MD2) Plant Water Requirement in Ghana: Implications for Planting Period Adjustment Field Experience Program

CROPWAT 8.0 model was used to estimate the water requirement of pineapple for the 2021/22 cropping period, considering all the months in the year. This was done to ascertain which months in the year have lower irrigation water requirements and where water savings can be made. For the months of December to August, irrigation requirement (mm) of 140, 145.1, 113.3, 110.8, 110.9, 145.9, 107.6 and 114, respectively was determined at the study site. However, sucker planting in September, October and November has shown requirements (mm) of 72.8, 61.3, 61.4 respectively, and adjusting the planting calendar to accommodate these months is recommended.

Persistent Mode of February-to-March Precipitation over Southern China: Variation, Mechanism, and Prediction

Abstract Precipitation variations over southern China in February and March have profound influences on local agricultural activities. Therefore, the leading intermonthly variation mode of February-to-March precipitation over southern China is investigated in this study using the extended empirical orthogonal function (EEOF) method. The first leading EEOF (EEOF1) pattern shows a persistent precipitation anomaly from February to March over southern China. Mechanistic analysis indicates that EEOF1’s different phases are related to different factors. A dipole sea surface temperature (SST) pattern with warm anomalies in the South China Sea (SCS) and cold anomalies in the western tropical Pacific (WTP) can lead to negative EEOF1 by exciting an anomalous Philippine Sea cyclone. In contrast, positive EEOF1 is influenced jointly by dipole patterns of SST in the southeast Indian Ocean (SEIO)–WTP area and atmospheric circulation over Europe and West Asia. The individual influence of the SST and atmospheric dipole patterns cannot induce persistent precipitation anomalies over southern China from February to March. Furthermore, we check the prediction skill of the National Center for Atmospheric Research (NCEP) Climate Forecast System (CFSv2) for positive and negative EEOF1. The evaluation results show that CFSv2 can predict the relationship between negative EEOF1 and the SCS–WTP dipole SST pattern, consequently showing skillful prediction for negative EEOF1 at the 1-month lead, with a correct rate of approximately 60%. However, CFSv2 cannot reproduce the combined effect of the aforementioned atmospheric dipole pattern and the SEIO–WTP dipole SST pattern and is therefore unskillful for predicting positive EEOF1, with a correct rate of only approximately 36%.

A new method based on additive vegetation index for mapping Huangtai algae coverage in Lake Ulansuhai.

Huangtai algal blooms are key indicators of eutrophication and lake-ecosystem damage. Understanding the spatiotemporal heterogeneity of their growth is critical for preserving the ecological environment. The dimidiate pixel model is commonly used to estimate vegetation coverage; however, indices such as the normalized difference vegetation index have not been specifically constructed for the Huangtai algae spectrum and thus are not specific or sufficiently precise for use as indicators. Therefore, we propose a new dimidiate pixel model based on a novel additive vegetation index to calculate the Huangtai algal coverage for each pixel using Landsat multispectral satellite images with 30-m resolution. The results showed that the additive vegetation index with R2 = 0.994 is a better indicator than the normalized difference vegetation index, enhanced vegetative index, and ratio vegetative index, with the accuracy of the new model reaching 86.61%. Monthly Landsat images from 2006 to 2016 were used to calculate the Huangtai algal coverage. Analysis of the inter-monthly variation indicated increased coverage from May to July, with an annual maximum and minimum of 14.43% and 0.33% in 2008 and 2013, respectively. This study provides a new reference map of Huangtai algal cover, which is important for monitoring and protecting the Lake Ulansuhai environment.

Modulation of European air quality by Euro-Atlantic weather regimes

This paper analyses the dependency of regional air pollution in Europe on the atmospheric circulation as represented by a set of 8 weather regimes (WRs). For this purpose, daily average PM2.5 concentrations and daily maximum concentrations of O3, NO2 and CO obtained from an atmospheric composition reanalysis have been used. The results indicate that the pollutant concentrations change substantially with the occurrence of each WR according to the associated circulation patterns. In the extended winter, the responses of PM2.5, NO2 and CO are quite similar, with zonal circulation regimes leading to better air quality than anticyclonic regimes linked to steady flows over Europe. On the other hand, the spatial patterns of O3 anomalies under most WRs resemble those of temperature in the extended summer as temperature enhancements favour the photochemical production of O3. The WR characterized by anticyclonic anomalies over Scandinavia leads to the highest concentrations at continental scale and is concurrent with an increased probability of exceeding the poor air quality limits of winter PM2.5 and summer O3 in most European countries. It is also found that a multiple linear regression model on the monthly frequency of WRs can explain a considerable fraction of the intermonthly variability of concentration anomalies over large parts of Europe regardless of the pollutant, with R2 values around 0.6–0.8 over large regions. Overall, the application of these models to sub-seasonal weather forecasts does not seem to bring major improvements to the prediction of month-ahead concentration anomalies as compared to the climatology. The poor skill of sub-seasonal forecasts in predicting the WRs for lead times beyond 15 days limits the forecast capability. However, these results open the possibility of extending current air quality forecast to scales of 5–10 days at a low cost in a near future.

