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interTwin co-designs and implements the prototype of an interdisciplinary Digital Twin Engine (DTE). The developed DTE will be an open source platform that includes software components for modelling and simulation to integrate application-specific Digital Twins. InterTwin WP6 will provide the core DTE modules to be integrated by WP7 and to be executed on infrastructure and components delivered by WP5. The current document consists of a report on the high-level description of WP6 components and the related design based on the C4 model. Additionally, this deliverable includes the set of requirements derived from the analysis of the use cases.

Remote sensing data play an important role in understanding the spatio-temporal variations in hydro-meteorological conditions at different spatial scales. In particular, one of the key processes of hydrological cycle for monitoring water loss from space is evapotranspiration (ET). In contrast to sparsely distributed in-situ measurements, development of two surface energy balance (TSEB) models forced by satellite observations has made a significant contribution to estimate ET with global coverage. In this regard, in the framework of ESA’s 4DMED-Hydrology project, we combine Copernicus data from Sentinel-2 (S2) Multispectral Instrument (MSI) and Sentinel-3 (S3) Land Surface Temperature Radiometer (SLSTR) with ERA5 climate reanalysis dataset derived within the period 2017-2021 for daily ET retrieval at high (100 m) spatial resolution. In this work, an open-source implementation of TSEB developed in the framework of the ESA’s Sen-ET project has been applied over wide areas represented by four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault and Medjerda). Considering large volume of satellite data and high computational requirements of the Sen-ET, all processes have been optimized to be run in the automatic manner by combining multiple steps into one processing workflow utilized in cloud computing platforms offered by EODC and ESA HPC of CloudFerro. First, due to incomplete time-series of S2 Level-2A, we pre-process Sentinel-2 data for further retrieval of 100-m reflectance and biophysical parameters needed for the ET estimation afterwards. Next, we downscale S3 land surface temperature (LST) product by exploiting relationships between 1-km Sentinel-3 and time-coincident 100-m S2 reflectances using decision trees (DT) algorithm. Apart from biophysical properties (e.g., leaf area index and fractional vegetation cover) and sharpened LST data, meteorological forcings and solar radiation from ERA5 have been generated for estimating instantaneous energy fluxes and daily evapotranspiration. Based on preliminary results over Po basin, DT algorithm allowed predicting 100-m LST with the average root mean square error (RMSE) of 3.2°C when compared to ground-derived skin temperature from two eddy covariance (EC) towers. Meanwhile, turbulent fluxes driven by downscaled LST resulted in RMSE equal to 52 Wm-2 and 108 Wm-2 for sensible and latent heat fluxes, respectively. Despite some limitations mainly related to the EC locations in complex mountain areas, ET estimates forced by satellite observations have potential for providing energy fluxes at wider scale.Keywords: evapotranspiration, Sentinel-3, land surface temperature, Mediterranean region

Ground surface temperature (GST), measured at approximately 5 cm into the ground is a key parameter controlling all the subsurface biophysical processes at the land-atmosphere boundary. Despite the GST significant importance, the current observational network for GST is sparse, particularly in mountain regions. This work exploits the relationship between the GST and satellite-based land surface temperature (LST) derived from MODerate resolution Imaging Spectroradiometer (MODIS). The GST and LST were compared at 14 weather stations in Mazia Valley, North-eastern Italian Alps. The 1-km MODIS LST was downscaled to a spatial resolution of 250-m using the random forest algorithm. The LST dataset covers the years 2014-2017 during the phenological cycle, between April and October. The in-situ GST measurements were recorded using Campbell Scientific CS655 data loggers. LSTs were usually larger than GSTs with temperature differences ranging from 0.1 to 22 °C and an average of 7.9 °C. The lowest and largest average difference was 4.49 °C (1823 m, pasture, south slope) and 10.27 °C (1778 m, forest, north slope), respectively. GST was positively correlated with LST with an R2 ranging from 0.24 to 0.52 and was above 0.45 for 57 % of the stations. The RMSE ranged between 6.05 and 11.05 °C, while for 71 % of the stations was below 9.3 °C. The statistics were influenced by the number of available pairwise for comparison that were ranging from 110 to 377 due to cloud contamination or logger malfunction. Although the RMSE was relatively high, the LST closely followed the pattern of the GST variability suggesting the possibility of linking GST to LST products.

