1,720,993 research outputs found
A combined index to characterize agricultural drought in Italy at municipality scale
Full text linkStudy region: Italy and in particular the provinces of Verona and Foggia.Study focus: The assessment of drought impacts at local scale requires adequately detailed spatiotemporal estimates of drought severity. Given the intrinsic uncertainty in drought severity estimates based on a single index, especially at high spatial resolution, the use of combined indices is preferable. However, the disagreement between the single indices needs to be addressed. We propose a methodology to combine the Standardized Precipitation-Evapotranspiration Index and the Soil Moisture Anomalies based on a double-entry matrix. The classification adopted to define five semi-quantitative severity classes is generalized by introducing an objective approach to assign values when the two base indices disagree. The methodology is tuned over two Italian provinces, Verona and Foggia, with focus on agricultural drought at the high spatial detail of single municipalities.New hydrological insights for the region: The methodology is proved to be skillful (Heidke Skill Score of 0.75) in capturing the spatio-temporal evolution of the major agricultural droughts observed in the two case study regions (2012, 2015 and 2017), which are benchmarked using data from drought impact databases. The temporal dynamics modeled by this index align with the timeline of the drought events, suggesting that the index is suitable for near-real time agricultural drought monitoring. The simplicity of the double-entry matrix approach allows for upscaling to the entire country
Corrigendum to "Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model" published in Hydrol. Earth Syst. Sci., 17, 39–62, 2013
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Exploiting In Situ and Remotely Sensed Data for Enhancing Hydrological Models Simulations
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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