Producing a Digital Hydrographic Map Aiming at Renewable Energy Potential Mapping of Lesotho

AbstractSome of the first outcomes of a project aiming at mapping the renewable energy potential in Lesotho are hereby presented. In particular, the present paper deals with the task of the project devoted to produce a digital hydrographic map of Lesotho and an associated geographic database. Different geographical, meteorological and hydrological data were collected in the first steps of the project. The hydrographic network was derived in vector format from a digital elevation model of Lesotho using geoprocessing tools in GIS environment. Results were compared with existing cartography and satellite images. Moreover, a methodology proposed in literature for the assessment of the theoretical maximum hydroelectric producibility at watershed level in Italy was applied to one of the main catchment areas of Lesotho. The activities planned to fulfil the objectives of the project are finally outlined

Investigating the Sediment Yield Predictability in Some Italian Rivers by Means of Hydro-Geomorphometric Variables

In the present work, preliminary results are reported from an ongoing research study aimed at developing an improved prediction model to estimate the sediment yield in Italian ungauged river basins. The statistical correlations between a set of hydro-geomorphometric parameters and suspended sediment yield (SSY) data from 30 Italian rivers were investigated. The main question is whether such variables are helpful to explain the behavior of fluvial systems in the sediment delivery process. To this aim, a broad set of variables, simply derived from digital cartographic sources and available data records, was utilized in order to take into account all the possible features and processes having some influence on sediment production and conveyance. A stepwise regression analysis pointed out that, among all possibilities, the catchment elevation range (Hr), the density of stream hierarchical anomaly (Da), and the stream channel slope ratio (ΔSs) are significantly linked to the SSY. The derived linear regression model equation was proven to be satisfactory (r2-adjusted = 0.72; F-significance = 5.7 × 10−8; ME = 0.61), however, the percentage standard error (40%) implies that the model is still affected by some uncertainties. These can be justified, on one hand, by the wide variance and, on the other hand, by the quality of the observed SSY data. Reducing these uncertainties will be the effort in the follow-up of the research

Assessment of a Simplified Connectivity Index and Specific Sediment Potential in River Basins by Means of Geomorphometric Tools

Sediment connectivity is a major topic in recent research because of its relevance in the characterization of the morphology of river systems and assessing of sediment transport and deposition. Currently, the connectivity indices found in the literature are generally dimensionless and need to be coupled with quantitative soil-loss data for land management and design purposes. In the present work, a simple methodology is proposed to assess two different indices, namely, the simplified connectivity index (SCI) and the specific sediment potential (SSP), based on geomorphometric tools that are commonly available in commercial and open-source geographic information system (GIS) platforms. The proposed metrics allows us to easily assess both the SCI and the SSP as functions of the estimated soil erosion per unit area of the catchment and of the inverse distance of each unit area from the river outlet, this distance being measured along the network path. The proposed indices have been devised to express, respectively, the potential sediment transfer ability and the sediment mass potentially available at a given section of the drainage network. In addition to other parameters used to describe the catchment characteristics potentially affecting the river sediment delivery capacity, the SCI and SSP indices can help to refine theoretical models in order to assess the sediment yield (SY) in ungauged river basins

Valutare l'erosione del suolo mediante l'applicazione del modello RUSLE in ambiente GIS

Predicting GIS erosion by the application of RUSLE model in a GIS environment The increase in soil erosion risk due to the current climate change is a focal point in sustainable land management. Being able to predict this risk is fundamental for impact mitigation and soil resource conservation. The state-of-the-art in geo-information technologies (GIS, remote sensing and image analysis) allows to perform, in semi-automatic way, assessments and predictions based on the integration of theoretical models with advanced techniques of geospatial and geostatistical analysis. One of the most successful models worldwide utilised, such as the Revised Universal Soil Loss Equation (RUSLE), can be now easily applied at multiple spatial and temporal scales, i.e. from the single catchment up to the regional scale and from interannual to long-term scenarios, by using GIS-based tools

Investigating the Sediment Yield Predictability in Some Italian Rivers by Means of Hydro-Geomorphometric Variables

In the present work, preliminary results are reported from an ongoing research study aimed at developing an improved prediction model to estimate the sediment yield in Italian ungauged river basins. The statistical correlations between a set of hydro-geomorphometric parameters and suspended sediment yield (SSY) data from 30 Italian rivers were investigated. The main question is whether such variables are helpful to explain the behavior of fluvial systems in the sediment delivery process. To this aim, a broad set of variables, simply derived from digital cartographic sources and available data records, was utilized in order to take into account all the possible features and processes having some influence on sediment production and conveyance. A stepwise regression analysis pointed out that, among all possibilities, the catchment elevation range (Hr), the density of stream hierarchical anomaly (Da), and the stream channel slope ratio (ΔSs) are significantly linked to the SSY. The derived linear regression model equation was proven to be satisfactory (r2-adjusted = 0.72; F-significance = 5.7 × 10−8; ME = 0.61), however, the percentage standard error (40%) implies that the model is still affected by some uncertainties. These can be justified, on one hand, by the wide variance and, on the other hand, by the quality of the observed SSY data. Reducing these uncertainties will be the effort in the follow-up of the research

Carta dell'erosione del suolo del Lazio meridionale

The present work was aimed to test the operability of the RUSLE prediction model, on the basis of available data, in combination with different interpolation methods in the area of southern Lazio (central Italy). The work was based on published rainfall, soil, land-cover and elevation data archives and on a quick supplementary soil sampling survey. The RUSLE factors were computed by means of different correlation formulae and algorithms. Despite the lack of information data, the obtained soil erosion map can provide a useful reference frame of the soil loss potential for regional planning purposes