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European forests are an important source for timber production, human welfare, income, protection and biodiversity. During the last two decades, Europe has experienced a number of droughts which have been exceptional within the last 500 years, both in terms of duration and intensity. These droughts seem to leave remarkable imprints on the mortality dynamics of European forests. However, systematic observations on tree decline, with emphasis on a single species, has been scarce so far so that our understanding of mortality dynamics and drought occurrence is still limited at a continental scale. Here, we make use of the ICP Forest crown defoliation dataset, permitting us to retrospectively monitor tree mortality for all major conifers, major broadleaves, as well as a pooled dataset of minor tree species in Europe. In total, we analysed more than three million observations gathered during the last 25 years and employed a high-resolution drought index which can assess soil moisture anomaly based on a hydrological water-balance and runoff model. We found overall and species-specific increasing trends in mortality rates, accompanied by decreasing soil moisture. A generalized linear mixed model identified a previous-year soil moisture anomaly as the most important driver of mortality patterns in conifers, but the response was not uniform across the numerous analysed plots. We conclude that mortality patterns in European forests are currently reaching a concerning upward trend which could be further accelerated by global change-type droughts in the near future.

Forests are currently experiencing an unprecedented period of progressively drier growing conditions around the globe, which is threatening many forest ecosystem functions. Trees as long-living organisms are able to withstand drought periods. Our understanding on critical drought severity resulting in substantial decline in net primary productivity and/or eventually tree mortality is underdeveloped. A wide range of remote sensing products and ground observations, including information on productivity, tree vitality, climate, and soil moisture with high temporal and spatial resolution are now available. Linking these data sources could improve our understanding of the complex relationship between forest growth and drought. We introduce here a conceptual framework using satellite remotely sensed net primary productivity (MOD17A3 and MODIS EURO), ground observations of tree mortality (ICP level I survey data), soil moisture anomaly (Copernicus European Drought Observatory), and spatially-downscaled daily climate data for entire Europe. This unique analysis will enable us to test the influence of biotic and abiotic covariates such as tree age, stand history, and drought legacy using historic droughts for model development. This conceptual framework, as evident from the preliminary results shown here, can help anticipating the effects of future droughts and optimize global climate models considering drought effects.

Studies have shown that the footprint of a single eddy covariance (EC) system may not yield representative measurements of the turbulent fluxes at the field scale for sparse vegetated surfaces, whereas scintillometry, due to its larger footprint, may be more suitable for this purpose. However, the latter approach strongly relies on the Monin-Obukhov similarity theory (MOST) that strictly applies in the inertial sub-layer only. This work aims at experimentally confirm the reliability of displaced-beam laser scintillometer (DBLS) measurements over an olive orchard against two EC systems during summer and autumn months of 2007 through 2009. It was found that the DBLS underestimated both the momentum and sensible heat fluxes by 15 to 20% when established retrieval procedures were applied. A new method to determine the sensible heat flux from the DBLS based on the addition of a single-height wind speed measurement was tested, yielding estimates that compare well with the EC observations, with discrepancies in sensible heat fluxes of about 30 to 40 W m(-2).

Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using surface energy balance (SEB) and soil-water balance models. Both modelling approaches use remotely sensed data to estimate evapotranspiration fluxes in a spatially distributed way. The first approach exploits visible (VIS), near-infrared (NIR) and thermal (TIR) observations to solve the surface energy balance equation whereas the soil-water balance model uses only VIS-NIR data to detect the spatial variability of crop parameters. Considering that the study area is characterized by typical spatially sparse Mediterranean vegetation, i.e. olive, citrus and vineyards, alternating bare soil and canopy, we focused the attention on the main conceptual differences between one-source and two-sources energy balance models. Two different models have been tested: the widely used one-source SEBAL model, where soil and vegetation are considered as the sole source (mostly appropriate in the case of uniform vegetation coverage) and the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared vs. the outputs of the agro-hydrological SWAP model, which was applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. Remote sensing data in the VIS and NIR spectral ranges have been used to infer spatially distributed vegetation parameters needed to set up the upper boundary condition of SWAP. Actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference to be used for energy balance models accuracy assessment. Airborne hyperspectral data acquired during a NERC (Natural Environment Research Council, UK) campaign in 2005 have been used. The results of this investigation seem to prove a slightly better agreement between SWAP and TSEB for some fields of the study area. Further investigations are programmed in order to confirm these indications.

