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Abstract Heatwaves and dry spells are major climate hazards with far-reaching implications for health, economy, agriculture, and ecosystems. The frequency of compound hot and dry summers in Europe has risen in recent years. Here we present an examination of past extreme summers and compare them to future climate conditions. We use reanalysis data (2001–2022) and model data at three global warming levels: +1.2 °C, +2 °C, and +3 °C for nine selected sub-regions. Key findings indicate a significant increase in the frequency of most extreme past occurrences under 2 °C and 3 °C warming scenarios. For specific summers, the occurrence probability rises by up to 5–6 times from 2 °C to 3 °C. Moreover, our analysis reveals a notable northward shift in the climatology of hot and dry summers under 3 °C warming. The hot and dry climate observed in Eastern Europe under current conditions is anticipated to extend into substantial parts of the Baltic coast, Finland, and Scandinavia.

The soil freeze–thaw (FT) cycle is a critical component of the terrestrial cryosphere and plays a significant role in hydrological, ecological, climatic, and biogeochemical processes within permafrost landscapes. The FT states can be monitored with in-situ field measurements, but these procedures are costly and limited to single chosen sites. Remote sensing data provides the opportunity to collect information repeatedly across extensive geographical areas. To explore a more effective way to monitor the FT states in the terrestrial cryosphere, in this study, we used microwave and optical remote sensing data and introduced the Deep Learning approach to simulate the soil FT states in the western part of Nunavik, Canada.Two networks, Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), were trained and tested with over 35,000 and approximately 54,000 randomly selected data samples, respectively. The trained CNN networks outperformed the MLP networks, achieving the highest testing accuracy of 95.67% and the highest validation accuracy of 87.28% based on ground truth data from 32 measurement stations from all seasons across the year. This study proposed the reference periods concept for convenient labeling in data preparation and tested different combinations of influence variables to achieve better transferability of the method for future studies. Our findings offer a more effective way to monitor FT states in the terrestrial cryosphere, offering valuable insights into the consequences of climate change on permafrost landscapes. Moreover, the suggested deep learning approach can be easily expanded when additional input sources are accessible. This expansion has the potential to further improve the model's performance for the FT retrieval.

Abstract Although prolonged heat periods have become a recurring feature of European climate, little knowledge is available on dominant spatial patterns of heatwaves and their influence on moisture-related processes. Increased knowledge will help to improve heatwave and drought prediction and mitigation. This study uses hierarchical agglomerative clustering to derive nine dominating spatial heatwave patterns from a 50-member regional climate model (Canadian Regional Climate Model version 5, CRCM5-LE). The heatwave patterns correspond well with clusters derived from an observational data set (E-OBS) and with extreme historical heatwave events. Moreover, we analyse the occurrence of heatwaves in the identified spatial patterns regarding a soil moisture deficit present before and after the event. We show that negative soil moisture anomalies in the preceding winter/spring (JFMA) can serve as a predictor for heatwaves in South Europe. For North Europe, we find a negative correlation between the number of heatwave days in summer and autumn (OND) soil moisture content.

In order to meet a stringent carbon budget, shared socioeconomic pathways (SSPs) aligned with the Paris Agreement typically require substantial land-use changes (LUC), such as large-scale forestation and bioenergy crop plantations. What if such a low-emission, intense-LUC scenario actually materialized? This paper quantifies the biophysical effects of LUC under SSP1-2.6 using an ensemble of regional climate simulations over Europe. We find that LUC projected over the 21st century, primarily broadleaf-tree forestation at the expense of grasslands, reduce summertime heat extremes significantly over large swaths of continental Europe. In fact, cooling from LUC trumps warming by greenhouse gas (GHG) emissions, resulting in milder heat extremes by 2100 for about half of the European population. Forestation brings heat relief by shifting the partition of turbulent energy fluxes away from sensible and towards latent heat fluxes. Impacts on the water cycle are then assessed. Forestation enhances precipitation recycling over continental Europe, but not enough to match the boost of evapotranspiration (green water flux). Run-off (blue water flux) is reduced as a consequence. Some regions experience severe drying in response. In other words, forestation turns blue water green, bringing heat relief but compromising water availability in some already-dry regions.

The response of flood risk in Bavaria, Germany to increases in rainfall extremes in a warming climate is modulated by land surface processes below a precipitation threshold, but not above, suggest ensemble simulations with a hydrological model.

