Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Diversity on the Interannual Variations of Spring Monthly Precipitation in Southern China and the Associated Tropical Sea Surface Temperature Anomalies

There is a continuous and relatively stable rainy period every spring in southern China(SC).This spring precipitation process is a unique weather and climate phenomenon in East Asia.Previously,the variation characteristics and associated mechanisms of this precipitation process have been mostly discussed from the perspective of seasonal mean.Based on the observed and reanalysis datasets from 1982 to 2021,this study investigates the diversity of the interannual variations of monthly precipitation in spring in SC,and focuses on the potential influence of the tropical sea surface temperature(SST)anomalies.The results show that the interannual variations of monthly precipitation in spring in SC have significant differences,and the correlations between each two months are very weak.All the interannual variations of precipitation in three months are related to a similar western North Pacific anomalous anticyclone(WNPAC),and the southwesterlies at the western flank of WNPAC bring abundant water vapor for the precipitation in SC.However,the WNPAC is influenced by tropical SST anomalies in different regions each month.The interannual variation of precipitation in March in SC is mainly influenced by the signal of El Nino-Southern Oscillation,and the associated SST anomalies in the equatorial central-eastern Pacific regulate the WNPAC through the Pacific-East Asia(PEA)teleconnection.In contrast,the WNPAC associated with the interannual variation of precipitation in April can be affected by the SST anomalies in the northwestern equatorial Pacific through a thermally induced Rossby wave response.The interannual variation of precipitation in May is regulated by the SST anomalies around the western Maritime Continent,which stimulates the development of low-level anomalous anticyclones over the South China Sea and east of the Philippine Sea by driving anomalous meridional vertical circulation.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and Forecasts to 2150

The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming.

Annual and Monthly Variations in Litter Macronutrients of Three Subalpine Forests in Western China

Macronutrients （N, P, K, Ca, Mg, and S） in litter of three primarily spruce （Picea purpurea Masters） （SF）, fir （Abies faxoniana Rehder ＆ E. H. Wilson） （FF）, and birch （Betula platyphylla Sukaczev） （BF） subalpine forests in western China were measured to understand the monthly variations in litter nutrient concentrations and annual and monthly nutrient returns via litteffall. Nutrient concentration in litter showed the rank order of Ca 〉 N 〉 Mg 〉 K 〉 S 〉 P. Monthly variations in nutrient concentrations were greater in leaf litter （LL） than other litter components. The highest and lowest concentrations of N, P, K, and S in LL were found in the growing season and the nongrowing season, respectively, but Ca and Mg were the opposite. Nutrient returns via litterfall showed a marked monthly pattern with a major peak in October and one or two small peaks in February and/or May, varying with the element and stand type, but no marked monthly variations in nutrient returns via woody litter, reproductive litter, except in May for the BF, and moss litter. Not only litter production but also nutrient concentration controlled the annual nutrient return and the monthly nutrient return pattern. The monthly patterns of the nutrient concentration and return were of ecological importance for nutrient cycling and plant growth in the subalpine forest ecosystems.

Statistical Downscaling Based on Dynamically Downscaled Predictors: Application to Monthly Precipitation in Sweden

A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical downscaling. The method uses predictors that are upscaled from a dynamical downscaling instead of predictors taken directly from a GCM simulation. The method is applied to downscaling of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the downscaled precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of downscaled precipitation. Due to the cost of the method and the limited improvements in the downscaling results, the three-step method is not justified to replace the one-step method for downscaling of Swedish precipitation.

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine

Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.

A Distributed Monthly Water Balance Model for Analyzing Impacts of Land Cover Change on Flow Regimes

The Miyun Reservoir is the most important water source for Beijing Municipality, the capital of China with a population of more than 12 million. In recent decades, the inflow to the reservoir has shown a decreasing trend, which has seriously threatened water use in Beijing. In order to analyze the influents of land use and cover change (LUCC) upon inflow to Miyun Reservoir, terrain and land use information from remote sensing were utilized with a revised evapotranspiration estimation formula; a water loss model under conditions of human impacts was introduced; and a distributed monthly water balance model was established and applied to the Chaobai River Basin controlled by the Miyun Reservoir. The model simulation suggested that not only the impact of land cover change on evapotranspiration, but also the extra water loss caused by human activities, such as the water and soil conservation development projects should be considered. Although these development projects were of great benefit to human and ecological protection, they could reallocate water resources in time and space, and in a sense thereby influence the stream flow.

