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As a key component in the water and energy cycle, estimates of precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. However, current satellite-based precipitation products have a coarse spatial resolution (from 10 to 50 km) not meeting the needs of several applications (e.g., flash floods and landslides). The implementation of spatial downscaling methods can be a suitable approach to overcome this shortcoming. In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the integrated multisatellite retrievals for global precipitation measurement (IMERG) V06B daily precipitation product over a complex topographic and climatic area in southwestern Europe (Iberian Peninsula) in the period 2016–2018. By exploiting the soil-water balance equation, high-resolution surface soil moisture (SSM) and normalized difference vegetation index (NDVI) products were used as auxiliary variables. The spatial resolution of the IMERG daily precipitation product was downscaled from 10 to 1 km. An evaluation using 1027 rain gauge stations highlighted the good performance of the downscaled 1 km IMERG product compared to the original 10 km product, with a correlation coefficient of 0.61, root mean square error (RMSE) of 4.83 mm and a relative bias of 5 %. Meanwhile, the 1 km downscaled results can also capture the typical temporal and spatial variation behaviors of precipitation in the study area during dry and wet seasons. Overall, the SMPD method greatly improves the spatial details of the original 10 km IMERG product also with a slight enhancement of accuracy. It shows good potential to be applied for the development of high-quality and high-resolution precipitation products in any region of interest. [ABSTRACT FROM AUTHOR]

Determining the spatiotemporal variability in the annual streamflow volume plays a relevant role in hydrology with regard to improving and implementing sustainable and resilient policies and practices of water resource management. This study investigates annual streamflow volume trends in a newly assembled, consolidated, and validated data set of daily mean river flow records from more than 3000 stations which cover near-natural basins in more than 40 countries across Europe. Although the data set contains streamflow time series from 1900 to 2013 in some stations, the statistical analyses were carried out by including observations from 1950 to 2013 in order to have a consistent and reliable data set over the continent. Trends were detected by calculating the slope of the Theil–Sen line over the annual anomalies of streamflow volume. The results show that annual streamflow volume trends have emerged at European scale, with a marked negative tendency in Mediterranean regions, with about -1×103 m 3 /(km 2 yr -2), and a generally positive trend in northern ones, with about 0.5×103 m 3 /(km -2 yr -2). The annual streamflow volume trend patterns appear to be in agreement with the continental-scale meteorological observations in response to climate change drivers. In the Mediterranean area, the decline of annual streamflow volumes started in 1965, and since the early 1980s, volumes have consistently been lower than the 1950–2013 average. The spatiotemporal annual streamflow volume patterns observed in this work can help to contextualize short-term trends and regional studies already available in the scientific literature, as well as to provide a valid benchmark for further accurate quantitative analysis of annual streamflow volumes. [ABSTRACT FROM AUTHOR]

Intermittent rivers are prevalent in many countries across Europe, but little is known about the temporal evolution of intermittence and its relationship with climate variability. Trend analysis of the annual and seasonal number of zero-flow days, the maximum duration of dry spells and the mean date of the zero-flow events is performed on a database of 452 rivers with varying degrees of intermittence between 1970 and 2010. The relationships between flow intermittence and climate are investigated using the standardized precipitation evapotranspiration index (SPEI) and climate indices describing large-scale atmospheric circulation. The results indicate a strong spatial variability of the seasonal patterns of intermittence and the annual and seasonal number of zero-flow days, highlighting the controls exerted by local catchment properties. Most of the detected trends indicate an increasing number of zero-flow days, which also tend to occur earlier in the year, particularly in southern Europe. The SPEI is found to be strongly related to the annual and seasonal zero-flow day occurrence in more than half of the stations for different accumulation times between 12 and 24 months. Conversely, there is a weaker dependence of river intermittence with large-scale circulation indices. Overall, these results suggest increased water stress in intermittent rivers that may affect their biota and biochemistry and also reduce available water resources. [ABSTRACT FROM AUTHOR]