Climate change is anticipated to alter the demand and supply of water at the earth's surface. Since many societal impacts from a lack of water happen under drought conditions, it is important to understand how droughts may develop with climate change. This study shows how hydrological droughts will change across Europe with increasing global warming levels (GWLs of 1.5, 2, and 3 K above pre-industrial temperature). We employed a low-flow analysis based on river discharge simulations of the LISFLOOD (De Roo et al., 2000) spatially distributed physically based hydrological and water use model, which was forced with a large ensemble of regional climate model projections under high emissions (RCP8.5) and moderate mitigation (RCP4.5) Representative Concentration Pathways. Different traits of drought, including severity, duration, and frequency, were investigated using the threshold level method. The projected changes in these traits identify four main sub-regions in Europe that are characterized by somehow homogeneous and distinct behaviours with a clear south-west–north-east contrast. The Mediterranean and Boreal sub-regions (defined in Sect. 3.1.1) of Europe show strong but opposite changes at all three GWLs, with the former area mostly characterized by stronger droughts (with larger differences at 3 K), while the latter is expected to experience a reduction in all drought traits. In the Atlantic and Continental sub-regions, the changes are expected to be less marked and characterized by a larger uncertainty, especially at the 1.5 and 2 K GWLs. Combining the projections in drought hazard with population and agricultural information shows that with 3 K global warming an additional 11 million people and 4.5 ×106 ha of agricultural land are projected to be exposed to droughts every year, on average, with the most affected areas located in the Mediterranean and Atlantic regions of Europe.

Among the indirect estimation approaches of soil water content in the upper layer of the soil, the &ldquo, triangle method&rdquo, is one of the most common that relies on the simple relationship between the optical and thermal features sensed via Earth Observation. These features are controlled by water content at the surface and within the root zone but also by meteorological forcing including air temperature and humidity, as well as solar radiation. Night- and day-time MODIS composites of land-surface temperature (LST) allowed applying a version of the triangle method that takes into account the temporal admittance of the soil. In this study, it has been applied to a long time-series of pair images to analyze the seasonal influence of the meteorological forcing on a triangle method index (or temperature&ndash, vegetation index, TVX), as well as to discuss extra challenges of the diachronic approach including seasonality effects and the variability of environmental forcing. The Imera Meridionale basin (Sicily, Italy) has been chosen to analyze the method over a time-series of 12 years. The analysis reveals that, under these specific environmental and climatic conditions (strong seasonality and rainfall out of phase with vegetation growth), Normalized Difference Vegetation Index (NDVI) and LST pairs move circularly in time within the optical vs. thermal feature space. Concordantly, the boundaries of the triangle move during the seasons. Results showed a strong correlation between TVX and rainfall normalized amplitudes of the power spectra (r2 ~0.8) over the range of frequencies of the main harmonics.

The statistical inference of the alternation of wet and dry periods in daily rainfall records can be achieved through the modelling of inter-arrival time-series, IT, defined as the succession of times elapsed from a rainy day and the one immediately preceding it. In this paper, under the hypothesis that ITs are independent and identically distributed random variables, a modelling framework based on a generalisation of the commonly adopted Bernoulli process is introduced. Within this framework, the capability of three discrete distributions, belonging to the Hurwitz–Lerch-Zeta family, to reproduce the main statistical features of IT time-series was tested. These distributions namely Lerch-series (Lerch), polylogarithmic- series (Polylog) and logarithmic-series (Log) were selected thanks to their capability to describe some peculiar properties usually observed in IT series derived from daily rainfall records: very high standard deviation and skewness, relatively high frequency associated to the unitary IT, monotonically decreasing frequencies with a slow decay. Both Polylog and Log distributions are special cases of the 3-parameter Lerch distribution with a decreasing number of free parameters (2- and 1-parameter, respectively). The analysis, performed on 55 raingauges located in Sicily (Italy) under a typical Mediterranean climate, suggests that a reliable statistical representation of IT can be attained with the 3-parameter Lerch distribution. Despite the marked seasonality of rainfall in the study area, a simple subdivision of the year into two 6-month periods, roughly corresponding to the dry ‘‘semester’’ (D-sem) and the wet ‘‘semester’’ (W-sem), allows a satisfactory reproduction of IT, as well as of wet spells (WS) and dry spells (DS), separately. It was also noticed that the 2-parameter Polylog distribution could be successfully used to reconstruct the DS frequency distribution only. This result suggests that the additional parameter of the Lerch distribution is required by the inclusion of WS into the analysis. Finally, considering that Polylog outperforms the commonly adopted Log, a noteworthy step forward in DS modelling can be achieved by using Polylog distribution rather than Log one.

