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In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km2) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018.

Two micrometeorological methods that utilize high frequency sampling of air temperature were tested against eddy covariance (EC) sensible heat flux (H) measurements at three sites representing agricultural, agro-forestry and forestry systems. The two methods encompass conventional and newly proposed forms of the flux-variance (FV) and surface renewal (SR) schemes. In terms of measurement setup, the sites represent surface, roughness and roughness to surface transitional layers, respectively. After the selection of the most reliable approaches, regression analyses against EC showed that both methods can estimate H with slopes within ±10 % from unity, and coefficient of determination R2 >0.9 across all three sites. The best performance, of both FV and SR, was at the agricultural field, where the measurements were within the surface layer. The worst performance occurred in the tall, relatively heterogeneous forest, where the measurements were taken in the roughness sublayer, the depth of which (with its inherent uncertainty) needs to be taken into account in the calculations. In addition to the evaluation of the FV and SR forms, an alternative perspective relating ramp-like structures to the vertical temperature gradients in the surface boundary layer is introduced here. Ramp-like structures carry much of the heat flux and temperature variance, representing opportunities to constrain the coefficients of the two methods. As a corollary, we introduce a novel approach emerging from bridging FV and SR methods that combines information about the coherent structures with the overall variance to obtain heat fluxes in a turbulent atmosphere. The proposed approach yields reliable H estimates without the need for site-specific calibration and instrumentation other than a single fast thermocouple. Acknowledgement: This study was conducted with support of SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) and USDA NIFA-AFRI Sustainable Bioenergy Program, 2011-67009-20089, Loblolly pine-switch grass intercropping for sustainable timber and biofuels production in the Southeastern United States. Funding for AmeriFlux core site US-NC4 (natural forested wetland) was provided by the USDA NIFA (Multi-agency A.5 Carbon Cycle Science Program) award 2014-67003-22068. Additional funding was provided by the DOE NICCR award 08-SC-NICCR-1072, the USDA Forest Service award 13-JV-11330110-081, and the DOE LBNL award DE-AC02-05CH11231.

The Svratka river basin represents an important water resource in the South Moravian Region of the Czech Republic. Due to its relatively low aridity index (the ratio of precipitation to potential evapotranspiration), it belongs to river basins sensitive to climate change. This is also supported by significant negative runoff trends over the past 40 years. The aim of this study is to evaluate the impacts of a hypothetical land use change on the hydrological processes of the Svratka river basin. We used a physically based and spatially distributed hydrological model Mike SHE. The Mike SHE model was calibrated and validated using measured river discharge data in the three hydrological profiles in the Svratka basin for the period 1981–2020. Several land use scenarios were tested against the reference scenario (i.e. current land use) to analyze the impacts of land use change. The land use scenarios encompassed the following hypothetical extreme changes where the entire basin in the model was changed to (i) grassland, (ii) mixed deciduous-coniferous forest, (iii) deciduous forest, or (iv) cropland. These extreme land use scenarios were tested for the baseline period 1981–2020 and also for several CMIP6 downscaled climate models and different socioeconomic pathways (emissions scenarios) up to the end of the 21st century. The results showed that the evapotranspiration was the highest for mixed deciduous-coniferous forest while the lowest was the grassland scenario and cropland . The lowest total runoff from the river basin was simulated for both forest scenarios (mixed forest and deciduous forest). The results also demonstrated that the dominant loss component for all scenarios of the water balance in the Svratka river basin is evapotranspiration. The sensitivity of the hydrological balance in the Czech landscape was also demonstrated, where a slight increase in the evapotranspiration value in the basin has a significant effect on the total runoff from the Svratka basin. The climate change scenarios additionally suggest further exacerbating the water balance and runoff decline in the region. The results of this study are a first step towards evaluating and designing nature-based adaptation measures to climate change in the Svratka river basin. Acknowledgment: The research infrastructure and CzechGlobe team was financially supported by the SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). The study was also supported by the Internal Grant Agency of the AgriSciences faculty at Mendel University in Brno (AF-IGA2023-IP-031).

Soil moisture is a key factor for plant growth and agricultural production. Therefore, it has become a fundamental part of agricultural drought monitoring systems developed for various spatial scales ranging from local to regional and global. Despite the development of large-scale soil moisture monitoring systems, in-situ soil moisture observations still remain inadequate for precise soil moisture monitoring, especially in remote areas, where there is a limited number of monitoring stations. Together with remote sensing technologies soil moisture modeling may provide an alternative to in-situ measurements that delivers spatially continuous estimates over large geographic areas. SoilClim is a semi-empirical water balance model that, together with other outputs (e.g., reference and actual evapotranspiration, soil temperature), estimates daily soil moisture in various depths of soil profile. The model has previously been validated on a total of 20 sites (5 in Central Europe and 15 in central USA) and is now used for global monitoring and prediction of soil moisture and drought intensity in an operational and interactive web platform (www.windy.com). Our study evaluates the SoilClim soil moisture global measurements with 0.1° spatial resolution using two independent sources of information: i) in-situ soil moisture measurements from the International Soil Moisture Network, and ii) the soil moisture derived from the Metop ASCAT sensors on Metop-A and Metop-B satellites. In the conference presentation we will introduce the SoilClim model and present results of the global validation including statistical spatial analysis and triple collocation.Acknowledgement: This study was conducted with support of SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).