Many satellite-derived evapotranspiration (ET) estimates rely on coarse resolution (CR) land surface temperature (LST) from 1-km thermal infrared bands offered by NASA and ESA instruments, like Terra MODIS and Sentinel-3 SLSTR. This affects prediction performance of ET, especially in complex regions, such as the Alps. Since most two-source energy balance (TSEB) models assume no thermal variability within a pixel, a major challenge in ET modelling is related to cell grid heterogeneity. Given this limitation, we investigate the potential of kernel-driven downscaling to obtain sub-kilometer LST products based on fine resolution (FR) sensors, i.e., Sentinel-2 MSI and MODIS VNIR, for estimating TSEB-based ET over South Tyrol, in the South-Eastern Alps. To this aim, we exploit relationships between CR LST and FR predictors using trees-based algorithms. Due to reduced capabilities of univariate models in complex ecosystems, multi-source predictors are considered, including multispectral reflectances, spectral indices, solar radiation, and topography. The performance of the TSEB model driven by disaggregated outputs is evaluated against original 1-km LST and ground-based fluxes from two eddy covariance towers. In general, turbulent fluxes forced with downscaled LST resulted in RMSE of 86 Wm-2 and mean bias of 55 Wm-2, which translated to 8% and 15% decrease in the respective estimates when compared to TSEB results with 1-km LST. Despite some limitations, mainly related to small-scale changes in landcover and topography that control LSTs and consequently affect TSEB-based ET estimates, the enhanced land surface temperature has potential for providing energy fluxes at finer spatial resolution in heterogenous ecosystems.

The main objective of this study is to exploit thermal remote sensing data for evapotranspiration (ET) modelling in the European Alps. This geographic region has been noted as a hot spot of climate change triggered by increasing number of drought events in recent years, with impacts on natural and agricultural vegetation. Evapotranspiration is considered as one of the major indicators for examining water anomalies in plants. The state-of-art ET models exploiting thermal remote sensing data have shown a large potential in water cycle monitoring. However, existing satellite-derived products do not provide adequate spatial resolution for mountain ecosystems affected by complex orography, common overcast and land-cover heterogeneity. Even though fine resolution imagery fills the gap regarding non-homogenous areas, its long revisit time and frequent cloud contamination hamper spatially continuous ET modelling. In this context, our aim is to overcome these limitations by downscaling and gap-filling 1-km MODIS LST (MOD11A1) to retrieve daily LST maps at 250 m spatial resolution, which can be considered a reasonable scale in the selected area. Firstly, we downscale MODIS LST images with the Random Forest (RF) algorithm by exploiting the relationship between coarse resolution MODIS LST and 250-m explanatory variables, including digital elevation model and normalized difference vegetation index. The 1-km MODIS LST and the downscaled product were compared with fine resolution Landsat LST images. The random forest results show an improvement of about 20% in the agreement between Landsat and 250-m MODIS LST compared to statistics obtained for MOD11A1. Secondly, we propose to recover missing values of LST pixels beneath the clouds. Considering local-scale climate variability of the study area, we present a novel approach based on investigating the relationships between LST and meteorological data under clear- and cloudy-sky conditions. The abovementioned improvements are planned to be used for energy balance modelling of ET with relevant implications on water availability assessment in the Alpine region.

Land ecosystems presently sequester around 25% of the carbon dioxide (CO2) that is emitted into the atmosphere by human activity and thus, along with the oceans (absorbing a similar fraction), slow down the increase of atmospheric CO2. Whether land ecosystems will be able to continue to sequester atmospheric CO2 at similar rates in the future or whether carbon cycle-climate feedbacks will cause the land sink to saturate or even turn into a source, is a topic of controversial discussion. While taking up CO2 through the stomata, plants inevitably lose water through transpiration. Terrestrial evapotranspiration (ET) can have a feedback to (local) precipitation and therefore modulate near-surface climate. The terrestrial carbon and water cycles are highly connected and controlled by complex interactions between biological and abiotic drivers. Mountain ecosystems in the European Alps are a hot spot of climate and land-use changes. Over the last century, temperatures have increased in the region with a rate double that of the global average and are expected to rise rapidly. In addition, precipitation changes are highly complex with an increasing and a decreasing trend in the northern and southern Alps, respectively and different seasonal patterns. Socio-economic development in the Alps during the past centuries have caused large-scale changes in land-use and its intensity, which has contributed to the uncertainty about future land-atmosphere interactions. The objective of the CYCLAMEN project is to quantify and project the resilience and vulnerability of carbon and water cycling in North and South Tyrol. We aim at providing information for predicting likely future changes in climate and land-use over the region.In the study we used a comprehensive and multidisciplinary approach to model biosphere-atmosphere interactions in the Alps. Data from eddy covariance stations spread across the region were chosen to test and calibrate the biosphere model SiB4. The meteorological data from the same stations was used to train a stochastic Weather Generator and simulate weather conditions under climate scenarios RCP8.5 and RCP2.6 until 2100. To account for future land- use/ land- cover (LULC) changes the SPA-LUCC model was used. Both the simulated weather conditions and the expected LULC were fed back to the SiB4 model to calculate ecosystem parameters, including carbon dioxide net ecosystem exchange and evapotranspiration. In parallel, an enhanced thermal remote sensing dataset was produced, specifically adapted for mountainous areas. This dataset will be the main driver for modelling ET with an energy balance model whose output will be cross compared with the one of the biosphere model SiB4.

