Pablo Durán-Barroso

Small and Micro-Hydropower Plants Location by Using Geographic Information System

Small-scale hydropower plants (SHP), and in particular the micro-hydropower plants (MHP) and pico-hydropower plants (PHP), are considering as an alternative energy resource based on the hydroelectric potential available in urban water cycle because of the excess of pressure existing in some urban water supply systems (WSS). Nowadays, pressure-reducing valves are necessary to reduce water pressure in WSS, so the use of a pump as turbine (PAT) can be considered as a proper way for reaching both an enough water head reduction and a hydropower generation possibility (self-consumption or energy recovery). MHPs are based on existing hydraulic resources where the PAT location is necessary, especially in those points with an excess of energy, which derives in an extra cost in terms of conservation and maintenance of the infrastructure or lead to the necessary installation of dissipation devices. The locations of these points are strongly influenced by the geographical and hydrological conditions, so a Geographic Information System (GIS) is a very useful tool for implementation of SHP and MHP or PHP projects. This paper describes the assessment and comparison of the methodology followed in the SHP and MHP locations: necessary data, GIS development, hydrologic model and hydropower potential.

Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

Sources of uncertainty in the NRCS CN model: Recognition and solutions

The Natural Resources Conservation Service Curve Number model (NRCS CN) is one of the most recognizable procedures in the field of rainfall-runoff estimation. It has been widely applied for different purposes in hydrological models. In spite of its widespread use, some uncertainties have not even clarified, and must be examined for its proper application. Particularly, choosing the most representative rainfall-runoff events, and the coefficient λ which relates the parameters of the model (curve number CN and initial abstraction Ia). In this research, an advanced analysis is developed to evaluate the influence of λ for a set of representative watersheds of the ARS-USDA (Agricultural Research Service of the United Stated Department of Agriculture). They are characterized by different soil properties, land uses and climatic conditions. Finally, two novel methodologies for the selection of the most representative rainfall-runoff events and for the adaptation of coefficient λ are included, based on the pattern of rainfall distribution.