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Soil bioavailable micronutrients have a significant impact on soil fertility, soil quality, maize production, and even environmental quality. However, minimal research has been conducted to characterize the spatial patterns of soil bioavailable micronutrients at the large spatial extent within the maize filed and to reveal the relative control of environmental factors on them at the global and local scale. In the study, topsoil samples (0–20 cm of plough depth, totaling 4,448) were collected from maize field to determine the spatial heterogeneity of soil bioavailable micronutrients including iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and boron (B), and to assess the relative effect of environmental indicators on them. Based on the 2-dimensional empirical mode decomposition (2D-EMD), the spatial pattern of micronutrients at the residues and their relationships with influencing indicators were explored. The results demonstrated that the distribution of Fe and Mn had the dominant longitudinal zonality, the distribution of Cu and Zn had the prominent vertical zonality, and the spatial characteristic of B did not exhibit any particular pattern at the large spatial extent. Based on the stepwise multivariate linear regression with the residues, environmental indicators had less global effect on the distribution of soil bioavailable micronutrients. However, based on the combination of geographically weighted regression (GWR) and 2D-EMD, environmental indicators had a good and significant interpretation on the dynamics of micronutrients bioavailability, which ranged from 53% to 75% in the study area. Specifically, the impact of environmental indicators on Fe, Mn, and Zn were greater, and the impact of human activities on B was greater. Our findings indicated that the local and scale effects on the soil available micronutrients should be integrated into the prediction methods, and the combined method of GWR and 2D-EMD would be a good choice for the high-quality digital mapping at large spatial extent.

Vegetation dynamic is sensitive to climatic warming, and is affected by individual or combined climatic factors at different temporal scale with different intensity. Previous studies have unraveled the relationships between vegetation condition and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamic was dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore the scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherency (BWC), multiple wavelet coherence (MWC), and multiple empirical model decomposition (MEMD). The results indicated that the significant vegetation dynamics were mainly located at scales of 1, 0.5, and 0.3 years. The combined explanatory power of the seven climatic factors on the vegetation were greater at the short-term and long-term scales, while the individual climatic factor might affect vegetation dynamic in the seasonal and medium-term scales at some climatic regions. The combined effect of climatic factors in grassland of Tibetan Plateau (TP) and Tempera grassland of Inner Mongolia (TGIM) regions were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate for crop dynamics between temperate humid & subhumid Northeast China (THSNC) and TP, warm-temperate humid & subhumid North China (WHSNC) and subtropical humid Central & South China (SHCSC), and TGIM and temperate & warm-temperate desert of Northwest China (TWDNC) were equivalent, which were around 47%, 45%, and 39%, respectively. Farming practices in cropland could alleviate the spatial variation of the relationships between climate and vegetation, while enhance the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly in the medium-term scale. Collectively, the results might provide alternative perspective for understanding vegetation evolution in response to climatic changes in China.

Land surface albedo is a key radiative forcing factor in Earth radiation budget, and its variation is highly related to land surface processes, such as land cover change. This study aimed to identify the contribution of human-induced albedo to regional land surface albedo change by analyzing the relationship between albedo and land cover change in northern China from 1992 to 2012. We first employed the time series decomposition method (TSDM) to decompose the satellite-based albedo to three components: seasonal cycle, trend, and residual information. We then analyzed the regional albedo characters over different land cover types and their corresponding variations induced by land cover conversion. The multi-year averaged albedo in northern China was 0.2285 ± 0.0646. The albedo over changed and unchanged area was 0.2435 ± 0.0840 and 0.2276 ± 0.0633, respectively. The contribution of human-induced albedo change to overall changes reached about 86%, which was mainly due to cultivation and urbanization. Trend analysis indicated that the albedo slightly increased in northern China, which may decrease regional solar radiation absorption. Fractional increases in croplands showed a positive correlation with albedo variation (R2 = 0.51). Human-induced albedo change would cause negative radiative forcing (-0.705 W/m2) and thus further promote cooling effects in northern China.

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland's boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May-September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.

The soil erosion and sediment load of the coarse sandy hilly catchment region of the Yellow River basin, which is the main source of sediment for the Yellow River, have become sources of concern for researchers as well as for the local population. Soil and water conservation measures, especially reforestation and check dam construction, have been progressively implemented in this region for several decades. During this period, the corresponding precipitation in the region is also changing dynamically. However, it is still unclear how precipitation variations, land use changes and check dams affect soil erosion and sediment yield in a typical watershed. A combination of field investigations and model simulations was employed to quantitatively identify the impacts of the above three factors on soil erosion and sediment yield in the Kuye River watershed. Significant land use changes, including the conversion of arable land and bare land into grassland, shrubland, forestland and construction land, occurred in the studied watershed from 1987 to 2016. In addition, 306 key dams were built in the watershed, with a total storage capacity of 316.64 Mm3, according to statistical data obtained in 2011. A hot spot analysis showed that the regions that were at high risk of soil erosion and sediment yield were mainly concentrated in the middle reaches of the watershed. The simulation results showed that check dams were the dominant factor affecting sediment load, reducing the total sediment load by 54% in 2006. However, from 1987 to 2016, the contributions of the three studied factors (precipitation variation, land use changes and check dams) to sediment reduction were 29%, 40% and 31%, respectively, which indicated that these factors all had significant influences on the sediment load. More attention should be paid to check dams, and corresponding measures should be taken to protect them, especially during flood periods. These results can serve as a reference for watershed management and policy implementation.

