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We present a global water cycle reanalysis that merges water balance estimates derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission, satellite water level altimetry and off-line estimates from several hydrological models. Error estimates for the sequential data assimilation scheme were derived from available uncertainty information and the triple collocation technique. Errors in four GRACE storage products were estimated to be 11–12 mm over land areas, while errors in monthly storage changes derived from five global hydrological models were estimated to be 17–28 mm. Prior and posterior water storage estimates were evaluated against independent observations of river water level and discharge, snow water storage and glacier mass loss. Data assimilation improved or maintained agreement overall, although results varied regionally. Uncertainties were greatest in regions where glacier mass loss and subsurface storage decline are both plausible but poorly constrained. We calculated a global water budget for 2003–2012. The main changes were a net loss of polar ice caps (−342 Gt yr−1) and mountain glaciers (−230 Gt yr−1), with an additional decrease in seasonal snowpack (−18 Gt yr−1). Storage increased due to new impoundments (+16 Gt yr−1), but this was compensated by decreases in other surface water bodies (−10 Gt yr−1). If the effect of groundwater depletion (−92 Gt yr−1) is considered separately, subsurface water storage increased by +202 Gt yr−1 due particularly to increased wetness in northern temperate regions and in the seasonally wet tropics of South America and southern Africa. The reanalysis results are publicly available via www.wenfo.org/wald/.

Drylands, covering nearly 30% of the global land surface, are characterized by high climate variability and sensitivity to land management. Here, two satellite-observed vegetation products were used to study the long-term (1988–2008) vegetation changes of global drylands: the widely used reflective-based Normalized Difference Vegetation Index (NDVI) and the recently developed passive-microwave-based Vegetation Optical Depth (VOD). The NDVI is sensitive to the chlorophyll concentrations in the canopy and the canopy cover fraction, while the VOD is sensitive to vegetation water content of both leafy and woody components. Therefore it can be expected that using both products helps to better characterize vegetation dynamics, particularly over regions with mixed herbaceous and woody vegetation. Linear regression analysis was performed between antecedent precipitation and observed NDVI and VOD independently to distinguish the contribution of climatic and non-climatic drivers in vegetation variations. Where possible, the contributions of fire, grazing, agriculture and CO2 level to vegetation trends were assessed. The results suggest that NDVI is more sensitive to fluctuations in herbaceous vegetation, which primarily uses shallow soil water, whereas VOD is more sensitive to woody vegetation, which additionally can exploit deeper water stores. Globally, evidence is found for woody encroachment over drylands. In the arid drylands, woody encroachment appears to be at the expense of herbaceous vegetation and a global driver is interpreted. Trends in semi-arid drylands vary widely between regions, suggesting that local rather than global drivers caused most of the vegetation response. In savannas, besides precipitation, fire regime plays an important role in shaping trends. Our results demonstrate that NDVI and VOD provide complementary information and allow new insights into dryland vegetation dynamics.

This study brings attention to the determinants of 21st-century skills and 21st-century digital skills. The following skills are investigated: technical, information, communication, collaboration, critical thinking, creativity, and problem-solving skills. To understand differences in the level of these skills among workers, we need to know the factors that determine an individual’s skill level. A systematic literature review was conducted to provide a comprehensive overview of empirical studies measuring skill determinants. The results show that there is strong need for research on determinants of communication and collaboration skills. In a digital context, determinants for creativity and critical thinking are hardly studied. Furthermore, the identified determinants of 21st-century skills studies are limited to personality and psychological determinants, neglecting, for example, social determinants such as social support. Although digital skills studies show more variety, they mostly cover demographic and socioeconomic determinants.

Aspergillus fumigatus is a saprophytic fungal pathogen that causes opportunistic infections in animals and humans. Azole resistance has been reported globally in human A. fumigatus isolates, but the prevalence of resistance in isolates from animals is largely unknown. A retrospective resistance surveillance study was performed using a collection of clinical A. fumigatus isolates from various animal species collected between 2015 and 2020. Agar-based azole resistance screening of all isolates was followed by in vitro antifungal susceptibility testing and cyp51A gene sequencing of the azole-resistant isolates. Over the 5 year period 16 (11.3%) of 142 A. fumigatus culture-positive animals harbored an azole-resistant isolate. Resistant isolates were found in birds (15%; 2/13), cats (21%; 6/28), dogs (8%; 6/75) and free-ranging harbor porpoise (33%; 2/6). Azole-resistance was cyp51A mediated in all isolates: 81.3% (T-67G/)TR 34 /L98H, 12.5% TR 46 /Y121F/T289A. In one azole-resistant A. fumigatus isolate a combination of C(-70)T/F46Y/C(intron7)T/C(intron66)T/M172V/E427K single-nucleotide polymorphisms in the cyp51A gene was found. Of the animals with an azole-resistant isolate and known azole exposure status 71.4% (10/14) were azole naive. Azole resistance in A. fumigatus isolates from animals in the Netherlands is present and predominantly cyp51A TR-mediated, supporting an environmental route of resistance selection. Our data supports the need to include veterinary isolates in resistance surveillance programs. Veterinarians should consider azole resistance as a reason for therapy failure when treating aspergillosis and consider resistance testing of relevant isolates.

Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastures and agricultural lands, followed by forest regeneration. This paper examines the possible impacts of these secondary forests on several Q characteristics for 12 mesoscale catchments (23–346 km2; mean precipitation 1720–3422 mm yr−1) with long (33–51 yr) and simultaneous records for Q, precipitation (P), potential evaporation (PET), and land cover. A simple spatially-lumped, conceptual rainfall–runoff model that uses daily P and PET time series as inputs (HBV-light) was used to simulate Q for each catchment. Annual time series of observed and simulated values of four Q characteristics were calculated. A least-squares trend was fitted through annual time series of the residual difference between observed and simulated time series of each Q characteristic. From this the total cumulative change (Â) was calculated, representing the change in each Q characteristic after controlling for climate variability and water storage carry-over effects between years. Negative values of  were found for most catchments and Q characteristics, suggesting enhanced actual evaporation overall following forest regeneration. However, correlations between changes in urban or forest area and values of  were insignificant (p 0.389) for all Q characteristics. This suggests there is no convincing evidence that changes in the chosen Q characteristics in these Puerto Rican catchments can be ascribed to changes in urban or forest area. The present results are in line with previous studies of meso- and macro-scale (sub-)tropical catchments, which generally found no significant change in Q that can be attributed to changes in forest cover. Possible explanations for the lack of a clear signal may include errors in the land cover, climate, Q, and/or catchment boundary data; changes in forest area occurring mainly in the less rainy lowlands; and heterogeneity in catchment response. Different results were obtained for different catchments, and using a smaller subset of catchments could have led to very different conclusions. This highlights the importance of including multiple catchments in land-cover impact analysis at the mesoscale.

Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 (\"transitional regions\"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

Background There is a growing concern regarding the increase of antimicrobial resistant bacteria in companion animals. Yet, there are no studies comparing the resistance levels of these organisms in European countries. The aim of this study was to investigate geographical and temporal trends of antimicrobial resistant bacteria causing urinary tract infection (UTI) in companion animals in Europe. The antimicrobial susceptibility of 22 256 bacteria isolated from dogs and cats with UTI was determined. Samples were collected between 2008 and 2013 from 16 laboratories of 14 European countries. The prevalence of antimicrobial resistance of the most common bacteria was determined for each country individually in the years 2012–2013 and temporal trends of bacteria resistance were established by logistic regression. Results The aetiology of uropathogenic bacteria differed between dogs and cats. For all bacterial species, Southern countries generally presented higher levels of antimicrobial resistance compared to Northern countries. Multidrug-resistant Escherichia coli were found to be more prevalent in Southern countries. During the study period, the level of fluoroquinolone-resistant E. coli isolated in Belgium, Denmark, France and the Netherlands decreased significantly. A temporal increase in resistance to amoxicillin-clavulanate and gentamicin was observed among E. coli isolates from the Netherlands and Switzerland, respectively. Other country-specific temporal increases were observed for fluoroquinolone-resistant Proteus spp. isolated from companion animals from Belgium. Conclusions This work brings new insights into the current status of antimicrobial resistance in bacteria isolated from companion animals with UTI in Europe and reinforces the need for strategies aiming to reduce resistance.

Many older adults are increasingly embracing digital technology in the Republic of Korea. This study investigated the relationship between the digital skills of Korean older adults and their perceived health status and digital technology application for health promotion. This mixed-method study comprised a community survey of 434 older adults aged ≥65 in two cities in South Korea, followed by focus group interviews. Five types of digital skills, ‘operational internet skills’, ‘information navigation skills’, ‘social skills’, ‘creative skills’, and ‘mobile skills’, were measured using the LSE digital skill measurement instrument. Multivariable analysis identified the influence of digital skills on health-related outcomes. Among them, ‘social skills’ associated positively with self-rated health (β 0.37, 95%CI 0.08, 0.65). ‘Information navigation skills’ contributed positively to the use of digital technology and the internet for a healthy lifestyle in terms of improving eating habits (β 0.43, 95%CI 0.09, 0.77), accessing healthcare (β 0.53, 95%CI 0.21, 0.85), and accessing long-term care services (β 0.45, 95%CI 0.11, 0.79). Thematic analysis revealed that the study participants use Korean language-based resources such as Naver and Kakao Talk for social connection to promote a healthy lifestyle. This study concludes that encouraging initial and sustained use of the internet and enhancing digital skills among Korean older adults can promote active and healthy aging.

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

The accuracy of three satellite rainfall products (TMPA 3B42RT, CMORPH and PERSIANN) was investigated through comparison with grid cell average ground station rainfall data in Indonesia, with a focus on their ability to detect patterns of low rainfall that may lead to drought conditions. Each of the three products underestimated rainfall in dry season months. The CMORPH and PERSIANN data differed most from ground station data and were also very different from the TMPA 3B42RT data. It proved possible to improve TMPA 3B42RT estimates by applying a single empirical bias correction equation that was uniform in space and time. For the six regions investigated, this reduced the root mean square error for estimates of dry season rainfall totals by a mean 9% (from 44 to 40 mm) and for annual totals by 14% (from 77 to 66 mm). The resulting errors represent 10% and 3% of mean dry season and annual rainfall, respectively. The accuracy of these bias corrected TMPA 3B42RT data is considered adequate for use in real-time drought monitoring in Indonesia. Compared to drought monitoring with only ground stations, this use of satellite-based rainfall estimates offers important advantages in terms of accuracy, spatial coverage, timeliness and cost efficiency.