The increasing frequency and intensity of severe droughts over recent decades have significantly impacted crop production in the Pannonian Basin in southeastern Europe. Related crop yield losses can be substantial and require logistic compensation on an international level. To plan such compensations, seasonal crop yield forecasts have proven to be a valuable tool to support decision-makers in taking timely action. However, the impact of severe droughts on crop yields is often underestimated by such forecasts. To address this issue, we developed a maize and wheat yield forecasting system based on extreme-gradient-boosting machine learning for 42 regions in the Pannonian Basin. The used predictors describe vegetation state, weather, and soil moisture conditions derived from Earth observation, reanalysis, in-situ data, and seasonal weather forecasts. The wide range of predictors was selected to represent the state of the crops and the conditions they are facing and are expected to face. We expected it to be crucial, especially during severe drought years, to provide the model with sufficient information about the drought and its impacts. Afterwards, the model was validated, with a focus on drought years. Our results show that crop yield anomaly estimates in the two months preceding harvest have better performance than earlier in the year (relative root mean square errors below 17%) in all years. The models have their clear strength in forecasting interannual variabilities but struggle to forecast differences between regions within individual years. This is related to spatial autocorrelations and a lower spatial than temporal variability of crop yields. In years of severe droughts, there is a clear improvement in the forecasts with a 2-month lead time over longer forecasts too. The crop yield losses remain underestimated, but the wheat model performs in drought years better than for average years with errors below 12%. The errors of the maize forecasts in drought years are larger than for non-drought years: 30% two months ahead and 20% one month ahead. The feature importance analysis shows that in general wheat yield anomalies are controlled by temperature and maize by water availability during the last two months before harvest. In severe drought years, soil moisture is the most important predictor for the maize model and the seasonal temperature forecast becomes key for wheat forecasts two months before harvest. Going forward, a finer spatial resolution of the predictors will be tested to better distinguish the yields between the different regions. In addition, longer time-series of crop yield data, including more data during severe drought years, will help to test the findings of this study.

The Thaya is a trans-national river basin that is situated in the Czech Republic and Austria. Different human activities in the basin and multiple water uses increase the water demand. This increase, combined with the recent droughts events in 2017 and 2018, has recently resulted in reconsidering the water management strategies for future climates. This contribution aims to evaluate the effect of climate change on the water balance of the Thaya. The aim is to apply two different hydrological models in an identical setting (the same model inputs, scenarios, and regional and water use data) and to identify water availability and its change under various climate and water use scenarios. The assessments compare BILAN and TUWmodel hydrological models coupled with the WATERRES water use module and a large sample of climate projections (the CMIP5 and CMIP6 models), which represent various socioeconomic pathways combined with projections of possible changes in water use. The results will demonstrate an insight into how the water balance in different parts of the Thaya basin has changed in the past and what are the possible effects of climate change on these water resources in the future.

Systematic and regular monitoring phenology over a larger area is often based on cooperation with volunteers or workers who monitor the onset of phenophases and are thus part of phenological societies or national phenological networks. As part of our work and teaching at Mendel University in Brno (Bioclimatology courses) we have been cooperating with students since 2015 till now and one of the parts of their required semester work is the observation of the phenological development of selected species. At the beginning of the semester, students are thoroughly familiarized with the observation methodology and choose the plant species they will observe at regular visits during the semester (field crops, trees, shrubs or vineyards). Each student observes at least two trees or shrubs and one field crop and needs to visit the localities every three days (or more frequently if possible) and record the phenological development with cameras (using personal smartphones). During the semester or at the end of the semester, students upload pictures and terms of phenophases through the website www.fenofaze.cz. On this website, students' results and observations are displayed in real-time and are open to the general public. Student observations are strictly controlled and unusable data are taken out. We cooperate with c. 200 students each semester (i. e. 600 observations) and the first outputs showed that 50% of all observations are usable. Terms of phenophases are observed at the cadastre level and are aggregated to the district level to evaluate the long-term changes. In most cases, students approach vegetation phenological monitoring responsibly and with enthusiasm, and we consider it a reliable way of recording phenological changes and dates.Acknowledgment: This research was funded by the Ministry of Education, Youth and Sports of the Czech Republic the project SustES—Adaptation strategies for the sustainability of ecosystem services and food security in adverse natural conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).