Electronic and nonlinear optical properties of endohedral metallofullerenes are presented. The endohedral metallofullerenes contain transition metal encapsulated in inorganic fullerenes X12Y12 (X = B, Al & Y = N, P). The endohedral metallofullerenes (endo-TM@X12Y12) possess quite interesting geometric and electronic properties, which are the function of the nature of the atom and the size of fullerene. NBO charge and frontier molecular orbital analyses reveal that the transition metal encapsulated Al12N12 fullerenes (endo-TM@Al12N12) are true metalides when the transition metals are Ni, Cu and Zn. Endo-Cr@Al12N12 and endo-Co@Al12N12 are at the borderline between metalides and electrides with predominantly electride characteristics. The other members of the series are excess electron systems, which offer interesting electronic and nonlinear optical properties. The diversity of nature possessed by endo-TM@Al12N12 is not prevalent for other fullerenes. Endo-TM@Al12P12 are true metalides when the transition metals are (Cr-Zn). HOMO-LUMO gaps (EH-L) are reduced significantly for these endohedral metallofullerenes, with a maximum percent decrease in EH-L of up to 70%. Many complexes show odd–even oscillating behavior for EH-L and dipole moments. Odd electron species contain large dipole moments and small EH-L, whereas even electron systems have the opposite behavior. Despite the decrease in EH-L, these systems show high kinetic and thermodynamic stabilities. The encapsulation of transition metals is a highly exergonic process. These endo-TM@X12Y12 possess remarkable nonlinear optical response in which the first hyperpolarizability reaches up to 2.79 × 105 au for endo-V@Al12N12. This study helps in the comparative analysis of the potential nonlinear optical responses of electrides, metalides and other excess electron systems. In general, the potential nonlinear optical response of electrides is higher than metalides but lower than those of simple excess electron compounds. The higher non-linear optical response and interesting electronic characteristics of endo-TM@Al12N12 complexes may be promising contenders for potential NLO applications.

Natural forests provide a wide range of ecosystem services especially water-related benefits. However, the effects of afforestation in different situations may lead to contradictory results. In some cases, afforestation considered as a land use activity that threatens water resources security. So, it is necessary to evaluate the afforestation development in fragile ecosystems, such as arid and semi-arid regions. The main goal of this study is to quantify the hydrological responses of Latian Dam watershed to afforestation projects. To reach the goal, SWAT model were employed combined with meteorological data (daily), hydrological data (monthly) and spatial data to simulate the out flow of Latian Dam watershed, located in northeast of Tehran-Iran, under two land use scenarios (baseline & afforestation). The SWAT model showed good performance in the Latian Dam watershed with acceptable values for Nash-Sutcliffe, P-factor and R-factor which were 0.82, 0.82, 0.68, 0.98 respectively for calibration and 0.83, 0.84, 0.78, 0.78 respectively for validation at the outlet of watershed. Simulation results under proposed scenarios demonstrated that as afforestation expansion 8.5% of the study area, runoff declined by 2.7% in downstream. This research quantitatively showed that afforestation will reduce the runoff in Latian watershed. The result of this study suggested that it is necessary to fully assess the effects of afforestation development in the given area, especially on hydrologic responses of watershed.

In this study, we determined the enthalpies of vaporisation for a suitable set of molecular and ionic liquids using modern techniques for vapour pressure measurements, such as the quartz crystal microbalance, thermogravimetric analysis (TGA), and gas chromatographic methods. This enabled us to measure reasonable vapour pressures, avoiding the problem of the decomposition of the ionic liquids at high temperatures. The enthalpies of vaporisation could be further analysed by applying the well-known “group contribution” methods for molecular liquids and the “centerpiece” method for ionic liquids. This combined approach allowed for the dissection of the enthalpies of vaporisation into different types of molecular interaction, including hydrogen bonding and the dispersion interaction in the liquid phase, without knowing the existing species in both the liquid and gas phases.