Rapid urbanization has resulted in the permanent conversion of large areas of cropland and natural vegetation to impervious surfaces and therefore greatly modified land surface properties and land-atmosphere interactions. This study sought to examine the urbanization process using Landsat images from 2001 to 2010 in metropolitan JingjinTang (JJT), a rapidly expanding urban cluster in northern China. We aggregated the original results of land use data as fractional cover information in 1 km and 10 km grids. Annual and seasonal land surface temperatures (LSTs) were processed from Moderate Resolution Imaging Spectroradiometer products. We used moving window and gradient analysis methods to examine the differences in LST between urban and other land types, further identifying LST increases in gradients of urbanization levels. Urban extent increased by 1.6 times, and approximately 45% newly developed areas were converted from croplands during this process. Emerging urban land in JJT has caused approximately 0.85 ± 0.68°C warming in terms of annual mean LST, and the greatest warming occurred in the summer. An increase in urban land of 10% in a 1 km grid in JJT would cause approximately a 0.21°C increase in annual LST. Urbanization also led to increases in daytime LSTs and nighttime LSTs by approximately 1.03 ± 1.38°C and 0.78 ± 1.02°C, respectively. The warming trend induced by urbanization exhibits clear seasonal and diurnal differences, and this warming trend is most likely caused by the cumulative effects of changes in land properties, radiation storage, and anthropogenic heat release by urbanization.

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In a forested country, such as Finland, over 50 % of its current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high spatial resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. Coarse spatial resolution estimates of the return levels of maximum wind speed based, e.g., on reanalysed meteorological data or climate scenarios can be downscaled to forest stand levels with the help of land cover and terrain elevation data. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 meter spatial resolution CORINE-land use dataset and high resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalysed data. These data were downscaled to 26 meteorological station locations to represent very diverse environments: Open Baltic Sea islands, agricultural land, forested areas, and Northern Finland treeless fells. Applying a comparison, the downscaled 10-year return levels explained 77 % of the observed variation among the stations examined. In addition, the spatial variation of wind multiplier downscaled 10-year return level wind was compared with the WAsP- model simulated wind. The heterogeneous test area was situated in Northern Finland, and it was found that the major features of the spatial variation were similar, but in the details, there were relatively large differences. However, for areas representing a typical Finnish forested landscape with no major topographic variation, both of the methods produced very similar results. Further fine-tuning of wind multipliers could improve the downscaling for the locations with large topographic variation. However, the current results already indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations having the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations., published version, peerReviewed

Droughts caused by a lack of precipitation are one of the major factors limiting agri- cultural crop production. It is thus important to assess the risk of such droughts in order to reduce their effect on agriculture. In the present study, the drought risk for crop production was assessed through an integrated approach that analyzed the relationship between crop yield and drought on the Henan Plain, China. We used the calibrated CERES-Wheat model to simulate 2 levels of wheat yield, the yield potential and the water-limited yield potential, at 66 weather stations. The yield gap between the yield potential and the water-limited yield potential was used as an indicator of the effects of a precipitation deficit on crop production under rain-fed conditions. A strong linear relationship between the yield gap and the amount of precipitation in the growing season was observed for each station during the period 1962�2009. A uniform criterion for drought severity thresholds for the entire Henan Plain was constructed based on the yield gap. For each station, the growing-season precipitation thresholds associated with different drought severities were then calculated based on the linear relationship between the yield gap and the amount of precipitation in the growing season. Drought frequencies derived from changes in the amount of precipitation during the growing season were also examined for all stations and spatially interpolated over the plain. The results showed diverse spatial patterns of frequency with respect to different drought types. Light droughts often occurred in the southern region, and moderate droughts occurred more frequently in the western and eastern regions. Severe drought displayed a generally decreasing trend from north to south.

In order to explore the trends in sunshine duration in the low-latitude belt of South China, long-term sunshine duration, total clouds, low clouds, and visibility were investigated for the period 1961–2005. The results show significant declines in sunshine duration, occurring on the average of −3.2% and −2.8% per decade under all-sky and clear-sky conditions, respectively. It is noted that increased air aerosol loading due to rapid socio-economic development is the drive behind the sharp declines in sunshine duration in the study area. Though cloud is the primary regulator of sunshine duration under all-sky conditions, sunshine is strongly correlated with visibility under clear-sky conditions. Relational analysis between sunshine and cloud amount suggests 0.4% change in clouds per decade, which is well in agreement with the trend of decline in sunshine duration under all-sky conditions in the study area. Increasing low-cloud opacity could be the primary factor driving the decline in sunshine duration in the low-latitude belt of South China.

The seasonal patterns of total cloud amount (TCA) responsible for El Nino/La Nina-Southern Oscillation (ENSO) Sea Surface Temperature (SST) anomalies were investigated using the ISCCP-D2 cloud and NOAA OI.v2 SST datasets for the period of July 1983 to June 2008. The results show three main ENSO-sensitive regions obtained by spatial overlapping of seasonal correlations, two in the western tropical Pacific and one in the central tropical Pacific. These regions were named WTP1, WTP2, and CTP. In all three regions, except the JJA (June–August) WTP2, the TCA changes were significantly correlated with the Nino 3.4 anomalies during the four seasons (December–January–February (DJF), March–April–May, JJA, and September–October–November (SON)). Remarkable differences in the seasonal variability of TCA were observed in these regions. In the WTP1, the DJF TCA always remained the highest value among the four seasons in all years. In the WTP2, the maximum TCA occurred during JJA in most years. In the CTP, the extreme value of TCA was mainly observed in DJF or SON near the peak time of ENSO. Seasonal cross-correlation analyses also showed significant relations between TCA and Nino 3.4 SST in these regions, which may be helpful for forecasting the evolution of ENSO.