Eco-hydrological models can be used to support effective land management and planning of forest resources. These models require a Digital Elevation Model (DEM), in order to accurately represent the morphological surface and to simulate catchment responses. This is particularly relevant on low altimetry catchments, where a high resolution DEM can result in a more accurate representation of terrain morphology (e.g., slope, flow direction), and therefore a better prediction of hydrological responses. This work intended to use Soil and Water Assessment Tool (SWAT) to assess the influence of DEM resolutions (1 m, 10 m and 30 m) on the accuracy of catchment representations and hydrological responses on a low relief forest catchment with a dry and hot summer Mediterranean climate. The catchment responses were simulated using independent SWAT models built up using three DEMs. These resolutions resulted in marked differences regarding the total number of channels, their length as well as the hierarchy. Model performance was increasingly improved using fine resolutions DEM, revealing a bR² (0.87, 0.85 and 0.85), NSE (0.84, 0.67 and 0.60) and Pbias (−14.1, −27.0 and −38.7), respectively, for 1 m, 10 m and 30 m resolutions. This translates into a better timing of the flow, improved volume simulation and significantly less underestimation of the flow.

Urban green/blue spaces are put under pressure as urban areas grow, develop and evolve. It is increasingly recognized, however, that green/blue spaces provide important ecosystem services, stimulate higher real estate prices and prevent flooding problems. This paper aims to assess and compare the socio-economic impacts of potential green/blue space, urban residential and road infrastructure development scenarios in the Lyon Confluence project area (France), using the Sustainable Urbanizing Landscape Development (SULD) hedonic pricing simulation model. Results show four major tendencies regarding the value-added of green/blue spaces in urban landscapes: (1) cities become more compact; (2) population densities increase; (3) real estate values rise; and (4) demographic distribution patterns change. The magnitude of these impacts depends, however, on the quality and size of the intervention, the social classes attracted to the intervention area and on the location of the intervention relative to existing residential areas, urban centres, road infrastructure and environmental amenities.