Spatiotemporal Modes Characteristics and SARIMA Prediction of Total Column Water Vapor over China during 2002–2022 Based on AIRS Dataset

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m2 per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed ‘bell-shaped’ curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region.

Run-Of-River Small Hydropower Plants as Hydro-Resilience Assets against Climate Change

Renewable energy sources, due to their direct (e.g., wind turbines) or indirect (e.g., hydropower, with precipitation being the generator of runoff) dependence on climatic variables, are foreseen to be affected by climate change. In this research, two run-of-river small hydropower plants (SHPPs) located at different water districts in Greece are being calibrated and validated, in order to be simulated in terms of future power production under climate change conditions. In doing so, future river discharges derived by the forcing of a hydrology model, by three Regional Climate Models under two Representative Concentration Pathways, are used as inputs for the simulation of the SHPPs. The research concludes, by comparing the outputs of short-term (2031–2060) and long-term (2071–2100) future periods to a reference period (1971–2000), that in the case of a significant projected decrease in river discharges (~25–30%), a relevant important decrease in the simulated future power generation is foreseen (~20–25%). On the other hand, in the decline projections of smaller discharges (up to ~15%) the generated energy depends on the intermonthly variations of the river runoff, establishing that runoff decreases in the wet months of the year have much lower impact on the produced energy than those occurring in the dry months. The latter is attributed to the non-existence of reservoirs that control the operation of run-of-river SHPPs; nevertheless, these types of hydropower plants can partially remediate the energy losses, since they are taking advantage of low flows for hydropower production. Hence, run-of-river SHPPs are designated as important hydro-resilience assets against the projected surface water availability decrease due to climate change.

Impact of climate variability on skipjack tuna (Katsuwonus pelamis) catches in the Indonesian Fisheries Management Area (FMA) 715

Marine and fisheries resources have trans-boundary nature and have great potential to be affected by global climate change. Tuna is one of the main export commodities from Indonesia. Patterns of fish life can not be separated from the existence of various environmental conditions. Fluctuations in environmental conditions have many influences on the seasonal migration period and the presence of fish in one place. This study aimed to determine the variability in peak season of tuna fishing to regional climate change, El Nino and La Nina, with the aim to optimize the utilization of Skipjack tuna. A descriptive qualitative study was conducted to investigate the direct correlation between Skipjack tuna catches during 2015 - 2019 and regional climate changes that occurred in those periods, i.e., El Nino (2015-2019) and La Nina (2015-2019). Sampling and measurement times were performed at the end of the East season, which was from September to December 2019. The results of this study show that regional climate change affected inter-monthly variation of the peak seasons of Skipjack fishing in the fisheries management (FMA) 715 area. During El Nino, FMA 715 was an ideal place for Skipjack tuna fishing. The peak fishing season was in October for the period from September to November. The regional climate change triggered from the Pacific Ocean caused the unsuitable water conditions in the FMA 715 for Skipjack tuna fishing. Variability of climate changes triggered by La Nina in the Pacific Ocean did not provide a favorable environment for fish migration to the FMA 715 waters.

Intra-seasonal variations in urban land surface temperature in two cities in Sierra Leone: the challenge of using a single-date image to represent a whole season

The use of a single date remotely sensed image to represent seasonal land surface temperature (LST) is a common practice whose reliability has not been tested, even though that might be unrepresentative of the season. Through remote sensing and geographic information system (GIS) techniques, this paper examined the effects of using a single date image to represent the whole season by quantifying the intra-seasonal (intra- and inter-month) LST variations in Freetown (coastal city) and Bo (inland city), Sierra Leone. Multi-date Landsat images within three months (two images per month) in the dry season were used to retrieve the LST using the Normalized Difference Vegetation Index (NDVI) threshold method. The results showed that the spatial structures of LST were not uniform on different dates during the same season in both cities. LST differed by as much as 2 °C for scenes within the same month and as much as 4 °C between scenes of different months. The results also showed that the highest intra- and inter-month LST variations were recorded in Freetown than in Bo. This is attributed to the combined influences of the proximity to the ocean, the mountain ranges and surface characteristics in Freetown. Thus, within a season, urban surface temperature varies not just in space based on the surface characteristics but also the variations between two urban areas could be significantly high. This renders surface temperature analysis based on a single date image unrepresentative due to the inability to incorporate such variability. The use of multi-date images could be more representative and can improve studies on urban LST.