Drought, caused by a prolonged deficit of precipitation, bears the risk of severe economic and ecological consequences for affected societies. The occurrence of this significant hydro-meteorological hazard is expected to strongly increase in many regions due to climate change, however, it is also subject to high internal climate variability. This calls for an assessment of climate trends and hot spots that considers the variations due to internal variability. In this study, the percent of normal index (PNI), an index that describes meteorological droughts by the deviation of a long-term reference mean, is analyzed in a single-model initial-condition large ensemble (SMILE) of the Canadian regional climate model version 5 (CRCM5) over Europe. A far future horizon under the Representative Concentration Pathway 8.5 is compared to the present-day climate and a pre-industrial reference, which is derived from pi-control runs of the CRCM5 representing a counterfactual world without anthropogenic climate change. Our analysis of the SMILE reveals a high internal variability of drought occurrence over Europe. Considering the high internal variability, our results show a clear overall increase in the duration, number and intensity of droughts toward the far future horizon. We furthermore find a strong seasonal divergence with a distinct increase in summer droughts and a decrease in winter droughts in most regions. Additionally, the percentage of summer droughts followed by wet winters is increasing in all regions except for the Iberian Peninsula. Because of particularly severe drying trends, the Alps, the Mediterranean, France and the Iberian Peninsula are suggested to be considered as drought hot spots. Due to the simplicity and intuitivity of the PNI, our results derived from this index are particularly appropriate for region-specific communication purposes and outreach.

The Northern Quebec landscape is typically covered by numerous lakes. The lowest emissivity of water bodies dominates the brightness temperature (Tb) data measured over this region. Thus, it is necessary to eliminate the effect of water bodies from Tb measurements. The primary objective of this study is to develop two approaches to correct the Soil Moisture Active Passive brightness temperature (SMAP L1C), collected between January and December of 2016, for the damping effect of water bodies within each 36 km by 36 km pixel. The first algorithm normalizes Tb with the intercept of its linear regression versus the water fraction of each pixel. A second algorithm used Tb regression with water fraction by vegetation classes for each scene. Surface soil temperature and moisture measured near Umiujaq were used to validate Tb correction. The proposed Tb corrections resolve the divergence observed with SMAP standard correction when the water fraction is higher than 20%. Corrected brightness temperature is then tested for mapping the soil freeze/thaw state using the Normalized Polarization Ratio. Agreements of up to 90% (ascending orbit) and 79% (descending orbit) were reached for the proposed approach versus 64% and 50% for the existing approach.

Multiple soil moisture (SM) products have been produced based on observations from microwave satellite sensors nowadays, allowing for the acquisition of global SM dynamics in a timely manner. Currently, only two blended microwave SM products, namely the Climate Change Initiative (CCI) from the European Space Agency and the Soil Moisture Operational Product System (SMOPS) from the National Oceanic and Atmospheric Administration, are available with either better temporal or better spatial coverage than those of other SM products derived from a single sensor. However, an assessment and especially a synchronous comparison of these two products are still lacking, making it difficult to determine a better alternative in actual applications. In the present study, a comprehensive assessment of the two blended products was conducted with reanalysis SM data from the European Centre for Medium-Range Weather Forecasts and in-situ measurements from the International Soil Moisture Network. The scaling strategy of cumulative distribution function matching was used to remove the systematic differences in spatial mismatch between the satellite pixels and ground in-situ observations. The results indicated that CCI reveals overall better accuracy than that of SMOPS with both in-situ measurements and reanalysis data under different climate patterns. Specifically, the overall root mean square error (RMSE) with the in-situ measurements were 0.042 m3/m3 and 0.046 m3/m3 for CCI and SMOPS, respectively. Further investigation also confirmed that SMOPS could be a potential alternative over the regions where CCI is not available, since SMOPS has better spatial coverage than CCI.

Ionic liquids (ILs) are recognized as an environmentally friendly alternative to replacing volatile molecular solvents. Knowledge of vaporization thermodynamics is crucial for practical applications. The vaporization thermodynamics of five ionic liquids containing a pyridinium cation and the [NTf2] anion were studied using a quartz crystal microbalance. Vapor pressure-temperature dependences were used to derive the enthalpies of vaporization of these ionic liquids. Vaporization enthalpies of the pyridinium-based ionic liquids available in the literature were collected and uniformly adjusted to the reference temperature T = 298.15 K. The consistent sets of evaluated vaporization enthalpies were used to develop the “centerpiece”-based group-additivity method for predicting enthalpies of vaporization of ionic compounds. The general transferability of the contributions to the enthalpy of vaporization from the molecular liquids to the ionic liquids was established. A small, but not negligible correction term was supposed to reconcile the estimated results with the experiment. The corrected “centerpiece” approach was recommended to predict the vaporization enthalpies of ILs.