This study summarizes seasonal monitoring of turbulent energy fluxes from eddy covariance (EC) and large aperture optical scintillometer, measuring in parallel. The site (agricultural field of app. 16.5 ha) is located in north-eastern Austria, Danube river lowland (48.21N, 16.622E); in 2019 covered subsequently by a winter wheat field, straw, and bare soil. The EC together with ancillary measurement was located at the 2.7 m height at the center of the field. The radiation balance components measurements consist of a 4-channel net radiometer for net radiation (Rn) installed at 3.5 m and three soil heat flux plates for soil heat flux (G) monitoring (0.05 m below surface), including thermocouples for quantification of the heat storage above the soil heat flux plates. The scintillometer transmitter and receiver units were fixed at 4 m height masts, facing towards each other from the NW and SE corners of the field, with a measurement path length of 570 m diagonally across the field. The EC method enables the determination of fluxes within a footprint centered around the point of measurement in the middle of the field, whereas the scintillometer provides an estimation of sensible heat flux (HSC), derived from air refractive index fluctuation integrated over the measurement path length. The scintillometer-based latent heat (LESC) is calculated as a residuum from available energy (Rn-G) and HSC, provided by the scintillometer. As the EC method provides direct measurements of sensible heat (HEC) and latent heat (LEEC) fluxes we use it as a reference method. During the period March to June (green canopy) the comparison of the EC-based turbulent fluxes (HEC+LEEC) and the available energy (Rn-G) showed a very good agreement, resulting in the energy balance closure of 0.97 (R2 = 0.94). This suggests the ability of the EC method to capture all scales of eddies responsible for energy transport at this site as well as the good accuracy and robustness of the measurement setup. During the period March to June (green canopy), the HEC, LEEC, HSC, and G fluxes accounted for 23 % (R2 = 0.55), 60 % (R2 = 0.72), 32 % (R2 = 0.56), and 12 % (R2 = 0.62) of the Rn flux, respectively. The comparison of methods indicates that HSC overestimated HEC by 20 % (R2 = 0.78). However, during the latter part of the season (straw and bare soil) we found that under highly unstable atmospheric stratification, HSC is even more overestimating HEC and sometimes is even exceeding the available energy. The main reason for such behavior can be the choice of the universal stability function in the computation of HSC. Recommendations for universal functions that are correcting this artifact in HSC will be discussed.Acknowledgment: This study was supported by SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).

This study aims at understanding the changes in the water balance in the Thaya river basin over the past 40 years. The Thaya River is one of the tributaries to the Danube basin with a catchment area of more than 13 000 km2. A number of hydroclimatic variables related to runoff were examined by a trend analysis based on Theil-Sen regression and Mann-Kendall tests for the two periods 1981–2020 and 2001–2020. The latter period was selected because it allows analysis of several relevant variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). These variables ecompass snow cover, leaf area index and land surface temperature based actual evapotranspiration. With our analyses we confirm previously found increasing trends in air temperature, ETo, and no trends in precipitation. We also found a consistent increase of ET during spring months and indication of summer decrease (not statistically significant). This change was associated with a significant increase of spring vegetation development followed by summer stagnation. We identified a significant trend decline in runoff, mainly in the upland sourcing areas. The correlation analysis reveals a different behavior along the elevation gradient, with evapotranspiration in the uplands being limited by energy and in the lowlands by water, especially in spring. During summer, however, the entire basin is often water-limited, with a more pronounced limitation in the lowlands. According to attribution analysis for the past 20 years, the significantly decreasing runoff is driven primarily by non-significantly decreasing precipitation, significantly increasing air temperature and vapor pressure deficit. Global radiation and wind speed affect the runoff only to a very limited extent. We conclude that complex adaptation measures reflecting the site specificity and elevation gradient are needed to sustain the water dependent sectors operating in the region facing increasing aridity.

