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Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [ABSTRACT FROM AUTHOR]

Land surface models (LSM) are an important tool for advancing our knowledge of the Earth system. LSM are constantly improved to represent the various terrestrial processes in more detail. High quality data, freely available from various observation networks, are providing being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSM with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for thirteen forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in-situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point scale measurements from, however for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that state-of-the-art LSM such as CLM5 still suffer from uncertainties in the representation of soil hydrological processes in forests, e.g. deep root water uptake, or highly uncertain vegetation parameters. [ABSTRACT FROM AUTHOR]

Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content. However, dedicated efforts are still required to develop workflows, executable on cloud-based platforms, for ingesting the satellite data, performing the targeted processes, and generating the desired products. In this study, an operational workflow is proposed, based on monthly Evaporative Stress Index (ESI) anomaly, and implemented in cloud-based online Virtual Earth Laboratory (VLab) platform, as a demonstration, to monitor European agricultural water stress. To this end, daily time-series of actual and reference evapotranspiration (ETa and ET0), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, were used to execute the proposed workflow successfully on VLab. The execution of the workflow resulted in obtaining one decade (2011–2020) of European monthly agricultural water stress maps at 0.04˚ spatial resolution and corresponding stress reports for each country. To support open science, all the workflow outputs are stored in GeoServer, documented in GeoNetwork, and made available through MapStore. This enables creating a dashboard for better visualization of the results for end-users. The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data. [ABSTRACT FROM AUTHOR]

This study quantifies the accuracy of evapotranspiration (ET) estimates from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary sensor onboard the Meteosat Second Generation (MSG) satellites, along seven key dimensions, i.e., diurnal cycle, daily, intra-annual, inter-annual, ecosystem, climate zone, and products intercomparison. In situ measurements were collected at 54 eddy covariance (EC) sites to evaluate the accuracy of SEVIRI actual ET products (diurnal and daily SEVIRI-ET a) as well as reference ET (daily SEVIRI-ET 0) covering the period from 2004 to 2018 across Europe. SEVIRI-ET a is produced by the Tiled ECMWF Surface Scheme of Exchange processes at the Land surface (TESSEL) model, while SEVIRI-ET 0 is estimated by a combination of a thermodynamically-based and an atmospheric boundary layer model. This evaluation is further separated according to the land cover heterogeneity of the SEVIRI pixels across all 54 EC sites, using MODIS land cover data. The Root Mean Squared Error (RMSE), along with the Kling-Gupta efficiency (KGE) and their respective decompositions, were employed to quantify the errors. For diurnal SEVIRI-ET a estimates, we found that the KGE (RMSE [mm hour−1]) varied between −1.6 (0.04) to 0.8 (0.14), with a median value of 0.26 (0.07). Higher accuracy for diurnal SEVIRI-ET a was obtained in the summer and during the mid-day time. For daily SEVIRI-ET a , the KGE (RMSE [mm day−1]) varied between −0.88 (0.43) to 0.93 (1.79), with a median value of 0.6 (0.77) and for daily SEVIRI-ET 0 the KGE (RMSE [mm day−1]) varied between 0.51 (0.40) to 0.94 (1.50), with a median value of 0.77 (0.57). For daily SEVIRI-ET a , intra-annual accuracy was low from January to March, increased in the mid-year, and then began to decline from November to December. Although accuracy remained relatively stable during the middle of the year, it varied considerably in the winter period. In the inter-annual dimension, the mid-year positive KGE values and distributions changed over time from 2004 to 2018. In spatial dimensions, the highest accuracy was in peat and grassland ecosystems, and the lowest in cropland ecosystem, with similar patterns observed in the boreal snow fully humid warm summer and warm temperate fully humid hot summer climate zones. Regarding SEVIRI-ET 0 results, similar to SEVIRI-ET a , intra-annual accuracy was low in the first quarter of the year and the last one but high in the mid-year. In the inter-annual dimension, unlike SEVIRI-ET a , almost an identical pattern was observed for the mid-year positive KGE values, demonstrating only a slight change in SEVIRI-ET 0 accuracy during 2004–2018. However, the highest accuracy was found in crop ecosystem, while the lowest was in forest ecosystem, reflecting similar trends in the warm temperate fully humid hot summer and warm temperate summer dry hot summer climate zones. The observed range of median RMSE changed between 0.4 and 1.5 mm day−1, also suggests a reasonable accuracy for SEVIRI-ET estimates in all spatial domains. Our results showed that the main trends in the accuracies (median KGEs) of SEVIRI-ET (both ET a and ET 0) remained similar in separated homogeneous and heterogeneous sites and were comparable to combined sites among the dimensions. Through error decomposition, we discerned that SEVIRI-ET estimates performed particularly well in explaining inter-annual and spatial variabilities. Furthermore, the intercomparison of ET products revealed that SEVIRI satellite-derived ET a exhibited the strongest correlation with in situ ET measurements across all ecosystem types and climate zones, outperforming other products (such as MODIS, PML, GLEAM, and BESS). The ET estimates from other products exhibited lower standard deviation errors and were in closer agreement with the in situ measurements. This study provides the first comprehensive evaluation of the accuracy of SEVIRI diurnal and daily ET products across Europe, which may serve as a stimulus for further optimized selection of these products by potential users for various applications. • Daily SEVIRI-ET a (ET 0) data accuracy separated into temporal and spatial dimensions. • Diurnal (sub-daily) SEVIRI-ET a data accuracy quantified. • SEVIRI-ET a (ET 0) performed well in explaining inter-annual and spatial variability. • SEVIRI-ET a showed the better performance compared to other relevant ET products. • This study findings can support the optimized selection of SEVIRI-ET a (ET 0) products. [ABSTRACT FROM AUTHOR]

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO 2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004-2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO 2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

A comparison was drawn between model predictions and experimentally determined volatilization rates to evaluate the volatilization approaches of European registration models. Volatilization rates of pesticides (14C-labeled parathion-methyl, fenpropimorph, and terbuthylazine and nonlabeled chlorpyrifos) were determined in a wind-tunnel experiment after simultaneous soil surface application on Gleyic Cambisol. Both continuous air sampling, which quantifies volatile losses of 14C-organic compounds and 14CO2 separately, and the detection of soil residues allow for a mass balance of radioactivity of the 14C-labeled pesticides. Recoveries were found to be > 94% of the applied radioactivity. The following descending order of cumulative volatilization was observed: chlorpyrifos > parathion-methyl > terbuthylazine > fenpropimorph. Due to its high air-water partitioning coefficient, nonlabeled chlorpyrifos was found to have the highest cumulative volatilization (44.4%) over the course of the experiment. Volatilization flux rates were measured up to 993 microg m(-2) h(-1) during the first hours after application. Parameterization of the Pesticide Emission Assessment at Regional and Local Scales (PEARL) model and the Pesticide Leaching Model (PELMO) was performed to mirror the experimental boundary conditions. In general, model predictions deviated markedly from measured volatilization rates and showed limitations of current volatilization models, such as the uppermost compartment thickness, making an enormous influence on predicted volatilization losses. Experimental findings revealed soil moisture to be an important factor influencing volatilization from soil, yet its influence was not reflected by the model calculations. Future versions of PEARL and PELMO ought to include improved descriptions of aerodynamic resistances and soil moisture dependent soil-air partitioning coefficients.