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We identified the climate zones where the climate has highest variation similarity to aid to climate data selection. The calculation of climate-growth correlations is the analytical foundation to study climate change influence on tree growth in dendrochronology. However, the majority of climate data used in climate-growth correlation analyses are not directly recorded on the sample sites, but obtained from nearby weather stations. We used a sample site in Saihanba region as a case study to address how correlation bias may occur if nearby climate products have no high correlation with the climate in the sample site. Temperatures in the sample site and from other data resources were highly correlated, suggesting that small potential bias in growth-temperature correlations when using temperatures from nearby climate stations. However, precipitation had large spatial variability, resulting in low correlation between precipitation of the sample site and precipitation from other resources. Large biases in growth-precipitation analysis would be expected when using precipitation from nearby stations, suggesting that precipitation records should be carefully chosen. To aid in this selection, we used a cluster analysis and multiple data-products across China to identify regions where station climate do and do not reflect accurately site conditions, and classified temperature and precipitation zones where climate has high correlation among grid cells of the same climate zone based on similarity of the macroclimate using a ~ 2.5 km resolution gridded climate dataset. Using climate stations located in the same cluster as the sample sites would help to prevent or reduce correlation biases in growth-climate analyses. The generated temperature and precipitation zones are freely available to download as GeoTIFF files in the online supplementary materials (Fig. 1S and Fig. 2S).

Forests are facing climate changes such as warmer temperatures, accelerated snowmelt, increased drought, as well as changing diurnal temperature ranges (DTR) and cloud cover regimes. How tree growth is influenced by the changes in daily to monthly temperatures and its associations with droughts has been extensively investigated, however, few studies have focused on how changes in sub-daily temperatures i.e., DTR, influence tree growth during drought events. Here, we used a network of Larix principis-rupprechtii tree-ring data from 1989 to 2018, covering most of the distribution of planted larch across North China, to investigate how DTR, cloud cover and their interactions influence the relationship between drought stress and tree growth. DTR showed a negative correlation with larch growth in 95 % of sites (r

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.

Urban heat island (UHI) can be characterized and quantified to understand the modification of urban surfaces on the local and regional climate. This study examines UHI variation across three megacities that are located in rapid urbanization regions in eastern China (Beijing, Shanghai and Guangzhou). These cities are located within a warm temperate climate zone, north subtropical climate zone, and lower subtropical climate zone, respectively. Satellite-based land surface temperature (LST) data and air temperature records from 2003 to 2016 were used to identify surface urban heat island (SUHI) and canopy urban heat island (CUHI), respectively. Generally, the average annual SUHI is higher than the CUHI, with the greatest UHIs appearing in Beijing (SUHI: 2.33 ± 0.18 °C, CUHI: 1.45 ± 0.54 °C). UHI changes across latitudes were negatively related to humidity variation, with higher UHI in drier climates. Seasonal UHI analysis suggests that a lower SUHI would occur in winter and a higher UHI in spring and summer, except for Guangzhou. CUHI in dry season was higher than in wet season for all three megacities, and the largest CUHI (2.10 ± 0.33 °C) appeared in winter in Beijing. Various patterns of seasonal cycles of SUHI and CUHI were related to monthly precipitation and solar insolation. Annual average daytime SUHI was higher than the nighttime SUHI, and larger daytime SUHI appeared in Guangzhou, contrasting with Shanghai and Beijing. The difference between SUHI and CUHI for all seasons was also high in Guangzhou. UHI changes were considered to be altered by warm and wet conditions in mega-cities of eastern China, and heat transportation from urban surface to urban canopy provided some possible understanding of the UHI change.