We compare Earth's surface energy balance components (monthly mean radiation and energy fluxes) from the ERA5-Land reanalysis with their measured equivalents from FLUXNET stations in the period 2001-2018. The comparison (FLUXNET station vs. the nearest grid point of ERA5-Land) is focused on Central European sites with an elevation under 1000 m above sea level during the summer half of the year (April - September) that roughly corresponds to the growing season in Central Europe. FLUXNET stations are further divided into four groups according to biomes (and surface types) that they represent: grass, crop, forest or wetland. We put special emphasis on the turbulent heat fluxes: sensible (H) and latent heat (LE) heat flux and their relative share in a form of evaporative fraction (EF, a ratio of latent heat flux, LE, to the sum of both turbulent fluxes, H + LE). There's a perfect fit between monthly FLUXNET observations and ERA5-Land data in case of some radiation fluxes: incoming shortwave (SWin) or outgoing longwave (LWout) radiation. Incoming longwave radiation (LWin) is underestimated by ERA5-Land, while the score of reflected shortwave radiation (SWout) is mixed. Latent heat flux (LE) is overestimated by the reanalysis, while sensible heat flux (H) is lower in the reanalysis than in FLUXNET observations. This leads to overestimation of the evaporative fraction (EF) by ERA5-Land in the growing season, with monthly values of EF being mostly between 0.7 - 0.9. On the other hand, FLUXNET's EF values range from 0.2 to 1.0. When focused on individual biomes, the best fit between ERA5-Land and FLUXNET is seen for grass. The shape of "half-annual" (Apr - Sep) cycle is also well captured for grass locations. In other major biomes (cropland or forest) we see not only disagreement in terms of absolute values of H and LE fluxes (or EF) but in the shape of "half-annual" cycle too. There's no significant decline of EF in summer for cropland locations in ERA5-Land. The magnitude of a steady EF increase in forest sites of FLUXNET is weaker in ERA5-Land.Acknowledgement: "SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797)”.

Thermal infrared (TIR) and visible/near-infrared (VNIR) surface reflectance imagery from remote sensing can be effectively combined in surface energy balance models to map evapotranspiration (ET) and vegetation stress, with broad applications in agriculture, forestry, and water resource management. Particularly valuable are ET retrievals at medium resolution (100 m or finer), resolving scales at which water and land are actively managed over much of the Earth’s surface. At this scale, TIR and VNIR data in the Landsat archive provide a 40-year and growing global record of coupled land and water use change. In this presentation we will discuss the unique information content conveyed by the land-surface temperature signal regarding the surface moisture status and vegetation health. We will explore applications for field-scale temperature and ET retrievals in promoting sustainable water use, forest health, and regenerative agricultural practices. Widespread and routine generation of ET data at this scale has been enabled by cloud computing technologies, with the OpenET ensemble modeling platform as an example of collaborative geospatial information development. Looking forward, integration of Landsat with new sources of medium-resolution TIR imagery (e.g., ECOSTRESS, LSTM, TRISHNA, SBG, Landsat-Next, and Hydrosat), as well as all-sky microwave-based temperature retrievals, will improve our ability to detect rapid changes in water use and availability – a key factor in real-time decision making.

The strong effects of climate change are expected to negatively impact the long-term resilience and function of forest ecosystems, which could lead to changes in forest carbon balance and productivity. However, these forest responses may vary with local conditions and forest types. Accordingly, this study was carried out to determine gross primary productivity (GPP) sensitivity to changes in environmental parameters. Central European beech (at Štítná) and spruce species (at Bílý Kr̆íz̆ and Rájec), growing under contrasting climatic conditions, were studied. The comparative analyses of GPP were based on a five-year-long dataset of eddy covariance fluxes during the main growing season (2012–2016). Results of forest GPP responses with changes in environmental factors from a traditional Stepwise multiple linear regression model (SMLR) were used and compared with Random forest (RF) analyses. To demonstrate how actual GPP trends compare to potential GPP (GPPpot) courses expected under near-optimal environmental conditions, we computed normalized GPP (GPPnorm) with values between 0 and 1 as the ratio of the estimated daily sum of GPP to GPPpot. The study confirmed the well-known effect of total intensity of the photosynthetically active radiation and its diffuse fraction on GPPnorm across all the forest types. However, the study also showed the secondary effects of other environmental variables on forest productivity depending on the species and local climatic conditions. The reduction in forest productivity at the beech forest in Štítná was presumed to be mainly induced by edaphic drought (anisohydric behaviour). In contrast, reduced forest productivity at the spruce forest sites was presumably induced by both meteorological and hydrological drought events, especially at the moderately dry climate in Rájec. Overall, our analyses call for more studies on forest productivity across different forest types and contrasting climatic conditions, as this productivity is strongly dependent on species type and site-specific environmental conditions.

The occurrence of extreme drought poses a severe threat to forest ecosystems and reduces their capability to sequester carbon dioxide. This study analysed the impacts of a central European summer drought in 2015 on gross primary productivity (GPP) at two Norway spruce forest sites representing two contrasting climatic conditions—cold and humid climate at Bílý Kříž (CZ-BK1) vs. moderately warm and dry climate at Rájec (CZ-RAJ). The comparative analyses of GPP was based on a three-year eddy covariance dataset, where 2014 and 2016 represented years with normal conditions, while 2015 was characterized by dry conditions. A significant decline in the forest GPP was found during the dry year of 2015, reaching 14% and 6% at CZ-BK1 and CZ-RAJ, respectively. The reduction in GPP coincided with high ecosystem respiration (Reco) during the dry year period, especially during July and August, when several heat waves hit the region. Additional analyses of GPP decline during the dry year period suggested that a vapour pressure deficit played a more important role than the soil volumetric water content at both investigated sites, highlighting the often neglected importance of considering the species hydraulic strategy (isohydric vs. anisohydric) in drought impact assessments. The study indicates the high vulnerability of the Norway spruce forest to drought stress, especially at sites with precipitation equal or smaller than the atmospheric evaporative demand. Since central Europe is currently experiencing large-scale dieback of Norway spruce forests in lowlands and uplands (such as for CZ-RAJ conditions), the findings of this study may help to quantitatively assess the fate of these widespread cultures under future climate projections, and may help to delimitate the areas of their sustainable production.