The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model, is a main research question. SRPs' accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g. threat score, false alarm ratio and probability of detection) and/or continuous (e.g. bias, root mean square error, Nash–Sutcliffe index, Kling–Gupta efficiency index and correlation coefficient) performance scores. However, whether these scores are informative about the associated performance in river discharge simulations (when the SRP is used as input to a hydrological model) is an under-discussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of river discharge simulation. That is, the following research questions are addressed: is there any performance score that can be used to select the best performing rainfall product for river discharge simulation? Are multiple scores needed? And, which are these scores? To answer these questions, three SRPs, namely the Tropical Rainfall Measurement Mission (TRRM) Multi-satellite Precipitation Analysis (TMPA), the Climate Prediction Center MORPHing (CMORPH) algorithm and the SM2RAIN algorithm applied to the Advanced SCATterometer (ASCAT) soil moisture product (SM2RAIN–ASCAT) have been used as input into a lumped hydrologic model, "Modello Idrologico Semi-Distribuito in continuo" (MISDc), for 1318 basins over Europe with different physiographic characteristics. Results suggest that, among the continuous scores, the correlation coefficient and Kling–Gupta efficiency index are not reliable indices to select the best performing rainfall product for hydrological modelling, whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to absolute values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling–Gupta efficiency index on river discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information for addressing the SRP selection for hydrological modelling. [ABSTRACT FROM AUTHOR]

In accordance with Article 6 of Regulation (EC) No 396/2005, the applicant Gowan Crop Protection Ltd submitted a request to the competent national authority in Greece to set an import tolerance for the active substance fenazaquin in almonds. The data submitted in support of the request were found to be sufficient to derive a maximum residue level (MRL) proposal for almonds. Adequate analytical methods for enforcement are available to control the residues of fenazaquin and its metabolites on the commodities under consideration at the validated limit of quantification (LOQ) of 0.01 mg/kg. Based on the risk assessment results, EFSA concluded that the short-term and long-term intake of residues resulting from the use fenazaquin according to the reported agricultural practices is unlikely to present a risk to European consumers. The chronic consumer risk assessment is affected by non-standard uncertainties due to the lack of information on the occurrence of the metabolite 2-(4-tert-butylphenyl) ethanol (TBPE) in crops where the use of fenazaquin is the authorised in Europe. The reliable end points, appropriate for use in regulatory risk assessment are presented. [ABSTRACT FROM AUTHOR]

Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range. SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies. [ABSTRACT FROM AUTHOR]

Complexity-reduction modelling can be useful for increasing the understanding of how the climate affects basin soil moisture response upon historical times not covered by detailed hydrological data. For this purpose, here is presented and assessed an empirical regression-based model, the European Soil Moisture Empirical Downscaling (ESMED), in which different climatic variables, easily available on the web, are addressed for simplifying the inherent complexity in the long-time studies. To accommodate this simplification, the Palmer Drought Severity Index, the precipitation, the elevation and the geographical location were used as input data in the ESMED model for predicting annual soil moisture budget. The test area was a large region including central Europe and Mediterranean countries, and the spatial resolution was initially set at 50 km. ESMED model calibration was made according to the soil moisture values retrieved from the Terrestrial Water Budget Data archive by selecting randomly 285 grid points (out of 2606). Once parameterized, ESMED model was performed at validation stage both spatially and temporally. The spatial validation was made for the grid points not selected in the calibration stage while the comparison with the soil moisture outputs of the Global Land Data Assimilation System-NOAH10 simulations upon the period 1950-2010 was carried out for the temporal validation. Moreover, ESMED results were found to be in good agreement with a root-zone soil moisture product obtained from active and passive microwave sensors from various satellite missions. ESMED model was thus found to be reliable for both the temporal and spatial validations and, hence, it might represent a useful tool to characterize the long-term dynamics of soil moisture-weather interaction. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation. Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~5cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction. [Copyright &y& Elsevier]

Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data. [ABSTRACT FROM AUTHOR]