Forests play an important role in sequestrating atmospheric carbon dioxide (CO2). Therefore, in order to understand the spatial–temporal variations and controlling mechanisms of global forest carbon (C) storage under future climate change, an improved individual-based forest ecosystem carbon budget model and remote sensing outputs in this study were applied to investigate the spatial–temporal dynamics of global forest (vegetation+soil) C storage in the future climate change scenario. The results showed that in the future RCP4.5 (representative concentration pathways) climate scenario, the total C storage per unit area per year in vegetation and soil of the global forest ecosystem showed a trend of first decreasing and then increasing from 2006 to 2100, with an average of 22.77 kg C m−2 year−1. However, the evolution trends of C storage changes in vegetation and soil were different. Moreover, the average soil C storage per unit area per year was 2.87 times higher than the average vegetation C storage. The impact of climate change on total C storage in vegetation and soil of the global forest ecosystems was positive, showing an obvious increase during 2006–2100. The total C storage varied significantly in spatial distribution. Spatially, the vegetation C storage and the soil organic C storage were projected to decrease significantly in most parts of South America and southern Africa in the Southern Hemisphere and increase in eastern North America, western Asia, and most areas of Europe in the Northern Hemisphere. Especially in the middle and high latitude regions of the Northern Hemisphere, the total forest C stock was projected to increase by 30–90% from 2046 to 2100. In the future, in these areas where forest C reserves were predicted to be reduced, it was suggested to increase afforestation, prohibit deforestation, and develop projects to increase forest C. Sustainable forest managements also offered opportunities for immediate mitigation and adaptation to climate change. Our findings provided not only a projection of C storage of global forest ecosystem responses to future climate change but also a useful methodology for estimating forest C storage at global levels.

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.

Accurate discrimination of common glycosaminoglycans (GAGs) before they are used in clinics is of great importance. Herein, a ratiometric sensor array Py-PP for discrimination of GAGs was constructed using three pyrene-porphyrin supramolecular complexes termed Py-PP1, Py-PP2 and Py-PP4. These complexes were readily synthesized by mixing pyrene-1-butyric acid (Py) and porphyrins PP1, PP2 and PP4 respectively. In the presence GAGs, the effective FRET from Py to porphyrin in the complex was influenced as a result of the competitive binding interactions between porphyrin and GAG. Controlled by the structural differences in the three porphyrins, complexes Py-PP1, Py-PP2 and Py-PP4 were determined to be cross-responsive towards tested GAGs including Hep, HA, Chs and DS. Distinctive fluorescence patterns were successfully generated for each GAG by the sensor array. The Py-PP sensor array was found to be powerful for discrimination of GAGs in both PBS and 5% serum media. Moreover, Py-PP was also successfully applied for reliable differentiation of Hep from other biological interferences and detection of trace GAG contaminants (0.1%, wt%) in Hep with 100% accuracy.

Heparin (Hep), widely used in clinics as an anticoagulant drug, has high degrees of heterogeneity and shares a similar disaccharide repeating unit with its GAG analogues. The development of reliable and convenient methods to discriminate Hep from its GAG analogues and detect trace GAG contaminants in Hep is meaningful for safe usage of Hep in clinics. Herein, five porphyrin-GO nanocomposites denoted as PP1-GO, PP2a-GO, PP2b-GO, PP3-GO, and PP4-GO were synthesized by assembling corresponding positively charged porphyrins onto the surface of GO. Controlled by a different number and position of the 4-N-methyl-pyridyl groups substituted at the porphyrins, these nanocomposites were determined to be cross-reactive toward Hep and other three commonly used GAGs including Chs, HA, and DS. A NIR sensor array PP-GO was thus constructed using these nanocomposites for GAGs discrimination and Hep quality control through pattern-based recognition. HCA and LDA calculated results indicated that PP-GO was powerful for discrimination of Hep and its GAG analogues in both PBS and even 10% serum media. Moreover, the PP-GO sensor array was successfully applied for the reliable discrimination of trace GAG contaminants in Hep with 100% accuracy.

Northeast China with seasonally frozen soil is quite sensitive to global warming. The changes in soil freezing and thawing processes initiated by global warming could alter the hydrological cycle of agricultural fields. A paired-plot experiment was conducted in frozen agricultural soils in Northeast China to examine the impacts of simulated warming on soil freezing and thawing processes and on soybean production. Infrared radiators were used to simulate global warming, rising surface soil temperature (5 cm depth) by 2.86 °C. We showed that, artificial warming caused the freeze duration shortened by 22 days, and the thaw duration shortened by 17 days resulting in the mean duration of soil freezing-thawing significantly shortened by 39 days and the maximum frost depth reduced by about 40 cm. Simulated warming had no significant effect on the average annual freeze-thaw cycle frequency. Warming induced a larger water accumulation in the 0–100 cm soil layer during 2014–2015 soil freezing period. In the dry year of 2015, warming did not significantly affect surface soil moisture during period from sowing date to VC (soybean cotyledon) date. Thus, warming-induced an increase in soybean yield in the dry year may be attributable to the positive effect of enhanced soil temperature on soybean growth (aboveground dry matter accumulation) and consequent on soybean production. In the wet year of 2014, warming decreased surface soil moisture from sowing date to the date of VC stage because warming advanced the soil thaw-end date in 20–60 cm layer by 15 days. This decline in surface soil water availability may potentially offset the positive effects of increased soil temperature on soybean yield, thus warming effects on soybean production was neutral in the wet year. Our findings highlight the potential role of seasonally soil freezing and thawing dynamics in regulating soybean to global warming and suggested that warming effects on soil water dynamics during soil freezing and thawing periods, and subsequent on the surface soil water availability at the early vegetative stage and soybean production were associated with the hydrological year. We conclude that under current precipitation patterns, the no response of soil surface water availability to warming during early vegetative growth, coupled with warming-mediated increases in soil temperature, might improve soybean production during dry years in Northeast China.