In 1994, a network of small catchments (GEOMON) was established in the Czech Republic to determine input–output element fluxes in semi‐natural forest ecosystems recovering from anthropogenic acidification. The network consists from 16 catchments and the primary observations of elements fluxes were complemented by monitoring of biomass stock, element pools in soil and vegetation, and the main water balance components. Over last three decades, reductions of SO₂, NOₓ and NH₃ emissions were followed by sulphur (S) and nitrogen (N) deposition reductions of 75% and 30%, respectively. Steeper declines of strong acid anion concentrations compared to cations (Ca, Mg, Na, K, NH₄) in precipitation resulted in precipitation pH increase from 4.5 to 5.2 in bulk precipitation and from 4.0 to 5.1 in spruce throughfall. Stream chemistry responded to changes in deposition: S leaching declined. However at majority of catchments soils acted as a net source of S to runoff, delaying recovery. Stream pH increased at acidic streams (pH

Our understanding of the carbon and water cycle was greatly improved through application of eddy covariance measurements in recent decades. Though powerful, this micrometeorological approach relies on a number of assumptions that can be affected by a selection of station location. Most importantly, terrain of the target area should be flat, target area should be homogeneous and adequate air mixing should be achieved. Although possible shortcomings can be reduced by careful site inspection before tower installation (flat terrain) or can be corrected for during data post-processing (filtering of periods with low mixing), preliminary assessment of target area homogeneity is difficult as well as correction of its impacts afterwards. The influence of such inhomogeneities can lead to a bias in the flux annual sums but also a bias in their relationships with environmental variables. Certain solutions were already proposed, but target area homogeneity was so far assessed only at a few selected sites. Here we aim to provide a suit of software tools that build on the existing software packages (REddyProc, Flux Footprint Prediction, openair, openeddy) and allow easy diagnosis of the situation at the given ecosystem station. We plan to provide directional analyses of variables of interest. This will allow to identify the wind sectors that show large deviations from the mean value of the whole target area. In a further step, we plan to combine footprint modeling with CO2 and energy flux measurements and thus provide attribution of mean (weighted) fluxes to their source area. Based on the differences with the directional analyses we will assess whether the higher computational expenses of footprint modeling are justified and bring additional information. Finally, we plan to separate the target area to a limited amount of wind sectors and attempt separate gap-filling and flux partitioning for areas identified by preceding homogeneity evaluation. The limitations and feasibility of this approach will be assessed.This work was supported by the Ministry of Education, Youth and Sports of CR within Mobility CzechGlobe2 (CZ.02.2.69/0.0/0.0/18_053/0016924).

Eddy covariance (EC) has become the standard method for determining energy fluxes at the soil-plant-atmosphere interface. However, the cost and complexity of EC often limit its widespread deployment, and therefore, alternatives need to be considered. Here, two alternative methods, flux-variance (FV) and surface renewal (SR), are evaluated in quantifying sensible heat flux at three sites representing agricultural (wheat field, straw and bare soil), agroforestry (pine-switchgrass intercroping) and natural forested wetland (mixed conifer-deciduous wetland forest) systems that span a broad range of canopy height and structural complexity. By considering the position of the sensors with respect to canopy, the measurements at these three sites were carried out in the atmospheric surface layer, roughness layer, and roughness to surface transitional layer, respectively. Since the introduction of FV and SR, several versions of these methods have been proposed, with significantly differing perspectives and assumptions. Until now, the differences between the methods have not been fully standardized or clarified. In principle, both methods require the monitoring of high frequency (e.g. 10 Hz) air temperature variation while some approaches require additional wind velocity measurements. This presentation provides an overview of the FV and SR approaches, including new perspectives as well as identifies the common framework of the methods rather than carrying out their mere comparison. We show that the frequently reported need for the calibration (e.g. against EC) cannot be fully overcome. However, it can be put in a more universal framework where the parameters of both methods requiring calibration are represented by joint physically based parameters such as surface aerodynamic properties rather than similarity constants in the case of FV or the mean volume over the area of the air parcels in the case of SR. After the selection of the most reliable approaches, regression analyses against EC shows that both methods can estimate sensible heat flux with slopes within ±10 % from unity and R2 >0.9 across all the three sites. The best performance of both FV and SR was at the agricultural field, where the measurements are well in the surface layer while the worst in the case of the tall forest where the measurements are still in the roughness sublayer and the roughness layer depth (with its inherent uncertainty) needs to be taken into account in the calculations. We conclude there may be opportunities to fill gaps in knowledge of ecosystem energy balance at substantial cost-savings in specialized circumstances where EC may not be appropriate using both FV and SR methods.Acknowledgement: This study was conducted with support of SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) and USDA NIFA-AFRI Sustainable Bioenergy Program, 2011-67009-20089, Loblolly pine-switch grass intercropping for sustainable timber and biofuels production in the Southeastern United States. Funding for AmeriFlux core site US-NC4 (natural forested wetland) was provided by the U.S. Department of Energy’s Office of Science.

