The Integrated Monitoring Programme (ICP IM) is part of the effect-oriented activities under the 1979 Convention on Long-range Transboundary Air Pollution, which covers the region of the United Nations Economic Commission for Europe (UNECE). The main aim of ICP IM is to provide a framework to observe and understand the complex changes occurring in natural/semi natural ecosystems. This report summarizes the work carried out by the ICP IM Programme Centre and several collaborating institutes. The emphasis of the report is in the work done during the programme year 2018/2019 including: - A short summary of previous data assessments - A status report of the ICP IM activities, content of the IM database, and geographical coverage of the monitoring network - An interim report on aluminium fractions in surface waters draining catchments of ICP Integrated Monitoring network - National Reports on ICP IM activities are presented as annexes.

We carried out a closure study of aerosol-cloud interactions during stratocumulus formation using a large eddy simulation model UCLALES-SALSA and observations from the 2020 cloud sampling campaign at the Puijo SMEAR IV station in Kuopio, Finland. The unique observational setup combining in situ and cloud remote sensing measurements allowed a closer look into the aerosol size-composition dependence of droplet activation and droplet growth in turbulent boundary layer driven by surface forcing and radiative cooling. UCLALES-SALSA uses spectral bin microphysics for aerosols and hydrometeors and incorporates a full description of their interactions into the turbulent-convective radiation-dynamical model of stratocumulus. Based on our results, the model successfully described the probability distribution of updraft velocities and consequently the size dependency of aerosol activation into cloud droplets, and further recreated the size distributions for both interstitial aerosol and cloud droplets. This is the first time such a detailed closure is achieved not only accounting for activation of cloud droplets in different updrafts, but also accounting for processes evaporating droplets and drizzle production through coagulation-coalescence. We studied two cases of cloud formation, one diurnal (24 September 2020) and one nocturnal (31 October 2020), with high and low aerosol loadings, respectively. Aerosol number concentrations differ more than 1 order of magnitude between cases and therefore, lead to cloud droplet number concentration (CDNC) values which range from less than 100cm-3 up to 1000cm-3. Different aerosol loadings affected supersaturation at the cloud base, and thus the size of aerosol particles activating to cloud droplets. Due to higher CDNC, the mean size of cloud droplets in the diurnal-high aerosol case was lower. Thus, droplet evaporation in downdrafts affected more the observed CDNC at Puijo altitude compared to the low aerosol case. In addition, in the low aerosol case, the presence of large aerosol particles in the accumulation mode played a significant role in the droplet spectrum evolution as it promoted the drizzle formation through collision and coalescence processes. Also, during the event, the formation of ice particles was observed due to subzero temperature at the cloud top. Although the modeled number concentration of ice hydrometeors was too low to be directly measured, the retrieval of hydrometeor sedimentation velocities with cloud radar allowed us to assess the realism of modeled ice particles. The studied cases are presented in detail and can be further used by the cloud modellers to test and validate their models in a well-characterized modelling setup. We also provide recommendations on how increasing amount of information on aerosol properties could improve the understanding of processes affecting cloud droplet number and liquid water content in stratiform clouds.

1. Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. 2. We describe a robot-enabled image-based identification machine, which can automate the process of invertebrate sample sorting, specimen identification and biomass estimation. We use the imaging device to generate a comprehensive image database of terrestrial arthropod species which is then used to test classification accuracy, that is, how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) to move forward with the best possible image quality. We use state-of-the-art Resnet-50 and InceptionV3 convolutional neural networks for the classification task. 3. The results for the initial dataset are very promising as we achieved an average classification accuracy of 0.980. While classification accuracy is high for most species, it is lower for species represented by less than 50 specimens. We found significant positive relationships between mean area of specimens derived from images and their dry weight for three species of Diptera. 4. The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.

This paper describes a new method for cloudcorrecting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 month. A weighted mean approach based on the CP values was shown to produce very-high-accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.

The ecological assessment of freshwaters is currently primarily based on biological communities and the reference condition approach (RCA). In the RCA, the communities in streams and lakes disturbed by humans are compared with communities in reference conditions with no or minimal anthropogenic influence. The currently favored rationale is using selected community metrics for which the expected values (E) for each site are typically estimated from environmental variables using a predictive model based on the reference data. The proportional differences between the observed values (O) and E are then derived, and the decision rules for status assessment are based on fixed (typically 10th or 25th) percentiles of the O/E ratios among reference sites. Based on mathematical formulations, illustrations by simulated data and real case studies representing such an assessment approach, we demonstrate that the use of a common quantile of O/E ratios will, under certain conditions, cause severe bias in decision making even if the predictive model would be unbiased. This is because the variance of O/E under these conditions, which seem to be quite common among the published applications, varies systematically with E. We propose a correction method for the bias and compare the novel approach to the conventional one in our case studies, with data from both reference and impacted sites. The results highlight a conceptual issue of employing ratios in the status assessment. In some cases using the absolute deviations instead provides a simple solution for the bias identified and might also be more ecologically relevant and defensible.