To extend the historical temperature record in the permafrost region of Northeast China, we reconstruct the regional ground surface temperature (GST) for the past four centuries based on a network of Dahurian larch (Larix gmelinii Rupr.) tree-ring width chronologies. Seven standard tree-ring chronologies, which correlate well with each other, are averaged to create a regional mean chronology. GST is the major limiting factor for tree growth in this region. The optimum range of GSTs is from 30 May to 26 August (summer GST), identified by combining the days on which tree growth was strongly influenced by the daily GST data. The summer GST was significantly correlated with the regional mean chronology (r = 0.704, p

Precipitation is the dominant factor that controls vegetation growth and land-use practices in the arid and semiarid Mongolian Plateau (MP), so the spatiotemporal heterogeneity of precipitation change has been an important scientific question in the region. This study investigated the spatiotemporal characteristics of annual and seasonal precipitation across the entire MP based on monthly precipitation data from 136 meteorological stations during 1961–2014 by using a modified Mann–Kendall test, Sen’s slope, Morlet Wavelet Transform, and geostatistical methods. Results show the following: 1) Annual precipitation decreased slightly from 1961 to 2014. Stations with positive and negative trends were 41.9% and 58.1%, respectively. Significant positive trends were mainly in the southwestern and northeastern regions of the plateau, whereas significant negative trends were in the northern and southeastern regions. 2) Precipitation decreased at rates of −5.65 and −0.41 mm/decade in summer and autumn, respectively, but increased at 1.91 and 0.51 mm/decade in spring and winter. The contribution of spring and winter precipitation to the annual amount increased significantly, but that of summer precipitation decreased significantly. 3) A large majority of stations (80.2%) showed decreasing trends in summer, whereas 89.7% and 83.1% of stations showed increasing trends in spring and winter. The spatial distribution of trend magnitude in seasonal precipitation amount was strongly heterogeneous. 4) By climatic zones, precipitation increased in humid and arid zones, but decreased in a semiarid zone. On the whole, the MP experienced a drying trend, with significant regional differentiation and seasonal variations.

The Palmer Drought Severity Index (PDSI) has been widely used to monitor drought. Its characteristics are more suitable for measuring droughts of longer timescales, and this fact has not received much attention. The Standardized Precipitation Evapotranspiration Index (SPEI) can better reflect the climatic water balance, owing to its combination of precipitation and potential evapotranspiration. In this study, we selected monthly average air temperature and precipitation data from 589 meteorological stations of China's National Meteorological Information Center, to compare the effects of applying a self-calibrating PDSI (SC-PDSI) and SPEI to monitor drought events in the station regions, with a special focus on differences of event timescale. The results show the following. 1) Comparative analysis using SC-PDSI and SPEI for drought years and characters of three dry periods from 1961 to 2011 in the Beijing region showed that durations of SC-PDSI-based dry spells were longer than those of 3-month and 6-month SPEIs, but equal to those of 12-month or longer timescale SPEIs. 2) For monitoring evolution of the fall 2009 to spring 2010 Southwest China drought and spring 2000 Huang-Huai drought, 3-month SPEI could better monitor the initiation, aggravation, alleviation and relief of drought in the two regions, whereas the SC-PDSI was insensitive to drought recovery because of its long-term memory of previous climate conditions. 3) Analysis of the relationship between SC-PDSI for different regions and SPEI for different timescales showed that correlation of the two indexes changed with region, and SC-PDSI was maximally correlated with SPEI of 9–19 months in China. Therefore, SC-PDSI is only suitable for monitoring mid- and long-term droughts, owing to the strong lagged autocorrelation such as 0.4786 for 12-month lagged ones in Beijing, whereas SPEI is suitable for both short- and long-term drought-monitoring and should have greater application prospects in China.