This work aims to assess a diagnostic approach which links evapotranspiration (ET) to land surface temperature (LST) measured by thermal remote sensing in the Alps. We estimated gridded ET, from field (30 m) to regional (1 km) scales, and we performed a specific study on grassland ecosystems in the Alps in South Tyrol (Italy), to evaluate the model sensitivity to different types of land management. The energy balance model TSEB ALEXI (Two Source Energy Balance Atmosphere Land EXchange Inverse) was first applied to Meteosat satellite data. Then ET was estimated by the flux disaggregation procedure DisALEXI, driven by MODIS and Landsat LST retrievals, which has never been applied before in a mountain region. We evaluated the model against eddy-covariance (EC) measurements from established stations in the Alps, and analyzed the main limitations which affect the model performance in mountainous regions. The TSEB model, applied in plot-scale mode using tower-based meteorological and LST input data, performed well with errors in daytime (6–18 UTC+1) latent heat flux around 30–60 W m−2 in comparison with flux measurements corrected for the lack of closure in the energy balance. For landscape ET retrievals, while Landsat resolution (30 m) is preferable for capturing small-scale heterogeneity in landscape moisture conditions, and for direct comparison with tower fluxes, persistent cloud cover resulted in no clear Landsat scenes during the study period. MODIS-based retrievals at 1 km resolution are too coarse to resolve the flux tower footprint in this complex landscape, yielding discrepancies of 100 W m−2 in model-measurement comparisons. Still, MODIS DisALEXI partitioning of the energy budget was reasonable and enabled to detect evaporative stress at regional scale expressed as the ratio between actual and potential ET, fPET. We evaluated fPET in comparison with a crop stress index based on cumulative air temperature and precipitation at different stations in the study area, and investigated ability to capture differential responses between managed and unmanaged grasslands. Results show that in the Alps i) moderate resolution thermal data can be used to monitor evaporative stress at the regional scale; ii) the spatial-temporal evolution of ET can be characterized from MODIS and Landsat thermal data with limitations which are due to the low availability of clear-sky scenes and to the small-scale (∼10 m) changes in soil moisture, topography and canopy density, which control ET patterns in mountainous regions; iii) solar radiation and leaf area index are critical variables which strongly affect the accuracy of the modeled energy fluxes.

This paper presents an improved algorithm for the retrieval of Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery that has been specifically customized for mountain grasslands in the Alps. The main features of the proposed algorithm, which is based on the inversion of a radiative transfer model, are: i) a higher spatial resolution (250 m) with respect to the corresponding standard MODIS product and ii) tuning the model to the spectral characteristics of mountain grasslands. To quantify the effects of the features of the proposed algorithm, the approach is first applied to a MODIS reflectance data time series from 2007 up-scaled to a 1 km spatial resolution for better comparison with the standard MODIS LAI product. In the next step, the benefit of the higher spatial resolution is assessed by applying the algorithm to a series of MODIS satellite images with a spatial resolution of 250 m acquired over the central Alps in the period 2005–2007. LAI estimates were validated for both temporal consistency and accuracy using ground measurement time series collected at three different study sites in the investigated area. The results obtained demonstrate the capability of the proposed algorithm to follow the expected temporal and range dynamics of LAI in this challenging environment, showing an overall RMSE accuracy of 1.68 (m 2 /m 2 ). This approach thus opens a promising avenue for the exploitation of moderate resolution satellite data for novel and more accurate monitoring studies at a regional scale in mountain environments.

