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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: This paper presents and assesses the Decadal Rainfall Erosive Multiscale Model (DREMM), in which extreme precipitation data (95th percentile) are used to estimate decadal-scale rainfall–runoff erosivity values compatible with the Universal Soil Loss Equation and its revision — (R)USLE. The test area was a large region including central Europe and Mediterranean countries, in which 111 decades from 88 weather stations (ranging from about 3 to 1680m above sea level) with rain and (R)USLE rainfall–runoff erosivity data were available. The construction of the model is simplified to a location-explicit term and to the understanding that the most erosive rainfalls are those recorded during the summertime and the beginning of autumn (May–October). These precipitation events are suitable for use in spatial and temporal climate variability studies on decadal time-scales. Once parameterized to capture decadal rainfall–runoff erosivity variability over central Europe and the Mediterranean, the DREMM was run to produce spatial patterns of rainfall–runoff erosivity in Germany and Bulgaria, compared to maps from the (R)USLE approach for 1961–1990. Implications for rainfall–runoff erosivity modelling were discussed concluding that a limited number of parameters may be sufficient to represent decadal rainfall–runoff erosivity for the central European and Mediterranean region. A transferable approach is recommended by employing the DREMM to assess the spatial impact of rainfall extremes. Its use to characterize the long-term dynamics of rainfall–runoff erosivity may also be of value in climate investigation. [Copyright &y& Elsevier]

Summary: This paper presents and assesses the model MedREM, in which annual-based climate and rainstorm data are used to estimate the long-term (R)Universal Soil Loss Equations and its Revisions (USLE) annual rainfall erosivity over a large region. Two known models of rainfall erosivity (named after the first author of the original paper, Torri and Yang) were also assessed and compared with the MedREM. Yang and Torri models are both based on the annual precipitation models. MedREM also takes annual maximum daily precipitation data into account, and incorporates a longitude-dependent coefficient. The test area was a large region centered on the Mediterranean basin, in which 66 weather stations were available – 43 Italian and 23 out-of-Italy sites (12 countries, about 30–50° latitude North, and 10–50° longitude East) – with multi-year data of (R)USLE annual erosivity. The three models were calibrated against (R)USLE rainfall erosivity data from 55 stations and evaluated over a validation dataset from 11 Italian stations. On the validation dataset, the MedREM estimates generally compared well with the (R)USLE data according to Nush–Sutcliffe coefficient (0.87 against 0.76 and 0.73 with Torri and Yang model, respectively). Implications for erosivity modelling were discussed in the context of climatic features concluding that accurate estimations of site-specific annual erosivity for the Mediterranean region require process-based model with spatially-explicit parameterization. [Copyright &y& Elsevier]