In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Active and passive microwave remote sensing has shown a principal capacity to provide effective monitoring of landscape FT dynamics. The study presents a seasonal threshold approach, which examines the timeseries progression of remote sensing measurements relative to signatures acquired during seasonal frozen and thawed reference states. This is used to estimate the FT state from the Sentinel-1 database and applied and evaluated for the region of Eastern Nunavik (Québec, Canada). An optimization process of the threshold is included. In situ measurements from the meteorological station network were used for the validation process. Overall, acceptable estimation accuracy (>70%) was achieved in most tests; on the best-performing sites, an accuracy higher than 90% was reached. The performance of the seasonal threshold approach over the study region was further discussed with consideration of land cover, spatial heterogeneity, and soil depth. This work is dedicated to providing more accurate data to capture the spatiotemporal heterogeneity of freeze/thaw transitions and to improving our understanding of related processes in permafrost landscapes.

Hydrometeorological compound events cause severe economical, societal and environmental damage, but their investigation is difficult as they occur rarely and are multivariate. Here we use 50 high-resolution climate simulations from the single model initial condition large ensemble CRCM5-LE to examine two such compound event types in southern Norway: (1) Heavy rainfall on saturated soil during the summer months (June, July, August, September; SES) and (2) Concurrent heavy rainfall and snowmelt (rain-on-snow; ROS). We compare present-day conditions (1980–2009) with future conditions under a high-emission scenario (2070–2099) and investigate the impact of climate change on the frequency and spatial distribution of SES and ROS events. We find that the probability of occurrence of SES events during the summer increases by 38% until 2070–2099 over the whole study area. The areas with the highest occurrence probability extend from the west coast into the interior. In contrast, the frequency of ROS is projected to decrease by 48% on average, largely driven by decreases in snowfall. Moreover, the spatial pattern of ROS are projected to change, with the most frequently affected areas shifting from the west coast towards the inner country. Our study highlights the benefits of single model large ensemble simulations for the analysis of compound events.

We show that carboxyl-functionalized ionic liquids (ILs) form doubly hydrogen-bonded cationic dimers (c+=c+) despite the repulsive forces between ions of like charge and competing hydrogen bonds between cation and anion (c+–a−). This structural motif as known for formic acid, the archetype of double hydrogen bridges, is present in the solid state of the IL 1−(carboxymethyl)pyridinium bis(trifluoromethylsulfonyl)imide [HOOC−CH2−py][NTf2]. By means of quantum chemical calculations, we explored different hydrogen-bonded isomers of neutral (HOOC–(CH2)n–py+)2(NTf2−)2, single-charged (HOOC–(CH2)n–py+)2(NTf2−), and double-charged (HOOC– (CH2)n−py+)2 complexes for demonstrating the paradoxical case of “anti-electrostatic” hydrogen bonding (AEHB) between ions of like charge. For the pure doubly hydrogen-bonded cationic dimers (HOOC– (CH2)n−py+)2, we report robust kinetic stability for n = 1–4. At n = 5, hydrogen bonding and dispersion fully compensate for the repulsive Coulomb forces between the cations, allowing for the quantification of the two equivalent hydrogen bonds and dispersion interaction in the order of 58.5 and 11 kJmol−1, respectively. For n = 6–8, we calculated negative free energies for temperatures below 47, 80, and 114 K, respectively. Quantum cluster equilibrium (QCE) theory predicts the equilibria between cationic monomers and dimers by considering the intermolecular interaction between the species, leading to thermodynamic stability at even higher temperatures. We rationalize the H-bond characteristics of the cationic dimers by the natural bond orbital (NBO) approach, emphasizing the strong correlation between NBO-based and spectroscopic descriptors, such as NMR chemical shifts and vibrational frequencies.

