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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. [ABSTRACT FROM AUTHOR]

ABSTRACT The Lisflood rainfall-runoff model was forced by the E- OBS () gridded meteorological dataset in order to produce 5-km resolution maps of root zone soil moisture for the period 1951-2013, which were used to evaluate the presence of significant trends in soil water deficit over Europe. The results of the trend analysis highlighted a clear subdivision of the European domain into two sub-regions: a southwest portion with mostly positive trends (drying of the soil) and a northeast part with decreasing soil water deficit. Overall, about 19% of the domain was affected by a significant linear trend according to the Mann-Kendall test and the false discovery rate ( FDR) procedure ( p = 0.05), with about 11.6% having a negative (wetting) and 7.6% a positive (drying) slope. A spatial cluster analysis identified 11 major areas (with extension > 10 000 km2) affected by significant trends, including the Ebro and Garonne basins (for positive slopes) and the Dniester watershed (for negative trends). A final analysis at national and sub-national levels highlights that most of the observed trends are also significant when aggregated at a regional ( NUTS-1) spatial scale, detecting the administrative areas where water management planes are most likely affected. As drying and wetting processes are occurring mainly in the already driest and wettest sub-areas of the modelling domain, respectively, the obtained results suggest a further polarization of the water deficit in Europe. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Thermal remote sensing methods for mapping evapotranspiration (ET) exploit the physical interconnection that exists between land-surface temperature (LST) and evaporative cooling, employing principles of surface energy balance (SEB). Unfortunately, while many applications in water resource management require ET information at daily and field spatial scales, current satellite-based thermal sensors are characterized by either low spatial resolution and high repeatability or by moderate/high spatial resolution and low frequency. Here we introduce a novel approach to ET mapping that fuses characteristics of both classes of sensors to provide optimal spatiotemporal coverage. In this approach, coarse resolution daily ET maps generated with a SEB model using geostationary satellite data are spatially disaggregated using daily MODIS (MODerate resolution Imaging Spectroradiometer) 1 km and biweekly Landsat LST imagery sharpened to 30 m. These ET fields are then fused to obtain daily ET maps at 30 m spatial resolution. The accuracy of the fused Landsat-MODIS daily ET maps was evaluated over Iowa using observations collected at eight flux towers sited in corn and soybean fields during the Soil Moisture Experiment of 2002, as well as in comparison with a Landsat-only retrieval. A significant improvement in ET accuracy (reducing errors from 0.75 to 0.58 mm d−1 on average) was obtained by fusing MODIS and Landsat data in comparison with the Landsat-only case, with most notable improvements when a rainfall event occurred between two successive Landsat acquisitions. The improvements are further evident at the seasonal timescale, where a 3% error is obtained using Landsat-MODIS fusion versus a 9% Landsat-only systematic underestimation. [ABSTRACT FROM AUTHOR]

We are proposing a new method for estimating soil surface water content from thermal inertia distributions retrieved from visible-near infrared (VIS-NIR) and thermal infrared (TIR) images. A drying experiment was conducted on three fine-textured soils while acquiring multispectral VIS-NIR and TIR images. Simultaneous measurements of soil water content and thermal inertia were conducted by the thermogravimetric method and the heat pulse technique, respectively. Direct measurements were used to test the thermal inertia approach proposed by Murray and Verhoef that requires only knowledge of soil porosity and can be easily inverted to derive soil water content from thermal inertia. For the three considered soils, the performance of the Murray and Verhoef model was practically equal to that of the traditional approach based on the direct estimation of thermal conductivity and heat capacity, which requires more detailed information about soil properties. With the aim of simplifying the estimation of thermal inertia from remotely sensed images, a modified Kersten function was proposed in which the normalized thermal inertia is substituted by the normalized apparent thermal inertia. Comparison between the two modified Kersten functions was satisfactory. The proposed approach allowed predictions of the surface soil moisture from apparent thermal inertia distributions with an acceptable level of accuracy for practical purposes (0.028 ≤ RMSE ≤ 0.043 m³ m-3 and therefore it can be considered a simple and effective tool for estimating the spatial and temporal distribution of surface soil moisture from VIS-NIR and TIR remotely sensed data. [ABSTRACT FROM AUTHOR]