The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).

In this study we compare turbulent energy fluxes obtained from eddy covariance (EC) (LI-7500A, LI-COR + Windmaster, Gill Instruments) and large aperture scintillometer (BLS900, Scintec) over an agricultural field (wheat field, straw and bare soil). As the EC method provides direct measurements of sensible heat (HEC) and latent heat (LEEC) fluxes we use it as a reference method. The EC method enables to determine fluxes within a footprint centered around the point of measurement in the middle of the field. The scintillometer provides an estimation of sensible heat flux (HSC), derived from air refractive index fluctuation integrated over the measurement path length, in this case 570 m diagonally across whole field. The reference measurements of the radiation balance components consist of 4-component net radiometer for net radiation (Rn) (NR01, Hukseflux), three soil heat flux plates for soil heat flux (G) monitoring (HFP01, Hukseflux), including thermocouples for quantification of the heat storage above the soil heat flux plates. The scintillometer-based latent heat (LESC) is calculated as a residuum from available energy (Rn-G) and HSC, provided by scintillometer. The measurement of radiation balance components was located at the top of 3.5 m mast with the EC system, while the soil heat flux plates were collocated around in 5 cm depth. The site is a flat, rectangular agricultural field (app. 16.5 ha), in the north-eastern Austria, Danube river lowland (48.21N, 16.622E), sown with winter wheat during growing season 2019. The measurement campaign was established in February 2019 with aim for multi-seasonal monitoring. The EC measurement height is 2.7 m, the scintillometer transmitter and receiver are fixed on 4 m masts, facing towards each other from NW and SE corners of the field.Comparison of the EC-based turbulent fluxes (HEC+LEEC) and the available energy (Rn-G) during the period March to Mid-June showed a very good agreement, resulting in the energy balance closure of 0.96 (R2 = 0.93). This suggest high accuracy and robustness of the measurement setup together with the ability of the EC method to capture all scales of eddies responsible for energy transport at this site. The comparison of methods indicates that HSC overestimated HEC by 10 % (R2 = 0.74) and LESC underestimated LEEC by 13 % (R2 = 0.81). Related to Rn, the HEC, LEEC and G fluxes accounted for 22 % (R2 = 0.53), 59 % (R2 = 0.70) and 15% (R2 = 0.62) of the Rn flux, respectively. We assume that the combination of EC and scintillometer method has a potential to bring deeper insight into the analysis of the energy balance closure problem.

The eddy covariance method provides important insights about CO2, water and energy exchange-related processes on the ecosystem scale level. Data are collected quasi-continuously with sampling frequency 10 Hz at minimum, often throughout multiple years, producing large datasets. Standard data processing methods are already devised but undergo continuous refinements that should be reflected in the available software. Currently, a suite of software packages is available for computation of half-hourly products from high frequency raw data. However, software packages consolidating the further post-processing computations are not yet that common. The post-processing steps can consist of quality control, footprint modelling, computation of storage fluxes, gap-filling, flux partitioning and data aggregation. Also they can be realized in different programming languages and require various input data formats. Users would therefore often evaluate only certain aspects of the dataset which limits the amount of extractable information from obtained data and they possibly omit the features that could affect data quality or interpretation. Here we present the free R software package openeddy (https://github.com/lsigut/openeddy) that provides utilities for input data handling, extended quality control checks, data aggregation and visualization and that includes a workflow (https://github.com/lsigut/EC_workflow) that attempts to integrate all post-processing steps through incorporation of other free software packages, such as REddyProc (https://github.com/bgctw/REddyProc/). The framework is designed for the standard set of eddy covariance fluxes, i.e. of momentum, latent and sensible heat as well as CO2. Special attention was paid to the visualization of results at different stages of processing and at different time resolutions and aggregation steps. This allows to quickly check that computations were performed as expected and it also helps to notice issues in the dataset itself. Finally, the proposed folder structure with defined post-processing steps allows to organize data in different stages of processing for improved ease of use. Produced workflow files document the whole processing chain and its possible adaptations for a given site. We believe that such a tool can be particularly useful for eddy-covariance novices, groups that cannot or do not contribute their data to regional networks for further processing or users that want to evaluate their data independently. This or similar efforts can also help to save human resources or speed up the development of new methods. This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the CzeCOS program, grant number LM2015061, and within Mobility CzechGlobe 2, grant number CZ.02.2.69/0.0/0.0/18_053/0016924.