Study region: The Mindel river catchment, gauge Offingen, Bavaria, Germany. Study focus: The study investigates the potential interference of climate change signals (CCS) in hydrological indicators due to the application of bias correction (BC) of regional climate models (RCM). A validated setup of the hydrological model WaSiM was used for runoff modeling. The CCS, gained by the application of three RCMs (CCLM, REMO-UBA, RACMO2) for a reference period (1971–2000) and a scenario period (2021–2050), are evaluated according to eight hydrological indicators derived from modeled runoff. Three different BC techniques (linear scaling, quantile mapping, local intensity scaling) are applied.New hydrological insights for the region: Runoff indicators are calculated for the investigated catchment using bias corrected RCM data. The quantile mapping approach proves superior to linear scaling and local intensity scaling and is recommended as the bias correction method of choice when assessing climate change impacts on catchment hydrology. Extreme flow indicators (high flows), however, are poorly represented by any bias corrected model results, as current approaches fail to properly capture extreme value statistics. The CCS of mean hydrological indicator values (e.g. mean flow) is well preserved by almost every BC technique. For extreme indicator values (e.g. high flows), the CCS shows distinct differences between the original RCM and BC data. Keywords: Bias correction, Regional climate model, Climate change signal, Hydrological modeling, Runoff indicators, Bavaria

Clean energy for all, as listed in the United Nation’s SDG7, is a key component for sustainable environmental development. Therefore, it is imperative to uncover the environmental implications of alternative energy technologies. SustainableGAS project simulates different process chains for the substitution of natural gas with renewable energies in the German gas market. The project follows an interdisciplinary approach, taking into account techno-social and environmental variabilities. However, this research highlights the project results from the environmental perspective. So far, a detailed assessment of the environmental costs of alternative gas technologies with a focus on the process of energy transition has remained rare. Although such data constitute key inputs for decision-making, this study helps to bridge a substantial knowledge gap. Competing land-use systems are examined to secure central ecosystem services. To fulfill this obligation, an Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) serves as the modelling tool. InVEST assesses ecosystem services (ES) that are or may be affected by alternative bioenergy technologies. Spatially explicit model results include the water provisioning from the Water Yield Model (WYM), soil erosion and sedimentation described by the Sediment Delivery Ratio (SDR), and nutrient fluxes (N) in response to changing land use are obtained through the Nutrient Delivery Ratio (NDR). The detailed model results are finally extrapolated, which provides a comprehensive image of the environmental impacts associated with bioenergy expansion in Germany from our combination of unique Renewable Gas Plants (RGPs). The final result shows that nutrient load will reduce in southern Germany by the year 2050 compared to the reference state, and biomass use reduced by 46% crops.

The range of sub-daily extreme precipitation due to internal variability is quantified within the single model initial-condition large ensemble featuring 50 members of the Canadian regional climate model, version 5 (CRCM5) under the high-emission scenario representative concentration pathway 8.5. Ten-year return levels of sub-daily precipitation are calculated for three future periods (2010–2039, 2040–2069, 2070–2099) and hourly to 24-hourly aggregations over a European domain. The return levels are found to increase by 4%–8% for every future 30 year period averaged for the study area, where short-duration rainfall intensities increase to a greater extent than longer-duration rainfall intensities. The ranges between the median of the 50 members and the 5th and 95th quantile amount to −15.6%–19.3%, −16.0%–20.1%, and −16.5%–20.9% for the near, mid and far future, respectively. It is also shown that the scaling of the precipitation increase with temperature (Clausius–Clapeyron scaling) exhibits substantial variations between the 50 CRCM5 members at regional aggregations. These findings illustrate the large impact of internal variability on the uncertainty of extreme precipitation return level estimates. Here, regions of significant changes are identified, where future median extreme precipitation exceeds the 95th quantile of the reference period (1980–2009). These regions are located in northern Europe, central Europe and the eastern part of the Mediterranean.