This study evaluates the suitability of the method HelioMont, developed by MeteoSwiss, for estimating solar radiation from geostationary satellite data over the Alpine region. The algorithm accounts for the influence of topography, clouds, snow cover and the atmosphere on incoming solar radiation. The main error sources are investigated for both direct and diffuse solar radiation components by comparison with ground-based measurement taken at three sites, namely Bolzano (IT), Davos (CH) and Payerne (CH), encompassing different topographic conditions. The comparison shows that the method provides high accuracy of the yearly cycle: the Mean Absolute Bias (MAB) is below 5 W m− 2 at the lowland station Payerne and below 12 W m− 2 at the other two mountainous stations for the monthly averages of global and diffuse radiation. For diffuse radiation the MAB is in the range 11–15 W m− 2 for daily means and 34–40 W m− 2 for hourly means. It is found that the largest errors in diffuse and direct radiation components on shorter time scales occur during summer and for cloud-free days. In both Bolzano and Davos the errors for daily-mean diffuse radiation can exceed 50 W m− 2 under such conditions. As HelioMont uses monthly climatological values of atmospheric aerosol characteristics, the effects of this approximation are investigated by simulating clear-sky solar radiation with the radiative transfer model (RTM) libRadtran using instantaneous aerosol measurements. Both ground-based and satellite-based data on aerosol optical properties and water vapor column amount are evaluated. When using daily atmospheric input the estimation of the hourly averages improves significantly and the mean error is reduced to 10–20 W m− 2. These results suggest the need for a more detailed characterization of the local-scale clear-sky atmospheric conditions for modeling solar radiation on daily and hourly time scales.

Photovoltaic (PV) module temperature predictions are crucial to accurately assess the efficiency of PV installations. In this study we focus on the cooling effect of wind on PV cell temperature. We show that for most of the technologies installed at a PV test facility in Bolzano (Italy), models including wind data predict PV cell temperature better than standard approaches which do not include wind data. Moreover, we show that wind data from numerical weather prediction models can replace in-situ wind measurements: when they are used as model input, the prediction also improves significantly compared to the standard approach., Energy Procedia, 40, ISSN:1876-6102, European Geosciences Union General Assembly 2013, EGUDivision Energy, Resources & the Environment, ERE

A systematic study of black carbon (BC) vertical profiles measured at high-resolution over three Italian basin valleys (Terni Valley, Po Valley and Passiria Valley) is presented. BC vertical profiles are scarcely available in literature. The campaign lasted 45 days and resulted in 120 measured vertical profiles. Besides the BC mass concentration, measurements along the vertical profiles also included aerosol size distributions in the optical particle counter range, chemical analysis of filter samples and a full set of meteorological parameters. Using the collected experimental data, we performed calculations of aerosol optical properties along the vertical profiles. The results, validated with AERONET data, were used as inputs to a radiative transfer model (libRadtran). The latter allowed an estimation of vertical profiles of the aerosol direct radiative effect, the atmospheric absorption and the heating rate in the lower troposphere. The present measurements revealed some common behaviors over the studied basin valleys. Specifically, at the mixing height, marked concentration drops of both BC (range: from −48.4 ± 5.3 to −69.1 ± 5.5%) and aerosols (range: from −23.9 ± 4.3 to −46.5 ± 7.3%) were found. The measured percentage decrease of BC was higher than that of aerosols: therefore, the BC aerosol fraction decreased upwards. Correspondingly, both the absorption and scattering coefficients decreased strongly across the mixing layer (range: from −47.6 ± 2.5 to −71.3 ± 3.0% and from −23.5 ± 0.8 to −61.2 ± 3.1%, respectively) resulting in a single-scattering albedo increase along height (range: from +4.9 ± 2.2 to +7.4 ± 1.0%). This behavior influenced the vertical distribution of the aerosol direct radiative effect and of the heating rate. In this respect, the highest atmospheric absorption of radiation was predicted below the mixing height (~ 2–3 times larger than above it) resulting in a heating rate characterized by a vertical negative gradient (range: from −2.6 ± 0.2 to −8.3 ± 1.2 K day−1 km−1). In conclusion, the present results suggest that the BC below the mixing height has the potential to promote a negative feedback on the atmospheric stability over basin valleys, weakening the ground-based thermal inversions and increasing the dispersal conditions.