Spatial distribution of surface temperature and land cover: A study concerning Sardinia, Italy

Land surface temperature (LST) is a key climate variable that has been studied mainly at the urban scale and in the context of urban heat islands. By analyzing the connection between LST and land covers, this study shows the potential of LST to analyze the relation between urbanization and heating phenomena at the regional level. Land cover data, drawn from Copernicus, and LST, retrieved from Landsat 8 satellite images, are analyzed through a methodology that couples GIS and regression analysis. By looking at the Italian island of Sardinia as a case study, this research shows that urbanization and the spatial dynamics of heating phenomena are closely connected, and that intensively farmed areas behave quite similarly to urban areas, whereas forests are the most effective land covers in mitigating LST, followed by areas covered with Mediterranean shrubs. This leads to key policy recommendations that decision makers could implement to mitigate LST at the regional scale and that can, in principle, be exported to regions with similar climate and land covers. The significance of this study can be summed up in a novel approach to analyze the relationship between LST and land covers that uses freely available spatial data and therefore can easily be replicated in other regional contexts to derive appropriate policy recommendations

A comparative study of the water budgets of lawns under three management scenarios

The fate of irrigation in urban ecosystems is highly uncertain, due to uncertainties in urban ecohydrology. We compared irrigation rates, soil moisture, evapotranspiration (ET), stomatal conductance, and water budgets of landscape ecosystems managed with different turfgrass species and irrigation technologies. The “Typical” landscape had a cool-season fescue and was irrigated by an automatic timer. The “Alternative1” landscape had a warm-season paspalum and a “smart” soil moisture sensor-based irrigation system. The “Alternative2” landscape had a cool-season native sedge and a “smart” weather station-based drip irrigation system. ET was measured with a portable closed chamber and modeled using a Penman-Monteith approach, and the two methods agreed well. The water applied to the Alternative1 was 54 % less than the water applied to the Typical landscape, and the water applied to the Alternative2 was 24 % less. Soil moisture was similar in the Typical and Alternative2, while Alternative1 was drier in spring. The stomatal conductance of sedge was lower than the other two species, but its ET was not lower due to higher leaf area. Irrigation efficiencies (ET/applied irrigation) were 57 - 58 %, 86 – 97 %, and 78 - 80 % for the Typical, Alternative1, and Alternative2 landscapes, respectively. Runoff was less than 2 % in each landscape, and excess irrigation primarily drained below the root zone. Differences in irrigation efficiency between landscapes were due mainly to irrigation application, which varied more than species water use. Smart irrigation systems provided substantial water savings relative to a timer-based system, and prevented significant drainage losses. The utilization of smart sensors was more important than the choice of turfgrass species for irrigation efficiency