Regional Environmental Change, 20 (4), ISSN:1436-3798, ISSN:1436-378X

Accurate estimation of energy fluxes and evapotranspiration (ET) in agricultural systems is critically needed, especially for water resource sustainability, soil moisture monitoring and irrigation. Numerous micrometeorological methods have become commercially available. Considering the eventual trade-off between cost and accuracy, it is important to evaluate these methods to provide recommendations for practical purposes. Therefore, we tested five different techniques at one field in the region of Central Europe dominated by rainfed farming but suffers from drought spells. In an intensive campaign, we used eddy covariance (EC), large aperture and surface layer scintillometers, the Bowen ratio energy balance (BREB), and the surface renewal (SR) methods to estimate the sensible (H) and latent (λET) heat fluxes of winter wheat and bare soil with harvest residues during two months in summer 2015. At the half-hourly level, the methods showed varying agreement under different field conditions. While H estimated by EC and scintillometry agreed well, there was an underestimation of λET by EC compared to the other methods, most likely due to energy balance non-closure. The λET estimated by the BREB method showed good agreement with the λET obtained by scintillometry when the Bowen ratio (β) was high and with the λET obtained by EC when β → 0. Our study confirmed good reliability of scintillometers across wide range of meteorological conditions. Although the SR method provided the most inferior agreement with other methods at half-hourly basis, it was deemed to be valuable when longer averaging periods were used. Over the entire experiment, mean daily ET estimated by scintillometry (2.6 mm d−1), BREB (2.3 mm d−1), and SR (2.9 mm d−1) showed reasonable agreement while EC (1.6 mm d−1) significantly underestimated. This indicates that low cost methods (BREB and SR) are sufficient for water management purposes when a daily and longer time scales are important. Further, parallel deploying of BREB and SR can provide additional diagnostics and increase the confidence in ET estimates.

We analyzed the effect of manipulated water availability on an accumulation of nutrients and metals, their stoichiometry, and allocation to roots or leaves in a short rotation coppice (SRC) poplar plantation. The aim of this study was also to clarify how these changes are related to the effects of drought on growth parameters. This study was conducted in Domaninek, Czech Republic in an SRC poplar clone J-105 (Populus nigra L. × P. Maximowiczii H.). This plantation was established as an uncoppiced (single stem) and later on converted into multi-stem (coppice). A rain-out shelter experiment (reduced throughfall) was established in the second year of coppice and the drought stress (DS) applied for 3 years. Water availability altered the accumulation and allocation of nutrients and metals in above and belowground biomass. Reduced water availability led, in particular, to the significantly lower accumulation of potassium (K) in both leaves and roots and a higher carbon (C) to potassium (K) ratio (C:K) in leaves. The significant decline of zinc (Zn) was also found in roots under reduced throughfall. Reduced water availability led to increased accumulation of cadmium (Cd) in leaves and decreased accumulation in roots. This resulted in significantly lower root:leaf ratio for Cd content. An opposite response was found for the allocation of copper (Cu). We also demonstrated that major changes in accumulation and allocation are associated with changes in growth. The results indicated that such knowledge may contribute to understanding the role of nutrient uptake and translocation in acclimation to DS and it may help in developing phytoextraction methods on contaminated soils.

Poplars are among the most widely used short rotation woody coppice (SRWC) species but due to their assumed high water use, concerns have been raised with respect to large-scale exploitation and potentially detrimental effects on water resources. Here we present a quantitative analysis of the water requirements of poplar SRWC using experimental data and a soil water balance modelling approach at three different sites across Europe. We used (i) eddy covariance (EC) measurements (2004–2006) at an irrigated SRWC grown on a previous rice paddy in northern Italy, (ii) Bowen ratio and energy balance (BREB) measurements (2008–2015) and EC (2011–2015) at a SRWC in rain-fed uplands in the Czech Republic, and (iii) EC measurements (2010–2013) at a SRWC on a previous agricultural land with a shallow water table in Belgium. Without any calibration against water balance component measurements, simulations by the newly developed soil water balance model R-4ET were compared with evapotranspiration (ET) measurements by EC and BREB with a resulting mean root mean square error (RMSE) of 0.75 mm day−1. In general, there was better agreement between EC and the model (RMSE = 0.66 mm day−1) when EC data were adjusted for lack of energy balance closure. A comparison of the simulated and measured soil water content yielded a mean RMSE of 0.03 m3 m−3. The mean annual crop coefficient, i.e. the ratio between actual and reference ET, was 0.82 (ranging from 0.65 to 0.95) while the monthly maxima reached 1.16. These values indicated that ET of poplar SRWC was significantly lower than ET of a well-watered grass cover at the annual time scale, but exceeded ET of the reference cover at shorter time scales during the growing season. We show that the model R-4ET is a simple, yet reliable tool for the assessment of water requirements of existing or planned SRWC. For very simple assessments on an annual basis, using a crop coefficient of 0.86 (adjusted to a sub-humid climate), representing an average value across the three sites in years with no evident drought stress, is supported by this analysis.