A remote sensing-based approach to estimate actual evapotranspiration (ET) was tested in an area covered by olive trees and characterized by Mediterranean climate. The methodology is a modified version of the standard FAO-56 dual crop coefficient procedure, in which the crop potential transpiration, T p, is obtained by directly applying the Penman-Monteith (PM) equation with actual canopy characteristics (i.e., leaf area index, albedo and canopy height) derived from optical remote sensing data. Due to the minimum requirement of in-situ ancillary inputs, the methodology is suitable also for applications on large areas where the use of tabled crop coefficient values become problematic, due to the need of corrections for specific crop parameters, i.e., percentage of ground cover, crop height, phenological cycles, etc. The methodology was applied using seven airborne remote sensing images acquired during spring-autumn 2008. The estimates based on PM approach always outperforms the ones obtained using simple crop coefficient constant values. Additionally, the comparison of simulated daily evapotranspiration and transpiration with the values observed by eddy correlation and sap flow techniques, respectively, shows a substantial agreement during both dry and wet days with an accuracy in the order of 0.5 and 0.3 mm d−1, respectively. The obtained results suggest the capability of the proposed approach to correctly partition evaporation and transpiration components during both the irrigation season and rainy period also under conditions of significant reduction of actual ET from the potential one.

Correct estimation of crop actual transpiration plays a key-role in precision irrigation scheduling, since crop growth and yield are associated to the water passing through the crop. Objective of the work was to assess how the combined use of micro-meteorological techniques (eddy covariance, EC) and physiological measurements (sap flow, SF) allows a better comprehension of the processes involving in the Soil–Plant–Atmosphere continuum. To this aim, an experimental dataset of actual evapotranspiration, plant transpiration, and soil water content measurements was collected in an olive orchard during the midseason phenological period of 2009 and 2010. It was demonstrated that the joint use of EC and SF techniques is effective to evaluate the components of actual evapotranspiration in an olive orchard characterized by sparse vegetation and a significant fraction of exposed bare soil. The availability of simultaneous soil water content measurements allowed to estimate the crop coefficients and to assess a simple crop water stress index, depending on actual transpiration that can be evaluated even in the absence of direct measurements of actual transpiration. The crop coefficients experimentally determined resulted very similar to those previously evaluated; in particular, in the absence of water stress, a seasonal average value of about 0.65 was obtained for the “single” crop coefficient, whereas values of a 0.34 and 0.41 were observed under limited water availability in the root zone. The comparison between the values of crop water stress index evaluated during the investigated periods evidenced systematically lower values (less crop water stress) in the first year compared to the second, according to the general trend of soil waters content in the root zone. Further researches are however necessary to extent the experimental dataset to periods characterized by values of soil evaporation higher than those observed, in order to verify the crop coefficients even under different conditions than those investigated.

Equations based on surface renewal (SR) analysis to estimate the sensible heat flux (H) require as input the mean ramp amplitude and period observed in the ramp‐like pattern of the air temperature measured at high frequency. A SR‐based method to estimate sensible heat flux (HSR‐LST) requiring only low‐frequency measurements of the air temperature, horizontal mean wind speed, and land‐surface temperature as input was derived and tested under unstable conditions over a heterogeneous canopy (olive grove). HSR‐LST assumes that the mean ramp amplitude can be inferred from the difference between land‐surface temperature and mean air temperature through a linear relationship and that the ramp frequency is related to a wind shear scale characteristic of the canopy flow. The land‐surface temperature was retrieved by integrating in situ sensing measures of thermal infrared energy emitted by the surface. The performance of HSR‐LST was analyzed against flux tower measurements collected at two heights (close to and well above the canopy top). Crucial parameters involved in HSR‐LST, which define the above mentioned linear relationship, were explained using the canopy height and the land surface temperature observed at sunrise and sunset. Although the olive grove can behave as either an isothermal or anisothermal surface, HSR‐LST performed close to H measured using the eddy covariance and the Bowen ratio energy balance methods. Root mean square differences between HSR‐LST and measured H were of about 55 W m−2. Thus, by using multitemporal thermal acquisitions, HSR‐LST appears to bypass inconsistency between land surface temperature and the mean aerodynamic temperature. The one‐source bulk transfer formulation for estimating H performed reliable after calibration against the eddy covariance method. After calibration, the latter performed similar to the proposed SR‐LST method. This research was funded by project CGL2012‐37416‐C04‐01 and CGL2015‐65627‐C3‐1‐R (Ministerio de Ciencia y Innovación of Spain), CEI Iberus, 2014 (Proyecto financiado por el Ministerio de Educación en el marco del Programa Campus de Excelencia Internacional of Spain), and Ayuda para estancias en centros extranjeros (Ministerio de Educación, Cultura y Deporte of Spain).

