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The standardized precipitation index (SPI) is the most commonly used index for detecting and characterizing meteorological droughts, and it is also extensively used as a proxy variable for soil moisture anomalies (SMA) for the purpose of monitoring agricultural drought in absence of long-term soil moisture observations. However, the potential capability of SPI to warn of the time-lagged soil water deficit—following the well-known "drought cascade" effect—is often overlooked in agricultural drought studies. In this research, a time-lagged correlation analysis is used to evaluate the relationship between the SMA dataset, generated as part of the Global Drought Observatory of the European Union's Copernicus Emergency Management Service, and a set of SPIs derived from the ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts. The possibility to achieve an optimal agreement between SPI and SMA that also preserves the early warning skills of SPI is evaluated. The results suggest that if only the correlation between SPI and SMA is considered, the maximum agreement is usually obtained with a zero lead time (almost 80% of the cases), with SPI-3 representing the best option in about 40% of the grid cells at global scale. By also accounting for the benefits of a positive lead time, short accumulation periods tend to be favored, with SPI-1 being the optimal choice in about one-half of the cases, and 10–20 days of lead time in more than 90% of the grid cells is achieved without any significant reduction in either correlation or skill in drought extreme detection. [ABSTRACT FROM AUTHOR]

Drought events evolve simultaneously in space and time; hence, a proper characterization of an event requires the tracking of its full spatiotemporal evolution. Here we present a generalized algorithm for the tracking of drought events based on a three-dimensional application of the DBSCAN (density-based spatial clustering of applications with noise) clustering approach. The need for a generalized and flexible algorithm is dictated by the absence of a unanimous consensus on the definition of a drought event, which often depends on the target of the study. The proposed methodology introduces a set of six parameters that control both the spatial and the temporal connectivity between cells under drought conditions, also accounting for the local intensity of the drought itself. The capability of the algorithm to adapt to different drought definitions is tested successfully over a study case in Australia in the period 2017–20 using a set of standardized precipitation index (SPI) data derived from the ERA5 precipitation reanalysis. Insights on the possible range of variability of the model parameters, as well as on their effects on the delineation of drought events, are provided for the case of meteorological droughts in order to incentivize further applications of the methodology. [ABSTRACT FROM AUTHOR]

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]

Hydrological drought is currently underrepresented in global monitoring systems, mainly due the shortage of near real-time estimates of river discharge at the global scale. In this study, the outputs of the Lisflood model are used to define a low-flow drought index, which shows a good correspondence with long-term records of the Global Runoff Data Centre in the period 1980–2014, as well as with verified information from the literature on six major drought events (covering different regions and watershed sizes). In contrast, the near real-time simulation (from 2015 onward) provides temporally inconsistent estimates over about 20% of the modelled cells (mostly over South America and Central Africa), even if reasonable results are obtained over other regions, as confirmed by intercomparison with the operational outcomes of the European Drought Observatory for the 2018 drought. In spite of the highlighted limitations, valuable information for operational drought monitoring can be retrieved from these simulations. [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]

Four upscaling methods for estimating daytime actual evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods assume self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggested that at-surface solar radiation was the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux yielded higher bias than the solar radiation method, although resulting errors showed similar lack of seasonal dependence. Finally, the commonly used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied. In general, the proposed methodology clearly showed the added value of an intercomparison of different upscaling methods under scenarios that account for the uncertainty in eddy covariance flux measurements due to closure errors. [ABSTRACT FROM AUTHOR]

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 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, 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. [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]

The article presents a study which examines the statistical analysis of inter-arrival time series of rainfall events for Mediterranean and Italian Sub-Alpine areas. The study uses the discrete probability polylog-series distribution as well as applied a simple qualitative trend analysis to some quantiles of inter-arrival times and to the average extent of rain clusters. The study reveals that the duration of dry periods for both environments increased.

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]

Thermal infrared (TIR) remote sensing of landsurface 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 (e.g., air temperature, advection) are affecting plant functioning. 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 to 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 on the order of 10m. 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, Africa and other continents with geostationary satellite coverage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using Surface Energy Balance and Agro-Hydrological models. Both modelling approaches require 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. To this end two different schemes have been tested: the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately, and the widely used one-source SEBAL model, where soil and vegetation are considered as a sole source. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared with the results of the Agro-Hydrological model SWAP, applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. In this latter model, 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. In the comparison presented here, actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference. Considering that the study area is characterized by typical Mediterranean sparse vegetation, i.e. olive, citrus and vineyards, we focused the attention on the main conceptual differences between SEBAL and TSEB. Airborne hyperspectral data acquired during a NERC campaign in 2005 have been used. The results of the investigation evidenced that the remote sensing two-sources approach used in TSEB model describes turbulent and radiative surface fluxes in a more realistic way than the one-source approach. [ABSTRACT FROM AUTHOR]