We examined the effect of throughfall exclusion on aboveground woody biomass (AGB) productivity in a short-rotation woody coppice plantation. This study was carried out between the 3rd and 7th year (2011–2015) of the second rotation cycle in hybrid poplar plantation (Populus nigra × P. maximowiczii) in the Czech Republic. A partial-throughfall exclusion (R) treatment and a control (C) treatment were applied. Throughfall exclusion was set to 40% in 2011–2012 and increased to 70% thereafter. The annual mean (standard error) AGB productivity for C and R during 2012–2015 were 11.2 (0.84) and 10.1 (0.89) Mg ha−1 y−1 of dry matter (DM), respectively. The lowest AGB productivity and most pronounced reduction (18.8%) in the annual AGB productivity by the R treatment occurred in 2014 - a year with the driest spring. The observed inter-annual variability in AGB productivity was not explained by precipitation nor by precipitation-treatment interaction. However, the annual AGB productivity variation depended (p = 0.002) on the throughfall totals during period May–July. The total harvestable biomass in 2015 reached 81.9 (7.2) and 75.0 (6.2) Mg ha−1 DM for the C and R treatments, respectively. Despite the non-negligible mean treatment difference (5.7 Mg ha−1 DM, i.e. 7.6%), the large between-plot variance and shifts in stand structure resulted in the nonsignificant differences. We suggest that once the root system is established, hybrid poplar plantations are relatively resilient to drought and might act as an income-stabilizing factor when traditional agricultural crops fail to deliver viable yields in drier climate.

Coastal forested wetlands provide important ecosystem services along the southeastern region of the United States, but are threatened by anthropogenic and natural disturbances. Here, we examined the species composition, mortality, aboveground biomass, and carbon content of vegetation and soils in natural pine forests of the lower coastal plain in eastern North Carolina, USA. We compared a forest clearly in decline (termed “ghost forest”) adjacent to a roadside canal that had been installed as drainage for a road next to an adjacent forest subject to “natural” hydrology, unaltered by human modification (termed “healthy forest”). We also assessed how soil organic carbon (SOC) accumulation changed over time using 14C radiocarbon dating of wood sampled at different depths within the peat profile. Our results showed that the ghost forest had a higher tree density at 687 trees ha−1, and was dominated by swamp bays (Persea palustric), compared to the healthy forest, which had 265 trees ha−1 dominated by pond pine (Pinus serotina Michx). Overstory tree mortality of the ghost forest was nearly ten times greater than the healthy forest (p < 0.05), which actually contributed to higher total aboveground biomass (55.9 ± 12.6 Mg C ha−1 vs. 27.9 ± 8.7 Mg ha−1 in healthy forest), as the dead standing tree biomass (snags) added to that of an encroaching woody shrub layer during ecosystem transition. Therefore, the total aboveground C content of the ghost forest, 33.98 ± 14.8 Mg C ha−1, was higher than the healthy forest, 24.7 ± 5.2 Mg C ha−1 (p < 0.05). The total SOC stock down to a 2.3 m depth in the ghost forest was 824.1 ± 46.2 Mg C ha−1, while that of the healthy forest was 749.0 ± 170.5 Mg C ha−1 (p > 0.05). Carbon dating of organic sediments indicated that, as the sample age approaches modern times (surface layer year 2015), the organic soil accumulation rate (1.11 to 1.13 mm year−1) is unable to keep pace with the estimated rate of recent sea level rise (2.1 to 2.4 mm year−1), suggesting a causative relationship with the ecosystem transition occurring at the site. Increasing hydrologic stress over recent decades appears to have been a major driver of ecosystem transition, that is, ghost forest formation and woody shrub encroachment, as indicated by the far higher overstory tree mortality adjacent to the drainage ditch, which allows the inland propagation of hydrologic/salinity forcing due to SLR and extreme storms. Our study documents C accumulation in a coastal wetland over the past two millennia, which is now threatened due to the recent increase in the rate of SLR exceeding the natural peat accumulation rate, causing an ecosystem transition with unknown consequences for the stored C; however, much of it will eventually be returned to the atmosphere. More studies are needed to determine the causes and consequences of coastal ecosystem transition to inform the modeling of future coastal wetland responses to environmental change and the estimation of regional terrestrial C stocks and flux.