The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) "Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modeling and Data Assimilation". Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).

Background The COVID-19-pandemic and especially the physical distancing measures drastically changed the conditions for providing outpatient care in adolescent psychiatry. Methods We investigated the outpatient services of adolescent psychiatry in the Helsinki University Hospital (HUH) from 1/1/2015 until 12/31/2020. We retrieved data from the in-house data software on the number of visits in total and categorized as in-person or remote visits, and analysed the data on a weekly basis. We further analysed these variables grouped according to the psychiatric diagnoses coded for visits. Data on the number of patients and on referrals from other health care providers were available on a monthly basis. We investigated the data descriptively and with a time-series analysis comparing the pre-pandemic period to the period of the COVID-19 pandemic. Results The total number of visits decreased slightly at the early stage of the COVID-19 pandemic in Spring 2020. Remote visits sharply increased starting in 3/2020 and remained at a high level compared with previous years. In-person visits decreased in Spring 2020, but gradually increased afterwards. The number of patients transiently fell in Spring 2020. Conclusions Rapid switch to remote visits in outpatient care of adolescent psychiatry made it possible to avoid a drastic drop in the number of visits despite the physical distancing measures during the COVID-19 pandemic.

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen-specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens-adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia-was matched by date with hydrometeorological variables from a global Earth observation dataset-precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non-linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7-day average temperatures-a relative risk of 0.76 (95% confidence interval: 0.69-0.85) above 28 degrees C-while ETEC risk increased by almost half, 1.43 (1.36-1.50), in the 20-35 degrees C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower-than-average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea-causing agents as the global climate changes. Plain Language Summary Diarrheal disease is a big health problem for children. It can be caused by different bugs, which can be caught more easily in certain weather conditions, though not much is understood about this because the climate varies so much from one place to the next. This study combined data from many different countries where diarrhea-causing bugs were diagnosed in children's stool. Satellites recorded what the weather was like on the day each sample was collected. Rotavirus is easiest to catch in cold weather and when water washes over the ground after rain. Dry weather also makes it and other viruses easy to catch. Bacteria spread best when the air is warm and humid, and the soil moist, though one type of E. coli can also be spread in rainwater. Climate change will make dry places drier, wet places wetter and everywhere warmer. This might lead to more diarrhea caused by bacteria and less by viruses in some places, though places with moist soil might see more of every kind of bug.

Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years. We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements. The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features. Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree. Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide.

Abstract Long records of phenological observations are commonly used as data in global change and palaeoclimate research and to analyse plants' responses to climatic changes. Here we delve into the historical archives of plant phenological observations (1750–1875) compiled and published previously by Professor Adolf Moberg (Imperial Alexander University of Finland). The digitized dataset represents 44,487 observations of 450 different plant species for their 15 different phenological phases made in 193 sites across Finland, and results in 662 different phenological variables. The five most frequently observed variables are the blooming of rye, the sowing of barley, the blooming of bird cherry, the leaf outbreak of birch, and the sowing of oat. The spring and summer observations demonstrate positive relationships between the onset date and the site latitude, this relationship becoming negative for observations made in the autumn. This latitudinal effect is evident in the raw data as demonstrated by the temporal correlations between the unadjusted mean phenological records and the mean latitude of the sites. After the latitudinal effect is removed from the original data such correlations are much reduced and the new set of phenological records based on the adjusted dates can be computed. The resulting mean phenological records correlate negatively and statistically significantly with the mean temperatures from April through July. Linear trends indicate (i) summer onsets having become delayed by more than one week over the full period and (ii) shortening of the growing seasons since 1846. The dataset is made available in an open repository.

