Search

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]

Earth ecosystems are not static, and they respond to environmental changes, particularly climatic and anthropogenic. Precipitation varying in its extremeness, with shifts to greater or lesser intensity of individual storms and/or to change in the length and frequency of wet and dry periods, can adversely affect both urban and rural ecosystems. Here, the authors review long-term precipitation records of the central Mediterranean area and employ a Web geographical information system (GIS)-based analytical approach to compare current rainfall impact with historical data on different spatial and temporal scales. Autumn (September–November) was recognized as the most hazardous season that marks the evidence of a changing climate, with a shift toward more intense rainfalls in recent times. In the first decade of the third millennium, areas of peninsular and insular Italy have been especially affected by extreme rains. A focus was put on the island of Sicily, where extraordinary rain events occurred in September 2009, discussed in the context of upcoming trends and climate histories. An improved knowledge and understanding of the scale at which changes on extremes occur is essential for dealing with the forthcoming challenges regarding soil and water conservation practices. The characteristics of changes in natural rainfall, its role on terrestrial ecosystems, and its effect on surface water erosion dynamics are discussed. It is argued that understanding these issues are major priorities for future research to promote a better understanding of the Earth interaction with water resources and related hydrological issues. [ABSTRACT FROM AUTHOR]

A crop simulation model must first be capable of representing the actual performance of crops grown in any region before it can be applied to the prediction of climate variability and change impacts. A cropping systems model (CropSyst) simulations of crop productivity in the sub-Saharan Central African (using Cameroon as the case study) region, under the current climate were compared with observed yields of maize, sorghum, groundnut, bambara groundnut and soybean from eight sites. The model produced both over-and-under estimates, but with a mean percentage difference of only –2.8%, ranging from –0.6% to –4.5%. Based on these results, we judged the CropSyst simulations sufficiently reliable to justify use of the model in assessing crop growth vulnerability to climatic changes in Cameroon and else where. [ABSTRACT FROM AUTHOR]

This work presents a modelling study where monthly-based climate data are used to estimate the Normalized Difference Vegetation Index (NDVI). The latter is a measure of vegetation greenness, usually derived from satellite-driven information. A model was developed to link NDVI data to rainfall and temperature measures. The test area was a 3 × 3 km grid centred to the top of Monte Pino hill (Southern Italy), for which multi-year (from 1996 to 2004) climate and satellite-derived NDVI data were available. The simulated NDVI data compared well with the remote-sensed measurements (e.g. modelling efficiency ∼0.80), thus showing a strong linking between vegetation greenness and climate patterns in spite of the many disturbances exerted from farming. The model was used to reconstruct an extended series of monthly NDVI values for a period antecedent 1996 (1972–1995). The analysis of long-term anomalies indicated a positive trend of NDVI over time, consistent with the air temperature increase registered in the same period. [ABSTRACT FROM AUTHOR]

Utilizing the weather generator ClimGen, daily solar radiation (Rs) and vapor pressure deficit (VPD) were estimated from temperature data and used to calculate evapotranspiration at five locations, representing tropical, temperate, semi-arid, and arid climates. ClimGen was calibrated for each location using the most recent 2 or 5 years of complete daily weather records. Actual and estimated values were compared on a daily and weekly (7-day running average) basis. Error indices were defined to indicate excellent to poor performance of the estimation methods. Overall in all locations, the ClimGen estimates for both daily Rs and VPD were poor to acceptable. The weekly analyses showed significant improvement in performance for both Rs and VPD estimations in arid and semi-arid locations. Daily reference crop evapotranspiration values using the FAO Penman-Monteith equation (PM ETo) were calculated using complete daily weather records. These values were compared with (1) ETo calculated with the PM model, actual temperature data, and ClimGen estimates of daily Rs, VPD, and generated wind speed (PMEst ETo), and (2) ETo calculated solely from actual daily temperature data using a calibrated version of the Hargreaves method (HGAdj ETo). The daily PMEst ETo results were poor to acceptable in all locations, but analyses for weekly periods showed improved performance to acceptable and good levels for arid and semi-arid locations. The performance of the HGAdj ETo method was also poor to acceptable for daily ET estimates in all locations, while weekly analyses showed improvement. A non-calibrated version of the Hargreaves method did not work for either daily or weekly periods. The PMEst ETo and HGadj ETo methods appeared suitable for weekly periods in arid and semi-arid locations provided that at least 2 years of complete weather records were available to calibrate the parameters required. There was no advantage in using 5 years of weather records for calibration. [ABSTRACT FROM AUTHOR]

Abstract: Multivariate geostatistical modeling can be used to generate spatial patterns of hydro-climate data over ungauged regions, but these models may be unsuitable when the hydro-climatological data available over large and remote areas are sparse and show different scales of resolution. In these cases, reduced complexity modeling can be better used in order to increase understanding of hydrological extremes at spatial scales and over time periods not covered by rainfall records. In this study we present and evaluate the African Rainfall Erosivity Subregional Empirical Downscaling (ARESED) model which has been developed based on hydro-climatological and geotopographic data from 46 stations across Africa with very varied climates and elevations. We spatially downscale the 85th percentile of monthly precipitation, based on several decades of data, from 50km to 10km grid squares in order to predict values of rainfall erosivity across Africa. This yields inputs comparable to values based on the standard Revised Universal Soil Loss Equation (RUSLE). The 46 African stations were chosen for model development because they are also sites for which there are RUSLE-based erosivity values. Once parameterized to capture mean rainfall erosivity over several decades, the ARESED model was run as a validation tool, comparing the output with actual erosivity data. On a continental scale and over decadal time scales, the ARESED model captures most of the important processes within the hydro-climatological system. Its reduced complexity structure also makes it suitable for application to regional management and environmental planning. [Copyright &y& Elsevier]

Abstract: Multivariate spatial statistics techniques can be efficiently applied to generate fine spatial patterns of climate data in presence of an appropriate multivariate spatial structure over ungauged mountainous basins. However, they can become unsuitable when the data available over complex regions are sparse and affected by discordant spatial scales in primary and (colocated)-auxiliary variables. This is the case of actual evapotranspiration (AET). Combining GIS and geoindicators (e.g., topographical and vegetational indices), we proposed an upscaling procedure to overcome this problem, transforming a preliminary-smoothed macro-scale pattern (AET grid-data), into a local-scale pattern. The procedure was applied to a cropland test site at Mediterranean sub-regional basin scale (Tammaro, South Italy) to develop a climatological baseline estimation of AET refined at slope scale. After the upscaling, the most frequent estimated AET values were about 550mmyr−1 (with quasi-normal distribution), while underestimations were observed in the preliminary, smoothed map (positively skewed distribution with mean 460mmyr−1). The upscaling allowed the influence of the topographic factor to emerge, with a wider range of values (about 300–900mmyr−1) being estimated and substantially not visible in the smoothed pattern. A temporal climate pattern of soil water depletion in the growing season was also shown as reflected in the increase of AET flux in the period 1991–2008 in comparison to the precedent climate (1961–1990). [Copyright &y& Elsevier]