Inter-monthly regional variation of sea surface salinity and influencing factors in the East China Sea and the adjacent Northwest Pacific Ocean

Based on 1871-2010 monthly sea surface salinity (SSS) and flow field data, and the global seabed topography data, the inter-monthly regional variation of SSS in different regions and their influencing factors are discussed after climate zoning in the East China Sea (ECS) and the adjacent Northwest Pacific Ocean (ANPO). Rresults show that: (1) the study area can be divided into the area of the ECS, the Kuroshio area of the East China Sea (KECS) and the ANPO I area, II area and III area. (2) In the continental shelf area of the ECS, the inter-monthly regional variation of SSS is significant, the inter-monthly variation range of feature points is large and the difference of SSS value at each point is great. (3) In the KECS, the inter-monthly regional variation of SSS is small. The SSS at feature points is high and the inter-monthly variation is small. From the inlet to the outlett of the KECS, the SSS value gradually increases. (4) In the ANPO, the inter-monthly regional variation of SSS is the smallest, the SSS at feature points is high, and their inter-monthly variation is small. The SSS increases with increasing longitude at the same latitude, and decreases with increasing latitude at the same longitude.

Source and drinking water organic and total iodine and correlation with water quality parameters

Iodinated disinfection by-products (I-DBPs) have recently emerged as part of the pool of DBPs of public health concern. Due to limitations in measuring individual I-DBPs in a water sample, the surrogate measure of total organic iodine (TOI) is often used to account for the sum of all I-DBPs. In this study, TOI and total iodine (TI) are quantified in raw and treated waters in treatment trains at three sites in the Northeast United States. The occurrence, magnitude, and seasonality of these species was investigated within each sampling train and across the different sites. A regression model was developed to explore how TOI occurrence varies with routinely measured physical and chemical parameters in a water sample. The TOI and TI concentration at the three sites ranged from below the method detection limit to 18 µg/L and from 3 and 18.9 µg/L, respectively. There was substantial inter-monthly variability in TOI without a clear seasonal signal, and the concentration of TOI did not increase upon treatment. The results of the multivariate regression model showed that dissolved organic carbon (DOC), specific UV254 absorbance (SUVA), combined chlorine residual (TCl2), and pH were all significantly related to TOI concentration to varying degrees. A Tobit model was fit to show TOI predictions against observed (measured) TOI values. The model could explain approximately 46% of the variance of TOI concentrations in the treated waters.

Open-Source Satellite-Derived Solar Resource Databases Comparison and Validation for Indonesia

Solar resource data derived from satellite imagery are widely available nowadays, either as an open-source or paid database. This article is intended to assess open-source databases, which cover the region of Indonesia. Here, four known solar resource databases, which spatially cover the Indonesian archipelago, have been used, namely, Prediction of Worldwide Energy Resource (POWER), Surface Solar Radiation–Heliosat-East (SARAH-E), CM SAF Cloud, Albedo, Radiation edition 2 (CLARA-A2), and SolarGIS. In addition, a minor portion of the Meteonorm database by Meteotest, around five sample points across Indonesia, has been assessed in terms of coherency to the four mentioned databases. Correlation coefficient and relative bias of the multiyear monthly mean annual cycle global horizontal irradiation (GHI) between pairs of databases are inspected. Three out of four databases are then validated through the available irradiation ground measurement data provided by the World Radiation Data Centre (WRDC). The correlation between each pair varies mostly between 0.7 and 1, which shows that the four databases to a certain extent agree on how the intermonthly variation would behave throughout the year. On the other hand, the validation result reveals that the three databases, i.e., POWER, CLARA-A2, and SARAH-E, are suffering from positive bias error ranging from 3% to 7%. Despite that fact, the correlation between measured and estimated values is still acceptable with SARAH-E showing the best performance among the three. Careful selections and adjustment enable the possibility of these databases to be utilized as a tool for depicting interannual and intermonthly variations of solar irradiation throughout the Indonesian archipelago.

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

AbstractHigh‐quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) (C‐ and X‐bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re‐Analysis‐Interim (ERA‐Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA‐Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA‐Interim products are closer to the in situ SM at seasonal scales, and AMSR‐E and NCEP perform worst in December–February and June–August, respectively. (c) Except for NCEP and ERA‐Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR‐E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differ obviously under different forest types, especially the AMSR‐E products. In summary, SM from ESA CCI is the best, followed by ERA‐Interim product, and precipitation is an important auxiliary data for selecting high‐quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products.