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.

Boreal forests are facing profound changes in their growth environment, including warming-induced water deficits, extended growing seasons, accelerated snowmelt, and permafrost thaw. The influence of warming on trees varies regionally, but in most boreal forests studied to date, tree growth has been found to be negatively affected by increasing temperatures. Here, we used a network of Pinus sylvestris tree-ring collections spanning a wide climate gradient the southern end of the boreal forest in Asia to assess their response to climate change for the period 1958-2014. Contrary to findings in other boreal regions, we found that previously negative effects of temperature on tree growth turned positive in the northern portion of the study network after the onset of rapid warming. Trees in the drier portion did not show this reversal in their climatic response during the period of rapid warming. Abundant water availability during the growing season, particularly in the early to mid-growing season (May-July), is key to the reversal of tree sensitivity to climate. Advancement in the onset of growth appears to allow trees to take advantage of snowmelt water, such that tree growth increases with increasing temperatures during the rapidly warming period. The region's monsoonal climate delivers limited precipitation during the early growing season, and thus snowmelt likely covers the water deficit so trees are less stressed from the onset of earlier growth. Our results indicate that the growth response of P. sylvestris to increasing temperatures strongly related to increased early season water availability. Hence, boreal forests with sufficient water available during crucial parts of the growing season might be more able to withstand or even increase growth during periods of rising temperatures. We suspect that other regions of the boreal forest may be affected by similar dynamics.

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.

Urbanization has changed the properties of the Earth’s surface and resulted in modification of the biogeochemical cycle and possible climate feedback at global and regional scales. Such climate effects are especially evident locally over short periods in megacity areas. Climate model simulation and urbanization process analysis are often limited by poor accuracy of land-cover products that largely neglect mixed urban-surface information below certain thresholds. The present study compares three urban land identification methods fractional cover, overlapping parabolic interpolation, and threshold used in remote sensing and climate model parameterization with Landsat Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer land-cover data sets in a systematic evaluation. We also analyse deviation induced by scaling effects and its influence on the urban radiation budget to better understand the implications of land-surface parameter deviation on regional climate analysis. A positive linear relationship is found between the spatial scale and urban-area deviation based on combined analysis of the three land identification methods, and deviation trends levelled off with an increase in the spatial scale. Coarse-resolution land-cover products could not capture well the urbanization process indicated by reference data from Beijing between 2000 and 2009, especially in urban fringe areas where major urban expansion was detected. Detailed sub-pixel information was possibly neglected by threshold methods, which resulted in strong deviation between land-cover products and actual conditions. The overlapping parabolic interpolation method used in climate models also produced deviation in surface parameter derivation during nested simulation work. This might further affect model performance at the regional scale and should be considered in climate model simulation.

Knowledge of spatio-temporal groundwater recharge (GR) is crucial for optimizing regional water management practices. Daily potential GR at 58 sites over the Chinese Loess Plateau (CLP) during 1981–2099 was simulated using the HYDRUS-1D and robust model inputs. The objective was to explore the impacts of soil, vegetation, and climate on potential GR at a regional scale. The median potential GR over the CLP during 1981–2010 was 1.8 cm, accounting for 4.1% of the annual precipitation (P). As dominated by P, the annual potential GR decreased from 18.8 cm (28% of P) at the southeast to 0.0 cm at the northwest. Temporally, consistent low-intensity of GR interspersed with extreme rainfall-induced high GR, being episodic or seasonal depending on sites and years. The lag (average of 5 months) between deep drainage at 3 m depth and rainfall was controlled by climate (i.e., P). From 1981 to 2010, annual potential GR significantly decreased as a result of increased ETp and leaf area index (LAI) over time. A warmer and wetter CLP at the end of this century as predicted by the HadCM3 model may decrease the potential GR because of the increased ETp. This study highlights the dominance of water input factor (P) on the spatial GR and water output factors (ETp and LAI) on the temporal GR. Measures such as water-saving practices and land use optimization should be taken to mitigate climate change effect on groundwater recharge.

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