Introduction: Fine particulate matter (PM2.5) emissions from vegetation fires can be transported over long distances and may cause significant air pollution episodes far from the fires. However, epidemiological evidence on health effects of vegetation-fire originated air pollution is limited, particularly for mortality and cardiovascular outcomes. Objective: We examined association between short-term exposure to long-range transported PM2.5 from vegetation fires and daily mortality due to non-accidental, cardiovascular, and respiratory causes and daily hospital admissions due to cardiovascular and respiratory causes in the Helsinki metropolitan area, Finland. Methods: Days significantly affected by smoke from vegetation fires between 2001 and 2010 were identified using air quality measurements at an urban background and a regional background monitoring station, and modelled data on surface concentrations of vegetation-fire smoke. Associations between daily PM2.5 concentration and health outcomes on i) smoke-affected days and ii) all other days (i.e. non smoke days) were analysed using Poisson time series regression. All statistical models were adjusted for daily temperature and relative humidity, influenza, pollen, and public holidays. Results: On smoke-affected days, 10 mu g/m(3) increase in PM2.5 was associated with a borderline statistically significant increase in cardiovascular mortality among total population at a lag of three days (12.4%, 95% CI -0.2% to 26.5%), and among the elderly (>= 65 years) following same-day exposure (13.8%, 95% CI -0.6% to 30.4%) and at a lag of three days (11.8%, 95% CI -2.2% to 27.7%). Smoke day PM2.5 was not associated with non-accidental mortality or hospital admissions due to cardiovascular causes. However, there was an indication of a positive association with hospital admissions due to respiratory causes among the elderly, and admissions due to chronic obstructive pulmonary disease or asthma among the total population. In contrast, on non-smoke days PM2.5 was generally not associated with the health outcomes, apart from suggestive small positive effects on non-accidental mortality at a lag of one day among the elderly and hospital admissions due to all respiratory causes following same-day exposure among the total population. Conclusions: Our research provides suggestive evidence for an association of exposure to long-range transported PM2.5 from vegetation fires with increased cardiovascular mortality, and to a lesser extent with increased hospital admissions due to respiratory causes. Hence, vegetation-fire originated air pollution may have adverse effects on public health over a distance of hundreds to thousands of kilometres from the fires. (C) 2016 The Authors. Published by Elsevier Inc.

Time-varying volatility and linear trends are common features of several macroeconomic time series. Recent articles have proposed panel unit root tests (PURTs) that are pivotal in the presence of volatility shifts, excluding linear trends, however. This article proposes a new PURT that works well for data that is both heteroskedastic and trending. Under the null hypothesis, the test statistic has a limiting Gaussian distribution. We derive the local asymptotic power to underpin the consistency of the test statistic. Simulation results reveal that the test performs well in small samples. As an empirical illustration, we examine the stationarity of energy use per capita in OECD economies. While the series are in general difference stationary, they could also be considered as trend stationary for specific time spans.

Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes.

Long-term trends of ambient gaseous pollutants and particulate matter in Helsinki metropolitan area were analyzed from 1994 to 2019. Measurement data from ten monitoring stations located in different types of urban environments including traffic, urban background, rural background, and suburban area were included in this study. We analyzed gas-phase air pollutants, such as NO, NO2, NOx, O3, SO2 and CO; and for aerosol pollutants, we explored mass concentrations for particles smaller than 10 µm and 2.5 µm in diameter (PM10 and PM2.5, respectively ). In order to quantify trends in the data, we deployed a non-parametric Mann-Kendall test and Theil-Sen method. The results were compared with the regional emissions trends and changes in meteorological conditions. Our analysis indicates that SO2 and CO in all stations have decreased to values corresponding to their regional background concentration levels and their role as urban air pollutants have diminished. Our results from the Helsinki Metropolitan area during the last 25 years show that the air quality improved and all the air pollutant concentrations show a decreasing trend, except ozone. Based on our analysis of the Air Quality Index (AQI) at traffic and urban background environments, NO2 concentration, which have typically represented the health effects resulting from vehicular traffic, is rapidly decreasing also in traffic environments. The current AQI standard therefore lacks clarity on the potential health risks from other air pollutants emitted from traffic exhaust. In addition, the air quality indicators currently considered in the AQI do not represent well enough residential wood burning and the possible health outcomes from its exposure. We suggest that the current AQI should be revised in a way that new air quality parameters would be considered, which would better represent the health effects resulting from these local combustion sources.

A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901-2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets. The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.