We explore quantum chemical calculations for studying clusters of hydroxyl-functionalized cations kinetically stabilized by hydrogen bonding despite strongly repulsive electrostatic forces. In a comprehensive study, we calculate clusters of ammonium, piperidinium, pyrrolidinium, imidazolium, pyridinium, and imidazolium cations, which are prominent constituents of ionic liquids. All cations are decorated with hydroxy-alkyl chains allowing H-bond formation between ions of like charge. The cluster topologies comprise linear and cyclic clusters up to the size of hexamers. The ring structures exhibit cooperative hydrogen bonds opposing the repulsive Coulomb forces and leading to kinetic stability of the clusters. We discuss the importance of hydrogen bonding and dispersion forces for the stability of the differently sized clusters. We find the largest clusters when hydrogen bonding is maximized in cyclic topologies and dispersion interaction is properly taken into account. The kinetic stability of the clusters with short-chained cations is studied for the different types of cations ranging from hard to polarizable or exhibiting additional functional groups such as the acidic C(2)-H position in the imidazolium-based cation. Increasing the alkyl chain length, the cation effect diminishes and the kinetic stability is exclusively governed by the alkyl chain tether increasing the distance between the positively charged rings of the cations. With adding the counterion tetrafluoroborate (BF4−) to the cationic clusters, the binding energies immediately switch from strongly positive to strongly negative. In the neutral clusters, the OH functional groups of the cations can interact either with other cations or with the anions. The hexamer cluster with the cyclic H-bond motive and “released” anions is almost as stable as the hexamer built by H-bonded ion pairs exclusively, which is in accord with recent IR spectra of similar ionic liquids detecting both types of hydrogen bonding. For the cationic and neutral clusters, we discuss geometric and spectroscopic properties as sensitive probes of opposite- and like-charge interaction. Finally, we show that NMR proton chemical shifts and deuteron quadrupole coupling constants can be related to each other, allowing to predict properties which are not easily accessible by experiment.

This study introduces a holistic approach for the hydrological modelling of peak flows for the major Bavarian river basins, referred to as Hydrological Bavaria. This approach, intended to develop a robust modelling framework to support water resources management under climate change conditions, comprises a regionalized parameterization of the water balance simulation model (WaSiM) for 98 catchments in high temporal (3 h) and spatial (500 m) resolution using spatially coherent information and an automatized calibration (dynamically dimensioned search–simulated annealing, DDS-SA) for storage components. The performance of the model was examined using common metrics (Nash & Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE)). The simulations provided the means for the calculation of a level of trust (LOT) by comparing observed and simulated high flows with a five, ten, and 20-year return period. These estimates were derived by the Generalized Pareto Distribution (GPD) applying the peak over threshold (POT) sampling method. Results show that the model overall performs well with regard to the selected objective measures, but also exhibits regional disparities mainly due to the availability of meteorological inputs or water management data. For most catchments, the LOT shows moderate to high confidence in the estimation of return periods with the hydrological model. Therefore, we consider the holistic modelling approach applicable for climate change impact studies concerned with dynamic alterations in peak flows.

This study assesses the change of the seasonal runoff characteristics in 98 catchments in central Europe between the reference period of 1981–2010, and in the near future (2011–2040), mid future (2041–2070) and far future (2071–2099). Therefore, a large ensemble of 50 hydrological simulations featuring the model WaSiM-ETH driven by a 50-member ensemble of the Canadian Regional Climate Model, version 5 (CRCM5) under the emission scenario Representative Concentration Pathway (RCP 8.5) is analyzed. A hierarchical cluster analysis is applied to group the runoff characteristics into six flow regime classes. In the study area, (glacio-)nival, nival (transition), nivo-pluvial and three different pluvial classes are identified. We find that the characteristics of all six regime groups are severely affected by climate change in terms of the amplitude and timing of the monthly peaks and sinks. According to our simulations, the monthly peak of nival regimes will occur earlier in the season and the relative importance of rainfall increases towards the future. Pluvial regimes will become less balanced with higher normalized monthly discharge during January to March and a strong decrease during May to October. In comparison to the reference period, 8% of catchments will shift to another regime class until 2011–2040, whereas until 2041–2070 and 2071–2099, 23% and 43% will shift to another class, respectively.

New thiosemicarbazide-linked acridines 3a–c were prepared and investigated as chemosensors for the detection of biologically and environmentally important anions. The compounds 3a–c were found selective for fluoride (F−) with no affinity for other anions, i.e. −OAc, Br−, I−, HSO4−, SO42−, PO43−, ClO3−, ClO4−, CN− and SCN−. Further, upon the gradual addition of a fluoride anion (F−) source (tetrabutylammonium fluoride), a well-defined change in colour of the solution of probes 3a–c was observed. The anion-sensing process was studied in detail via UV–visible absorption, fluorescence and 1H-NMR experiments. Moreover, during the synthesis of acridine probes 3a–c nickel fluoride (NiF2), a rarely explored transition metal fluoride salt, was used as the catalyst. Theoretical studies via density functional theory were also carried out to further investigate the sensing and anion (F−) selectivity pattern of these probes.