In this work a set of time-series of inter-arrival times of rainfall events, at daily scale, was analysed, with the aim to verify the issue of increasing duration of dry periods. The set consists of 12 time-series recorded at rain gauges in 1926–2005, six of them belong to an Italian Sub-Alpine area (Piedmont) and six to a Mediterranean one (Sicily). In order to overcome the problem related to limited sample size for high values of inter-arrival times, the discrete probability polylog-series distribution was used to fit the empirical data from partial (20 yr) time-series. Moreover, a simple qualitative trend analysis was applied to some high quantiles of inter-arrival times as well as to the average extent of rain clusters. The preliminary analysis seems to confirm the issue of increasing duration of dry periods for both environments, which is limited to the ''cold'' season.

Summary An operational use of the actual evapotranspiration assessed by remote sensing approaches requires the integration of instantaneous fluxes to daily values. This is commonly achieved under the hypotheses of daytime self-preservation of evaporative fraction and negligible daily ground heat flux. The aim of this study is to evaluate the effect of these assumptions on estimate daily evapotranspiration over a full phenological cycle, including phases characterized by significant changes both in net radiation and vegetation cover. To assess the reliability of these hypotheses, the observations made by a flux tower, installed within a homogeneous field of cereal located in the valley part of the Imera Meridionale basin, were analyzed. Additionally, the widely-known SEBAL (Surface Energy Balance Algorithm for Land) model was applied on the same study area by means of four MODIS (MODerate-resolution Imaging Spectroradiometer) images selected across a three-rainfall events period in March–April 2007 with the aim to analyze the consistency of its estimates in an operational study case under different conditions of water availability. The analysis of in situ data highlights errors on 24-h evapotranspiration characterized by an average value of 20% due to daily soil heat flux neglecting; whereas, the hypothesis of evaporative fraction self-preservation causes an average error equal to −16%. Moreover, the analysis of the observations suggests that a compensation effect of the errors related to each hypothesis occurs in most cases (56%), and this makes suitable the approach for practical daily integration purposes. The application of the SEBAL model at basin scale shows a good capability to detect the increase of the actual 24-h evapotranspiration under the tested hypotheses, also in the case of instantaneous evaporative fraction and daily net radiation not derived form in situ observations.

Summary A coupled energy/hydrologic model was applied to simulate the exchange of energy and water in the soil–plant-atmosphere system (SPA). The model, which uses a “two-source” approach to estimate the energy fluxes, and the “force-restore” approach to represent the water balance, was validated by means of evapotranspiration measurements collected via scintillometry and soil moisture measurements collected via time domain reflectometry (TDR) in a Sicilian olive grove. The comparison between measured and estimated fluxes values at an hourly scale showed good agreement. Additional comparisons on a daily timescale confirmed the model’s applicability for quantifying crop water requirements. Also in terms of daily evapotranspiration and soil water content values, the obtained results confirmed the model’s applicability for those practical applications aiming to quantify the crop water requirement. Moreover, further studies should be conducted to test the feasibility of using this model for long term simulations over a broad range of conditions.

Soil humidity plays a key-role in hydrological and agricultural processes. In the rainfall-runoff processes the knowledge of its spatial distribution is fundamental to accurately model these phenomena. Furthermore in agronomy and agricultural sciences, assessing the water content of the root zone is required in order to optimize the plant productivity and to improve the irrigation systems management. Despite the importance of this variable the in situ measurements techniques based on Time Domain Reflectometry (TDR) or on the standard thermo-gravimetric methods, are neither cost-effective nor representative of its spatial and temporal variability. Indirect estimations via Earth Observation (EO) images include the triangle method, which shows that Land Surface Temperature (LST) is prevalently controlled by surface and root zone humidity in bare and vegetated soils respectively. The effects of pre-processing techniques correcting for altimetry and seasonality are analyzed by means of shortwave and longwave airborne images acquired on a vineyard during a whole phenological period. The paper also discusses the advantages induced by replacing the absolute temperatures with relative values, that were obtained subtracting the temperatures measured by micrometeorological station or the surface temperature of high thermal inertia surfaces (as small irrigation reservoir) chosen as reference values. The validation with in situ data also highlights that a higher spatial resolution not necessarily imply a higher accuracy.