According to metacommunity theory (Leibold et al., 2004), the structure of local communities results from the interplay between local factors (e.g., environmental filtering, species interactions) and regional factors (e.g., dispersal rates, landscape configuration). The relative importance of these factors is highly dependent on the organisms’ biological traits, landscape connectivity, and the spatial and temporal scales considered (Heino et al., 2015; Tonkin et al., 2018; Viana and Chase, 2019; Almeida-Gomes et al., 2020; Cañedo-Argüelles et al., 2020; Lansac-Tôha et al., 2021). However, the differences in metacommunity assembly mechanisms found among studies are far from being fully understood. The evaluation of temporal dynamics of metacommunities has only emerged recently (Cañedo-Argüelles et al., 2020; Jabot et al., 2020; Li et al., 2020; Lindholm et al., 2021) and the application of the metacommunity theory in other fields, such as biomonitoring, conservation biology or ecosystem restoration, is yet to be fully explored (Bengtsson, 2010; Heino, 2013; Leibold and Chase, 2018; Chase et al., 2020; Cid et al., 2020; Heino et al., 2021). In this Research Topic, our aim was to invite researchers working in different biogeographic regions and ecological systems (Figure 1) to publish a number of innovative papers on metacommunity spatio-temporal dynamics. We expect to obtain a better understanding of how the factors and processes that structure metacommunities vary in space and time, as well as the implications of such dynamics for biodiversity conservation and ecosystem management.

Algal blooms increasingly threaten lake and reservoir water quality at the global scale, caused by ongoing climate change and nutrient loading. To anticipate these algal blooms, models to project future algal blooms worldwide are required. Here we present the state-of-the-art in algal projection modelling and explore the requirements of an ideal algal projection model. Based on this, we identify current challenges and opportunities for such model development. Since most building blocks are present, we foresee that algal projection models for any lake on earth can be developed in the near future. Finally, we think that algal bloom projection models at a global scale will provide a valuable contribution to global policymaking, in particular with respect to SDG 6 (clean water and sanitation).

Climate change adaptation is a complex human process, framed by uncertainties and constraints, which is difficult to capture in existing assessment models. Attempts to improve model representations are hampered by a shortage of systematic descriptions of adaptation processes and their relevance to models. This paper reviews the scientific literature to investigate conceptualisations and models of climate change adaptation, and the ways in which representation of adaptation in models can be improved. The review shows that real-world adaptive responses can be differentiated along a number of dimensions including intent or purpose, timescale, spatial scale, beneficiaries and providers, type of action, and sector. However, models of climate change consequences for land use and water management currently provide poor coverage of these dimensions, instead modelling adaptation in an artificial and subjective manner. While different modelling approaches do capture distinct aspects of the adaptive process, they have done so in relative isolation, without producing improved unified representations. Furthermore, adaptation is often assumed to be objective, effective and consistent through time, with only a minority of models taking account of the human decisions underpinning the choice of adaptation measures (14%), the triggers that motivate actions (38%) or the time-lags and constraints that may limit their uptake and effectiveness (14%). No models included adaptation to take advantage of beneficial opportunities of climate change. Based on these insights, transferable recommendations are made on directions for future model development that may enhance realism within models, while also advancing our understanding of the processes and effectiveness of adaptation to a changing climate.

Although mining companies have long been conscious of water related risks, they still face environmental management challenges. Several recent environmental incidents in Finnish mines have raised questions regarding mine site environmental and water management practices. This has increased public awareness of mining threats to the environment and resulted in stricter permits and longer permitting procedures. Water balance modelling aids in predictive water management and reduces risks caused by an excess or shortage of water at a mining site. In this study the primary objective was to exploit online water quantity and water quality measurements to better serve water balance management. The second objective was to develop and test mathematical models to calculate the water balance in mining operations. The third objective was to determine how monitoring and modelling tools can be integrated into the management system and process control. According to the experience gained from monitoring water balances, the main recommendation is that the data should be stored in a database where it is easily available for water balance calculations. For real-time simulations, online measurements should be available from strategically defined positions in the mine site. Groundwater may also act as a source or sink with respect to mine site surface water, and therefore monitoring and investigations should be designed to account for the full water balance. In Finland it is possible to calculate water balance for planning or for operative purposes by using the Watershed Simulation and Forecasting System (WSFS) developed at the Finnish Environment Institute (SYKE). This system covers every sub-basin (10-50 km2) over the whole of Finland. WSFS automatically obtains the latest observations of temperature, precipitation, water level, discharge and other needed data provided by the Finnish Meteorological Institute (FMI), SYKE, as well as other sources. The system also uses these observations to follow-up on simulation and forecasting accuracy. The water balance model was further developed to simulate and forecast the water balance at the Yara Siilinjärvi mine site. The WSFS-model was also extended with one-way coupling to the groundwater flow model. The model is operated via a web-based user interface and can produce water-balance forecasts automatically, if necessary, several times a day. The water balance and water flow in the area are simulated using real-time weather observations. The model enables forecasting water levels and planning discharges and pumping at the mine site. Possible uses of the model include preparation for spring floods by emptying ponds for storage of water from snow melt, estimation of the effect of heavy rainfall and calculating the required outflow from the mine site reservoir. Thus, overflows and dam-breaks can be avoided and consequently prevent the leakage of contaminated water. Furthermore, as the model can be modified to simulate changes at the mine site, it can also be beneficial during the mine site-planning process. The water balance model is currently operational for Yara Siilinjärvi mine site and hydrological forecasts are produced on a daily basis. Water level, discharge and pumping data, essential for modelling the area, are provided by the mine operator and EHP-Tekniikka Ltd. The model uses meteorological observations and forecasts from FMI as inputs for the simulations and forecasts. In addition to the accurate weather forecasts, the real time observations are a key factor in the accuracy of the model forecasts. GoldSim is the most popular commercial simulation software solution chosen, not only by mines worldwide, but also in many other sectors. One of the main reasons for its extensive use is its versatility and the ability to expand the program as the needs of the mine require. As the mine project progresses, one of GoldSim’s strongest assets is risk analysis at different phases during both the planning and execution of mine operations. The use of the GoldSim platform was tested during the project and some new features were developed. The project has paid special attention to commercialization of the developed products and well thought out policies for possible joint bids.