Monthly variation in litterfall and the amount of nutrients in an Aleurites montana plantation

In this study, the dynamics of monthly variation in litterfall and the amount of nutrients, i.e., organic C, N, P and K, in an Aleurites montana plantation were analyzed, based on a field study and experiments over one year. The results show that the litterfall mass of A. montana collected generally presents an ascending trend with maximum defoliation occurring in the autumn and winter （October-December）, accounting for 75.67% of the total amount of annual litterfalk The sequence in the amount of nutrients in A. montana litter was as follows： organic C 〉 N 〉 K 〉 P; their monthly amounts show various dynamic curves. Similar to the dynamics of the mass of monthly litterfall, the monthly returns of C, N, P and K generally show an ascending trend with their peak values all occurring in December. The mass of A. montana litterfall and the dynamics of its monthly nutrient return provide, to a certain degree, a scientific reference for planting and fertilizing A. montana.

Characteristics of Spatial and Temporal Variations of Monthly Mean Surface Air Temperature over Qinghai-Tibet Plateau

The recorded meteorological data of monthly mean surface air temperature from 72 meteorological stations over the Qinghal-Tibet Plateau in the period of 1960-2003 have been analyzed by using Empirical Orthogonal Function （EOF） method, to understand the detailed features of its temporal and spatial variations. The results show that there was a high consistency of the monthly mean surface air temperature, with a secondarily different variation between the north and the south of the plateau. Warming trend has existed at all stations since the 1960s, while the warming rates were different in various zones. The source regions of big rivers had intense warming tendency. June, November and December were the top three fast-warming months since the 1960s; while April, July and September presented dramatic warming tendency during the last decade.

Monthly Variation in Crustacean Assemblage(Decapod and Stomatopod) and Its Relationships with Environmental Variables in Laizhou Bay,China

In this study,we investigated the community structure of crustaceans(decapod and stomatopod) inhabiting the sandy mud bottoms of Laizhou Bay(northeastern China) monthly from May 2011 to April 2012.Investigation was stopped from December 2011 to February 2012 because of the extreme weather and sea ice.A total of 205,057 specimens belonging to 31 species(shrimp,15;crab,15;and stomatopod,1) were collected in 148 hauls.From 2011 to 2012,Oratosquilla oratoria was the dominant biomass species(47.80%),followed by Charybdis japonica(15.49%),Alpheus japonicas(12.61%),Portunus trituberculatus(6.46%),and Crangon spp.(4.19%).Crangon spp.was the most dominant species by individual(32.55%).O.oratoria was the most-frequently encountered species(81.76%),followed by Palaemon gravieri(70.95%),C.japonica(65.54%),A.japonicas(62.16%),and P.trituberculatus(54.73%).The biomass density increased from August to September 2011 and decreased from March 2012 to April 2012.The dynamics of the ecological indices evolve in a similar manner,with high values of diversity and evenness and rich species from May to June 2011 and low values from September to October 2011.O.oratoria,C.japonica,and P.trituberculatus differed by biomass data between groups I(samples obtained from September to October 2011) and II(samples in other months).These species contributed more than 70% to the similarity of the crustacean community structure.Furthermore,the subsets of environmental variables that best matched the crustacean-assemblage structure were as follows:water depth(WD) in summer(June to August);sea surface temperature(SST),dissolved oxygen(DO),and WD in autumn(September to November);and DO,salinity,and WD in spring(March to May).The calculated correlation coefficients and significance level were higher in the period of July to August 2011 than in other months.Comparing 2011 to 2012 with 1982 to 1983,the species composition remained stable.However,the dominant species changed significantly.High value and large species,such as F.chinensis,P.trituberculatus,and T.curvirostris,have been replaced by low value and small species(i.e.,Crangon spp.,P.gravieri,and C.japonica).

Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China

This paper presents the application of autoregressive integrated moving average （ARIMA）, seasonal ARIMA （SARIMA）, and Jordan-Elman artificial neural networks （ANN） models in forecasting the monthly streamflow of the Kizil River in Xinjiang, China. Two different types of monthly streamflow data （original and deseasonalized data） were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors. The one-month-ahead forecasting performances of all models for the testing period （1998-2005） were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River. The Jordan-Elman ANN models, using previous flow conditions as inputs, resulted in no significant improvement over time series models in one-month-ahead forecasting. The results suggest that the simple time series models （ARIMA and SARIMA） can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

Monthly Changes in the Influence of the Arctic Oscillation on Surface Air Temperature over China

Partial Least Squares Regression （PLSR） is used to study monthly changes in the influence of the Arctic Oscillation （AO） on spring, summer and autumn air temperature over China with the January 500 hPa geopotential height data from 1951 to 2004 and monthly temperature data from January to November at 160 stations in China. Several AO indices have been defined with the 500-hPa geopotential data and the index defined as the first principal component of the normalized geopotential data is best to be used to study the influence of the AO on SAT （surface air temperature） in China. There are three modes through which the AO in winter influences SAT in China. The influence of the AO on SAT in China changes monthly and is stronger in spring and summer than in autumn. The main influenced regions are Northeast China and the Changjiang River drainage area.

Comparison of semivariogram models for kriging monthly rainfall in eastern China

An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.

Monthly Variations of Atmospheric Circulations Associated with Haze Pollution in the Yangtze River Delta and North China

Haze pollution in early winter(December and January) in the Yangtze River Delta(YRD) and in North China(NC)are both severe;however, their monthly variations are significantly different. In this study, the dominant large-scale atmospheric circulations and local meteorological conditions were investigated and compared over the YRD and NC in each month. Results showed that the YRD(NC) is dominated by the so-called Scandinavia(East Atlantic/West Russia)pattern in December, and these circulations weaken in January. The East Asian December and January monsoons over the YRD and NC have negative correlations with the number of haze days. The local descending motion facilitates less removal of haze pollution over the YRD, while the local ascending motion facilitates less removal of haze pollution over NC in January, despite a weaker relationship in December. Additionally, the monthly variations of atmospheric circulations showed that adverse meteorological conditions restrict the vertical(horizontal) dispersion of haze pollution in December(January) over the YRD, while the associated local weather conditions are similar in these two months over NC.

Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.

Overview of global monthly surface temperature data in the past century and preliminary integration

This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.

Reversal of monthly East Asian winter air temperature in 2020/21 and its predictability

In this study,the reversal of monthly East Asian winter air temperature(EAWT) in 2020/21 and its predictability were investigated.The reversal of monthly EAWT in 2020/21 was characterized by colder temperatures in early winter(December 2020 to mid-January 2021) and warmer temperatures in late winter(mid-January to February 2021).Results show that the reversal in the intensity of the Siberian high(SH) also occurred between early and late winter in 2020/21.In early winter,as the Barents-Laptev sea ice in the previous September(i.e., in2020) reached a minimum for the period 1981-2020,the SH was strengthaned via a reduction of the meridional gradient between the Arctic and East Asia.In late winter,as a sudden stratospheric warming occurred on 5 January 2021,the stratospheric polar vortex weakened,with the weakest center shifting to North America in January.Subsequently,the negative Arctic Oscillation-like structure shifted towards North America in the middle and lower troposphere,which weakened the SH in late winter.Furthermore,the predictability of the reversal in EAWT in 2020/21 was validated based on monthly and daily predictions from NCEP-CFSv2(National Centers for Environment Prediction-Climate Forecast System,version 2).The results showed that the model was unable to reproduce the monthly reversal of EAWT.However,it was able to forecast the reversal date(18 January 2021)of EAWT at lead times of 1-20 days on the daily scale.