Spatial-Temporal Distribution of Absorptive Aerosols in the Yangtze River Delta

Based on the ozone monitoring instrument (OMI)/Aura L2 OMAERUV data from 2008 to 2017, the spatial-temporal distribution of absorptive aerosols during the past 10 years were studied. The results are as follows. ① In the temporal distribution, the inter-annual variation of absorptive aerosol optical depth (AAOD) first increased and then decreased, reaching the highest value of 0.056 in 2011; this is consistent with the aerosol optical depth (AOD) of 0.702 in the Yangtze River Delta. The inter-monthly variation shows that the high value of AAOD appeared mostly in January, March, and June and increased significantly from November to January. ② In the spatial distribution, the AAOD was higher in the north than in the south in the Yangtze River Delta, and the AOD was similar to the AAOD. High values of AAOD above 0.05 were concentrated mainly in northern Anhui and Jiangsu provinces and in Nanjing, Hangzhou, and Jinhua. The seasonal spatial distribution of AAOD and AOD was higher in spring and winter and lower in autumn, although the AOD was very high and the AAOD was low in summer. The contribution of black carbon in the Yangtze River Delta was consistent with the annual spatial distribution of the AAOD and AOD.

Long-term and inter-monthly dynamics of aquatic vegetation and its relation with environmental factors in Taihu Lake, China

This paper discussed the long-term and inter-monthly variation in the distribution area of aquatic macrophytes in Taihu Lake, as well as the relationship between these variations and environmental factors. The findings were of great significance to the protection and environmental remediation of lake ecosystems. This paper presented data from 92 periods during 1980 to 2017 on the distribution area of aquatic macrophytes (including submerged macrophytes and floating-leaved macrophytes, but excluding emergent macrophytes) in Taihu Lake. Data were acquired by remote-sensing and subsequent image interpretation. The analysis of the inter-monthly variation indicated that the area occupied by aquatic macrophytes first increased and then decreased from January to December. Specifically, the distribution area was very small from January to March, began to increase gradually from April to August, reached its maximum in September, and decreased gradually from October to December. The analysis of the long-term variation showed that the distribution and area of aquatic macrophytes experienced two stages during the years 1980 to 2017: 1) gradual increase, 2) sharp decrease. In the first stage (1980 to 2014), the area occupied by aquatic macrophytes increased by 9 times, the maximum distribution area was 206.27 km2 (in May), 307.92 km2 (in September) and 277.33 km2 (in October). In the second stage (2015 to 2017), the distribution area of aquatic macrophytes decreased sharply to 50 km2 or less. The distribution area of aquatic macrophytes during the months of January to December had a significant positive correlation with monthly average temperature, CODMn value, secchi disk depth(SDD), area of cyanobacteria and chlorophyll-a (Chl-a) concentration, a significant negative correlation with water quality indices such as dissolved oxygen (DO) value and NH3-N concentration, but no significant correlation with water quality indices such as pH values and total suspended matter (TSM) concentration. The distribution area of aquatic macrophytes from 1980 to 2017 had a significant positive correlation with annual average temperature, annual minimum water level, pH value, SDD, area of cyanobacteria and Chl-a concentration, but no significant correlation with water quality indices such as DO value, CODMn value, NH3-N concentration and TSM concentration. The sharp decrease in the distribution area of aquatic macrophytes in 2015 and subsequent years was primarily due to the mechanized salvage of aquatic plants.

The Climatic Characteristics of Surface Salinity in the South China Sea and the Adjacent Northwest Pacific Ocean

Based on the high-resolution SODA oceanographic hydrological reanalysis data, analyzing the climatic characteristics of the surface salinity in the South China Sea and the adjacent Northwest Pacific Ocean during the new climatic baseline period from 1981 to 2010 in this paper. The results showed that: Sea surface salinity (SSS) in the research area could be obviously divided into two areas, the South China Sea and the Northwest Pacific Ocean. The SSS in the South China Sea was lower than that in the Northwest Pacific Ocean. The annual amplitude of SSS in the South China Sea varied in different sea areas, but it was smaller and almost same in the Northwest Pacific Ocean area. The inter-monthly variations of SSS were the same at continental shelf side and the sea area near the island in the South China Sea, however differences existed with that in the central of the South China Sea. The inter-monthly variation of SSS in the Northwest Pacific Ocean varied from low latitude to high latitude and the salinity field showed “high-low-high” distribution. According to the climatic characteristics of salinity, the research area could be divided into five climatic zones. Among them, the South China Sea was divided into areas A and B, and the Northwest Pacific Ocean was divided into areas C, D, and E. As water invading from the Northwest Pacific Ocean to the South China Sea area that caused a higher salinity in area B than in area A. The northern equatorial current influenced on the Northwest Pacific Ocean, that caused the salinity in area D lower than in areas C and E.