Soil moisture is difficult to quantify because of its high spatial variability. Consequently, great efforts have been undertaken by the research community to develop practical remote sensing approaches to estimate the spatial distribution of surface soil moisture over large areas and with high spatial detail. Many methodologies have been developed using remote sensing data acquiring information in different parts of the electromagnetic spectrum. Conventional field measurement techniques (including gravimetric and time-domain reflectometry) are point-based, involve on-site operators, are time expensive and, in any case, do not provide exhaustive information on the spatial distribution of soil moisture because it strongly depends on pedology, soil roughness and vegetation cover. The technological development of imaging sensors acquiring in the visible (VIS), near infrared (NIR) and thermal infrared (TIR), renewed the research interest in setting up remote sensed based techniques aimed to retrieve soil water content variability in the soil-plant-atmosphere system (SPA). In this context different approaches have been widely applied at regional scale throughout synthetic indexes based on VIS, NIR and TIR spectral bands. A laboratory experiment has been carried out to verify a physically based model based on the remote estimation of the soil thermal inertia, P, to indirectly retrieve the soil surface water content, θ. The paper shows laboratory retrievals using simultaneously a FLIR A320G thermal camera, a six bands customized TETRACAM MCA II (Multiple Camera Array) multispectral camera working in the VIS/NIR part of the spectrum. Using these two type of sensors a set of VIS/NIR and TIR images were acquired as the main input dataset to retrieve the spatial variability of the thermal inertia values. Moreover, given that the accuracy of the proposed approach strongly depends on the accurate estimation of the soil thermal conductivity, a Decagon Device KD2 PRO thermal analyzer was used to verify the remotely estimate of thermal conductivity. Remotely estimated water contents were validated using the gravimetric method. The considered thermal inertia approach allowed prediction of the spatial distribution of the soil water with a satisfactory level of accuracy.

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (soil moisture, advection, air temperature) are affecting plant stress. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5–10 km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI), spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions of 30 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe and, Africa and other continents with geostationary satellite coverage.

The rate at which the net radiation is transferred to the soil as ground heat flux varies with surface characteristics. Surface energy balance algorithms use empirical relationships taking into account the effects of the canopy cover to insulate the soil through vegetation indexes, the soil capacity to absorb incoming net radiation via the albedo, and the surface temperature promoting the energy transfer. However empirical relationships are often dependent on local conditions, such as the soil humidity and vegetation type. Ground heat flux assumes a minimum value in case of full canopy cover and a maximum value for dry bare soil. Aim of the present research is the critical analysis of some ground heat flux equations on a homogeneous field of cereal using measured data acquired between February and May 2008. The study period covers almost a full phenological cycle, including phases characterised by a significant change in both reflected radiation and vegetation cover. The dataset begins with the emergence phase, in November, within which shoots emerge from the ground and finishes with the flowering phase, in May, when tiny white stems begin to come-out; moreover the dataset includes a bare soil period (from September up to November). The daily evapotranspiration is calculated in energy balance models under the hypotheses of negligible daily ground heat flux and constant daily evaporative fraction. Actually micrometeorological data show that daily average ground heat flux is not null but characterised by an increasing or decreasing transient. As a consequence, it is particular important to assess the effects of neglecting the daily ground heat flux on daily evapotranspiration estimation.

Daily high spatial resolution assessment of actual evapotranspiration is essential for water management and crop water requirement estimation under stress conditions. The application of energy balance models usually requires satellite observations of radiometric surface temperat ure with high geometrical and temporal resolutions. By now, however, high spatial resolution (~ 100 m) is available with low time fre quency (approximately every two weeks); at the opposite daily acquisition are characterised by poor spatial resolution. The analysis of vegetation index (VI) and land surface temperature (LST) spatial relationship, shows in substance a scale invariant behaviour [1] ; this consideration allows the application of spatial sharpening algorithms of thermal data, by means of a combination of high spatial resolution data in VIS/NIR range with high temporal acquisition on TIR. In this paper, a sharpening algorithm was applied using the thermal bands of MODIS ( MOderate resolution Imaging Spectroradiometer ) and vegetation indices derived by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer ) sensor; the choice of this sensors is justified by the simultaneous acquisition time. The results of this sharpening process was firstly compared against LST estimation (at the same spatial resolution) by means of the ASTER simultaneous data; then the derived high spatial resolution LST distribution was used in order to investigate the effect of the disaggregation on the outputs of surface energy balance models. The above described application was performed on a Sicilian study area. Keywords: land surface temperature, disaggregation, actual ev apotranspiration, surface energy balance models.