The monitoring of soil freezing and thawing states over large areas is very challenging on ground. In order to investigate the potential and the limitations of space-borne SAR polarimetry at C-band for soil state survey, analyses were conducted on an entire winter time series of fully polarimetric RADARSAT-2 data from 2011/2012 to identify freezing as well as thawing states within the soil. The polarimetric data were acquired over the Sodankylä test site in Finland together with in situ measurements of the soil and the snow cover. The analyses indicate clearly that the dynamics of the polarimetric entropy and mean scattering alpha angle are directly correlated to soil freezing and thawing states, even under distinct dry snow cover. First modeling attempts using the Extended Bragg soil scattering model justify the observed trends, which indicate surface-like scattering during frozen soil conditions and multiple/volume scattering for thawed soils. Hence, these first investigations at C-band foster motivation to work towards a robust polarimetric detection of soil freezing and thawing states as well as their transition phase.

We developed a holistic intelligent decision-support system (IDSS) to provide decision-support for all steps in planning, managing and optimizing water quality monitoring programs (WQMPs). The IDSS is connected to a previously developed database, EnkiTM. The IDSS integrates tacit and explicit knowledge on WQMPs to standardize decision making and to make decisions transparent and transferable. The optimization features of the IDSS were tested on a lake and a river WQMP from two case studies in Canada. We illustrate how the IDSS provides decision support to understanding the underlying rationale of the existing WQMPs, validating and storing data, selecting optimization procedures proposed in the literature, applying the optimization procedures and finalizing the optimization procedure. We demonstrated that the IDSS/EnkiTM is necessary to take and document decisions during all phases of a WQMP to obtain a clear idea of when and why changes are made and determine actionable tasks in the optimization process.

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9−53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

Groundwater and surface water are strongly connected. Therefore, understanding their interactions is important when studying the water balance of a complex aquatic system. This paper aims to present an integrated approach to study such processes, including a better understanding of the hydrological system behavior in the Ljubljansko polje (Slovenia). The study is based on multivariate statistical analyses of data collected over a long period, including the isotopic composition of groundwater, river water, and precipitation. The hydrology in the study domain was also simulated using a comprehensive modelling framework. Since boundary conditions are essential for simulating groundwater flow in a sensitive aquifer, a modelling system of rivers and channels (MIKE 11) and water flow and balance simulation model (WaSiM) were used to model river dynamics and the percolation of local precipitation, respectively. The results were then used as boundary conditions imposed on a transient state groundwater flow model performed in finite element subsurface flow simulation system (FEFLOW 6.2). Both the locations of recharge areas in the study domain and the calculated fluxes between the Sava River and the aquifer are graphically presented. The study revealed that a combination of the MIKE 11-FEFLOW-WaSiM tools offers a good solution for performing parallel simulations of groundwater and surface water dynamics.

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.

Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6–22 m2 and decomposed them into a dual scale process by using geostatistical techniques. For the characterization of soil surface roughness, we calculated two different roughness indices (the RMS height s and the autocorrelation length l) differing significantly for each scale. While we could relate the small scale roughness pattern clearly to the seedbed rows, the larger second scale pattern could be related to the appearance of wheel tracks of the tillage machine used. As a result, major progress was made in the understanding of the different scales in soil surface roughness characterization and its quantification possibilities.

The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

Accurate spatial and temporal representation of precipitation is of utmost importance for hydrological applications. Uncertainties in available data sets increase with spatial resolution due to small-scale processes over complex terrain. As previous studies revealed high regional differences in the performance of gridded precipitation data sets, it is important to assess the related uncertainties at the catchment scale, where these data sets are typically applied, e.g., for hydrological modeling. In this study, the uncertainty of eight gridded precipitation data sets from various sources is investigated over an alpine catchment. A high resolution reference data set is constructed from station data and applied to quantify the contribution of spatial resolution to the overall uncertainty. While the results demonstrate that the data sets reasonably capture inter-annual variability, they show large seasonal differences. These increase for daily indicators assessing dry and wet spells as well as heavy precipitation. Although the higher resolution data sets, independent of their source, show a better agreement, the coarser data sets showed great potential especially in the representation of the overall climatology. To bridge the gaps in data scarce areas and to overcome the issues with observational data sets (e.g., undercatch and station density) it is important to include a variety of data sets and select an ensemble for a robust representation of catchment precipitation. However, the study highlights the importance of a thorough assessment and a careful selection of the data sets, which should be tailored to the desired application.