The event runoff coefficient (i.e. the ratio between event runoff and precipitation that originated the runoff) is a key factor for understanding basin response to precipitation events. Runoff coefficient depends on precipitation intensity and duration but also on specific basin geohydrology attributes (including soil type, geology, land cover, topography) and last but not least, antecedent (or pre-storm) conditions (i.e., the amount of water stored in the different hydrological compartments, like the river, groundwater, soil and snowpack). The relation between runoff coefficient and basin pre-storm conditions is critical for flood forecasting, yet, the understanding of where, when and how much basin pre-storm conditions control runoff coefficients is still an open question. Here, we tested the control of basin pre-storm conditions on runoff coefficient for 60620 flood events across 284 basins in Europe. To do so, we derived basin pre-storm conditions from different proxies, namely: antecedent precipitation; surface and root zone soil moisture from hydrological models, reanalyses and land surface models also ingesting satellite observations; pre-storm river discharge, and pre-storm total water storage anomalies. We evaluated the coupling strength between runoff coefficient and pre-storm conditions proxies in relation to five classes of European basins, defined based on land use and soil type (as indexed by the Soil Conservation Service curve number CN), topography, hydrology and long-term climate and tested their ability to explain stormflow volume variability. We found that precipitation explains relatively well the stormflow volumes for both small and large events but not very well the peak discharge, especially for large floods. The runoff coefficient of events shows different distributions for the five different classes and correlates well with deep soil storages (such as root-zone soil moisture and pre-storm total water storage anomalies), pre-storm river discharge, and pre-storm snow water equivalent. Overall, these correlations depend on the class. Poor correlations are found against antecedent precipitation index despite its wide use in the hydrological community. Seasonal and interannual climate variability exert a key role on the coupling strength between runoff coefficient and pre-storm conditions by inducing sharp changes in the correlation with season and climate. These results increase our understanding of the coupling between pre-storm conditions and runoff coefficients. This will aid flood forecasting, hydrological and land surface model calibration, and data assimilation. Furthermore, these findings can help us to better interpret future flood projections in Europe based on expected changes in long and short-term climatic drivers. • Different basin pre-storm proxies explain runoff coefficient variability in Europe • Basin hydrophysioclimatic characteristics control basin pre-storm condition importance • Seasonal and climate variabilities impact basin pre-storm condition importance [ABSTRACT FROM AUTHOR]

Rainfall is the primary input for hydrologic models that simulate the rainfall-runoff processes at basin scale. Because rainfall is highly variable in space and time, accurate hydrological simulations require accurate rainfall data at the best possible resolution. The conventional rain gauge observations in many parts of the world are sparse and unevenly distributed. Satellite-based rainfall products (SRPs) could be an alternative to traditional rain gauge observations and nowadays are available on a global scale at ever increasing spatial and temporal resolution.This study proposes a comprehensive assessment of SRPs for flood modeling in Europe. For this purpose, multiple SRPs (i.e., the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis TMPA; the Climate Prediction Center (CPC) Morphing algorithm, CMORPH, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, PERSIANN; the SM2RAIN‐ASCAT rainfall product obtained from ASCAT satellite soil moisture through the SM2RAIN algorithm) will be used to force different lumped hydrologic models (e.g., MISDc, GR4J, HYMOD) over several (+900) basins throughout Europe with different sizes and physiographic characteristics. In particular, this study will allow to: 1) assess the quality of different SRPs for flood modelling and its relationship with climatic/geomorphological conditions; 2) explore the connection between the accuracy of SRPs and their performance in terms of flood modeling taking into account the rainfall-runoff model structure as well. Preliminary results indicated that: 1) satellite rainfall products are not completely reliable for flood forecasting; 2) the hydrological performances of satellite rainfall products depend both on the product and on the selected hydrological model making general guidelines for the optimal use of SRPs in flood modeling difficult to be drawn. To overcome this issue a multimodel-multiproduct approach would help to exploit relative skills of each satellite product-hydrological model configuration and would bring to a more reliable flood forecasting system. [ABSTRACT FROM AUTHOR]